From 3f803a9421fddf10a30745fc145d565d9737bd40 Mon Sep 17 00:00:00 2001 From: frreiss Date: Fri, 28 Sep 2018 17:18:01 -0700 Subject: [PATCH 001/873] Make add_n() handle a single IndexedSlices argument properly --- tensorflow/python/ops/math_ops.py | 4 +++- tensorflow/python/ops/math_ops_test.py | 11 +++++++++++ 2 files changed, 14 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py index f57abf6704..ebdfa592d3 100644 --- a/tensorflow/python/ops/math_ops.py +++ b/tensorflow/python/ops/math_ops.py @@ -2135,6 +2135,8 @@ def _as_indexed_slices_list(inputs, optimize=True): def add_n(inputs, name=None): """Adds all input tensors element-wise. + Converts `IndexedSlices` objects into dense tensors prior to adding. + Args: inputs: A list of `Tensor` or `IndexedSlices` objects, each with same shape and type. @@ -2157,7 +2159,7 @@ def add_n(inputs, name=None): if len(inputs) == 1: if isinstance(inputs[0], ops.IndexedSlices): - values = inputs[0].values + values = ops.convert_to_tensor(inputs[0]) else: values = inputs[0] if name: diff --git a/tensorflow/python/ops/math_ops_test.py b/tensorflow/python/ops/math_ops_test.py index f051850d92..cd9c89e519 100644 --- a/tensorflow/python/ops/math_ops_test.py +++ b/tensorflow/python/ops/math_ops_test.py @@ -359,6 +359,17 @@ class AddNTest(test_util.TensorFlowTestCase): [g.eval() for g in add_n_grad]) + def testIndexedSlices(self): + slc = tf.IndexedSlices(array_ops.constant([1, 2], shape=[1, 2]), + array_ops.constant([2]), array_ops.constant([2,2]) + slc_as_dense = np.array([[0, 0], [1, 2]]) + with self.test_session(use_gpu=True): + # add_n currently always converts IndexedSlices to dense + self.assertAllEqual(slc_as_dense, math_ops.add_n([slc]).eval()) + self.assertAllEqual(2 * slc_as_dense, math_ops.add_n([slc, slc]).eval()) + + + class DivAndModTest(test_util.TensorFlowTestCase): # TODO(aselle): Test more types before exposing new division operators. -- GitLab From ca7105c42182f6ef562d18a7843090a2ef458b83 Mon Sep 17 00:00:00 2001 From: frreiss Date: Mon, 1 Oct 2018 17:25:33 -0700 Subject: [PATCH 002/873] Oops, missing paren --- tensorflow/python/ops/math_ops_test.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/python/ops/math_ops_test.py b/tensorflow/python/ops/math_ops_test.py index cd9c89e519..fbae792cd0 100644 --- a/tensorflow/python/ops/math_ops_test.py +++ b/tensorflow/python/ops/math_ops_test.py @@ -360,8 +360,8 @@ class AddNTest(test_util.TensorFlowTestCase): def testIndexedSlices(self): - slc = tf.IndexedSlices(array_ops.constant([1, 2], shape=[1, 2]), - array_ops.constant([2]), array_ops.constant([2,2]) + slc = ops.IndexedSlices(array_ops.constant([1, 2], shape=[1, 2]), + array_ops.constant([1]), array_ops.constant([2,2])) slc_as_dense = np.array([[0, 0], [1, 2]]) with self.test_session(use_gpu=True): # add_n currently always converts IndexedSlices to dense -- GitLab From 2918d022954d4ce75e2b2ce4cd30c7f06d820444 Mon Sep 17 00:00:00 2001 From: frreiss Date: Mon, 1 Oct 2018 18:27:06 -0700 Subject: [PATCH 003/873] Remove extra blank line --- tensorflow/python/ops/math_ops_test.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tensorflow/python/ops/math_ops_test.py b/tensorflow/python/ops/math_ops_test.py index fbae792cd0..06abdcfc54 100644 --- a/tensorflow/python/ops/math_ops_test.py +++ b/tensorflow/python/ops/math_ops_test.py @@ -369,7 +369,6 @@ class AddNTest(test_util.TensorFlowTestCase): self.assertAllEqual(2 * slc_as_dense, math_ops.add_n([slc, slc]).eval()) - class DivAndModTest(test_util.TensorFlowTestCase): # TODO(aselle): Test more types before exposing new division operators. -- GitLab From aa9bb45cc8d534e5b1cec8613bea4b4e30f622de Mon Sep 17 00:00:00 2001 From: Todd Wang Date: Fri, 12 Oct 2018 17:55:19 -0700 Subject: [PATCH 004/873] Explicitly set jdk8 in ci_parameterized_build.sh (#22956) PiperOrigin-RevId: 216946217 --- tensorflow/tools/ci_build/ci_parameterized_build.sh | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/tensorflow/tools/ci_build/ci_parameterized_build.sh b/tensorflow/tools/ci_build/ci_parameterized_build.sh index 489722c0e9..bc9cb4e9a1 100755 --- a/tensorflow/tools/ci_build/ci_parameterized_build.sh +++ b/tensorflow/tools/ci_build/ci_parameterized_build.sh @@ -149,8 +149,12 @@ BAZEL_TEST_FLAGS=""\ "--test_env=TF_PER_DEVICE_MEMORY_LIMIT_MB=${TF_PER_DEVICE_MEMORY_LIMIT_MB}" BAZEL_BUILD_FLAGS="--keep_going" -BAZEL_CMD="bazel test ${BAZEL_TEST_FLAGS}" -BAZEL_BUILD_ONLY_CMD="bazel build ${BAZEL_BUILD_FLAGS}" +# Explicitly set jdk8 since that's what's installed in our images. Note that +# bazel 0.16 and higher defaults to jdk9, which causes failures. See b/117634064 +BAZEL_JAVA_FLAGS="--java_toolchain=@bazel_tools//tools/jdk:toolchain_hostjdk8" + +BAZEL_CMD="bazel test ${BAZEL_TEST_FLAGS} ${BAZEL_JAVA_FLAGS}" +BAZEL_BUILD_ONLY_CMD="bazel build ${BAZEL_BUILD_FLAGS} ${BAZEL_JAVA_FLAGS}" BAZEL_CLEAN_CMD="bazel clean" PIP_CMD="${CI_BUILD_DIR}/builds/pip.sh" -- GitLab From 5ffddda5b707099fb62097aae00ba9403adedd13 Mon Sep 17 00:00:00 2001 From: frreiss Date: Mon, 15 Oct 2018 15:05:58 -0700 Subject: [PATCH 005/873] lint issues --- tensorflow/python/ops/math_ops_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/ops/math_ops_test.py b/tensorflow/python/ops/math_ops_test.py index 06abdcfc54..0973e707a7 100644 --- a/tensorflow/python/ops/math_ops_test.py +++ b/tensorflow/python/ops/math_ops_test.py @@ -361,7 +361,7 @@ class AddNTest(test_util.TensorFlowTestCase): def testIndexedSlices(self): slc = ops.IndexedSlices(array_ops.constant([1, 2], shape=[1, 2]), - array_ops.constant([1]), array_ops.constant([2,2])) + array_ops.constant([1]), array_ops.constant([2, 2])) slc_as_dense = np.array([[0, 0], [1, 2]]) with self.test_session(use_gpu=True): # add_n currently always converts IndexedSlices to dense -- GitLab From 7b081981131bf6da32065b8ecc3b8c5bd1280c4a Mon Sep 17 00:00:00 2001 From: Goldie Gadde Date: Tue, 16 Oct 2018 10:14:23 -0700 Subject: [PATCH 006/873] Update version information in preparation for 1.12.0-rc1 (#23028) --- tensorflow/core/public/version.h | 2 +- tensorflow/tools/pip_package/setup.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h index 07eeeb4f03..592dd5da16 100644 --- a/tensorflow/core/public/version.h +++ b/tensorflow/core/public/version.h @@ -24,7 +24,7 @@ limitations under the License. // 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 "-rc1" #define TF_STR_HELPER(x) #x #define TF_STR(x) TF_STR_HELPER(x) diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index 3632ee2076..7593cfb58b 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 = '1.12.0-rc0' +_VERSION = '1.12.0-rc1' REQUIRED_PACKAGES = [ 'absl-py >= 0.1.6', -- GitLab From b379cecbdc4a9e6a0f8e468e0877888956e35dd5 Mon Sep 17 00:00:00 2001 From: annarev Date: Thu, 18 Oct 2018 14:04:48 -0700 Subject: [PATCH 007/873] Include .inc files for absl headers (#23081) --- tensorflow/tools/pip_package/setup.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index 7593cfb58b..8c3bd4ac70 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -232,6 +232,8 @@ headers = (list(find_files('*.h', 'tensorflow/core')) + list(find_files('*', 'third_party/eigen3')) + list(find_files('*.h', 'tensorflow/include/external/com_google_absl')) + + list(find_files('*.inc', + 'tensorflow/include/external/com_google_absl')) + list(find_files('*', 'tensorflow/include/external/eigen_archive'))) setup( -- GitLab From 2aaf639173420403b804a7216f8f1c51027b6240 Mon Sep 17 00:00:00 2001 From: Goldie Gadde Date: Fri, 19 Oct 2018 09:00:12 -0700 Subject: [PATCH 008/873] Update relnotes with Ignite information --- RELEASE.md | 1 + 1 file changed, 1 insertion(+) diff --git a/RELEASE.md b/RELEASE.md index 58d918895c..dbe34db0bb 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -4,6 +4,7 @@ * Keras models can now be directly exported to the SavedModel format(`tf.contrib.saved_model.save_keras_model()`) and used with Tensorflow Serving. * Keras models now support evaluating with a `tf.data.Dataset`. * TensorFlow binaries are built with XLA support linked in by default. +* Ignite Dataset added to contrib/ignite that allows to work with Apache Ignite. ## Bug Fixes and Other Changes -- GitLab From 878e98c1abd6cbd5bd044ddf8660c55e0c2a1634 Mon Sep 17 00:00:00 2001 From: Goldie Gadde Date: Fri, 19 Oct 2018 13:52:44 -0700 Subject: [PATCH 009/873] Update TF 1.12 version to 1.12-rc2 --- tensorflow/core/public/version.h | 2 +- tensorflow/tools/pip_package/setup.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h index 592dd5da16..500ec8f97b 100644 --- a/tensorflow/core/public/version.h +++ b/tensorflow/core/public/version.h @@ -24,7 +24,7 @@ limitations under the License. // TF_VERSION_SUFFIX is non-empty for pre-releases (e.g. "-alpha", "-alpha.1", // "-beta", "-rc", "-rc.1") -#define TF_VERSION_SUFFIX "-rc1" +#define TF_VERSION_SUFFIX "-rc2" #define TF_STR_HELPER(x) #x #define TF_STR(x) TF_STR_HELPER(x) diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index 8c3bd4ac70..b7eed56695 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 = '1.12.0-rc1' +_VERSION = '1.12.0-rc2' REQUIRED_PACKAGES = [ 'absl-py >= 0.1.6', -- GitLab From 4b4052c90e17c2c5bed45dc47c2d59d22f341b48 Mon Sep 17 00:00:00 2001 From: Todd Wang Date: Sat, 20 Oct 2018 16:17:55 -0700 Subject: [PATCH 010/873] Check for the presence of a Worker machine when reassigning hooks in distributed (#23116) training jobs. PiperOrigin-RevId: 217407558 --- tensorflow/python/estimator/estimator.py | 6 ++ tensorflow/python/estimator/estimator_test.py | 61 +++++++++++++++++++ 2 files changed, 67 insertions(+) diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py index e6d82f0db7..8b957288c3 100644 --- a/tensorflow/python/estimator/estimator.py +++ b/tensorflow/python/estimator/estimator.py @@ -1424,7 +1424,13 @@ class Estimator(object): # evaluations. save_summary_steps = self._config.save_summary_steps log_step_count_steps = self._config.log_step_count_steps + + # Check existence of appropriate cluster spec fields, as well as master and + # worker nodes. As master also performs evaluation, summary writing must + # occur on a different node. The presence of a worker is also checked to + # prevent reassigning hooks for single-replica jobs with just a master node. if (self._config.cluster_spec and self._config.cluster_spec.jobs and + (run_config.TaskType.WORKER in self._config.cluster_spec.jobs) and (run_config.TaskType.MASTER in self._config.cluster_spec.jobs)): # Update config values to prevent the default hooks from being created on # the master or other workers. diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py index 246dfb1a4b..c26b3e6509 100644 --- a/tensorflow/python/estimator/estimator_test.py +++ b/tensorflow/python/estimator/estimator_test.py @@ -1063,6 +1063,67 @@ class EstimatorTrainTest(test.TestCase): self.assertEqual(0, mock_sess.call_args[1]['save_summaries_steps']) self.assertIsNone(mock_sess.call_args[1]['log_step_count_steps']) + def test_master_hooks_single_replica(self): + tf_config = json.dumps({ + 'cluster': { + run_config.TaskType.MASTER: ['localhost:1234'] + }, + 'task': { + 'type': run_config.TaskType.MASTER, + 'index': 0 + } + }) + with test.mock.patch.dict('os.environ', {'TF_CONFIG': tf_config}): + est = estimator.Estimator( + model_fn=model_fn_global_step_incrementer, + config=run_config.RunConfig( + save_summary_steps=100, log_step_count_steps=200)) + + with test.mock.patch.object(training, + 'MonitoredTrainingSession') as mock_sess: + est.train(dummy_input_fn, steps=1) + self.assertFalse( + any( + isinstance(hook, basic_session_run_hooks.SummarySaverHook) + for hook in mock_sess.call_args[1]['hooks'])) + self.assertFalse( + any( + isinstance(hook, basic_session_run_hooks.StepCounterHook) + for hook in mock_sess.call_args[1]['hooks'])) + self.assertEqual(100, mock_sess.call_args[1]['save_summaries_steps']) + self.assertEqual(200, mock_sess.call_args[1]['log_step_count_steps']) + + def test_master_hooks_single_replica_with_ps(self): + tf_config = json.dumps({ + 'cluster': { + run_config.TaskType.MASTER: ['localhost:1234'], + run_config.TaskType.PS: ['localhost: 1235'], + }, + 'task': { + 'type': run_config.TaskType.MASTER, + 'index': 0 + } + }) + with test.mock.patch.dict('os.environ', {'TF_CONFIG': tf_config}): + est = estimator.Estimator( + model_fn=model_fn_global_step_incrementer, + config=run_config.RunConfig( + save_summary_steps=100, log_step_count_steps=200)) + + with test.mock.patch.object(training, + 'MonitoredTrainingSession') as mock_sess: + est.train(dummy_input_fn, steps=1) + self.assertFalse( + any( + isinstance(hook, basic_session_run_hooks.SummarySaverHook) + for hook in mock_sess.call_args[1]['hooks'])) + self.assertFalse( + any( + isinstance(hook, basic_session_run_hooks.StepCounterHook) + for hook in mock_sess.call_args[1]['hooks'])) + self.assertEqual(100, mock_sess.call_args[1]['save_summaries_steps']) + self.assertEqual(200, mock_sess.call_args[1]['log_step_count_steps']) + def _model_fn_with_eval_metric_ops(features, labels, mode, params): _, _ = features, labels -- GitLab From 20b53f7fe512a022ccbf97c71da4bd49f2fd5a04 Mon Sep 17 00:00:00 2001 From: Todd Wang Date: Sat, 20 Oct 2018 18:02:12 -0700 Subject: [PATCH 011/873] Fix triggering of asynchronous checkpoints. (#23138) PiperOrigin-RevId: 217570792 --- .../contrib/tpu/python/tpu/async_checkpoint.py | 15 ++++++--------- 1 file changed, 6 insertions(+), 9 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/async_checkpoint.py b/tensorflow/contrib/tpu/python/tpu/async_checkpoint.py index 20b7ba0997..700598d2f4 100644 --- a/tensorflow/contrib/tpu/python/tpu/async_checkpoint.py +++ b/tensorflow/contrib/tpu/python/tpu/async_checkpoint.py @@ -114,15 +114,12 @@ class AsyncCheckpointSaverHook(basic_session_run_hooks.CheckpointSaverHook): return SessionRunArgs(self._global_step_tensor) def after_run(self, run_context, run_values): - stale_global_step = run_values.results - if self._timer.should_trigger_for_step(stale_global_step + - self._steps_per_run): - # get the real value after train op. - global_step = run_context.session.run(self._global_step_tensor) - if self._timer.should_trigger_for_step(global_step): - self._timer.update_last_triggered_step(global_step) - if self._save(run_context.session, global_step): - run_context.request_stop() + global_step = run_context.session.run(self._global_step_tensor) + if self._timer.should_trigger_for_step(global_step): + self._timer.update_last_triggered_step(global_step) + logging.info("Triggering checkpoint. %s", global_step) + if self._save(run_context.session, global_step): + run_context.request_stop() def end(self, session): if self._save_thread: -- GitLab From 238bf3f5a503227befb15ba3dd8a861eb30c6f5c Mon Sep 17 00:00:00 2001 From: Todd Wang Date: Sat, 20 Oct 2018 18:22:35 -0700 Subject: [PATCH 012/873] Async checkpointing: Save the graph in a background thread. (#23139) PiperOrigin-RevId: 217747382 --- .../contrib/tpu/python/tpu/async_checkpoint.py | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/async_checkpoint.py b/tensorflow/contrib/tpu/python/tpu/async_checkpoint.py index 700598d2f4..78253d83fc 100644 --- a/tensorflow/contrib/tpu/python/tpu/async_checkpoint.py +++ b/tensorflow/contrib/tpu/python/tpu/async_checkpoint.py @@ -69,6 +69,7 @@ class AsyncCheckpointSaverHook(basic_session_run_hooks.CheckpointSaverHook): raise ValueError("You cannot provide both saver and scaffold.") self._saver = saver self._save_thread = None + self._write_graph_thread = None self._checkpoint_dir = checkpoint_dir self._save_path = os.path.join(checkpoint_dir, checkpoint_basename) self._scaffold = scaffold @@ -97,9 +98,13 @@ class AsyncCheckpointSaverHook(basic_session_run_hooks.CheckpointSaverHook): # We do write graph and saver_def at the first call of before_run. # We cannot do this in begin, since we let other hooks to change graph and # add variables in begin. Graph is finalized after all begin calls. - training_util.write_graph( - ops.get_default_graph().as_graph_def(add_shapes=True), - self._checkpoint_dir, "graph.pbtxt") + def _write_graph_fn(self): + training_util.write_graph( + ops.get_default_graph().as_graph_def(add_shapes=True), + self._checkpoint_dir, "graph.pbtxt") + self._write_graph_thread = threading.Thread(target=_write_graph_fn) + self._write_graph_thread.start() + saver_def = self._get_saver().saver_def if self._get_saver() else None graph = ops.get_default_graph() meta_graph_def = meta_graph.create_meta_graph_def( @@ -125,6 +130,9 @@ class AsyncCheckpointSaverHook(basic_session_run_hooks.CheckpointSaverHook): if self._save_thread: logging.info("Waiting for any pending checkpoints to finish.") self._save_thread.join() + if self._write_graph_thread: + logging.info("Waiting for any pending write_graph to finish.") + self._write_graph_thread.join() last_step = session.run(self._global_step_tensor) -- GitLab From e40642fb03f96881c6e046e8b84606f29ab5d2b1 Mon Sep 17 00:00:00 2001 From: Todd Wang Date: Sat, 20 Oct 2018 18:43:02 -0700 Subject: [PATCH 013/873] Support fp16 types in ScatterNd GPU version (#23141) PiperOrigin-RevId: 217749577 --- tensorflow/core/kernels/scatter_nd_op.cc | 4 +- .../kernel_tests/scatter_nd_ops_test.py | 176 +++++++++--------- 2 files changed, 88 insertions(+), 92 deletions(-) diff --git a/tensorflow/core/kernels/scatter_nd_op.cc b/tensorflow/core/kernels/scatter_nd_op.cc index 2f8aede427..fd54c6d6d7 100644 --- a/tensorflow/core/kernels/scatter_nd_op.cc +++ b/tensorflow/core/kernels/scatter_nd_op.cc @@ -297,8 +297,7 @@ TF_CALL_bool(REGISTER_SCATTER_ND_CPU); 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_GPU_NUMBER_TYPES(REGISTER_SCATTER_ND_ALL_GPU); TF_CALL_complex64(REGISTER_SCATTER_ND_ALL_GPU); TF_CALL_complex128(REGISTER_SCATTER_ND_ALL_GPU); @@ -587,7 +586,6 @@ namespace functor { 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); TF_CALL_complex128(DECLARE_GPU_SPECS); diff --git a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py index 4b92309e4d..49d83fb1d5 100644 --- a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py +++ b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py @@ -36,6 +36,9 @@ from tensorflow.python.ops import variables from tensorflow.python.platform import test +GRADIENT_TESTS_DTYPES = (dtypes.float16, dtypes.float32, dtypes.float64) + + def _AsType(v, vtype): return v.astype(vtype) if isinstance(v, np.ndarray) else vtype(v) @@ -144,9 +147,8 @@ 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.int32, np.float16, 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 +225,7 @@ 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.int32, np.float16, np.float32, np.float64): for itype in (np.int32, np.int64): self._VariableRankTest( np_scatter, tf_scatter, vtype, itype, repeat_indices=True) @@ -520,97 +522,93 @@ class ScatterNdTest(test.TestCase): self.scatter_nd(indices, updates, shape) def testGradientsRank2ElementUpdate(self): - indices = constant_op.constant([[0, 0], [1, 1]], dtype=dtypes.int32) - updates = constant_op.constant([1, 4], dtype=dtypes.float64) - shape = constant_op.constant([2, 2], dtype=dtypes.int32) - input_ = array_ops.zeros(shape, dtype=dtypes.float64) - outputs = self.scatter_nd(indices, updates, shape, input_) - - grad_vals = constant_op.constant([[1, 2], [3, 4]], dtype=dtypes.float64) - updates_grad, input_grad = gradients_impl.gradients( - [outputs], [updates, input_], [grad_vals]) - expected_updates_grad = np.array([1, 4], dtype=np.float64) - expected_input_grad = np.array([[1, 2], [3, 4]], dtype=np.float64) - with self.cached_session(): - self.assertAllEqual(expected_updates_grad, updates_grad.eval()) - if self.non_aliasing_add_test: - self.assertAllEqual(expected_input_grad, input_grad.eval()) + for dtype in GRADIENT_TESTS_DTYPES: + indices = constant_op.constant([[0, 0], [1, 1]], dtype=dtypes.int32) + updates = constant_op.constant([1, 4], dtype=dtype) + shape = constant_op.constant([2, 2], dtype=dtypes.int32) + input_ = array_ops.zeros(shape, dtype=dtype) + outputs = self.scatter_nd(indices, updates, shape, input_) + + grad_vals = constant_op.constant([[1, 2], [3, 4]], dtype=dtype) + updates_grad, input_grad = gradients_impl.gradients( + [outputs], [updates, input_], [grad_vals]) + expected_updates_grad = np.array([1, 4], dtype=dtype.as_numpy_dtype()) + expected_input_grad = np.array([[1, 2], [3, 4]], + dtype=dtype.as_numpy_dtype()) + with self.cached_session(): + self.assertAllEqual(expected_updates_grad, updates_grad.eval()) + if self.non_aliasing_add_test: + self.assertAllEqual(expected_input_grad, input_grad.eval()) def testGradientsRank2SliceUpdate(self): - indices = constant_op.constant([[1], [0]], dtype=dtypes.int32) - updates = constant_op.constant([[3, 4], [1, 2]], dtype=dtypes.float64) - shape = constant_op.constant([2, 2], dtype=dtypes.int32) - input_ = array_ops.zeros(shape, dtype=dtypes.float64) - outputs = self.scatter_nd(indices, updates, shape, input_) - - grad_vals = constant_op.constant([[3, 4], [1, 2]], dtype=dtypes.float64) - updates_grad, input_grad = gradients_impl.gradients( - [outputs], [updates, input_], [grad_vals]) - expected_updates_grad = np.array([[1, 2], [3, 4]], dtype=np.float64) - expected_input_grad = np.array([[3, 4], [1, 2]], dtype=np.float64) - with self.cached_session(): - self.assertAllEqual(expected_updates_grad, updates_grad.eval()) - if self.non_aliasing_add_test: - self.assertAllEqual(expected_input_grad, input_grad.eval()) + for dtype in GRADIENT_TESTS_DTYPES: + indices = constant_op.constant([[1], [0]], dtype=dtypes.int32) + updates = constant_op.constant([[3, 4], [1, 2]], dtype=dtype) + shape = constant_op.constant([2, 2], dtype=dtypes.int32) + input_ = array_ops.zeros(shape, dtype=dtype) + outputs = self.scatter_nd(indices, updates, shape, input_) + + grad_vals = constant_op.constant([[3, 4], [1, 2]], dtype=dtype) + updates_grad, input_grad = gradients_impl.gradients( + [outputs], [updates, input_], [grad_vals]) + expected_updates_grad = np.array([[1, 2], [3, 4]], + dtype=dtype.as_numpy_dtype()) + expected_input_grad = np.array([[3, 4], [1, 2]], + dtype=dtype.as_numpy_dtype()) + with self.cached_session(): + self.assertAllEqual(expected_updates_grad, updates_grad.eval()) + if self.non_aliasing_add_test: + self.assertAllEqual(expected_input_grad, input_grad.eval()) def testGradientsRank3SliceUpdate(self): - indices = constant_op.constant( - [[[0, 1], [1, 0]], [[0, 0], [1, 1]]], dtype=dtypes.int32) - updates = constant_op.constant( - [[[5, 7], [2, 4]], [[1, 3], [6, 8]]], dtype=dtypes.float64) - shape = constant_op.constant([2, 2, 2], dtype=dtypes.int32) - input_ = array_ops.zeros(shape, dtype=dtypes.float64) - outputs = self.scatter_nd(indices, updates, shape, input_) - - grad_vals = constant_op.constant( - [[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=dtypes.float64) - updates_grad, input_grad = gradients_impl.gradients( - [outputs], [updates, input_], [grad_vals]) - expected_updates_grad = np.array( - [[[3, 4], [5, 6]], [[1, 2], [7, 8]]], dtype=np.float64) - expected_input_grad = np.array( - [[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=np.float64) - with self.cached_session(): - self.assertAllEqual(expected_updates_grad, updates_grad.eval()) - if self.non_aliasing_add_test: - self.assertAllEqual(expected_input_grad, input_grad.eval()) + for dtype in GRADIENT_TESTS_DTYPES: + indices = constant_op.constant([[[0, 1], [1, 0]], [[0, 0], [1, 1]]], + dtype=dtypes.int32) + updates = constant_op.constant([[[5, 7], [2, 4]], [[1, 3], [6, 8]]], + dtype=dtype) + shape = constant_op.constant([2, 2, 2], dtype=dtypes.int32) + input_ = array_ops.zeros(shape, dtype=dtype) + outputs = self.scatter_nd(indices, updates, shape, input_) + + grad_vals = constant_op.constant([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], + dtype=dtype) + updates_grad, input_grad = gradients_impl.gradients( + [outputs], [updates, input_], [grad_vals]) + expected_updates_grad = np.array([[[3, 4], [5, 6]], [[1, 2], [7, 8]]], + dtype=dtype.as_numpy_dtype()) + expected_input_grad = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], + dtype=dtype.as_numpy_dtype()) + with self.cached_session(): + self.assertAllEqual(expected_updates_grad, updates_grad.eval()) + if self.non_aliasing_add_test: + self.assertAllEqual(expected_input_grad, input_grad.eval()) def testGradientsRank7SliceUpdate(self): - indices = constant_op.constant( - [[[ - [[[[0, 0, 0, 0, 0, 1], [0, 0, 1, 0, 0, 0]]]], - [[[[0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 1]]]] - ]]], dtype=dtypes.int32) - updates = constant_op.constant( - [[[ - [[[[5, 6], [2, 4]]]], - [[[[1, 3], [6, 8]]]] - ]]], dtype=dtypes.float64) - shape = constant_op.constant([1, 1, 2, 1, 1, 2, 2], dtype=dtypes.int32) - input_ = array_ops.zeros(shape, dtype=dtypes.float64) - outputs = self.scatter_nd(indices, updates, shape, input_) - - grad_vals = constant_op.constant( - [[[ - [[[[1, 2], [3, 4]]]], - [[[[5, 6], [7, 8]]]] - ]]], dtype=dtypes.float64) - updates_grad, input_grad = gradients_impl.gradients( - [outputs], [updates, input_], [grad_vals]) - expected_updates_grad = np.array( - [[[ - [[[[3, 4], [5, 6]]]], - [[[[1, 2], [7, 8]]]] - ]]], dtype=np.float64) - expected_input_grad = np.array( - [[[ - [[[[1, 2], [3, 4]]]], - [[[[5, 6], [7, 8]]]] - ]]], dtype=np.float64) - with self.cached_session(): - self.assertAllEqual(expected_updates_grad, updates_grad.eval()) - if self.non_aliasing_add_test: - self.assertAllEqual(expected_input_grad, input_grad.eval()) + for dtype in GRADIENT_TESTS_DTYPES: + indices = constant_op.constant( + [[[[[[[0, 0, 0, 0, 0, 1], [0, 0, 1, 0, 0, 0]]]], + [[[[0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 1]]]]]]], + dtype=dtypes.int32) + updates = constant_op.constant( + [[[[[[[5, 6], [2, 4]]]], [[[[1, 3], [6, 8]]]]]]], dtype=dtype) + shape = constant_op.constant([1, 1, 2, 1, 1, 2, 2], dtype=dtypes.int32) + input_ = array_ops.zeros(shape, dtype=dtype) + outputs = self.scatter_nd(indices, updates, shape, input_) + + grad_vals = constant_op.constant( + [[[[[[[1, 2], [3, 4]]]], [[[[5, 6], [7, 8]]]]]]], dtype=dtype) + updates_grad, input_grad = gradients_impl.gradients( + [outputs], [updates, input_], [grad_vals]) + expected_updates_grad = np.array( + [[[[[[[3, 4], [5, 6]]]], [[[[1, 2], [7, 8]]]]]]], + dtype=dtype.as_numpy_dtype()) + expected_input_grad = np.array( + [[[[[[[1, 2], [3, 4]]]], [[[[5, 6], [7, 8]]]]]]], + dtype=dtype.as_numpy_dtype()) + with self.cached_session(): + self.assertAllEqual(expected_updates_grad, updates_grad.eval()) + if self.non_aliasing_add_test: + self.assertAllEqual(expected_input_grad, input_grad.eval()) def testScatterNdRepatedIndicesAdd(self): indices = array_ops.zeros([100000, 1], dtypes.int32) -- GitLab From 3f9564a8b901c94eab2a21a764d8e177a45af12f Mon Sep 17 00:00:00 2001 From: Anna R Date: Mon, 22 Oct 2018 14:12:18 -0700 Subject: [PATCH 014/873] Merging confusion_matrix naming --- tensorflow/python/ops/confusion_matrix.py | 3 +-- tensorflow/tools/api/golden/v1/tensorflow.train.pbtxt | 4 ---- 2 files changed, 1 insertion(+), 6 deletions(-) diff --git a/tensorflow/python/ops/confusion_matrix.py b/tensorflow/python/ops/confusion_matrix.py index 8259142456..3c55ae68ac 100644 --- a/tensorflow/python/ops/confusion_matrix.py +++ b/tensorflow/python/ops/confusion_matrix.py @@ -90,8 +90,7 @@ def remove_squeezable_dimensions( return labels, predictions -@tf_export('train.confusion_matrix', 'confusion_matrix') -@deprecation.deprecated_endpoints('confusion_matrix') +@tf_export('confusion_matrix') def confusion_matrix(labels, predictions, num_classes=None, dtype=dtypes.int32, name=None, weights=None): """Computes the confusion matrix from predictions and labels. diff --git a/tensorflow/tools/api/golden/v1/tensorflow.train.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.train.pbtxt index 45c81fdd3b..9f35395284 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.train.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.train.pbtxt @@ -272,10 +272,6 @@ tf_module { name: "checkpoint_exists" argspec: "args=[\'checkpoint_prefix\'], varargs=None, keywords=None, defaults=None" } - member_method { - name: "confusion_matrix" - argspec: "args=[\'labels\', \'predictions\', \'num_classes\', \'dtype\', \'name\', \'weights\'], varargs=None, keywords=None, defaults=[\'None\', \"\", \'None\', \'None\'], " - } member_method { name: "cosine_decay" argspec: "args=[\'learning_rate\', \'global_step\', \'decay_steps\', \'alpha\', \'name\'], varargs=None, keywords=None, defaults=[\'0.0\', \'None\'], " -- GitLab From 9fa2e774d3aa3f53592cf5e0d3fe26cb40e3d6a1 Mon Sep 17 00:00:00 2001 From: Anna Revinskaya Date: Mon, 22 Oct 2018 18:47:25 -0700 Subject: [PATCH 015/873] Removed unused import --- tensorflow/python/ops/confusion_matrix.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tensorflow/python/ops/confusion_matrix.py b/tensorflow/python/ops/confusion_matrix.py index 3c55ae68ac..c09154129f 100644 --- a/tensorflow/python/ops/confusion_matrix.py +++ b/tensorflow/python/ops/confusion_matrix.py @@ -26,7 +26,6 @@ from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import sparse_ops -from tensorflow.python.util import deprecation from tensorflow.python.util.tf_export import tf_export -- GitLab From 185ae29da792ee8d42fa153e819c75787717174e Mon Sep 17 00:00:00 2001 From: Anna Revinskaya Date: Mon, 22 Oct 2018 19:06:15 -0700 Subject: [PATCH 016/873] Update V2 golden as well --- tensorflow/tools/api/golden/v2/tensorflow.train.pbtxt | 4 ---- 1 file changed, 4 deletions(-) diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.pbtxt index 7e980fe44d..cb6da5088b 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.train.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.pbtxt @@ -252,10 +252,6 @@ tf_module { name: "checkpoint_exists" argspec: "args=[\'checkpoint_prefix\'], varargs=None, keywords=None, defaults=None" } - member_method { - name: "confusion_matrix" - argspec: "args=[\'labels\', \'predictions\', \'num_classes\', \'dtype\', \'name\', \'weights\'], varargs=None, keywords=None, defaults=[\'None\', \"\", \'None\', \'None\'], " - } member_method { name: "cosine_decay" argspec: "args=[\'learning_rate\', \'global_step\', \'decay_steps\', \'alpha\', \'name\'], varargs=None, keywords=None, defaults=[\'0.0\', \'None\'], " -- GitLab From da1b48ddd04875995098f3c5c3fe0740b72518b8 Mon Sep 17 00:00:00 2001 From: Todd Wang Date: Wed, 24 Oct 2018 16:39:49 -0700 Subject: [PATCH 017/873] Declare that stateless random ops are not differentiable in C++ code. (#23227) PiperOrigin-RevId: 215935319 --- tensorflow/core/BUILD | 1 + tensorflow/core/ops/stateless_random_grad.cc | 23 ++++++++++++++++++++ 2 files changed, 24 insertions(+) create mode 100644 tensorflow/core/ops/stateless_random_grad.cc diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 6a3ee3c1cb..900a0e11c4 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -1242,6 +1242,7 @@ cc_library( srcs = [ "ops/math_grad.cc", "ops/random_grad.cc", + "ops/stateless_random_grad.cc", ], linkstatic = 1, # Needed since alwayslink is broken in bazel b/27630669 visibility = ["//visibility:public"], diff --git a/tensorflow/core/ops/stateless_random_grad.cc b/tensorflow/core/ops/stateless_random_grad.cc new file mode 100644 index 0000000000..331e1d0152 --- /dev/null +++ b/tensorflow/core/ops/stateless_random_grad.cc @@ -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. +==============================================================================*/ + +#include "tensorflow/core/framework/function.h" + +namespace tensorflow { +REGISTER_OP_NO_GRADIENT("StatelessRandomUniform"); +REGISTER_OP_NO_GRADIENT("StatelessRandomNormal"); +REGISTER_OP_NO_GRADIENT("StatelessTruncatedNormal"); +REGISTER_OP_NO_GRADIENT("StatelessMultinomial"); +} // end namespace tensorflow -- GitLab From e72c9ebe78a119715541f40ea99b1a8c89639968 Mon Sep 17 00:00:00 2001 From: Todd Wang Date: Wed, 24 Oct 2018 17:46:03 -0700 Subject: [PATCH 018/873] 1.12.0-rc2 cherry-pick request: Various XLA scatter improvements. (#23235) * [XLA] Update Tf2Xla bridge to use Scatter HLO. PiperOrigin-RevId: 215687800 * [XLA:GPU] Add an implementation of scatter for GPU This simple has a kernel that runs on every element of the updates tensor, figure out the right indices to perform the update, and applies it with an atomic operation. Currently we emit a CAS for plain (i.e. non-add) updates, which is inefficient. Also TuplePointsToAnalysis doesn't know that it should alias the operand and output buffers of a scatter, which would avoid a copy. PiperOrigin-RevId: 216412467 * [XLA] Allow scatter to share the operand buffer with the output This avoids a copy. PiperOrigin-RevId: 216437329 * [XLA:GPU] Elide the SequentialThunk when emitting scatter with no copy We have a 1-element thunk sequence if we're not copying. That's still two thunks and hlo profiling gets confused if it sees two thunks for the same instruction and one of them claims to be the whole instruction. PiperOrigin-RevId: 216448063 * [XLA:GPU] Allow input fusion into scatter We fuse everything into the scatter now, and emit two kernels. The first kernel fills the output buffer with the computation fused into the scatter operand. The second kernel is a regular scatter, which also contains the fused operations from the updates and scatter_indices inputs. PiperOrigin-RevId: 216624225 * [XLA:GPU] Adding a test case for Scatter where GPU implementation fails. PiperOrigin-RevId: 216798034 * [XLA:GPU] Fix scatter oob check computation This was comparing the index after adding it to the window, and then comparing against the window dimension. This means that the bounds check was only correct for the first element of a window. Instead compare the scatter index, which is the same for all elements of a window. PiperOrigin-RevId: 216921512 * [XLA:GPU] Elide tuple roots of the entry computation The tuple buffer is never read, so stop emitting code to fill it. A typical root tuple consists of a H2D memcpy and a host callback, both of which are somewhat slow. This helps tiny models and inference benchmarks, where the host/device syncs can be a significant part of the runtime of the entire computation. PiperOrigin-RevId: 216968475 --- tensorflow/compiler/tf2xla/lib/scatter.cc | 213 +++++++++------- tensorflow/compiler/tf2xla/lib/scatter.h | 6 +- tensorflow/compiler/xla/client/xla_builder.cc | 3 + tensorflow/compiler/xla/service/gpu/BUILD | 1 - .../xla/service/gpu/instruction_fusion.cc | 9 +- .../service/gpu/instruction_fusion_test.cc | 39 +++ .../xla/service/gpu/ir_emitter_unnested.cc | 241 +++++++++++++++++- .../xla/service/gpu/ir_emitter_unnested.h | 9 + .../xla/service/gpu/nvptx_compiler.cc | 3 - .../xla/service/hlo_dataflow_analysis.cc | 1 + .../xla/service/hlo_dataflow_analysis_test.cc | 38 +++ .../compiler/xla/service/hlo_matchers.h | 1 + tensorflow/compiler/xla/service/hlo_module.cc | 3 +- tensorflow/compiler/xla/service/inliner.cc | 32 ++- .../compiler/xla/service/inliner_test.cc | 30 +++ .../compiler/xla/service/layout_assignment.cc | 2 +- .../xla/service/tuple_points_to_analysis.cc | 1 + .../service/tuple_points_to_analysis_test.cc | 38 +++ tensorflow/compiler/xla/tests/scatter_test.cc | 62 +++++ 19 files changed, 613 insertions(+), 119 deletions(-) diff --git a/tensorflow/compiler/tf2xla/lib/scatter.cc b/tensorflow/compiler/tf2xla/lib/scatter.cc index 38dfde165d..2b1c2ced92 100644 --- a/tensorflow/compiler/tf2xla/lib/scatter.cc +++ b/tensorflow/compiler/tf2xla/lib/scatter.cc @@ -38,12 +38,10 @@ xla::StatusOr XlaScatter( combiner, xla::XlaBuilder* builder) { TF_ASSIGN_OR_RETURN(xla::Shape buffer_shape, builder->GetShape(buffer)); - TF_RETURN_IF_ERROR(builder->GetShape(updates).status()); + TF_ASSIGN_OR_RETURN(xla::Shape updates_shape, builder->GetShape(updates)); TF_ASSIGN_OR_RETURN(xla::Shape indices_shape, builder->GetShape(indices)); absl::Span indices_dims = xla::AsInt64Slice(indices_shape.dimensions()); - absl::Span buffer_dims = - xla::AsInt64Slice(buffer_shape.dimensions()); // If the indices are N-dimensional, the minor dimension of indices contains // the indices to update. Otherwise the indices are all scalars. @@ -81,104 +79,129 @@ xla::StatusOr XlaScatter( } } - // Shape of the non-indexed dimensions of the buffer. - std::vector buffer_shape_post_axes( - buffer_dims.begin() + num_index_dims, buffer_dims.end()); - - // Flatten the major dimensions of indices and updates into a single dimension - // for ease of iteration. - std::vector flat_indices_shape({num_indices}); - if (indices_are_vectors) { - flat_indices_shape.push_back(num_index_dims); + // Example of a 1-D scatter that updates two [3,1] tensors in a tensor of + // shape [3,3]: + // NOTE: ***This case will not be generated by any of the tf.scatter ops.*** + // + // operand = s32[3,3] parameter(0) + // indices = s32[2] parameter(1) + // updates = s32[3,2] parameter(2) + // scatter = s32[3,3] scatter(operand, indices, updates), + // to_apply=update_computation, + // update_window_dims={0}, + // inserted_window_dims={1}, + // scatter_dims_to_operand_dims={1}, + // index_vector_dim=1 + // + // + // Example of a 1-D scatter that updates two [1,3] tensors in a tensor of + // shape [3,3]: + // + // operand = s32[3,3] parameter(0) + // indices = s32[2] parameter(1) + // updates = s32[2,3] parameter(2) + // scatter = s32[3,3] scatter(operand, indices, updates), + // to_apply=update_computation, + // update_window_dims={1}, + // inserted_window_dims={0}, + // scatter_dims_to_operand_dims={0}, + // index_vector_dim=1 + // + // + // Example of an N-D scatter updating slices of shape [1,1,2] in a tensor of + // shape [3,3,2] + // + // operand = s32[3,3,2] parameter(0) + // indices = s32[2,2] parameter(1) + // updates = s32[2,2] parameter(2) + // scatter = s32[3,3,2] scatter(operand, indices, updates), + // to_apply=update_computation, + // update_window_dims={1}, + // inserted_window_dims={0,1}, + // scatter_dims_to_operand_dims={0,1}, + // index_vector_dim=1 + // + // + // Example of a scatter updating slices of shape [] in a tensor of shape [1,1] + // + // operand = s32[1,1] parameter(0) + // indices = s32[1] parameter(1) + // updates = s32[1] parameter(2) + // scatter = s32[1,1] scatter(operand, indices, updates), + // to_apply=update_computation, + // update_window_dims={}, + // inserted_window_dims={0,1}, + // scatter_dims_to_operand_dims={0}, + // index_vector_dim=1 + // Note that updates operand would be broadcasted into [1] in this case. + // + + xla::ScatterDimensionNumbers dim_numbers; + dim_numbers.set_index_vector_dim(indices_are_vectors + ? indices_shape.dimensions_size() - 1 + : indices_shape.dimensions_size()); + + int64 updates_rank = xla::ShapeUtil::Rank(updates_shape); + int64 buffer_rank = xla::ShapeUtil::Rank(buffer_shape); + int64 num_window_dims_in_updates = buffer_rank - num_index_dims; + + // If the rank of `updates` is 0 and does not match the expected rank of + // updates, broadcast `updates` to the expected shape of updates. + auto new_updates = updates; + std::vector expected_updates_dims(indices_dims.begin(), + indices_dims.end()); + for (int64 dim = num_index_dims; dim < buffer_rank; ++dim) { + expected_updates_dims.push_back(buffer_shape.dimensions(dim)); + } + int64 expected_updates_rank = expected_updates_dims.size(); + if (updates_rank == 0 && expected_updates_rank != 0) { + new_updates = xla::Broadcast(updates, expected_updates_dims); + TF_ASSIGN_OR_RETURN(updates_shape, builder->GetShape(new_updates)); + updates_rank = xla::ShapeUtil::Rank(updates_shape); } - std::vector flat_updates_shape({num_indices}); - flat_updates_shape.insert(flat_updates_shape.end(), - buffer_shape_post_axes.begin(), - buffer_shape_post_axes.end()); - - // Construct the initial values of the loop-carried Tensors. - auto flat_indices = xla::Reshape(indices, flat_indices_shape); - auto flat_updates = xla::Reshape(updates, flat_updates_shape); - auto init = {flat_indices, flat_updates, buffer}; - - // Constructs the loop body. The implementation of scatter is essentially: - // for i in range(num_indices): - // index = dynamic-slice(indices, i) - // update = dynamic-slice(updates, i) - // buffer = dynamic-update-slice(buffer, update, index) - auto body_fn = [&](xla::XlaOp i, absl::Span loop_vars, - xla::XlaBuilder* body_builder) { - auto indices = loop_vars[0]; - auto updates = loop_vars[1]; - auto buffer = loop_vars[2]; - - auto zero_index = xla::ConstantLiteral( - body_builder, xla::LiteralUtil::Zero(indices_shape.element_type())); - - // Slice the i-th index from the indices array. - xla::XlaOp index; - auto indices_offset = xla::Reshape(i, {1}); - if (indices_are_vectors) { - indices_offset = xla::Pad(indices_offset, zero_index, - xla::MakeEdgePaddingConfig({{0, 1}})); - - index = xla::DynamicSlice(indices, indices_offset, {1, num_index_dims}); - index = xla::Collapse(index, {0, 1}); - } else { - index = xla::DynamicSlice(indices, indices_offset, {1}); + if (updates_rank > 0) { + for (int64 i = (updates_rank - num_window_dims_in_updates); + i < updates_rank; ++i) { + dim_numbers.add_update_window_dims(i); } + } - // Discard updates with negative indices, since some users expect this. - auto index_in_range = xla::ReduceAll( - xla::Le(zero_index, index), xla::ConstantR0(body_builder, true), - xla::CreateScalarAndComputation(xla::PRED, body_builder)); - - // Make the index in bounds to prevent implementation defined behavior. - index = xla::Max(index, zero_index); - index = xla::Pad( - index, zero_index, - xla::MakeEdgePaddingConfig({{0, buffer_shape_post_axes.size()}})); - - // Slice the i-th index from the updates array. - auto updates_offset = xla::Reshape(i, {1}); - updates_offset = xla::Pad( - updates_offset, zero_index, - xla::MakeEdgePaddingConfig({{0, buffer_shape_post_axes.size()}})); - std::vector flat_updates_slice_shape({1}); - flat_updates_slice_shape.insert(flat_updates_slice_shape.end(), - buffer_shape_post_axes.begin(), - buffer_shape_post_axes.end()); - auto update = - xla::DynamicSlice(updates, updates_offset, flat_updates_slice_shape); - - // Unflatten the major (iteration) dimensions of the slice to their - // original shape. - std::vector updates_slice_shape(num_index_dims, 1); - updates_slice_shape.insert(updates_slice_shape.end(), - buffer_shape_post_axes.begin(), - buffer_shape_post_axes.end()); - update = xla::Reshape(update, updates_slice_shape); - - // Apply the update to the buffer. If there is a combiner, use it to merge - // the current values with the update. - auto current_value = xla::DynamicSlice(buffer, index, updates_slice_shape); + for (int64 i = 0; i < num_index_dims; ++i) { + dim_numbers.add_inserted_window_dims(i); + dim_numbers.add_scatter_dims_to_operand_dims(i); + } + + // Build the combiner computation. + xla::XlaComputation combiner_computation; + { + xla::XlaBuilder cb("scatter-combiner"); + auto xla_scalar_shape = + xla::ShapeUtil::MakeShape(buffer_shape.element_type(), {}); + auto p0 = xla::Parameter(&cb, 0, xla_scalar_shape, "p0"); + auto p1 = xla::Parameter(&cb, 1, xla_scalar_shape, "p1"); if (combiner) { - update = combiner(current_value, update, body_builder); + combiner(p0, p1, &cb); } - // Use the current value instead of the update if the index is out of - // bounds. - update = xla::Select(index_in_range, update, current_value); - // Apply the update. - buffer = xla::DynamicUpdateSlice(buffer, update, index); - - return std::vector{indices, updates, buffer}; - }; - - TF_ASSIGN_OR_RETURN(auto outputs, - XlaForEachIndex(num_indices, indices_shape.element_type(), - body_fn, init, "scatter", builder)); - return outputs[2]; + combiner_computation = cb.Build().ConsumeValueOrDie(); + } + + VLOG(3) << "Scatter op:"; + VLOG(3) << " Input: " << xla::ShapeUtil::HumanString(buffer_shape); + VLOG(3) << " Indices: " << xla::ShapeUtil::HumanString(indices_shape); + VLOG(3) << " Updates: " << xla::ShapeUtil::HumanString(updates_shape); + VLOG(3) << " Scatter Dimension Numbers: "; + VLOG(3) << " index_vector_dim: " << dim_numbers.index_vector_dim(); + VLOG(3) << " update_window_dims: [" + << absl::StrJoin(dim_numbers.update_window_dims(), ",") << "]"; + VLOG(3) << " inserted_window_dims: [" + << absl::StrJoin(dim_numbers.inserted_window_dims(), ",") << "]"; + VLOG(3) << " scatter_dims_to_operand_dims: [" + << absl::StrJoin(dim_numbers.scatter_dims_to_operand_dims(), ",") + << "]"; + + return xla::Scatter(buffer, indices, new_updates, combiner_computation, + dim_numbers); } } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/scatter.h b/tensorflow/compiler/tf2xla/lib/scatter.h index 13a5f1b850..4cf478c4b9 100644 --- a/tensorflow/compiler/tf2xla/lib/scatter.h +++ b/tensorflow/compiler/tf2xla/lib/scatter.h @@ -34,7 +34,11 @@ namespace tensorflow { // Otherwise, `indices_are_vectors`, then indices are multidimensional and the // minor dimension of `indices` represents a vector of indices. // -// If any indices are negative, the corresponding update is discarded. +// If `updates` is a scalar, then it will be broadcasted into the expected shape +// of updates. +// +// If any part of the update region is out-of-bounds, the corresponding update +// is discarded. // // If a `combiner` is provided, updates are combined with the existing values in // the buffer using the combiner function. Otherwise, the updates replace the diff --git a/tensorflow/compiler/xla/client/xla_builder.cc b/tensorflow/compiler/xla/client/xla_builder.cc index e0ec91dba1..d196252db1 100644 --- a/tensorflow/compiler/xla/client/xla_builder.cc +++ b/tensorflow/compiler/xla/client/xla_builder.cc @@ -208,6 +208,9 @@ void XlaBuilder::IsConstantVisitor(const int64 op_handle, case HloOpcode::kWhile: // TODO(b/32495713): We aren't checking the condition and body // computations themselves. + case HloOpcode::kScatter: + // TODO(b/32495713): We aren't checking the embedded computation in + // Scatter. case HloOpcode::kSend: case HloOpcode::kRecv: case HloOpcode::kParameter: diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD index a838464cae..dde0cc7459 100644 --- a/tensorflow/compiler/xla/service/gpu/BUILD +++ b/tensorflow/compiler/xla/service/gpu/BUILD @@ -704,7 +704,6 @@ cc_library( "//tensorflow/compiler/xla/service:llvm_compiler", "//tensorflow/compiler/xla/service:reduce_precision_insertion", "//tensorflow/compiler/xla/service:reshape_mover", - "//tensorflow/compiler/xla/service:scatter_expander", "//tensorflow/compiler/xla/service:transpose_folding", "//tensorflow/compiler/xla/service:tuple_simplifier", "//tensorflow/compiler/xla/service:while_loop_constant_sinking", diff --git a/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc b/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc index b61f038739..1d66787d89 100644 --- a/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc +++ b/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc @@ -47,6 +47,7 @@ bool IsFusible(const HloInstruction& hlo) { hlo.opcode() == HloOpcode::kReduce || hlo.opcode() == HloOpcode::kReduceWindow || hlo.opcode() == HloOpcode::kReshape || + hlo.opcode() == HloOpcode::kScatter || hlo.opcode() == HloOpcode::kSlice || hlo.opcode() == HloOpcode::kTranspose; } @@ -223,6 +224,11 @@ bool GpuInstructionFusion::ShouldFuse(HloInstruction* consumer, return false; } + // Scatter is only supported at the root of a kInput fusion. + if (producer->opcode() == HloOpcode::kScatter) { + return false; + } + // Do not fuse into reduce input fusions if the resulting kernel would suffer // from poor data locality (due to unfriendly input layouts). if (IsInputFusibleReduction(*consumer) && @@ -285,7 +291,8 @@ bool GpuInstructionFusion::ShouldFuseIntoMultiOutput(HloInstruction* consumer, HloInstruction::FusionKind GpuInstructionFusion::ChooseKind( const HloInstruction* producer, const HloInstruction* consumer) { - if (IsReductionToVector(*consumer)) { + if (IsReductionToVector(*consumer) || + consumer->opcode() == HloOpcode::kScatter) { return HloInstruction::FusionKind::kInput; } if (producer->opcode() == HloOpcode::kDot || diff --git a/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc b/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc index 96bfe0c12e..fd9b7cee80 100644 --- a/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc +++ b/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc @@ -709,5 +709,44 @@ TEST_F(InstructionFusionTest, AvoidsLargeFusion) { } } +TEST_F(InstructionFusionTest, FuseIntoScatter) { + auto module = ParseHloString(R"( + HloModule test_module + + add { + lhs = f32[] parameter(0) + rhs = f32[] parameter(1) + ROOT add = f32[] add(lhs, rhs) + } + + ENTRY FuseIntoScatter { + p0 = s32[3,3] parameter(0) + operand = s32[3,3] add(p0, p0) + p1 = s32[2] parameter(1) + indices = s32[2] add(p1, p1) + p2 = s32[2,3] parameter(2) + updates = s32[2,3] add(p2, p2) + scatter = s32[3,3] scatter(operand, indices, updates), + to_apply=add, + update_window_dims={1}, + inserted_window_dims={0}, + scatter_dims_to_operand_dims={0}, + index_vector_dim=1 + ROOT add = s32[3,3] add(scatter, scatter) + })") + .ValueOrDie(); + + EXPECT_TRUE(GpuInstructionFusion(/*may_duplicate=*/true) + .Run(module.get()) + .ValueOrDie()); + + HloInstruction* root = module->entry_computation()->root_instruction(); + EXPECT_THAT(root, op::Add(op::Fusion(), op::Fusion())); + EXPECT_EQ(root->operand(0)->fusion_kind(), + HloInstruction::FusionKind::kInput); + EXPECT_THAT(root->operand(0)->fused_expression_root(), + op::Scatter(op::Add(), op::Add(), op::Add())); +} + } // namespace gpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index c792dd2ddb..2951f7a65f 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -493,13 +493,68 @@ Status IrEmitterUnnested::HandleFft(HloInstruction* fft) { Status IrEmitterUnnested::HandleFusion(HloInstruction* fusion) { HloInstruction* root = fusion->fused_expression_root(); - // HandleFusion specializes reduction from a multi-dimensional array to a 1D - // array. The specialized version requires a initializer thunk that - // initializes the output array to the initial value of the reduce. if (HloInstruction::FusionKind::kInput == fusion->fusion_kind()) { switch (root->opcode()) { + case HloOpcode::kScatter: { + std::vector> thunks; + // The initialization from 'operand' is using different loop bounds, so + // emit it in a separate kernel. Treat it like a loop fusion, writing to + // the output buffer. + { + int unroll_factor = ComputeMaxUnrollFactor(fusion); + thunks.push_back(BuildKernelThunk( + fusion, /*implements_whole_instruction=*/false, unroll_factor)); + + std::vector operand_parameter_arrays; + for (HloInstruction* operand : fusion->operands()) { + operand_parameter_arrays.push_back(GetIrArray(*operand, *fusion)); + } + GpuElementalIrEmitter operand_elemental_emitter( + hlo_module_config_, ir_emitter_context_->llvm_module(), &b_, + GetNestedComputer()); + FusedIrEmitter operand_fused_emitter(operand_parameter_arrays, + &operand_elemental_emitter); + TF_RETURN_IF_ERROR( + root->mutable_operand(0)->Accept(&operand_fused_emitter)); + + TF_RETURN_IF_ERROR(EmitTargetElementLoopInThunk( + *fusion, operand_fused_emitter.GetGenerator(root->operand(0)), + static_cast(thunks.back().get()))); + } + + // Now build the actual scatter, reading and writing to the freshly + // filled output buffer. + { + thunks.push_back( + BuildKernelThunk(fusion, + /*implements_whole_instruction=*/false)); + // Spin up a new fused emitter for the scatter kernel and emit it. + std::vector scatter_parameter_arrays; + for (HloInstruction* operand : fusion->operands()) { + scatter_parameter_arrays.push_back(GetIrArray(*operand, *fusion)); + } + GpuElementalIrEmitter scatter_elemental_emitter( + hlo_module_config_, ir_emitter_context_->llvm_module(), &b_, + GetNestedComputer()); + FusedIrEmitter scatter_fused_emitter(scatter_parameter_arrays, + &scatter_elemental_emitter); + TF_RETURN_IF_ERROR(root->Accept(&scatter_fused_emitter)); + TF_RETURN_IF_ERROR(EmitScatter( + thunks.back().get(), root, + /*scatter_indices_gen=*/ + scatter_fused_emitter.GetGenerator(root->operand(1)), + /*updates_gen=*/ + scatter_fused_emitter.GetGenerator(root->operand(2)))); + } + thunk_sequence_->emplace_back( + absl::make_unique(std::move(thunks), fusion)); + return Status::OK(); + } case HloOpcode::kTuple: case HloOpcode::kReduce: { + // HandleFusion specializes reduction from a multi-dimensional array to + // a 1D array. The specialized version requires a initializer thunk that + // initializes the output array to the initial value of the reduce. if (root->opcode() == HloOpcode::kReduce && ShapeUtil::IsTuple(root->shape())) { // TODO(b/112040122): Support variadic reduce. @@ -1672,6 +1727,14 @@ Status IrEmitterUnnested::HandleReduce(HloInstruction* reduce) { } Status IrEmitterUnnested::HandleTuple(HloInstruction* tuple) { + // For the root node of the entry computation we can elide writing the tuple + // buffer. We can always figure out the contents of the tuples from buffer + // assignment because we insert copies to ensure non-ambiguous output buffers. + // GpuExecutable never reads the tuple buffer. + if (tuple == + tuple->parent()->parent()->entry_computation()->root_instruction()) { + return Status::OK(); + } bool all_tuple_elements_have_buffer = absl::c_all_of(tuple->operands(), [&](HloInstruction* tuple_element) { return ir_emitter_context_->buffer_assignment() @@ -1958,6 +2021,178 @@ Status IrEmitterUnnested::HandleRng(HloInstruction* rng) { return Status::OK(); } +Status IrEmitterUnnested::HandleScatter(HloInstruction* scatter) { + const HloInstruction* operand = scatter->operand(0); + const HloInstruction* scatter_indices = scatter->operand(1); + const HloInstruction* updates = scatter->operand(2); + + std::vector> thunks; + + // Copy the operand into the output if it's not the same buffer already. + auto operand_buffer = GetAllocationSlice(*operand); + auto destination_buffer = GetAllocationSlice(*scatter); + if (operand_buffer != destination_buffer) { + thunks.push_back(absl::make_unique( + /*source_address=*/operand_buffer, + /*destination_buffer=*/destination_buffer, + /*mem_size=*/ShapeUtil::ByteSizeOf(operand->shape()), scatter)); + } + + thunks.push_back( + BuildKernelThunk(scatter, + /*implements_whole_instruction=*/thunks.empty())); + + TF_RETURN_IF_ERROR( + EmitScatter(thunks.back().get(), scatter, + /*scatter_indices_gen=*/ + [=](const IrArray::Index& index) { + return GetIrArray(*scatter_indices, *scatter) + .EmitReadArrayElement(index, &b_, "scatter_index"); + }, + /*updates_gen=*/ + [=](const IrArray::Index& index) { + return GetIrArray(*updates, *scatter) + .EmitReadArrayElement(index, &b_, "update"); + })); + + // Elide the sequential thunk if there's no copy. + if (thunks.size() == 1) { + thunk_sequence_->push_back(std::move(thunks[0])); + } else { + thunk_sequence_->emplace_back( + absl::make_unique(std::move(thunks), scatter)); + } + return Status::OK(); +} + +Status IrEmitterUnnested::EmitScatter( + Thunk* thunk, HloInstruction* scatter, + const llvm_ir::ElementGenerator& scatter_indices_gen, + const llvm_ir::ElementGenerator& updates_gen) { + const HloInstruction* operand = scatter->operand(0); + const HloInstruction* scatter_indices = scatter->operand(1); + const HloInstruction* updates = scatter->operand(2); + const ScatterDimensionNumbers& dim_numbers = + scatter->scatter_dimension_numbers(); + CHECK(ShapeUtil::Equal(scatter->shape(), operand->shape())); + + auto loop_body_emitter = [&](const IrArray::Index& index) -> Status { + std::vector raw_window_multidim; + std::vector input_scatter_multidim; + std::vector raw_window_bounds; + + // Partition the index into window indices and scatter indices. + for (int64 i = 0, e = index.size(); i != e; ++i) { + // For window indices also remember the window size, this comes in handy + // later. + if (absl::c_binary_search(dim_numbers.update_window_dims(), i)) { + raw_window_multidim.push_back(index[i]); + raw_window_bounds.push_back(updates->shape().dimensions(i)); + } else { + input_scatter_multidim.push_back(index[i]); + } + } + DCHECK_EQ(raw_window_multidim.size(), + dim_numbers.update_window_dims_size()); + + // Apply inserted_window_dims to the window dimensions. + int64 raw_window_multidim_idx = 0; + std::vector input_window_multidim; + std::vector input_window_bounds; + for (int64 i = 0, e = ShapeUtil::Rank(operand->shape()); i != e; ++i) { + if (absl::c_binary_search(dim_numbers.inserted_window_dims(), i)) { + input_window_bounds.push_back(1); // Trivial dimension. + input_window_multidim.push_back(index.GetConstantWithIndexType(0)); + } else { + input_window_bounds.push_back( + raw_window_bounds[raw_window_multidim_idx]); + input_window_multidim.push_back( + raw_window_multidim[raw_window_multidim_idx]); + ++raw_window_multidim_idx; + } + } + DCHECK_EQ(input_window_multidim.size(), ShapeUtil::Rank(operand->shape())); + + // Insert a 1 dimension at the end if index_vector_dim requests one. + Shape scatter_indices_shape = scatter_indices->shape(); + if (dim_numbers.index_vector_dim() == + ShapeUtil::Rank(scatter_indices_shape)) { + scatter_indices_shape.add_dimensions(1); + scatter_indices_shape.mutable_layout()->add_minor_to_major( + dim_numbers.index_vector_dim()); + } + + // Now load the indices corresponding to the current window from + // scatter_indices. + llvm_ir::IrArray::Index raw_scatter_index_index(input_scatter_multidim, + index.GetType()); + raw_scatter_index_index.InsertAt(dim_numbers.index_vector_dim(), nullptr); + llvm::Value* is_in_bounds = b_.getTrue(); + for (int64 i = 0, e = dim_numbers.scatter_dims_to_operand_dims_size(); + i != e; ++i) { + // Our index is stored along index_vector_dim, insert that into the lookup + // index into scatter_indices. + raw_scatter_index_index[dim_numbers.index_vector_dim()] = + raw_scatter_index_index.GetConstantWithIndexType(i); + + int64 operand_dim = dim_numbers.scatter_dims_to_operand_dims(i); + TF_ASSIGN_OR_RETURN( + llvm::Value* const loaded_scatter_index, + scatter_indices_gen(raw_scatter_index_index.SourceIndexOfReshape( + scatter_indices_shape, scatter_indices->shape(), &b_))); + // And add the index to our window index. This yields the output index. + llvm::Value* casted_scatter_index = + IntCast(loaded_scatter_index, index.GetType(), + /*isSigned=*/true); + llvm::Value* dim_offset = + Add(input_window_multidim[operand_dim], casted_scatter_index); + input_window_multidim[operand_dim] = dim_offset; + + // Also do the bounds check now. + int64 max_index = operand->shape().dimensions(operand_dim) - + input_window_bounds[operand_dim] + 1; + // is_in_bounds = index >= 0 && index < dim_size-window_size+1 + // --> index u< dim_size-window_size+1 + is_in_bounds = + And(is_in_bounds, ICmpULT(casted_scatter_index, + index.GetConstantWithIndexType(max_index))); + } + + llvm_ir::LlvmIfData if_window_in_bounds_data = llvm_ir::EmitIfThenElse( + is_in_bounds, "scatter.in_bounds", &b_, /*emit_else=*/false); + llvm_ir::SetToFirstInsertPoint(if_window_in_bounds_data.true_block, &b_); + // All done, now just read from the calculated input from the window, and do + // an atomic store to the calculated location in the output. + llvm_ir::IrArray::Index input_window_index(input_window_multidim, + index.GetType()); + HloInstruction* output_hlo = + scatter->IsFused() ? scatter->parent()->FusionInstruction() : scatter; + llvm::Value* output_address = + GetIrArray(*output_hlo, *output_hlo) + .EmitArrayElementAddress(input_window_index, &b_); + llvm::Value* input_address = Alloca(llvm_ir::PrimitiveTypeToIrType( + updates->shape().element_type(), module_)); + TF_ASSIGN_OR_RETURN(llvm::Value* const input_ir_value, updates_gen(index)); + Store(input_ir_value, input_address); + return EmitAtomicOperationForNestedComputation( + *scatter->to_apply(), output_address, input_address); + }; + + // Launch a kernel that reads every element in the updates tensor. We could + // also do one kernel per window instead if bounds checks turn out to be a + // bottleneck. + LaunchDimensions launch_dimensions = CalculateLaunchDimensions( + updates->shape(), ir_emitter_context_->device_description()); + UpdateLaunchDimensions(launch_dimensions, thunk, + ir_emitter_context_->llvm_module()); + + return ParallelLoopEmitter(loop_body_emitter, updates->shape(), + launch_dimensions, &b_) + .EmitLoop(IrName(scatter), + GetIndexTypeForKernel(scatter, launch_dimensions.launch_bound(), + &b_)); +} + Status IrEmitterUnnested::HandleSelect(HloInstruction* select) { thunk_sequence_->push_back( BuildKernelThunk(select, /*implements_whole_instruction=*/true)); diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h index bd5db72051..93f11c069a 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h @@ -76,6 +76,7 @@ class IrEmitterUnnested : public IrEmitter { Status HandleInfeed(HloInstruction* xla_infeed) override; Status HandleOutfeed(HloInstruction* outfeed) override; Status HandleRng(HloInstruction* random) override; + Status HandleScatter(HloInstruction* scatter) override; Status HandleSelect(HloInstruction* select) override; Status HandleSort(HloInstruction* sort) override; Status HandleTupleSelect(HloInstruction* tuple_select) override; @@ -184,6 +185,14 @@ class IrEmitterUnnested : public IrEmitter { absl::Span> extra_output_gens); + // Emits code for an in-place scatter, modifying `thunk`s launch dimensions in + // the process. `scatter` may be fused, scatter indices are taken from + // `scatter_indices_gen`, updates from`updates_gen`. The output buffer is + // expected to have the operand values in it already. + Status EmitScatter(Thunk* thunk, HloInstruction* scatter, + const llvm_ir::ElementGenerator& scatter_indices_gen, + const llvm_ir::ElementGenerator& updates_gen); + // Returns true if a 0-2-1 tiling algorithm is already used to emit the kernel // for the hlo instruction. bool CheckAndEmitHloWithTile021(HloInstruction* hlo); diff --git a/tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc b/tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc index b4ae2e42c7..89c5f2b128 100644 --- a/tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc +++ b/tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc @@ -75,7 +75,6 @@ limitations under the License. #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" #include "tensorflow/compiler/xla/service/reduce_precision_insertion.h" #include "tensorflow/compiler/xla/service/reshape_mover.h" -#include "tensorflow/compiler/xla/service/scatter_expander.h" #include "tensorflow/compiler/xla/service/transpose_folding.h" #include "tensorflow/compiler/xla/service/tuple_simplifier.h" #include "tensorflow/compiler/xla/service/while_loop_constant_sinking.h" @@ -176,8 +175,6 @@ Status OptimizeHloModule(HloModule* hlo_module, se::StreamExecutor* stream_exec, // elimination has to come after that pass. pipeline.AddPass(); - pipeline.AddPass(); - pass.AddPass( /*is_layout_sensitive=*/false, [](const Shape&, const Shape&) { return false; }); diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc index 44cde4a3d2..1f7d4205ab 100644 --- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc +++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc @@ -1072,6 +1072,7 @@ bool HloDataflowAnalysis::CanShareOperandBufferWithUser( } if (user->opcode() == HloOpcode::kDynamicUpdateSlice || + user->opcode() == HloOpcode::kScatter || user->opcode() == HloOpcode::kWhile) { // We eliminated other users in BufferLiveness::live_range_strictly_before, // so here we just need to check that the use is at operand index 0. diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc index 510d6360a1..d27786d160 100644 --- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc +++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc @@ -2283,6 +2283,44 @@ TEST_F(CanShareOperandBufferWithUserTest, DynamicUpdateSliceCanShare) { dataflow_analysis_->CanShareOperandBufferWithUser(starts, {}, dus, {})); } +TEST_F(CanShareOperandBufferWithUserTest, ScatterCanShare) { + const char* hlo_text = R"( + HloModule TensorFlowScatterV1 + + update_s32 (lhs: s32[], rhs: s32[]) -> s32[] { + lhs = s32[] parameter(0) + ROOT rhs = s32[] parameter(1) + } + + ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2] parameter(1) + updates = s32[2,3] parameter(2) + ROOT scatter = s32[3,3] scatter(operand, indices, updates), + to_apply=update_s32, + update_window_dims={1}, + inserted_window_dims={0}, + scatter_dims_to_operand_dims={0}, + index_vector_dim=1 + } + )"; + TF_ASSERT_OK_AND_ASSIGN(module_, ParseHloString(hlo_text)); + computation_ = module_->entry_computation(); + RunAnalysis(); + + HloInstruction* operand_param = computation_->parameter_instruction(0); + HloInstruction* indices_param = computation_->parameter_instruction(1); + HloInstruction* updates_param = computation_->parameter_instruction(2); + HloInstruction* scatter = computation_->root_instruction(); + + EXPECT_TRUE(dataflow_analysis_->CanShareOperandBufferWithUser( + operand_param, {}, scatter, {})); + EXPECT_FALSE(dataflow_analysis_->CanShareOperandBufferWithUser( + indices_param, {}, scatter, {})); + EXPECT_FALSE(dataflow_analysis_->CanShareOperandBufferWithUser( + updates_param, {}, scatter, {})); +} + TEST_F(CanShareOperandBufferWithUserTest, SortCanShare) { auto builder = HloComputation::Builder(TestName()); diff --git a/tensorflow/compiler/xla/service/hlo_matchers.h b/tensorflow/compiler/xla/service/hlo_matchers.h index 5502e565b6..ab901b435a 100644 --- a/tensorflow/compiler/xla/service/hlo_matchers.h +++ b/tensorflow/compiler/xla/service/hlo_matchers.h @@ -216,6 +216,7 @@ HLO_MATCHER(Remainder); HLO_MATCHER(Reshape); HLO_MATCHER(Reverse); HLO_MATCHER(Rng); +HLO_MATCHER(Scatter); HLO_MATCHER(Select); HLO_MATCHER(SelectAndScatter); HLO_MATCHER(Send); diff --git a/tensorflow/compiler/xla/service/hlo_module.cc b/tensorflow/compiler/xla/service/hlo_module.cc index 7527e35c95..93e04eb3db 100644 --- a/tensorflow/compiler/xla/service/hlo_module.cc +++ b/tensorflow/compiler/xla/service/hlo_module.cc @@ -146,7 +146,8 @@ void HloModule::ReplaceComputations( case HloOpcode::kCall: case HloOpcode::kMap: case HloOpcode::kReduce: - case HloOpcode::kReduceWindow: { + case HloOpcode::kReduceWindow: + case HloOpcode::kScatter: { HloComputation* new_arg = tensorflow::gtl::FindWithDefault( replacements, instruction->to_apply(), nullptr); if (new_arg != nullptr) { diff --git a/tensorflow/compiler/xla/service/inliner.cc b/tensorflow/compiler/xla/service/inliner.cc index 5fd779ebf9..50c408f5bb 100644 --- a/tensorflow/compiler/xla/service/inliner.cc +++ b/tensorflow/compiler/xla/service/inliner.cc @@ -71,26 +71,23 @@ Status InlinerVisitor::HandleMap(HloInstruction* map) { // profitability model for inlining is defined. if (hlo_query::AllOperandsAreParameters(root)) { if (root.opcode() == HloOpcode::kFusion || - root.opcode() == HloOpcode::kParameter || root.opcode() == HloOpcode::kTrace) { // Cloning not supported for these instructions. return Status::OK(); } VLOG(10) << "inlining map({X ... Y}, op) => : op(X ... Y) with function " << root.ToShortString(); - // If the input is a constant then the shape of the constant could be - // different than the map shape. Hence, a broadcast is needed, else the - // cloned operand with new shape and operands work. - if (root.opcode() != HloOpcode::kConstant) { - std::vector params; - for (int64 o = 0; o < root.operands().size(); o++) { - params.push_back(map->operands()[root.operand(o)->parameter_number()]); - } - HloInstruction* placed_instruction = computation_->AddInstruction( - root.CloneWithNewOperands(map->shape(), params)); + if (root.opcode() == HloOpcode::kParameter) { + // If the root is a parameter, then use the corresponding operand as the + // result of the computation. TF_RETURN_IF_ERROR( - computation_->ReplaceInstruction(map, placed_instruction)); - } else { + map->ReplaceAllUsesWith(map->operands()[root.parameter_number()])); + TF_RETURN_IF_ERROR(computation_->RemoveInstruction(map)); + } else if (root.opcode() == HloOpcode::kConstant) { + // If the input is a constant then the shape of the constant could be + // different than the map shape. Hence, a broadcast is needed, else the + // cloned operand with new shape and operands work. + // // The constant is in an embedded computation and needs to be recreated // as part of the computation that the broadcast is inserted into. HloInstruction* constant = computation_->AddInstruction(root.Clone()); @@ -98,6 +95,15 @@ Status InlinerVisitor::HandleMap(HloInstruction* map) { HloInstruction::CreateBroadcast(map->shape(), constant, {})); TF_RETURN_IF_ERROR( computation_->ReplaceInstruction(map, placed_instruction)); + } else { + std::vector params; + for (int64 o = 0; o < root.operands().size(); o++) { + params.push_back(map->operands()[root.operand(o)->parameter_number()]); + } + HloInstruction* placed_instruction = computation_->AddInstruction( + root.CloneWithNewOperands(map->shape(), params)); + TF_RETURN_IF_ERROR( + computation_->ReplaceInstruction(map, placed_instruction)); } changed_ = true; return Status::OK(); diff --git a/tensorflow/compiler/xla/service/inliner_test.cc b/tensorflow/compiler/xla/service/inliner_test.cc index 7e967f035c..98e0f2cfd7 100644 --- a/tensorflow/compiler/xla/service/inliner_test.cc +++ b/tensorflow/compiler/xla/service/inliner_test.cc @@ -146,6 +146,36 @@ TEST_F(InlinerTest, MapSubtractOppositeOrder) { EXPECT_TRUE(LiteralTestUtil::Equal(result, expected)); } +TEST_F(InlinerTest, MapParameter) { + Shape r0f32 = ShapeUtil::MakeShape(F32, {}); + + auto param_builder = HloComputation::Builder(TestName()); + param_builder.AddInstruction(HloInstruction::CreateParameter(0, r0f32, "p0")); + param_builder.AddInstruction(HloInstruction::CreateParameter(1, r0f32, "p1")); + auto param_f32 = param_builder.Build(); + + auto builder = HloComputation::Builder("MapParamFunction"); + auto lhs = builder.AddInstruction( + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1))); + auto rhs = builder.AddInstruction( + HloInstruction::CreateConstant(LiteralUtil::CreateR0(4))); + builder.AddInstruction( + HloInstruction::CreateMap(lhs->shape(), {lhs, rhs}, param_f32.get())); + + auto computation = builder.Build(); + auto hlo_module = CreateNewVerifiedModule(); + hlo_module->AddEmbeddedComputation(std::move(param_f32)); + hlo_module->AddEntryComputation(std::move(computation)); + + Inliner inliner; + EXPECT_TRUE(inliner.Run(hlo_module.get()).ValueOrDie()); + EXPECT_THAT(hlo_module->entry_computation()->root_instruction(), rhs); + + // Verify execution on CPU. + auto result = ExecuteAndTransfer(hlo_module->Clone(), {}); + auto expected = LiteralUtil::CreateR0(4); + EXPECT_TRUE(LiteralTestUtil::Equal(result, expected)); +} } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/service/layout_assignment.cc b/tensorflow/compiler/xla/service/layout_assignment.cc index 395e01fb59..9ebb603ca5 100644 --- a/tensorflow/compiler/xla/service/layout_assignment.cc +++ b/tensorflow/compiler/xla/service/layout_assignment.cc @@ -1862,6 +1862,7 @@ bool LayoutAssignment::InstructionCanChangeLayout( case HloOpcode::kRemainder: case HloOpcode::kReverse: case HloOpcode::kRoundNearestAfz: + case HloOpcode::kScatter: case HloOpcode::kSelect: case HloOpcode::kSelectAndScatter: case HloOpcode::kShiftLeft: @@ -1899,7 +1900,6 @@ bool LayoutAssignment::InstructionCanChangeLayout( case HloOpcode::kReduce: case HloOpcode::kReshape: case HloOpcode::kRng: - case HloOpcode::kScatter: case HloOpcode::kSend: case HloOpcode::kSendDone: case HloOpcode::kAfterAll: diff --git a/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc b/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc index 6fed7c76d0..6ef6b58e50 100644 --- a/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc +++ b/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc @@ -771,6 +771,7 @@ bool TuplePointsToAnalysis::CanShareOperandBufferWithUser( } } if (user->opcode() == HloOpcode::kDynamicUpdateSlice || + user->opcode() == HloOpcode::kScatter || user->opcode() == HloOpcode::kWhile) { // We eliminated other users in BufferLiveness::live_range_strictly_before, // so here we just need to check that the use is at operand index 0. diff --git a/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc b/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc index e9a07b14ed..a571bd571b 100644 --- a/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc +++ b/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc @@ -1010,6 +1010,44 @@ TEST_F(CanShareOperandBufferWithUserTest, DynamicUpdateSliceCanShare) { points_to_analysis_->CanShareOperandBufferWithUser(starts, {}, dus, {})); } +TEST_F(CanShareOperandBufferWithUserTest, ScatterCanShare) { + const char* hlo_text = R"( + HloModule TensorFlowScatterV1 + + update_s32 (lhs: s32[], rhs: s32[]) -> s32[] { + lhs = s32[] parameter(0) + ROOT rhs = s32[] parameter(1) + } + + ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2] parameter(1) + updates = s32[2,3] parameter(2) + ROOT scatter = s32[3,3] scatter(operand, indices, updates), + to_apply=update_s32, + update_window_dims={1}, + inserted_window_dims={0}, + scatter_dims_to_operand_dims={0}, + index_vector_dim=1 + } + )"; + TF_ASSERT_OK_AND_ASSIGN(module_, ParseHloString(hlo_text)); + computation_ = module_->entry_computation(); + RunAnalysis(); + + HloInstruction* operand_param = computation_->parameter_instruction(0); + HloInstruction* indices_param = computation_->parameter_instruction(1); + HloInstruction* updates_param = computation_->parameter_instruction(2); + HloInstruction* scatter = computation_->root_instruction(); + + EXPECT_TRUE(points_to_analysis_->CanShareOperandBufferWithUser( + operand_param, {}, scatter, {})); + EXPECT_FALSE(points_to_analysis_->CanShareOperandBufferWithUser( + indices_param, {}, scatter, {})); + EXPECT_FALSE(points_to_analysis_->CanShareOperandBufferWithUser( + updates_param, {}, scatter, {})); +} + TEST_F(CanShareOperandBufferWithUserTest, SortCanShare) { auto builder = HloComputation::Builder(TestName()); diff --git a/tensorflow/compiler/xla/tests/scatter_test.cc b/tensorflow/compiler/xla/tests/scatter_test.cc index b21dd56045..7e1f4aa0eb 100644 --- a/tensorflow/compiler/xla/tests/scatter_test.cc +++ b/tensorflow/compiler/xla/tests/scatter_test.cc @@ -69,6 +69,37 @@ ENTRY main { RunTest(hlo_text, &operand, &scatter_indices, &updates); } +XLA_TEST_F(ScatterTest, TensorFlowScatterV1_WithFusedAdds) { + const string hlo_text = R"( +HloModule TensorFlowScatterV1 + +update_s32 (lhs: s32[], rhs: s32[]) -> s32[] { + lhs = s32[] parameter(0) + ROOT rhs = s32[] parameter(1) +} + +ENTRY main { + p0 = s32[3,3] parameter(0) + operand = s32[3,3] add(p0, p0) + p1 = s32[2] parameter(1) + indices = s32[2] add(p1, p1) + p2 = s32[2,3] parameter(2) + updates = s32[2,3] add(p2, p2) + ROOT scatter = s32[3,3] scatter(operand, indices, updates), + to_apply=update_s32, + update_window_dims={1}, + inserted_window_dims={0}, + scatter_dims_to_operand_dims={0}, + index_vector_dim=1 +} +)"; + Literal operand = + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + Literal scatter_indices = LiteralUtil::CreateR1({0, 1}); + Literal updates = LiteralUtil::CreateR2({{10, 20, 30}, {70, 80, 90}}); + RunTest(hlo_text, &operand, &scatter_indices, &updates); +} + XLA_TEST_F(ScatterTest, TensorFlowScatterV2_Update) { const char* hlo_text = R"( HloModule TensorFlowScatterV2 @@ -98,6 +129,37 @@ ENTRY main { RunTest(hlo_text, &operand, &scatter_indices, &updates); } +XLA_TEST_F(ScatterTest, SimpleR4) { + const char* hlo_text = R"( +HloModule SimpleR4 + +add_f32 (lhs: f32[], rhs: f32[]) -> f32[] { + lhs = f32[] parameter(0) + rhs = f32[] parameter(1) + ROOT add = f32[] add(f32[] lhs, f32[] rhs) +} + +ENTRY main { + operand = f32[1,2,2,1] parameter(0) + indices = s32[1,3] parameter(1) + updates = f32[1,2,2,1] parameter(2) + ROOT scatter = f32[1,2,2,1] scatter(operand, indices, updates), + to_apply=add_f32, + update_window_dims={1,2,3}, + inserted_window_dims={0}, + scatter_dims_to_operand_dims={0, 2, 1}, + index_vector_dim=1 +} +)"; + + Literal operand = + LiteralUtil::CreateR4({{{{0.f}, {0.f}}, {{0.f}, {0.f}}}}); + Literal updates = + LiteralUtil::CreateR4({{{{0.12}, {0.28}}, {{0.018}, {0.42}}}}); + Literal scatter_indices = LiteralUtil::CreateR2({{0, 0, 0}}); + RunTest(hlo_text, &operand, &scatter_indices, &updates); +} + XLA_TEST_F(ScatterTest, TensorFlowScatter_Add) { const string hlo_text = R"( HloModule TensorFlowScatter_Add -- GitLab From e3f4d32490e9a28cba0bfa5614255dc5d517ca91 Mon Sep 17 00:00:00 2001 From: Nick Felt Date: Wed, 24 Oct 2018 18:01:36 -0700 Subject: [PATCH 019/873] Update tensorboard dependency to 1.12.x (#23230) Also updated tb-nightly to +1 minor version, 1.13.x. PiperOrigin-RevId: 218582588 --- tensorflow/tools/pip_package/setup.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index b7eed56695..ceaa96b690 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -56,7 +56,7 @@ REQUIRED_PACKAGES = [ 'numpy >= 1.13.3', 'six >= 1.10.0', 'protobuf >= 3.6.1', - 'tensorboard >= 1.11.0, < 1.12.0', + 'tensorboard >= 1.12.0, < 1.13.0', 'termcolor >= 1.1.0', ] @@ -85,7 +85,7 @@ else: if 'tf_nightly' in project_name: for i, pkg in enumerate(REQUIRED_PACKAGES): if 'tensorboard' in pkg: - REQUIRED_PACKAGES[i] = 'tb-nightly >= 1.12.0a0, < 1.13.0a0' + REQUIRED_PACKAGES[i] = 'tb-nightly >= 1.13.0a0, < 1.14.0a0' break # weakref.finalize and enum were introduced in Python 3.4 -- GitLab From 43ec5a3d6ee49eadc98835d1ab18c62cafa5043d Mon Sep 17 00:00:00 2001 From: Todd Wang Date: Wed, 24 Oct 2018 18:29:31 -0700 Subject: [PATCH 020/873] Fix string comparison (#23237) PiperOrigin-RevId: 218607372 --- tensorflow/tools/ci_build/builds/configured | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/tools/ci_build/builds/configured b/tensorflow/tools/ci_build/builds/configured index 3eee11fd7e..f8a9311918 100755 --- a/tensorflow/tools/ci_build/builds/configured +++ b/tensorflow/tools/ci_build/builds/configured @@ -33,7 +33,7 @@ COMMAND=("$@") export CI_BUILD_PYTHON="${CI_BUILD_PYTHON:-python}" export PYTHON_BIN_PATH="${PYTHON_BIN_PATH:-$(which ${CI_BUILD_PYTHON})}" # XLA currently does not build under Android, so disable it for now. -if [[ "${CONTAINER_TYPE}" -eq 'android' ]]; then +if [[ "${CONTAINER_TYPE}" == 'android' ]]; then export TF_ENABLE_XLA=0 fi -- GitLab From dd9ebe12df7906a3211b8db2d21fa73c4504d118 Mon Sep 17 00:00:00 2001 From: "Meng, Peng" Date: Thu, 25 Oct 2018 11:03:59 +0800 Subject: [PATCH 021/873] fix softmax dims error Change-Id: I3303f368053a691787a0922098ee75e3b0c26219 Conflicts: tensorflow/core/kernels/mkl_softmax_op.cc --- tensorflow/core/kernels/mkl_softmax_op.cc | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tensorflow/core/kernels/mkl_softmax_op.cc b/tensorflow/core/kernels/mkl_softmax_op.cc index cfab529662..92167e06d5 100644 --- a/tensorflow/core/kernels/mkl_softmax_op.cc +++ b/tensorflow/core/kernels/mkl_softmax_op.cc @@ -50,8 +50,8 @@ class MklSoftmaxOp : public OpKernel { // src_tensor now points to the 0-th input of global data struct "context" size_t src_idx = 0; const Tensor& src_tensor = MklGetInput(context, src_idx); - const int input_dims = src_tensor.dims(); - + //const int input_dims = src_tensor.dims(); + // printf("input_dims = %d\n", input_dims); // Add: get MklShape MklDnnShape src_mkl_shape; GetMklShape(context, src_idx, &src_mkl_shape); @@ -61,6 +61,7 @@ class MklSoftmaxOp : public OpKernel { auto src_tf_shape = src_mkl_shape.IsMklTensor() ? src_mkl_shape.GetTfShape() : src_tensor.shape(); + const int input_dims = src_tf_shape.dims(); auto src_dims = TFShapeToMklDnnDims(src_tf_shape); auto output_dims = src_dims; memory::format layout_type; -- GitLab From ec31b13690118d1998824ba4d350fcbc22fbfb60 Mon Sep 17 00:00:00 2001 From: Todd Wang Date: Thu, 25 Oct 2018 09:22:14 -0700 Subject: [PATCH 022/873] Explicitly quote every command piece. (#23259) PiperOrigin-RevId: 218399942 --- third_party/repo.bzl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/third_party/repo.bzl b/third_party/repo.bzl index 6e30618d39..391622e237 100644 --- a/third_party/repo.bzl +++ b/third_party/repo.bzl @@ -26,7 +26,7 @@ def _wrap_bash_cmd(ctx, cmd): bazel_sh = _get_env_var(ctx, "BAZEL_SH") if not bazel_sh: fail("BAZEL_SH environment variable is not set") - cmd = [bazel_sh, "-l", "-c", " ".join(cmd)] + cmd = [bazel_sh, "-l", "-c", " ".join(["\"%s\"" % s for s in cmd])] return cmd def _get_env_var(ctx, name): -- GitLab From a315296d577b09eca88fe1a6cd36a13502d72067 Mon Sep 17 00:00:00 2001 From: Todd Wang Date: Thu, 25 Oct 2018 10:44:18 -0700 Subject: [PATCH 023/873] Don't set TF_PER_DEVICE_MEMORY_LIMIT_MB as a --test_env if it isn't specified. (#23258) PiperOrigin-RevId: 218634344 --- .../tools/ci_build/ci_parameterized_build.sh | 21 ++++++++++++++----- 1 file changed, 16 insertions(+), 5 deletions(-) diff --git a/tensorflow/tools/ci_build/ci_parameterized_build.sh b/tensorflow/tools/ci_build/ci_parameterized_build.sh index bc9cb4e9a1..435ec7ca68 100755 --- a/tensorflow/tools/ci_build/ci_parameterized_build.sh +++ b/tensorflow/tools/ci_build/ci_parameterized_build.sh @@ -128,8 +128,9 @@ NO_DOCKER_OPT_FLAG="--genrule_strategy=standalone" DO_DOCKER=1 -# Bazel uses defaults for all test sizes when given `-1`. -TF_BUILD_TEST_TIMEOUT=${TF_BUILD_TEST_TIMEOUT:--1} +# Default values for various settings. +TF_BUILD_TEST_TIMEOUT=${TF_BUILD_TEST_TIMEOUT:--1} # Use bazel defaults +TF_GPU_COUNT=${TF_GPU_COUNT:-4} # Helpful flags: # --test_summary=detailed: Tell us more about which targets are being built @@ -144,9 +145,20 @@ TF_BUILD_TEST_TIMEOUT=${TF_BUILD_TEST_TIMEOUT:--1} BAZEL_TEST_FLAGS=""\ "--test_summary=detailed --build_tests_only --keep_going "\ "--test_timeout=${TF_BUILD_TEST_TIMEOUT} "\ -"--test_env=TF_GPU_COUNT=${TF_GPU_COUNT} "\ -"--test_env=TF_TESTS_PER_GPU=${TF_TESTS_PER_GPU} "\ +"--test_env=TF_GPU_COUNT=${TF_GPU_COUNT}" + +# Only set these environment variables if they're specified, to avoid causing +# problems like b/118404869, where an envvar set to the empty string has +# different semantics from an unset envvar. +if [ -n "${TF_TESTS_PER_GPU}" ]; then + BAZEL_TEST_FLAGS="${BAZEL_TEST_FLAGS} "\ +"--test_env=TF_TESTS_PER_GPU=${TF_TESTS_PER_GPU}" +fi +if [ -n "${TF_PER_DEVICE_MEMORY_LIMIT_MB}" ]; then + BAZEL_TEST_FLAGS="${BAZEL_TEST_FLAGS} "\ "--test_env=TF_PER_DEVICE_MEMORY_LIMIT_MB=${TF_PER_DEVICE_MEMORY_LIMIT_MB}" +fi + BAZEL_BUILD_FLAGS="--keep_going" # Explicitly set jdk8 since that's what's installed in our images. Note that @@ -163,7 +175,6 @@ PIP_INTEGRATION_TESTS_FLAG="--integration_tests" ANDROID_CMD="${CI_BUILD_DIR}/builds/android.sh" ANDROID_FULL_CMD="${CI_BUILD_DIR}/builds/android_full.sh" -TF_GPU_COUNT=${TF_GPU_COUNT:-4} PARALLEL_GPU_TEST_CMD='//tensorflow/tools/ci_build/gpu_build:parallel_gpu_execute' BENCHMARK_CMD="${CI_BUILD_DIR}/builds/benchmark.sh" -- GitLab From 37a2e36733b0f12102133e8ff5fb516573bdf7ec Mon Sep 17 00:00:00 2001 From: Todd Wang Date: Thu, 25 Oct 2018 12:49:30 -0700 Subject: [PATCH 024/873] Upgrade setuptools before installing absl-py in remaining scripts. (#23264) PiperOrigin-RevId: 218730741 --- .../ci_build/install/install_python3.5_pip_packages.sh | 10 ++++------ .../ci_build/install/install_python3.6_pip_packages.sh | 6 ++++-- 2 files changed, 8 insertions(+), 8 deletions(-) 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 61d4fe3fe8..62e04df717 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 @@ -41,6 +41,10 @@ fi set -e pip3.5 install --upgrade pip +# Install last working version of setuptools. This must happen before we install +# absl-py, which uses install_requires notation introduced in setuptools 20.5. +pip3.5 install --upgrade setuptools==39.1.0 + pip3.5 install --upgrade virtualenv # Install six. @@ -81,15 +85,9 @@ pip3.5 install --upgrade astor pip3.5 install --upgrade gast pip3.5 install --upgrade termcolor -# Install last working version of setuptools. -pip3.5 install --upgrade setuptools==39.1.0 - # Keras pip3.5 install keras_applications==1.0.6 pip3.5 install keras_preprocessing==1.0.5 pip3.5 install --upgrade h5py==2.8.0 -# 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 8949af8a88..48d556b1dd 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 @@ -51,6 +51,10 @@ ln -s /usr/local/bin/pip3.6 /usr/local/bin/pip3 pip3 install --upgrade pip +# Install last working version of setuptools. This must happen before we install +# absl-py, which uses install_requires notation introduced in setuptools 20.5. +pip3 install --upgrade setuptools==39.1.0 + pip3 install --upgrade virtualenv set -e @@ -97,8 +101,6 @@ pip3 install --upgrade astor pip3 install --upgrade gast pip3 install --upgrade termcolor -# Install last working version of setuptools. -pip3 install --upgrade setuptools==39.1.0 pip3 install --upgrade h5py==2.8.0 # Keras -- GitLab From 405b34608005bc17c50dbbe915e4d68a694274ca Mon Sep 17 00:00:00 2001 From: Todd Wang Date: Thu, 25 Oct 2018 13:56:08 -0700 Subject: [PATCH 025/873] Fp16 LSTMBlocKCell and LSTMBlockFusedCell (#23267) PiperOrigin-RevId: 216632480 --- tensorflow/contrib/rnn/kernels/blas_gemm.cc | 7 +- tensorflow/contrib/rnn/kernels/blas_gemm.h | 9 +- tensorflow/contrib/rnn/kernels/lstm_ops.cc | 163 +++++++++--------- tensorflow/contrib/rnn/kernels/lstm_ops.h | 34 ++-- .../contrib/rnn/kernels/lstm_ops_gpu.cu.cc | 80 +++++++-- tensorflow/contrib/rnn/ops/lstm_ops.cc | 8 +- tensorflow/contrib/rnn/python/ops/lstm_ops.py | 5 +- 7 files changed, 185 insertions(+), 121 deletions(-) diff --git a/tensorflow/contrib/rnn/kernels/blas_gemm.cc b/tensorflow/contrib/rnn/kernels/blas_gemm.cc index 45d22b739b..56ec86418d 100644 --- a/tensorflow/contrib/rnn/kernels/blas_gemm.cc +++ b/tensorflow/contrib/rnn/kernels/blas_gemm.cc @@ -38,8 +38,9 @@ namespace functor { template void TensorCuBlasGemm::operator()(OpKernelContext* ctx, bool transa, bool transb, uint64 m, uint64 n, uint64 k, - T alpha, const T* a, int lda, const T* b, - int ldb, T beta, T* c, int ldc) { + float alpha, const T* a, int lda, + const T* b, int ldb, float beta, T* c, + int ldc) { #if GOOGLE_CUDA se::blas::Transpose trans[] = {se::blas::Transpose::kNoTranspose, se::blas::Transpose::kTranspose}; @@ -60,8 +61,8 @@ void TensorCuBlasGemm::operator()(OpKernelContext* ctx, bool transa, #endif } +template struct TensorCuBlasGemm; template struct TensorCuBlasGemm; -template struct TensorCuBlasGemm; } // end namespace functor } // end namespace tensorflow diff --git a/tensorflow/contrib/rnn/kernels/blas_gemm.h b/tensorflow/contrib/rnn/kernels/blas_gemm.h index a52c934233..9535a76566 100644 --- a/tensorflow/contrib/rnn/kernels/blas_gemm.h +++ b/tensorflow/contrib/rnn/kernels/blas_gemm.h @@ -28,8 +28,8 @@ namespace functor { template struct TensorCuBlasGemm { void operator()(OpKernelContext* ctx, bool transa, bool transb, uint64 m, - uint64 n, uint64 k, T alpha, const T* a, int lda, const T* b, - int ldb, T beta, T* c, int ldc); + uint64 n, uint64 k, float alpha, const T* a, int lda, + const T* b, int ldb, float beta, T* c, int ldc); }; template @@ -38,8 +38,9 @@ struct TensorBlasGemm; template struct TensorBlasGemm { static void compute(OpKernelContext* ctx, const Device& d, bool transa, - bool transb, T alpha, typename TTypes::ConstMatrix a, - typename TTypes::ConstMatrix b, T beta, + bool transb, float alpha, + typename TTypes::ConstMatrix a, + typename TTypes::ConstMatrix b, float beta, typename TTypes::Matrix c) { int64 m = c.dimensions()[0]; int64 n = c.dimensions()[1]; diff --git a/tensorflow/contrib/rnn/kernels/lstm_ops.cc b/tensorflow/contrib/rnn/kernels/lstm_ops.cc index 5e7cf0ce84..ee08d306f8 100644 --- a/tensorflow/contrib/rnn/kernels/lstm_ops.cc +++ b/tensorflow/contrib/rnn/kernels/lstm_ops.cc @@ -44,7 +44,7 @@ namespace functor { template void LSTMBlockCellFpropWithEigen( const LSTMBlockCell& cell, OpKernelContext* ctx, const CPUDevice& d, - const T forget_bias, const T cell_clip, bool use_peephole, + const float forget_bias, const float cell_clip, bool use_peephole, typename TTypes::ConstMatrix x, typename TTypes::ConstMatrix cs_prev, typename TTypes::ConstMatrix h_prev, typename TTypes::ConstMatrix w, typename TTypes::ConstVec wci, typename TTypes::ConstVec wcf, @@ -177,50 +177,51 @@ void LSTMBlockCellBpropWithEigen( } } -#define DEFINE_CPU_SPECS(T) \ - template <> \ - void LSTMBlockCellFprop::operator()( \ - OpKernelContext* ctx, const CPUDevice& d, const T forget_bias, \ - const T cell_clip, bool use_peephole, typename TTypes::ConstMatrix x, \ - typename TTypes::ConstMatrix cs_prev, \ - typename TTypes::ConstMatrix h_prev, \ - typename TTypes::ConstMatrix w, typename TTypes::ConstVec wci, \ - typename TTypes::ConstVec wcf, typename TTypes::ConstVec wco, \ - typename TTypes::ConstVec b, typename TTypes::Matrix xh, \ - typename TTypes::Matrix i, typename TTypes::Matrix cs, \ - typename TTypes::Matrix f, typename TTypes::Matrix o, \ - typename TTypes::Matrix ci, typename TTypes::Matrix co, \ - typename TTypes::Matrix icfo, typename TTypes::Matrix h) { \ - LSTMBlockCellFpropWithEigen( \ - *this, ctx, d, forget_bias, cell_clip, use_peephole, x, cs_prev, \ - h_prev, w, wci, wcf, wco, b, xh, i, cs, f, o, ci, co, icfo, h); \ - } \ - template <> \ - void LSTMBlockCellBprop::operator()( \ - OpKernelContext* ctx, const CPUDevice& d, bool use_peephole, \ - typename TTypes::ConstMatrix x, \ - typename TTypes::ConstMatrix cs_prev, \ - typename TTypes::ConstMatrix h_prev, \ - typename TTypes::ConstMatrix w, typename TTypes::ConstVec wci, \ - typename TTypes::ConstVec wcf, typename TTypes::ConstVec wco, \ - typename TTypes::ConstVec b, typename TTypes::ConstMatrix i, \ - typename TTypes::ConstMatrix cs, typename TTypes::ConstMatrix f, \ - typename TTypes::ConstMatrix o, typename TTypes::ConstMatrix ci, \ - typename TTypes::ConstMatrix co, \ - typename TTypes::ConstMatrix cs_grad, \ - typename TTypes::ConstMatrix h_grad, typename TTypes::Matrix do_, \ - typename TTypes::Matrix dcs, typename TTypes::Matrix dci, \ - typename TTypes::Matrix df, typename TTypes::Matrix di, \ - typename TTypes::Matrix dicfo, \ - typename TTypes::Matrix cs_prev_grad, \ - typename TTypes::Vec wci_grad, typename TTypes::Vec wcf_grad, \ - typename TTypes::Vec wco_grad) { \ - LSTMBlockCellBpropWithEigen( \ - *this, ctx, d, use_peephole, x, cs_prev, h_prev, w, wci, wcf, wco, b, \ - i, cs, f, o, ci, co, cs_grad, h_grad, do_, dcs, dci, df, di, dicfo, \ - cs_prev_grad, wci_grad, wcf_grad, wco_grad); \ - } \ - template struct LSTMBlockCellFprop; \ +#define DEFINE_CPU_SPECS(T) \ + template <> \ + void LSTMBlockCellFprop::operator()( \ + OpKernelContext* ctx, const CPUDevice& d, const float forget_bias, \ + const float cell_clip, bool use_peephole, \ + typename TTypes::ConstMatrix x, \ + typename TTypes::ConstMatrix cs_prev, \ + typename TTypes::ConstMatrix h_prev, \ + typename TTypes::ConstMatrix w, typename TTypes::ConstVec wci, \ + typename TTypes::ConstVec wcf, typename TTypes::ConstVec wco, \ + typename TTypes::ConstVec b, typename TTypes::Matrix xh, \ + typename TTypes::Matrix i, typename TTypes::Matrix cs, \ + typename TTypes::Matrix f, typename TTypes::Matrix o, \ + typename TTypes::Matrix ci, typename TTypes::Matrix co, \ + typename TTypes::Matrix icfo, typename TTypes::Matrix h) { \ + LSTMBlockCellFpropWithEigen( \ + *this, ctx, d, forget_bias, cell_clip, use_peephole, x, cs_prev, \ + h_prev, w, wci, wcf, wco, b, xh, i, cs, f, o, ci, co, icfo, h); \ + } \ + template <> \ + void LSTMBlockCellBprop::operator()( \ + OpKernelContext* ctx, const CPUDevice& d, bool use_peephole, \ + typename TTypes::ConstMatrix x, \ + typename TTypes::ConstMatrix cs_prev, \ + typename TTypes::ConstMatrix h_prev, \ + typename TTypes::ConstMatrix w, typename TTypes::ConstVec wci, \ + typename TTypes::ConstVec wcf, typename TTypes::ConstVec wco, \ + typename TTypes::ConstVec b, typename TTypes::ConstMatrix i, \ + typename TTypes::ConstMatrix cs, typename TTypes::ConstMatrix f, \ + typename TTypes::ConstMatrix o, typename TTypes::ConstMatrix ci, \ + typename TTypes::ConstMatrix co, \ + typename TTypes::ConstMatrix cs_grad, \ + typename TTypes::ConstMatrix h_grad, typename TTypes::Matrix do_, \ + typename TTypes::Matrix dcs, typename TTypes::Matrix dci, \ + typename TTypes::Matrix df, typename TTypes::Matrix di, \ + typename TTypes::Matrix dicfo, \ + typename TTypes::Matrix cs_prev_grad, \ + typename TTypes::Vec wci_grad, typename TTypes::Vec wcf_grad, \ + typename TTypes::Vec wco_grad) { \ + LSTMBlockCellBpropWithEigen( \ + *this, ctx, d, use_peephole, x, cs_prev, h_prev, w, wci, wcf, wco, b, \ + i, cs, f, o, ci, co, cs_grad, h_grad, do_, dcs, dci, df, di, dicfo, \ + cs_prev_grad, wci_grad, wcf_grad, wco_grad); \ + } \ + template struct LSTMBlockCellFprop; \ template struct LSTMBlockCellBprop; DEFINE_CPU_SPECS(float); @@ -377,24 +378,26 @@ REGISTER_KERNEL(float); #if GOOGLE_CUDA namespace functor { -#define DECLARE_GPU_SPEC(T) \ - template <> \ - void LSTMBlockCellFprop::operator()( \ - OpKernelContext* ctx, const GPUDevice& d, const T forget_bias, \ - const T cell_clip, bool use_peephole, typename TTypes::ConstMatrix x, \ - typename TTypes::ConstMatrix cs_prev, \ - typename TTypes::ConstMatrix h_prev, \ - typename TTypes::ConstMatrix w, typename TTypes::ConstVec wci, \ - typename TTypes::ConstVec wcf, typename TTypes::ConstVec wco, \ - typename TTypes::ConstVec b, typename TTypes::Matrix xh, \ - typename TTypes::Matrix i, typename TTypes::Matrix cs, \ - typename TTypes::Matrix f, typename TTypes::Matrix o, \ - typename TTypes::Matrix ci, typename TTypes::Matrix co, \ - typename TTypes::Matrix icfo, typename TTypes::Matrix h); \ - \ +#define DECLARE_GPU_SPEC(T) \ + template <> \ + void LSTMBlockCellFprop::operator()( \ + OpKernelContext* ctx, const GPUDevice& d, const float forget_bias, \ + const float cell_clip, bool use_peephole, \ + typename TTypes::ConstMatrix x, \ + typename TTypes::ConstMatrix cs_prev, \ + typename TTypes::ConstMatrix h_prev, \ + typename TTypes::ConstMatrix w, typename TTypes::ConstVec wci, \ + typename TTypes::ConstVec wcf, typename TTypes::ConstVec wco, \ + typename TTypes::ConstVec b, typename TTypes::Matrix xh, \ + typename TTypes::Matrix i, typename TTypes::Matrix cs, \ + typename TTypes::Matrix f, typename TTypes::Matrix o, \ + typename TTypes::Matrix ci, typename TTypes::Matrix co, \ + typename TTypes::Matrix icfo, typename TTypes::Matrix h); \ + \ extern template struct LSTMBlockCellFprop; DECLARE_GPU_SPEC(float); +DECLARE_GPU_SPEC(Eigen::half); // DECLARE_GPU_SPEC(double); #undef DECLARE_GPU_SPEC } // end namespace functor @@ -405,6 +408,7 @@ DECLARE_GPU_SPEC(float); LSTMBlockCellOp); REGISTER_GPU_KERNEL(float); +REGISTER_GPU_KERNEL(Eigen::half); // REGISTER_GPU_KERNEL(double); #undef REGISTER_GPU_KERNEL #endif // GOOGLE_CUDA @@ -629,9 +633,9 @@ class LSTMBlockCellGradOp : public OpKernel { const Device& device = ctx->eigen_device(); - functor::TensorZero()(device, wci_grad_tensor->flat()); - functor::TensorZero()(device, wcf_grad_tensor->flat()); - functor::TensorZero()(device, wco_grad_tensor->flat()); + functor::TensorZero()(device, wci_grad_tensor->flat()); + functor::TensorZero()(device, wcf_grad_tensor->flat()); + functor::TensorZero()(device, wco_grad_tensor->flat()); functor::LSTMBlockCellBprop(batch_size, input_size, cell_size)( @@ -688,6 +692,7 @@ namespace functor { true /* USE_CUBLAS */>; DECLARE_GPU_SPEC(float); +DECLARE_GPU_SPEC(Eigen::half); // DECLARE_GPU_SPEC(double); #undef DECLARE_GPU_SPEC } // namespace functor @@ -698,6 +703,7 @@ DECLARE_GPU_SPEC(float); LSTMBlockCellGradOp); REGISTER_GPU_KERNEL(float); +REGISTER_GPU_KERNEL(Eigen::half); // REGISTER_GPU_KERNEL(double); #undef REGISTER_GPU_KERNEL #endif // GOOGLE_CUDA @@ -984,10 +990,10 @@ class BlockLSTMOp : public OpKernel { Tensor cs_tensor = cs_out->Slice(seq_len_max, timelen); Tensor h_tensor = h_out->Slice(seq_len_max, timelen); - functor::TensorUnalignedZero()( - device, cs_tensor.unaligned_flat()); - functor::TensorUnalignedZero()( - device, h_tensor.unaligned_flat()); + functor::TensorUnalignedZero()(device, + cs_tensor.unaligned_flat()); + functor::TensorUnalignedZero()(device, + h_tensor.unaligned_flat()); } } @@ -1021,6 +1027,7 @@ namespace functor { extern template struct TensorUnalignedZero; DECLARE_GPU_SPEC(float); +DECLARE_GPU_SPEC(Eigen::half); // DECLARE_GPU_SPEC(double); #undef DECLARE_GPU_SPEC } // end namespace functor @@ -1033,6 +1040,7 @@ DECLARE_GPU_SPEC(float); BlockLSTMOp); REGISTER_GPU_KERNEL(float); +REGISTER_GPU_KERNEL(Eigen::half); // REGISTER_GPU_KERNEL(double); #undef REGISTER_GPU_KERNEL #endif // GOOGLE_CUDA @@ -1195,16 +1203,15 @@ class BlockLSTMGradOp : public OpKernel { const Device& device = ctx->eigen_device(); - functor::TensorZero()(device, cs_grad_tensor.flat()); - functor::TensorZero()(device, - cs_prev_grad_tensor->flat()); - functor::TensorZero()(device, h_grad_tensor.flat()); - functor::TensorZero()(device, h_prev_grad_tensor->flat()); - functor::TensorZero()(device, w_grad_tensor->flat()); - functor::TensorZero()(device, wci_grad_tensor->flat()); - functor::TensorZero()(device, wcf_grad_tensor->flat()); - functor::TensorZero()(device, wco_grad_tensor->flat()); - functor::TensorZero()(device, b_grad_tensor->flat()); + functor::TensorZero()(device, cs_grad_tensor.flat()); + functor::TensorZero()(device, cs_prev_grad_tensor->flat()); + functor::TensorZero()(device, h_grad_tensor.flat()); + functor::TensorZero()(device, h_prev_grad_tensor->flat()); + functor::TensorZero()(device, w_grad_tensor->flat()); + functor::TensorZero()(device, wci_grad_tensor->flat()); + functor::TensorZero()(device, wcf_grad_tensor->flat()); + functor::TensorZero()(device, wco_grad_tensor->flat()); + functor::TensorZero()(device, b_grad_tensor->flat()); const int64 seq_len_max = seq_len_max_tensor->scalar()(); SliceHelper slicer(ctx); @@ -1331,6 +1338,7 @@ namespace functor { extern template struct BlockLSTMBprop; DECLARE_GPU_SPEC(float); +DECLARE_GPU_SPEC(Eigen::half); // DECLARE_GPU_SPEC(double); #undef DECLARE_GPU_SPEC } // end namespace functor @@ -1343,6 +1351,7 @@ DECLARE_GPU_SPEC(float); BlockLSTMGradOp); REGISTER_GPU_KERNEL(float); +REGISTER_GPU_KERNEL(Eigen::half); // REGISTER_GPU_KERNEL(double); #undef REGISTER_GPU_KERNEL #endif // GOOGLE_CUDA diff --git a/tensorflow/contrib/rnn/kernels/lstm_ops.h b/tensorflow/contrib/rnn/kernels/lstm_ops.h index d23cedc234..5ca1dad655 100644 --- a/tensorflow/contrib/rnn/kernels/lstm_ops.h +++ b/tensorflow/contrib/rnn/kernels/lstm_ops.h @@ -77,8 +77,7 @@ template struct TensorZeroPadding { void operator()(const Device& d, const int64 time_idx, typename TTypes::ConstVec seq_len, - typename TTypes::Vec mask, - typename TTypes::Matrix m) { + typename TTypes::Vec mask, typename TTypes::Matrix m) { // mask is shape [batch_size]. mask.device(d) = seq_len.constant(time_idx) < seq_len; @@ -154,18 +153,21 @@ struct LSTMBlockCellFprop : public LSTMBlockCell { const int cell_size) : LSTMBlockCell(batch_size, input_size, cell_size) {} - void operator()( - OpKernelContext* ctx, const Device& d, const T forget_bias, - const T cell_clip, bool use_peephole, typename TTypes::ConstMatrix x, - typename TTypes::ConstMatrix cs_prev, - typename TTypes::ConstMatrix h_prev, typename TTypes::ConstMatrix w, - typename TTypes::ConstVec wci, typename TTypes::ConstVec wcf, - typename TTypes::ConstVec wco, typename TTypes::ConstVec b, - typename TTypes::Matrix xh, typename TTypes::Matrix i, - typename TTypes::Matrix cs, typename TTypes::Matrix f, - typename TTypes::Matrix o, typename TTypes::Matrix ci, - typename TTypes::Matrix co, typename TTypes::Matrix icfo, - typename TTypes::Matrix h); + void operator()(OpKernelContext* ctx, const Device& d, + const float forget_bias, const float cell_clip, + bool use_peephole, typename TTypes::ConstMatrix x, + typename TTypes::ConstMatrix cs_prev, + typename TTypes::ConstMatrix h_prev, + typename TTypes::ConstMatrix w, + typename TTypes::ConstVec wci, + typename TTypes::ConstVec wcf, + typename TTypes::ConstVec wco, + typename TTypes::ConstVec b, typename TTypes::Matrix xh, + typename TTypes::Matrix i, typename TTypes::Matrix cs, + typename TTypes::Matrix f, typename TTypes::Matrix o, + typename TTypes::Matrix ci, typename TTypes::Matrix co, + typename TTypes::Matrix icfo, + typename TTypes::Matrix h); }; // See lstm_ops.cc for CPUDevice implementation and lstm_ops_gpu.cu.cc for @@ -261,7 +263,7 @@ struct BlockLSTMBprop : public LSTMBlockCell { typename TTypes::ConstMatrix const_dicfo(dicfo.data(), dicfo.dimensions()); TensorBlasGemm::compute( - ctx, d, false, true, T(1), const_dicfo, w, T(0), xh_grad); + ctx, d, false, true, 1.f, const_dicfo, w, 0.f, xh_grad); // xh. xh.slice(xh_x_offsets(), xh_x_extents()).device(d) = x; @@ -274,7 +276,7 @@ struct BlockLSTMBprop : public LSTMBlockCell { // w_grad. TensorBlasGemm::compute( - ctx, d, true, false, T(1), const_xh, const_dicfo, T(1), w_grad); + ctx, d, true, false, 1.f, const_xh, const_dicfo, 1.f, w_grad); // b_grad. b_grad.device(d) += dicfo.sum(Eigen::array({0})); diff --git a/tensorflow/contrib/rnn/kernels/lstm_ops_gpu.cu.cc b/tensorflow/contrib/rnn/kernels/lstm_ops_gpu.cu.cc index 6d3758fef1..b664b0f45e 100644 --- a/tensorflow/contrib/rnn/kernels/lstm_ops_gpu.cu.cc +++ b/tensorflow/contrib/rnn/kernels/lstm_ops_gpu.cu.cc @@ -31,6 +31,49 @@ typedef Eigen::GpuDevice GPUDevice; namespace { +struct FloatToHalf { + __host__ __device__ EIGEN_STRONG_INLINE Eigen::half operator()( + const float& x) const { + return Eigen::half_impl::float_to_half_rtne(x); + } +}; + +template +__host__ __device__ EIGEN_STRONG_INLINE + typename std::enable_if::value, U>::type + strict_cast(T t); + +template +__host__ __device__ EIGEN_STRONG_INLINE + typename std::enable_if::value, U>::type + strict_cast(T t) { + return t; +} + +template <> +__host__ __device__ EIGEN_STRONG_INLINE Eigen::half +strict_cast(float t) { + return FloatToHalf()(t); +} + +} // namespace + +template +struct TensorZero { + void operator()(const GPUDevice& d, typename TTypes::Flat t) { + t.device(d) = t.constant(strict_cast(0.f)); + } +}; + +template +struct TensorUnalignedZero { + void operator()(const GPUDevice& d, typename TTypes::UnalignedFlat t) { + t.device(d) = t.constant(strict_cast(0.f)); + } +}; + +namespace { + // Adds bias, applies non-linearities and gates. // // Launch with a 2D setup such that there is one thread per (example, @@ -42,12 +85,15 @@ namespace { template __global__ void lstm_gates(const T* icfo, const T* b, const T* cs_prev, const T* wci, const T* wcf, const T* wco, T* o, T* h, - T* ci, T* cs, T* co, T* i, T* f, const T forget_bias, - const T cell_clip, const int batch_size, - const int cell_size) { + T* ci, T* cs, T* co, T* i, T* f, + const float forget_bias, const float cell_clip, + const int batch_size, const int cell_size) { const int batch_id = blockIdx.x * blockDim.x + threadIdx.x; const int act_id = blockIdx.y * blockDim.y + threadIdx.y; + T forget_bias_t = strict_cast(forget_bias); + T cell_clip_t = strict_cast(cell_clip); + if (batch_id >= batch_size || act_id >= cell_size) return; // The following code assumes the input arrays are of the following @@ -115,16 +161,16 @@ __global__ void lstm_gates(const T* icfo, const T* b, const T* cs_prev, T f_local; if (use_peephole) { f_local = sigmoid_op(icfo[2 * cell_size + gid] + b[2 * cell_size + act_id] + - forget_bias + cs_prev[cid] * wcf[act_id]); + forget_bias_t + cs_prev[cid] * wcf[act_id]); } else { f_local = sigmoid_op(icfo[2 * cell_size + gid] + b[2 * cell_size + act_id] + - forget_bias); + forget_bias_t); } f[cid] = f_local; T cs_local = i_local * ci_local + f_local * cs_prev[cid]; - if (cell_clip > 0.0) { - cs_local = clip_op(cs_local, cell_clip); + if (cell_clip_t > strict_cast(0.0f)) { + cs_local = clip_op(cs_local, cell_clip_t); } cs[cid] = cs_local; @@ -174,8 +220,8 @@ __global__ void concat_xh(T* xh, const T* x, const T* h_prev, template void LSTMBlockCellFpropWithCUDA( - OpKernelContext* ctx, const GPUDevice& d, const T forget_bias, - const T cell_clip, bool use_peephole, typename TTypes::ConstMatrix x, + OpKernelContext* ctx, const GPUDevice& d, const float forget_bias, + const float cell_clip, bool use_peephole, typename TTypes::ConstMatrix x, typename TTypes::ConstMatrix cs_prev, typename TTypes::ConstMatrix h_prev, typename TTypes::ConstMatrix w, typename TTypes::ConstVec wci, typename TTypes::ConstVec wcf, @@ -202,7 +248,7 @@ void LSTMBlockCellFpropWithCUDA( // states1 = xh * w typename TTypes::ConstMatrix const_xh(xh.data(), xh.dimensions()); TensorBlasGemm::compute( - ctx, d, false, false, T(1), const_xh, w, T(0), icfo); + ctx, d, false, false, 1.f, const_xh, w, 0.f, icfo); // Add bias, apply non-linearities and gating. // @@ -357,8 +403,9 @@ void LSTMBlockCellBpropWithCUDA( template struct TensorAdd; \ template <> \ void LSTMBlockCellFprop::operator()( \ - OpKernelContext* ctx, const GPUDevice& d, const T forget_bias, \ - const T cell_clip, bool use_peephole, typename TTypes::ConstMatrix x, \ + OpKernelContext* ctx, const GPUDevice& d, const float forget_bias, \ + const float cell_clip, bool use_peephole, \ + typename TTypes::ConstMatrix x, \ typename TTypes::ConstMatrix cs_prev, \ typename TTypes::ConstMatrix h_prev, \ typename TTypes::ConstMatrix w, typename TTypes::ConstVec wci, \ @@ -368,10 +415,10 @@ void LSTMBlockCellBpropWithCUDA( typename TTypes::Matrix f, typename TTypes::Matrix o, \ typename TTypes::Matrix ci, typename TTypes::Matrix co, \ typename TTypes::Matrix icfo, typename TTypes::Matrix h) { \ - LSTMBlockCellFpropWithCUDA(ctx, d, forget_bias, cell_clip, use_peephole, \ - x, cs_prev, h_prev, w, wci, wcf, wco, b, xh, i, \ - cs, f, o, ci, co, icfo, h, batch_size_, \ - cell_size_, input_size_); \ + LSTMBlockCellFpropWithCUDA(ctx, d, forget_bias, cell_clip, \ + use_peephole, x, cs_prev, h_prev, w, wci, \ + wcf, wco, b, xh, i, cs, f, o, ci, co, icfo, \ + h, batch_size_, cell_size_, input_size_); \ } \ template <> \ void LSTMBlockCellBprop::operator()( \ @@ -403,6 +450,7 @@ void LSTMBlockCellBpropWithCUDA( template struct BlockLSTMBprop; DEFINE_GPU_SPECS(float); +DEFINE_GPU_SPECS(Eigen::half); // DEFINE_GPU_SPECS(double); #undef DEFINE_GPU_SPECS diff --git a/tensorflow/contrib/rnn/ops/lstm_ops.cc b/tensorflow/contrib/rnn/ops/lstm_ops.cc index 699cc6c88a..1679e35518 100644 --- a/tensorflow/contrib/rnn/ops/lstm_ops.cc +++ b/tensorflow/contrib/rnn/ops/lstm_ops.cc @@ -41,7 +41,7 @@ REGISTER_OP("LSTMBlockCell") .Attr("forget_bias: float = 1.0") .Attr("cell_clip: float = 3.0") .Attr("use_peephole: bool = false") - .Attr("T: {float}") + .Attr("T: {half, float}") .SetShapeFn([](InferenceContext* c) { ShapeHandle x, cs_prev; TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &x)); @@ -128,7 +128,7 @@ REGISTER_OP("LSTMBlockCellGrad") .Output("wcf_grad: T") .Output("wco_grad: T") .Attr("use_peephole: bool") - .Attr("T: {float}") + .Attr("T: {half, float}") .SetShapeFn([](InferenceContext* c) { ShapeHandle x, cs_prev; TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &x)); @@ -196,7 +196,7 @@ REGISTER_OP("BlockLSTM") .Attr("forget_bias: float = 1.0") .Attr("cell_clip: float = 3.0") .Attr("use_peephole: bool = false") - .Attr("T: {float}") + .Attr("T: {half, float}") .SetShapeFn([](InferenceContext* c) { ShapeHandle x, b; TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 3, &x)); @@ -288,7 +288,7 @@ REGISTER_OP("BlockLSTMGrad") .Output("wco_grad: T") .Output("b_grad: T") .Attr("use_peephole: bool") - .Attr("T: {float}") + .Attr("T: {half, float}") .SetShapeFn([](InferenceContext* c) { ShapeHandle x, cs_prev, h_prev, w, wci, wco, wcf, b; TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 3, &x)); diff --git a/tensorflow/contrib/rnn/python/ops/lstm_ops.py b/tensorflow/contrib/rnn/python/ops/lstm_ops.py index 9e61fc54d1..f645165efe 100644 --- a/tensorflow/contrib/rnn/python/ops/lstm_ops.py +++ b/tensorflow/contrib/rnn/python/ops/lstm_ops.py @@ -596,6 +596,7 @@ class LSTMBlockFusedCell(LSTMBlockWrapper): cell_clip=None, use_peephole=False, reuse=None, + dtype=None, name="lstm_fused_cell"): """Initialize the LSTM cell. @@ -607,12 +608,14 @@ class LSTMBlockFusedCell(LSTMBlockWrapper): reuse: (optional) boolean describing whether to reuse variables in an existing scope. If not `True`, and the existing scope already has the given variables, an error is raised. + dtype: the dtype of variables of this layer. name: String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require reuse=True in such cases. By default this is "lstm_cell", for variable-name compatibility with `tf.nn.rnn_cell.LSTMCell`. """ - super(LSTMBlockFusedCell, self).__init__(_reuse=reuse, name=name) + super(LSTMBlockFusedCell, self).__init__( + _reuse=reuse, name=name, dtype=dtype) self._num_units = num_units self._forget_bias = forget_bias self._cell_clip = cell_clip if cell_clip is not None else -1 -- GitLab From 40dd7b0096f3e344444766169617a57ce410fd17 Mon Sep 17 00:00:00 2001 From: Todd Wang Date: Thu, 25 Oct 2018 14:06:44 -0700 Subject: [PATCH 026/873] Upgrade setuptools before installing absl-py. (#23266) PiperOrigin-RevId: 218471042 --- .../tools/ci_build/install/install_pip_packages.sh | 13 +++++-------- 1 file changed, 5 insertions(+), 8 deletions(-) diff --git a/tensorflow/tools/ci_build/install/install_pip_packages.sh b/tensorflow/tools/ci_build/install/install_pip_packages.sh index 7f293e8604..2c142041f3 100755 --- a/tensorflow/tools/ci_build/install/install_pip_packages.sh +++ b/tensorflow/tools/ci_build/install/install_pip_packages.sh @@ -29,6 +29,11 @@ easy_install3 -U pip==9.0.3 pip2 install wheel==0.31.1 pip3 install wheel==0.31.1 +# Install last working version of setuptools. This must happen before we install +# absl-py, which uses install_requires notation introduced in setuptools 20.5. +pip2 install --upgrade setuptools==39.1.0 +pip3 install --upgrade setuptools==39.1.0 + pip2 install virtualenv pip3 install virtualenv @@ -112,10 +117,6 @@ pip3 install --upgrade gast pip2 install --upgrade termcolor pip3 install --upgrade termcolor -# Install last working version of setuptools. -pip2 install --upgrade setuptools==39.1.0 -pip3 install --upgrade setuptools==39.1.0 - # Keras pip2 install keras_applications==1.0.6 --no-deps pip3 install keras_applications==1.0.6 --no-deps @@ -123,7 +124,3 @@ pip2 install keras_preprocessing==1.0.5 --no-deps pip3 install keras_preprocessing==1.0.5 --no-deps pip2 install --upgrade h5py==2.8.0 pip3 install --upgrade h5py==2.8.0 - -# Install last working version of setuptools. -pip2 install --upgrade setuptools==39.1.0 -pip3 install --upgrade setuptools==39.1.0 -- GitLab From b58290fc603760724dc4fb55585ad81094204f56 Mon Sep 17 00:00:00 2001 From: Todd Wang Date: Thu, 25 Oct 2018 18:11:56 -0700 Subject: [PATCH 027/873] Allow empty GCS tokens to be cached. (#23275) PiperOrigin-RevId: 217159671 --- tensorflow/core/platform/cloud/google_auth_provider.cc | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tensorflow/core/platform/cloud/google_auth_provider.cc b/tensorflow/core/platform/cloud/google_auth_provider.cc index 6ffe51e897..e15400780a 100644 --- a/tensorflow/core/platform/cloud/google_auth_provider.cc +++ b/tensorflow/core/platform/cloud/google_auth_provider.cc @@ -135,8 +135,7 @@ Status GoogleAuthProvider::GetToken(string* t) { mutex_lock lock(mu_); const uint64 now_sec = env_->NowSeconds(); - if (!current_token_.empty() && - now_sec + kExpirationTimeMarginSec < expiration_timestamp_sec_) { + if (now_sec + kExpirationTimeMarginSec < expiration_timestamp_sec_) { *t = current_token_; return Status::OK(); } -- GitLab From f90c2141ce5417e26bbf3dbcae426a8987cb60f1 Mon Sep 17 00:00:00 2001 From: Todd Wang Date: Thu, 25 Oct 2018 18:36:15 -0700 Subject: [PATCH 028/873] Upgrade setuptools before clean pip install pulls in absl-py. (#23276) absl-py recently added a version dependency to the package, causing install to fail on the old setuptools PiperOrigin-RevId: 218783878 --- tensorflow/tools/ci_build/builds/pip.sh | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/tensorflow/tools/ci_build/builds/pip.sh b/tensorflow/tools/ci_build/builds/pip.sh index 6543779022..d1fad98ed7 100755 --- a/tensorflow/tools/ci_build/builds/pip.sh +++ b/tensorflow/tools/ci_build/builds/pip.sh @@ -321,6 +321,12 @@ create_activate_virtualenv_and_install_tensorflow() { # some versions in python curl https://bootstrap.pypa.io/get-pip.py | python + # Force upgrade of setuptools. This must happen before the pip install of the + # WHL_PATH, which pulls in absl-py, which uses install_requires notation + # introduced in setuptools >=20.5. The default version of setuptools is 5.5.1, + # which is too old for absl-py. + pip install --upgrade setuptools==39.1.0 + # Force tensorflow reinstallation. Otherwise it may not get installed from # last build if it had the same version number as previous build. PIP_FLAGS="--upgrade --force-reinstall" @@ -328,9 +334,11 @@ create_activate_virtualenv_and_install_tensorflow() { die "pip install (forcing to reinstall tensorflow) FAILED" echo "Successfully installed pip package ${TF_WHEEL_PATH}" - # Force downgrade setuptools. + # Force downgrade of setuptools. This must happen after the pip install of the + # WHL_PATH, which ends up upgrading to the latest version of setuptools. + # Versions of setuptools >= 39.1.0 will cause tests to fail like this: + # ImportError: cannot import name py31compat pip install --upgrade setuptools==39.1.0 - } ################################################################################ -- GitLab From 748435b8ef55a554e011e97a9f893304e737775a Mon Sep 17 00:00:00 2001 From: Todd Wang Date: Thu, 25 Oct 2018 20:26:00 -0700 Subject: [PATCH 029/873] Fixed the issue that each invocation of model.fit/evaluate/predict modifies the (#23280) graph. PiperOrigin-RevId: 218793646 --- .../contrib/tpu/python/tpu/keras_support.py | 17 ++++++++++++++--- 1 file changed, 14 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/keras_support.py b/tensorflow/contrib/tpu/python/tpu/keras_support.py index d628258b9d..a8eb3aa892 100644 --- a/tensorflow/contrib/tpu/python/tpu/keras_support.py +++ b/tensorflow/contrib/tpu/python/tpu/keras_support.py @@ -97,14 +97,25 @@ from tensorflow.python.platform import tf_logging as logging # TODO(b/114775106): temporary shim to optionally initialize the TPU # This increases the odds our session is initialized, but shouldn't be needed. +_TEST_REWRITE_OP = None + + def _maybe_initialize_tpu(session): """Initialize the TPU if it has not already been initialized.""" + global _TEST_REWRITE_OP try: + # Try to use cached version to avoid another ground of graph optimization. + test_rewrite_op = _TEST_REWRITE_OP + if (test_rewrite_op is None or + test_rewrite_op[0].graph != ops.get_default_graph()): + + def test_op(): + return constant_op.constant(1) + constant_op.constant(1) - def test_op(): - return constant_op.constant(1) + constant_op.constant(1) + test_rewrite_op = tpu.rewrite(test_op) + _TEST_REWRITE_OP = test_rewrite_op - session.run(tpu.rewrite(test_op)) + session.run(test_rewrite_op) except errors.FailedPreconditionError as _: session.run(tpu.initialize_system()) -- GitLab From 0fb33d8f232eff875aac4379a2bd347fbd0ef8e1 Mon Sep 17 00:00:00 2001 From: "Meng, Peng" Date: Thu, 1 Nov 2018 23:00:07 +0800 Subject: [PATCH 030/873] fix softmax Change-Id: Ic882c0c071c650400a3aadb9025b37381c762262 --- tensorflow/core/kernels/mkl_softmax_op.cc | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/tensorflow/core/kernels/mkl_softmax_op.cc b/tensorflow/core/kernels/mkl_softmax_op.cc index 92167e06d5..6ff27b1957 100644 --- a/tensorflow/core/kernels/mkl_softmax_op.cc +++ b/tensorflow/core/kernels/mkl_softmax_op.cc @@ -63,7 +63,13 @@ class MklSoftmaxOp : public OpKernel { : src_tensor.shape(); const int input_dims = src_tf_shape.dims(); auto src_dims = TFShapeToMklDnnDims(src_tf_shape); - auto output_dims = src_dims; + memory::dims output_dims; + if(src_mkl_shape.IsMklTensor()) { + output_dims = src_mkl_shape.GetSizesAsMklDnnDims(); + } + else { + output_dims = src_dims; //nhwc + } memory::format layout_type; // In MKL, data format passed to mkl softmax op depends on dimension of the input tensor. // Here "x" data format in MKL is used for 1 dim tensor, "nc" for 2 dim tensor, @@ -82,10 +88,10 @@ class MklSoftmaxOp : public OpKernel { layout_type = memory::format::tnc; break; case 4: - layout_type = memory::format::nchw; + layout_type = memory::format::nhwc; break; case 5: - layout_type = memory::format::ncdhw; + layout_type = memory::format::ndhwc; break; default: OP_REQUIRES_OK(context, errors::Aborted("Input dims must be <= 5 and >=1")); -- GitLab From 4cdcadc62394e3f07520e0a04208a6916f178f42 Mon Sep 17 00:00:00 2001 From: Todd Wang Date: Thu, 1 Nov 2018 16:56:56 -0700 Subject: [PATCH 031/873] AsyncCheckpoints: Add missing 'self' arg to write_graph_fn. (#23439) PiperOrigin-RevId: 219365527 --- tensorflow/contrib/tpu/python/tpu/async_checkpoint.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/tpu/python/tpu/async_checkpoint.py b/tensorflow/contrib/tpu/python/tpu/async_checkpoint.py index 78253d83fc..c32bd5997c 100644 --- a/tensorflow/contrib/tpu/python/tpu/async_checkpoint.py +++ b/tensorflow/contrib/tpu/python/tpu/async_checkpoint.py @@ -102,7 +102,8 @@ class AsyncCheckpointSaverHook(basic_session_run_hooks.CheckpointSaverHook): training_util.write_graph( ops.get_default_graph().as_graph_def(add_shapes=True), self._checkpoint_dir, "graph.pbtxt") - self._write_graph_thread = threading.Thread(target=_write_graph_fn) + self._write_graph_thread = threading.Thread(target=_write_graph_fn, + args=[self]) self._write_graph_thread.start() saver_def = self._get_saver().saver_def if self._get_saver() else None -- GitLab From 8ce231a8ebc73be5be53ccd90387fc68b187bcec Mon Sep 17 00:00:00 2001 From: Todd Wang Date: Thu, 1 Nov 2018 18:12:13 -0700 Subject: [PATCH 032/873] Update version to 1.12.0 final (#23444) --- tensorflow/core/public/version.h | 2 +- tensorflow/tools/pip_package/setup.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h index 500ec8f97b..a55fe17dd5 100644 --- a/tensorflow/core/public/version.h +++ b/tensorflow/core/public/version.h @@ -24,7 +24,7 @@ limitations under the License. // TF_VERSION_SUFFIX is non-empty for pre-releases (e.g. "-alpha", "-alpha.1", // "-beta", "-rc", "-rc.1") -#define TF_VERSION_SUFFIX "-rc2" +#define TF_VERSION_SUFFIX "" #define TF_STR_HELPER(x) #x #define TF_STR(x) TF_STR_HELPER(x) diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index ceaa96b690..036830dd22 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 = '1.12.0-rc2' +_VERSION = '1.12.0' REQUIRED_PACKAGES = [ 'absl-py >= 0.1.6', -- GitLab From a6d8ffae097d0132989ae4688d224121ec6d8f35 Mon Sep 17 00:00:00 2001 From: Todd Wang Date: Thu, 1 Nov 2018 18:35:10 -0700 Subject: [PATCH 033/873] Fix a bug in tpu.py and xla.py that while creating an identity node for control input edges under rewrite context, the parent control flow context is lost. (#23446) PiperOrigin-RevId: 219724472 --- tensorflow/contrib/compiler/xla.py | 13 +++++-------- tensorflow/contrib/tpu/python/tpu/tpu.py | 13 +++++-------- 2 files changed, 10 insertions(+), 16 deletions(-) diff --git a/tensorflow/contrib/compiler/xla.py b/tensorflow/contrib/compiler/xla.py index 873b03580d..83d9d8c54a 100644 --- a/tensorflow/contrib/compiler/xla.py +++ b/tensorflow/contrib/compiler/xla.py @@ -179,14 +179,11 @@ class XLACompileContext(control_flow_ops.XLAControlFlowContext): if external_control_inputs: # Use an identity to pull control inputs as data inputs. Note that we # ignore ops which don't have outputs. TODO(phawkins): fix that. - with ops.control_dependencies(None): - self.Enter() - external_control_inputs = [ - array_ops.identity(x.outputs[0]).op - for x in external_control_inputs - if x.outputs - ] - self.Exit() + external_control_inputs = [ + array_ops.identity(x.outputs[0]).op + for x in external_control_inputs + if x.outputs + ] # pylint: disable=protected-access op._add_control_inputs(external_control_inputs) # pylint: enable=protected-access diff --git a/tensorflow/contrib/tpu/python/tpu/tpu.py b/tensorflow/contrib/tpu/python/tpu/tpu.py index 11aaa1c66a..a5ccaa071b 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu.py @@ -371,14 +371,11 @@ class TPUReplicateContext(control_flow_ops.XLAControlFlowContext): if external_control_inputs: # Use an identity to pull control inputs as data inputs. Note that we # ignore ops which don't have outputs. TODO(phawkins): fix that. - with ops.control_dependencies(None): - self.Enter() - external_control_inputs = [ - array_ops.identity(x.outputs[0]).op - for x in external_control_inputs - if x.outputs - ] - self.Exit() + external_control_inputs = [ + array_ops.identity(x.outputs[0]).op + for x in external_control_inputs + if x.outputs + ] # pylint: disable=protected-access op._add_control_inputs(external_control_inputs) # pylint: enable=protected-access -- GitLab From c475ede7a02ff9a3e919ecbb9545be9377013bf1 Mon Sep 17 00:00:00 2001 From: George Sterpu Date: Fri, 2 Nov 2018 17:11:03 +0000 Subject: [PATCH 034/873] Update beam_search_decoder.py #22172 probably not the neatest way to update my previous pull request... --- tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py b/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py index ab36848f13..8f8f057702 100644 --- a/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py +++ b/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py @@ -921,6 +921,7 @@ def _get_scores(log_probs, sequence_lengths, length_penalty_weight, """ length_penalty_ = _length_penalty( sequence_lengths=sequence_lengths, penalty_factor=length_penalty_weight) + length_penalty_ = math_ops.cast(length_penalty_, dtype=log_probs.dtype) scores = log_probs / length_penalty_ coverage_penalty_weight = ops.convert_to_tensor( -- GitLab From 090cb450e25f14942e70c53d0d82ea8f9d164d57 Mon Sep 17 00:00:00 2001 From: Bhavani Subramanian Date: Mon, 5 Nov 2018 15:30:04 -0800 Subject: [PATCH 035/873] Fix for build failure (#424) Temporarily merging fix into our master so testing can progress --- tensorflow/core/BUILD | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index afe4c46c8e..26dd295d0c 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -383,6 +383,7 @@ cc_library( ":lib_platform", ":platform_base", "//tensorflow/core/platform/default/build_config:port", + "@com_google_absl//absl/base", "@snappy", ], ) -- GitLab From 88026690778a4960c23019d13572f0f346f19916 Mon Sep 17 00:00:00 2001 From: "Meng, Peng" Date: Tue, 6 Nov 2018 21:13:00 +0800 Subject: [PATCH 036/873] update mkl_softmax comments Change-Id: I95428c0e1d4df73f984b3b1f0e9770ec14688dd1 --- tensorflow/core/kernels/mkl_softmax_op.cc | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/core/kernels/mkl_softmax_op.cc b/tensorflow/core/kernels/mkl_softmax_op.cc index 6ff27b1957..c8b78f6187 100644 --- a/tensorflow/core/kernels/mkl_softmax_op.cc +++ b/tensorflow/core/kernels/mkl_softmax_op.cc @@ -50,8 +50,6 @@ class MklSoftmaxOp : public OpKernel { // src_tensor now points to the 0-th input of global data struct "context" size_t src_idx = 0; const Tensor& src_tensor = MklGetInput(context, src_idx); - //const int input_dims = src_tensor.dims(); - // printf("input_dims = %d\n", input_dims); // Add: get MklShape MklDnnShape src_mkl_shape; GetMklShape(context, src_idx, &src_mkl_shape); @@ -122,6 +120,8 @@ class MklSoftmaxOp : public OpKernel { // creating a memory descriptor // passing outermost dim as default axis, where the softmax is applied + // If axis is not the last dimension, python op will do a transpose so that we can + // still perform softmax on its last dimension. int axis = input_dims - 1; auto softmax_fwd_desc = softmax_forward::desc(prop_kind::forward_scoring, src.GetOpMemDesc(), axis); -- GitLab From 7f642e5afd7ddaad5215958ce3f22523ccb08a9c Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Mon, 27 Nov 2017 11:28:59 -0800 Subject: [PATCH 037/873] Fix issue in tf.nn.softmax where negative dims could only be -1 This fix tries to address the issue raised in 14916 where negative dims could only be -1 in tf.nn.softmax. The issue was that dims=-1 was handled as a case of "last dim" with `is_last_dim = (dim is -1) or (dim == shape.ndims - 1)` but the generic negative dims were never processed. This fix adds `dim += shape.ndims` for generic negative dims. This fix fixes 14916. Signed-off-by: Yong Tang --- tensorflow/python/ops/nn_ops.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py index bc195993c2..0b6d8e836f 100644 --- a/tensorflow/python/ops/nn_ops.py +++ b/tensorflow/python/ops/nn_ops.py @@ -1698,6 +1698,10 @@ def _softmax(logits, compute_op, dim=-1, name=None): # If dim is not the last dimension, we have to do a transpose so that we can # still perform softmax on its last dimension. + # In case dim is negative (and is not last dimension -1), add shape.ndims + if dim < 0: + dim += shape.ndims + # Swap logits' dimension of dim and its last dimension. input_rank = array_ops.rank(logits) dim_axis = dim % shape.ndims -- GitLab From e459d7ed9e843d2e6cad5cee2cfd0cbeb9d0c462 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Mon, 27 Nov 2017 11:35:21 -0800 Subject: [PATCH 038/873] Add test case for negative dims (other than -1) for tf.nn.softmax Signed-off-by: Yong Tang --- tensorflow/python/kernel_tests/softmax_op_test.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/tensorflow/python/kernel_tests/softmax_op_test.py b/tensorflow/python/kernel_tests/softmax_op_test.py index ef9301d4e3..c87b6728aa 100644 --- a/tensorflow/python/kernel_tests/softmax_op_test.py +++ b/tensorflow/python/kernel_tests/softmax_op_test.py @@ -200,6 +200,15 @@ class SoftmaxTest(test.TestCase): use_gpu=False) self._testOverflow(use_gpu=False) + def testAlongNegativeDimension(self): + self._testSoftmax( + np.array([[[1., 1., 1., 1.], [1., 2., 3., 4.]], + [[2., 3., 4., 5.], [6., 7., 8., 9.]], + [[5., 4., 3., 2.], [1., 2., 3., 4.]]]).astype(np.float32), + dim=-2, + use_gpu=False) + self._testOverflow(use_gpu=False) + def testShapeInference(self): op = nn_ops.softmax([[[1., 1., 1., 1.], [1., 2., 3., 4.]], [[2., 3., 4., 5.], [6., 7., 8., 9.]], -- GitLab From 2ec6dcb7fe33ffac1dc55b9d7f6f23c417cb3dc1 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Tue, 6 Nov 2018 23:12:59 +0000 Subject: [PATCH 039/873] Fix broken test Signed-off-by: Yong Tang --- tensorflow/python/ops/nn_ops.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py index 0b6d8e836f..a2305cefba 100644 --- a/tensorflow/python/ops/nn_ops.py +++ b/tensorflow/python/ops/nn_ops.py @@ -1699,7 +1699,7 @@ def _softmax(logits, compute_op, dim=-1, name=None): # still perform softmax on its last dimension. # In case dim is negative (and is not last dimension -1), add shape.ndims - if dim < 0: + if not isinstance(dim, ops.Tensor) and dim < 0: dim += shape.ndims # Swap logits' dimension of dim and its last dimension. -- GitLab From 8e4ec9ae62135adbc523470af1546c178a7f97c5 Mon Sep 17 00:00:00 2001 From: frreiss Date: Tue, 6 Nov 2018 12:55:29 -0800 Subject: [PATCH 040/873] Add missing random seed field to OrderedEnqueuer Fix whitespace Simplify changes Simplify changeset --- tensorflow/python/keras/utils/data_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/keras/utils/data_utils.py b/tensorflow/python/keras/utils/data_utils.py index 01a9d61a84..8e36d4dea7 100644 --- a/tensorflow/python/keras/utils/data_utils.py +++ b/tensorflow/python/keras/utils/data_utils.py @@ -598,7 +598,7 @@ class OrderedEnqueuer(SequenceEnqueuer): def pool_fn(seqs): return multiprocessing.Pool(workers, initializer=init_pool_generator, - initargs=(seqs, self.random_seed)) + initargs=(seqs, None)) return pool_fn def _wait_queue(self): -- GitLab From 117d30b9e313f93a39f17883e2e64960b4015c15 Mon Sep 17 00:00:00 2001 From: "Meng, Peng" Date: Thu, 8 Nov 2018 22:33:42 +0800 Subject: [PATCH 041/873] use different layout for mkl and tf Change-Id: Id148c006fa74ca0382af8e67c6437f551fbba1b7 --- tensorflow/core/kernels/mkl_softmax_op.cc | 17 ++++++++++++++--- 1 file changed, 14 insertions(+), 3 deletions(-) diff --git a/tensorflow/core/kernels/mkl_softmax_op.cc b/tensorflow/core/kernels/mkl_softmax_op.cc index c8b78f6187..ca78164ac9 100644 --- a/tensorflow/core/kernels/mkl_softmax_op.cc +++ b/tensorflow/core/kernels/mkl_softmax_op.cc @@ -62,7 +62,7 @@ class MklSoftmaxOp : public OpKernel { const int input_dims = src_tf_shape.dims(); auto src_dims = TFShapeToMklDnnDims(src_tf_shape); memory::dims output_dims; - if(src_mkl_shape.IsMklTensor()) { + if (src_mkl_shape.IsMklTensor()) { output_dims = src_mkl_shape.GetSizesAsMklDnnDims(); } else { @@ -75,6 +75,7 @@ class MklSoftmaxOp : public OpKernel { // Each of the simbols has the following meaning: // n = batch, c = channels, t = sequence lenght, h = height, // w = width, d = depth + switch (input_dims) { case 1: layout_type = memory::format::x; @@ -86,10 +87,20 @@ class MklSoftmaxOp : public OpKernel { layout_type = memory::format::tnc; break; case 4: - layout_type = memory::format::nhwc; + if (src_mkl_shape.IsMklTensor()) { + layout_type = memory::format::nhwc; + } + else { + layout_type = memory::format::nchw; + } break; case 5: - layout_type = memory::format::ndhwc; + if (src_mkl_shape.IsMklTensor()) { + layout_type = memory::format::ndhwc; + } + else { + layout_type = memory::format::ncdhw; + } break; default: OP_REQUIRES_OK(context, errors::Aborted("Input dims must be <= 5 and >=1")); -- GitLab From 902b080a85fc78816f0ca0c8b66d80411b372579 Mon Sep 17 00:00:00 2001 From: "Meng, Peng" Date: Mon, 12 Nov 2018 00:48:03 +0800 Subject: [PATCH 042/873] fix layout error Change-Id: I24d66af494a9e96cfa13c885b3765f3f74dc2976 --- tensorflow/core/kernels/mkl_softmax_op.cc | 21 ++++++--------------- 1 file changed, 6 insertions(+), 15 deletions(-) diff --git a/tensorflow/core/kernels/mkl_softmax_op.cc b/tensorflow/core/kernels/mkl_softmax_op.cc index ca78164ac9..6d644fba69 100644 --- a/tensorflow/core/kernels/mkl_softmax_op.cc +++ b/tensorflow/core/kernels/mkl_softmax_op.cc @@ -62,10 +62,13 @@ class MklSoftmaxOp : public OpKernel { const int input_dims = src_tf_shape.dims(); auto src_dims = TFShapeToMklDnnDims(src_tf_shape); memory::dims output_dims; + int axis; if (src_mkl_shape.IsMklTensor()) { + axis = 1; output_dims = src_mkl_shape.GetSizesAsMklDnnDims(); } else { + axis = input_dims - 1; output_dims = src_dims; //nhwc } memory::format layout_type; @@ -87,20 +90,10 @@ class MklSoftmaxOp : public OpKernel { layout_type = memory::format::tnc; break; case 4: - if (src_mkl_shape.IsMklTensor()) { - layout_type = memory::format::nhwc; - } - else { - layout_type = memory::format::nchw; - } + layout_type = memory::format::nchw; break; case 5: - if (src_mkl_shape.IsMklTensor()) { - layout_type = memory::format::ndhwc; - } - else { - layout_type = memory::format::ncdhw; - } + layout_type = memory::format::ncdhw; break; default: OP_REQUIRES_OK(context, errors::Aborted("Input dims must be <= 5 and >=1")); @@ -127,15 +120,13 @@ class MklSoftmaxOp : public OpKernel { // data format is "nc" for src and dst; since the src and dst buffer is // always in 2D shape src.SetUsrMem(src_md, &src_tensor); - src.SetOpMemDesc(src_dims, layout_type); // creating a memory descriptor // passing outermost dim as default axis, where the softmax is applied // If axis is not the last dimension, python op will do a transpose so that we can // still perform softmax on its last dimension. - int axis = input_dims - 1; auto softmax_fwd_desc = softmax_forward::desc(prop_kind::forward_scoring, - src.GetOpMemDesc(), axis); + src.GetUsrMemDesc(), axis); auto softmax_fwd_pd = softmax_forward::primitive_desc(softmax_fwd_desc, cpu_engine); -- GitLab From 669698caf6e886c27d4a9494760078ef3f4f1d40 Mon Sep 17 00:00:00 2001 From: "Meng, Peng" Date: Wed, 14 Nov 2018 09:26:32 +0800 Subject: [PATCH 043/873] update comments Change-Id: Ie781dba3b07cee43bf1864ab5155a710d322aa19 --- tensorflow/core/kernels/mkl_softmax_op.cc | 11 ++--------- 1 file changed, 2 insertions(+), 9 deletions(-) diff --git a/tensorflow/core/kernels/mkl_softmax_op.cc b/tensorflow/core/kernels/mkl_softmax_op.cc index 6d644fba69..4e093cbf4b 100644 --- a/tensorflow/core/kernels/mkl_softmax_op.cc +++ b/tensorflow/core/kernels/mkl_softmax_op.cc @@ -69,7 +69,7 @@ class MklSoftmaxOp : public OpKernel { } else { axis = input_dims - 1; - output_dims = src_dims; //nhwc + output_dims = src_dims; } memory::format layout_type; // In MKL, data format passed to mkl softmax op depends on dimension of the input tensor. @@ -113,18 +113,11 @@ class MklSoftmaxOp : public OpKernel { ? src_mkl_shape.GetMklLayout() : memory::desc(src_dims, MklDnnType(), layout_type); - // src: setting memory descriptor and op memory descriptor - // Basically following two functions maps the TF "src_tensor" to mkl - // tensor object "src" + // src: setting memory descriptor // following functions are in mkl_util.h - // data format is "nc" for src and dst; since the src and dst buffer is - // always in 2D shape src.SetUsrMem(src_md, &src_tensor); // creating a memory descriptor - // passing outermost dim as default axis, where the softmax is applied - // If axis is not the last dimension, python op will do a transpose so that we can - // still perform softmax on its last dimension. auto softmax_fwd_desc = softmax_forward::desc(prop_kind::forward_scoring, src.GetUsrMemDesc(), axis); auto softmax_fwd_pd = -- GitLab From 489e181be77b83b2b631f48968aaf40897001838 Mon Sep 17 00:00:00 2001 From: Siju Date: Fri, 16 Nov 2018 11:56:50 +0530 Subject: [PATCH 044/873] Update graph_transformations.h --- .../lite/toco/graph_transformations/graph_transformations.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/lite/toco/graph_transformations/graph_transformations.h b/tensorflow/lite/toco/graph_transformations/graph_transformations.h index 73a90c8239..187b584b69 100644 --- a/tensorflow/lite/toco/graph_transformations/graph_transformations.h +++ b/tensorflow/lite/toco/graph_transformations/graph_transformations.h @@ -139,7 +139,7 @@ DECLARE_GRAPH_TRANSFORMATION(MakeInitialDequantizeOperator) DECLARE_GRAPH_TRANSFORMATION(MoveBinaryOperatorBeforeReshape) DECLARE_GRAPH_TRANSFORMATION(PropagateActivationFunctionIntoConstants) DECLARE_GRAPH_TRANSFORMATION(PropagateArrayDataTypes) -DECLARE_GRAPH_TRANSFORMATION(PropagateFakeQuantNumBits); +DECLARE_GRAPH_TRANSFORMATION(PropagateFakeQuantNumBits) DECLARE_GRAPH_TRANSFORMATION(PropagateFixedSizes) DECLARE_GRAPH_TRANSFORMATION(HardcodeMinMax) DECLARE_GRAPH_TRANSFORMATION(Quantize) -- GitLab From 2427ff8fe9a24f4d9581716af46ef07f99408e0f Mon Sep 17 00:00:00 2001 From: "Meng, Peng" Date: Wed, 24 Oct 2018 15:49:46 +0800 Subject: [PATCH 045/873] fix layout error when src tensor is mkl Change-Id: I6bcfc8981867f1b60591c65fde77c92cff298694 --- tensorflow/core/kernels/mkl_softmax_op.cc | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/tensorflow/core/kernels/mkl_softmax_op.cc b/tensorflow/core/kernels/mkl_softmax_op.cc index 4e093cbf4b..25c0c7b078 100644 --- a/tensorflow/core/kernels/mkl_softmax_op.cc +++ b/tensorflow/core/kernels/mkl_softmax_op.cc @@ -90,10 +90,20 @@ class MklSoftmaxOp : public OpKernel { layout_type = memory::format::tnc; break; case 4: - layout_type = memory::format::nchw; + if (src_mkl_shape.IsMklTensor()) { + layout_type = memory::format::nhwc; + } + else { + layout_type = memory::format::nchw; + } break; case 5: - layout_type = memory::format::ncdhw; + if (src_mkl_shape.IsMklTensor()) { + layout_type = memory::format::ndhwc; + } + else { + layout_type = memory::format::ncdhw; + } break; default: OP_REQUIRES_OK(context, errors::Aborted("Input dims must be <= 5 and >=1")); -- GitLab From 437aeb55cc89fade6e386205b30148bc21471bb1 Mon Sep 17 00:00:00 2001 From: Castiel Date: Fri, 23 Nov 2018 07:31:57 +1030 Subject: [PATCH 046/873] Minor change in word2vec_basic tutorial --- tensorflow/examples/tutorials/word2vec/word2vec_basic.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py index b09ee99768..bbcfc32098 100644 --- a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py +++ b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py @@ -91,7 +91,7 @@ vocabulary_size = 50000 def build_dataset(words, n_words): """Process raw inputs into a dataset.""" - count = [['UNK', -1]] + count = [('UNK', -1)] count.extend(collections.Counter(words).most_common(n_words - 1)) dictionary = dict() for word, _ in count: @@ -125,6 +125,7 @@ data_index = 0 # Step 3: Function to generate a training batch for the skip-gram model. def generate_batch(batch_size, num_skips, skip_window): + global data global data_index assert batch_size % num_skips == 0 assert num_skips <= 2 * skip_window -- GitLab From 8c9dc06e8e2566de4e3c2d6067d8f1b8bc077307 Mon Sep 17 00:00:00 2001 From: Karl Lessard Date: Mon, 26 Nov 2018 09:34:16 -0500 Subject: [PATCH 047/873] Define API defs for Java --- tensorflow/core/api_def/java_api/api_def_Abort.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Abs.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_AccumulateNV2.pbtxt | 6 ++++++ .../api_def/java_api/api_def_AccumulatorApplyGradient.pbtxt | 6 ++++++ .../java_api/api_def_AccumulatorNumAccumulated.pbtxt | 6 ++++++ .../api_def/java_api/api_def_AccumulatorSetGlobalStep.pbtxt | 6 ++++++ .../api_def/java_api/api_def_AccumulatorTakeGradient.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Acos.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Acosh.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Add.pbtxt | 6 ++++++ .../java_api/api_def_AddManySparseToTensorsMap.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_AddN.pbtxt | 6 ++++++ .../api_def/java_api/api_def_AddSparseToTensorsMap.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_AddV2.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_AdjustContrast.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_AdjustContrastv2.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_AdjustHue.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_AdjustSaturation.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_All.pbtxt | 3 +++ .../core/api_def/java_api/api_def_AllCandidateSampler.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Angle.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_AnonymousIterator.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Any.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_ApplyAdaMax.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ApplyAdadelta.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_ApplyAdagrad.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ApplyAdagradDA.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_ApplyAdam.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_ApplyAddSign.pbtxt | 6 ++++++ .../api_def/java_api/api_def_ApplyCenteredRMSProp.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_ApplyFtrl.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_ApplyFtrlV2.pbtxt | 6 ++++++ .../api_def/java_api/api_def_ApplyGradientDescent.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ApplyMomentum.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ApplyPowerSign.pbtxt | 6 ++++++ .../api_def/java_api/api_def_ApplyProximalAdagrad.pbtxt | 6 ++++++ .../java_api/api_def_ApplyProximalGradientDescent.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_ApplyRMSProp.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ApproximateEqual.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_ArgMax.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_ArgMin.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_AsString.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Asin.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Asinh.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Assert.pbtxt | 6 ++++-- tensorflow/core/api_def/java_api/api_def_Assign.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_AssignAdd.pbtxt | 3 +++ .../core/api_def/java_api/api_def_AssignAddVariableOp.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_AssignSub.pbtxt | 3 +++ .../core/api_def/java_api/api_def_AssignSubVariableOp.pbtxt | 3 +++ .../core/api_def/java_api/api_def_AssignVariableOp.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Atan.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Atan2.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Atanh.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_AudioSpectrogram.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_AudioSummary.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_AudioSummaryV2.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_AvgPool.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_AvgPool3D.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_AvgPool3DGrad.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_AvgPoolGrad.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Barrier.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_BarrierClose.pbtxt | 3 +++ .../api_def/java_api/api_def_BarrierIncompleteSize.pbtxt | 3 +++ .../core/api_def/java_api/api_def_BarrierInsertMany.pbtxt | 3 +++ .../core/api_def/java_api/api_def_BarrierReadySize.pbtxt | 3 +++ .../core/api_def/java_api/api_def_BarrierTakeMany.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Batch.pbtxt | 3 +++ .../core/api_def/java_api/api_def_BatchCholesky.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_BatchCholeskyGrad.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_BatchDataset.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_BatchDatasetV2.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_BatchFFT.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_BatchFFT2D.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_BatchFFT3D.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_BatchFunction.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_BatchIFFT.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_BatchIFFT2D.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_BatchIFFT3D.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_BatchMatMul.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_BatchMatrixBandPart.pbtxt | 6 ++++++ .../api_def/java_api/api_def_BatchMatrixDeterminant.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_BatchMatrixDiag.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_BatchMatrixDiagPart.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_BatchMatrixInverse.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_BatchMatrixSetDiag.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_BatchMatrixSolve.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_BatchMatrixSolveLs.pbtxt | 6 ++++++ .../java_api/api_def_BatchMatrixTriangularSolve.pbtxt | 6 ++++++ .../java_api/api_def_BatchNormWithGlobalNormalization.pbtxt | 6 ++++++ .../api_def_BatchNormWithGlobalNormalizationGrad.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_BatchSelfAdjointEig.pbtxt | 6 ++++++ .../api_def/java_api/api_def_BatchSelfAdjointEigV2.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_BatchSvd.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_BatchToSpace.pbtxt | 3 +++ .../core/api_def/java_api/api_def_BatchToSpaceND.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_BesselI0e.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_BesselI1e.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Betainc.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_BiasAdd.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_BiasAddGrad.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_BiasAddV1.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_BigQueryReader.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Bincount.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Bitcast.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_BitwiseAnd.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_BitwiseOr.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_BitwiseXor.pbtxt | 6 ++++++ .../api_def/java_api/api_def_BoostedTreesBucketize.pbtxt | 3 +++ .../api_def_BoostedTreesCalculateBestGainsPerFeature.pbtxt | 3 +++ .../api_def/java_api/api_def_BoostedTreesCenterBias.pbtxt | 3 +++ .../java_api/api_def_BoostedTreesCreateEnsemble.pbtxt | 3 +++ .../api_def_BoostedTreesCreateQuantileStreamResource.pbtxt | 3 +++ .../java_api/api_def_BoostedTreesDeserializeEnsemble.pbtxt | 3 +++ .../api_def_BoostedTreesEnsembleResourceHandleOp.pbtxt | 3 +++ .../java_api/api_def_BoostedTreesExampleDebugOutputs.pbtxt | 3 +++ .../java_api/api_def_BoostedTreesGetEnsembleStates.pbtxt | 3 +++ .../api_def_BoostedTreesMakeQuantileSummaries.pbtxt | 3 +++ .../java_api/api_def_BoostedTreesMakeStatsSummary.pbtxt | 3 +++ .../core/api_def/java_api/api_def_BoostedTreesPredict.pbtxt | 3 +++ ...def_BoostedTreesQuantileStreamResourceAddSummaries.pbtxt | 3 +++ ..._def_BoostedTreesQuantileStreamResourceDeserialize.pbtxt | 3 +++ .../api_def_BoostedTreesQuantileStreamResourceFlush.pbtxt | 3 +++ ...stedTreesQuantileStreamResourceGetBucketBoundaries.pbtxt | 3 +++ ...api_def_BoostedTreesQuantileStreamResourceHandleOp.pbtxt | 3 +++ .../java_api/api_def_BoostedTreesSerializeEnsemble.pbtxt | 3 +++ .../java_api/api_def_BoostedTreesTrainingPredict.pbtxt | 3 +++ .../java_api/api_def_BoostedTreesUpdateEnsemble.pbtxt | 3 +++ .../core/api_def/java_api/api_def_BroadcastArgs.pbtxt | 3 +++ .../api_def/java_api/api_def_BroadcastGradientArgs.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_BroadcastTo.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Bucketize.pbtxt | 3 +++ .../java_api/api_def_BytesProducedStatsDataset.pbtxt | 6 ++++++ .../api_def/java_api/api_def_CTCBeamSearchDecoder.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_CTCGreedyDecoder.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_CTCLoss.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_CacheDataset.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Cast.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Ceil.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_CheckNumerics.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Cholesky.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_CholeskyGrad.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_ClipByValue.pbtxt | 3 +++ .../core/api_def/java_api/api_def_CloseSummaryWriter.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_CollectiveBcastRecv.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_CollectiveBcastSend.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_CollectiveReduce.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_CompareAndBitpack.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Complex.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_ComplexAbs.pbtxt | 6 ++++++ .../api_def/java_api/api_def_ComputeAccidentalHits.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Concat.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_ConcatOffset.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_ConcatV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_ConcatenateDataset.pbtxt | 6 ++++++ .../api_def/java_api/api_def_ConditionalAccumulator.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Conj.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ConjugateTranspose.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Const.pbtxt | 4 ++-- .../core/api_def/java_api/api_def_ConsumeMutexLock.pbtxt | 3 +++ .../core/api_def/java_api/api_def_ControlTrigger.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Conv2D.pbtxt | 6 ++++++ .../api_def/java_api/api_def_Conv2DBackpropFilter.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_Conv2DBackpropInput.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Conv3D.pbtxt | 6 ++++++ .../api_def/java_api/api_def_Conv3DBackpropFilter.pbtxt | 6 ++++++ .../api_def/java_api/api_def_Conv3DBackpropFilterV2.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_Conv3DBackpropInput.pbtxt | 6 ++++++ .../api_def/java_api/api_def_Conv3DBackpropInputV2.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Cos.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Cosh.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_CountUpTo.pbtxt | 3 +++ .../api_def/java_api/api_def_CreateSummaryDbWriter.pbtxt | 6 ++++++ .../api_def/java_api/api_def_CreateSummaryFileWriter.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_CropAndResize.pbtxt | 6 ++++++ .../api_def/java_api/api_def_CropAndResizeGradBoxes.pbtxt | 6 ++++++ .../api_def/java_api/api_def_CropAndResizeGradImage.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Cross.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_CudnnRNN.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_CudnnRNNBackprop.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_CudnnRNNBackpropV2.pbtxt | 6 ++++++ .../java_api/api_def_CudnnRNNCanonicalToParams.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_CudnnRNNParamsSize.pbtxt | 6 ++++++ .../java_api/api_def_CudnnRNNParamsToCanonical.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_CudnnRNNV2.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Cumprod.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Cumsum.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_DataFormatDimMap.pbtxt | 6 ++++++ .../api_def/java_api/api_def_DataFormatVecPermute.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_DatasetToGraph.pbtxt | 6 ++++++ .../api_def/java_api/api_def_DatasetToSingleElement.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_DatasetToTFRecord.pbtxt | 6 ++++++ .../api_def/java_api/api_def_DebugGradientIdentity.pbtxt | 4 ++++ .../api_def/java_api/api_def_DebugGradientRefIdentity.pbtxt | 4 ++++ .../core/api_def/java_api/api_def_DecodeAndCropJpeg.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_DecodeBase64.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_DecodeBmp.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_DecodeCSV.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_DecodeCompressed.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_DecodeGif.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_DecodeJSONExample.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_DecodeJpeg.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_DecodePng.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_DecodeProtoV2.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_DecodeRaw.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_DecodeWav.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_DeepCopy.pbtxt | 3 +++ .../core/api_def/java_api/api_def_DeleteSessionTensor.pbtxt | 3 +++ .../api_def/java_api/api_def_DenseToDenseSetOperation.pbtxt | 6 ++++++ .../java_api/api_def_DenseToSparseBatchDataset.pbtxt | 6 ++++++ .../java_api/api_def_DenseToSparseSetOperation.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_DepthToSpace.pbtxt | 6 ++++++ .../api_def/java_api/api_def_DepthwiseConv2dNative.pbtxt | 6 ++++++ .../api_def_DepthwiseConv2dNativeBackpropFilter.pbtxt | 6 ++++++ .../api_def_DepthwiseConv2dNativeBackpropInput.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Dequantize.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_DeserializeIterator.pbtxt | 6 ++++++ .../api_def/java_api/api_def_DeserializeManySparse.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_DeserializeSparse.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_DestroyResourceOp.pbtxt | 3 +++ .../api_def/java_api/api_def_DestroyTemporaryVariable.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Diag.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_DiagPart.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Digamma.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Dilation2D.pbtxt | 6 ++++++ .../api_def/java_api/api_def_Dilation2DBackpropFilter.pbtxt | 6 ++++++ .../api_def/java_api/api_def_Dilation2DBackpropInput.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Div.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_DivNoNan.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_DrawBoundingBoxes.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_DynamicPartition.pbtxt | 3 +++ .../core/api_def/java_api/api_def_DynamicStitch.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_EagerPyFunc.pbtxt | 4 ++++ tensorflow/core/api_def/java_api/api_def_EditDistance.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Elu.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_EluGrad.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Empty.pbtxt | 3 +++ .../core/api_def/java_api/api_def_EmptyTensorList.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_EncodeBase64.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_EncodeJpeg.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_EncodePng.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_EncodeProto.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_EncodeWav.pbtxt | 6 ++++++ .../api_def/java_api/api_def_EnqueueInQueueDataset.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_EnsureShape.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Enter.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Equal.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Erf.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Erfc.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Exit.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Exp.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_ExpandDims.pbtxt | 3 +++ .../java_api/api_def_ExperimentalAssertNextDataset.pbtxt | 6 ++++++ .../api_def/java_api/api_def_ExperimentalCSVDataset.pbtxt | 6 ++++++ .../api_def_ExperimentalDirectedInterleaveDataset.pbtxt | 6 ++++++ .../api_def_ExperimentalFunctionBufferingResource.pbtxt | 6 ++++++ ...i_def_ExperimentalFunctionBufferingResourceGetNext.pbtxt | 6 ++++++ ...api_def_ExperimentalFunctionBufferingResourceReset.pbtxt | 6 ++++++ .../api_def_ExperimentalIdentityIndexedDataset.pbtxt | 6 ++++++ .../java_api/api_def_ExperimentalIgnoreErrorsDataset.pbtxt | 6 ++++++ .../java_api/api_def_ExperimentalIndexedDatasetGet.pbtxt | 6 ++++++ .../api_def_ExperimentalIndexedDatasetMaterialize.pbtxt | 6 ++++++ .../java_api/api_def_ExperimentalIteratorGetDevice.pbtxt | 6 ++++++ .../api_def/java_api/api_def_ExperimentalLMDBDataset.pbtxt | 6 ++++++ .../api_def/java_api/api_def_ExperimentalMapDataset.pbtxt | 6 ++++++ ...api_def_ExperimentalMaterializedIndexDatasetHandle.pbtxt | 6 ++++++ .../api_def_ExperimentalNonSerializableDataset.pbtxt | 6 ++++++ .../api_def_ExperimentalNumaMapAndBatchDataset.pbtxt | 6 ++++++ .../api_def/java_api/api_def_ExperimentalSleepDataset.pbtxt | 6 ++++++ .../java_api/api_def_ExperimentalThreadPoolDataset.pbtxt | 6 ++++++ .../java_api/api_def_ExperimentalThreadPoolHandle.pbtxt | 6 ++++++ .../java_api/api_def_ExperimentalUniqueDataset.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Expm1.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ExtractGlimpse.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ExtractImagePatches.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ExtractJpegShape.pbtxt | 6 ++++++ .../api_def/java_api/api_def_ExtractVolumePatches.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_FFT.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_FFT2D.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_FFT3D.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_FIFOQueue.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_FIFOQueueV2.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Fact.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_FakeParam.pbtxt | 3 +++ .../api_def/java_api/api_def_FakeQuantWithMinMaxArgs.pbtxt | 6 ++++++ .../java_api/api_def_FakeQuantWithMinMaxArgsGradient.pbtxt | 6 ++++++ .../api_def/java_api/api_def_FakeQuantWithMinMaxVars.pbtxt | 6 ++++++ .../java_api/api_def_FakeQuantWithMinMaxVarsGradient.pbtxt | 6 ++++++ .../api_def_FakeQuantWithMinMaxVarsPerChannel.pbtxt | 6 ++++++ .../api_def_FakeQuantWithMinMaxVarsPerChannelGradient.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_FakeQueue.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Fill.pbtxt | 3 +++ .../java_api/api_def_FilterByLastComponentDataset.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_FilterDataset.pbtxt | 6 ++++++ .../api_def/java_api/api_def_FixedLengthRecordDataset.pbtxt | 6 ++++++ .../java_api/api_def_FixedLengthRecordDatasetV2.pbtxt | 6 ++++++ .../api_def/java_api/api_def_FixedLengthRecordReader.pbtxt | 6 ++++++ .../java_api/api_def_FixedLengthRecordReaderV2.pbtxt | 6 ++++++ .../java_api/api_def_FixedUnigramCandidateSampler.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_FlatMapDataset.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Floor.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_FloorDiv.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_FloorMod.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_FlushSummaryWriter.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_For.pbtxt | 3 +++ .../core/api_def/java_api/api_def_FractionalAvgPool.pbtxt | 6 ++++++ .../api_def/java_api/api_def_FractionalAvgPoolGrad.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_FractionalMaxPool.pbtxt | 6 ++++++ .../api_def/java_api/api_def_FractionalMaxPoolGrad.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_FusedBatchNorm.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_FusedBatchNormGrad.pbtxt | 6 ++++++ .../api_def/java_api/api_def_FusedBatchNormGradV2.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_FusedBatchNormV2.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_FusedPadConv2D.pbtxt | 6 ++++++ .../api_def/java_api/api_def_FusedResizeAndPadConv2D.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Gather.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_GatherNd.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_GatherV2.pbtxt | 3 +++ .../api_def/java_api/api_def_GcsConfigureBlockCache.pbtxt | 3 +++ .../api_def/java_api/api_def_GcsConfigureCredentials.pbtxt | 3 +++ .../java_api/api_def_GenerateBigQueryReaderPartitions.pbtxt | 3 +++ .../api_def/java_api/api_def_GenerateVocabRemapping.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_GeneratorDataset.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_GetSessionHandle.pbtxt | 3 +++ .../core/api_def/java_api/api_def_GetSessionHandleV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_GetSessionTensor.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Greater.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_GreaterEqual.pbtxt | 6 ++++++ .../api_def/java_api/api_def_GroupByReducerDataset.pbtxt | 3 +++ .../api_def/java_api/api_def_GroupByWindowDataset.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_GuaranteeConst.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_HSVToRGB.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_HashTable.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_HashTableV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_HistogramFixedWidth.pbtxt | 3 +++ .../core/api_def/java_api/api_def_HistogramSummary.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_HostConst.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_IFFT.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_IFFT2D.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_IFFT3D.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_IRFFT.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_IRFFT2D.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_IRFFT3D.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Identity.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_IdentityN.pbtxt | 3 +++ .../core/api_def/java_api/api_def_IdentityReader.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_IdentityReaderV2.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_If.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Igamma.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_IgammaGradA.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Igammac.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Imag.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_ImageSummary.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ImmutableConst.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_ImportEvent.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_InTopK.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_InTopKV2.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_InitializeTable.pbtxt | 3 +++ .../java_api/api_def_InitializeTableFromTextFile.pbtxt | 3 +++ .../java_api/api_def_InitializeTableFromTextFileV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_InitializeTableV2.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_InplaceAdd.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_InplaceSub.pbtxt | 3 +++ .../core/api_def/java_api/api_def_InplaceUpdate.pbtxt | 3 +++ .../core/api_def/java_api/api_def_InterleaveDataset.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Inv.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_InvGrad.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Invert.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_InvertPermutation.pbtxt | 6 ++++++ .../api_def_IsBoostedTreesEnsembleInitialized.pbtxt | 3 +++ ...ef_IsBoostedTreesQuantileStreamResourceInitialized.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_IsFinite.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_IsInf.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_IsNan.pbtxt | 6 ++++++ .../api_def/java_api/api_def_IsVariableInitialized.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Iterator.pbtxt | 6 ++++++ .../api_def/java_api/api_def_IteratorFromStringHandle.pbtxt | 6 ++++++ .../java_api/api_def_IteratorFromStringHandleV2.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_IteratorGetNext.pbtxt | 6 ++++++ .../java_api/api_def_IteratorGetNextAsOptional.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_IteratorGetNextSync.pbtxt | 6 ++++++ .../api_def/java_api/api_def_IteratorToStringHandle.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_IteratorV2.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_L2Loss.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_LMDBReader.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_LRN.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_LRNGrad.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_LatencyStatsDataset.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_LeakyRelu.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_LeakyReluGrad.pbtxt | 6 ++++++ .../java_api/api_def_LearnedUnigramCandidateSampler.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_LeftShift.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Less.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_LessEqual.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Lgamma.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_LinSpace.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_ListDiff.pbtxt | 3 +++ .../core/api_def/java_api/api_def_LoadAndRemapMatrix.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Log.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Log1p.pbtxt | 6 ++++++ .../api_def/java_api/api_def_LogMatrixDeterminant.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_LogSoftmax.pbtxt | 6 ++++++ .../java_api/api_def_LogUniformCandidateSampler.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_LogicalAnd.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_LogicalNot.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_LogicalOr.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_LookupTableExport.pbtxt | 3 +++ .../core/api_def/java_api/api_def_LookupTableExportV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_LookupTableFind.pbtxt | 3 +++ .../core/api_def/java_api/api_def_LookupTableFindV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_LookupTableImport.pbtxt | 3 +++ .../core/api_def/java_api/api_def_LookupTableImportV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_LookupTableInsert.pbtxt | 3 +++ .../core/api_def/java_api/api_def_LookupTableInsertV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_LookupTableRemoveV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_LookupTableSize.pbtxt | 3 +++ .../core/api_def/java_api/api_def_LookupTableSizeV2.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_LoopCond.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_LowerBound.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_MakeIterator.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_MapAndBatchDataset.pbtxt | 6 ++++++ .../api_def/java_api/api_def_MapAndBatchDatasetV2.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_MapClear.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_MapDataset.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_MapDefun.pbtxt | 3 +++ .../core/api_def/java_api/api_def_MapIncompleteSize.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_MapPeek.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_MapSize.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_MapStage.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_MapUnstage.pbtxt | 3 +++ .../core/api_def/java_api/api_def_MapUnstageNoKey.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_MatMul.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_MatchingFiles.pbtxt | 6 ++++++ .../api_def/java_api/api_def_MatchingFilesDataset.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_MatrixBandPart.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_MatrixDeterminant.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_MatrixDiag.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_MatrixDiagPart.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_MatrixExponential.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_MatrixInverse.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_MatrixLogarithm.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_MatrixSetDiag.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_MatrixSolve.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_MatrixSolveLs.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_MatrixSquareRoot.pbtxt | 6 ++++++ .../api_def/java_api/api_def_MatrixTriangularSolve.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Max.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_MaxPool.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_MaxPool3D.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_MaxPool3DGrad.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_MaxPool3DGradGrad.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_MaxPoolGrad.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_MaxPoolGradGrad.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_MaxPoolGradGradV2.pbtxt | 6 ++++++ .../java_api/api_def_MaxPoolGradGradWithArgmax.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_MaxPoolGradV2.pbtxt | 6 ++++++ .../api_def/java_api/api_def_MaxPoolGradWithArgmax.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_MaxPoolV2.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_MaxPoolWithArgmax.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Maximum.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Mean.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Merge.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_MergeSummary.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_MergeV2Checkpoints.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Mfcc.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Min.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Minimum.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_MirrorPad.pbtxt | 3 +++ .../core/api_def/java_api/api_def_MirrorPadGrad.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Mod.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_ModelDataset.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Mul.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_MultiDeviceIterator.pbtxt | 6 ++++++ .../api_def_MultiDeviceIteratorFromStringHandle.pbtxt | 6 ++++++ .../api_def_MultiDeviceIteratorGetNextFromShard.pbtxt | 6 ++++++ .../api_def/java_api/api_def_MultiDeviceIteratorInit.pbtxt | 6 ++++++ .../api_def_MultiDeviceIteratorToStringHandle.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Multinomial.pbtxt | 6 ++++++ .../api_def/java_api/api_def_MutableDenseHashTable.pbtxt | 3 +++ .../api_def/java_api/api_def_MutableDenseHashTableV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_MutableHashTable.pbtxt | 3 +++ .../java_api/api_def_MutableHashTableOfTensors.pbtxt | 3 +++ .../java_api/api_def_MutableHashTableOfTensorsV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_MutableHashTableV2.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_MutexLock.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_MutexV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_NcclAllReduce.pbtxt | 3 +++ .../core/api_def/java_api/api_def_NcclBroadcast.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_NcclReduce.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Neg.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_NegTrain.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_NextIteration.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_NoOp.pbtxt | 3 +++ .../core/api_def/java_api/api_def_NonMaxSuppression.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_NonMaxSuppressionV2.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_NonMaxSuppressionV3.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_NonMaxSuppressionV4.pbtxt | 6 ++++++ .../java_api/api_def_NonMaxSuppressionWithOverlaps.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_NotEqual.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_NthElement.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_OneHot.pbtxt | 3 +++ .../core/api_def/java_api/api_def_OneShotIterator.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_OnesLike.pbtxt | 3 +++ .../core/api_def/java_api/api_def_OptimizeDataset.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_OptionalFromValue.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_OptionalGetValue.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_OptionalHasValue.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_OptionalNone.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_OrderedMapClear.pbtxt | 3 +++ .../api_def/java_api/api_def_OrderedMapIncompleteSize.pbtxt | 3 +++ .../core/api_def/java_api/api_def_OrderedMapPeek.pbtxt | 3 +++ .../core/api_def/java_api/api_def_OrderedMapSize.pbtxt | 3 +++ .../core/api_def/java_api/api_def_OrderedMapStage.pbtxt | 3 +++ .../core/api_def/java_api/api_def_OrderedMapUnstage.pbtxt | 3 +++ .../api_def/java_api/api_def_OrderedMapUnstageNoKey.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Pack.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Pad.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_PadV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_PaddedBatchDataset.pbtxt | 6 ++++++ .../api_def/java_api/api_def_PaddedBatchDatasetV2.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_PaddingFIFOQueue.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_PaddingFIFOQueueV2.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ParallelConcat.pbtxt | 3 +++ .../api_def/java_api/api_def_ParallelDynamicStitch.pbtxt | 3 +++ .../java_api/api_def_ParallelInterleaveDataset.pbtxt | 6 ++++++ .../java_api/api_def_ParallelInterleaveDatasetV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_ParallelMapDataset.pbtxt | 6 ++++++ .../java_api/api_def_ParameterizedTruncatedNormal.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_ParseExample.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ParseExampleDataset.pbtxt | 6 ++++++ .../api_def/java_api/api_def_ParseSequenceExample.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ParseSingleExample.pbtxt | 6 ++++++ .../java_api/api_def_ParseSingleSequenceExample.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_ParseTensor.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_PartitionedCall.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Placeholder.pbtxt | 3 +++ .../core/api_def/java_api/api_def_PlaceholderV2.pbtxt | 3 +++ .../api_def/java_api/api_def_PlaceholderWithDefault.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Polygamma.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_PopulationCount.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Pow.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_PrefetchDataset.pbtxt | 6 ++++++ .../api_def_PrependFromQueueAndPaddedBatchDataset.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_PreventGradient.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Print.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_PrintV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_PriorityQueue.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_PriorityQueueV2.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Prod.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_PyFunc.pbtxt | 4 ++++ .../core/api_def/java_api/api_def_PyFuncStateless.pbtxt | 4 ++++ tensorflow/core/api_def/java_api/api_def_Qr.pbtxt | 6 ++++++ .../api_def/java_api/api_def_QuantizeAndDequantize.pbtxt | 6 ++++++ .../api_def/java_api/api_def_QuantizeAndDequantizeV2.pbtxt | 6 ++++++ .../api_def/java_api/api_def_QuantizeAndDequantizeV3.pbtxt | 6 ++++++ .../java_api/api_def_QuantizeDownAndShrinkRange.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_QuantizeV2.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_QuantizedAdd.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_QuantizedAvgPool.pbtxt | 6 ++++++ .../api_def_QuantizedBatchNormWithGlobalNormalization.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_QuantizedBiasAdd.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_QuantizedConcat.pbtxt | 3 +++ .../core/api_def/java_api/api_def_QuantizedConv2D.pbtxt | 6 ++++++ .../api_def/java_api/api_def_QuantizedInstanceNorm.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_QuantizedMatMul.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_QuantizedMaxPool.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_QuantizedMul.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_QuantizedRelu.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_QuantizedRelu6.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_QuantizedReluX.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_QuantizedReshape.pbtxt | 3 +++ .../api_def/java_api/api_def_QuantizedResizeBilinear.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_QueueClose.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_QueueCloseV2.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_QueueDequeue.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_QueueDequeueMany.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_QueueDequeueManyV2.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_QueueDequeueUpTo.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_QueueDequeueUpToV2.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_QueueDequeueV2.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_QueueEnqueue.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_QueueEnqueueMany.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_QueueEnqueueManyV2.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_QueueEnqueueV2.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_QueueIsClosed.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_QueueIsClosedV2.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_QueueSize.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_QueueSizeV2.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_RFFT.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_RFFT2D.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_RFFT3D.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_RGBToHSV.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_RaggedGather.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_RaggedRange.pbtxt | 3 +++ .../api_def/java_api/api_def_RaggedTensorToSparse.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_RandomCrop.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_RandomDataset.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_RandomGamma.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_RandomGammaGrad.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_RandomPoisson.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_RandomPoissonV2.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_RandomShuffle.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_RandomShuffleQueue.pbtxt | 6 ++++++ .../api_def/java_api/api_def_RandomShuffleQueueV2.pbtxt | 6 ++++++ .../api_def/java_api/api_def_RandomStandardNormal.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_RandomUniform.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_RandomUniformInt.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Range.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_RangeDataset.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Rank.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_ReadFile.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ReadVariableOp.pbtxt | 3 +++ .../api_def/java_api/api_def_ReaderNumRecordsProduced.pbtxt | 6 ++++++ .../java_api/api_def_ReaderNumRecordsProducedV2.pbtxt | 6 ++++++ .../java_api/api_def_ReaderNumWorkUnitsCompleted.pbtxt | 6 ++++++ .../java_api/api_def_ReaderNumWorkUnitsCompletedV2.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_ReaderRead.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ReaderReadUpTo.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ReaderReadUpToV2.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_ReaderReadV2.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_ReaderReset.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ReaderResetV2.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ReaderRestoreState.pbtxt | 6 ++++++ .../api_def/java_api/api_def_ReaderRestoreStateV2.pbtxt | 6 ++++++ .../api_def/java_api/api_def_ReaderSerializeState.pbtxt | 6 ++++++ .../api_def/java_api/api_def_ReaderSerializeStateV2.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Real.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_RealDiv.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Reciprocal.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ReciprocalGrad.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_RecordInput.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ReduceDataset.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_ReduceJoin.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_RefEnter.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_RefExit.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_RefIdentity.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_RefMerge.pbtxt | 3 +++ .../core/api_def/java_api/api_def_RefNextIteration.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_RefSelect.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_RefSwitch.pbtxt | 3 +++ .../core/api_def/java_api/api_def_RegexFullMatch.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_RegexReplace.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Relu.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Relu6.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Relu6Grad.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_ReluGrad.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_RemoteCall.pbtxt | 3 +++ .../api_def/java_api/api_def_RemoteFusedGraphExecute.pbtxt | 3 +++ .../core/api_def/java_api/api_def_RepeatDataset.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_RequantizationRange.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Requantize.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Reshape.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_ResizeArea.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ResizeBicubic.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ResizeBicubicGrad.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ResizeBilinear.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ResizeBilinearGrad.pbtxt | 6 ++++++ .../api_def/java_api/api_def_ResizeNearestNeighbor.pbtxt | 6 ++++++ .../java_api/api_def_ResizeNearestNeighborGrad.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ResourceApplyAdaMax.pbtxt | 6 ++++++ .../api_def/java_api/api_def_ResourceApplyAdadelta.pbtxt | 6 ++++++ .../api_def/java_api/api_def_ResourceApplyAdagrad.pbtxt | 6 ++++++ .../api_def/java_api/api_def_ResourceApplyAdagradDA.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ResourceApplyAdam.pbtxt | 6 ++++++ .../api_def/java_api/api_def_ResourceApplyAddSign.pbtxt | 6 ++++++ .../java_api/api_def_ResourceApplyCenteredRMSProp.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ResourceApplyFtrl.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ResourceApplyFtrlV2.pbtxt | 6 ++++++ .../java_api/api_def_ResourceApplyGradientDescent.pbtxt | 6 ++++++ .../api_def/java_api/api_def_ResourceApplyMomentum.pbtxt | 6 ++++++ .../api_def/java_api/api_def_ResourceApplyPowerSign.pbtxt | 6 ++++++ .../java_api/api_def_ResourceApplyProximalAdagrad.pbtxt | 6 ++++++ .../api_def_ResourceApplyProximalGradientDescent.pbtxt | 6 ++++++ .../api_def/java_api/api_def_ResourceApplyRMSProp.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ResourceCountUpTo.pbtxt | 3 +++ .../core/api_def/java_api/api_def_ResourceGather.pbtxt | 3 +++ .../core/api_def/java_api/api_def_ResourceScatterAdd.pbtxt | 3 +++ .../core/api_def/java_api/api_def_ResourceScatterDiv.pbtxt | 3 +++ .../core/api_def/java_api/api_def_ResourceScatterMax.pbtxt | 3 +++ .../core/api_def/java_api/api_def_ResourceScatterMin.pbtxt | 3 +++ .../core/api_def/java_api/api_def_ResourceScatterMul.pbtxt | 3 +++ .../api_def/java_api/api_def_ResourceScatterNdAdd.pbtxt | 3 +++ .../api_def/java_api/api_def_ResourceScatterNdUpdate.pbtxt | 3 +++ .../core/api_def/java_api/api_def_ResourceScatterSub.pbtxt | 3 +++ .../api_def/java_api/api_def_ResourceScatterUpdate.pbtxt | 3 +++ .../java_api/api_def_ResourceSparseApplyAdadelta.pbtxt | 6 ++++++ .../java_api/api_def_ResourceSparseApplyAdagrad.pbtxt | 6 ++++++ .../java_api/api_def_ResourceSparseApplyAdagradDA.pbtxt | 6 ++++++ .../api_def_ResourceSparseApplyCenteredRMSProp.pbtxt | 6 ++++++ .../api_def/java_api/api_def_ResourceSparseApplyFtrl.pbtxt | 6 ++++++ .../java_api/api_def_ResourceSparseApplyFtrlV2.pbtxt | 6 ++++++ .../java_api/api_def_ResourceSparseApplyMomentum.pbtxt | 6 ++++++ .../api_def_ResourceSparseApplyProximalAdagrad.pbtxt | 6 ++++++ ...api_def_ResourceSparseApplyProximalGradientDescent.pbtxt | 6 ++++++ .../java_api/api_def_ResourceSparseApplyRMSProp.pbtxt | 6 ++++++ .../java_api/api_def_ResourceStridedSliceAssign.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Restore.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_RestoreSlice.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_RestoreV2.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Reverse.pbtxt | 3 +++ .../core/api_def/java_api/api_def_ReverseSequence.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_ReverseV2.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_RightShift.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Rint.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Roll.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Round.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Rpc.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Rsqrt.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_RsqrtGrad.pbtxt | 6 ++++++ .../java_api/api_def_SampleDistortedBoundingBox.pbtxt | 6 ++++++ .../java_api/api_def_SampleDistortedBoundingBoxV2.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Save.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_SaveSlices.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_SaveV2.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ScalarSummary.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_ScanDataset.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_ScatterAdd.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_ScatterDiv.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_ScatterMax.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_ScatterMin.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_ScatterMul.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_ScatterNd.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_ScatterNdAdd.pbtxt | 3 +++ .../api_def/java_api/api_def_ScatterNdNonAliasingAdd.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_ScatterNdSub.pbtxt | 3 +++ .../core/api_def/java_api/api_def_ScatterNdUpdate.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_ScatterSub.pbtxt | 3 +++ .../core/api_def/java_api/api_def_ScatterUpdate.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_SdcaFprint.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SdcaOptimizer.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SdcaOptimizerV2.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_SdcaShrinkL1.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_SegmentMax.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_SegmentMean.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_SegmentMin.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_SegmentProd.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_SegmentSum.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Select.pbtxt | 3 +++ .../core/api_def/java_api/api_def_SelfAdjointEig.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SelfAdjointEigV2.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Selu.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_SeluGrad.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SerializeIterator.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SerializeManySparse.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SerializeSparse.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SerializeTensor.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_SetSize.pbtxt | 3 +++ .../java_api/api_def_SetStatsAggregatorDataset.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Shape.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_ShapeN.pbtxt | 3 +++ .../core/api_def/java_api/api_def_ShardedFilename.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ShardedFilespec.pbtxt | 6 ++++++ .../api_def/java_api/api_def_ShuffleAndRepeatDataset.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_ShuffleDataset.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Sigmoid.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_SigmoidGrad.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Sign.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Sin.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Sinh.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Size.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_SkipDataset.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Skipgram.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Slice.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_SlideDataset.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Snapshot.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Softmax.pbtxt | 6 ++++++ .../java_api/api_def_SoftmaxCrossEntropyWithLogits.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Softplus.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_SoftplusGrad.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Softsign.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_SoftsignGrad.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_SpaceToBatch.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SpaceToBatchND.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_SpaceToDepth.pbtxt | 6 ++++++ .../java_api/api_def_SparseAccumulatorApplyGradient.pbtxt | 6 ++++++ .../java_api/api_def_SparseAccumulatorTakeGradient.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_SparseAdd.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SparseAddGrad.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SparseApplyAdadelta.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SparseApplyAdagrad.pbtxt | 6 ++++++ .../api_def/java_api/api_def_SparseApplyAdagradDA.pbtxt | 6 ++++++ .../java_api/api_def_SparseApplyCenteredRMSProp.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SparseApplyFtrl.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SparseApplyFtrlV2.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SparseApplyMomentum.pbtxt | 6 ++++++ .../java_api/api_def_SparseApplyProximalAdagrad.pbtxt | 6 ++++++ .../api_def_SparseApplyProximalGradientDescent.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SparseApplyRMSProp.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_SparseConcat.pbtxt | 6 ++++++ .../java_api/api_def_SparseConditionalAccumulator.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_SparseCross.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SparseDenseCwiseAdd.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SparseDenseCwiseDiv.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SparseDenseCwiseMul.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SparseFillEmptyRows.pbtxt | 6 ++++++ .../api_def/java_api/api_def_SparseFillEmptyRowsGrad.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_SparseMatMul.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SparseReduceMax.pbtxt | 6 ++++++ .../api_def/java_api/api_def_SparseReduceMaxSparse.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SparseReduceSum.pbtxt | 6 ++++++ .../api_def/java_api/api_def_SparseReduceSumSparse.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SparseReorder.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SparseReshape.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SparseSegmentMean.pbtxt | 6 ++++++ .../api_def/java_api/api_def_SparseSegmentMeanGrad.pbtxt | 6 ++++++ .../java_api/api_def_SparseSegmentMeanWithNumSegments.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SparseSegmentSqrtN.pbtxt | 6 ++++++ .../api_def/java_api/api_def_SparseSegmentSqrtNGrad.pbtxt | 6 ++++++ .../api_def_SparseSegmentSqrtNWithNumSegments.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SparseSegmentSum.pbtxt | 6 ++++++ .../java_api/api_def_SparseSegmentSumWithNumSegments.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_SparseSlice.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SparseSliceGrad.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SparseSoftmax.pbtxt | 6 ++++++ .../api_def_SparseSoftmaxCrossEntropyWithLogits.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SparseSparseMaximum.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SparseSparseMinimum.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_SparseSplit.pbtxt | 6 ++++++ .../api_def/java_api/api_def_SparseTensorDenseAdd.pbtxt | 6 ++++++ .../api_def/java_api/api_def_SparseTensorDenseMatMul.pbtxt | 6 ++++++ .../api_def/java_api/api_def_SparseTensorSliceDataset.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SparseToDense.pbtxt | 6 ++++++ .../java_api/api_def_SparseToSparseSetOperation.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Split.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_SplitV.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_SqlDataset.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Sqrt.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_SqrtGrad.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Square.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_SquaredDifference.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Squeeze.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Stack.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_StackClose.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_StackCloseV2.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_StackPop.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_StackPopV2.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_StackPush.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_StackPushV2.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_StackV2.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Stage.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_StageClear.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_StagePeek.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_StageSize.pbtxt | 3 +++ .../api_def/java_api/api_def_StatefulPartitionedCall.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_StatelessIf.pbtxt | 3 +++ .../api_def/java_api/api_def_StatelessMultinomial.pbtxt | 6 ++++++ .../api_def/java_api/api_def_StatelessRandomNormal.pbtxt | 6 ++++++ .../api_def/java_api/api_def_StatelessRandomUniform.pbtxt | 6 ++++++ .../java_api/api_def_StatelessRandomUniformInt.pbtxt | 6 ++++++ .../api_def/java_api/api_def_StatelessTruncatedNormal.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_StatelessWhile.pbtxt | 3 +++ .../api_def/java_api/api_def_StaticRegexFullMatch.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_StaticRegexReplace.pbtxt | 6 ++++++ .../api_def/java_api/api_def_StatsAggregatorHandle.pbtxt | 6 ++++++ .../api_def/java_api/api_def_StatsAggregatorSummary.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_StopGradient.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_StridedSlice.pbtxt | 3 +++ .../core/api_def/java_api/api_def_StridedSliceAssign.pbtxt | 3 +++ .../core/api_def/java_api/api_def_StridedSliceGrad.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_StringFormat.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_StringJoin.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_StringLength.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_StringSplit.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_StringSplitV2.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_StringStrip.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_StringToHashBucket.pbtxt | 6 ++++++ .../api_def/java_api/api_def_StringToHashBucketFast.pbtxt | 6 ++++++ .../api_def/java_api/api_def_StringToHashBucketStrong.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_StringToNumber.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Sub.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Substr.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Sum.pbtxt | 3 +++ .../core/api_def/java_api/api_def_SummaryWriter.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Svd.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Switch.pbtxt | 6 ++++-- .../core/api_def/java_api/api_def_SymbolicGradient.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_TFRecordDataset.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_TFRecordReader.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_TFRecordReaderV2.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_TakeDataset.pbtxt | 6 ++++++ .../java_api/api_def_TakeManySparseFromTensorsMap.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Tan.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Tanh.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_TanhGrad.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_TemporaryVariable.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_TensorArray.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayClose.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayCloseV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayCloseV3.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayConcat.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayConcatV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayConcatV3.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayGather.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayGatherV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayGatherV3.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayGrad.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayGradV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayGradV3.pbtxt | 3 +++ .../api_def/java_api/api_def_TensorArrayGradWithShape.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayPack.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayRead.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayReadV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayReadV3.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayScatter.pbtxt | 3 +++ .../api_def/java_api/api_def_TensorArrayScatterV2.pbtxt | 3 +++ .../api_def/java_api/api_def_TensorArrayScatterV3.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArraySize.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArraySizeV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArraySizeV3.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArraySplit.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArraySplitV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArraySplitV3.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayUnpack.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayV3.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayWrite.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayWriteV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayWriteV3.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorDataset.pbtxt | 6 ++++++ .../java_api/api_def_TensorForestCreateTreeVariable.pbtxt | 3 +++ .../java_api/api_def_TensorForestTreeDeserialize.pbtxt | 3 +++ .../java_api/api_def_TensorForestTreeIsInitializedOp.pbtxt | 3 +++ .../api_def/java_api/api_def_TensorForestTreePredict.pbtxt | 3 +++ .../java_api/api_def_TensorForestTreeResourceHandleOp.pbtxt | 3 +++ .../java_api/api_def_TensorForestTreeSerialize.pbtxt | 3 +++ .../api_def/java_api/api_def_TensorForestTreeSize.pbtxt | 3 +++ .../api_def/java_api/api_def_TensorListConcatLists.pbtxt | 3 +++ .../api_def/java_api/api_def_TensorListElementShape.pbtxt | 3 +++ .../api_def/java_api/api_def_TensorListFromTensor.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorListGather.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorListGetItem.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorListLength.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorListPopBack.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorListPushBack.pbtxt | 3 +++ .../api_def/java_api/api_def_TensorListPushBackBatch.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorListReserve.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorListScatter.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorListSetItem.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorListStack.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorSliceDataset.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_TensorSummary.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_TensorSummaryV2.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_TextLineDataset.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_TextLineReader.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_TextLineReaderV2.pbtxt | 6 ++++++ .../api_def_ThreadUnsafeUnigramCandidateSampler.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Tile.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_TileGrad.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Timestamp.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_TopK.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_TopKV2.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Transpose.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_TruncateDiv.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_TruncateMod.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_TruncatedNormal.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_TryRpc.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Unbatch.pbtxt | 3 +++ .../core/api_def/java_api/api_def_UnbatchDataset.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_UnbatchGrad.pbtxt | 3 +++ .../api_def/java_api/api_def_UnicodeDecodeWithOffsets.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_UnicodeScript.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_UnicodeTranscode.pbtxt | 6 ++++++ .../api_def/java_api/api_def_UniformCandidateSampler.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Unique.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_UniqueV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_UniqueWithCounts.pbtxt | 3 +++ .../core/api_def/java_api/api_def_UniqueWithCountsV2.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Unpack.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_UnravelIndex.pbtxt | 3 +++ .../core/api_def/java_api/api_def_UnsortedSegmentMax.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_UnsortedSegmentMin.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_UnsortedSegmentProd.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_UnsortedSegmentSum.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_Unstage.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_UpperBound.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_VarHandleOp.pbtxt | 3 +++ .../core/api_def/java_api/api_def_VarIsInitializedOp.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Variable.pbtxt | 3 +++ .../core/api_def/java_api/api_def_VariableShape.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_VariableV2.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Where.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_While.pbtxt | 3 +++ .../core/api_def/java_api/api_def_WholeFileReader.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_WholeFileReaderV2.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_WindowDataset.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_WriteAudioSummary.pbtxt | 6 ++++++ tensorflow/core/api_def/java_api/api_def_WriteFile.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_WriteGraphSummary.pbtxt | 6 ++++++ .../api_def/java_api/api_def_WriteHistogramSummary.pbtxt | 6 ++++++ .../core/api_def/java_api/api_def_WriteImageSummary.pbtxt | 6 ++++++ 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tensorflow/core/api_def/java_api/api_def_WholeFileReader.pbtxt create mode 100644 tensorflow/core/api_def/java_api/api_def_WholeFileReaderV2.pbtxt create mode 100644 tensorflow/core/api_def/java_api/api_def_WindowDataset.pbtxt create mode 100644 tensorflow/core/api_def/java_api/api_def_WriteAudioSummary.pbtxt create mode 100644 tensorflow/core/api_def/java_api/api_def_WriteFile.pbtxt create mode 100644 tensorflow/core/api_def/java_api/api_def_WriteGraphSummary.pbtxt create mode 100644 tensorflow/core/api_def/java_api/api_def_WriteHistogramSummary.pbtxt create mode 100644 tensorflow/core/api_def/java_api/api_def_WriteImageSummary.pbtxt create mode 100644 tensorflow/core/api_def/java_api/api_def_WriteScalarSummary.pbtxt create mode 100644 tensorflow/core/api_def/java_api/api_def_WriteSummary.pbtxt create mode 100644 tensorflow/core/api_def/java_api/api_def_Xdivy.pbtxt create mode 100644 tensorflow/core/api_def/java_api/api_def_Xlogy.pbtxt create mode 100644 tensorflow/core/api_def/java_api/api_def_ZerosLike.pbtxt create mode 100644 tensorflow/core/api_def/java_api/api_def_Zeta.pbtxt create mode 100644 tensorflow/core/api_def/java_api/api_def_ZipDataset.pbtxt diff --git a/tensorflow/core/api_def/java_api/api_def_Abort.pbtxt b/tensorflow/core/api_def/java_api/api_def_Abort.pbtxt new file mode 100644 index 0000000000..58448c2d17 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Abort.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Abort" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Abs.pbtxt b/tensorflow/core/api_def/java_api/api_def_Abs.pbtxt new file mode 100644 index 0000000000..ece45cf73f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Abs.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Abs" + endpoint { + name: "math.Abs" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_AccumulateNV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_AccumulateNV2.pbtxt new file mode 100644 index 0000000000..09e7a155e0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_AccumulateNV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "AccumulateNV2" + endpoint { + name: "math.AccumulateNV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_AccumulatorApplyGradient.pbtxt b/tensorflow/core/api_def/java_api/api_def_AccumulatorApplyGradient.pbtxt new file mode 100644 index 0000000000..49b7acad7d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_AccumulatorApplyGradient.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "AccumulatorApplyGradient" + endpoint { + name: "train.AccumulatorApplyGradient" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_AccumulatorNumAccumulated.pbtxt b/tensorflow/core/api_def/java_api/api_def_AccumulatorNumAccumulated.pbtxt new file mode 100644 index 0000000000..1c42e819bf --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_AccumulatorNumAccumulated.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "AccumulatorNumAccumulated" + endpoint { + name: "train.AccumulatorNumAccumulated" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_AccumulatorSetGlobalStep.pbtxt b/tensorflow/core/api_def/java_api/api_def_AccumulatorSetGlobalStep.pbtxt new file mode 100644 index 0000000000..ca85302cdb --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_AccumulatorSetGlobalStep.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "AccumulatorSetGlobalStep" + endpoint { + name: "train.AccumulatorSetGlobalStep" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_AccumulatorTakeGradient.pbtxt b/tensorflow/core/api_def/java_api/api_def_AccumulatorTakeGradient.pbtxt new file mode 100644 index 0000000000..4883802c63 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_AccumulatorTakeGradient.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "AccumulatorTakeGradient" + endpoint { + name: "train.AccumulatorTakeGradient" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Acos.pbtxt b/tensorflow/core/api_def/java_api/api_def_Acos.pbtxt new file mode 100644 index 0000000000..847986b429 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Acos.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Acos" + endpoint { + name: "math.Acos" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Acosh.pbtxt b/tensorflow/core/api_def/java_api/api_def_Acosh.pbtxt new file mode 100644 index 0000000000..76d8f5fad0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Acosh.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Acosh" + endpoint { + name: "math.Acosh" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Add.pbtxt b/tensorflow/core/api_def/java_api/api_def_Add.pbtxt new file mode 100644 index 0000000000..4f78ccc9ea --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Add.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Add" + endpoint { + name: "math.Add" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_AddManySparseToTensorsMap.pbtxt b/tensorflow/core/api_def/java_api/api_def_AddManySparseToTensorsMap.pbtxt new file mode 100644 index 0000000000..e009ba19d3 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_AddManySparseToTensorsMap.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "AddManySparseToTensorsMap" + endpoint { + name: "sparse.AddManySparseToTensorsMap" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_AddN.pbtxt b/tensorflow/core/api_def/java_api/api_def_AddN.pbtxt new file mode 100644 index 0000000000..20d469ae73 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_AddN.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "AddN" + endpoint { + name: "math.AddN" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_AddSparseToTensorsMap.pbtxt b/tensorflow/core/api_def/java_api/api_def_AddSparseToTensorsMap.pbtxt new file mode 100644 index 0000000000..0bb20186de --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_AddSparseToTensorsMap.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "AddSparseToTensorsMap" + endpoint { + name: "sparse.AddSparseToTensorsMap" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_AddV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_AddV2.pbtxt new file mode 100644 index 0000000000..f42a2add07 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_AddV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "AddV2" + endpoint { + name: "math.AddV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_AdjustContrast.pbtxt b/tensorflow/core/api_def/java_api/api_def_AdjustContrast.pbtxt new file mode 100644 index 0000000000..ff49aec2db --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_AdjustContrast.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "AdjustContrast" + endpoint { + name: "image.AdjustContrast" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_AdjustContrastv2.pbtxt b/tensorflow/core/api_def/java_api/api_def_AdjustContrastv2.pbtxt new file mode 100644 index 0000000000..dd6647b137 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_AdjustContrastv2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "AdjustContrastv2" + endpoint { + name: "image.AdjustContrastv2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_AdjustHue.pbtxt b/tensorflow/core/api_def/java_api/api_def_AdjustHue.pbtxt new file mode 100644 index 0000000000..0847cad403 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_AdjustHue.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "AdjustHue" + endpoint { + name: "image.AdjustHue" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_AdjustSaturation.pbtxt b/tensorflow/core/api_def/java_api/api_def_AdjustSaturation.pbtxt new file mode 100644 index 0000000000..d685636eb1 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_AdjustSaturation.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "AdjustSaturation" + endpoint { + name: "image.AdjustSaturation" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_All.pbtxt b/tensorflow/core/api_def/java_api/api_def_All.pbtxt new file mode 100644 index 0000000000..a6459c56b7 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_All.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "All" +} diff --git a/tensorflow/core/api_def/java_api/api_def_AllCandidateSampler.pbtxt b/tensorflow/core/api_def/java_api/api_def_AllCandidateSampler.pbtxt new file mode 100644 index 0000000000..607c208a46 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_AllCandidateSampler.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "AllCandidateSampler" + endpoint { + name: "random.AllCandidateSampler" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Angle.pbtxt b/tensorflow/core/api_def/java_api/api_def_Angle.pbtxt new file mode 100644 index 0000000000..a92ccf357d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Angle.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Angle" + endpoint { + name: "math.Angle" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_AnonymousIterator.pbtxt b/tensorflow/core/api_def/java_api/api_def_AnonymousIterator.pbtxt new file mode 100644 index 0000000000..894f85ae88 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_AnonymousIterator.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "AnonymousIterator" + endpoint { + name: "data.AnonymousIterator" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Any.pbtxt b/tensorflow/core/api_def/java_api/api_def_Any.pbtxt new file mode 100644 index 0000000000..20b36eda3f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Any.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Any" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ApplyAdaMax.pbtxt b/tensorflow/core/api_def/java_api/api_def_ApplyAdaMax.pbtxt new file mode 100644 index 0000000000..583f164e06 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ApplyAdaMax.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ApplyAdaMax" + endpoint { + name: "train.ApplyAdaMax" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ApplyAdadelta.pbtxt b/tensorflow/core/api_def/java_api/api_def_ApplyAdadelta.pbtxt new file mode 100644 index 0000000000..e672a8ef03 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ApplyAdadelta.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ApplyAdadelta" + endpoint { + name: "train.ApplyAdadelta" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ApplyAdagrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_ApplyAdagrad.pbtxt new file mode 100644 index 0000000000..980c57c5fe --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ApplyAdagrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ApplyAdagrad" + endpoint { + name: "train.ApplyAdagrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ApplyAdagradDA.pbtxt b/tensorflow/core/api_def/java_api/api_def_ApplyAdagradDA.pbtxt new file mode 100644 index 0000000000..d2b38707fb --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ApplyAdagradDA.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ApplyAdagradDA" + endpoint { + name: "train.ApplyAdagradDA" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ApplyAdam.pbtxt b/tensorflow/core/api_def/java_api/api_def_ApplyAdam.pbtxt new file mode 100644 index 0000000000..56461b1d3d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ApplyAdam.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ApplyAdam" + endpoint { + name: "train.ApplyAdam" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ApplyAddSign.pbtxt b/tensorflow/core/api_def/java_api/api_def_ApplyAddSign.pbtxt new file mode 100644 index 0000000000..b54ff6eca4 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ApplyAddSign.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ApplyAddSign" + endpoint { + name: "train.ApplyAddSign" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ApplyCenteredRMSProp.pbtxt b/tensorflow/core/api_def/java_api/api_def_ApplyCenteredRMSProp.pbtxt new file mode 100644 index 0000000000..6ed0660165 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ApplyCenteredRMSProp.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ApplyCenteredRMSProp" + endpoint { + name: "train.ApplyCenteredRMSProp" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ApplyFtrl.pbtxt b/tensorflow/core/api_def/java_api/api_def_ApplyFtrl.pbtxt new file mode 100644 index 0000000000..388e39c6ba --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ApplyFtrl.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ApplyFtrl" + endpoint { + name: "train.ApplyFtrl" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ApplyFtrlV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_ApplyFtrlV2.pbtxt new file mode 100644 index 0000000000..8463ad163b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ApplyFtrlV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ApplyFtrlV2" + endpoint { + name: "train.ApplyFtrlV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ApplyGradientDescent.pbtxt b/tensorflow/core/api_def/java_api/api_def_ApplyGradientDescent.pbtxt new file mode 100644 index 0000000000..1fa569ed32 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ApplyGradientDescent.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ApplyGradientDescent" + endpoint { + name: "train.ApplyGradientDescent" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ApplyMomentum.pbtxt b/tensorflow/core/api_def/java_api/api_def_ApplyMomentum.pbtxt new file mode 100644 index 0000000000..96c21199f0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ApplyMomentum.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ApplyMomentum" + endpoint { + name: "train.ApplyMomentum" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ApplyPowerSign.pbtxt b/tensorflow/core/api_def/java_api/api_def_ApplyPowerSign.pbtxt new file mode 100644 index 0000000000..e5c2234755 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ApplyPowerSign.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ApplyPowerSign" + endpoint { + name: "train.ApplyPowerSign" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ApplyProximalAdagrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_ApplyProximalAdagrad.pbtxt new file mode 100644 index 0000000000..a52d8c3591 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ApplyProximalAdagrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ApplyProximalAdagrad" + endpoint { + name: "train.ApplyProximalAdagrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ApplyProximalGradientDescent.pbtxt b/tensorflow/core/api_def/java_api/api_def_ApplyProximalGradientDescent.pbtxt new file mode 100644 index 0000000000..74ea29cf88 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ApplyProximalGradientDescent.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ApplyProximalGradientDescent" + endpoint { + name: "train.ApplyProximalGradientDescent" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ApplyRMSProp.pbtxt b/tensorflow/core/api_def/java_api/api_def_ApplyRMSProp.pbtxt new file mode 100644 index 0000000000..a1397b16fc --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ApplyRMSProp.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ApplyRMSProp" + endpoint { + name: "train.ApplyRMSProp" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ApproximateEqual.pbtxt b/tensorflow/core/api_def/java_api/api_def_ApproximateEqual.pbtxt new file mode 100644 index 0000000000..029dc6d293 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ApproximateEqual.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ApproximateEqual" + endpoint { + name: "math.ApproximateEqual" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ArgMax.pbtxt b/tensorflow/core/api_def/java_api/api_def_ArgMax.pbtxt new file mode 100644 index 0000000000..f9effd49c4 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ArgMax.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ArgMax" + endpoint { + name: "math.ArgMax" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ArgMin.pbtxt b/tensorflow/core/api_def/java_api/api_def_ArgMin.pbtxt new file mode 100644 index 0000000000..5ff04c0d1a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ArgMin.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ArgMin" + endpoint { + name: "math.ArgMin" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_AsString.pbtxt b/tensorflow/core/api_def/java_api/api_def_AsString.pbtxt new file mode 100644 index 0000000000..e8c875ea81 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_AsString.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "AsString" + endpoint { + name: "dtypes.AsString" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Asin.pbtxt b/tensorflow/core/api_def/java_api/api_def_Asin.pbtxt new file mode 100644 index 0000000000..8ffc8e3e57 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Asin.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Asin" + endpoint { + name: "math.Asin" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Asinh.pbtxt b/tensorflow/core/api_def/java_api/api_def_Asinh.pbtxt new file mode 100644 index 0000000000..e3b30dd512 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Asinh.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Asinh" + endpoint { + name: "math.Asinh" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Assert.pbtxt b/tensorflow/core/api_def/java_api/api_def_Assert.pbtxt index b1f868897d..a9e107b478 100644 --- a/tensorflow/core/api_def/java_api/api_def_Assert.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Assert.pbtxt @@ -1,4 +1,6 @@ op { - graph_op_name: "Assert" #TODO(karllessard) escape that reserved name - visibility: HIDDEN + graph_op_name: "Assert" + endpoint { + name: "AssertThat" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_Assign.pbtxt b/tensorflow/core/api_def/java_api/api_def_Assign.pbtxt new file mode 100644 index 0000000000..15d778f61e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Assign.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Assign" +} diff --git a/tensorflow/core/api_def/java_api/api_def_AssignAdd.pbtxt b/tensorflow/core/api_def/java_api/api_def_AssignAdd.pbtxt new file mode 100644 index 0000000000..a4118b64af --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_AssignAdd.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "AssignAdd" +} diff --git a/tensorflow/core/api_def/java_api/api_def_AssignAddVariableOp.pbtxt b/tensorflow/core/api_def/java_api/api_def_AssignAddVariableOp.pbtxt new file mode 100644 index 0000000000..05fecb191b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_AssignAddVariableOp.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "AssignAddVariableOp" +} diff --git a/tensorflow/core/api_def/java_api/api_def_AssignSub.pbtxt b/tensorflow/core/api_def/java_api/api_def_AssignSub.pbtxt new file mode 100644 index 0000000000..aaf9246a6a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_AssignSub.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "AssignSub" +} diff --git a/tensorflow/core/api_def/java_api/api_def_AssignSubVariableOp.pbtxt b/tensorflow/core/api_def/java_api/api_def_AssignSubVariableOp.pbtxt new file mode 100644 index 0000000000..6e8791aed2 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_AssignSubVariableOp.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "AssignSubVariableOp" +} diff --git a/tensorflow/core/api_def/java_api/api_def_AssignVariableOp.pbtxt b/tensorflow/core/api_def/java_api/api_def_AssignVariableOp.pbtxt new file mode 100644 index 0000000000..980e696826 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_AssignVariableOp.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "AssignVariableOp" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Atan.pbtxt b/tensorflow/core/api_def/java_api/api_def_Atan.pbtxt new file mode 100644 index 0000000000..e51aee9abc --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Atan.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Atan" + endpoint { + name: "math.Atan" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Atan2.pbtxt b/tensorflow/core/api_def/java_api/api_def_Atan2.pbtxt new file mode 100644 index 0000000000..302b05f9dc --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Atan2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Atan2" + endpoint { + name: "math.Atan2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Atanh.pbtxt b/tensorflow/core/api_def/java_api/api_def_Atanh.pbtxt new file mode 100644 index 0000000000..b9c4a41154 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Atanh.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Atanh" + endpoint { + name: "math.Atanh" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_AudioSpectrogram.pbtxt b/tensorflow/core/api_def/java_api/api_def_AudioSpectrogram.pbtxt new file mode 100644 index 0000000000..bd8f3a5e33 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_AudioSpectrogram.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "AudioSpectrogram" + endpoint { + name: "audio.AudioSpectrogram" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_AudioSummary.pbtxt b/tensorflow/core/api_def/java_api/api_def_AudioSummary.pbtxt new file mode 100644 index 0000000000..026ec79dd9 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_AudioSummary.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "AudioSummary" + endpoint { + name: "summary.AudioSummary" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_AudioSummaryV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_AudioSummaryV2.pbtxt new file mode 100644 index 0000000000..07d24ec3a3 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_AudioSummaryV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "AudioSummaryV2" + endpoint { + name: "summary.AudioSummaryV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_AvgPool.pbtxt b/tensorflow/core/api_def/java_api/api_def_AvgPool.pbtxt new file mode 100644 index 0000000000..10d87802f0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_AvgPool.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "AvgPool" + endpoint { + name: "nn.AvgPool" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_AvgPool3D.pbtxt b/tensorflow/core/api_def/java_api/api_def_AvgPool3D.pbtxt new file mode 100644 index 0000000000..1ae2794f48 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_AvgPool3D.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "AvgPool3D" + endpoint { + name: "nn.AvgPool3d" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_AvgPool3DGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_AvgPool3DGrad.pbtxt new file mode 100644 index 0000000000..ac51715095 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_AvgPool3DGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "AvgPool3DGrad" + endpoint { + name: "nn.AvgPool3DGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_AvgPoolGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_AvgPoolGrad.pbtxt new file mode 100644 index 0000000000..fc8fec89b9 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_AvgPoolGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "AvgPoolGrad" + endpoint { + name: "nn.AvgPoolGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Barrier.pbtxt b/tensorflow/core/api_def/java_api/api_def_Barrier.pbtxt new file mode 100644 index 0000000000..6e282ca7b3 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Barrier.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Barrier" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BarrierClose.pbtxt b/tensorflow/core/api_def/java_api/api_def_BarrierClose.pbtxt new file mode 100644 index 0000000000..0307318763 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BarrierClose.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BarrierClose" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BarrierIncompleteSize.pbtxt b/tensorflow/core/api_def/java_api/api_def_BarrierIncompleteSize.pbtxt new file mode 100644 index 0000000000..fb11b18e95 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BarrierIncompleteSize.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BarrierIncompleteSize" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BarrierInsertMany.pbtxt b/tensorflow/core/api_def/java_api/api_def_BarrierInsertMany.pbtxt new file mode 100644 index 0000000000..32e29f0015 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BarrierInsertMany.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BarrierInsertMany" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BarrierReadySize.pbtxt b/tensorflow/core/api_def/java_api/api_def_BarrierReadySize.pbtxt new file mode 100644 index 0000000000..0ed50b2579 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BarrierReadySize.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BarrierReadySize" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BarrierTakeMany.pbtxt b/tensorflow/core/api_def/java_api/api_def_BarrierTakeMany.pbtxt new file mode 100644 index 0000000000..21f08878c6 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BarrierTakeMany.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BarrierTakeMany" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Batch.pbtxt b/tensorflow/core/api_def/java_api/api_def_Batch.pbtxt new file mode 100644 index 0000000000..2c21faf72d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Batch.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Batch" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BatchCholesky.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchCholesky.pbtxt new file mode 100644 index 0000000000..15048109fd --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BatchCholesky.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BatchCholesky" + endpoint { + name: "linalg.BatchCholesky" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BatchCholeskyGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchCholeskyGrad.pbtxt new file mode 100644 index 0000000000..eb0e2c6bc8 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BatchCholeskyGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BatchCholeskyGrad" + endpoint { + name: "linalg.BatchCholeskyGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BatchDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchDataset.pbtxt new file mode 100644 index 0000000000..fe47605f37 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BatchDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BatchDataset" + endpoint { + name: "data.BatchDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BatchDatasetV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchDatasetV2.pbtxt new file mode 100644 index 0000000000..079efe146f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BatchDatasetV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BatchDatasetV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BatchFFT.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchFFT.pbtxt new file mode 100644 index 0000000000..62bc804c7e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BatchFFT.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BatchFFT" + endpoint { + name: "signal.BatchFFT" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BatchFFT2D.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchFFT2D.pbtxt new file mode 100644 index 0000000000..89479537a2 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BatchFFT2D.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BatchFFT2D" + endpoint { + name: "signal.BatchFFT2D" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BatchFFT3D.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchFFT3D.pbtxt new file mode 100644 index 0000000000..e9cff897bb --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BatchFFT3D.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BatchFFT3D" + endpoint { + name: "signal.BatchFFT3D" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BatchFunction.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchFunction.pbtxt new file mode 100644 index 0000000000..8789dc6acb --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BatchFunction.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BatchFunction" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BatchIFFT.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchIFFT.pbtxt new file mode 100644 index 0000000000..f156a6df39 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BatchIFFT.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BatchIFFT" + endpoint { + name: "signal.BatchIFFT" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BatchIFFT2D.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchIFFT2D.pbtxt new file mode 100644 index 0000000000..7cce88f8f7 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BatchIFFT2D.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BatchIFFT2D" + endpoint { + name: "signal.BatchIFFT2D" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BatchIFFT3D.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchIFFT3D.pbtxt new file mode 100644 index 0000000000..b30e596d75 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BatchIFFT3D.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BatchIFFT3D" + endpoint { + name: "signal.BatchIFFT3D" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BatchMatMul.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchMatMul.pbtxt new file mode 100644 index 0000000000..95aa644615 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BatchMatMul.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BatchMatMul" + endpoint { + name: "linalg.BatchMatMul" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BatchMatrixBandPart.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchMatrixBandPart.pbtxt new file mode 100644 index 0000000000..de989c6d52 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BatchMatrixBandPart.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BatchMatrixBandPart" + endpoint { + name: "linalg.BatchMatrixBandPart" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BatchMatrixDeterminant.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchMatrixDeterminant.pbtxt new file mode 100644 index 0000000000..a45fe25d10 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BatchMatrixDeterminant.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BatchMatrixDeterminant" + endpoint { + name: "linalg.BatchMatrixDeterminant" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BatchMatrixDiag.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchMatrixDiag.pbtxt new file mode 100644 index 0000000000..d85d76f02f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BatchMatrixDiag.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BatchMatrixDiag" + endpoint { + name: "linalg.BatchMatrixDiag" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BatchMatrixDiagPart.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchMatrixDiagPart.pbtxt new file mode 100644 index 0000000000..4b5350b11e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BatchMatrixDiagPart.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BatchMatrixDiagPart" + endpoint { + name: "linalg.BatchMatrixDiagPart" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BatchMatrixInverse.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchMatrixInverse.pbtxt new file mode 100644 index 0000000000..f40ea50d4b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BatchMatrixInverse.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BatchMatrixInverse" + endpoint { + name: "linalg.BatchMatrixInverse" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BatchMatrixSetDiag.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchMatrixSetDiag.pbtxt new file mode 100644 index 0000000000..ac4cd6889b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BatchMatrixSetDiag.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BatchMatrixSetDiag" + endpoint { + name: "linalg.BatchMatrixSetDiag" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BatchMatrixSolve.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchMatrixSolve.pbtxt new file mode 100644 index 0000000000..97435acb4e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BatchMatrixSolve.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BatchMatrixSolve" + endpoint { + name: "linalg.BatchMatrixSolve" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BatchMatrixSolveLs.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchMatrixSolveLs.pbtxt new file mode 100644 index 0000000000..aee0b4add3 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BatchMatrixSolveLs.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BatchMatrixSolveLs" + endpoint { + name: "linalg.BatchMatrixSolveLs" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BatchMatrixTriangularSolve.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchMatrixTriangularSolve.pbtxt new file mode 100644 index 0000000000..554eff1574 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BatchMatrixTriangularSolve.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BatchMatrixTriangularSolve" + endpoint { + name: "linalg.BatchMatrixTriangularSolve" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BatchNormWithGlobalNormalization.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchNormWithGlobalNormalization.pbtxt new file mode 100644 index 0000000000..8285ac284d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BatchNormWithGlobalNormalization.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BatchNormWithGlobalNormalization" + endpoint { + name: "nn.BatchNormWithGlobalNormalization" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BatchNormWithGlobalNormalizationGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchNormWithGlobalNormalizationGrad.pbtxt new file mode 100644 index 0000000000..7b18bf52ac --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BatchNormWithGlobalNormalizationGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BatchNormWithGlobalNormalizationGrad" + endpoint { + name: "nn.BatchNormWithGlobalNormalizationGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BatchSelfAdjointEig.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchSelfAdjointEig.pbtxt new file mode 100644 index 0000000000..58d60fa962 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BatchSelfAdjointEig.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BatchSelfAdjointEig" + endpoint { + name: "linalg.BatchSelfAdjointEig" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BatchSelfAdjointEigV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchSelfAdjointEigV2.pbtxt new file mode 100644 index 0000000000..fb18b2a000 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BatchSelfAdjointEigV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BatchSelfAdjointEigV2" + endpoint { + name: "linalg.BatchSelfAdjointEigV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BatchSvd.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchSvd.pbtxt new file mode 100644 index 0000000000..8696359df8 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BatchSvd.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BatchSvd" + endpoint { + name: "linalg.BatchSvd" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BatchToSpace.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchToSpace.pbtxt new file mode 100644 index 0000000000..affbc519e5 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BatchToSpace.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BatchToSpace" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BatchToSpaceND.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchToSpaceND.pbtxt new file mode 100644 index 0000000000..6c7d2fbdb9 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BatchToSpaceND.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BatchToSpaceND" + endpoint { + name: "BatchToSpaceNd" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BesselI0e.pbtxt b/tensorflow/core/api_def/java_api/api_def_BesselI0e.pbtxt new file mode 100644 index 0000000000..84eb3b5e71 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BesselI0e.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BesselI0e" + endpoint { + name: "math.BesselI0e" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BesselI1e.pbtxt b/tensorflow/core/api_def/java_api/api_def_BesselI1e.pbtxt new file mode 100644 index 0000000000..43f9113b0b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BesselI1e.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BesselI1e" + endpoint { + name: "math.BesselI1e" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Betainc.pbtxt b/tensorflow/core/api_def/java_api/api_def_Betainc.pbtxt new file mode 100644 index 0000000000..6e9956d9ec --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Betainc.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Betainc" + endpoint { + name: "math.Betainc" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BiasAdd.pbtxt b/tensorflow/core/api_def/java_api/api_def_BiasAdd.pbtxt new file mode 100644 index 0000000000..eb3be23bd9 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BiasAdd.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BiasAdd" + endpoint { + name: "nn.BiasAdd" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BiasAddGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_BiasAddGrad.pbtxt new file mode 100644 index 0000000000..4e040bf6df --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BiasAddGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BiasAddGrad" + endpoint { + name: "nn.BiasAddGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BiasAddV1.pbtxt b/tensorflow/core/api_def/java_api/api_def_BiasAddV1.pbtxt new file mode 100644 index 0000000000..76ae8ec8ff --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BiasAddV1.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BiasAddV1" + endpoint { + name: "nn.BiasAddV1" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BigQueryReader.pbtxt b/tensorflow/core/api_def/java_api/api_def_BigQueryReader.pbtxt new file mode 100644 index 0000000000..5b6e11687a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BigQueryReader.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BigQueryReader" + endpoint { + name: "io.BigQueryReader" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Bincount.pbtxt b/tensorflow/core/api_def/java_api/api_def_Bincount.pbtxt new file mode 100644 index 0000000000..b894fd6ec5 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Bincount.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Bincount" + endpoint { + name: "math.Bincount" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Bitcast.pbtxt b/tensorflow/core/api_def/java_api/api_def_Bitcast.pbtxt new file mode 100644 index 0000000000..9d2db26851 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Bitcast.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Bitcast" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BitwiseAnd.pbtxt b/tensorflow/core/api_def/java_api/api_def_BitwiseAnd.pbtxt new file mode 100644 index 0000000000..db5fada246 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BitwiseAnd.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BitwiseAnd" + endpoint { + name: "bitwise.BitwiseAnd" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BitwiseOr.pbtxt b/tensorflow/core/api_def/java_api/api_def_BitwiseOr.pbtxt new file mode 100644 index 0000000000..8f9d1bc2fe --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BitwiseOr.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BitwiseOr" + endpoint { + name: "bitwise.BitwiseOr" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BitwiseXor.pbtxt b/tensorflow/core/api_def/java_api/api_def_BitwiseXor.pbtxt new file mode 100644 index 0000000000..28f405b8ad --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BitwiseXor.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BitwiseXor" + endpoint { + name: "bitwise.BitwiseXor" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_BoostedTreesBucketize.pbtxt b/tensorflow/core/api_def/java_api/api_def_BoostedTreesBucketize.pbtxt new file mode 100644 index 0000000000..acec845ca4 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BoostedTreesBucketize.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BoostedTreesBucketize" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BoostedTreesCalculateBestGainsPerFeature.pbtxt b/tensorflow/core/api_def/java_api/api_def_BoostedTreesCalculateBestGainsPerFeature.pbtxt new file mode 100644 index 0000000000..fa22216ed5 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BoostedTreesCalculateBestGainsPerFeature.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BoostedTreesCalculateBestGainsPerFeature" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BoostedTreesCenterBias.pbtxt b/tensorflow/core/api_def/java_api/api_def_BoostedTreesCenterBias.pbtxt new file mode 100644 index 0000000000..1b65775a12 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BoostedTreesCenterBias.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BoostedTreesCenterBias" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BoostedTreesCreateEnsemble.pbtxt b/tensorflow/core/api_def/java_api/api_def_BoostedTreesCreateEnsemble.pbtxt new file mode 100644 index 0000000000..194251d433 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BoostedTreesCreateEnsemble.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BoostedTreesCreateEnsemble" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BoostedTreesCreateQuantileStreamResource.pbtxt b/tensorflow/core/api_def/java_api/api_def_BoostedTreesCreateQuantileStreamResource.pbtxt new file mode 100644 index 0000000000..7d9c8c9229 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BoostedTreesCreateQuantileStreamResource.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BoostedTreesCreateQuantileStreamResource" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BoostedTreesDeserializeEnsemble.pbtxt b/tensorflow/core/api_def/java_api/api_def_BoostedTreesDeserializeEnsemble.pbtxt new file mode 100644 index 0000000000..2ed17ca30f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BoostedTreesDeserializeEnsemble.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BoostedTreesDeserializeEnsemble" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BoostedTreesEnsembleResourceHandleOp.pbtxt b/tensorflow/core/api_def/java_api/api_def_BoostedTreesEnsembleResourceHandleOp.pbtxt new file mode 100644 index 0000000000..a44c86614a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BoostedTreesEnsembleResourceHandleOp.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BoostedTreesEnsembleResourceHandleOp" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BoostedTreesExampleDebugOutputs.pbtxt b/tensorflow/core/api_def/java_api/api_def_BoostedTreesExampleDebugOutputs.pbtxt new file mode 100644 index 0000000000..4d6f276911 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BoostedTreesExampleDebugOutputs.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BoostedTreesExampleDebugOutputs" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BoostedTreesGetEnsembleStates.pbtxt b/tensorflow/core/api_def/java_api/api_def_BoostedTreesGetEnsembleStates.pbtxt new file mode 100644 index 0000000000..a591013fb7 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BoostedTreesGetEnsembleStates.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BoostedTreesGetEnsembleStates" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BoostedTreesMakeQuantileSummaries.pbtxt b/tensorflow/core/api_def/java_api/api_def_BoostedTreesMakeQuantileSummaries.pbtxt new file mode 100644 index 0000000000..4e147765a7 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BoostedTreesMakeQuantileSummaries.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BoostedTreesMakeQuantileSummaries" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BoostedTreesMakeStatsSummary.pbtxt b/tensorflow/core/api_def/java_api/api_def_BoostedTreesMakeStatsSummary.pbtxt new file mode 100644 index 0000000000..bbee8bb47c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BoostedTreesMakeStatsSummary.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BoostedTreesMakeStatsSummary" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BoostedTreesPredict.pbtxt b/tensorflow/core/api_def/java_api/api_def_BoostedTreesPredict.pbtxt new file mode 100644 index 0000000000..e25d43a18f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BoostedTreesPredict.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BoostedTreesPredict" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BoostedTreesQuantileStreamResourceAddSummaries.pbtxt b/tensorflow/core/api_def/java_api/api_def_BoostedTreesQuantileStreamResourceAddSummaries.pbtxt new file mode 100644 index 0000000000..d9fe96d3cd --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BoostedTreesQuantileStreamResourceAddSummaries.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BoostedTreesQuantileStreamResourceAddSummaries" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BoostedTreesQuantileStreamResourceDeserialize.pbtxt b/tensorflow/core/api_def/java_api/api_def_BoostedTreesQuantileStreamResourceDeserialize.pbtxt new file mode 100644 index 0000000000..86e8090241 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BoostedTreesQuantileStreamResourceDeserialize.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BoostedTreesQuantileStreamResourceDeserialize" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BoostedTreesQuantileStreamResourceFlush.pbtxt b/tensorflow/core/api_def/java_api/api_def_BoostedTreesQuantileStreamResourceFlush.pbtxt new file mode 100644 index 0000000000..c98375bb24 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BoostedTreesQuantileStreamResourceFlush.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BoostedTreesQuantileStreamResourceFlush" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BoostedTreesQuantileStreamResourceGetBucketBoundaries.pbtxt b/tensorflow/core/api_def/java_api/api_def_BoostedTreesQuantileStreamResourceGetBucketBoundaries.pbtxt new file mode 100644 index 0000000000..e0421be40a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BoostedTreesQuantileStreamResourceGetBucketBoundaries.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BoostedTreesQuantileStreamResourceGetBucketBoundaries" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BoostedTreesQuantileStreamResourceHandleOp.pbtxt b/tensorflow/core/api_def/java_api/api_def_BoostedTreesQuantileStreamResourceHandleOp.pbtxt new file mode 100644 index 0000000000..b7f5e8aa65 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BoostedTreesQuantileStreamResourceHandleOp.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BoostedTreesQuantileStreamResourceHandleOp" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BoostedTreesSerializeEnsemble.pbtxt b/tensorflow/core/api_def/java_api/api_def_BoostedTreesSerializeEnsemble.pbtxt new file mode 100644 index 0000000000..db5807344b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BoostedTreesSerializeEnsemble.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BoostedTreesSerializeEnsemble" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BoostedTreesTrainingPredict.pbtxt b/tensorflow/core/api_def/java_api/api_def_BoostedTreesTrainingPredict.pbtxt new file mode 100644 index 0000000000..b722233953 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BoostedTreesTrainingPredict.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BoostedTreesTrainingPredict" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BoostedTreesUpdateEnsemble.pbtxt b/tensorflow/core/api_def/java_api/api_def_BoostedTreesUpdateEnsemble.pbtxt new file mode 100644 index 0000000000..fb642dd430 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BoostedTreesUpdateEnsemble.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BoostedTreesUpdateEnsemble" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BroadcastArgs.pbtxt b/tensorflow/core/api_def/java_api/api_def_BroadcastArgs.pbtxt new file mode 100644 index 0000000000..484742a2d0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BroadcastArgs.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BroadcastArgs" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BroadcastGradientArgs.pbtxt b/tensorflow/core/api_def/java_api/api_def_BroadcastGradientArgs.pbtxt new file mode 100644 index 0000000000..50f631b2a6 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BroadcastGradientArgs.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BroadcastGradientArgs" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BroadcastTo.pbtxt b/tensorflow/core/api_def/java_api/api_def_BroadcastTo.pbtxt new file mode 100644 index 0000000000..127458816c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BroadcastTo.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "BroadcastTo" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Bucketize.pbtxt b/tensorflow/core/api_def/java_api/api_def_Bucketize.pbtxt new file mode 100644 index 0000000000..5a99712fd6 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Bucketize.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Bucketize" +} diff --git a/tensorflow/core/api_def/java_api/api_def_BytesProducedStatsDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_BytesProducedStatsDataset.pbtxt new file mode 100644 index 0000000000..cd7f24d961 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_BytesProducedStatsDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "BytesProducedStatsDataset" + endpoint { + name: "data.BytesProducedStatsDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_CTCBeamSearchDecoder.pbtxt b/tensorflow/core/api_def/java_api/api_def_CTCBeamSearchDecoder.pbtxt new file mode 100644 index 0000000000..79dae81df9 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_CTCBeamSearchDecoder.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "CTCBeamSearchDecoder" + endpoint { + name: "nn.CTCBeamSearchDecoder" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_CTCGreedyDecoder.pbtxt b/tensorflow/core/api_def/java_api/api_def_CTCGreedyDecoder.pbtxt new file mode 100644 index 0000000000..6a58e628bd --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_CTCGreedyDecoder.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "CTCGreedyDecoder" + endpoint { + name: "nn.CTCGreedyDecoder" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_CTCLoss.pbtxt b/tensorflow/core/api_def/java_api/api_def_CTCLoss.pbtxt new file mode 100644 index 0000000000..361270e1f7 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_CTCLoss.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "CTCLoss" + endpoint { + name: "nn.CTCLoss" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_CacheDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_CacheDataset.pbtxt new file mode 100644 index 0000000000..11c26c1dfc --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_CacheDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "CacheDataset" + endpoint { + name: "data.CacheDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Cast.pbtxt b/tensorflow/core/api_def/java_api/api_def_Cast.pbtxt new file mode 100644 index 0000000000..ea9f812e2a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Cast.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Cast" + endpoint { + name: "dtypes.Cast" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Ceil.pbtxt b/tensorflow/core/api_def/java_api/api_def_Ceil.pbtxt new file mode 100644 index 0000000000..d1a75f27d9 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Ceil.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Ceil" + endpoint { + name: "math.Ceil" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_CheckNumerics.pbtxt b/tensorflow/core/api_def/java_api/api_def_CheckNumerics.pbtxt new file mode 100644 index 0000000000..731e9030a0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_CheckNumerics.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "CheckNumerics" + endpoint { + name: "math.CheckNumerics" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Cholesky.pbtxt b/tensorflow/core/api_def/java_api/api_def_Cholesky.pbtxt new file mode 100644 index 0000000000..a60c4e3663 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Cholesky.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Cholesky" + endpoint { + name: "linalg.Cholesky" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_CholeskyGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_CholeskyGrad.pbtxt new file mode 100644 index 0000000000..2601d41554 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_CholeskyGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "CholeskyGrad" + endpoint { + name: "linalg.CholeskyGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ClipByValue.pbtxt b/tensorflow/core/api_def/java_api/api_def_ClipByValue.pbtxt new file mode 100644 index 0000000000..c4e055c117 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ClipByValue.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ClipByValue" +} diff --git a/tensorflow/core/api_def/java_api/api_def_CloseSummaryWriter.pbtxt b/tensorflow/core/api_def/java_api/api_def_CloseSummaryWriter.pbtxt new file mode 100644 index 0000000000..d5fbe557db --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_CloseSummaryWriter.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "CloseSummaryWriter" + endpoint { + name: "summary.CloseSummaryWriter" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_CollectiveBcastRecv.pbtxt b/tensorflow/core/api_def/java_api/api_def_CollectiveBcastRecv.pbtxt new file mode 100644 index 0000000000..8ada333e44 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_CollectiveBcastRecv.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "CollectiveBcastRecv" + endpoint { + name: "collective.BroadcastRecv" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_CollectiveBcastSend.pbtxt b/tensorflow/core/api_def/java_api/api_def_CollectiveBcastSend.pbtxt new file mode 100644 index 0000000000..18b4bef345 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_CollectiveBcastSend.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "CollectiveBcastSend" + endpoint { + name: "collective.BroadcastSend" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_CollectiveReduce.pbtxt b/tensorflow/core/api_def/java_api/api_def_CollectiveReduce.pbtxt new file mode 100644 index 0000000000..6226cc05ec --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_CollectiveReduce.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "CollectiveReduce" + endpoint { + name: "collective.AllReduce" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_CompareAndBitpack.pbtxt b/tensorflow/core/api_def/java_api/api_def_CompareAndBitpack.pbtxt new file mode 100644 index 0000000000..d744fbbc90 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_CompareAndBitpack.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "CompareAndBitpack" + endpoint { + name: "math.CompareAndBitpack" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Complex.pbtxt b/tensorflow/core/api_def/java_api/api_def_Complex.pbtxt new file mode 100644 index 0000000000..4889360a96 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Complex.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Complex" + endpoint { + name: "dtypes.Complex" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ComplexAbs.pbtxt b/tensorflow/core/api_def/java_api/api_def_ComplexAbs.pbtxt new file mode 100644 index 0000000000..42a6a3c6a1 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ComplexAbs.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ComplexAbs" + endpoint { + name: "math.ComplexAbs" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ComputeAccidentalHits.pbtxt b/tensorflow/core/api_def/java_api/api_def_ComputeAccidentalHits.pbtxt new file mode 100644 index 0000000000..ca9e590fbc --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ComputeAccidentalHits.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ComputeAccidentalHits" + endpoint { + name: "nn.ComputeAccidentalHits" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Concat.pbtxt b/tensorflow/core/api_def/java_api/api_def_Concat.pbtxt new file mode 100644 index 0000000000..b13eb820ec --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Concat.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Concat" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ConcatOffset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ConcatOffset.pbtxt new file mode 100644 index 0000000000..e8e23cf559 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ConcatOffset.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ConcatOffset" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ConcatV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_ConcatV2.pbtxt new file mode 100644 index 0000000000..a600c31ed9 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ConcatV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ConcatV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ConcatenateDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ConcatenateDataset.pbtxt new file mode 100644 index 0000000000..ec8253e9b5 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ConcatenateDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ConcatenateDataset" + endpoint { + name: "data.ConcatenateDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ConditionalAccumulator.pbtxt b/tensorflow/core/api_def/java_api/api_def_ConditionalAccumulator.pbtxt new file mode 100644 index 0000000000..08431982da --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ConditionalAccumulator.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ConditionalAccumulator" + endpoint { + name: "train.ConditionalAccumulator" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Conj.pbtxt b/tensorflow/core/api_def/java_api/api_def_Conj.pbtxt new file mode 100644 index 0000000000..7de199b55f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Conj.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Conj" + endpoint { + name: "math.Conj" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ConjugateTranspose.pbtxt b/tensorflow/core/api_def/java_api/api_def_ConjugateTranspose.pbtxt new file mode 100644 index 0000000000..42173088ae --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ConjugateTranspose.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ConjugateTranspose" + endpoint { + name: "linalg.ConjugateTranspose" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Const.pbtxt b/tensorflow/core/api_def/java_api/api_def_Const.pbtxt index 2dbdca34e0..a73f1e6c3a 100644 --- a/tensorflow/core/api_def/java_api/api_def_Const.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Const.pbtxt @@ -1,4 +1,4 @@ op { - graph_op_name: "Const" #TODO(karllessard) escape that reserved name - visibility: HIDDEN + graph_op_name: "Const" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_ConsumeMutexLock.pbtxt b/tensorflow/core/api_def/java_api/api_def_ConsumeMutexLock.pbtxt new file mode 100644 index 0000000000..1e0d136bc2 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ConsumeMutexLock.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ConsumeMutexLock" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ControlTrigger.pbtxt b/tensorflow/core/api_def/java_api/api_def_ControlTrigger.pbtxt new file mode 100644 index 0000000000..4517b4373f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ControlTrigger.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ControlTrigger" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Conv2D.pbtxt b/tensorflow/core/api_def/java_api/api_def_Conv2D.pbtxt new file mode 100644 index 0000000000..04c4fcec16 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Conv2D.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Conv2D" + endpoint { + name: "nn.Conv2D" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Conv2DBackpropFilter.pbtxt b/tensorflow/core/api_def/java_api/api_def_Conv2DBackpropFilter.pbtxt new file mode 100644 index 0000000000..990a54262e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Conv2DBackpropFilter.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Conv2DBackpropFilter" + endpoint { + name: "nn.Conv2DBackpropFilter" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Conv2DBackpropInput.pbtxt b/tensorflow/core/api_def/java_api/api_def_Conv2DBackpropInput.pbtxt new file mode 100644 index 0000000000..11bbaa455c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Conv2DBackpropInput.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Conv2DBackpropInput" + endpoint { + name: "nn.Conv2DBackpropInput" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Conv3D.pbtxt b/tensorflow/core/api_def/java_api/api_def_Conv3D.pbtxt new file mode 100644 index 0000000000..0d97065704 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Conv3D.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Conv3D" + endpoint { + name: "nn.Conv3D" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropFilter.pbtxt b/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropFilter.pbtxt new file mode 100644 index 0000000000..279cd5867e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropFilter.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Conv3DBackpropFilter" + endpoint { + name: "nn.Conv3DBackpropFilter" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropFilterV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropFilterV2.pbtxt new file mode 100644 index 0000000000..0643cc14a9 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropFilterV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Conv3DBackpropFilterV2" + endpoint { + name: "nn.Conv3dBackpropFilter" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropInput.pbtxt b/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropInput.pbtxt new file mode 100644 index 0000000000..5be32ebe3c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropInput.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Conv3DBackpropInput" + endpoint { + name: "nn.Conv3DBackpropInput" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropInputV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropInputV2.pbtxt new file mode 100644 index 0000000000..2ce9cf134b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropInputV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Conv3DBackpropInputV2" + endpoint { + name: "nn.Conv3DBackpropInputV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Cos.pbtxt b/tensorflow/core/api_def/java_api/api_def_Cos.pbtxt new file mode 100644 index 0000000000..db1f62806e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Cos.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Cos" + endpoint { + name: "math.Cos" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Cosh.pbtxt b/tensorflow/core/api_def/java_api/api_def_Cosh.pbtxt new file mode 100644 index 0000000000..a4b5e752bf --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Cosh.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Cosh" + endpoint { + name: "math.Cosh" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_CountUpTo.pbtxt b/tensorflow/core/api_def/java_api/api_def_CountUpTo.pbtxt new file mode 100644 index 0000000000..eb9f328ce0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_CountUpTo.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "CountUpTo" +} diff --git a/tensorflow/core/api_def/java_api/api_def_CreateSummaryDbWriter.pbtxt b/tensorflow/core/api_def/java_api/api_def_CreateSummaryDbWriter.pbtxt new file mode 100644 index 0000000000..299f881dd4 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_CreateSummaryDbWriter.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "CreateSummaryDbWriter" + endpoint { + name: "summary.CreateSummaryDbWriter" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_CreateSummaryFileWriter.pbtxt b/tensorflow/core/api_def/java_api/api_def_CreateSummaryFileWriter.pbtxt new file mode 100644 index 0000000000..26c7941ce5 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_CreateSummaryFileWriter.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "CreateSummaryFileWriter" + endpoint { + name: "summary.CreateSummaryFileWriter" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_CropAndResize.pbtxt b/tensorflow/core/api_def/java_api/api_def_CropAndResize.pbtxt new file mode 100644 index 0000000000..cbf9aa8f99 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_CropAndResize.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "CropAndResize" + endpoint { + name: "image.CropAndResize" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_CropAndResizeGradBoxes.pbtxt b/tensorflow/core/api_def/java_api/api_def_CropAndResizeGradBoxes.pbtxt new file mode 100644 index 0000000000..44354bdfa0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_CropAndResizeGradBoxes.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "CropAndResizeGradBoxes" + endpoint { + name: "image.CropAndResizeGradBoxes" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_CropAndResizeGradImage.pbtxt b/tensorflow/core/api_def/java_api/api_def_CropAndResizeGradImage.pbtxt new file mode 100644 index 0000000000..0618db9a8d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_CropAndResizeGradImage.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "CropAndResizeGradImage" + endpoint { + name: "image.CropAndResizeGradImage" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Cross.pbtxt b/tensorflow/core/api_def/java_api/api_def_Cross.pbtxt new file mode 100644 index 0000000000..c027884250 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Cross.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Cross" + endpoint { + name: "linalg.Cross" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_CudnnRNN.pbtxt b/tensorflow/core/api_def/java_api/api_def_CudnnRNN.pbtxt new file mode 100644 index 0000000000..c3d7f17596 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_CudnnRNN.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "CudnnRNN" + endpoint { + name: "nn.CudnnRNN" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_CudnnRNNBackprop.pbtxt b/tensorflow/core/api_def/java_api/api_def_CudnnRNNBackprop.pbtxt new file mode 100644 index 0000000000..371b6a18db --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_CudnnRNNBackprop.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "CudnnRNNBackprop" + endpoint { + name: "nn.CudnnRNNBackprop" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_CudnnRNNBackpropV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_CudnnRNNBackpropV2.pbtxt new file mode 100644 index 0000000000..756abdb71f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_CudnnRNNBackpropV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "CudnnRNNBackpropV2" + endpoint { + name: "nn.CudnnRNNBackpropV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_CudnnRNNCanonicalToParams.pbtxt b/tensorflow/core/api_def/java_api/api_def_CudnnRNNCanonicalToParams.pbtxt new file mode 100644 index 0000000000..86d7045db7 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_CudnnRNNCanonicalToParams.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "CudnnRNNCanonicalToParams" + endpoint { + name: "nn.CudnnRNNCanonicalToParams" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_CudnnRNNParamsSize.pbtxt b/tensorflow/core/api_def/java_api/api_def_CudnnRNNParamsSize.pbtxt new file mode 100644 index 0000000000..73922b0343 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_CudnnRNNParamsSize.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "CudnnRNNParamsSize" + endpoint { + name: "nn.CudnnRNNParamsSize" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_CudnnRNNParamsToCanonical.pbtxt b/tensorflow/core/api_def/java_api/api_def_CudnnRNNParamsToCanonical.pbtxt new file mode 100644 index 0000000000..e1c567163a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_CudnnRNNParamsToCanonical.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "CudnnRNNParamsToCanonical" + endpoint { + name: "nn.CudnnRNNParamsToCanonical" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_CudnnRNNV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_CudnnRNNV2.pbtxt new file mode 100644 index 0000000000..d535f35459 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_CudnnRNNV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "CudnnRNNV2" + endpoint { + name: "nn.CudnnRNNV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Cumprod.pbtxt b/tensorflow/core/api_def/java_api/api_def_Cumprod.pbtxt new file mode 100644 index 0000000000..0cb7862413 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Cumprod.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Cumprod" + endpoint { + name: "math.Cumprod" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Cumsum.pbtxt b/tensorflow/core/api_def/java_api/api_def_Cumsum.pbtxt new file mode 100644 index 0000000000..e7d9076532 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Cumsum.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Cumsum" + endpoint { + name: "math.Cumsum" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DataFormatDimMap.pbtxt b/tensorflow/core/api_def/java_api/api_def_DataFormatDimMap.pbtxt new file mode 100644 index 0000000000..36ea17793f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DataFormatDimMap.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DataFormatDimMap" + endpoint { + name: "nn.DataFormatDimMap" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DataFormatVecPermute.pbtxt b/tensorflow/core/api_def/java_api/api_def_DataFormatVecPermute.pbtxt new file mode 100644 index 0000000000..b6b7e2dc76 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DataFormatVecPermute.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DataFormatVecPermute" + endpoint { + name: "nn.DataFormatVecPermute" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DatasetToGraph.pbtxt b/tensorflow/core/api_def/java_api/api_def_DatasetToGraph.pbtxt new file mode 100644 index 0000000000..2e7d48961d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DatasetToGraph.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DatasetToGraph" + endpoint { + name: "data.DatasetToGraph" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DatasetToSingleElement.pbtxt b/tensorflow/core/api_def/java_api/api_def_DatasetToSingleElement.pbtxt new file mode 100644 index 0000000000..0ac42e0e93 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DatasetToSingleElement.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DatasetToSingleElement" + endpoint { + name: "data.DatasetToSingleElement" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DatasetToTFRecord.pbtxt b/tensorflow/core/api_def/java_api/api_def_DatasetToTFRecord.pbtxt new file mode 100644 index 0000000000..5884fe960d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DatasetToTFRecord.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DatasetToTFRecord" + endpoint { + name: "data.DatasetToTFRecord" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DebugGradientIdentity.pbtxt b/tensorflow/core/api_def/java_api/api_def_DebugGradientIdentity.pbtxt new file mode 100644 index 0000000000..7d50c5c868 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DebugGradientIdentity.pbtxt @@ -0,0 +1,4 @@ +op { + graph_op_name: "DebugGradientIdentity" + visibility: HIDDEN +} diff --git a/tensorflow/core/api_def/java_api/api_def_DebugGradientRefIdentity.pbtxt b/tensorflow/core/api_def/java_api/api_def_DebugGradientRefIdentity.pbtxt new file mode 100644 index 0000000000..5e14e5fffd --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DebugGradientRefIdentity.pbtxt @@ -0,0 +1,4 @@ +op { + graph_op_name: "DebugGradientRefIdentity" + visibility: HIDDEN +} diff --git a/tensorflow/core/api_def/java_api/api_def_DecodeAndCropJpeg.pbtxt b/tensorflow/core/api_def/java_api/api_def_DecodeAndCropJpeg.pbtxt new file mode 100644 index 0000000000..c07bb7a1bd --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DecodeAndCropJpeg.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DecodeAndCropJpeg" + endpoint { + name: "image.DecodeAndCropJpeg" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DecodeBase64.pbtxt b/tensorflow/core/api_def/java_api/api_def_DecodeBase64.pbtxt new file mode 100644 index 0000000000..49c93453f7 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DecodeBase64.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DecodeBase64" + endpoint { + name: "io.DecodeBase64" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DecodeBmp.pbtxt b/tensorflow/core/api_def/java_api/api_def_DecodeBmp.pbtxt new file mode 100644 index 0000000000..049cfa153d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DecodeBmp.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DecodeBmp" + endpoint { + name: "image.DecodeBmp" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DecodeCSV.pbtxt b/tensorflow/core/api_def/java_api/api_def_DecodeCSV.pbtxt new file mode 100644 index 0000000000..2c738cf4dd --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DecodeCSV.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DecodeCSV" + endpoint { + name: "io.DecodeCSV" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DecodeCompressed.pbtxt b/tensorflow/core/api_def/java_api/api_def_DecodeCompressed.pbtxt new file mode 100644 index 0000000000..91327a92ec --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DecodeCompressed.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DecodeCompressed" + endpoint { + name: "io.DecodeCompressed" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DecodeGif.pbtxt b/tensorflow/core/api_def/java_api/api_def_DecodeGif.pbtxt new file mode 100644 index 0000000000..355643ff77 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DecodeGif.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DecodeGif" + endpoint { + name: "image.DecodeGif" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DecodeJSONExample.pbtxt b/tensorflow/core/api_def/java_api/api_def_DecodeJSONExample.pbtxt new file mode 100644 index 0000000000..6ecba5ab05 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DecodeJSONExample.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DecodeJSONExample" + endpoint { + name: "io.DecodeJsonExample" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DecodeJpeg.pbtxt b/tensorflow/core/api_def/java_api/api_def_DecodeJpeg.pbtxt new file mode 100644 index 0000000000..c0ebf2e315 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DecodeJpeg.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DecodeJpeg" + endpoint { + name: "image.DecodeJpeg" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DecodePng.pbtxt b/tensorflow/core/api_def/java_api/api_def_DecodePng.pbtxt new file mode 100644 index 0000000000..d94537dc92 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DecodePng.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DecodePng" + endpoint { + name: "image.DecodePng" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DecodeProtoV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_DecodeProtoV2.pbtxt new file mode 100644 index 0000000000..ff6531a3ac --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DecodeProtoV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "DecodeProtoV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_DecodeRaw.pbtxt b/tensorflow/core/api_def/java_api/api_def_DecodeRaw.pbtxt new file mode 100644 index 0000000000..73067173ed --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DecodeRaw.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DecodeRaw" + endpoint { + name: "io.DecodeRaw" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DecodeWav.pbtxt b/tensorflow/core/api_def/java_api/api_def_DecodeWav.pbtxt new file mode 100644 index 0000000000..9b249cc6e9 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DecodeWav.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DecodeWav" + endpoint { + name: "audio.DecodeWav" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DeepCopy.pbtxt b/tensorflow/core/api_def/java_api/api_def_DeepCopy.pbtxt new file mode 100644 index 0000000000..88a87c9291 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DeepCopy.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "DeepCopy" +} diff --git a/tensorflow/core/api_def/java_api/api_def_DeleteSessionTensor.pbtxt b/tensorflow/core/api_def/java_api/api_def_DeleteSessionTensor.pbtxt new file mode 100644 index 0000000000..1865b461de --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DeleteSessionTensor.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "DeleteSessionTensor" +} diff --git a/tensorflow/core/api_def/java_api/api_def_DenseToDenseSetOperation.pbtxt b/tensorflow/core/api_def/java_api/api_def_DenseToDenseSetOperation.pbtxt new file mode 100644 index 0000000000..f85def92ee --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DenseToDenseSetOperation.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DenseToDenseSetOperation" + endpoint { + name: "sparse.DenseToDenseSetOperation" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DenseToSparseBatchDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_DenseToSparseBatchDataset.pbtxt new file mode 100644 index 0000000000..76f6ba0b8a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DenseToSparseBatchDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DenseToSparseBatchDataset" + endpoint { + name: "data.DenseToSparseBatchDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DenseToSparseSetOperation.pbtxt b/tensorflow/core/api_def/java_api/api_def_DenseToSparseSetOperation.pbtxt new file mode 100644 index 0000000000..11fbef8ff1 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DenseToSparseSetOperation.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DenseToSparseSetOperation" + endpoint { + name: "sparse.DenseToSparseSetOperation" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DepthToSpace.pbtxt b/tensorflow/core/api_def/java_api/api_def_DepthToSpace.pbtxt new file mode 100644 index 0000000000..0d2cbd2b90 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DepthToSpace.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DepthToSpace" + endpoint { + name: "nn.DepthToSpace" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DepthwiseConv2dNative.pbtxt b/tensorflow/core/api_def/java_api/api_def_DepthwiseConv2dNative.pbtxt new file mode 100644 index 0000000000..1aaa480fef --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DepthwiseConv2dNative.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DepthwiseConv2dNative" + endpoint { + name: "nn.DepthwiseConv2dNative" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DepthwiseConv2dNativeBackpropFilter.pbtxt b/tensorflow/core/api_def/java_api/api_def_DepthwiseConv2dNativeBackpropFilter.pbtxt new file mode 100644 index 0000000000..1a62d8cf63 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DepthwiseConv2dNativeBackpropFilter.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DepthwiseConv2dNativeBackpropFilter" + endpoint { + name: "nn.DepthwiseConv2dNativeBackpropFilter" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DepthwiseConv2dNativeBackpropInput.pbtxt b/tensorflow/core/api_def/java_api/api_def_DepthwiseConv2dNativeBackpropInput.pbtxt new file mode 100644 index 0000000000..9106dd2f8f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DepthwiseConv2dNativeBackpropInput.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DepthwiseConv2dNativeBackpropInput" + endpoint { + name: "nn.DepthwiseConv2dNativeBackpropInput" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Dequantize.pbtxt b/tensorflow/core/api_def/java_api/api_def_Dequantize.pbtxt new file mode 100644 index 0000000000..8ee4daa2f7 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Dequantize.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Dequantize" + endpoint { + name: "quantization.Dequantize" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DeserializeIterator.pbtxt b/tensorflow/core/api_def/java_api/api_def_DeserializeIterator.pbtxt new file mode 100644 index 0000000000..bdd03f5dc6 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DeserializeIterator.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DeserializeIterator" + endpoint { + name: "data.DeserializeIterator" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DeserializeManySparse.pbtxt b/tensorflow/core/api_def/java_api/api_def_DeserializeManySparse.pbtxt new file mode 100644 index 0000000000..826d49f546 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DeserializeManySparse.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DeserializeManySparse" + endpoint { + name: "io.DeserializeManySparse" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DeserializeSparse.pbtxt b/tensorflow/core/api_def/java_api/api_def_DeserializeSparse.pbtxt new file mode 100644 index 0000000000..e6f24bb625 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DeserializeSparse.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DeserializeSparse" + endpoint { + name: "sparse.DeserializeSparse" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DestroyResourceOp.pbtxt b/tensorflow/core/api_def/java_api/api_def_DestroyResourceOp.pbtxt new file mode 100644 index 0000000000..733e5e5029 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DestroyResourceOp.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "DestroyResourceOp" +} diff --git a/tensorflow/core/api_def/java_api/api_def_DestroyTemporaryVariable.pbtxt b/tensorflow/core/api_def/java_api/api_def_DestroyTemporaryVariable.pbtxt new file mode 100644 index 0000000000..bd416eb68f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DestroyTemporaryVariable.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "DestroyTemporaryVariable" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Diag.pbtxt b/tensorflow/core/api_def/java_api/api_def_Diag.pbtxt new file mode 100644 index 0000000000..374b3c97e1 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Diag.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Diag" + endpoint { + name: "linalg.TensorDiag" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DiagPart.pbtxt b/tensorflow/core/api_def/java_api/api_def_DiagPart.pbtxt new file mode 100644 index 0000000000..70db2357d0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DiagPart.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DiagPart" + endpoint { + name: "linalg.TensorDiagPart" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Digamma.pbtxt b/tensorflow/core/api_def/java_api/api_def_Digamma.pbtxt new file mode 100644 index 0000000000..68dc74c64e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Digamma.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Digamma" + endpoint { + name: "math.Digamma" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Dilation2D.pbtxt b/tensorflow/core/api_def/java_api/api_def_Dilation2D.pbtxt new file mode 100644 index 0000000000..914ea29812 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Dilation2D.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Dilation2D" + endpoint { + name: "nn.Dilation2d" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Dilation2DBackpropFilter.pbtxt b/tensorflow/core/api_def/java_api/api_def_Dilation2DBackpropFilter.pbtxt new file mode 100644 index 0000000000..ab8b79d681 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Dilation2DBackpropFilter.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Dilation2DBackpropFilter" + endpoint { + name: "nn.Dilation2DBackpropFilter" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Dilation2DBackpropInput.pbtxt b/tensorflow/core/api_def/java_api/api_def_Dilation2DBackpropInput.pbtxt new file mode 100644 index 0000000000..9dece4569c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Dilation2DBackpropInput.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Dilation2DBackpropInput" + endpoint { + name: "nn.Dilation2DBackpropInput" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Div.pbtxt b/tensorflow/core/api_def/java_api/api_def_Div.pbtxt new file mode 100644 index 0000000000..2abba7f05f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Div.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Div" + endpoint { + name: "math.Div" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DivNoNan.pbtxt b/tensorflow/core/api_def/java_api/api_def_DivNoNan.pbtxt new file mode 100644 index 0000000000..c124044604 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DivNoNan.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DivNoNan" + endpoint { + name: "math.DivNoNan" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DrawBoundingBoxes.pbtxt b/tensorflow/core/api_def/java_api/api_def_DrawBoundingBoxes.pbtxt new file mode 100644 index 0000000000..2e7954e2b7 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DrawBoundingBoxes.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "DrawBoundingBoxes" + endpoint { + name: "image.DrawBoundingBoxes" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_DynamicPartition.pbtxt b/tensorflow/core/api_def/java_api/api_def_DynamicPartition.pbtxt new file mode 100644 index 0000000000..cc585676e4 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DynamicPartition.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "DynamicPartition" +} diff --git a/tensorflow/core/api_def/java_api/api_def_DynamicStitch.pbtxt b/tensorflow/core/api_def/java_api/api_def_DynamicStitch.pbtxt new file mode 100644 index 0000000000..ac1fef4b6a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_DynamicStitch.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "DynamicStitch" +} diff --git a/tensorflow/core/api_def/java_api/api_def_EagerPyFunc.pbtxt b/tensorflow/core/api_def/java_api/api_def_EagerPyFunc.pbtxt new file mode 100644 index 0000000000..e097041d73 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_EagerPyFunc.pbtxt @@ -0,0 +1,4 @@ +op { + graph_op_name: "EagerPyFunc" + visibility: SKIP +} diff --git a/tensorflow/core/api_def/java_api/api_def_EditDistance.pbtxt b/tensorflow/core/api_def/java_api/api_def_EditDistance.pbtxt new file mode 100644 index 0000000000..ca65c2c6e5 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_EditDistance.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "EditDistance" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Elu.pbtxt b/tensorflow/core/api_def/java_api/api_def_Elu.pbtxt new file mode 100644 index 0000000000..bfe8d972cf --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Elu.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Elu" + endpoint { + name: "nn.Elu" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_EluGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_EluGrad.pbtxt new file mode 100644 index 0000000000..3757357c00 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_EluGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "EluGrad" + endpoint { + name: "nn.EluGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Empty.pbtxt b/tensorflow/core/api_def/java_api/api_def_Empty.pbtxt new file mode 100644 index 0000000000..6522f51d9d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Empty.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Empty" +} diff --git a/tensorflow/core/api_def/java_api/api_def_EmptyTensorList.pbtxt b/tensorflow/core/api_def/java_api/api_def_EmptyTensorList.pbtxt new file mode 100644 index 0000000000..ef3f533964 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_EmptyTensorList.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "EmptyTensorList" +} diff --git a/tensorflow/core/api_def/java_api/api_def_EncodeBase64.pbtxt b/tensorflow/core/api_def/java_api/api_def_EncodeBase64.pbtxt new file mode 100644 index 0000000000..66f19def9a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_EncodeBase64.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "EncodeBase64" + endpoint { + name: "io.EncodeBase64" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_EncodeJpeg.pbtxt b/tensorflow/core/api_def/java_api/api_def_EncodeJpeg.pbtxt new file mode 100644 index 0000000000..1e151665f8 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_EncodeJpeg.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "EncodeJpeg" + endpoint { + name: "image.EncodeJpeg" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_EncodePng.pbtxt b/tensorflow/core/api_def/java_api/api_def_EncodePng.pbtxt new file mode 100644 index 0000000000..7a8d713c86 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_EncodePng.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "EncodePng" + endpoint { + name: "image.EncodePng" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_EncodeProto.pbtxt b/tensorflow/core/api_def/java_api/api_def_EncodeProto.pbtxt new file mode 100644 index 0000000000..ac6a04b4bc --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_EncodeProto.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "EncodeProto" +} diff --git a/tensorflow/core/api_def/java_api/api_def_EncodeWav.pbtxt b/tensorflow/core/api_def/java_api/api_def_EncodeWav.pbtxt new file mode 100644 index 0000000000..f3b22fde66 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_EncodeWav.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "EncodeWav" + endpoint { + name: "audio.EncodeWav" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_EnqueueInQueueDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_EnqueueInQueueDataset.pbtxt new file mode 100644 index 0000000000..26051ab446 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_EnqueueInQueueDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "EnqueueInQueueDataset" + endpoint { + name: "data.EnqueueInQueueDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_EnsureShape.pbtxt b/tensorflow/core/api_def/java_api/api_def_EnsureShape.pbtxt new file mode 100644 index 0000000000..6238947598 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_EnsureShape.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "EnsureShape" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Enter.pbtxt b/tensorflow/core/api_def/java_api/api_def_Enter.pbtxt new file mode 100644 index 0000000000..ffc10c91be --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Enter.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Enter" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Equal.pbtxt b/tensorflow/core/api_def/java_api/api_def_Equal.pbtxt new file mode 100644 index 0000000000..c2256c2433 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Equal.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Equal" + endpoint { + name: "math.Equal" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Erf.pbtxt b/tensorflow/core/api_def/java_api/api_def_Erf.pbtxt new file mode 100644 index 0000000000..9efcc3983c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Erf.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Erf" + endpoint { + name: "math.Erf" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Erfc.pbtxt b/tensorflow/core/api_def/java_api/api_def_Erfc.pbtxt new file mode 100644 index 0000000000..c0f4db61ff --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Erfc.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Erfc" + endpoint { + name: "math.Erfc" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Exit.pbtxt b/tensorflow/core/api_def/java_api/api_def_Exit.pbtxt new file mode 100644 index 0000000000..6215cd2229 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Exit.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Exit" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Exp.pbtxt b/tensorflow/core/api_def/java_api/api_def_Exp.pbtxt new file mode 100644 index 0000000000..b2790c8306 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Exp.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Exp" + endpoint { + name: "math.Exp" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ExpandDims.pbtxt b/tensorflow/core/api_def/java_api/api_def_ExpandDims.pbtxt new file mode 100644 index 0000000000..66902ccb5b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ExpandDims.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ExpandDims" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ExperimentalAssertNextDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ExperimentalAssertNextDataset.pbtxt new file mode 100644 index 0000000000..cec4c229e4 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ExperimentalAssertNextDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ExperimentalAssertNextDataset" + endpoint { + name: "data.ExperimentalAssertNextDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ExperimentalCSVDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ExperimentalCSVDataset.pbtxt new file mode 100644 index 0000000000..2c555c3f1a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ExperimentalCSVDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ExperimentalCSVDataset" + endpoint { + name: "data.ExperimentalCSVDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ExperimentalDirectedInterleaveDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ExperimentalDirectedInterleaveDataset.pbtxt new file mode 100644 index 0000000000..77fe42fd94 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ExperimentalDirectedInterleaveDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ExperimentalDirectedInterleaveDataset" + endpoint { + name: "data.ExperimentalDirectedInterleaveDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ExperimentalFunctionBufferingResource.pbtxt b/tensorflow/core/api_def/java_api/api_def_ExperimentalFunctionBufferingResource.pbtxt new file mode 100644 index 0000000000..320f4affb5 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ExperimentalFunctionBufferingResource.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ExperimentalFunctionBufferingResource" + endpoint { + name: "data.ExperimentalFunctionBufferingResource" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ExperimentalFunctionBufferingResourceGetNext.pbtxt b/tensorflow/core/api_def/java_api/api_def_ExperimentalFunctionBufferingResourceGetNext.pbtxt new file mode 100644 index 0000000000..d3dbe7600a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ExperimentalFunctionBufferingResourceGetNext.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ExperimentalFunctionBufferingResourceGetNext" + endpoint { + name: "data.ExperimentalFunctionBufferingResourceGetNext" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ExperimentalFunctionBufferingResourceReset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ExperimentalFunctionBufferingResourceReset.pbtxt new file mode 100644 index 0000000000..6909e8678a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ExperimentalFunctionBufferingResourceReset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ExperimentalFunctionBufferingResourceReset" + endpoint { + name: "data.ExperimentalFunctionBufferingResourceReset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ExperimentalIdentityIndexedDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ExperimentalIdentityIndexedDataset.pbtxt new file mode 100644 index 0000000000..c49c6de217 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ExperimentalIdentityIndexedDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ExperimentalIdentityIndexedDataset" + endpoint { + name: "data.ExperimentalIdentityIndexedDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ExperimentalIgnoreErrorsDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ExperimentalIgnoreErrorsDataset.pbtxt new file mode 100644 index 0000000000..7750a43de2 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ExperimentalIgnoreErrorsDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ExperimentalIgnoreErrorsDataset" + endpoint { + name: "data.ExperimentalIgnoreErrorsDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ExperimentalIndexedDatasetGet.pbtxt b/tensorflow/core/api_def/java_api/api_def_ExperimentalIndexedDatasetGet.pbtxt new file mode 100644 index 0000000000..96a3befe8b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ExperimentalIndexedDatasetGet.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ExperimentalIndexedDatasetGet" + endpoint { + name: "data.ExperimentalIndexedDatasetGet" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ExperimentalIndexedDatasetMaterialize.pbtxt b/tensorflow/core/api_def/java_api/api_def_ExperimentalIndexedDatasetMaterialize.pbtxt new file mode 100644 index 0000000000..731309d329 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ExperimentalIndexedDatasetMaterialize.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ExperimentalIndexedDatasetMaterialize" + endpoint { + name: "data.ExperimentalIndexedDatasetMaterialize" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ExperimentalIteratorGetDevice.pbtxt b/tensorflow/core/api_def/java_api/api_def_ExperimentalIteratorGetDevice.pbtxt new file mode 100644 index 0000000000..838d579ef7 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ExperimentalIteratorGetDevice.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ExperimentalIteratorGetDevice" + endpoint { + name: "data.ExperimentalIteratorGetDevice" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ExperimentalLMDBDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ExperimentalLMDBDataset.pbtxt new file mode 100644 index 0000000000..dd24ff544f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ExperimentalLMDBDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ExperimentalLMDBDataset" + endpoint { + name: "data.ExperimentalLMDBDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ExperimentalMapDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ExperimentalMapDataset.pbtxt new file mode 100644 index 0000000000..bea6dffd9c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ExperimentalMapDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ExperimentalMapDataset" + endpoint { + name: "data.ExperimentalMapDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ExperimentalMaterializedIndexDatasetHandle.pbtxt b/tensorflow/core/api_def/java_api/api_def_ExperimentalMaterializedIndexDatasetHandle.pbtxt new file mode 100644 index 0000000000..06632e9041 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ExperimentalMaterializedIndexDatasetHandle.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ExperimentalMaterializedIndexDatasetHandle" + endpoint { + name: "data.ExperimentalMaterializedIndexDatasetHandle" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ExperimentalNonSerializableDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ExperimentalNonSerializableDataset.pbtxt new file mode 100644 index 0000000000..7b26cf129c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ExperimentalNonSerializableDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ExperimentalNonSerializableDataset" + endpoint { + name: "data.ExperimentalNonSerializableDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ExperimentalNumaMapAndBatchDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ExperimentalNumaMapAndBatchDataset.pbtxt new file mode 100644 index 0000000000..a2c0d92f89 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ExperimentalNumaMapAndBatchDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ExperimentalNumaMapAndBatchDataset" + endpoint { + name: "data.ExperimentalNumaMapAndBatchDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ExperimentalSleepDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ExperimentalSleepDataset.pbtxt new file mode 100644 index 0000000000..9e95c55ac2 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ExperimentalSleepDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ExperimentalSleepDataset" + endpoint { + name: "data.ExperimentalSleepDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ExperimentalThreadPoolDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ExperimentalThreadPoolDataset.pbtxt new file mode 100644 index 0000000000..3e4aef1c68 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ExperimentalThreadPoolDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ExperimentalThreadPoolDataset" + endpoint { + name: "data.ExperimentalThreadPoolDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ExperimentalThreadPoolHandle.pbtxt b/tensorflow/core/api_def/java_api/api_def_ExperimentalThreadPoolHandle.pbtxt new file mode 100644 index 0000000000..73cf6767b3 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ExperimentalThreadPoolHandle.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ExperimentalThreadPoolHandle" + endpoint { + name: "data.ExperimentalThreadPoolHandle" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ExperimentalUniqueDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ExperimentalUniqueDataset.pbtxt new file mode 100644 index 0000000000..0d834193dd --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ExperimentalUniqueDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ExperimentalUniqueDataset" + endpoint { + name: "data.ExperimentalUniqueDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Expm1.pbtxt b/tensorflow/core/api_def/java_api/api_def_Expm1.pbtxt new file mode 100644 index 0000000000..71a8fcf022 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Expm1.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Expm1" + endpoint { + name: "math.Expm1" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ExtractGlimpse.pbtxt b/tensorflow/core/api_def/java_api/api_def_ExtractGlimpse.pbtxt new file mode 100644 index 0000000000..3591f93f71 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ExtractGlimpse.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ExtractGlimpse" + endpoint { + name: "image.ExtractGlimpse" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ExtractImagePatches.pbtxt b/tensorflow/core/api_def/java_api/api_def_ExtractImagePatches.pbtxt new file mode 100644 index 0000000000..7cdcfd7528 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ExtractImagePatches.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ExtractImagePatches" + endpoint { + name: "image.ExtractImagePatches" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ExtractJpegShape.pbtxt b/tensorflow/core/api_def/java_api/api_def_ExtractJpegShape.pbtxt new file mode 100644 index 0000000000..c95fcc9cef --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ExtractJpegShape.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ExtractJpegShape" + endpoint { + name: "image.ExtractJpegShape" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ExtractVolumePatches.pbtxt b/tensorflow/core/api_def/java_api/api_def_ExtractVolumePatches.pbtxt new file mode 100644 index 0000000000..6f61c83210 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ExtractVolumePatches.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ExtractVolumePatches" +} diff --git a/tensorflow/core/api_def/java_api/api_def_FFT.pbtxt b/tensorflow/core/api_def/java_api/api_def_FFT.pbtxt new file mode 100644 index 0000000000..d965590308 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FFT.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FFT" + endpoint { + name: "signal.Fft" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FFT2D.pbtxt b/tensorflow/core/api_def/java_api/api_def_FFT2D.pbtxt new file mode 100644 index 0000000000..474103076b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FFT2D.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FFT2D" + endpoint { + name: "signal.Fft2d" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FFT3D.pbtxt b/tensorflow/core/api_def/java_api/api_def_FFT3D.pbtxt new file mode 100644 index 0000000000..8e1606b8f9 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FFT3D.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FFT3D" + endpoint { + name: "signal.Fft3d" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FIFOQueue.pbtxt b/tensorflow/core/api_def/java_api/api_def_FIFOQueue.pbtxt new file mode 100644 index 0000000000..e1760b685b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FIFOQueue.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FIFOQueue" + endpoint { + name: "io.FIFOQueue" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FIFOQueueV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_FIFOQueueV2.pbtxt new file mode 100644 index 0000000000..fe260ca2be --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FIFOQueueV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FIFOQueueV2" + endpoint { + name: "io.FIFOQueueV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Fact.pbtxt b/tensorflow/core/api_def/java_api/api_def_Fact.pbtxt new file mode 100644 index 0000000000..436664e554 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Fact.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Fact" + endpoint { + name: "math.Fact" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FakeParam.pbtxt b/tensorflow/core/api_def/java_api/api_def_FakeParam.pbtxt new file mode 100644 index 0000000000..ac8f751442 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FakeParam.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "FakeParam" +} diff --git a/tensorflow/core/api_def/java_api/api_def_FakeQuantWithMinMaxArgs.pbtxt b/tensorflow/core/api_def/java_api/api_def_FakeQuantWithMinMaxArgs.pbtxt new file mode 100644 index 0000000000..809d231a55 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FakeQuantWithMinMaxArgs.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FakeQuantWithMinMaxArgs" + endpoint { + name: "quantization.FakeQuantWithMinMaxArgs" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FakeQuantWithMinMaxArgsGradient.pbtxt b/tensorflow/core/api_def/java_api/api_def_FakeQuantWithMinMaxArgsGradient.pbtxt new file mode 100644 index 0000000000..50d0f51a14 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FakeQuantWithMinMaxArgsGradient.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FakeQuantWithMinMaxArgsGradient" + endpoint { + name: "quantization.FakeQuantWithMinMaxArgsGradient" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FakeQuantWithMinMaxVars.pbtxt b/tensorflow/core/api_def/java_api/api_def_FakeQuantWithMinMaxVars.pbtxt new file mode 100644 index 0000000000..b86258aab2 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FakeQuantWithMinMaxVars.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FakeQuantWithMinMaxVars" + endpoint { + name: "quantization.FakeQuantWithMinMaxVars" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FakeQuantWithMinMaxVarsGradient.pbtxt b/tensorflow/core/api_def/java_api/api_def_FakeQuantWithMinMaxVarsGradient.pbtxt new file mode 100644 index 0000000000..3c1343423c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FakeQuantWithMinMaxVarsGradient.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FakeQuantWithMinMaxVarsGradient" + endpoint { + name: "quantization.FakeQuantWithMinMaxVarsGradient" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FakeQuantWithMinMaxVarsPerChannel.pbtxt b/tensorflow/core/api_def/java_api/api_def_FakeQuantWithMinMaxVarsPerChannel.pbtxt new file mode 100644 index 0000000000..afe45a290d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FakeQuantWithMinMaxVarsPerChannel.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FakeQuantWithMinMaxVarsPerChannel" + endpoint { + name: "quantization.FakeQuantWithMinMaxVarsPerChannel" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FakeQuantWithMinMaxVarsPerChannelGradient.pbtxt b/tensorflow/core/api_def/java_api/api_def_FakeQuantWithMinMaxVarsPerChannelGradient.pbtxt new file mode 100644 index 0000000000..9dd62fdffd --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FakeQuantWithMinMaxVarsPerChannelGradient.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FakeQuantWithMinMaxVarsPerChannelGradient" + endpoint { + name: "quantization.FakeQuantWithMinMaxVarsPerChannelGradient" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FakeQueue.pbtxt b/tensorflow/core/api_def/java_api/api_def_FakeQueue.pbtxt new file mode 100644 index 0000000000..8960966f08 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FakeQueue.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FakeQueue" + endpoint { + name: "io.FakeQueue" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Fill.pbtxt b/tensorflow/core/api_def/java_api/api_def_Fill.pbtxt new file mode 100644 index 0000000000..3997328ed3 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Fill.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Fill" +} diff --git a/tensorflow/core/api_def/java_api/api_def_FilterByLastComponentDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_FilterByLastComponentDataset.pbtxt new file mode 100644 index 0000000000..b7111f48fa --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FilterByLastComponentDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FilterByLastComponentDataset" + endpoint { + name: "data.FilterByLastComponentDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FilterDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_FilterDataset.pbtxt new file mode 100644 index 0000000000..930fff4191 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FilterDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FilterDataset" + endpoint { + name: "data.FilterDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FixedLengthRecordDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_FixedLengthRecordDataset.pbtxt new file mode 100644 index 0000000000..8f38364f85 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FixedLengthRecordDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FixedLengthRecordDataset" + endpoint { + name: "data.FixedLengthRecordDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FixedLengthRecordDatasetV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_FixedLengthRecordDatasetV2.pbtxt new file mode 100644 index 0000000000..723248019d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FixedLengthRecordDatasetV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FixedLengthRecordDatasetV2" + endpoint { + name: "data.FixedLengthRecordDatasetV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FixedLengthRecordReader.pbtxt b/tensorflow/core/api_def/java_api/api_def_FixedLengthRecordReader.pbtxt new file mode 100644 index 0000000000..295526d074 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FixedLengthRecordReader.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FixedLengthRecordReader" + endpoint { + name: "io.FixedLengthRecordReader" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FixedLengthRecordReaderV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_FixedLengthRecordReaderV2.pbtxt new file mode 100644 index 0000000000..0cfefe2075 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FixedLengthRecordReaderV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FixedLengthRecordReaderV2" + endpoint { + name: "io.FixedLengthRecordReaderV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FixedUnigramCandidateSampler.pbtxt b/tensorflow/core/api_def/java_api/api_def_FixedUnigramCandidateSampler.pbtxt new file mode 100644 index 0000000000..eb9c68d4db --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FixedUnigramCandidateSampler.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FixedUnigramCandidateSampler" + endpoint { + name: "nn.FixedUnigramCandidateSampler" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FlatMapDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_FlatMapDataset.pbtxt new file mode 100644 index 0000000000..d6e96cb4e0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FlatMapDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FlatMapDataset" + endpoint { + name: "data.FlatMapDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Floor.pbtxt b/tensorflow/core/api_def/java_api/api_def_Floor.pbtxt new file mode 100644 index 0000000000..a2b80f97e0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Floor.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Floor" + endpoint { + name: "math.Floor" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FloorDiv.pbtxt b/tensorflow/core/api_def/java_api/api_def_FloorDiv.pbtxt new file mode 100644 index 0000000000..054d85f55c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FloorDiv.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FloorDiv" + endpoint { + name: "math.FloorDiv" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FloorMod.pbtxt b/tensorflow/core/api_def/java_api/api_def_FloorMod.pbtxt new file mode 100644 index 0000000000..ff2216a935 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FloorMod.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FloorMod" + endpoint { + name: "math.FloorMod" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FlushSummaryWriter.pbtxt b/tensorflow/core/api_def/java_api/api_def_FlushSummaryWriter.pbtxt new file mode 100644 index 0000000000..feaa3a6dc2 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FlushSummaryWriter.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FlushSummaryWriter" + endpoint { + name: "summary.FlushSummaryWriter" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_For.pbtxt b/tensorflow/core/api_def/java_api/api_def_For.pbtxt new file mode 100644 index 0000000000..30363d1e96 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_For.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "For" +} diff --git a/tensorflow/core/api_def/java_api/api_def_FractionalAvgPool.pbtxt b/tensorflow/core/api_def/java_api/api_def_FractionalAvgPool.pbtxt new file mode 100644 index 0000000000..fc2e6ca54b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FractionalAvgPool.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FractionalAvgPool" + endpoint { + name: "nn.FractionalAvgPool" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FractionalAvgPoolGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_FractionalAvgPoolGrad.pbtxt new file mode 100644 index 0000000000..4e11d5e395 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FractionalAvgPoolGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FractionalAvgPoolGrad" + endpoint { + name: "nn.FractionalAvgPoolGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FractionalMaxPool.pbtxt b/tensorflow/core/api_def/java_api/api_def_FractionalMaxPool.pbtxt new file mode 100644 index 0000000000..061b358ec2 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FractionalMaxPool.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FractionalMaxPool" + endpoint { + name: "nn.FractionalMaxPool" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FractionalMaxPoolGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_FractionalMaxPoolGrad.pbtxt new file mode 100644 index 0000000000..c70e6d721e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FractionalMaxPoolGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FractionalMaxPoolGrad" + endpoint { + name: "nn.FractionalMaxPoolGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FusedBatchNorm.pbtxt b/tensorflow/core/api_def/java_api/api_def_FusedBatchNorm.pbtxt new file mode 100644 index 0000000000..058c82c177 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FusedBatchNorm.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FusedBatchNorm" + endpoint { + name: "nn.FusedBatchNorm" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FusedBatchNormGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_FusedBatchNormGrad.pbtxt new file mode 100644 index 0000000000..69baf2a8e3 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FusedBatchNormGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FusedBatchNormGrad" + endpoint { + name: "nn.FusedBatchNormGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FusedBatchNormGradV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_FusedBatchNormGradV2.pbtxt new file mode 100644 index 0000000000..81da6f4bb2 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FusedBatchNormGradV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FusedBatchNormGradV2" + endpoint { + name: "nn.FusedBatchNormGradV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FusedBatchNormV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_FusedBatchNormV2.pbtxt new file mode 100644 index 0000000000..e81d78ef5c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FusedBatchNormV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FusedBatchNormV2" + endpoint { + name: "nn.FusedBatchNormV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FusedPadConv2D.pbtxt b/tensorflow/core/api_def/java_api/api_def_FusedPadConv2D.pbtxt new file mode 100644 index 0000000000..e4836df290 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FusedPadConv2D.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FusedPadConv2D" + endpoint { + name: "nn.FusedPadConv2D" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_FusedResizeAndPadConv2D.pbtxt b/tensorflow/core/api_def/java_api/api_def_FusedResizeAndPadConv2D.pbtxt new file mode 100644 index 0000000000..6cd0caa088 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_FusedResizeAndPadConv2D.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "FusedResizeAndPadConv2D" + endpoint { + name: "nn.FusedResizeAndPadConv2D" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Gather.pbtxt b/tensorflow/core/api_def/java_api/api_def_Gather.pbtxt new file mode 100644 index 0000000000..6848641714 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Gather.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Gather" +} diff --git a/tensorflow/core/api_def/java_api/api_def_GatherNd.pbtxt b/tensorflow/core/api_def/java_api/api_def_GatherNd.pbtxt new file mode 100644 index 0000000000..257c0316ea --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_GatherNd.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "GatherNd" + endpoint { + name: "GatherNd" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_GatherV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_GatherV2.pbtxt new file mode 100644 index 0000000000..f5b2cb92f6 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_GatherV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "GatherV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_GcsConfigureBlockCache.pbtxt b/tensorflow/core/api_def/java_api/api_def_GcsConfigureBlockCache.pbtxt new file mode 100644 index 0000000000..1ba3044d4c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_GcsConfigureBlockCache.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "GcsConfigureBlockCache" +} diff --git a/tensorflow/core/api_def/java_api/api_def_GcsConfigureCredentials.pbtxt b/tensorflow/core/api_def/java_api/api_def_GcsConfigureCredentials.pbtxt new file mode 100644 index 0000000000..98bd555fb8 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_GcsConfigureCredentials.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "GcsConfigureCredentials" +} diff --git a/tensorflow/core/api_def/java_api/api_def_GenerateBigQueryReaderPartitions.pbtxt b/tensorflow/core/api_def/java_api/api_def_GenerateBigQueryReaderPartitions.pbtxt new file mode 100644 index 0000000000..956f40762d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_GenerateBigQueryReaderPartitions.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "GenerateBigQueryReaderPartitions" +} diff --git a/tensorflow/core/api_def/java_api/api_def_GenerateVocabRemapping.pbtxt b/tensorflow/core/api_def/java_api/api_def_GenerateVocabRemapping.pbtxt new file mode 100644 index 0000000000..9aac3b17f3 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_GenerateVocabRemapping.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "GenerateVocabRemapping" + endpoint { + name: "train.GenerateVocabRemapping" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_GeneratorDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_GeneratorDataset.pbtxt new file mode 100644 index 0000000000..b1719005e9 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_GeneratorDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "GeneratorDataset" + endpoint { + name: "data.GeneratorDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_GetSessionHandle.pbtxt b/tensorflow/core/api_def/java_api/api_def_GetSessionHandle.pbtxt new file mode 100644 index 0000000000..84b14a3335 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_GetSessionHandle.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "GetSessionHandle" +} diff --git a/tensorflow/core/api_def/java_api/api_def_GetSessionHandleV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_GetSessionHandleV2.pbtxt new file mode 100644 index 0000000000..28488ac79a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_GetSessionHandleV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "GetSessionHandleV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_GetSessionTensor.pbtxt b/tensorflow/core/api_def/java_api/api_def_GetSessionTensor.pbtxt new file mode 100644 index 0000000000..34b6e627cd --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_GetSessionTensor.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "GetSessionTensor" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Greater.pbtxt b/tensorflow/core/api_def/java_api/api_def_Greater.pbtxt new file mode 100644 index 0000000000..594f9276be --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Greater.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Greater" + endpoint { + name: "math.Greater" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_GreaterEqual.pbtxt b/tensorflow/core/api_def/java_api/api_def_GreaterEqual.pbtxt new file mode 100644 index 0000000000..17ea8696b0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_GreaterEqual.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "GreaterEqual" + endpoint { + name: "math.GreaterEqual" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_GroupByReducerDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_GroupByReducerDataset.pbtxt new file mode 100644 index 0000000000..1bd2c8f531 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_GroupByReducerDataset.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "GroupByReducerDataset" +} diff --git a/tensorflow/core/api_def/java_api/api_def_GroupByWindowDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_GroupByWindowDataset.pbtxt new file mode 100644 index 0000000000..9e4c4cd4ff --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_GroupByWindowDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "GroupByWindowDataset" + endpoint { + name: "data.GroupByWindowDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_GuaranteeConst.pbtxt b/tensorflow/core/api_def/java_api/api_def_GuaranteeConst.pbtxt new file mode 100644 index 0000000000..8cac25787d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_GuaranteeConst.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "GuaranteeConst" +} diff --git a/tensorflow/core/api_def/java_api/api_def_HSVToRGB.pbtxt b/tensorflow/core/api_def/java_api/api_def_HSVToRGB.pbtxt new file mode 100644 index 0000000000..95b042d5d6 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_HSVToRGB.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "HSVToRGB" + endpoint { + name: "image.HsvToRgb" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_HashTable.pbtxt b/tensorflow/core/api_def/java_api/api_def_HashTable.pbtxt new file mode 100644 index 0000000000..f733b277ea --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_HashTable.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "HashTable" +} diff --git a/tensorflow/core/api_def/java_api/api_def_HashTableV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_HashTableV2.pbtxt new file mode 100644 index 0000000000..74fda0380a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_HashTableV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "HashTableV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_HistogramFixedWidth.pbtxt b/tensorflow/core/api_def/java_api/api_def_HistogramFixedWidth.pbtxt new file mode 100644 index 0000000000..f64d9ae1d2 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_HistogramFixedWidth.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "HistogramFixedWidth" +} diff --git a/tensorflow/core/api_def/java_api/api_def_HistogramSummary.pbtxt b/tensorflow/core/api_def/java_api/api_def_HistogramSummary.pbtxt new file mode 100644 index 0000000000..97f28335bb --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_HistogramSummary.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "HistogramSummary" + endpoint { + name: "summary.HistogramSummary" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_HostConst.pbtxt b/tensorflow/core/api_def/java_api/api_def_HostConst.pbtxt new file mode 100644 index 0000000000..ba589e73e7 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_HostConst.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "HostConst" +} diff --git a/tensorflow/core/api_def/java_api/api_def_IFFT.pbtxt b/tensorflow/core/api_def/java_api/api_def_IFFT.pbtxt new file mode 100644 index 0000000000..4a15ebec7f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_IFFT.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "IFFT" + endpoint { + name: "signal.Ifft" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_IFFT2D.pbtxt b/tensorflow/core/api_def/java_api/api_def_IFFT2D.pbtxt new file mode 100644 index 0000000000..35d696ee73 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_IFFT2D.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "IFFT2D" + endpoint { + name: "signal.Ifft2d" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_IFFT3D.pbtxt b/tensorflow/core/api_def/java_api/api_def_IFFT3D.pbtxt new file mode 100644 index 0000000000..76a3164e6a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_IFFT3D.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "IFFT3D" + endpoint { + name: "signal.Ifft3d" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_IRFFT.pbtxt b/tensorflow/core/api_def/java_api/api_def_IRFFT.pbtxt new file mode 100644 index 0000000000..d2ade5b16e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_IRFFT.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "IRFFT" + endpoint { + name: "signal.IRFFT" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_IRFFT2D.pbtxt b/tensorflow/core/api_def/java_api/api_def_IRFFT2D.pbtxt new file mode 100644 index 0000000000..3a5a5b0c71 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_IRFFT2D.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "IRFFT2D" + endpoint { + name: "signal.IRFFT2D" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_IRFFT3D.pbtxt b/tensorflow/core/api_def/java_api/api_def_IRFFT3D.pbtxt new file mode 100644 index 0000000000..fc5a5451b4 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_IRFFT3D.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "IRFFT3D" + endpoint { + name: "signal.IRFFT3D" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Identity.pbtxt b/tensorflow/core/api_def/java_api/api_def_Identity.pbtxt new file mode 100644 index 0000000000..b6df3c6cfe --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Identity.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Identity" +} diff --git a/tensorflow/core/api_def/java_api/api_def_IdentityN.pbtxt b/tensorflow/core/api_def/java_api/api_def_IdentityN.pbtxt new file mode 100644 index 0000000000..827df10c65 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_IdentityN.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "IdentityN" +} diff --git a/tensorflow/core/api_def/java_api/api_def_IdentityReader.pbtxt b/tensorflow/core/api_def/java_api/api_def_IdentityReader.pbtxt new file mode 100644 index 0000000000..1e1314213d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_IdentityReader.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "IdentityReader" + endpoint { + name: "io.IdentityReader" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_IdentityReaderV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_IdentityReaderV2.pbtxt new file mode 100644 index 0000000000..2973807a9c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_IdentityReaderV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "IdentityReaderV2" + endpoint { + name: "io.IdentityReaderV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_If.pbtxt b/tensorflow/core/api_def/java_api/api_def_If.pbtxt new file mode 100644 index 0000000000..a3bc33ac2c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_If.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "If" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Igamma.pbtxt b/tensorflow/core/api_def/java_api/api_def_Igamma.pbtxt new file mode 100644 index 0000000000..cbdd8b984c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Igamma.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Igamma" + endpoint { + name: "math.Igamma" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_IgammaGradA.pbtxt b/tensorflow/core/api_def/java_api/api_def_IgammaGradA.pbtxt new file mode 100644 index 0000000000..0659c80c39 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_IgammaGradA.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "IgammaGradA" + endpoint { + name: "math.IgammaGradA" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Igammac.pbtxt b/tensorflow/core/api_def/java_api/api_def_Igammac.pbtxt new file mode 100644 index 0000000000..94f6085e1a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Igammac.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Igammac" + endpoint { + name: "math.Igammac" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Imag.pbtxt b/tensorflow/core/api_def/java_api/api_def_Imag.pbtxt new file mode 100644 index 0000000000..4227c7078f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Imag.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Imag" + endpoint { + name: "math.Imag" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ImageSummary.pbtxt b/tensorflow/core/api_def/java_api/api_def_ImageSummary.pbtxt new file mode 100644 index 0000000000..1871e6b655 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ImageSummary.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ImageSummary" + endpoint { + name: "summary.ImageSummary" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ImmutableConst.pbtxt b/tensorflow/core/api_def/java_api/api_def_ImmutableConst.pbtxt new file mode 100644 index 0000000000..fd0384dc45 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ImmutableConst.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ImmutableConst" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ImportEvent.pbtxt b/tensorflow/core/api_def/java_api/api_def_ImportEvent.pbtxt new file mode 100644 index 0000000000..c2d8d2eba3 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ImportEvent.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ImportEvent" + endpoint { + name: "summary.ImportEvent" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_InTopK.pbtxt b/tensorflow/core/api_def/java_api/api_def_InTopK.pbtxt new file mode 100644 index 0000000000..1cca92e0ea --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_InTopK.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "InTopK" + endpoint { + name: "nn.InTopK" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_InTopKV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_InTopKV2.pbtxt new file mode 100644 index 0000000000..3dc9bff289 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_InTopKV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "InTopKV2" + endpoint { + name: "nn.InTopKV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_InitializeTable.pbtxt b/tensorflow/core/api_def/java_api/api_def_InitializeTable.pbtxt new file mode 100644 index 0000000000..49496f29a0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_InitializeTable.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "InitializeTable" +} diff --git a/tensorflow/core/api_def/java_api/api_def_InitializeTableFromTextFile.pbtxt b/tensorflow/core/api_def/java_api/api_def_InitializeTableFromTextFile.pbtxt new file mode 100644 index 0000000000..8cc206b0f0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_InitializeTableFromTextFile.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "InitializeTableFromTextFile" +} diff --git a/tensorflow/core/api_def/java_api/api_def_InitializeTableFromTextFileV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_InitializeTableFromTextFileV2.pbtxt new file mode 100644 index 0000000000..2a665348a7 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_InitializeTableFromTextFileV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "InitializeTableFromTextFileV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_InitializeTableV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_InitializeTableV2.pbtxt new file mode 100644 index 0000000000..8d9bec2f2f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_InitializeTableV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "InitializeTableV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_InplaceAdd.pbtxt b/tensorflow/core/api_def/java_api/api_def_InplaceAdd.pbtxt new file mode 100644 index 0000000000..3d157ab7f8 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_InplaceAdd.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "InplaceAdd" +} diff --git a/tensorflow/core/api_def/java_api/api_def_InplaceSub.pbtxt b/tensorflow/core/api_def/java_api/api_def_InplaceSub.pbtxt new file mode 100644 index 0000000000..b2ed549643 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_InplaceSub.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "InplaceSub" +} diff --git a/tensorflow/core/api_def/java_api/api_def_InplaceUpdate.pbtxt b/tensorflow/core/api_def/java_api/api_def_InplaceUpdate.pbtxt new file mode 100644 index 0000000000..91041b43ab --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_InplaceUpdate.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "InplaceUpdate" +} diff --git a/tensorflow/core/api_def/java_api/api_def_InterleaveDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_InterleaveDataset.pbtxt new file mode 100644 index 0000000000..25e40ac2db --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_InterleaveDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "InterleaveDataset" + endpoint { + name: "data.InterleaveDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Inv.pbtxt b/tensorflow/core/api_def/java_api/api_def_Inv.pbtxt new file mode 100644 index 0000000000..49f3e6c042 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Inv.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Inv" + endpoint { + name: "linalg.Inv" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_InvGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_InvGrad.pbtxt new file mode 100644 index 0000000000..d3bfa78e99 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_InvGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "InvGrad" + endpoint { + name: "nn.InvGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Invert.pbtxt b/tensorflow/core/api_def/java_api/api_def_Invert.pbtxt new file mode 100644 index 0000000000..9898bfa003 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Invert.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Invert" + endpoint { + name: "bitwise.Invert" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_InvertPermutation.pbtxt b/tensorflow/core/api_def/java_api/api_def_InvertPermutation.pbtxt new file mode 100644 index 0000000000..9ee103f554 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_InvertPermutation.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "InvertPermutation" + endpoint { + name: "math.InvertPermutation" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_IsBoostedTreesEnsembleInitialized.pbtxt b/tensorflow/core/api_def/java_api/api_def_IsBoostedTreesEnsembleInitialized.pbtxt new file mode 100644 index 0000000000..35f17b7924 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_IsBoostedTreesEnsembleInitialized.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "IsBoostedTreesEnsembleInitialized" +} diff --git a/tensorflow/core/api_def/java_api/api_def_IsBoostedTreesQuantileStreamResourceInitialized.pbtxt b/tensorflow/core/api_def/java_api/api_def_IsBoostedTreesQuantileStreamResourceInitialized.pbtxt new file mode 100644 index 0000000000..d6cd2dcc45 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_IsBoostedTreesQuantileStreamResourceInitialized.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "IsBoostedTreesQuantileStreamResourceInitialized" +} diff --git a/tensorflow/core/api_def/java_api/api_def_IsFinite.pbtxt b/tensorflow/core/api_def/java_api/api_def_IsFinite.pbtxt new file mode 100644 index 0000000000..fce5890399 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_IsFinite.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "IsFinite" + endpoint { + name: "math.IsFinite" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_IsInf.pbtxt b/tensorflow/core/api_def/java_api/api_def_IsInf.pbtxt new file mode 100644 index 0000000000..823c1d7281 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_IsInf.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "IsInf" + endpoint { + name: "math.IsInf" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_IsNan.pbtxt b/tensorflow/core/api_def/java_api/api_def_IsNan.pbtxt new file mode 100644 index 0000000000..58805bf99f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_IsNan.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "IsNan" + endpoint { + name: "math.IsNan" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_IsVariableInitialized.pbtxt b/tensorflow/core/api_def/java_api/api_def_IsVariableInitialized.pbtxt new file mode 100644 index 0000000000..7bf51da2da --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_IsVariableInitialized.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "IsVariableInitialized" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Iterator.pbtxt b/tensorflow/core/api_def/java_api/api_def_Iterator.pbtxt new file mode 100644 index 0000000000..7d4b5e6328 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Iterator.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Iterator" + endpoint { + name: "data.Iterator" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_IteratorFromStringHandle.pbtxt b/tensorflow/core/api_def/java_api/api_def_IteratorFromStringHandle.pbtxt new file mode 100644 index 0000000000..b2fe71a0c1 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_IteratorFromStringHandle.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "IteratorFromStringHandle" + endpoint { + name: "data.IteratorFromStringHandle" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_IteratorFromStringHandleV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_IteratorFromStringHandleV2.pbtxt new file mode 100644 index 0000000000..c77959f34c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_IteratorFromStringHandleV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "IteratorFromStringHandleV2" + endpoint { + name: "data.IteratorFromStringHandleV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_IteratorGetNext.pbtxt b/tensorflow/core/api_def/java_api/api_def_IteratorGetNext.pbtxt new file mode 100644 index 0000000000..2248ff9f5f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_IteratorGetNext.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "IteratorGetNext" + endpoint { + name: "data.IteratorGetNext" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_IteratorGetNextAsOptional.pbtxt b/tensorflow/core/api_def/java_api/api_def_IteratorGetNextAsOptional.pbtxt new file mode 100644 index 0000000000..ae02a0e017 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_IteratorGetNextAsOptional.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "IteratorGetNextAsOptional" + endpoint { + name: "data.IteratorGetNextAsOptional" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_IteratorGetNextSync.pbtxt b/tensorflow/core/api_def/java_api/api_def_IteratorGetNextSync.pbtxt new file mode 100644 index 0000000000..4aa7c07a77 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_IteratorGetNextSync.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "IteratorGetNextSync" + endpoint { + name: "data.IteratorGetNextSync" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_IteratorToStringHandle.pbtxt b/tensorflow/core/api_def/java_api/api_def_IteratorToStringHandle.pbtxt new file mode 100644 index 0000000000..7413ec846e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_IteratorToStringHandle.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "IteratorToStringHandle" + endpoint { + name: "data.IteratorToStringHandle" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_IteratorV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_IteratorV2.pbtxt new file mode 100644 index 0000000000..7892b096fe --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_IteratorV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "IteratorV2" + endpoint { + name: "data.IteratorV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_L2Loss.pbtxt b/tensorflow/core/api_def/java_api/api_def_L2Loss.pbtxt new file mode 100644 index 0000000000..c348e0f0e0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_L2Loss.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "L2Loss" + endpoint { + name: "nn.L2Loss" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_LMDBReader.pbtxt b/tensorflow/core/api_def/java_api/api_def_LMDBReader.pbtxt new file mode 100644 index 0000000000..226fa21953 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LMDBReader.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "LMDBReader" + endpoint { + name: "io.LMDBReader" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_LRN.pbtxt b/tensorflow/core/api_def/java_api/api_def_LRN.pbtxt new file mode 100644 index 0000000000..d16fea3184 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LRN.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "LRN" + endpoint { + name: "nn.LocalResponseNormalization" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_LRNGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_LRNGrad.pbtxt new file mode 100644 index 0000000000..a50e738d78 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LRNGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "LRNGrad" + endpoint { + name: "nn.LocalResponseNormalizationGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_LatencyStatsDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_LatencyStatsDataset.pbtxt new file mode 100644 index 0000000000..bf0bf2a5ed --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LatencyStatsDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "LatencyStatsDataset" + endpoint { + name: "data.LatencyStatsDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_LeakyRelu.pbtxt b/tensorflow/core/api_def/java_api/api_def_LeakyRelu.pbtxt new file mode 100644 index 0000000000..31a4f01167 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LeakyRelu.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "LeakyRelu" + endpoint { + name: "nn.LeakyRelu" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_LeakyReluGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_LeakyReluGrad.pbtxt new file mode 100644 index 0000000000..9899c64c13 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LeakyReluGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "LeakyReluGrad" + endpoint { + name: "data.LeakyReluGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_LearnedUnigramCandidateSampler.pbtxt b/tensorflow/core/api_def/java_api/api_def_LearnedUnigramCandidateSampler.pbtxt new file mode 100644 index 0000000000..5f193da1be --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LearnedUnigramCandidateSampler.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "LearnedUnigramCandidateSampler" + endpoint { + name: "nn.LearnedUnigramCandidateSampler" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_LeftShift.pbtxt b/tensorflow/core/api_def/java_api/api_def_LeftShift.pbtxt new file mode 100644 index 0000000000..44a8727e40 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LeftShift.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "LeftShift" + endpoint { + name: "bitwise.LeftShift" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Less.pbtxt b/tensorflow/core/api_def/java_api/api_def_Less.pbtxt new file mode 100644 index 0000000000..577d2556b8 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Less.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Less" + endpoint { + name: "math.Less" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_LessEqual.pbtxt b/tensorflow/core/api_def/java_api/api_def_LessEqual.pbtxt new file mode 100644 index 0000000000..6cad35c622 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LessEqual.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "LessEqual" + endpoint { + name: "math.LessEqual" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Lgamma.pbtxt b/tensorflow/core/api_def/java_api/api_def_Lgamma.pbtxt new file mode 100644 index 0000000000..eb7bc9660c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Lgamma.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Lgamma" + endpoint { + name: "math.Lgamma" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_LinSpace.pbtxt b/tensorflow/core/api_def/java_api/api_def_LinSpace.pbtxt new file mode 100644 index 0000000000..599c310021 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LinSpace.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "LinSpace" + endpoint { + name: "LinSpace" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ListDiff.pbtxt b/tensorflow/core/api_def/java_api/api_def_ListDiff.pbtxt new file mode 100644 index 0000000000..bbbd3bb3ec --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ListDiff.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ListDiff" +} diff --git a/tensorflow/core/api_def/java_api/api_def_LoadAndRemapMatrix.pbtxt b/tensorflow/core/api_def/java_api/api_def_LoadAndRemapMatrix.pbtxt new file mode 100644 index 0000000000..54ee68fde4 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LoadAndRemapMatrix.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "LoadAndRemapMatrix" + endpoint { + name: "linalg.LoadAndRemapMatrix" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Log.pbtxt b/tensorflow/core/api_def/java_api/api_def_Log.pbtxt new file mode 100644 index 0000000000..9d11c26c71 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Log.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Log" + endpoint { + name: "math.Log" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Log1p.pbtxt b/tensorflow/core/api_def/java_api/api_def_Log1p.pbtxt new file mode 100644 index 0000000000..6cc1d6e6c8 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Log1p.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Log1p" + endpoint { + name: "math.Log1p" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_LogMatrixDeterminant.pbtxt b/tensorflow/core/api_def/java_api/api_def_LogMatrixDeterminant.pbtxt new file mode 100644 index 0000000000..5e52d9eced --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LogMatrixDeterminant.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "LogMatrixDeterminant" + endpoint { + name: "linalg.LogMatrixDeterminant" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_LogSoftmax.pbtxt b/tensorflow/core/api_def/java_api/api_def_LogSoftmax.pbtxt new file mode 100644 index 0000000000..19518a71ea --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LogSoftmax.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "LogSoftmax" + endpoint { + name: "nn.LogSoftmax" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_LogUniformCandidateSampler.pbtxt b/tensorflow/core/api_def/java_api/api_def_LogUniformCandidateSampler.pbtxt new file mode 100644 index 0000000000..bdcf01c20f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LogUniformCandidateSampler.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "LogUniformCandidateSampler" + endpoint { + name: "random.LogUniformCandidateSampler" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_LogicalAnd.pbtxt b/tensorflow/core/api_def/java_api/api_def_LogicalAnd.pbtxt new file mode 100644 index 0000000000..12921dd932 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LogicalAnd.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "LogicalAnd" + endpoint { + name: "math.LogicalAnd" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_LogicalNot.pbtxt b/tensorflow/core/api_def/java_api/api_def_LogicalNot.pbtxt new file mode 100644 index 0000000000..9e0960958e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LogicalNot.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "LogicalNot" + endpoint { + name: "math.LogicalNot" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_LogicalOr.pbtxt b/tensorflow/core/api_def/java_api/api_def_LogicalOr.pbtxt new file mode 100644 index 0000000000..6c834e4641 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LogicalOr.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "LogicalOr" + endpoint { + name: "math.LogicalOr" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_LookupTableExport.pbtxt b/tensorflow/core/api_def/java_api/api_def_LookupTableExport.pbtxt new file mode 100644 index 0000000000..49637da997 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LookupTableExport.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "LookupTableExport" +} diff --git a/tensorflow/core/api_def/java_api/api_def_LookupTableExportV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_LookupTableExportV2.pbtxt new file mode 100644 index 0000000000..fce8a6e3cc --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LookupTableExportV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "LookupTableExportV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_LookupTableFind.pbtxt b/tensorflow/core/api_def/java_api/api_def_LookupTableFind.pbtxt new file mode 100644 index 0000000000..f8637744ae --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LookupTableFind.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "LookupTableFind" +} diff --git a/tensorflow/core/api_def/java_api/api_def_LookupTableFindV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_LookupTableFindV2.pbtxt new file mode 100644 index 0000000000..cb78732e9c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LookupTableFindV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "LookupTableFindV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_LookupTableImport.pbtxt b/tensorflow/core/api_def/java_api/api_def_LookupTableImport.pbtxt new file mode 100644 index 0000000000..4cf7971f56 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LookupTableImport.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "LookupTableImport" +} diff --git a/tensorflow/core/api_def/java_api/api_def_LookupTableImportV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_LookupTableImportV2.pbtxt new file mode 100644 index 0000000000..3216346961 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LookupTableImportV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "LookupTableImportV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_LookupTableInsert.pbtxt b/tensorflow/core/api_def/java_api/api_def_LookupTableInsert.pbtxt new file mode 100644 index 0000000000..51fe22ba60 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LookupTableInsert.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "LookupTableInsert" +} diff --git a/tensorflow/core/api_def/java_api/api_def_LookupTableInsertV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_LookupTableInsertV2.pbtxt new file mode 100644 index 0000000000..429bf25b0a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LookupTableInsertV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "LookupTableInsertV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_LookupTableRemoveV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_LookupTableRemoveV2.pbtxt new file mode 100644 index 0000000000..274dd236e3 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LookupTableRemoveV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "LookupTableRemoveV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_LookupTableSize.pbtxt b/tensorflow/core/api_def/java_api/api_def_LookupTableSize.pbtxt new file mode 100644 index 0000000000..70329e9e90 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LookupTableSize.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "LookupTableSize" +} diff --git a/tensorflow/core/api_def/java_api/api_def_LookupTableSizeV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_LookupTableSizeV2.pbtxt new file mode 100644 index 0000000000..d5681c3fa0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LookupTableSizeV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "LookupTableSizeV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_LoopCond.pbtxt b/tensorflow/core/api_def/java_api/api_def_LoopCond.pbtxt new file mode 100644 index 0000000000..492f78f62e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LoopCond.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "LoopCond" +} diff --git a/tensorflow/core/api_def/java_api/api_def_LowerBound.pbtxt b/tensorflow/core/api_def/java_api/api_def_LowerBound.pbtxt new file mode 100644 index 0000000000..31f1d3038c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_LowerBound.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "LowerBound" +} diff --git a/tensorflow/core/api_def/java_api/api_def_MakeIterator.pbtxt b/tensorflow/core/api_def/java_api/api_def_MakeIterator.pbtxt new file mode 100644 index 0000000000..9dfa761370 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MakeIterator.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MakeIterator" + endpoint { + name: "data.MakeIterator" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MapAndBatchDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_MapAndBatchDataset.pbtxt new file mode 100644 index 0000000000..b8f23009d7 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MapAndBatchDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MapAndBatchDataset" + endpoint { + name: "data.MapAndBatchDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MapAndBatchDatasetV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_MapAndBatchDatasetV2.pbtxt new file mode 100644 index 0000000000..a0bc306c52 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MapAndBatchDatasetV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "MapAndBatchDatasetV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_MapClear.pbtxt b/tensorflow/core/api_def/java_api/api_def_MapClear.pbtxt new file mode 100644 index 0000000000..3ed9bf8a5d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MapClear.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "MapClear" +} diff --git a/tensorflow/core/api_def/java_api/api_def_MapDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_MapDataset.pbtxt new file mode 100644 index 0000000000..fdec9eb857 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MapDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MapDataset" + endpoint { + name: "data.MapDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MapDefun.pbtxt b/tensorflow/core/api_def/java_api/api_def_MapDefun.pbtxt new file mode 100644 index 0000000000..43b1dc722c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MapDefun.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "MapDefun" +} diff --git a/tensorflow/core/api_def/java_api/api_def_MapIncompleteSize.pbtxt b/tensorflow/core/api_def/java_api/api_def_MapIncompleteSize.pbtxt new file mode 100644 index 0000000000..659993e42b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MapIncompleteSize.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "MapIncompleteSize" +} diff --git a/tensorflow/core/api_def/java_api/api_def_MapPeek.pbtxt b/tensorflow/core/api_def/java_api/api_def_MapPeek.pbtxt new file mode 100644 index 0000000000..eb1bd158f0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MapPeek.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "MapPeek" +} diff --git a/tensorflow/core/api_def/java_api/api_def_MapSize.pbtxt b/tensorflow/core/api_def/java_api/api_def_MapSize.pbtxt new file mode 100644 index 0000000000..4da151152c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MapSize.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "MapSize" +} diff --git a/tensorflow/core/api_def/java_api/api_def_MapStage.pbtxt b/tensorflow/core/api_def/java_api/api_def_MapStage.pbtxt new file mode 100644 index 0000000000..6d9f66cfc4 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MapStage.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "MapStage" +} diff --git a/tensorflow/core/api_def/java_api/api_def_MapUnstage.pbtxt b/tensorflow/core/api_def/java_api/api_def_MapUnstage.pbtxt new file mode 100644 index 0000000000..bb118f0fcb --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MapUnstage.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "MapUnstage" +} diff --git a/tensorflow/core/api_def/java_api/api_def_MapUnstageNoKey.pbtxt b/tensorflow/core/api_def/java_api/api_def_MapUnstageNoKey.pbtxt new file mode 100644 index 0000000000..1004e96482 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MapUnstageNoKey.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "MapUnstageNoKey" +} diff --git a/tensorflow/core/api_def/java_api/api_def_MatMul.pbtxt b/tensorflow/core/api_def/java_api/api_def_MatMul.pbtxt new file mode 100644 index 0000000000..fe4b8405b9 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MatMul.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MatMul" + endpoint { + name: "linalg.MatMul" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MatchingFiles.pbtxt b/tensorflow/core/api_def/java_api/api_def_MatchingFiles.pbtxt new file mode 100644 index 0000000000..bb7b096895 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MatchingFiles.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MatchingFiles" + endpoint { + name: "io.MatchingFiles" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MatchingFilesDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_MatchingFilesDataset.pbtxt new file mode 100644 index 0000000000..749257c37b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MatchingFilesDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MatchingFilesDataset" + endpoint { + name: "data.MatchingFilesDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MatrixBandPart.pbtxt b/tensorflow/core/api_def/java_api/api_def_MatrixBandPart.pbtxt new file mode 100644 index 0000000000..eaf426c00e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MatrixBandPart.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MatrixBandPart" + endpoint { + name: "linalg.BandPart" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MatrixDeterminant.pbtxt b/tensorflow/core/api_def/java_api/api_def_MatrixDeterminant.pbtxt new file mode 100644 index 0000000000..b56d2dad3a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MatrixDeterminant.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MatrixDeterminant" + endpoint { + name: "linalg.Det" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MatrixDiag.pbtxt b/tensorflow/core/api_def/java_api/api_def_MatrixDiag.pbtxt new file mode 100644 index 0000000000..839cd82b89 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MatrixDiag.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MatrixDiag" + endpoint { + name: "linalg.Diag" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MatrixDiagPart.pbtxt b/tensorflow/core/api_def/java_api/api_def_MatrixDiagPart.pbtxt new file mode 100644 index 0000000000..008f75c1e9 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MatrixDiagPart.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MatrixDiagPart" + endpoint { + name: "linalg.DiagPart" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MatrixExponential.pbtxt b/tensorflow/core/api_def/java_api/api_def_MatrixExponential.pbtxt new file mode 100644 index 0000000000..fb232dab98 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MatrixExponential.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MatrixExponential" + endpoint { + name: "linalg.MatrixExponential" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MatrixInverse.pbtxt b/tensorflow/core/api_def/java_api/api_def_MatrixInverse.pbtxt new file mode 100644 index 0000000000..68721fc78d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MatrixInverse.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MatrixInverse" + endpoint { + name: "linalg.Inv" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MatrixLogarithm.pbtxt b/tensorflow/core/api_def/java_api/api_def_MatrixLogarithm.pbtxt new file mode 100644 index 0000000000..04137ffae7 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MatrixLogarithm.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MatrixLogarithm" + endpoint { + name: "linalg.MatrixLogarithm" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MatrixSetDiag.pbtxt b/tensorflow/core/api_def/java_api/api_def_MatrixSetDiag.pbtxt new file mode 100644 index 0000000000..61001fa38c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MatrixSetDiag.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MatrixSetDiag" + endpoint { + name: "linalg.SetDiag" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MatrixSolve.pbtxt b/tensorflow/core/api_def/java_api/api_def_MatrixSolve.pbtxt new file mode 100644 index 0000000000..02c21448bb --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MatrixSolve.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MatrixSolve" + endpoint { + name: "linalg.Solve" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MatrixSolveLs.pbtxt b/tensorflow/core/api_def/java_api/api_def_MatrixSolveLs.pbtxt new file mode 100644 index 0000000000..9cee578ec2 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MatrixSolveLs.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MatrixSolveLs" + endpoint { + name: "linalg.MatrixSolveLs" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MatrixSquareRoot.pbtxt b/tensorflow/core/api_def/java_api/api_def_MatrixSquareRoot.pbtxt new file mode 100644 index 0000000000..14c7624fe3 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MatrixSquareRoot.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MatrixSquareRoot" + endpoint { + name: "linalg.Sqrtm" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MatrixTriangularSolve.pbtxt b/tensorflow/core/api_def/java_api/api_def_MatrixTriangularSolve.pbtxt new file mode 100644 index 0000000000..1f61e99efe --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MatrixTriangularSolve.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MatrixTriangularSolve" + endpoint { + name: "linalg.TriangularSolve" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Max.pbtxt b/tensorflow/core/api_def/java_api/api_def_Max.pbtxt new file mode 100644 index 0000000000..03868720ed --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Max.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Max" +} diff --git a/tensorflow/core/api_def/java_api/api_def_MaxPool.pbtxt b/tensorflow/core/api_def/java_api/api_def_MaxPool.pbtxt new file mode 100644 index 0000000000..17c17b5699 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MaxPool.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MaxPool" + endpoint { + name: "nn.MaxPool" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MaxPool3D.pbtxt b/tensorflow/core/api_def/java_api/api_def_MaxPool3D.pbtxt new file mode 100644 index 0000000000..17aeb6a8c9 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MaxPool3D.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MaxPool3D" + endpoint { + name: "nn.MaxPool3d" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MaxPool3DGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_MaxPool3DGrad.pbtxt new file mode 100644 index 0000000000..7f5ab71845 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MaxPool3DGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MaxPool3DGrad" + endpoint { + name: "nn.MaxPool3DGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MaxPool3DGradGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_MaxPool3DGradGrad.pbtxt new file mode 100644 index 0000000000..4d7211add2 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MaxPool3DGradGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MaxPool3DGradGrad" + endpoint { + name: "nn.MaxPool3DGradGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MaxPoolGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_MaxPoolGrad.pbtxt new file mode 100644 index 0000000000..c8b783ee7a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MaxPoolGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MaxPoolGrad" + endpoint { + name: "nn.MaxPoolGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MaxPoolGradGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_MaxPoolGradGrad.pbtxt new file mode 100644 index 0000000000..2dd7fdf229 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MaxPoolGradGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MaxPoolGradGrad" + endpoint { + name: "nn.MaxPoolGradGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MaxPoolGradGradV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_MaxPoolGradGradV2.pbtxt new file mode 100644 index 0000000000..e2cf7927ca --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MaxPoolGradGradV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MaxPoolGradGradV2" + endpoint { + name: "nn.MaxPoolGradGradV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MaxPoolGradGradWithArgmax.pbtxt b/tensorflow/core/api_def/java_api/api_def_MaxPoolGradGradWithArgmax.pbtxt new file mode 100644 index 0000000000..d43cf7447c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MaxPoolGradGradWithArgmax.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MaxPoolGradGradWithArgmax" + endpoint { + name: "nn.MaxPoolGradGradWithArgmax" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MaxPoolGradV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_MaxPoolGradV2.pbtxt new file mode 100644 index 0000000000..1e47cd13e3 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MaxPoolGradV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MaxPoolGradV2" + endpoint { + name: "nn.MaxPoolGradV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MaxPoolGradWithArgmax.pbtxt b/tensorflow/core/api_def/java_api/api_def_MaxPoolGradWithArgmax.pbtxt new file mode 100644 index 0000000000..c10701f555 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MaxPoolGradWithArgmax.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MaxPoolGradWithArgmax" + endpoint { + name: "nn.MaxPoolGradWithArgmax" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MaxPoolV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_MaxPoolV2.pbtxt new file mode 100644 index 0000000000..bd885135d5 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MaxPoolV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MaxPoolV2" + endpoint { + name: "nn.MaxPoolV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MaxPoolWithArgmax.pbtxt b/tensorflow/core/api_def/java_api/api_def_MaxPoolWithArgmax.pbtxt new file mode 100644 index 0000000000..43630534cb --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MaxPoolWithArgmax.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MaxPoolWithArgmax" + endpoint { + name: "nn.MaxPoolWithArgmax" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Maximum.pbtxt b/tensorflow/core/api_def/java_api/api_def_Maximum.pbtxt new file mode 100644 index 0000000000..1df9c60530 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Maximum.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Maximum" + endpoint { + name: "math.Maximum" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Mean.pbtxt b/tensorflow/core/api_def/java_api/api_def_Mean.pbtxt new file mode 100644 index 0000000000..7bdcdc3d74 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Mean.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Mean" + endpoint { + name: "math.Mean" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Merge.pbtxt b/tensorflow/core/api_def/java_api/api_def_Merge.pbtxt new file mode 100644 index 0000000000..954d5085ad --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Merge.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Merge" +} diff --git a/tensorflow/core/api_def/java_api/api_def_MergeSummary.pbtxt b/tensorflow/core/api_def/java_api/api_def_MergeSummary.pbtxt new file mode 100644 index 0000000000..f52c7c0996 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MergeSummary.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MergeSummary" + endpoint { + name: "summary.MergeSummary" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MergeV2Checkpoints.pbtxt b/tensorflow/core/api_def/java_api/api_def_MergeV2Checkpoints.pbtxt new file mode 100644 index 0000000000..8899c8c4ed --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MergeV2Checkpoints.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MergeV2Checkpoints" + endpoint { + name: "train.MergeV2Checkpoints" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Mfcc.pbtxt b/tensorflow/core/api_def/java_api/api_def_Mfcc.pbtxt new file mode 100644 index 0000000000..6cb04e73ff --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Mfcc.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Mfcc" + endpoint { + name: "audio.Mfcc" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Min.pbtxt b/tensorflow/core/api_def/java_api/api_def_Min.pbtxt new file mode 100644 index 0000000000..72894c1ffd --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Min.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Min" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Minimum.pbtxt b/tensorflow/core/api_def/java_api/api_def_Minimum.pbtxt new file mode 100644 index 0000000000..69f76a9829 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Minimum.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Minimum" + endpoint { + name: "math.Minimum" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MirrorPad.pbtxt b/tensorflow/core/api_def/java_api/api_def_MirrorPad.pbtxt new file mode 100644 index 0000000000..e1cb766f8f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MirrorPad.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "MirrorPad" +} diff --git a/tensorflow/core/api_def/java_api/api_def_MirrorPadGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_MirrorPadGrad.pbtxt new file mode 100644 index 0000000000..ddd8ab3ba1 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MirrorPadGrad.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "MirrorPadGrad" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Mod.pbtxt b/tensorflow/core/api_def/java_api/api_def_Mod.pbtxt new file mode 100644 index 0000000000..76fbbe97a8 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Mod.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Mod" + endpoint { + name: "math.Mod" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ModelDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ModelDataset.pbtxt new file mode 100644 index 0000000000..143c7afd72 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ModelDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ModelDataset" + endpoint { + name: "data.ModelDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Mul.pbtxt b/tensorflow/core/api_def/java_api/api_def_Mul.pbtxt new file mode 100644 index 0000000000..605e110931 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Mul.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Mul" + endpoint { + name: "math.Mul" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MultiDeviceIterator.pbtxt b/tensorflow/core/api_def/java_api/api_def_MultiDeviceIterator.pbtxt new file mode 100644 index 0000000000..81eabf9bdc --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MultiDeviceIterator.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MultiDeviceIterator" + endpoint { + name: "data.MultiDeviceIterator" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MultiDeviceIteratorFromStringHandle.pbtxt b/tensorflow/core/api_def/java_api/api_def_MultiDeviceIteratorFromStringHandle.pbtxt new file mode 100644 index 0000000000..4006f72d52 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MultiDeviceIteratorFromStringHandle.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MultiDeviceIteratorFromStringHandle" + endpoint { + name: "data.MultiDeviceIteratorFromStringHandle" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MultiDeviceIteratorGetNextFromShard.pbtxt b/tensorflow/core/api_def/java_api/api_def_MultiDeviceIteratorGetNextFromShard.pbtxt new file mode 100644 index 0000000000..a7e6fc1508 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MultiDeviceIteratorGetNextFromShard.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MultiDeviceIteratorGetNextFromShard" + endpoint { + name: "data.MultiDeviceIteratorGetNextFromShard" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MultiDeviceIteratorInit.pbtxt b/tensorflow/core/api_def/java_api/api_def_MultiDeviceIteratorInit.pbtxt new file mode 100644 index 0000000000..1663bc5c22 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MultiDeviceIteratorInit.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MultiDeviceIteratorInit" + endpoint { + name: "data.MultiDeviceIteratorInit" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MultiDeviceIteratorToStringHandle.pbtxt b/tensorflow/core/api_def/java_api/api_def_MultiDeviceIteratorToStringHandle.pbtxt new file mode 100644 index 0000000000..ff061da390 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MultiDeviceIteratorToStringHandle.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "MultiDeviceIteratorToStringHandle" + endpoint { + name: "data.MultiDeviceIteratorToStringHandle" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Multinomial.pbtxt b/tensorflow/core/api_def/java_api/api_def_Multinomial.pbtxt new file mode 100644 index 0000000000..bd98bb10b4 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Multinomial.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Multinomial" + endpoint { + name: "random.Multinomial" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_MutableDenseHashTable.pbtxt b/tensorflow/core/api_def/java_api/api_def_MutableDenseHashTable.pbtxt new file mode 100644 index 0000000000..15803468ff --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MutableDenseHashTable.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "MutableDenseHashTable" +} diff --git a/tensorflow/core/api_def/java_api/api_def_MutableDenseHashTableV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_MutableDenseHashTableV2.pbtxt new file mode 100644 index 0000000000..03fdeddb95 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MutableDenseHashTableV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "MutableDenseHashTableV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_MutableHashTable.pbtxt b/tensorflow/core/api_def/java_api/api_def_MutableHashTable.pbtxt new file mode 100644 index 0000000000..1a80874495 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MutableHashTable.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "MutableHashTable" +} diff --git a/tensorflow/core/api_def/java_api/api_def_MutableHashTableOfTensors.pbtxt b/tensorflow/core/api_def/java_api/api_def_MutableHashTableOfTensors.pbtxt new file mode 100644 index 0000000000..bf2fa065dc --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MutableHashTableOfTensors.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "MutableHashTableOfTensors" +} diff --git a/tensorflow/core/api_def/java_api/api_def_MutableHashTableOfTensorsV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_MutableHashTableOfTensorsV2.pbtxt new file mode 100644 index 0000000000..53d780e925 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MutableHashTableOfTensorsV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "MutableHashTableOfTensorsV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_MutableHashTableV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_MutableHashTableV2.pbtxt new file mode 100644 index 0000000000..99007df253 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MutableHashTableV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "MutableHashTableV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_MutexLock.pbtxt b/tensorflow/core/api_def/java_api/api_def_MutexLock.pbtxt new file mode 100644 index 0000000000..75c7be5286 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MutexLock.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "MutexLock" +} diff --git a/tensorflow/core/api_def/java_api/api_def_MutexV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_MutexV2.pbtxt new file mode 100644 index 0000000000..93988914aa --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_MutexV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "MutexV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_NcclAllReduce.pbtxt b/tensorflow/core/api_def/java_api/api_def_NcclAllReduce.pbtxt new file mode 100644 index 0000000000..c7133d4a4a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_NcclAllReduce.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "NcclAllReduce" +} diff --git a/tensorflow/core/api_def/java_api/api_def_NcclBroadcast.pbtxt b/tensorflow/core/api_def/java_api/api_def_NcclBroadcast.pbtxt new file mode 100644 index 0000000000..b9b1a345c9 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_NcclBroadcast.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "NcclBroadcast" +} diff --git a/tensorflow/core/api_def/java_api/api_def_NcclReduce.pbtxt b/tensorflow/core/api_def/java_api/api_def_NcclReduce.pbtxt new file mode 100644 index 0000000000..18dc890177 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_NcclReduce.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "NcclReduce" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Neg.pbtxt b/tensorflow/core/api_def/java_api/api_def_Neg.pbtxt new file mode 100644 index 0000000000..c7e9ede2a5 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Neg.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Neg" + endpoint { + name: "math.Neg" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_NegTrain.pbtxt b/tensorflow/core/api_def/java_api/api_def_NegTrain.pbtxt new file mode 100644 index 0000000000..eb62186362 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_NegTrain.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "NegTrain" + endpoint { + name: "train.NegTrain" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_NextIteration.pbtxt b/tensorflow/core/api_def/java_api/api_def_NextIteration.pbtxt new file mode 100644 index 0000000000..bc63f6ada1 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_NextIteration.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "NextIteration" +} diff --git a/tensorflow/core/api_def/java_api/api_def_NoOp.pbtxt b/tensorflow/core/api_def/java_api/api_def_NoOp.pbtxt new file mode 100644 index 0000000000..337fb5da14 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_NoOp.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "NoOp" +} diff --git a/tensorflow/core/api_def/java_api/api_def_NonMaxSuppression.pbtxt b/tensorflow/core/api_def/java_api/api_def_NonMaxSuppression.pbtxt new file mode 100644 index 0000000000..d7156b0a3a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_NonMaxSuppression.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "NonMaxSuppression" + endpoint { + name: "image.NonMaxSuppression" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_NonMaxSuppressionV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_NonMaxSuppressionV2.pbtxt new file mode 100644 index 0000000000..d78eb9745d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_NonMaxSuppressionV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "NonMaxSuppressionV2" + endpoint { + name: "image.NonMaxSuppressionV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_NonMaxSuppressionV3.pbtxt b/tensorflow/core/api_def/java_api/api_def_NonMaxSuppressionV3.pbtxt new file mode 100644 index 0000000000..e3fde0a566 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_NonMaxSuppressionV3.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "NonMaxSuppressionV3" + endpoint { + name: "image.NonMaxSuppressionV3" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_NonMaxSuppressionV4.pbtxt b/tensorflow/core/api_def/java_api/api_def_NonMaxSuppressionV4.pbtxt new file mode 100644 index 0000000000..98776e8e7f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_NonMaxSuppressionV4.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "NonMaxSuppressionV4" + endpoint { + name: "image.NonMaxSuppressionV4" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_NonMaxSuppressionWithOverlaps.pbtxt b/tensorflow/core/api_def/java_api/api_def_NonMaxSuppressionWithOverlaps.pbtxt new file mode 100644 index 0000000000..06fa52920d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_NonMaxSuppressionWithOverlaps.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "NonMaxSuppressionWithOverlaps" + endpoint { + name: "image.NonMaxSuppressionWithOverlaps" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_NotEqual.pbtxt b/tensorflow/core/api_def/java_api/api_def_NotEqual.pbtxt new file mode 100644 index 0000000000..0d99af40b5 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_NotEqual.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "NotEqual" + endpoint { + name: "math.NotEqual" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_NthElement.pbtxt b/tensorflow/core/api_def/java_api/api_def_NthElement.pbtxt new file mode 100644 index 0000000000..57097e634a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_NthElement.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "NthElement" + endpoint { + name: "nn.NthElement" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_OneHot.pbtxt b/tensorflow/core/api_def/java_api/api_def_OneHot.pbtxt new file mode 100644 index 0000000000..66872d5eb8 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_OneHot.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "OneHot" +} diff --git a/tensorflow/core/api_def/java_api/api_def_OneShotIterator.pbtxt b/tensorflow/core/api_def/java_api/api_def_OneShotIterator.pbtxt new file mode 100644 index 0000000000..39af8cefde --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_OneShotIterator.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "OneShotIterator" + endpoint { + name: "data.OneShotIterator" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_OnesLike.pbtxt b/tensorflow/core/api_def/java_api/api_def_OnesLike.pbtxt new file mode 100644 index 0000000000..97abe0814a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_OnesLike.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "OnesLike" +} diff --git a/tensorflow/core/api_def/java_api/api_def_OptimizeDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_OptimizeDataset.pbtxt new file mode 100644 index 0000000000..e7ddf97d1a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_OptimizeDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "OptimizeDataset" + endpoint { + name: "data.OptimizeDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_OptionalFromValue.pbtxt b/tensorflow/core/api_def/java_api/api_def_OptionalFromValue.pbtxt new file mode 100644 index 0000000000..d251fd5d94 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_OptionalFromValue.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "OptionalFromValue" + endpoint { + name: "data.OptionalFromValue" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_OptionalGetValue.pbtxt b/tensorflow/core/api_def/java_api/api_def_OptionalGetValue.pbtxt new file mode 100644 index 0000000000..7fcdb5ac69 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_OptionalGetValue.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "OptionalGetValue" + endpoint { + name: "data.OptionalGetValue" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_OptionalHasValue.pbtxt b/tensorflow/core/api_def/java_api/api_def_OptionalHasValue.pbtxt new file mode 100644 index 0000000000..4ffa15b564 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_OptionalHasValue.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "OptionalHasValue" + endpoint { + name: "data.OptionalHasValue" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_OptionalNone.pbtxt b/tensorflow/core/api_def/java_api/api_def_OptionalNone.pbtxt new file mode 100644 index 0000000000..cec29a42ae --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_OptionalNone.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "OptionalNone" + endpoint { + name: "data.OptionalNone" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_OrderedMapClear.pbtxt b/tensorflow/core/api_def/java_api/api_def_OrderedMapClear.pbtxt new file mode 100644 index 0000000000..e36b2aa3e4 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_OrderedMapClear.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "OrderedMapClear" +} diff --git a/tensorflow/core/api_def/java_api/api_def_OrderedMapIncompleteSize.pbtxt b/tensorflow/core/api_def/java_api/api_def_OrderedMapIncompleteSize.pbtxt new file mode 100644 index 0000000000..c609e9e50a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_OrderedMapIncompleteSize.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "OrderedMapIncompleteSize" +} diff --git a/tensorflow/core/api_def/java_api/api_def_OrderedMapPeek.pbtxt b/tensorflow/core/api_def/java_api/api_def_OrderedMapPeek.pbtxt new file mode 100644 index 0000000000..06fc218277 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_OrderedMapPeek.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "OrderedMapPeek" +} diff --git a/tensorflow/core/api_def/java_api/api_def_OrderedMapSize.pbtxt b/tensorflow/core/api_def/java_api/api_def_OrderedMapSize.pbtxt new file mode 100644 index 0000000000..7beef3f376 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_OrderedMapSize.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "OrderedMapSize" +} diff --git a/tensorflow/core/api_def/java_api/api_def_OrderedMapStage.pbtxt b/tensorflow/core/api_def/java_api/api_def_OrderedMapStage.pbtxt new file mode 100644 index 0000000000..8b579d21a0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_OrderedMapStage.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "OrderedMapStage" +} diff --git a/tensorflow/core/api_def/java_api/api_def_OrderedMapUnstage.pbtxt b/tensorflow/core/api_def/java_api/api_def_OrderedMapUnstage.pbtxt new file mode 100644 index 0000000000..d3d6862fbe --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_OrderedMapUnstage.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "OrderedMapUnstage" +} diff --git a/tensorflow/core/api_def/java_api/api_def_OrderedMapUnstageNoKey.pbtxt b/tensorflow/core/api_def/java_api/api_def_OrderedMapUnstageNoKey.pbtxt new file mode 100644 index 0000000000..3d275c85d9 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_OrderedMapUnstageNoKey.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "OrderedMapUnstageNoKey" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Pack.pbtxt b/tensorflow/core/api_def/java_api/api_def_Pack.pbtxt new file mode 100644 index 0000000000..d9e9897d77 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Pack.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Pack" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Pad.pbtxt b/tensorflow/core/api_def/java_api/api_def_Pad.pbtxt new file mode 100644 index 0000000000..a9de5541ac --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Pad.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Pad" +} diff --git a/tensorflow/core/api_def/java_api/api_def_PadV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_PadV2.pbtxt new file mode 100644 index 0000000000..1554cd0b64 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_PadV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "PadV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_PaddedBatchDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_PaddedBatchDataset.pbtxt new file mode 100644 index 0000000000..2d734539da --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_PaddedBatchDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "PaddedBatchDataset" + endpoint { + name: "data.PaddedBatchDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_PaddedBatchDatasetV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_PaddedBatchDatasetV2.pbtxt new file mode 100644 index 0000000000..d4b85550e9 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_PaddedBatchDatasetV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "PaddedBatchDatasetV2" + endpoint { + name: "data.PaddedBatchDatasetV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_PaddingFIFOQueue.pbtxt b/tensorflow/core/api_def/java_api/api_def_PaddingFIFOQueue.pbtxt new file mode 100644 index 0000000000..151ab0f872 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_PaddingFIFOQueue.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "PaddingFIFOQueue" + endpoint { + name: "io.PaddingFIFOQueue" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_PaddingFIFOQueueV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_PaddingFIFOQueueV2.pbtxt new file mode 100644 index 0000000000..4b589a9afb --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_PaddingFIFOQueueV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "PaddingFIFOQueueV2" + endpoint { + name: "io.PaddingFIFOQueueV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ParallelConcat.pbtxt b/tensorflow/core/api_def/java_api/api_def_ParallelConcat.pbtxt new file mode 100644 index 0000000000..0b17c7d256 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ParallelConcat.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ParallelConcat" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ParallelDynamicStitch.pbtxt b/tensorflow/core/api_def/java_api/api_def_ParallelDynamicStitch.pbtxt new file mode 100644 index 0000000000..79a55b763f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ParallelDynamicStitch.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ParallelDynamicStitch" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ParallelInterleaveDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ParallelInterleaveDataset.pbtxt new file mode 100644 index 0000000000..3ccc2a6bf7 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ParallelInterleaveDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ParallelInterleaveDataset" + endpoint { + name: "data.ParallelInterleaveDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ParallelInterleaveDatasetV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_ParallelInterleaveDatasetV2.pbtxt new file mode 100644 index 0000000000..56b05cc2f6 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ParallelInterleaveDatasetV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ParallelInterleaveDatasetV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ParallelMapDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ParallelMapDataset.pbtxt new file mode 100644 index 0000000000..5091bb9cec --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ParallelMapDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ParallelMapDataset" + endpoint { + name: "data.ParallelMapDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ParameterizedTruncatedNormal.pbtxt b/tensorflow/core/api_def/java_api/api_def_ParameterizedTruncatedNormal.pbtxt new file mode 100644 index 0000000000..26ca2fc86f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ParameterizedTruncatedNormal.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ParameterizedTruncatedNormal" + endpoint { + name: "random.ParameterizedTruncatedNormal" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ParseExample.pbtxt b/tensorflow/core/api_def/java_api/api_def_ParseExample.pbtxt new file mode 100644 index 0000000000..6e7384f2e3 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ParseExample.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ParseExample" + endpoint { + name: "io.ParseExample" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ParseExampleDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ParseExampleDataset.pbtxt new file mode 100644 index 0000000000..4309645093 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ParseExampleDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ParseExampleDataset" + endpoint { + name: "data.ParseExampleDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ParseSequenceExample.pbtxt b/tensorflow/core/api_def/java_api/api_def_ParseSequenceExample.pbtxt new file mode 100644 index 0000000000..09ee715ac7 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ParseSequenceExample.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ParseSequenceExample" + endpoint { + name: "io.ParseSequenceExample" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ParseSingleExample.pbtxt b/tensorflow/core/api_def/java_api/api_def_ParseSingleExample.pbtxt new file mode 100644 index 0000000000..7559957b35 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ParseSingleExample.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ParseSingleExample" + endpoint { + name: "io.ParseSingleExample" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ParseSingleSequenceExample.pbtxt b/tensorflow/core/api_def/java_api/api_def_ParseSingleSequenceExample.pbtxt new file mode 100644 index 0000000000..00eb325b2a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ParseSingleSequenceExample.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ParseSingleSequenceExample" + endpoint { + name: "io.ParseSingleSequenceExample" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ParseTensor.pbtxt b/tensorflow/core/api_def/java_api/api_def_ParseTensor.pbtxt new file mode 100644 index 0000000000..a78cdc7f5c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ParseTensor.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ParseTensor" + endpoint { + name: "io.ParseTensor" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_PartitionedCall.pbtxt b/tensorflow/core/api_def/java_api/api_def_PartitionedCall.pbtxt new file mode 100644 index 0000000000..1ac10b6028 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_PartitionedCall.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "PartitionedCall" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Placeholder.pbtxt b/tensorflow/core/api_def/java_api/api_def_Placeholder.pbtxt new file mode 100644 index 0000000000..5e6daa2ae4 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Placeholder.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Placeholder" +} diff --git a/tensorflow/core/api_def/java_api/api_def_PlaceholderV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_PlaceholderV2.pbtxt new file mode 100644 index 0000000000..39012d4aa2 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_PlaceholderV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "PlaceholderV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_PlaceholderWithDefault.pbtxt b/tensorflow/core/api_def/java_api/api_def_PlaceholderWithDefault.pbtxt new file mode 100644 index 0000000000..59067a9c68 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_PlaceholderWithDefault.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "PlaceholderWithDefault" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Polygamma.pbtxt b/tensorflow/core/api_def/java_api/api_def_Polygamma.pbtxt new file mode 100644 index 0000000000..746b3375a0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Polygamma.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Polygamma" + endpoint { + name: "math.Polygamma" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_PopulationCount.pbtxt b/tensorflow/core/api_def/java_api/api_def_PopulationCount.pbtxt new file mode 100644 index 0000000000..6aacdf4d12 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_PopulationCount.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "PopulationCount" + endpoint { + name: "math.PopulationCount" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Pow.pbtxt b/tensorflow/core/api_def/java_api/api_def_Pow.pbtxt new file mode 100644 index 0000000000..e7eaaed695 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Pow.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Pow" + endpoint { + name: "math.Pow" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_PrefetchDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_PrefetchDataset.pbtxt new file mode 100644 index 0000000000..beaad84d15 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_PrefetchDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "PrefetchDataset" + endpoint { + name: "data.PrefetchDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_PrependFromQueueAndPaddedBatchDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_PrependFromQueueAndPaddedBatchDataset.pbtxt new file mode 100644 index 0000000000..7c9d509b16 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_PrependFromQueueAndPaddedBatchDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "PrependFromQueueAndPaddedBatchDataset" + endpoint { + name: "data.PrependFromQueueAndPaddedBatchDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_PreventGradient.pbtxt b/tensorflow/core/api_def/java_api/api_def_PreventGradient.pbtxt new file mode 100644 index 0000000000..4731f21af4 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_PreventGradient.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "PreventGradient" + endpoint { + name: "train.PreventGradient" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Print.pbtxt b/tensorflow/core/api_def/java_api/api_def_Print.pbtxt new file mode 100644 index 0000000000..5b837135fd --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Print.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Print" +} diff --git a/tensorflow/core/api_def/java_api/api_def_PrintV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_PrintV2.pbtxt new file mode 100644 index 0000000000..c6e406d08f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_PrintV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "PrintV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_PriorityQueue.pbtxt b/tensorflow/core/api_def/java_api/api_def_PriorityQueue.pbtxt new file mode 100644 index 0000000000..cee973139d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_PriorityQueue.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "PriorityQueue" + endpoint { + name: "io.PriorityQueue" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_PriorityQueueV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_PriorityQueueV2.pbtxt new file mode 100644 index 0000000000..27e89f92be --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_PriorityQueueV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "PriorityQueueV2" + endpoint { + name: "io.PriorityQueueV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Prod.pbtxt b/tensorflow/core/api_def/java_api/api_def_Prod.pbtxt new file mode 100644 index 0000000000..7a98972a87 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Prod.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Prod" +} diff --git a/tensorflow/core/api_def/java_api/api_def_PyFunc.pbtxt b/tensorflow/core/api_def/java_api/api_def_PyFunc.pbtxt new file mode 100644 index 0000000000..5f1f7c47ca --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_PyFunc.pbtxt @@ -0,0 +1,4 @@ +op { + graph_op_name: "PyFunc" + visibility: SKIP +} diff --git a/tensorflow/core/api_def/java_api/api_def_PyFuncStateless.pbtxt b/tensorflow/core/api_def/java_api/api_def_PyFuncStateless.pbtxt new file mode 100644 index 0000000000..684ef58d1b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_PyFuncStateless.pbtxt @@ -0,0 +1,4 @@ +op { + graph_op_name: "PyFuncStateless" + visibility: SKIP +} diff --git a/tensorflow/core/api_def/java_api/api_def_Qr.pbtxt b/tensorflow/core/api_def/java_api/api_def_Qr.pbtxt new file mode 100644 index 0000000000..c0d31e7977 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Qr.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Qr" + endpoint { + name: "linalg.Qr" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QuantizeAndDequantize.pbtxt b/tensorflow/core/api_def/java_api/api_def_QuantizeAndDequantize.pbtxt new file mode 100644 index 0000000000..fe8401d8f9 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QuantizeAndDequantize.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QuantizeAndDequantize" + endpoint { + name: "quantization.QuantizeAndDequantize" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QuantizeAndDequantizeV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_QuantizeAndDequantizeV2.pbtxt new file mode 100644 index 0000000000..0535993d40 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QuantizeAndDequantizeV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QuantizeAndDequantizeV2" + endpoint { + name: "quantization.QuantizeAndDequantize" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QuantizeAndDequantizeV3.pbtxt b/tensorflow/core/api_def/java_api/api_def_QuantizeAndDequantizeV3.pbtxt new file mode 100644 index 0000000000..d056b320c8 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QuantizeAndDequantizeV3.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QuantizeAndDequantizeV3" + endpoint { + name: "quantization.QuantizeAndDequantizeV3" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QuantizeDownAndShrinkRange.pbtxt b/tensorflow/core/api_def/java_api/api_def_QuantizeDownAndShrinkRange.pbtxt new file mode 100644 index 0000000000..7119f53cb2 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QuantizeDownAndShrinkRange.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QuantizeDownAndShrinkRange" + endpoint { + name: "quantization.QuantizeDownAndShrinkRange" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QuantizeV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_QuantizeV2.pbtxt new file mode 100644 index 0000000000..d04a01726f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QuantizeV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QuantizeV2" + endpoint { + name: "quantization.QuantizeV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QuantizedAdd.pbtxt b/tensorflow/core/api_def/java_api/api_def_QuantizedAdd.pbtxt new file mode 100644 index 0000000000..1a2bfa36ed --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QuantizedAdd.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QuantizedAdd" + endpoint { + name: "math.QuantizedAdd" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QuantizedAvgPool.pbtxt b/tensorflow/core/api_def/java_api/api_def_QuantizedAvgPool.pbtxt new file mode 100644 index 0000000000..7f16fb046d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QuantizedAvgPool.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QuantizedAvgPool" + endpoint { + name: "nn.QuantizedAvgPool" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QuantizedBatchNormWithGlobalNormalization.pbtxt b/tensorflow/core/api_def/java_api/api_def_QuantizedBatchNormWithGlobalNormalization.pbtxt new file mode 100644 index 0000000000..2101ce5d69 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QuantizedBatchNormWithGlobalNormalization.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QuantizedBatchNormWithGlobalNormalization" + endpoint { + name: "nn.QuantizedBatchNormWithGlobalNormalization" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QuantizedBiasAdd.pbtxt b/tensorflow/core/api_def/java_api/api_def_QuantizedBiasAdd.pbtxt new file mode 100644 index 0000000000..c18fb1c574 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QuantizedBiasAdd.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QuantizedBiasAdd" + endpoint { + name: "nn.QuantizedBiasAdd" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QuantizedConcat.pbtxt b/tensorflow/core/api_def/java_api/api_def_QuantizedConcat.pbtxt new file mode 100644 index 0000000000..cb5d0c0fda --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QuantizedConcat.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "QuantizedConcat" +} diff --git a/tensorflow/core/api_def/java_api/api_def_QuantizedConv2D.pbtxt b/tensorflow/core/api_def/java_api/api_def_QuantizedConv2D.pbtxt new file mode 100644 index 0000000000..8345892762 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QuantizedConv2D.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QuantizedConv2D" + endpoint { + name: "nn.QuantizedConv2D" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QuantizedInstanceNorm.pbtxt b/tensorflow/core/api_def/java_api/api_def_QuantizedInstanceNorm.pbtxt new file mode 100644 index 0000000000..bbd2e7fc5e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QuantizedInstanceNorm.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QuantizedInstanceNorm" + endpoint { + name: "nn.QuantizedInstanceNorm" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QuantizedMatMul.pbtxt b/tensorflow/core/api_def/java_api/api_def_QuantizedMatMul.pbtxt new file mode 100644 index 0000000000..7962cbade6 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QuantizedMatMul.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QuantizedMatMul" + endpoint { + name: "linalg.QuantizedMatMul" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QuantizedMaxPool.pbtxt b/tensorflow/core/api_def/java_api/api_def_QuantizedMaxPool.pbtxt new file mode 100644 index 0000000000..57e900494e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QuantizedMaxPool.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QuantizedMaxPool" + endpoint { + name: "nn.QuantizedMaxPool" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QuantizedMul.pbtxt b/tensorflow/core/api_def/java_api/api_def_QuantizedMul.pbtxt new file mode 100644 index 0000000000..be23ef706e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QuantizedMul.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QuantizedMul" + endpoint { + name: "math.QuantizedMul" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QuantizedRelu.pbtxt b/tensorflow/core/api_def/java_api/api_def_QuantizedRelu.pbtxt new file mode 100644 index 0000000000..7b9a11640b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QuantizedRelu.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QuantizedRelu" + endpoint { + name: "nn.QuantizedRelu" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QuantizedRelu6.pbtxt b/tensorflow/core/api_def/java_api/api_def_QuantizedRelu6.pbtxt new file mode 100644 index 0000000000..6a60e2112e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QuantizedRelu6.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QuantizedRelu6" + endpoint { + name: "nn.QuantizedRelu6" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QuantizedReluX.pbtxt b/tensorflow/core/api_def/java_api/api_def_QuantizedReluX.pbtxt new file mode 100644 index 0000000000..cc47d322b2 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QuantizedReluX.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QuantizedReluX" + endpoint { + name: "nn.QuantizedReluX" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QuantizedReshape.pbtxt b/tensorflow/core/api_def/java_api/api_def_QuantizedReshape.pbtxt new file mode 100644 index 0000000000..4557853d94 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QuantizedReshape.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "QuantizedReshape" +} diff --git a/tensorflow/core/api_def/java_api/api_def_QuantizedResizeBilinear.pbtxt b/tensorflow/core/api_def/java_api/api_def_QuantizedResizeBilinear.pbtxt new file mode 100644 index 0000000000..81dca49094 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QuantizedResizeBilinear.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QuantizedResizeBilinear" + endpoint { + name: "image.QuantizedResizeBilinear" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QueueClose.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueClose.pbtxt new file mode 100644 index 0000000000..70e559c773 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QueueClose.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QueueClose" + endpoint { + name: "io.QueueClose" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QueueCloseV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueCloseV2.pbtxt new file mode 100644 index 0000000000..01460f27e2 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QueueCloseV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QueueCloseV2" + endpoint { + name: "io.QueueCloseV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QueueDequeue.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueDequeue.pbtxt new file mode 100644 index 0000000000..9cd77b4ca3 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QueueDequeue.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QueueDequeue" + endpoint { + name: "io.QueueDequeue" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QueueDequeueMany.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueDequeueMany.pbtxt new file mode 100644 index 0000000000..ceb2e82394 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QueueDequeueMany.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QueueDequeueMany" + endpoint { + name: "io.QueueDequeueMany" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QueueDequeueManyV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueDequeueManyV2.pbtxt new file mode 100644 index 0000000000..ac27fcb620 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QueueDequeueManyV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QueueDequeueManyV2" + endpoint { + name: "io.QueueDequeueManyV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QueueDequeueUpTo.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueDequeueUpTo.pbtxt new file mode 100644 index 0000000000..657c63363f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QueueDequeueUpTo.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QueueDequeueUpTo" + endpoint { + name: "io.QueueDequeueUpTo" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QueueDequeueUpToV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueDequeueUpToV2.pbtxt new file mode 100644 index 0000000000..19b3fff653 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QueueDequeueUpToV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QueueDequeueUpToV2" + endpoint { + name: "io.QueueDequeueUpToV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QueueDequeueV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueDequeueV2.pbtxt new file mode 100644 index 0000000000..8aca207816 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QueueDequeueV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QueueDequeueV2" + endpoint { + name: "io.QueueDequeueV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QueueEnqueue.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueEnqueue.pbtxt new file mode 100644 index 0000000000..e516dab297 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QueueEnqueue.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QueueEnqueue" + endpoint { + name: "io.QueueEnqueue" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QueueEnqueueMany.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueEnqueueMany.pbtxt new file mode 100644 index 0000000000..b9c0e4fd84 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QueueEnqueueMany.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QueueEnqueueMany" + endpoint { + name: "io.QueueEnqueueMany" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QueueEnqueueManyV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueEnqueueManyV2.pbtxt new file mode 100644 index 0000000000..153700a646 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QueueEnqueueManyV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QueueEnqueueManyV2" + endpoint { + name: "io.QueueEnqueueManyV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QueueEnqueueV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueEnqueueV2.pbtxt new file mode 100644 index 0000000000..68bacd7b5b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QueueEnqueueV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QueueEnqueueV2" + endpoint { + name: "io.QueueEnqueueV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QueueIsClosed.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueIsClosed.pbtxt new file mode 100644 index 0000000000..86914c7124 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QueueIsClosed.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QueueIsClosed" + endpoint { + name: "io.QueueIsClosed" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QueueIsClosedV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueIsClosedV2.pbtxt new file mode 100644 index 0000000000..ce33b2498b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QueueIsClosedV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QueueIsClosedV2" + endpoint { + name: "io.QueueIsClosedV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QueueSize.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueSize.pbtxt new file mode 100644 index 0000000000..5592d58f9f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QueueSize.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QueueSize" + endpoint { + name: "io.QueueSize" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_QueueSizeV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueSizeV2.pbtxt new file mode 100644 index 0000000000..68364aa605 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_QueueSizeV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "QueueSizeV2" + endpoint { + name: "io.QueueSizeV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_RFFT.pbtxt b/tensorflow/core/api_def/java_api/api_def_RFFT.pbtxt new file mode 100644 index 0000000000..e56a0ba30a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RFFT.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "RFFT" + endpoint { + name: "signal.RFFT" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_RFFT2D.pbtxt b/tensorflow/core/api_def/java_api/api_def_RFFT2D.pbtxt new file mode 100644 index 0000000000..eb193e638d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RFFT2D.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "RFFT2D" + endpoint { + name: "signal.RFFT2D" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_RFFT3D.pbtxt b/tensorflow/core/api_def/java_api/api_def_RFFT3D.pbtxt new file mode 100644 index 0000000000..292b957f3e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RFFT3D.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "RFFT3D" + endpoint { + name: "signal.RFFT3D" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_RGBToHSV.pbtxt b/tensorflow/core/api_def/java_api/api_def_RGBToHSV.pbtxt new file mode 100644 index 0000000000..1b35891ae2 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RGBToHSV.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "RGBToHSV" + endpoint { + name: "image.RgbToHsv" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_RaggedGather.pbtxt b/tensorflow/core/api_def/java_api/api_def_RaggedGather.pbtxt new file mode 100644 index 0000000000..f060daeb65 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RaggedGather.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "RaggedGather" +} diff --git a/tensorflow/core/api_def/java_api/api_def_RaggedRange.pbtxt b/tensorflow/core/api_def/java_api/api_def_RaggedRange.pbtxt new file mode 100644 index 0000000000..b1a5bab0ac --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RaggedRange.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "RaggedRange" +} diff --git a/tensorflow/core/api_def/java_api/api_def_RaggedTensorToSparse.pbtxt b/tensorflow/core/api_def/java_api/api_def_RaggedTensorToSparse.pbtxt new file mode 100644 index 0000000000..f049f47b46 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RaggedTensorToSparse.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "RaggedTensorToSparse" +} diff --git a/tensorflow/core/api_def/java_api/api_def_RandomCrop.pbtxt b/tensorflow/core/api_def/java_api/api_def_RandomCrop.pbtxt new file mode 100644 index 0000000000..a3b8a3cecd --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RandomCrop.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "RandomCrop" + endpoint { + name: "image.RandomCrop" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_RandomDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_RandomDataset.pbtxt new file mode 100644 index 0000000000..43921e6eaf --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RandomDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "RandomDataset" + endpoint { + name: "data.RandomDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_RandomGamma.pbtxt b/tensorflow/core/api_def/java_api/api_def_RandomGamma.pbtxt new file mode 100644 index 0000000000..927f2c5693 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RandomGamma.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "RandomGamma" + endpoint { + name: "random.RandomGamma" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_RandomGammaGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_RandomGammaGrad.pbtxt new file mode 100644 index 0000000000..9257495c9b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RandomGammaGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "RandomGammaGrad" + endpoint { + name: "random.RandomGammaGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_RandomPoisson.pbtxt b/tensorflow/core/api_def/java_api/api_def_RandomPoisson.pbtxt new file mode 100644 index 0000000000..42ce1a5fb1 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RandomPoisson.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "RandomPoisson" + endpoint { + name: "random.RandomPoisson" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_RandomPoissonV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_RandomPoissonV2.pbtxt new file mode 100644 index 0000000000..adc5441abc --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RandomPoissonV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "RandomPoissonV2" + endpoint { + name: "random.RandomPoissonV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_RandomShuffle.pbtxt b/tensorflow/core/api_def/java_api/api_def_RandomShuffle.pbtxt new file mode 100644 index 0000000000..6dcd12fd37 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RandomShuffle.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "RandomShuffle" + endpoint { + name: "random.RandomShuffle" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_RandomShuffleQueue.pbtxt b/tensorflow/core/api_def/java_api/api_def_RandomShuffleQueue.pbtxt new file mode 100644 index 0000000000..f622eb4e0d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RandomShuffleQueue.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "RandomShuffleQueue" + endpoint { + name: "io.RandomShuffleQueue" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_RandomShuffleQueueV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_RandomShuffleQueueV2.pbtxt new file mode 100644 index 0000000000..c88c2a4631 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RandomShuffleQueueV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "RandomShuffleQueueV2" + endpoint { + name: "io.RandomShuffleQueueV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_RandomStandardNormal.pbtxt b/tensorflow/core/api_def/java_api/api_def_RandomStandardNormal.pbtxt new file mode 100644 index 0000000000..413fc87bdf --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RandomStandardNormal.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "RandomStandardNormal" + endpoint { + name: "random.RandomStandardNormal" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_RandomUniform.pbtxt b/tensorflow/core/api_def/java_api/api_def_RandomUniform.pbtxt new file mode 100644 index 0000000000..2a93df83df --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RandomUniform.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "RandomUniform" + endpoint { + name: "random.RandomUniform" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_RandomUniformInt.pbtxt b/tensorflow/core/api_def/java_api/api_def_RandomUniformInt.pbtxt new file mode 100644 index 0000000000..a1383f406a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RandomUniformInt.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "RandomUniformInt" + endpoint { + name: "random.RandomUniformInt" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Range.pbtxt b/tensorflow/core/api_def/java_api/api_def_Range.pbtxt new file mode 100644 index 0000000000..24f3787a8e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Range.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Range" +} diff --git a/tensorflow/core/api_def/java_api/api_def_RangeDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_RangeDataset.pbtxt new file mode 100644 index 0000000000..b6180261b0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RangeDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "RangeDataset" + endpoint { + name: "data.RangeDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Rank.pbtxt b/tensorflow/core/api_def/java_api/api_def_Rank.pbtxt new file mode 100644 index 0000000000..baa84aab10 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Rank.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Rank" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ReadFile.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReadFile.pbtxt new file mode 100644 index 0000000000..f74250d42f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ReadFile.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ReadFile" + endpoint { + name: "io.ReadFile" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ReadVariableOp.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReadVariableOp.pbtxt new file mode 100644 index 0000000000..018886d5b8 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ReadVariableOp.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ReadVariableOp" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ReaderNumRecordsProduced.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReaderNumRecordsProduced.pbtxt new file mode 100644 index 0000000000..67dbe1035e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ReaderNumRecordsProduced.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ReaderNumRecordsProduced" + endpoint { + name: "io.ReaderNumRecordsProduced" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ReaderNumRecordsProducedV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReaderNumRecordsProducedV2.pbtxt new file mode 100644 index 0000000000..5063706eb0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ReaderNumRecordsProducedV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ReaderNumRecordsProducedV2" + endpoint { + name: "io.ReaderNumRecordsProducedV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ReaderNumWorkUnitsCompleted.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReaderNumWorkUnitsCompleted.pbtxt new file mode 100644 index 0000000000..63bb2d4108 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ReaderNumWorkUnitsCompleted.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ReaderNumWorkUnitsCompleted" + endpoint { + name: "io.ReaderNumWorkUnitsCompleted" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ReaderNumWorkUnitsCompletedV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReaderNumWorkUnitsCompletedV2.pbtxt new file mode 100644 index 0000000000..cf72ecc562 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ReaderNumWorkUnitsCompletedV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ReaderNumWorkUnitsCompletedV2" + endpoint { + name: "io.ReaderNumWorkUnitsCompletedV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ReaderRead.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReaderRead.pbtxt new file mode 100644 index 0000000000..47e9679f80 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ReaderRead.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ReaderRead" + endpoint { + name: "io.ReaderRead" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ReaderReadUpTo.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReaderReadUpTo.pbtxt new file mode 100644 index 0000000000..985d0035b0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ReaderReadUpTo.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ReaderReadUpTo" + endpoint { + name: "io.ReaderReadUpTo" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ReaderReadUpToV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReaderReadUpToV2.pbtxt new file mode 100644 index 0000000000..a001349e2d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ReaderReadUpToV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ReaderReadUpToV2" + endpoint { + name: "io.ReaderReadUpToV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ReaderReadV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReaderReadV2.pbtxt new file mode 100644 index 0000000000..a64349d15e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ReaderReadV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ReaderReadV2" + endpoint { + name: "io.ReaderReadV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ReaderReset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReaderReset.pbtxt new file mode 100644 index 0000000000..9a387753f5 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ReaderReset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ReaderReset" + endpoint { + name: "io.ReaderReset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ReaderResetV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReaderResetV2.pbtxt new file mode 100644 index 0000000000..6e31d5a8ff --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ReaderResetV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ReaderResetV2" + endpoint { + name: "io.ReaderResetV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ReaderRestoreState.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReaderRestoreState.pbtxt new file mode 100644 index 0000000000..d148adde6a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ReaderRestoreState.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ReaderRestoreState" + endpoint { + name: "io.ReaderRestoreState" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ReaderRestoreStateV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReaderRestoreStateV2.pbtxt new file mode 100644 index 0000000000..3d78d8eb00 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ReaderRestoreStateV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ReaderRestoreStateV2" + endpoint { + name: "io.ReaderRestoreStateV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ReaderSerializeState.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReaderSerializeState.pbtxt new file mode 100644 index 0000000000..fd12a4c784 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ReaderSerializeState.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ReaderSerializeState" + endpoint { + name: "io.ReaderSerializeState" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ReaderSerializeStateV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReaderSerializeStateV2.pbtxt new file mode 100644 index 0000000000..28c5048fc9 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ReaderSerializeStateV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ReaderSerializeStateV2" + endpoint { + name: "io.ReaderSerializeStateV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Real.pbtxt b/tensorflow/core/api_def/java_api/api_def_Real.pbtxt new file mode 100644 index 0000000000..3aaea928de --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Real.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Real" + endpoint { + name: "math.Real" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_RealDiv.pbtxt b/tensorflow/core/api_def/java_api/api_def_RealDiv.pbtxt new file mode 100644 index 0000000000..415bd29da0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RealDiv.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "RealDiv" + endpoint { + name: "math.RealDiv" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Reciprocal.pbtxt b/tensorflow/core/api_def/java_api/api_def_Reciprocal.pbtxt new file mode 100644 index 0000000000..1c0d787c24 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Reciprocal.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Reciprocal" + endpoint { + name: "math.Reciprocal" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ReciprocalGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReciprocalGrad.pbtxt new file mode 100644 index 0000000000..68879669b5 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ReciprocalGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ReciprocalGrad" + endpoint { + name: "math.ReciprocalGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_RecordInput.pbtxt b/tensorflow/core/api_def/java_api/api_def_RecordInput.pbtxt new file mode 100644 index 0000000000..c4807c68de --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RecordInput.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "RecordInput" + endpoint { + name: "random.RecordInput" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ReduceDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReduceDataset.pbtxt new file mode 100644 index 0000000000..b16c5dbb96 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ReduceDataset.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ReduceDataset" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ReduceJoin.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReduceJoin.pbtxt new file mode 100644 index 0000000000..7919322201 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ReduceJoin.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ReduceJoin" + endpoint { + name: "strings.ReduceJoin" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_RefEnter.pbtxt b/tensorflow/core/api_def/java_api/api_def_RefEnter.pbtxt new file mode 100644 index 0000000000..9cd2281bc6 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RefEnter.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "RefEnter" +} diff --git a/tensorflow/core/api_def/java_api/api_def_RefExit.pbtxt b/tensorflow/core/api_def/java_api/api_def_RefExit.pbtxt new file mode 100644 index 0000000000..67e8d39c9a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RefExit.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "RefExit" +} diff --git a/tensorflow/core/api_def/java_api/api_def_RefIdentity.pbtxt b/tensorflow/core/api_def/java_api/api_def_RefIdentity.pbtxt new file mode 100644 index 0000000000..53483bd1bb --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RefIdentity.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "RefIdentity" +} diff --git a/tensorflow/core/api_def/java_api/api_def_RefMerge.pbtxt b/tensorflow/core/api_def/java_api/api_def_RefMerge.pbtxt new file mode 100644 index 0000000000..6ea3145841 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RefMerge.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "RefMerge" +} diff --git a/tensorflow/core/api_def/java_api/api_def_RefNextIteration.pbtxt b/tensorflow/core/api_def/java_api/api_def_RefNextIteration.pbtxt new file mode 100644 index 0000000000..5d008204b7 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RefNextIteration.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "RefNextIteration" +} diff --git a/tensorflow/core/api_def/java_api/api_def_RefSelect.pbtxt b/tensorflow/core/api_def/java_api/api_def_RefSelect.pbtxt new file mode 100644 index 0000000000..d7cda2d5b3 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RefSelect.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "RefSelect" +} diff --git a/tensorflow/core/api_def/java_api/api_def_RefSwitch.pbtxt b/tensorflow/core/api_def/java_api/api_def_RefSwitch.pbtxt new file mode 100644 index 0000000000..78261d8b7e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RefSwitch.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "RefSwitch" +} diff --git a/tensorflow/core/api_def/java_api/api_def_RegexFullMatch.pbtxt b/tensorflow/core/api_def/java_api/api_def_RegexFullMatch.pbtxt new file mode 100644 index 0000000000..7f88e24eac --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RegexFullMatch.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "RegexFullMatch" + endpoint { + name: "strings.RegexFullMatch" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_RegexReplace.pbtxt b/tensorflow/core/api_def/java_api/api_def_RegexReplace.pbtxt new file mode 100644 index 0000000000..01c9e93cab --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RegexReplace.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "RegexReplace" + endpoint { + name: "strings.RegexReplace" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Relu.pbtxt b/tensorflow/core/api_def/java_api/api_def_Relu.pbtxt new file mode 100644 index 0000000000..39d7fec452 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Relu.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Relu" + endpoint { + name: "nn.Relu" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Relu6.pbtxt b/tensorflow/core/api_def/java_api/api_def_Relu6.pbtxt new file mode 100644 index 0000000000..fcc012b503 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Relu6.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Relu6" + endpoint { + name: "nn.Relu6" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Relu6Grad.pbtxt b/tensorflow/core/api_def/java_api/api_def_Relu6Grad.pbtxt new file mode 100644 index 0000000000..33e959cc7b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Relu6Grad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Relu6Grad" + endpoint { + name: "nn.Relu6Grad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ReluGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReluGrad.pbtxt new file mode 100644 index 0000000000..ec4a8b5f97 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ReluGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ReluGrad" + endpoint { + name: "nn.ReluGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_RemoteCall.pbtxt b/tensorflow/core/api_def/java_api/api_def_RemoteCall.pbtxt new file mode 100644 index 0000000000..a0ddb01784 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RemoteCall.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "RemoteCall" +} diff --git a/tensorflow/core/api_def/java_api/api_def_RemoteFusedGraphExecute.pbtxt b/tensorflow/core/api_def/java_api/api_def_RemoteFusedGraphExecute.pbtxt new file mode 100644 index 0000000000..b73e633ef2 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RemoteFusedGraphExecute.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "RemoteFusedGraphExecute" +} diff --git a/tensorflow/core/api_def/java_api/api_def_RepeatDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_RepeatDataset.pbtxt new file mode 100644 index 0000000000..871824f886 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RepeatDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "RepeatDataset" + endpoint { + name: "data.RepeatDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_RequantizationRange.pbtxt b/tensorflow/core/api_def/java_api/api_def_RequantizationRange.pbtxt new file mode 100644 index 0000000000..d365e89925 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RequantizationRange.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "RequantizationRange" + endpoint { + name: "quantization.RequantizationRange" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Requantize.pbtxt b/tensorflow/core/api_def/java_api/api_def_Requantize.pbtxt new file mode 100644 index 0000000000..d397cde4db --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Requantize.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Requantize" + endpoint { + name: "quantization.Requantize" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Reshape.pbtxt b/tensorflow/core/api_def/java_api/api_def_Reshape.pbtxt new file mode 100644 index 0000000000..4bf3a409d1 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Reshape.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Reshape" + endpoint { + name: "Reshape" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResizeArea.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResizeArea.pbtxt new file mode 100644 index 0000000000..5358c18d4b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResizeArea.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResizeArea" + endpoint { + name: "image.ResizeArea" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResizeBicubic.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResizeBicubic.pbtxt new file mode 100644 index 0000000000..0d0942e266 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResizeBicubic.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResizeBicubic" + endpoint { + name: "image.ResizeBicubic" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResizeBicubicGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResizeBicubicGrad.pbtxt new file mode 100644 index 0000000000..12e61dc823 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResizeBicubicGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResizeBicubicGrad" + endpoint { + name: "image.ResizeBicubicGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResizeBilinear.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResizeBilinear.pbtxt new file mode 100644 index 0000000000..ad123744a9 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResizeBilinear.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResizeBilinear" + endpoint { + name: "image.ResizeBilinear" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResizeBilinearGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResizeBilinearGrad.pbtxt new file mode 100644 index 0000000000..04f3e9f19e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResizeBilinearGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResizeBilinearGrad" + endpoint { + name: "image.ResizeBilinearGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResizeNearestNeighbor.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResizeNearestNeighbor.pbtxt new file mode 100644 index 0000000000..86ad39a517 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResizeNearestNeighbor.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResizeNearestNeighbor" + endpoint { + name: "image.ResizeNearestNeighbor" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResizeNearestNeighborGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResizeNearestNeighborGrad.pbtxt new file mode 100644 index 0000000000..70eeb906fa --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResizeNearestNeighborGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResizeNearestNeighborGrad" + endpoint { + name: "image.ResizeNearestNeighborGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceApplyAdaMax.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceApplyAdaMax.pbtxt new file mode 100644 index 0000000000..ff57bd5849 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceApplyAdaMax.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResourceApplyAdaMax" + endpoint { + name: "train.ResourceApplyAdaMax" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceApplyAdadelta.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceApplyAdadelta.pbtxt new file mode 100644 index 0000000000..d4369f0ead --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceApplyAdadelta.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResourceApplyAdadelta" + endpoint { + name: "train.ResourceApplyAdadelta" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceApplyAdagrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceApplyAdagrad.pbtxt new file mode 100644 index 0000000000..9bf7c20ee7 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceApplyAdagrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResourceApplyAdagrad" + endpoint { + name: "train.ResourceApplyAdagrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceApplyAdagradDA.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceApplyAdagradDA.pbtxt new file mode 100644 index 0000000000..904e434b8a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceApplyAdagradDA.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResourceApplyAdagradDA" + endpoint { + name: "train.ResourceApplyAdagradDA" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceApplyAdam.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceApplyAdam.pbtxt new file mode 100644 index 0000000000..390bd999c4 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceApplyAdam.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResourceApplyAdam" + endpoint { + name: "train.ResourceApplyAdam" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceApplyAddSign.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceApplyAddSign.pbtxt new file mode 100644 index 0000000000..bf944477be --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceApplyAddSign.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResourceApplyAddSign" + endpoint { + name: "train.ResourceApplyAddSign" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceApplyCenteredRMSProp.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceApplyCenteredRMSProp.pbtxt new file mode 100644 index 0000000000..58027823e0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceApplyCenteredRMSProp.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResourceApplyCenteredRMSProp" + endpoint { + name: "train.ResourceApplyCenteredRMSProp" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceApplyFtrl.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceApplyFtrl.pbtxt new file mode 100644 index 0000000000..db64e86fd4 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceApplyFtrl.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResourceApplyFtrl" + endpoint { + name: "train.ResourceApplyFtrl" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceApplyFtrlV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceApplyFtrlV2.pbtxt new file mode 100644 index 0000000000..547041e649 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceApplyFtrlV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResourceApplyFtrlV2" + endpoint { + name: "train.ResourceApplyFtrlV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceApplyGradientDescent.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceApplyGradientDescent.pbtxt new file mode 100644 index 0000000000..a54fed14d1 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceApplyGradientDescent.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResourceApplyGradientDescent" + endpoint { + name: "train.ResourceApplyGradientDescent" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceApplyMomentum.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceApplyMomentum.pbtxt new file mode 100644 index 0000000000..debb0a8131 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceApplyMomentum.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResourceApplyMomentum" + endpoint { + name: "train.ResourceApplyMomentum" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceApplyPowerSign.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceApplyPowerSign.pbtxt new file mode 100644 index 0000000000..96df22c81f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceApplyPowerSign.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResourceApplyPowerSign" + endpoint { + name: "train.ResourceApplyPowerSign" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceApplyProximalAdagrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceApplyProximalAdagrad.pbtxt new file mode 100644 index 0000000000..809b8b3af3 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceApplyProximalAdagrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResourceApplyProximalAdagrad" + endpoint { + name: "train.ResourceApplyProximalAdagrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceApplyProximalGradientDescent.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceApplyProximalGradientDescent.pbtxt new file mode 100644 index 0000000000..c9ff5a499d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceApplyProximalGradientDescent.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResourceApplyProximalGradientDescent" + endpoint { + name: "train.ResourceApplyProximalGradientDescent" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceApplyRMSProp.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceApplyRMSProp.pbtxt new file mode 100644 index 0000000000..74a3d77531 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceApplyRMSProp.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResourceApplyRMSProp" + endpoint { + name: "train.ResourceApplyRMSProp" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceCountUpTo.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceCountUpTo.pbtxt new file mode 100644 index 0000000000..439c1f1755 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceCountUpTo.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ResourceCountUpTo" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceGather.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceGather.pbtxt new file mode 100644 index 0000000000..79c6e8abbc --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceGather.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ResourceGather" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceScatterAdd.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceScatterAdd.pbtxt new file mode 100644 index 0000000000..e4184e33bf --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceScatterAdd.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ResourceScatterAdd" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceScatterDiv.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceScatterDiv.pbtxt new file mode 100644 index 0000000000..3e21c24a58 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceScatterDiv.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ResourceScatterDiv" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceScatterMax.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceScatterMax.pbtxt new file mode 100644 index 0000000000..d25b14272d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceScatterMax.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ResourceScatterMax" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceScatterMin.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceScatterMin.pbtxt new file mode 100644 index 0000000000..6243cc1ae3 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceScatterMin.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ResourceScatterMin" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceScatterMul.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceScatterMul.pbtxt new file mode 100644 index 0000000000..393e5556c0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceScatterMul.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ResourceScatterMul" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceScatterNdAdd.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceScatterNdAdd.pbtxt new file mode 100644 index 0000000000..2fd38f7be8 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceScatterNdAdd.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ResourceScatterNdAdd" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceScatterNdUpdate.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceScatterNdUpdate.pbtxt new file mode 100644 index 0000000000..732de5f1cc --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceScatterNdUpdate.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ResourceScatterNdUpdate" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceScatterSub.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceScatterSub.pbtxt new file mode 100644 index 0000000000..77081dda4d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceScatterSub.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ResourceScatterSub" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceScatterUpdate.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceScatterUpdate.pbtxt new file mode 100644 index 0000000000..9c2cc0ec21 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceScatterUpdate.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ResourceScatterUpdate" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyAdadelta.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyAdadelta.pbtxt new file mode 100644 index 0000000000..c6e3ae2219 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyAdadelta.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResourceSparseApplyAdadelta" + endpoint { + name: "train.ResourceSparseApplyAdadelta" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyAdagrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyAdagrad.pbtxt new file mode 100644 index 0000000000..5be4d6199b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyAdagrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResourceSparseApplyAdagrad" + endpoint { + name: "train.ResourceSparseApplyAdagrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyAdagradDA.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyAdagradDA.pbtxt new file mode 100644 index 0000000000..bd66d64160 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyAdagradDA.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResourceSparseApplyAdagradDA" + endpoint { + name: "train.ResourceSparseApplyAdagradDA" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyCenteredRMSProp.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyCenteredRMSProp.pbtxt new file mode 100644 index 0000000000..afda4adb0d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyCenteredRMSProp.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResourceSparseApplyCenteredRMSProp" + endpoint { + name: "train.ResourceSparseApplyCenteredRMSProp" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyFtrl.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyFtrl.pbtxt new file mode 100644 index 0000000000..195198fc9a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyFtrl.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResourceSparseApplyFtrl" + endpoint { + name: "train.ResourceSparseApplyFtrl" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyFtrlV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyFtrlV2.pbtxt new file mode 100644 index 0000000000..49c6d34dcc --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyFtrlV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResourceSparseApplyFtrlV2" + endpoint { + name: "train.ResourceSparseApplyFtrlV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyMomentum.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyMomentum.pbtxt new file mode 100644 index 0000000000..7e00039e01 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyMomentum.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResourceSparseApplyMomentum" + endpoint { + name: "train.ResourceSparseApplyMomentum" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyProximalAdagrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyProximalAdagrad.pbtxt new file mode 100644 index 0000000000..04fe8504e5 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyProximalAdagrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResourceSparseApplyProximalAdagrad" + endpoint { + name: "train.ResourceSparseApplyProximalAdagrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyProximalGradientDescent.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyProximalGradientDescent.pbtxt new file mode 100644 index 0000000000..27df43c9c5 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyProximalGradientDescent.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResourceSparseApplyProximalGradientDescent" + endpoint { + name: "train.ResourceSparseApplyProximalGradientDescent" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyRMSProp.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyRMSProp.pbtxt new file mode 100644 index 0000000000..3e78f6f06d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyRMSProp.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ResourceSparseApplyRMSProp" + endpoint { + name: "train.ResourceSparseApplyRMSProp" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceStridedSliceAssign.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceStridedSliceAssign.pbtxt new file mode 100644 index 0000000000..83805389b9 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ResourceStridedSliceAssign.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ResourceStridedSliceAssign" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Restore.pbtxt b/tensorflow/core/api_def/java_api/api_def_Restore.pbtxt new file mode 100644 index 0000000000..ca79fc10db --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Restore.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Restore" + endpoint { + name: "train.Restore" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_RestoreSlice.pbtxt b/tensorflow/core/api_def/java_api/api_def_RestoreSlice.pbtxt new file mode 100644 index 0000000000..0728f5908b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RestoreSlice.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "RestoreSlice" + endpoint { + name: "train.RestoreSlice" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_RestoreV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_RestoreV2.pbtxt new file mode 100644 index 0000000000..e877ff9869 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RestoreV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "RestoreV2" + endpoint { + name: "train.RestoreV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Reverse.pbtxt b/tensorflow/core/api_def/java_api/api_def_Reverse.pbtxt new file mode 100644 index 0000000000..2bd25417aa --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Reverse.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Reverse" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ReverseSequence.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReverseSequence.pbtxt new file mode 100644 index 0000000000..87638c0dcc --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ReverseSequence.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ReverseSequence" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ReverseV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReverseV2.pbtxt new file mode 100644 index 0000000000..71efbe1892 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ReverseV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ReverseV2" + endpoint { + name: "Reverse" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_RightShift.pbtxt b/tensorflow/core/api_def/java_api/api_def_RightShift.pbtxt new file mode 100644 index 0000000000..68fab3e8cf --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RightShift.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "RightShift" + endpoint { + name: "bitwise.RightShift" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Rint.pbtxt b/tensorflow/core/api_def/java_api/api_def_Rint.pbtxt new file mode 100644 index 0000000000..48fbcc7c34 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Rint.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Rint" + endpoint { + name: "math.Rint" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Roll.pbtxt b/tensorflow/core/api_def/java_api/api_def_Roll.pbtxt new file mode 100644 index 0000000000..50f7915a65 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Roll.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Roll" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Round.pbtxt b/tensorflow/core/api_def/java_api/api_def_Round.pbtxt new file mode 100644 index 0000000000..dd612a33d6 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Round.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Round" + endpoint { + name: "math.Round" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Rpc.pbtxt b/tensorflow/core/api_def/java_api/api_def_Rpc.pbtxt new file mode 100644 index 0000000000..0d1e2b90e6 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Rpc.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Rpc" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Rsqrt.pbtxt b/tensorflow/core/api_def/java_api/api_def_Rsqrt.pbtxt new file mode 100644 index 0000000000..06b1b81ecd --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Rsqrt.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Rsqrt" + endpoint { + name: "math.Rsqrt" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_RsqrtGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_RsqrtGrad.pbtxt new file mode 100644 index 0000000000..88073b6f25 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_RsqrtGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "RsqrtGrad" + endpoint { + name: "math.RsqrtGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SampleDistortedBoundingBox.pbtxt b/tensorflow/core/api_def/java_api/api_def_SampleDistortedBoundingBox.pbtxt new file mode 100644 index 0000000000..e7c1c90ea6 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SampleDistortedBoundingBox.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SampleDistortedBoundingBox" + endpoint { + name: "image.SampleDistortedBoundingBox" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SampleDistortedBoundingBoxV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_SampleDistortedBoundingBoxV2.pbtxt new file mode 100644 index 0000000000..8656977bf6 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SampleDistortedBoundingBoxV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SampleDistortedBoundingBoxV2" + endpoint { + name: "image.SampleDistortedBoundingBoxV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Save.pbtxt b/tensorflow/core/api_def/java_api/api_def_Save.pbtxt new file mode 100644 index 0000000000..87dab6dc1f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Save.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Save" + endpoint { + name: "train.Save" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SaveSlices.pbtxt b/tensorflow/core/api_def/java_api/api_def_SaveSlices.pbtxt new file mode 100644 index 0000000000..b336035680 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SaveSlices.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SaveSlices" + endpoint { + name: "train.SaveSlices" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SaveV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_SaveV2.pbtxt new file mode 100644 index 0000000000..6417252f45 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SaveV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SaveV2" + endpoint { + name: "train.SaveV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ScalarSummary.pbtxt b/tensorflow/core/api_def/java_api/api_def_ScalarSummary.pbtxt new file mode 100644 index 0000000000..c339ce0a7a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ScalarSummary.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ScalarSummary" + endpoint { + name: "summary.ScalarSummary" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ScanDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ScanDataset.pbtxt new file mode 100644 index 0000000000..89b63c53f7 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ScanDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ScanDataset" + endpoint { + name: "data.ScanDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ScatterAdd.pbtxt b/tensorflow/core/api_def/java_api/api_def_ScatterAdd.pbtxt new file mode 100644 index 0000000000..41c63dc0a4 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ScatterAdd.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ScatterAdd" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ScatterDiv.pbtxt b/tensorflow/core/api_def/java_api/api_def_ScatterDiv.pbtxt new file mode 100644 index 0000000000..5754249eaf --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ScatterDiv.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ScatterDiv" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ScatterMax.pbtxt b/tensorflow/core/api_def/java_api/api_def_ScatterMax.pbtxt new file mode 100644 index 0000000000..aa6375cbd7 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ScatterMax.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ScatterMax" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ScatterMin.pbtxt b/tensorflow/core/api_def/java_api/api_def_ScatterMin.pbtxt new file mode 100644 index 0000000000..ea007120c3 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ScatterMin.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ScatterMin" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ScatterMul.pbtxt b/tensorflow/core/api_def/java_api/api_def_ScatterMul.pbtxt new file mode 100644 index 0000000000..f1d91258e4 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ScatterMul.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ScatterMul" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ScatterNd.pbtxt b/tensorflow/core/api_def/java_api/api_def_ScatterNd.pbtxt new file mode 100644 index 0000000000..8ef01b2fca --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ScatterNd.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ScatterNd" + endpoint { + name: "ScatterNd" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ScatterNdAdd.pbtxt b/tensorflow/core/api_def/java_api/api_def_ScatterNdAdd.pbtxt new file mode 100644 index 0000000000..bea152a9da --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ScatterNdAdd.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ScatterNdAdd" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ScatterNdNonAliasingAdd.pbtxt b/tensorflow/core/api_def/java_api/api_def_ScatterNdNonAliasingAdd.pbtxt new file mode 100644 index 0000000000..4dd756bfc0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ScatterNdNonAliasingAdd.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ScatterNdNonAliasingAdd" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ScatterNdSub.pbtxt b/tensorflow/core/api_def/java_api/api_def_ScatterNdSub.pbtxt new file mode 100644 index 0000000000..384e79d64e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ScatterNdSub.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ScatterNdSub" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ScatterNdUpdate.pbtxt b/tensorflow/core/api_def/java_api/api_def_ScatterNdUpdate.pbtxt new file mode 100644 index 0000000000..92fce7f0ac --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ScatterNdUpdate.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ScatterNdUpdate" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ScatterSub.pbtxt b/tensorflow/core/api_def/java_api/api_def_ScatterSub.pbtxt new file mode 100644 index 0000000000..5baaa4f604 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ScatterSub.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ScatterSub" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ScatterUpdate.pbtxt b/tensorflow/core/api_def/java_api/api_def_ScatterUpdate.pbtxt new file mode 100644 index 0000000000..83ac128ed6 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ScatterUpdate.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ScatterUpdate" +} diff --git a/tensorflow/core/api_def/java_api/api_def_SdcaFprint.pbtxt b/tensorflow/core/api_def/java_api/api_def_SdcaFprint.pbtxt new file mode 100644 index 0000000000..ce179918cd --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SdcaFprint.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SdcaFprint" + endpoint { + name: "train.SdcaFprint" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SdcaOptimizer.pbtxt b/tensorflow/core/api_def/java_api/api_def_SdcaOptimizer.pbtxt new file mode 100644 index 0000000000..68eb8cf1f0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SdcaOptimizer.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SdcaOptimizer" + endpoint { + name: "train.SdcaOptimizer" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SdcaOptimizerV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_SdcaOptimizerV2.pbtxt new file mode 100644 index 0000000000..4e90531060 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SdcaOptimizerV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SdcaOptimizerV2" + endpoint { + name: "train.SdcaOptimizerV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SdcaShrinkL1.pbtxt b/tensorflow/core/api_def/java_api/api_def_SdcaShrinkL1.pbtxt new file mode 100644 index 0000000000..83993bcf14 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SdcaShrinkL1.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SdcaShrinkL1" + endpoint { + name: "train.SdcaShrinkL1" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SegmentMax.pbtxt b/tensorflow/core/api_def/java_api/api_def_SegmentMax.pbtxt new file mode 100644 index 0000000000..6ac26c9e9e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SegmentMax.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SegmentMax" + endpoint { + name: "math.SegmentMax" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SegmentMean.pbtxt b/tensorflow/core/api_def/java_api/api_def_SegmentMean.pbtxt new file mode 100644 index 0000000000..982db87bf0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SegmentMean.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SegmentMean" + endpoint { + name: "math.SegmentMean" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SegmentMin.pbtxt b/tensorflow/core/api_def/java_api/api_def_SegmentMin.pbtxt new file mode 100644 index 0000000000..7a403b6c63 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SegmentMin.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SegmentMin" + endpoint { + name: "math.SegmentMin" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SegmentProd.pbtxt b/tensorflow/core/api_def/java_api/api_def_SegmentProd.pbtxt new file mode 100644 index 0000000000..1bf280edc4 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SegmentProd.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SegmentProd" + endpoint { + name: "math.SegmentProd" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SegmentSum.pbtxt b/tensorflow/core/api_def/java_api/api_def_SegmentSum.pbtxt new file mode 100644 index 0000000000..3dcbc35225 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SegmentSum.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SegmentSum" + endpoint { + name: "math.SegmentSum" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Select.pbtxt b/tensorflow/core/api_def/java_api/api_def_Select.pbtxt new file mode 100644 index 0000000000..eeff79284c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Select.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Select" +} diff --git a/tensorflow/core/api_def/java_api/api_def_SelfAdjointEig.pbtxt b/tensorflow/core/api_def/java_api/api_def_SelfAdjointEig.pbtxt new file mode 100644 index 0000000000..552d397ad8 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SelfAdjointEig.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SelfAdjointEig" + endpoint { + name: "linalg.SelfAdjointEig" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SelfAdjointEigV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_SelfAdjointEigV2.pbtxt new file mode 100644 index 0000000000..4b610f437c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SelfAdjointEigV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SelfAdjointEigV2" + endpoint { + name: "linalg.SelfAdjointEigV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Selu.pbtxt b/tensorflow/core/api_def/java_api/api_def_Selu.pbtxt new file mode 100644 index 0000000000..7002d5be12 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Selu.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Selu" + endpoint { + name: "nn.Selu" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SeluGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_SeluGrad.pbtxt new file mode 100644 index 0000000000..b34e2f2239 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SeluGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SeluGrad" + endpoint { + name: "nn.SeluGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SerializeIterator.pbtxt b/tensorflow/core/api_def/java_api/api_def_SerializeIterator.pbtxt new file mode 100644 index 0000000000..37789c753b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SerializeIterator.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SerializeIterator" + endpoint { + name: "data.SerializeIterator" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SerializeManySparse.pbtxt b/tensorflow/core/api_def/java_api/api_def_SerializeManySparse.pbtxt new file mode 100644 index 0000000000..567a8e4b7f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SerializeManySparse.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SerializeManySparse" + endpoint { + name: "io.SerializeManySparse" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SerializeSparse.pbtxt b/tensorflow/core/api_def/java_api/api_def_SerializeSparse.pbtxt new file mode 100644 index 0000000000..ad86c7095c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SerializeSparse.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SerializeSparse" + endpoint { + name: "io.SerializeSparse" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SerializeTensor.pbtxt b/tensorflow/core/api_def/java_api/api_def_SerializeTensor.pbtxt new file mode 100644 index 0000000000..169120a063 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SerializeTensor.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SerializeTensor" + endpoint { + name: "io.SerializeTensor" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SetSize.pbtxt b/tensorflow/core/api_def/java_api/api_def_SetSize.pbtxt new file mode 100644 index 0000000000..1c000e9c8a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SetSize.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "SetSize" +} diff --git a/tensorflow/core/api_def/java_api/api_def_SetStatsAggregatorDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_SetStatsAggregatorDataset.pbtxt new file mode 100644 index 0000000000..f57abe5a66 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SetStatsAggregatorDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SetStatsAggregatorDataset" + endpoint { + name: "data.SetStatsAggregatorDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Shape.pbtxt b/tensorflow/core/api_def/java_api/api_def_Shape.pbtxt new file mode 100644 index 0000000000..5074000b53 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Shape.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Shape" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ShapeN.pbtxt b/tensorflow/core/api_def/java_api/api_def_ShapeN.pbtxt new file mode 100644 index 0000000000..b4bfb91118 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ShapeN.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ShapeN" +} diff --git a/tensorflow/core/api_def/java_api/api_def_ShardedFilename.pbtxt b/tensorflow/core/api_def/java_api/api_def_ShardedFilename.pbtxt new file mode 100644 index 0000000000..8efd0afb8b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ShardedFilename.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ShardedFilename" + endpoint { + name: "io.ShardedFilename" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ShardedFilespec.pbtxt b/tensorflow/core/api_def/java_api/api_def_ShardedFilespec.pbtxt new file mode 100644 index 0000000000..e31cac1040 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ShardedFilespec.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ShardedFilespec" + endpoint { + name: "io.ShardedFilespec" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ShuffleAndRepeatDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ShuffleAndRepeatDataset.pbtxt new file mode 100644 index 0000000000..543d5a109c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ShuffleAndRepeatDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ShuffleAndRepeatDataset" + endpoint { + name: "data.ShuffleAndRepeatDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ShuffleDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ShuffleDataset.pbtxt new file mode 100644 index 0000000000..36f4979c96 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ShuffleDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ShuffleDataset" + endpoint { + name: "data.ShuffleDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Sigmoid.pbtxt b/tensorflow/core/api_def/java_api/api_def_Sigmoid.pbtxt new file mode 100644 index 0000000000..b6e16a4110 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Sigmoid.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Sigmoid" + endpoint { + name: "math.Sigmoid" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SigmoidGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_SigmoidGrad.pbtxt new file mode 100644 index 0000000000..bb141abe3b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SigmoidGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SigmoidGrad" + endpoint { + name: "math.SigmoidGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Sign.pbtxt b/tensorflow/core/api_def/java_api/api_def_Sign.pbtxt new file mode 100644 index 0000000000..435fb9e825 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Sign.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Sign" + endpoint { + name: "math.Sign" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Sin.pbtxt b/tensorflow/core/api_def/java_api/api_def_Sin.pbtxt new file mode 100644 index 0000000000..2fc95755ba --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Sin.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Sin" + endpoint { + name: "math.Sin" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Sinh.pbtxt b/tensorflow/core/api_def/java_api/api_def_Sinh.pbtxt new file mode 100644 index 0000000000..f343685e80 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Sinh.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Sinh" + endpoint { + name: "math.Sinh" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Size.pbtxt b/tensorflow/core/api_def/java_api/api_def_Size.pbtxt new file mode 100644 index 0000000000..a41cddd8ac --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Size.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Size" +} diff --git a/tensorflow/core/api_def/java_api/api_def_SkipDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_SkipDataset.pbtxt new file mode 100644 index 0000000000..39bce67a3e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SkipDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SkipDataset" + endpoint { + name: "data.SkipDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Skipgram.pbtxt b/tensorflow/core/api_def/java_api/api_def_Skipgram.pbtxt new file mode 100644 index 0000000000..d095c7b61b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Skipgram.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Skipgram" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Slice.pbtxt b/tensorflow/core/api_def/java_api/api_def_Slice.pbtxt new file mode 100644 index 0000000000..adfe6fa4fd --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Slice.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Slice" +} diff --git a/tensorflow/core/api_def/java_api/api_def_SlideDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_SlideDataset.pbtxt new file mode 100644 index 0000000000..bc284c2833 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SlideDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SlideDataset" + endpoint { + name: "data.SlideDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Snapshot.pbtxt b/tensorflow/core/api_def/java_api/api_def_Snapshot.pbtxt new file mode 100644 index 0000000000..6e49c1a543 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Snapshot.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Snapshot" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Softmax.pbtxt b/tensorflow/core/api_def/java_api/api_def_Softmax.pbtxt new file mode 100644 index 0000000000..cb27a04d21 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Softmax.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Softmax" + endpoint { + name: "nn.Softmax" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SoftmaxCrossEntropyWithLogits.pbtxt b/tensorflow/core/api_def/java_api/api_def_SoftmaxCrossEntropyWithLogits.pbtxt new file mode 100644 index 0000000000..e064562c0f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SoftmaxCrossEntropyWithLogits.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SoftmaxCrossEntropyWithLogits" + endpoint { + name: "nn.SoftmaxCrossEntropyWithLogits" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Softplus.pbtxt b/tensorflow/core/api_def/java_api/api_def_Softplus.pbtxt new file mode 100644 index 0000000000..671656c287 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Softplus.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Softplus" + endpoint { + name: "math.Softplus" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SoftplusGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_SoftplusGrad.pbtxt new file mode 100644 index 0000000000..d1b074a9b7 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SoftplusGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SoftplusGrad" + endpoint { + name: "math.SoftplusGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Softsign.pbtxt b/tensorflow/core/api_def/java_api/api_def_Softsign.pbtxt new file mode 100644 index 0000000000..23aa1e3c58 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Softsign.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Softsign" + endpoint { + name: "nn.Softsign" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SoftsignGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_SoftsignGrad.pbtxt new file mode 100644 index 0000000000..73faf74511 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SoftsignGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SoftsignGrad" + endpoint { + name: "nn.SoftsignGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SpaceToBatch.pbtxt b/tensorflow/core/api_def/java_api/api_def_SpaceToBatch.pbtxt new file mode 100644 index 0000000000..4fe7232e54 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SpaceToBatch.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SpaceToBatch" + endpoint { + name: "nn.SpaceToBatch" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SpaceToBatchND.pbtxt b/tensorflow/core/api_def/java_api/api_def_SpaceToBatchND.pbtxt new file mode 100644 index 0000000000..6612b48286 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SpaceToBatchND.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SpaceToBatchND" + endpoint { + name: "SpaceToBatchNd" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SpaceToDepth.pbtxt b/tensorflow/core/api_def/java_api/api_def_SpaceToDepth.pbtxt new file mode 100644 index 0000000000..cb421c75db --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SpaceToDepth.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SpaceToDepth" + endpoint { + name: "nn.SpaceToDepth" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseAccumulatorApplyGradient.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseAccumulatorApplyGradient.pbtxt new file mode 100644 index 0000000000..cdb0b14b67 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseAccumulatorApplyGradient.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseAccumulatorApplyGradient" + endpoint { + name: "sparse.SparseAccumulatorApplyGradient" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseAccumulatorTakeGradient.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseAccumulatorTakeGradient.pbtxt new file mode 100644 index 0000000000..8fc1e70959 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseAccumulatorTakeGradient.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseAccumulatorTakeGradient" + endpoint { + name: "sparse.SparseAccumulatorTakeGradient" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseAdd.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseAdd.pbtxt new file mode 100644 index 0000000000..0761f2ed16 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseAdd.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseAdd" + endpoint { + name: "sparse.SparseAdd" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseAddGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseAddGrad.pbtxt new file mode 100644 index 0000000000..6529c46a17 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseAddGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseAddGrad" + endpoint { + name: "sparse.SparseAddGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseApplyAdadelta.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseApplyAdadelta.pbtxt new file mode 100644 index 0000000000..7122f210a4 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseApplyAdadelta.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseApplyAdadelta" + endpoint { + name: "train.SparseApplyAdadelta" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseApplyAdagrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseApplyAdagrad.pbtxt new file mode 100644 index 0000000000..184a8cfb2f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseApplyAdagrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseApplyAdagrad" + endpoint { + name: "train.SparseApplyAdagrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseApplyAdagradDA.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseApplyAdagradDA.pbtxt new file mode 100644 index 0000000000..56d9b1c49b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseApplyAdagradDA.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseApplyAdagradDA" + endpoint { + name: "train.SparseApplyAdagradDA" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseApplyCenteredRMSProp.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseApplyCenteredRMSProp.pbtxt new file mode 100644 index 0000000000..fa35db29b0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseApplyCenteredRMSProp.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseApplyCenteredRMSProp" + endpoint { + name: "train.SparseApplyCenteredRMSProp" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseApplyFtrl.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseApplyFtrl.pbtxt new file mode 100644 index 0000000000..bb669aa773 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseApplyFtrl.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseApplyFtrl" + endpoint { + name: "train.SparseApplyFtrl" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseApplyFtrlV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseApplyFtrlV2.pbtxt new file mode 100644 index 0000000000..673f71c34b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseApplyFtrlV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseApplyFtrlV2" + endpoint { + name: "train.SparseApplyFtrlV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseApplyMomentum.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseApplyMomentum.pbtxt new file mode 100644 index 0000000000..f7e79c5e7e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseApplyMomentum.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseApplyMomentum" + endpoint { + name: "train.SparseApplyMomentum" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseApplyProximalAdagrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseApplyProximalAdagrad.pbtxt new file mode 100644 index 0000000000..8ac6cf771c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseApplyProximalAdagrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseApplyProximalAdagrad" + endpoint { + name: "train.SparseApplyProximalAdagrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseApplyProximalGradientDescent.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseApplyProximalGradientDescent.pbtxt new file mode 100644 index 0000000000..926ed2c1d4 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseApplyProximalGradientDescent.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseApplyProximalGradientDescent" + endpoint { + name: "train.SparseApplyProximalGradientDescent" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseApplyRMSProp.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseApplyRMSProp.pbtxt new file mode 100644 index 0000000000..94646093af --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseApplyRMSProp.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseApplyRMSProp" + endpoint { + name: "train.SparseApplyRMSProp" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseConcat.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseConcat.pbtxt new file mode 100644 index 0000000000..8ceb600a42 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseConcat.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseConcat" + endpoint { + name: "sparse.SparseConcat" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseConditionalAccumulator.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseConditionalAccumulator.pbtxt new file mode 100644 index 0000000000..3dc2c1ea8a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseConditionalAccumulator.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseConditionalAccumulator" + endpoint { + name: "sparse.SparseConditionalAccumulator" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseCross.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseCross.pbtxt new file mode 100644 index 0000000000..130f333d35 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseCross.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseCross" + endpoint { + name: "sparse.SparseCross" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseDenseCwiseAdd.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseDenseCwiseAdd.pbtxt new file mode 100644 index 0000000000..013b7eede9 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseDenseCwiseAdd.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseDenseCwiseAdd" + endpoint { + name: "sparse.SparseDenseCwiseAdd" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseDenseCwiseDiv.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseDenseCwiseDiv.pbtxt new file mode 100644 index 0000000000..8cf56d7b41 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseDenseCwiseDiv.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseDenseCwiseDiv" + endpoint { + name: "sparse.SparseDenseCwiseDiv" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseDenseCwiseMul.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseDenseCwiseMul.pbtxt new file mode 100644 index 0000000000..37c50f2327 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseDenseCwiseMul.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseDenseCwiseMul" + endpoint { + name: "sparse.SparseDenseCwiseMul" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseFillEmptyRows.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseFillEmptyRows.pbtxt new file mode 100644 index 0000000000..3adddbd34b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseFillEmptyRows.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseFillEmptyRows" + endpoint { + name: "sparse.SparseFillEmptyRows" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseFillEmptyRowsGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseFillEmptyRowsGrad.pbtxt new file mode 100644 index 0000000000..708069d028 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseFillEmptyRowsGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseFillEmptyRowsGrad" + endpoint { + name: "sparse.SparseFillEmptyRowsGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseMatMul.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseMatMul.pbtxt new file mode 100644 index 0000000000..445d53b023 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseMatMul.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseMatMul" + endpoint { + name: "sparse.SparseMatMul" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseReduceMax.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseReduceMax.pbtxt new file mode 100644 index 0000000000..a7467b9b47 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseReduceMax.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseReduceMax" + endpoint { + name: "sparse.SparseReduceMax" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseReduceMaxSparse.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseReduceMaxSparse.pbtxt new file mode 100644 index 0000000000..987e98467e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseReduceMaxSparse.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseReduceMaxSparse" + endpoint { + name: "sparse.SparseReduceMaxSparse" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseReduceSum.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseReduceSum.pbtxt new file mode 100644 index 0000000000..739fb5fb95 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseReduceSum.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseReduceSum" + endpoint { + name: "sparse.SparseReduceSum" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseReduceSumSparse.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseReduceSumSparse.pbtxt new file mode 100644 index 0000000000..64d6d45f1a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseReduceSumSparse.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseReduceSumSparse" + endpoint { + name: "sparse.SparseReduceSumSparse" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseReorder.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseReorder.pbtxt new file mode 100644 index 0000000000..202066e76f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseReorder.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseReorder" + endpoint { + name: "sparse.SparseReorder" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseReshape.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseReshape.pbtxt new file mode 100644 index 0000000000..0a393a6105 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseReshape.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseReshape" + endpoint { + name: "sparse.SparseReshape" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseSegmentMean.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseSegmentMean.pbtxt new file mode 100644 index 0000000000..9187dbed67 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseSegmentMean.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseSegmentMean" + endpoint { + name: "sparse.SparseSegmentMean" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseSegmentMeanGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseSegmentMeanGrad.pbtxt new file mode 100644 index 0000000000..1b7d5bbcf0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseSegmentMeanGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseSegmentMeanGrad" + endpoint { + name: "sparse.SparseSegmentMeanGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseSegmentMeanWithNumSegments.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseSegmentMeanWithNumSegments.pbtxt new file mode 100644 index 0000000000..bb3ac2256d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseSegmentMeanWithNumSegments.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseSegmentMeanWithNumSegments" + endpoint { + name: "sparse.SparseSegmentMeanWithNumSegments" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseSegmentSqrtN.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseSegmentSqrtN.pbtxt new file mode 100644 index 0000000000..2cecff503f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseSegmentSqrtN.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseSegmentSqrtN" + endpoint { + name: "sparse.SparseSegmentSqrtN" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseSegmentSqrtNGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseSegmentSqrtNGrad.pbtxt new file mode 100644 index 0000000000..e6973eb773 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseSegmentSqrtNGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseSegmentSqrtNGrad" + endpoint { + name: "sparse.SparseSegmentSqrtNGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseSegmentSqrtNWithNumSegments.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseSegmentSqrtNWithNumSegments.pbtxt new file mode 100644 index 0000000000..78aed85f0a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseSegmentSqrtNWithNumSegments.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseSegmentSqrtNWithNumSegments" + endpoint { + name: "sparse.SparseSegmentSqrtNWithNumSegments" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseSegmentSum.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseSegmentSum.pbtxt new file mode 100644 index 0000000000..f18f351667 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseSegmentSum.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseSegmentSum" + endpoint { + name: "sparse.SparseSegmentSum" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseSegmentSumWithNumSegments.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseSegmentSumWithNumSegments.pbtxt new file mode 100644 index 0000000000..8e384857cc --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseSegmentSumWithNumSegments.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseSegmentSumWithNumSegments" + endpoint { + name: "sparse.SparseSegmentSumWithNumSegments" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseSlice.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseSlice.pbtxt new file mode 100644 index 0000000000..1d794df925 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseSlice.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseSlice" + endpoint { + name: "sparse.SparseSlice" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseSliceGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseSliceGrad.pbtxt new file mode 100644 index 0000000000..979326c0fc --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseSliceGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseSliceGrad" + endpoint { + name: "sparse.SparseSliceGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseSoftmax.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseSoftmax.pbtxt new file mode 100644 index 0000000000..56c96640cb --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseSoftmax.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseSoftmax" + endpoint { + name: "sparse.SparseSoftmax" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseSoftmaxCrossEntropyWithLogits.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseSoftmaxCrossEntropyWithLogits.pbtxt new file mode 100644 index 0000000000..7627d5f607 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseSoftmaxCrossEntropyWithLogits.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseSoftmaxCrossEntropyWithLogits" + endpoint { + name: "nn.SparseSoftmaxCrossEntropyWithLogits" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseSparseMaximum.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseSparseMaximum.pbtxt new file mode 100644 index 0000000000..3143096213 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseSparseMaximum.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseSparseMaximum" + endpoint { + name: "sparse.SparseSparseMaximum" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseSparseMinimum.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseSparseMinimum.pbtxt new file mode 100644 index 0000000000..fc04bb4fed --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseSparseMinimum.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseSparseMinimum" + endpoint { + name: "sparse.SparseSparseMinimum" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseSplit.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseSplit.pbtxt new file mode 100644 index 0000000000..0afc95199a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseSplit.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseSplit" + endpoint { + name: "sparse.SparseSplit" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseTensorDenseAdd.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseTensorDenseAdd.pbtxt new file mode 100644 index 0000000000..fc5c882f83 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseTensorDenseAdd.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseTensorDenseAdd" + endpoint { + name: "sparse.SparseTensorDenseAdd" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseTensorDenseMatMul.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseTensorDenseMatMul.pbtxt new file mode 100644 index 0000000000..cd1f1e09e9 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseTensorDenseMatMul.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseTensorDenseMatMul" + endpoint { + name: "sparse.SparseTensorDenseMatMul" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseTensorSliceDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseTensorSliceDataset.pbtxt new file mode 100644 index 0000000000..bb0d1d7a94 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseTensorSliceDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseTensorSliceDataset" + endpoint { + name: "data.SparseTensorSliceDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseToDense.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseToDense.pbtxt new file mode 100644 index 0000000000..68df155e46 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseToDense.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseToDense" + endpoint { + name: "sparse.SparseToDense" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SparseToSparseSetOperation.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseToSparseSetOperation.pbtxt new file mode 100644 index 0000000000..fb04366fea --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SparseToSparseSetOperation.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SparseToSparseSetOperation" + endpoint { + name: "sparse.SparseToSparseSetOperation" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Split.pbtxt b/tensorflow/core/api_def/java_api/api_def_Split.pbtxt new file mode 100644 index 0000000000..ffb7b52e09 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Split.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Split" +} diff --git a/tensorflow/core/api_def/java_api/api_def_SplitV.pbtxt b/tensorflow/core/api_def/java_api/api_def_SplitV.pbtxt new file mode 100644 index 0000000000..94f4a08d70 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SplitV.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "SplitV" +} diff --git a/tensorflow/core/api_def/java_api/api_def_SqlDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_SqlDataset.pbtxt new file mode 100644 index 0000000000..8764e81af2 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SqlDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SqlDataset" + endpoint { + name: "data.SqlDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Sqrt.pbtxt b/tensorflow/core/api_def/java_api/api_def_Sqrt.pbtxt new file mode 100644 index 0000000000..daa2a1ee86 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Sqrt.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Sqrt" + endpoint { + name: "math.Sqrt" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SqrtGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_SqrtGrad.pbtxt new file mode 100644 index 0000000000..8eca1b89b3 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SqrtGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SqrtGrad" + endpoint { + name: "math.SqrtGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Square.pbtxt b/tensorflow/core/api_def/java_api/api_def_Square.pbtxt new file mode 100644 index 0000000000..561e10e020 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Square.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Square" + endpoint { + name: "math.Square" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_SquaredDifference.pbtxt b/tensorflow/core/api_def/java_api/api_def_SquaredDifference.pbtxt new file mode 100644 index 0000000000..752dbcce7b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SquaredDifference.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SquaredDifference" + endpoint { + name: "math.SquaredDifference" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Squeeze.pbtxt b/tensorflow/core/api_def/java_api/api_def_Squeeze.pbtxt new file mode 100644 index 0000000000..264c384603 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Squeeze.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Squeeze" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Stack.pbtxt b/tensorflow/core/api_def/java_api/api_def_Stack.pbtxt new file mode 100644 index 0000000000..522e5efec5 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Stack.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Stack" +} diff --git a/tensorflow/core/api_def/java_api/api_def_StackClose.pbtxt b/tensorflow/core/api_def/java_api/api_def_StackClose.pbtxt new file mode 100644 index 0000000000..1cba682de9 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StackClose.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "StackClose" +} diff --git a/tensorflow/core/api_def/java_api/api_def_StackCloseV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_StackCloseV2.pbtxt new file mode 100644 index 0000000000..be5a0f535a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StackCloseV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "StackCloseV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_StackPop.pbtxt b/tensorflow/core/api_def/java_api/api_def_StackPop.pbtxt new file mode 100644 index 0000000000..4439e73843 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StackPop.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "StackPop" +} diff --git a/tensorflow/core/api_def/java_api/api_def_StackPopV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_StackPopV2.pbtxt new file mode 100644 index 0000000000..b8babb4c56 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StackPopV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "StackPopV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_StackPush.pbtxt b/tensorflow/core/api_def/java_api/api_def_StackPush.pbtxt new file mode 100644 index 0000000000..41792e327f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StackPush.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "StackPush" +} diff --git a/tensorflow/core/api_def/java_api/api_def_StackPushV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_StackPushV2.pbtxt new file mode 100644 index 0000000000..4920152259 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StackPushV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "StackPushV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_StackV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_StackV2.pbtxt new file mode 100644 index 0000000000..5232e0425e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StackV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "StackV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Stage.pbtxt b/tensorflow/core/api_def/java_api/api_def_Stage.pbtxt new file mode 100644 index 0000000000..87e0c7d981 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Stage.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Stage" +} diff --git a/tensorflow/core/api_def/java_api/api_def_StageClear.pbtxt b/tensorflow/core/api_def/java_api/api_def_StageClear.pbtxt new file mode 100644 index 0000000000..26890a55b3 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StageClear.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "StageClear" +} diff --git a/tensorflow/core/api_def/java_api/api_def_StagePeek.pbtxt b/tensorflow/core/api_def/java_api/api_def_StagePeek.pbtxt new file mode 100644 index 0000000000..7c3ed3dc91 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StagePeek.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "StagePeek" +} diff --git a/tensorflow/core/api_def/java_api/api_def_StageSize.pbtxt b/tensorflow/core/api_def/java_api/api_def_StageSize.pbtxt new file mode 100644 index 0000000000..d8188c3e0b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StageSize.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "StageSize" +} diff --git a/tensorflow/core/api_def/java_api/api_def_StatefulPartitionedCall.pbtxt b/tensorflow/core/api_def/java_api/api_def_StatefulPartitionedCall.pbtxt new file mode 100644 index 0000000000..2e6decf19a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StatefulPartitionedCall.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "StatefulPartitionedCall" +} diff --git a/tensorflow/core/api_def/java_api/api_def_StatelessIf.pbtxt b/tensorflow/core/api_def/java_api/api_def_StatelessIf.pbtxt new file mode 100644 index 0000000000..37c7b9a962 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StatelessIf.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "StatelessIf" +} diff --git a/tensorflow/core/api_def/java_api/api_def_StatelessMultinomial.pbtxt b/tensorflow/core/api_def/java_api/api_def_StatelessMultinomial.pbtxt new file mode 100644 index 0000000000..8cfbbfb2c2 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StatelessMultinomial.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "StatelessMultinomial" + endpoint { + name: "random.StatelessMultinomial" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_StatelessRandomNormal.pbtxt b/tensorflow/core/api_def/java_api/api_def_StatelessRandomNormal.pbtxt new file mode 100644 index 0000000000..4e648cfa69 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StatelessRandomNormal.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "StatelessRandomNormal" + endpoint { + name: "random.StatelessRandomNormal" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_StatelessRandomUniform.pbtxt b/tensorflow/core/api_def/java_api/api_def_StatelessRandomUniform.pbtxt new file mode 100644 index 0000000000..6f8f328b41 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StatelessRandomUniform.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "StatelessRandomUniform" + endpoint { + name: "random.StatelessRandomUniform" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_StatelessRandomUniformInt.pbtxt b/tensorflow/core/api_def/java_api/api_def_StatelessRandomUniformInt.pbtxt new file mode 100644 index 0000000000..2fec4a7cc6 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StatelessRandomUniformInt.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "StatelessRandomUniformInt" + endpoint { + name: "random.StatelessRandomUniformInt" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_StatelessTruncatedNormal.pbtxt b/tensorflow/core/api_def/java_api/api_def_StatelessTruncatedNormal.pbtxt new file mode 100644 index 0000000000..c05071dd73 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StatelessTruncatedNormal.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "StatelessTruncatedNormal" + endpoint { + name: "random.StatelessTruncatedNormal" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_StatelessWhile.pbtxt b/tensorflow/core/api_def/java_api/api_def_StatelessWhile.pbtxt new file mode 100644 index 0000000000..1a4252c90a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StatelessWhile.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "StatelessWhile" +} diff --git a/tensorflow/core/api_def/java_api/api_def_StaticRegexFullMatch.pbtxt b/tensorflow/core/api_def/java_api/api_def_StaticRegexFullMatch.pbtxt new file mode 100644 index 0000000000..66b841ad74 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StaticRegexFullMatch.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "StaticRegexFullMatch" + endpoint { + name: "strings.StaticRegexFullMatch" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_StaticRegexReplace.pbtxt b/tensorflow/core/api_def/java_api/api_def_StaticRegexReplace.pbtxt new file mode 100644 index 0000000000..305bc8a3ca --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StaticRegexReplace.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "StaticRegexReplace" + endpoint { + name: "strings.StaticRegexReplace" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_StatsAggregatorHandle.pbtxt b/tensorflow/core/api_def/java_api/api_def_StatsAggregatorHandle.pbtxt new file mode 100644 index 0000000000..301dc982e3 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StatsAggregatorHandle.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "StatsAggregatorHandle" + endpoint { + name: "data.StatsAggregatorHandle" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_StatsAggregatorSummary.pbtxt b/tensorflow/core/api_def/java_api/api_def_StatsAggregatorSummary.pbtxt new file mode 100644 index 0000000000..f83c8f5a4f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StatsAggregatorSummary.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "StatsAggregatorSummary" + endpoint { + name: "summary.StatsAggregatorSummary" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_StopGradient.pbtxt b/tensorflow/core/api_def/java_api/api_def_StopGradient.pbtxt new file mode 100644 index 0000000000..ad8473e816 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StopGradient.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "StopGradient" +} diff --git a/tensorflow/core/api_def/java_api/api_def_StridedSlice.pbtxt b/tensorflow/core/api_def/java_api/api_def_StridedSlice.pbtxt new file mode 100644 index 0000000000..b0c301dab1 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StridedSlice.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "StridedSlice" +} diff --git a/tensorflow/core/api_def/java_api/api_def_StridedSliceAssign.pbtxt b/tensorflow/core/api_def/java_api/api_def_StridedSliceAssign.pbtxt new file mode 100644 index 0000000000..6850dc2d1b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StridedSliceAssign.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "StridedSliceAssign" +} diff --git a/tensorflow/core/api_def/java_api/api_def_StridedSliceGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_StridedSliceGrad.pbtxt new file mode 100644 index 0000000000..b03204bcc4 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StridedSliceGrad.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "StridedSliceGrad" +} diff --git a/tensorflow/core/api_def/java_api/api_def_StringFormat.pbtxt b/tensorflow/core/api_def/java_api/api_def_StringFormat.pbtxt new file mode 100644 index 0000000000..cdd0313996 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StringFormat.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "StringFormat" + endpoint { + name: "strings.StringFormat" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_StringJoin.pbtxt b/tensorflow/core/api_def/java_api/api_def_StringJoin.pbtxt new file mode 100644 index 0000000000..b43ff157cd --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StringJoin.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "StringJoin" + endpoint { + name: "strings.Join" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_StringLength.pbtxt b/tensorflow/core/api_def/java_api/api_def_StringLength.pbtxt new file mode 100644 index 0000000000..c8eb48cc3c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StringLength.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "StringLength" + endpoint { + name: "strings.StringLength" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_StringSplit.pbtxt b/tensorflow/core/api_def/java_api/api_def_StringSplit.pbtxt new file mode 100644 index 0000000000..f16e196a6f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StringSplit.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "StringSplit" + endpoint { + name: "strings.StringSplit" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_StringSplitV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_StringSplitV2.pbtxt new file mode 100644 index 0000000000..0779eb7f71 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StringSplitV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "StringSplitV2" + endpoint { + name: "strings.StringSplitV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_StringStrip.pbtxt b/tensorflow/core/api_def/java_api/api_def_StringStrip.pbtxt new file mode 100644 index 0000000000..01691211ff --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StringStrip.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "StringStrip" + endpoint { + name: "strings.Strip" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_StringToHashBucket.pbtxt b/tensorflow/core/api_def/java_api/api_def_StringToHashBucket.pbtxt new file mode 100644 index 0000000000..765517578d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StringToHashBucket.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "StringToHashBucket" + endpoint { + name: "strings.ToHashBucket" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_StringToHashBucketFast.pbtxt b/tensorflow/core/api_def/java_api/api_def_StringToHashBucketFast.pbtxt new file mode 100644 index 0000000000..de08bc2d36 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StringToHashBucketFast.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "StringToHashBucketFast" + endpoint { + name: "strings.ToHashBucketFast" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_StringToHashBucketStrong.pbtxt b/tensorflow/core/api_def/java_api/api_def_StringToHashBucketStrong.pbtxt new file mode 100644 index 0000000000..15b9138238 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StringToHashBucketStrong.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "StringToHashBucketStrong" + endpoint { + name: "strings.ToHashBucketStrong" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_StringToNumber.pbtxt b/tensorflow/core/api_def/java_api/api_def_StringToNumber.pbtxt new file mode 100644 index 0000000000..196f694da5 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_StringToNumber.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "StringToNumber" + endpoint { + name: "strings.ToNumber" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Sub.pbtxt b/tensorflow/core/api_def/java_api/api_def_Sub.pbtxt new file mode 100644 index 0000000000..e79c6a0036 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Sub.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Sub" + endpoint { + name: "math.Sub" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Substr.pbtxt b/tensorflow/core/api_def/java_api/api_def_Substr.pbtxt new file mode 100644 index 0000000000..78f34550a5 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Substr.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Substr" + endpoint { + name: "strings.Substr" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Sum.pbtxt b/tensorflow/core/api_def/java_api/api_def_Sum.pbtxt new file mode 100644 index 0000000000..3999fa6ed1 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Sum.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Sum" +} diff --git a/tensorflow/core/api_def/java_api/api_def_SummaryWriter.pbtxt b/tensorflow/core/api_def/java_api/api_def_SummaryWriter.pbtxt new file mode 100644 index 0000000000..8338c0fa18 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SummaryWriter.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SummaryWriter" + endpoint { + name: "summary.SummaryWriter" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Svd.pbtxt b/tensorflow/core/api_def/java_api/api_def_Svd.pbtxt new file mode 100644 index 0000000000..889d416281 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Svd.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Svd" + endpoint { + name: "linalg.Svd" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Switch.pbtxt b/tensorflow/core/api_def/java_api/api_def_Switch.pbtxt index 0d3362a91e..edd9255452 100644 --- a/tensorflow/core/api_def/java_api/api_def_Switch.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Switch.pbtxt @@ -1,4 +1,6 @@ op { - graph_op_name: "Switch" #TODO(karllessard) escape that reserved name - visibility: HIDDEN + graph_op_name: "Switch" + endpoint { + name: "SwitchCond" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_SymbolicGradient.pbtxt b/tensorflow/core/api_def/java_api/api_def_SymbolicGradient.pbtxt new file mode 100644 index 0000000000..6c6e68ae74 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_SymbolicGradient.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "SymbolicGradient" + endpoint { + name: "train.SymbolicGradient" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_TFRecordDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_TFRecordDataset.pbtxt new file mode 100644 index 0000000000..76c077f452 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TFRecordDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "TFRecordDataset" + endpoint { + name: "data.TFRecordDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_TFRecordReader.pbtxt b/tensorflow/core/api_def/java_api/api_def_TFRecordReader.pbtxt new file mode 100644 index 0000000000..72d02db279 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TFRecordReader.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "TFRecordReader" + endpoint { + name: "io.TFRecordReader" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_TFRecordReaderV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TFRecordReaderV2.pbtxt new file mode 100644 index 0000000000..beb7f58862 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TFRecordReaderV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "TFRecordReaderV2" + endpoint { + name: "io.TFRecordReaderV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_TakeDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_TakeDataset.pbtxt new file mode 100644 index 0000000000..2b0c0544fb --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TakeDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "TakeDataset" + endpoint { + name: "data.TakeDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_TakeManySparseFromTensorsMap.pbtxt b/tensorflow/core/api_def/java_api/api_def_TakeManySparseFromTensorsMap.pbtxt new file mode 100644 index 0000000000..620e9fb012 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TakeManySparseFromTensorsMap.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "TakeManySparseFromTensorsMap" + endpoint { + name: "sparse.TakeManySparseFromTensorsMap" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Tan.pbtxt b/tensorflow/core/api_def/java_api/api_def_Tan.pbtxt new file mode 100644 index 0000000000..560ca546b7 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Tan.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Tan" + endpoint { + name: "math.Tan" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Tanh.pbtxt b/tensorflow/core/api_def/java_api/api_def_Tanh.pbtxt new file mode 100644 index 0000000000..90e441808f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Tanh.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Tanh" + endpoint { + name: "math.Tanh" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_TanhGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_TanhGrad.pbtxt new file mode 100644 index 0000000000..c0837d0487 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TanhGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "TanhGrad" + endpoint { + name: "math.TanhGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_TemporaryVariable.pbtxt b/tensorflow/core/api_def/java_api/api_def_TemporaryVariable.pbtxt new file mode 100644 index 0000000000..8ce760f9c9 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TemporaryVariable.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TemporaryVariable" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArray.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArray.pbtxt new file mode 100644 index 0000000000..7eaa468130 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArray.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArray" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayClose.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayClose.pbtxt new file mode 100644 index 0000000000..e866250d3a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayClose.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArrayClose" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayCloseV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayCloseV2.pbtxt new file mode 100644 index 0000000000..5add953e66 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayCloseV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArrayCloseV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayCloseV3.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayCloseV3.pbtxt new file mode 100644 index 0000000000..b881089718 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayCloseV3.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArrayCloseV3" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayConcat.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayConcat.pbtxt new file mode 100644 index 0000000000..e72b58de1c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayConcat.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArrayConcat" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayConcatV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayConcatV2.pbtxt new file mode 100644 index 0000000000..abc3e60f0c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayConcatV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArrayConcatV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayConcatV3.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayConcatV3.pbtxt new file mode 100644 index 0000000000..d1a91e90d7 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayConcatV3.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArrayConcatV3" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayGather.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayGather.pbtxt new file mode 100644 index 0000000000..d4d179874f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayGather.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArrayGather" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayGatherV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayGatherV2.pbtxt new file mode 100644 index 0000000000..dd94fc4ef5 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayGatherV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArrayGatherV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayGatherV3.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayGatherV3.pbtxt new file mode 100644 index 0000000000..b8f01e6ac1 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayGatherV3.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArrayGatherV3" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayGrad.pbtxt new file mode 100644 index 0000000000..517461edba --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayGrad.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArrayGrad" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayGradV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayGradV2.pbtxt new file mode 100644 index 0000000000..b7278b5ffa --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayGradV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArrayGradV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayGradV3.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayGradV3.pbtxt new file mode 100644 index 0000000000..b7c3f143ef --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayGradV3.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArrayGradV3" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayGradWithShape.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayGradWithShape.pbtxt new file mode 100644 index 0000000000..d6804fb697 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayGradWithShape.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArrayGradWithShape" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayPack.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayPack.pbtxt new file mode 100644 index 0000000000..030950b06f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayPack.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArrayPack" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayRead.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayRead.pbtxt new file mode 100644 index 0000000000..1b62f7fac7 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayRead.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArrayRead" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayReadV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayReadV2.pbtxt new file mode 100644 index 0000000000..ba02f61028 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayReadV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArrayReadV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayReadV3.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayReadV3.pbtxt new file mode 100644 index 0000000000..c30428c23a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayReadV3.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArrayReadV3" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayScatter.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayScatter.pbtxt new file mode 100644 index 0000000000..a3e8d1625e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayScatter.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArrayScatter" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayScatterV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayScatterV2.pbtxt new file mode 100644 index 0000000000..38080410e6 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayScatterV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArrayScatterV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayScatterV3.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayScatterV3.pbtxt new file mode 100644 index 0000000000..6f42524af8 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayScatterV3.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArrayScatterV3" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArraySize.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArraySize.pbtxt new file mode 100644 index 0000000000..fb3a6fae1c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArraySize.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArraySize" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArraySizeV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArraySizeV2.pbtxt new file mode 100644 index 0000000000..03bf061f8b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArraySizeV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArraySizeV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArraySizeV3.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArraySizeV3.pbtxt new file mode 100644 index 0000000000..0c14360641 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArraySizeV3.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArraySizeV3" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArraySplit.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArraySplit.pbtxt new file mode 100644 index 0000000000..3eb8d6c7ff --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArraySplit.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArraySplit" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArraySplitV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArraySplitV2.pbtxt new file mode 100644 index 0000000000..34740aa2ef --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArraySplitV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArraySplitV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArraySplitV3.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArraySplitV3.pbtxt new file mode 100644 index 0000000000..4b22f2bdf6 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArraySplitV3.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArraySplitV3" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayUnpack.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayUnpack.pbtxt new file mode 100644 index 0000000000..a9011de23e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayUnpack.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArrayUnpack" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayV2.pbtxt new file mode 100644 index 0000000000..d18517725e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArrayV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayV3.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayV3.pbtxt new file mode 100644 index 0000000000..06e65ef93c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayV3.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArrayV3" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayWrite.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayWrite.pbtxt new file mode 100644 index 0000000000..92ab1764ec --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayWrite.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArrayWrite" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayWriteV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayWriteV2.pbtxt new file mode 100644 index 0000000000..10c505cff4 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayWriteV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArrayWriteV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayWriteV3.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayWriteV3.pbtxt new file mode 100644 index 0000000000..2fe2d02127 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayWriteV3.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorArrayWriteV3" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorDataset.pbtxt new file mode 100644 index 0000000000..ed0ead6e7a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "TensorDataset" + endpoint { + name: "data.TensorDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorForestCreateTreeVariable.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorForestCreateTreeVariable.pbtxt new file mode 100644 index 0000000000..8e2410a0dd --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorForestCreateTreeVariable.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorForestCreateTreeVariable" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorForestTreeDeserialize.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorForestTreeDeserialize.pbtxt new file mode 100644 index 0000000000..724bdb282d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorForestTreeDeserialize.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorForestTreeDeserialize" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorForestTreeIsInitializedOp.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorForestTreeIsInitializedOp.pbtxt new file mode 100644 index 0000000000..7e93af8508 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorForestTreeIsInitializedOp.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorForestTreeIsInitializedOp" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorForestTreePredict.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorForestTreePredict.pbtxt new file mode 100644 index 0000000000..73770fa291 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorForestTreePredict.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorForestTreePredict" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorForestTreeResourceHandleOp.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorForestTreeResourceHandleOp.pbtxt new file mode 100644 index 0000000000..c2ef0ee5dd --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorForestTreeResourceHandleOp.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorForestTreeResourceHandleOp" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorForestTreeSerialize.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorForestTreeSerialize.pbtxt new file mode 100644 index 0000000000..d10f9e632b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorForestTreeSerialize.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorForestTreeSerialize" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorForestTreeSize.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorForestTreeSize.pbtxt new file mode 100644 index 0000000000..9d81f1ea8c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorForestTreeSize.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorForestTreeSize" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorListConcatLists.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorListConcatLists.pbtxt new file mode 100644 index 0000000000..3fa6265e10 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorListConcatLists.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorListConcatLists" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorListElementShape.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorListElementShape.pbtxt new file mode 100644 index 0000000000..396a0cfa8f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorListElementShape.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorListElementShape" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorListFromTensor.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorListFromTensor.pbtxt new file mode 100644 index 0000000000..3179feddd6 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorListFromTensor.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorListFromTensor" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorListGather.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorListGather.pbtxt new file mode 100644 index 0000000000..580d34b68f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorListGather.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorListGather" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorListGetItem.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorListGetItem.pbtxt new file mode 100644 index 0000000000..2c47208fa0 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorListGetItem.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorListGetItem" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorListLength.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorListLength.pbtxt new file mode 100644 index 0000000000..2ec689d71c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorListLength.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorListLength" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorListPopBack.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorListPopBack.pbtxt new file mode 100644 index 0000000000..8d0d6ed55b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorListPopBack.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorListPopBack" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorListPushBack.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorListPushBack.pbtxt new file mode 100644 index 0000000000..285351cf4f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorListPushBack.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorListPushBack" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorListPushBackBatch.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorListPushBackBatch.pbtxt new file mode 100644 index 0000000000..1f33d49260 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorListPushBackBatch.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorListPushBackBatch" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorListReserve.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorListReserve.pbtxt new file mode 100644 index 0000000000..5336600688 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorListReserve.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorListReserve" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorListScatter.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorListScatter.pbtxt new file mode 100644 index 0000000000..f3a56f1292 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorListScatter.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorListScatter" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorListSetItem.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorListSetItem.pbtxt new file mode 100644 index 0000000000..002e2a9bd3 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorListSetItem.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorListSetItem" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorListStack.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorListStack.pbtxt new file mode 100644 index 0000000000..b4b03c86e5 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorListStack.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TensorListStack" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorSliceDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorSliceDataset.pbtxt new file mode 100644 index 0000000000..3729a025e6 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorSliceDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "TensorSliceDataset" + endpoint { + name: "data.TensorSliceDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorSummary.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorSummary.pbtxt new file mode 100644 index 0000000000..a720ca9076 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorSummary.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "TensorSummary" + endpoint { + name: "summary.TensorSummary" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_TensorSummaryV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorSummaryV2.pbtxt new file mode 100644 index 0000000000..ba5131c85e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TensorSummaryV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "TensorSummaryV2" + endpoint { + name: "summary.TensorSummaryV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_TextLineDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_TextLineDataset.pbtxt new file mode 100644 index 0000000000..c3c75d5703 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TextLineDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "TextLineDataset" + endpoint { + name: "data.TextLineDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_TextLineReader.pbtxt b/tensorflow/core/api_def/java_api/api_def_TextLineReader.pbtxt new file mode 100644 index 0000000000..17eb20cffe --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TextLineReader.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "TextLineReader" + endpoint { + name: "io.TextLineReader" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_TextLineReaderV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TextLineReaderV2.pbtxt new file mode 100644 index 0000000000..745c85fac9 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TextLineReaderV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "TextLineReaderV2" + endpoint { + name: "io.TextLineReaderV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ThreadUnsafeUnigramCandidateSampler.pbtxt b/tensorflow/core/api_def/java_api/api_def_ThreadUnsafeUnigramCandidateSampler.pbtxt new file mode 100644 index 0000000000..2ef4a83478 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ThreadUnsafeUnigramCandidateSampler.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ThreadUnsafeUnigramCandidateSampler" + endpoint { + name: "random.ThreadUnsafeUnigramCandidateSampler" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Tile.pbtxt b/tensorflow/core/api_def/java_api/api_def_Tile.pbtxt new file mode 100644 index 0000000000..8e4e63a222 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Tile.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Tile" + endpoint { + name: "Tile" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_TileGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_TileGrad.pbtxt new file mode 100644 index 0000000000..7cd975c02f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TileGrad.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "TileGrad" + endpoint { + name: "train.TileGrad" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Timestamp.pbtxt b/tensorflow/core/api_def/java_api/api_def_Timestamp.pbtxt new file mode 100644 index 0000000000..9ebc664ae1 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Timestamp.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Timestamp" +} diff --git a/tensorflow/core/api_def/java_api/api_def_TopK.pbtxt b/tensorflow/core/api_def/java_api/api_def_TopK.pbtxt new file mode 100644 index 0000000000..108701adf1 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TopK.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "TopK" + endpoint { + name: "nn.TopK" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_TopKV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TopKV2.pbtxt new file mode 100644 index 0000000000..2aa89adcce --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TopKV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "TopKV2" + endpoint { + name: "nn.TopKV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Transpose.pbtxt b/tensorflow/core/api_def/java_api/api_def_Transpose.pbtxt new file mode 100644 index 0000000000..ad7900c00b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Transpose.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Transpose" + endpoint { + name: "linalg.Transpose" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_TruncateDiv.pbtxt b/tensorflow/core/api_def/java_api/api_def_TruncateDiv.pbtxt new file mode 100644 index 0000000000..4fbd682340 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TruncateDiv.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "TruncateDiv" + endpoint { + name: "math.TruncateDiv" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_TruncateMod.pbtxt b/tensorflow/core/api_def/java_api/api_def_TruncateMod.pbtxt new file mode 100644 index 0000000000..7d1ae9a14f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TruncateMod.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "TruncateMod" + endpoint { + name: "math.TruncateMod" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_TruncatedNormal.pbtxt b/tensorflow/core/api_def/java_api/api_def_TruncatedNormal.pbtxt new file mode 100644 index 0000000000..b2dd52c955 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TruncatedNormal.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "TruncatedNormal" + endpoint { + name: "random.TruncatedNormal" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_TryRpc.pbtxt b/tensorflow/core/api_def/java_api/api_def_TryRpc.pbtxt new file mode 100644 index 0000000000..7ca476086a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_TryRpc.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "TryRpc" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Unbatch.pbtxt b/tensorflow/core/api_def/java_api/api_def_Unbatch.pbtxt new file mode 100644 index 0000000000..513b05593a --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Unbatch.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Unbatch" +} diff --git a/tensorflow/core/api_def/java_api/api_def_UnbatchDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_UnbatchDataset.pbtxt new file mode 100644 index 0000000000..24907c804b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_UnbatchDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "UnbatchDataset" + endpoint { + name: "data.UnbatchDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_UnbatchGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_UnbatchGrad.pbtxt new file mode 100644 index 0000000000..ce612f84f6 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_UnbatchGrad.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "UnbatchGrad" +} diff --git a/tensorflow/core/api_def/java_api/api_def_UnicodeDecodeWithOffsets.pbtxt b/tensorflow/core/api_def/java_api/api_def_UnicodeDecodeWithOffsets.pbtxt new file mode 100644 index 0000000000..16cc033140 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_UnicodeDecodeWithOffsets.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "UnicodeDecodeWithOffsets" + endpoint { + name: "strings.UnicodeDecodeWithOffsets" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_UnicodeScript.pbtxt b/tensorflow/core/api_def/java_api/api_def_UnicodeScript.pbtxt new file mode 100644 index 0000000000..a873151d5f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_UnicodeScript.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "UnicodeScript" + endpoint { + name: "strings.UnicodeScript" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_UnicodeTranscode.pbtxt b/tensorflow/core/api_def/java_api/api_def_UnicodeTranscode.pbtxt new file mode 100644 index 0000000000..131cc6169c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_UnicodeTranscode.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "UnicodeTranscode" + endpoint { + name: "strings.UnicodeTranscode" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_UniformCandidateSampler.pbtxt b/tensorflow/core/api_def/java_api/api_def_UniformCandidateSampler.pbtxt new file mode 100644 index 0000000000..d7a56c2a6e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_UniformCandidateSampler.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "UniformCandidateSampler" + endpoint { + name: "random.UniformCandidateSampler" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Unique.pbtxt b/tensorflow/core/api_def/java_api/api_def_Unique.pbtxt new file mode 100644 index 0000000000..5c4262b5fb --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Unique.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Unique" +} diff --git a/tensorflow/core/api_def/java_api/api_def_UniqueV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_UniqueV2.pbtxt new file mode 100644 index 0000000000..4d43500a0d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_UniqueV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "UniqueV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_UniqueWithCounts.pbtxt b/tensorflow/core/api_def/java_api/api_def_UniqueWithCounts.pbtxt new file mode 100644 index 0000000000..1ce5314157 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_UniqueWithCounts.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "UniqueWithCounts" +} diff --git a/tensorflow/core/api_def/java_api/api_def_UniqueWithCountsV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_UniqueWithCountsV2.pbtxt new file mode 100644 index 0000000000..abfd496b8e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_UniqueWithCountsV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "UniqueWithCountsV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Unpack.pbtxt b/tensorflow/core/api_def/java_api/api_def_Unpack.pbtxt new file mode 100644 index 0000000000..c3ad63e8f8 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Unpack.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Unpack" +} diff --git a/tensorflow/core/api_def/java_api/api_def_UnravelIndex.pbtxt b/tensorflow/core/api_def/java_api/api_def_UnravelIndex.pbtxt new file mode 100644 index 0000000000..1a08dc8f1e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_UnravelIndex.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "UnravelIndex" +} diff --git a/tensorflow/core/api_def/java_api/api_def_UnsortedSegmentMax.pbtxt b/tensorflow/core/api_def/java_api/api_def_UnsortedSegmentMax.pbtxt new file mode 100644 index 0000000000..a58d8740d5 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_UnsortedSegmentMax.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "UnsortedSegmentMax" + endpoint { + name: "math.UnsortedSegmentMax" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_UnsortedSegmentMin.pbtxt b/tensorflow/core/api_def/java_api/api_def_UnsortedSegmentMin.pbtxt new file mode 100644 index 0000000000..2f17ab4624 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_UnsortedSegmentMin.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "UnsortedSegmentMin" + endpoint { + name: "math.UnsortedSegmentMin" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_UnsortedSegmentProd.pbtxt b/tensorflow/core/api_def/java_api/api_def_UnsortedSegmentProd.pbtxt new file mode 100644 index 0000000000..c594941bcc --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_UnsortedSegmentProd.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "UnsortedSegmentProd" + endpoint { + name: "math.UnsortedSegmentProd" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_UnsortedSegmentSum.pbtxt b/tensorflow/core/api_def/java_api/api_def_UnsortedSegmentSum.pbtxt new file mode 100644 index 0000000000..e10cf8a6c2 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_UnsortedSegmentSum.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "UnsortedSegmentSum" + endpoint { + name: "math.UnsortedSegmentSum" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Unstage.pbtxt b/tensorflow/core/api_def/java_api/api_def_Unstage.pbtxt new file mode 100644 index 0000000000..31a97cf84d --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Unstage.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Unstage" +} diff --git a/tensorflow/core/api_def/java_api/api_def_UpperBound.pbtxt b/tensorflow/core/api_def/java_api/api_def_UpperBound.pbtxt new file mode 100644 index 0000000000..229a6ddfc3 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_UpperBound.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "UpperBound" +} diff --git a/tensorflow/core/api_def/java_api/api_def_VarHandleOp.pbtxt b/tensorflow/core/api_def/java_api/api_def_VarHandleOp.pbtxt new file mode 100644 index 0000000000..30bdace0e6 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_VarHandleOp.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "VarHandleOp" +} diff --git a/tensorflow/core/api_def/java_api/api_def_VarIsInitializedOp.pbtxt b/tensorflow/core/api_def/java_api/api_def_VarIsInitializedOp.pbtxt new file mode 100644 index 0000000000..a3992019bc --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_VarIsInitializedOp.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "VarIsInitializedOp" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Variable.pbtxt b/tensorflow/core/api_def/java_api/api_def_Variable.pbtxt new file mode 100644 index 0000000000..abc2f9c954 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Variable.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Variable" +} diff --git a/tensorflow/core/api_def/java_api/api_def_VariableShape.pbtxt b/tensorflow/core/api_def/java_api/api_def_VariableShape.pbtxt new file mode 100644 index 0000000000..38c63b5b70 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_VariableShape.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "VariableShape" +} diff --git a/tensorflow/core/api_def/java_api/api_def_VariableV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_VariableV2.pbtxt new file mode 100644 index 0000000000..221a80f760 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_VariableV2.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "VariableV2" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Where.pbtxt b/tensorflow/core/api_def/java_api/api_def_Where.pbtxt new file mode 100644 index 0000000000..f130181a6e --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Where.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "Where" +} diff --git a/tensorflow/core/api_def/java_api/api_def_While.pbtxt b/tensorflow/core/api_def/java_api/api_def_While.pbtxt new file mode 100644 index 0000000000..9d0f3b0763 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_While.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "While" +} diff --git a/tensorflow/core/api_def/java_api/api_def_WholeFileReader.pbtxt b/tensorflow/core/api_def/java_api/api_def_WholeFileReader.pbtxt new file mode 100644 index 0000000000..67a8933efb --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_WholeFileReader.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "WholeFileReader" + endpoint { + name: "io.WholeFileReader" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_WholeFileReaderV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_WholeFileReaderV2.pbtxt new file mode 100644 index 0000000000..172268a122 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_WholeFileReaderV2.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "WholeFileReaderV2" + endpoint { + name: "io.WholeFileReaderV2" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_WindowDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_WindowDataset.pbtxt new file mode 100644 index 0000000000..69f12c55e1 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_WindowDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "WindowDataset" + endpoint { + name: "data.WindowDataset" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_WriteAudioSummary.pbtxt b/tensorflow/core/api_def/java_api/api_def_WriteAudioSummary.pbtxt new file mode 100644 index 0000000000..fcd0df85c2 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_WriteAudioSummary.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "WriteAudioSummary" + endpoint { + name: "summary.WriteAudioSummary" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_WriteFile.pbtxt b/tensorflow/core/api_def/java_api/api_def_WriteFile.pbtxt new file mode 100644 index 0000000000..a2d6a5bace --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_WriteFile.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "WriteFile" + endpoint { + name: "io.WriteFile" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_WriteGraphSummary.pbtxt b/tensorflow/core/api_def/java_api/api_def_WriteGraphSummary.pbtxt new file mode 100644 index 0000000000..8e461bbba3 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_WriteGraphSummary.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "WriteGraphSummary" + endpoint { + name: "summary.WriteGraphSummary" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_WriteHistogramSummary.pbtxt b/tensorflow/core/api_def/java_api/api_def_WriteHistogramSummary.pbtxt new file mode 100644 index 0000000000..c991079032 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_WriteHistogramSummary.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "WriteHistogramSummary" + endpoint { + name: "summary.WriteHistogramSummary" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_WriteImageSummary.pbtxt b/tensorflow/core/api_def/java_api/api_def_WriteImageSummary.pbtxt new file mode 100644 index 0000000000..08bf0adb2f --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_WriteImageSummary.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "WriteImageSummary" + endpoint { + name: "summary.WriteImageSummary" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_WriteScalarSummary.pbtxt b/tensorflow/core/api_def/java_api/api_def_WriteScalarSummary.pbtxt new file mode 100644 index 0000000000..7bc7c16a0c --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_WriteScalarSummary.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "WriteScalarSummary" + endpoint { + name: "summary.WriteScalarSummary" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_WriteSummary.pbtxt b/tensorflow/core/api_def/java_api/api_def_WriteSummary.pbtxt new file mode 100644 index 0000000000..1141cb6dbb --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_WriteSummary.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "WriteSummary" + endpoint { + name: "summary.WriteSummary" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Xdivy.pbtxt b/tensorflow/core/api_def/java_api/api_def_Xdivy.pbtxt new file mode 100644 index 0000000000..13a94b8a56 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Xdivy.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Xdivy" + endpoint { + name: "math.Xdivy" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_Xlogy.pbtxt b/tensorflow/core/api_def/java_api/api_def_Xlogy.pbtxt new file mode 100644 index 0000000000..52f457d645 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Xlogy.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Xlogy" + endpoint { + name: "math.Xlogy" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ZerosLike.pbtxt b/tensorflow/core/api_def/java_api/api_def_ZerosLike.pbtxt new file mode 100644 index 0000000000..8ef17aba9b --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ZerosLike.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "ZerosLike" +} diff --git a/tensorflow/core/api_def/java_api/api_def_Zeta.pbtxt b/tensorflow/core/api_def/java_api/api_def_Zeta.pbtxt new file mode 100644 index 0000000000..b602bbc7e1 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_Zeta.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "Zeta" + endpoint { + name: "math.Zeta" + } +} diff --git a/tensorflow/core/api_def/java_api/api_def_ZipDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ZipDataset.pbtxt new file mode 100644 index 0000000000..e32362bef2 --- /dev/null +++ b/tensorflow/core/api_def/java_api/api_def_ZipDataset.pbtxt @@ -0,0 +1,6 @@ +op { + graph_op_name: "ZipDataset" + endpoint { + name: "data.ZipDataset" + } +} -- GitLab From b381d3c10bef3d3bd87cf691a40502986b3f35dc Mon Sep 17 00:00:00 2001 From: Karl Lessard Date: Mon, 26 Nov 2018 22:45:29 -0500 Subject: [PATCH 048/873] Use lower-camelcase in ops names and fix uniqueness --- tensorflow/core/api_def/java_api/api_def_ApplyAdagradDA.pbtxt | 2 +- .../core/api_def/java_api/api_def_ApplyCenteredRMSProp.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_ApplyRMSProp.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_AvgPool3DGrad.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_BatchFFT.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_BatchFFT2D.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_BatchFFT3D.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_BatchIFFT.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_BatchIFFT2D.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_BatchIFFT3D.pbtxt | 2 +- .../core/api_def/java_api/api_def_CTCBeamSearchDecoder.pbtxt | 2 +- .../core/api_def/java_api/api_def_CTCGreedyDecoder.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_CTCLoss.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_Conv2D.pbtxt | 2 +- .../core/api_def/java_api/api_def_Conv2DBackpropFilter.pbtxt | 2 +- .../core/api_def/java_api/api_def_Conv2DBackpropInput.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_Conv3D.pbtxt | 2 +- .../core/api_def/java_api/api_def_Conv3DBackpropFilter.pbtxt | 2 +- .../core/api_def/java_api/api_def_Conv3DBackpropFilterV2.pbtxt | 2 +- .../core/api_def/java_api/api_def_Conv3DBackpropInput.pbtxt | 2 +- .../core/api_def/java_api/api_def_Conv3DBackpropInputV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_CudnnRNN.pbtxt | 2 +- .../core/api_def/java_api/api_def_CudnnRNNBackprop.pbtxt | 2 +- .../core/api_def/java_api/api_def_CudnnRNNBackpropV2.pbtxt | 2 +- .../api_def/java_api/api_def_CudnnRNNCanonicalToParams.pbtxt | 2 +- .../core/api_def/java_api/api_def_CudnnRNNParamsSize.pbtxt | 2 +- .../api_def/java_api/api_def_CudnnRNNParamsToCanonical.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_CudnnRNNV2.pbtxt | 2 +- .../core/api_def/java_api/api_def_DatasetToTFRecord.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_DecodeCSV.pbtxt | 2 +- .../api_def/java_api/api_def_Dilation2DBackpropFilter.pbtxt | 2 +- .../api_def/java_api/api_def_Dilation2DBackpropInput.pbtxt | 2 +- .../core/api_def/java_api/api_def_ExperimentalCSVDataset.pbtxt | 2 +- .../api_def/java_api/api_def_ExperimentalLMDBDataset.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_FIFOQueue.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_FIFOQueueV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_FusedPadConv2D.pbtxt | 2 +- .../api_def/java_api/api_def_FusedResizeAndPadConv2D.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_IRFFT.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_IRFFT2D.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_IRFFT3D.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_LMDBReader.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_ListDiff.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_MaxPool3DGrad.pbtxt | 2 +- .../core/api_def/java_api/api_def_MaxPool3DGradGrad.pbtxt | 2 +- .../core/api_def/java_api/api_def_PaddingFIFOQueue.pbtxt | 2 +- .../core/api_def/java_api/api_def_PaddingFIFOQueueV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_QuantizedConv2D.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_RFFT.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_RFFT2D.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_RFFT3D.pbtxt | 2 +- .../core/api_def/java_api/api_def_ResourceApplyAdagradDA.pbtxt | 2 +- .../java_api/api_def_ResourceApplyCenteredRMSProp.pbtxt | 2 +- .../core/api_def/java_api/api_def_ResourceApplyRMSProp.pbtxt | 2 +- .../java_api/api_def_ResourceSparseApplyAdagradDA.pbtxt | 2 +- .../java_api/api_def_ResourceSparseApplyCenteredRMSProp.pbtxt | 2 +- .../api_def/java_api/api_def_ResourceSparseApplyRMSProp.pbtxt | 2 +- .../core/api_def/java_api/api_def_SparseApplyAdagradDA.pbtxt | 2 +- .../api_def/java_api/api_def_SparseApplyCenteredRMSProp.pbtxt | 2 +- .../core/api_def/java_api/api_def_SparseApplyRMSProp.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_TFRecordDataset.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_TFRecordReader.pbtxt | 2 +- .../core/api_def/java_api/api_def_TFRecordReaderV2.pbtxt | 2 +- 63 files changed, 65 insertions(+), 62 deletions(-) diff --git a/tensorflow/core/api_def/java_api/api_def_ApplyAdagradDA.pbtxt b/tensorflow/core/api_def/java_api/api_def_ApplyAdagradDA.pbtxt index d2b38707fb..815df985ef 100644 --- a/tensorflow/core/api_def/java_api/api_def_ApplyAdagradDA.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ApplyAdagradDA.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "ApplyAdagradDA" endpoint { - name: "train.ApplyAdagradDA" + name: "train.ApplyAdagradDa" } } diff --git a/tensorflow/core/api_def/java_api/api_def_ApplyCenteredRMSProp.pbtxt b/tensorflow/core/api_def/java_api/api_def_ApplyCenteredRMSProp.pbtxt index 6ed0660165..1b831bca43 100644 --- a/tensorflow/core/api_def/java_api/api_def_ApplyCenteredRMSProp.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ApplyCenteredRMSProp.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "ApplyCenteredRMSProp" endpoint { - name: "train.ApplyCenteredRMSProp" + name: "train.ApplyCenteredRmsProp" } } diff --git a/tensorflow/core/api_def/java_api/api_def_ApplyRMSProp.pbtxt b/tensorflow/core/api_def/java_api/api_def_ApplyRMSProp.pbtxt index a1397b16fc..90171ccc75 100644 --- a/tensorflow/core/api_def/java_api/api_def_ApplyRMSProp.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ApplyRMSProp.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "ApplyRMSProp" endpoint { - name: "train.ApplyRMSProp" + name: "train.ApplyRmsProp" } } diff --git a/tensorflow/core/api_def/java_api/api_def_AvgPool3DGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_AvgPool3DGrad.pbtxt index ac51715095..09aba78ca2 100644 --- a/tensorflow/core/api_def/java_api/api_def_AvgPool3DGrad.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_AvgPool3DGrad.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "AvgPool3DGrad" endpoint { - name: "nn.AvgPool3DGrad" + name: "nn.AvgPool3dGrad" } } diff --git a/tensorflow/core/api_def/java_api/api_def_BatchFFT.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchFFT.pbtxt index 62bc804c7e..4dda7c1fb6 100644 --- a/tensorflow/core/api_def/java_api/api_def_BatchFFT.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_BatchFFT.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "BatchFFT" endpoint { - name: "signal.BatchFFT" + name: "signal.BatchFft" } } diff --git a/tensorflow/core/api_def/java_api/api_def_BatchFFT2D.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchFFT2D.pbtxt index 89479537a2..e11860138a 100644 --- a/tensorflow/core/api_def/java_api/api_def_BatchFFT2D.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_BatchFFT2D.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "BatchFFT2D" endpoint { - name: "signal.BatchFFT2D" + name: "signal.BatchFft2d" } } diff --git a/tensorflow/core/api_def/java_api/api_def_BatchFFT3D.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchFFT3D.pbtxt index e9cff897bb..3be0b516d0 100644 --- a/tensorflow/core/api_def/java_api/api_def_BatchFFT3D.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_BatchFFT3D.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "BatchFFT3D" endpoint { - name: "signal.BatchFFT3D" + name: "signal.BatchFft3d" } } diff --git a/tensorflow/core/api_def/java_api/api_def_BatchIFFT.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchIFFT.pbtxt index f156a6df39..de37ada148 100644 --- a/tensorflow/core/api_def/java_api/api_def_BatchIFFT.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_BatchIFFT.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "BatchIFFT" endpoint { - name: "signal.BatchIFFT" + name: "signal.BatchIfft" } } diff --git a/tensorflow/core/api_def/java_api/api_def_BatchIFFT2D.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchIFFT2D.pbtxt index 7cce88f8f7..4ae7fb4cb0 100644 --- a/tensorflow/core/api_def/java_api/api_def_BatchIFFT2D.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_BatchIFFT2D.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "BatchIFFT2D" endpoint { - name: "signal.BatchIFFT2D" + name: "signal.BatchIfft2d" } } diff --git a/tensorflow/core/api_def/java_api/api_def_BatchIFFT3D.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchIFFT3D.pbtxt index b30e596d75..0ecb52714b 100644 --- a/tensorflow/core/api_def/java_api/api_def_BatchIFFT3D.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_BatchIFFT3D.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "BatchIFFT3D" endpoint { - name: "signal.BatchIFFT3D" + name: "signal.BatchIfft3d" } } diff --git a/tensorflow/core/api_def/java_api/api_def_CTCBeamSearchDecoder.pbtxt b/tensorflow/core/api_def/java_api/api_def_CTCBeamSearchDecoder.pbtxt index 79dae81df9..39739f03a3 100644 --- a/tensorflow/core/api_def/java_api/api_def_CTCBeamSearchDecoder.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_CTCBeamSearchDecoder.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "CTCBeamSearchDecoder" endpoint { - name: "nn.CTCBeamSearchDecoder" + name: "nn.CtcBeamSearchDecoder" } } diff --git a/tensorflow/core/api_def/java_api/api_def_CTCGreedyDecoder.pbtxt b/tensorflow/core/api_def/java_api/api_def_CTCGreedyDecoder.pbtxt index 6a58e628bd..009742f097 100644 --- a/tensorflow/core/api_def/java_api/api_def_CTCGreedyDecoder.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_CTCGreedyDecoder.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "CTCGreedyDecoder" endpoint { - name: "nn.CTCGreedyDecoder" + name: "nn.CtcGreedyDecoder" } } diff --git a/tensorflow/core/api_def/java_api/api_def_CTCLoss.pbtxt b/tensorflow/core/api_def/java_api/api_def_CTCLoss.pbtxt index 361270e1f7..dbeefa4017 100644 --- a/tensorflow/core/api_def/java_api/api_def_CTCLoss.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_CTCLoss.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "CTCLoss" endpoint { - name: "nn.CTCLoss" + name: "nn.CtcLoss" } } diff --git a/tensorflow/core/api_def/java_api/api_def_Conv2D.pbtxt b/tensorflow/core/api_def/java_api/api_def_Conv2D.pbtxt index 04c4fcec16..21d1398e09 100644 --- a/tensorflow/core/api_def/java_api/api_def_Conv2D.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Conv2D.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "Conv2D" endpoint { - name: "nn.Conv2D" + name: "nn.Conv2d" } } diff --git a/tensorflow/core/api_def/java_api/api_def_Conv2DBackpropFilter.pbtxt b/tensorflow/core/api_def/java_api/api_def_Conv2DBackpropFilter.pbtxt index 990a54262e..30eb55c6f2 100644 --- a/tensorflow/core/api_def/java_api/api_def_Conv2DBackpropFilter.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Conv2DBackpropFilter.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "Conv2DBackpropFilter" endpoint { - name: "nn.Conv2DBackpropFilter" + name: "nn.Conv2dBackpropFilter" } } diff --git a/tensorflow/core/api_def/java_api/api_def_Conv2DBackpropInput.pbtxt b/tensorflow/core/api_def/java_api/api_def_Conv2DBackpropInput.pbtxt index 11bbaa455c..7c98646c13 100644 --- a/tensorflow/core/api_def/java_api/api_def_Conv2DBackpropInput.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Conv2DBackpropInput.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "Conv2DBackpropInput" endpoint { - name: "nn.Conv2DBackpropInput" + name: "nn.Conv2dBackpropInput" } } diff --git a/tensorflow/core/api_def/java_api/api_def_Conv3D.pbtxt b/tensorflow/core/api_def/java_api/api_def_Conv3D.pbtxt index 0d97065704..6ee1befcff 100644 --- a/tensorflow/core/api_def/java_api/api_def_Conv3D.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Conv3D.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "Conv3D" endpoint { - name: "nn.Conv3D" + name: "nn.Conv3d" } } diff --git a/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropFilter.pbtxt b/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropFilter.pbtxt index 279cd5867e..f66034aec1 100644 --- a/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropFilter.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropFilter.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "Conv3DBackpropFilter" endpoint { - name: "nn.Conv3DBackpropFilter" + name: "nn.Conv3dBackpropFilter" } } diff --git a/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropFilterV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropFilterV2.pbtxt index 0643cc14a9..13db85238f 100644 --- a/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropFilterV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropFilterV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "Conv3DBackpropFilterV2" endpoint { - name: "nn.Conv3dBackpropFilter" + name: "nn.Conv3dBackpropFilterV2" } } diff --git a/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropInput.pbtxt b/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropInput.pbtxt index 5be32ebe3c..fdb2a9d66c 100644 --- a/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropInput.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropInput.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "Conv3DBackpropInput" endpoint { - name: "nn.Conv3DBackpropInput" + name: "nn.Conv3dBackpropInput" } } diff --git a/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropInputV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropInputV2.pbtxt index 2ce9cf134b..7f25b7b9af 100644 --- a/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropInputV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropInputV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "Conv3DBackpropInputV2" endpoint { - name: "nn.Conv3DBackpropInputV2" + name: "nn.Conv3dBackpropInputV2" } } diff --git a/tensorflow/core/api_def/java_api/api_def_CudnnRNN.pbtxt b/tensorflow/core/api_def/java_api/api_def_CudnnRNN.pbtxt index c3d7f17596..4cd8e4fa0f 100644 --- a/tensorflow/core/api_def/java_api/api_def_CudnnRNN.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_CudnnRNN.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "CudnnRNN" endpoint { - name: "nn.CudnnRNN" + name: "nn.CudnnRnn" } } diff --git a/tensorflow/core/api_def/java_api/api_def_CudnnRNNBackprop.pbtxt b/tensorflow/core/api_def/java_api/api_def_CudnnRNNBackprop.pbtxt index 371b6a18db..b2a222e03a 100644 --- a/tensorflow/core/api_def/java_api/api_def_CudnnRNNBackprop.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_CudnnRNNBackprop.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "CudnnRNNBackprop" endpoint { - name: "nn.CudnnRNNBackprop" + name: "nn.CudnnRnnBackprop" } } diff --git a/tensorflow/core/api_def/java_api/api_def_CudnnRNNBackpropV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_CudnnRNNBackpropV2.pbtxt index 756abdb71f..880a3b2bc4 100644 --- a/tensorflow/core/api_def/java_api/api_def_CudnnRNNBackpropV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_CudnnRNNBackpropV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "CudnnRNNBackpropV2" endpoint { - name: "nn.CudnnRNNBackpropV2" + name: "nn.CudnnRnnBackpropV2" } } diff --git a/tensorflow/core/api_def/java_api/api_def_CudnnRNNCanonicalToParams.pbtxt b/tensorflow/core/api_def/java_api/api_def_CudnnRNNCanonicalToParams.pbtxt index 86d7045db7..8d351fa860 100644 --- a/tensorflow/core/api_def/java_api/api_def_CudnnRNNCanonicalToParams.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_CudnnRNNCanonicalToParams.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "CudnnRNNCanonicalToParams" endpoint { - name: "nn.CudnnRNNCanonicalToParams" + name: "nn.CudnnRnnCanonicalToParams" } } diff --git a/tensorflow/core/api_def/java_api/api_def_CudnnRNNParamsSize.pbtxt b/tensorflow/core/api_def/java_api/api_def_CudnnRNNParamsSize.pbtxt index 73922b0343..3f1193fe60 100644 --- a/tensorflow/core/api_def/java_api/api_def_CudnnRNNParamsSize.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_CudnnRNNParamsSize.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "CudnnRNNParamsSize" endpoint { - name: "nn.CudnnRNNParamsSize" + name: "nn.CudnnRnnParamsSize" } } diff --git a/tensorflow/core/api_def/java_api/api_def_CudnnRNNParamsToCanonical.pbtxt b/tensorflow/core/api_def/java_api/api_def_CudnnRNNParamsToCanonical.pbtxt index e1c567163a..d2e4c6201e 100644 --- a/tensorflow/core/api_def/java_api/api_def_CudnnRNNParamsToCanonical.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_CudnnRNNParamsToCanonical.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "CudnnRNNParamsToCanonical" endpoint { - name: "nn.CudnnRNNParamsToCanonical" + name: "nn.CudnnRnnParamsToCanonical" } } diff --git a/tensorflow/core/api_def/java_api/api_def_CudnnRNNV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_CudnnRNNV2.pbtxt index d535f35459..7086d3c9d2 100644 --- a/tensorflow/core/api_def/java_api/api_def_CudnnRNNV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_CudnnRNNV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "CudnnRNNV2" endpoint { - name: "nn.CudnnRNNV2" + name: "nn.CudnnRnnV2" } } diff --git a/tensorflow/core/api_def/java_api/api_def_DatasetToTFRecord.pbtxt b/tensorflow/core/api_def/java_api/api_def_DatasetToTFRecord.pbtxt index 5884fe960d..3d38857063 100644 --- a/tensorflow/core/api_def/java_api/api_def_DatasetToTFRecord.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_DatasetToTFRecord.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "DatasetToTFRecord" endpoint { - name: "data.DatasetToTFRecord" + name: "data.DatasetToTfRecord" } } diff --git a/tensorflow/core/api_def/java_api/api_def_DecodeCSV.pbtxt b/tensorflow/core/api_def/java_api/api_def_DecodeCSV.pbtxt index 2c738cf4dd..1d60107ada 100644 --- a/tensorflow/core/api_def/java_api/api_def_DecodeCSV.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_DecodeCSV.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "DecodeCSV" endpoint { - name: "io.DecodeCSV" + name: "io.DecodeCsv" } } diff --git a/tensorflow/core/api_def/java_api/api_def_Dilation2DBackpropFilter.pbtxt b/tensorflow/core/api_def/java_api/api_def_Dilation2DBackpropFilter.pbtxt index ab8b79d681..db3c68e088 100644 --- a/tensorflow/core/api_def/java_api/api_def_Dilation2DBackpropFilter.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Dilation2DBackpropFilter.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "Dilation2DBackpropFilter" endpoint { - name: "nn.Dilation2DBackpropFilter" + name: "nn.Dilation2dBackpropFilter" } } diff --git a/tensorflow/core/api_def/java_api/api_def_Dilation2DBackpropInput.pbtxt b/tensorflow/core/api_def/java_api/api_def_Dilation2DBackpropInput.pbtxt index 9dece4569c..c935144f7a 100644 --- a/tensorflow/core/api_def/java_api/api_def_Dilation2DBackpropInput.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Dilation2DBackpropInput.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "Dilation2DBackpropInput" endpoint { - name: "nn.Dilation2DBackpropInput" + name: "nn.Dilation2dBackpropInput" } } diff --git a/tensorflow/core/api_def/java_api/api_def_ExperimentalCSVDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ExperimentalCSVDataset.pbtxt index 2c555c3f1a..51fdd9f0b0 100644 --- a/tensorflow/core/api_def/java_api/api_def_ExperimentalCSVDataset.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ExperimentalCSVDataset.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "ExperimentalCSVDataset" endpoint { - name: "data.ExperimentalCSVDataset" + name: "data.ExperimentalCsvDataset" } } diff --git a/tensorflow/core/api_def/java_api/api_def_ExperimentalLMDBDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ExperimentalLMDBDataset.pbtxt index dd24ff544f..a092b1cf39 100644 --- a/tensorflow/core/api_def/java_api/api_def_ExperimentalLMDBDataset.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ExperimentalLMDBDataset.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "ExperimentalLMDBDataset" endpoint { - name: "data.ExperimentalLMDBDataset" + name: "data.ExperimentalLmdbDataset" } } diff --git a/tensorflow/core/api_def/java_api/api_def_FIFOQueue.pbtxt b/tensorflow/core/api_def/java_api/api_def_FIFOQueue.pbtxt index e1760b685b..18c3ed4979 100644 --- a/tensorflow/core/api_def/java_api/api_def_FIFOQueue.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_FIFOQueue.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "FIFOQueue" endpoint { - name: "io.FIFOQueue" + name: "io.FifoQueue" } } diff --git a/tensorflow/core/api_def/java_api/api_def_FIFOQueueV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_FIFOQueueV2.pbtxt index fe260ca2be..f892d7291d 100644 --- a/tensorflow/core/api_def/java_api/api_def_FIFOQueueV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_FIFOQueueV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "FIFOQueueV2" endpoint { - name: "io.FIFOQueueV2" + name: "io.FifoQueueV2" } } diff --git a/tensorflow/core/api_def/java_api/api_def_FusedPadConv2D.pbtxt b/tensorflow/core/api_def/java_api/api_def_FusedPadConv2D.pbtxt index e4836df290..e7ee10e0c5 100644 --- a/tensorflow/core/api_def/java_api/api_def_FusedPadConv2D.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_FusedPadConv2D.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "FusedPadConv2D" endpoint { - name: "nn.FusedPadConv2D" + name: "nn.FusedPadConv2d" } } diff --git a/tensorflow/core/api_def/java_api/api_def_FusedResizeAndPadConv2D.pbtxt b/tensorflow/core/api_def/java_api/api_def_FusedResizeAndPadConv2D.pbtxt index 6cd0caa088..6948fc1b87 100644 --- a/tensorflow/core/api_def/java_api/api_def_FusedResizeAndPadConv2D.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_FusedResizeAndPadConv2D.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "FusedResizeAndPadConv2D" endpoint { - name: "nn.FusedResizeAndPadConv2D" + name: "nn.FusedResizeAndPadConv2d" } } diff --git a/tensorflow/core/api_def/java_api/api_def_IRFFT.pbtxt b/tensorflow/core/api_def/java_api/api_def_IRFFT.pbtxt index d2ade5b16e..7a68b01524 100644 --- a/tensorflow/core/api_def/java_api/api_def_IRFFT.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_IRFFT.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "IRFFT" endpoint { - name: "signal.IRFFT" + name: "signal.Irfft" } } diff --git a/tensorflow/core/api_def/java_api/api_def_IRFFT2D.pbtxt b/tensorflow/core/api_def/java_api/api_def_IRFFT2D.pbtxt index 3a5a5b0c71..239ec445d0 100644 --- a/tensorflow/core/api_def/java_api/api_def_IRFFT2D.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_IRFFT2D.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "IRFFT2D" endpoint { - name: "signal.IRFFT2D" + name: "signal.Irfft2d" } } diff --git a/tensorflow/core/api_def/java_api/api_def_IRFFT3D.pbtxt b/tensorflow/core/api_def/java_api/api_def_IRFFT3D.pbtxt index fc5a5451b4..87969436b7 100644 --- a/tensorflow/core/api_def/java_api/api_def_IRFFT3D.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_IRFFT3D.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "IRFFT3D" endpoint { - name: "signal.IRFFT3D" + name: "signal.Irfft3d" } } diff --git a/tensorflow/core/api_def/java_api/api_def_LMDBReader.pbtxt b/tensorflow/core/api_def/java_api/api_def_LMDBReader.pbtxt index 226fa21953..b6cd7bdbb7 100644 --- a/tensorflow/core/api_def/java_api/api_def_LMDBReader.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_LMDBReader.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "LMDBReader" endpoint { - name: "io.LMDBReader" + name: "io.LmdbReader" } } diff --git a/tensorflow/core/api_def/java_api/api_def_ListDiff.pbtxt b/tensorflow/core/api_def/java_api/api_def_ListDiff.pbtxt index bbbd3bb3ec..aa94c958f1 100644 --- a/tensorflow/core/api_def/java_api/api_def_ListDiff.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ListDiff.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "ListDiff" + endpoint { + name: "SetDiff1d" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_MaxPool3DGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_MaxPool3DGrad.pbtxt index 7f5ab71845..ca7a7be835 100644 --- a/tensorflow/core/api_def/java_api/api_def_MaxPool3DGrad.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_MaxPool3DGrad.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "MaxPool3DGrad" endpoint { - name: "nn.MaxPool3DGrad" + name: "nn.MaxPool3dGrad" } } diff --git a/tensorflow/core/api_def/java_api/api_def_MaxPool3DGradGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_MaxPool3DGradGrad.pbtxt index 4d7211add2..c70aa3fe30 100644 --- a/tensorflow/core/api_def/java_api/api_def_MaxPool3DGradGrad.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_MaxPool3DGradGrad.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "MaxPool3DGradGrad" endpoint { - name: "nn.MaxPool3DGradGrad" + name: "nn.MaxPool3dGradGrad" } } diff --git a/tensorflow/core/api_def/java_api/api_def_PaddingFIFOQueue.pbtxt b/tensorflow/core/api_def/java_api/api_def_PaddingFIFOQueue.pbtxt index 151ab0f872..952516cfff 100644 --- a/tensorflow/core/api_def/java_api/api_def_PaddingFIFOQueue.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_PaddingFIFOQueue.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "PaddingFIFOQueue" endpoint { - name: "io.PaddingFIFOQueue" + name: "io.PaddingFifoQueue" } } diff --git a/tensorflow/core/api_def/java_api/api_def_PaddingFIFOQueueV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_PaddingFIFOQueueV2.pbtxt index 4b589a9afb..80c53e7ee9 100644 --- a/tensorflow/core/api_def/java_api/api_def_PaddingFIFOQueueV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_PaddingFIFOQueueV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "PaddingFIFOQueueV2" endpoint { - name: "io.PaddingFIFOQueueV2" + name: "io.PaddingFifoQueueV2" } } diff --git a/tensorflow/core/api_def/java_api/api_def_QuantizedConv2D.pbtxt b/tensorflow/core/api_def/java_api/api_def_QuantizedConv2D.pbtxt index 8345892762..9909157603 100644 --- a/tensorflow/core/api_def/java_api/api_def_QuantizedConv2D.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_QuantizedConv2D.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "QuantizedConv2D" endpoint { - name: "nn.QuantizedConv2D" + name: "nn.QuantizedConv2d" } } diff --git a/tensorflow/core/api_def/java_api/api_def_RFFT.pbtxt b/tensorflow/core/api_def/java_api/api_def_RFFT.pbtxt index e56a0ba30a..9576600e75 100644 --- a/tensorflow/core/api_def/java_api/api_def_RFFT.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_RFFT.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "RFFT" endpoint { - name: "signal.RFFT" + name: "signal.Rfft" } } diff --git a/tensorflow/core/api_def/java_api/api_def_RFFT2D.pbtxt b/tensorflow/core/api_def/java_api/api_def_RFFT2D.pbtxt index eb193e638d..41d638b26a 100644 --- a/tensorflow/core/api_def/java_api/api_def_RFFT2D.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_RFFT2D.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "RFFT2D" endpoint { - name: "signal.RFFT2D" + name: "signal.Rfft2d" } } diff --git a/tensorflow/core/api_def/java_api/api_def_RFFT3D.pbtxt b/tensorflow/core/api_def/java_api/api_def_RFFT3D.pbtxt index 292b957f3e..7a762d22e5 100644 --- a/tensorflow/core/api_def/java_api/api_def_RFFT3D.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_RFFT3D.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "RFFT3D" endpoint { - name: "signal.RFFT3D" + name: "signal.Rfft3d" } } diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceApplyAdagradDA.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceApplyAdagradDA.pbtxt index 904e434b8a..afddaaff57 100644 --- a/tensorflow/core/api_def/java_api/api_def_ResourceApplyAdagradDA.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ResourceApplyAdagradDA.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "ResourceApplyAdagradDA" endpoint { - name: "train.ResourceApplyAdagradDA" + name: "train.ResourceApplyAdagradDa" } } diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceApplyCenteredRMSProp.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceApplyCenteredRMSProp.pbtxt index 58027823e0..85c97b430a 100644 --- a/tensorflow/core/api_def/java_api/api_def_ResourceApplyCenteredRMSProp.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ResourceApplyCenteredRMSProp.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "ResourceApplyCenteredRMSProp" endpoint { - name: "train.ResourceApplyCenteredRMSProp" + name: "train.ResourceApplyCenteredRmsProp" } } diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceApplyRMSProp.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceApplyRMSProp.pbtxt index 74a3d77531..fa3adf759e 100644 --- a/tensorflow/core/api_def/java_api/api_def_ResourceApplyRMSProp.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ResourceApplyRMSProp.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "ResourceApplyRMSProp" endpoint { - name: "train.ResourceApplyRMSProp" + name: "train.ResourceApplyRmsProp" } } diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyAdagradDA.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyAdagradDA.pbtxt index bd66d64160..0547687d64 100644 --- a/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyAdagradDA.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyAdagradDA.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "ResourceSparseApplyAdagradDA" endpoint { - name: "train.ResourceSparseApplyAdagradDA" + name: "train.ResourceSparseApplyAdagradDa" } } diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyCenteredRMSProp.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyCenteredRMSProp.pbtxt index afda4adb0d..632b0ab4c2 100644 --- a/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyCenteredRMSProp.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyCenteredRMSProp.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "ResourceSparseApplyCenteredRMSProp" endpoint { - name: "train.ResourceSparseApplyCenteredRMSProp" + name: "train.ResourceSparseApplyCenteredRmsProp" } } diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyRMSProp.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyRMSProp.pbtxt index 3e78f6f06d..ec8910a88a 100644 --- a/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyRMSProp.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyRMSProp.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "ResourceSparseApplyRMSProp" endpoint { - name: "train.ResourceSparseApplyRMSProp" + name: "train.ResourceSparseApplyRmsProp" } } diff --git a/tensorflow/core/api_def/java_api/api_def_SparseApplyAdagradDA.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseApplyAdagradDA.pbtxt index 56d9b1c49b..950dc00dd3 100644 --- a/tensorflow/core/api_def/java_api/api_def_SparseApplyAdagradDA.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_SparseApplyAdagradDA.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "SparseApplyAdagradDA" endpoint { - name: "train.SparseApplyAdagradDA" + name: "train.SparseApplyAdagradDa" } } diff --git a/tensorflow/core/api_def/java_api/api_def_SparseApplyCenteredRMSProp.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseApplyCenteredRMSProp.pbtxt index fa35db29b0..090536f5eb 100644 --- a/tensorflow/core/api_def/java_api/api_def_SparseApplyCenteredRMSProp.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_SparseApplyCenteredRMSProp.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "SparseApplyCenteredRMSProp" endpoint { - name: "train.SparseApplyCenteredRMSProp" + name: "train.SparseApplyCenteredRmsProp" } } diff --git a/tensorflow/core/api_def/java_api/api_def_SparseApplyRMSProp.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseApplyRMSProp.pbtxt index 94646093af..3e39f4ffa5 100644 --- a/tensorflow/core/api_def/java_api/api_def_SparseApplyRMSProp.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_SparseApplyRMSProp.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "SparseApplyRMSProp" endpoint { - name: "train.SparseApplyRMSProp" + name: "train.SparseApplyRmsProp" } } diff --git a/tensorflow/core/api_def/java_api/api_def_TFRecordDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_TFRecordDataset.pbtxt index 76c077f452..f1d42edd63 100644 --- a/tensorflow/core/api_def/java_api/api_def_TFRecordDataset.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TFRecordDataset.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "TFRecordDataset" endpoint { - name: "data.TFRecordDataset" + name: "data.TfRecordDataset" } } diff --git a/tensorflow/core/api_def/java_api/api_def_TFRecordReader.pbtxt b/tensorflow/core/api_def/java_api/api_def_TFRecordReader.pbtxt index 72d02db279..c93d53fa1a 100644 --- a/tensorflow/core/api_def/java_api/api_def_TFRecordReader.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TFRecordReader.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "TFRecordReader" endpoint { - name: "io.TFRecordReader" + name: "io.TfRecordReader" } } diff --git a/tensorflow/core/api_def/java_api/api_def_TFRecordReaderV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TFRecordReaderV2.pbtxt index beb7f58862..84df23f6e2 100644 --- a/tensorflow/core/api_def/java_api/api_def_TFRecordReaderV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TFRecordReaderV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "TFRecordReaderV2" endpoint { - name: "io.TFRecordReaderV2" + name: "io.TfRecordReaderV2" } } -- GitLab From ce619f2697afd683813264ae2d068a1038acab77 Mon Sep 17 00:00:00 2001 From: Clayne Robison Date: Wed, 28 Nov 2018 08:43:33 -0700 Subject: [PATCH 049/873] [Intel MKL] Updating README.md with new links to Intel(R) Optimized Tensorflow --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 8af5370bef..02a40c49b0 100644 --- a/README.md +++ b/README.md @@ -115,7 +115,7 @@ Build Type **IBM ppc64le GPU** Nightly | [![Build Status](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_GPU_Nightly_Artifact/badge/icon)](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_GPU_Nightly_Artifact/) | [Nightly](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_GPU_Nightly_Artifact/) **IBM ppc64le GPU** Stable Release | [![Build Status](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_GPU_Release_Build/badge/icon)](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_GPU_Release_Build/) | [Release](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_GPU_Release_Build/) **Linux CPU with Intel® MKL-DNN** Nightly | [![Build Status](https://tensorflow-ci.intel.com/job/tensorflow-mkl-linux-cpu/badge/icon)](https://tensorflow-ci.intel.com/job/tensorflow-mkl-linux-cpu/) | [Nightly](https://tensorflow-ci.intel.com/job/tensorflow-mkl-build-whl-nightly/) -**Linux CPU with Intel® MKL-DNN** Python 2.7
**Linux CPU with Intel® MKL-DNN** Python 3.4
**Linux CPU with Intel® MKL-DNN** Python 3.5
**Linux CPU with Intel® MKL-DNN** Python 3.6 | [![Build Status](https://tensorflow-ci.intel.com/job/tensorflow-mkl-build-release-whl/badge/icon)](https://tensorflow-ci.intel.com/job/tensorflow-mkl-build-release-whl/lastStableBuild) | [1.11.0 py2.7](https://storage.googleapis.com/intel-optimized-tensorflow/tensorflow-1.11.0-cp27-cp27mu-linux_x86_64.whl)
[1.11.0 py3.4](https://storage.googleapis.com/intel-optimized-tensorflow/tensorflow-1.11.0-cp34-cp34m-linux_x86_64.whl)
[1.11.0 py3.5](https://storage.googleapis.com/intel-optimized-tensorflow/tensorflow-1.11.0-cp35-cp35m-linux_x86_64.whl)
[1.11.0 py3.6](https://storage.googleapis.com/intel-optimized-tensorflow/tensorflow-1.11.0-cp36-cp36m-linux_x86_64.whl) +**Linux CPU with Intel® MKL-DNN** Python 2.7
**Linux CPU with Intel® MKL-DNN** Python 3.4
**Linux CPU with Intel® MKL-DNN** Python 3.5
**Linux CPU with Intel® MKL-DNN** Python 3.6 | [![Build Status](https://tensorflow-ci.intel.com/job/tensorflow-mkl-build-release-whl/badge/icon)](https://tensorflow-ci.intel.com/job/tensorflow-mkl-build-release-whl/lastStableBuild) | [1.12.0 py2.7](https://storage.googleapis.com/intel-optimized-tensorflow/tensorflow-1.12.0-cp27-cp27mu-linux_x86_64.whl)
[1.12.0 py3.4](https://storage.googleapis.com/intel-optimized-tensorflow/tensorflow-1.12.0-cp34-cp34m-linux_x86_64.whl)
[1.12.0 py3.5](https://storage.googleapis.com/intel-optimized-tensorflow/tensorflow-1.12.0-cp35-cp35m-linux_x86_64.whl)
[1.12.0 py3.6](https://storage.googleapis.com/intel-optimized-tensorflow/tensorflow-1.12.0-cp36-cp36m-linux_x86_64.whl) ## For more information * [TensorFlow Website](https://www.tensorflow.org) -- GitLab From 046d08eff5cad0231c738dd362c85abf0877aa1d Mon Sep 17 00:00:00 2001 From: Karl Lessard Date: Wed, 28 Nov 2018 23:02:18 -0500 Subject: [PATCH 050/873] Only keep latest version available for each ops, skipping others. --- tensorflow/core/api_def/java_api/api_def_AccumulateNV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_Add.pbtxt | 4 +--- tensorflow/core/api_def/java_api/api_def_AddV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_AdjustContrast.pbtxt | 4 +--- .../core/api_def/java_api/api_def_AdjustContrastv2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_ApplyFtrl.pbtxt | 4 +--- tensorflow/core/api_def/java_api/api_def_ApplyFtrlV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_AudioSummary.pbtxt | 4 +--- tensorflow/core/api_def/java_api/api_def_AudioSummaryV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_BatchDataset.pbtxt | 4 +--- tensorflow/core/api_def/java_api/api_def_BatchDatasetV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_BatchSelfAdjointEig.pbtxt | 4 +--- .../core/api_def/java_api/api_def_BatchSelfAdjointEigV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_BiasAddV1.pbtxt | 4 +--- tensorflow/core/api_def/java_api/api_def_Concat.pbtxt | 1 + tensorflow/core/api_def/java_api/api_def_ConcatV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_Conv3DBackpropFilter.pbtxt | 4 +--- .../api_def/java_api/api_def_Conv3DBackpropFilterV2.pbtxt | 2 +- .../core/api_def/java_api/api_def_Conv3DBackpropInput.pbtxt | 4 +--- .../core/api_def/java_api/api_def_Conv3DBackpropInputV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_CudnnRNN.pbtxt | 4 +--- .../core/api_def/java_api/api_def_CudnnRNNBackprop.pbtxt | 4 +--- .../core/api_def/java_api/api_def_CudnnRNNBackpropV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_CudnnRNNV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_DecodeProtoV2.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_FIFOQueue.pbtxt | 4 +--- tensorflow/core/api_def/java_api/api_def_FIFOQueueV2.pbtxt | 2 +- .../api_def/java_api/api_def_FixedLengthRecordDataset.pbtxt | 4 +--- .../api_def/java_api/api_def_FixedLengthRecordDatasetV2.pbtxt | 2 +- .../api_def/java_api/api_def_FixedLengthRecordReader.pbtxt | 4 +--- .../api_def/java_api/api_def_FixedLengthRecordReaderV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_FusedBatchNorm.pbtxt | 4 +--- .../core/api_def/java_api/api_def_FusedBatchNormGrad.pbtxt | 4 +--- .../core/api_def/java_api/api_def_FusedBatchNormGradV2.pbtxt | 2 +- .../core/api_def/java_api/api_def_FusedBatchNormV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_Gather.pbtxt | 1 + tensorflow/core/api_def/java_api/api_def_GatherV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_GetSessionHandle.pbtxt | 1 + .../core/api_def/java_api/api_def_GetSessionHandleV2.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_HashTable.pbtxt | 1 + tensorflow/core/api_def/java_api/api_def_HashTableV2.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_IdentityReader.pbtxt | 4 +--- .../core/api_def/java_api/api_def_IdentityReaderV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_InTopK.pbtxt | 4 +--- tensorflow/core/api_def/java_api/api_def_InTopKV2.pbtxt | 2 +- .../core/api_def/java_api/api_def_InitializeTable.pbtxt | 1 + .../java_api/api_def_InitializeTableFromTextFile.pbtxt | 1 + .../java_api/api_def_InitializeTableFromTextFileV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_InitializeTableV2.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Iterator.pbtxt | 4 +--- .../api_def/java_api/api_def_IteratorFromStringHandle.pbtxt | 4 +--- .../api_def/java_api/api_def_IteratorFromStringHandleV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_IteratorV2.pbtxt | 2 +- .../core/api_def/java_api/api_def_LookupTableExport.pbtxt | 1 + .../core/api_def/java_api/api_def_LookupTableExportV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_LookupTableFind.pbtxt | 1 + .../core/api_def/java_api/api_def_LookupTableFindV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_LookupTableImport.pbtxt | 1 + .../core/api_def/java_api/api_def_LookupTableImportV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_LookupTableInsert.pbtxt | 1 + .../core/api_def/java_api/api_def_LookupTableInsertV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_LookupTableRemoveV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_LookupTableSize.pbtxt | 1 + .../core/api_def/java_api/api_def_LookupTableSizeV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_MapAndBatchDataset.pbtxt | 4 +--- .../core/api_def/java_api/api_def_MapAndBatchDatasetV2.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_MaxPool.pbtxt | 4 +--- tensorflow/core/api_def/java_api/api_def_MaxPoolGrad.pbtxt | 4 +--- .../core/api_def/java_api/api_def_MaxPoolGradGrad.pbtxt | 4 +--- .../core/api_def/java_api/api_def_MaxPoolGradGradV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_MaxPoolGradV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_MaxPoolV2.pbtxt | 2 +- .../core/api_def/java_api/api_def_MutableDenseHashTable.pbtxt | 1 + .../api_def/java_api/api_def_MutableDenseHashTableV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_MutableHashTable.pbtxt | 1 + .../api_def/java_api/api_def_MutableHashTableOfTensors.pbtxt | 1 + .../java_api/api_def_MutableHashTableOfTensorsV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_MutableHashTableV2.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_MutexV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_NonMaxSuppression.pbtxt | 4 +--- .../core/api_def/java_api/api_def_NonMaxSuppressionV2.pbtxt | 4 +--- .../core/api_def/java_api/api_def_NonMaxSuppressionV3.pbtxt | 4 +--- .../core/api_def/java_api/api_def_NonMaxSuppressionV4.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_Pad.pbtxt | 1 + tensorflow/core/api_def/java_api/api_def_PadV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_PaddedBatchDataset.pbtxt | 4 +--- .../core/api_def/java_api/api_def_PaddedBatchDatasetV2.pbtxt | 2 +- .../core/api_def/java_api/api_def_PaddingFIFOQueue.pbtxt | 4 +--- .../core/api_def/java_api/api_def_PaddingFIFOQueueV2.pbtxt | 2 +- .../api_def/java_api/api_def_ParallelInterleaveDataset.pbtxt | 4 +--- .../java_api/api_def_ParallelInterleaveDatasetV2.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Placeholder.pbtxt | 1 + tensorflow/core/api_def/java_api/api_def_PlaceholderV2.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Print.pbtxt | 1 + tensorflow/core/api_def/java_api/api_def_PrintV2.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_PriorityQueue.pbtxt | 4 +--- .../core/api_def/java_api/api_def_PriorityQueueV2.pbtxt | 2 +- .../core/api_def/java_api/api_def_QuantizeAndDequantize.pbtxt | 4 +--- .../api_def/java_api/api_def_QuantizeAndDequantizeV2.pbtxt | 4 +--- .../api_def/java_api/api_def_QuantizeAndDequantizeV3.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_QuantizeV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_QueueClose.pbtxt | 4 +--- tensorflow/core/api_def/java_api/api_def_QueueCloseV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_QueueDequeue.pbtxt | 4 +--- .../core/api_def/java_api/api_def_QueueDequeueMany.pbtxt | 4 +--- .../core/api_def/java_api/api_def_QueueDequeueManyV2.pbtxt | 2 +- .../core/api_def/java_api/api_def_QueueDequeueUpTo.pbtxt | 4 +--- .../core/api_def/java_api/api_def_QueueDequeueUpToV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_QueueDequeueV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_QueueEnqueue.pbtxt | 4 +--- .../core/api_def/java_api/api_def_QueueEnqueueMany.pbtxt | 4 +--- .../core/api_def/java_api/api_def_QueueEnqueueManyV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_QueueEnqueueV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_QueueIsClosed.pbtxt | 4 +--- .../core/api_def/java_api/api_def_QueueIsClosedV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_QueueSize.pbtxt | 4 +--- tensorflow/core/api_def/java_api/api_def_QueueSizeV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_RandomPoisson.pbtxt | 4 +--- .../core/api_def/java_api/api_def_RandomPoissonV2.pbtxt | 2 +- .../core/api_def/java_api/api_def_RandomShuffleQueue.pbtxt | 4 +--- .../core/api_def/java_api/api_def_RandomShuffleQueueV2.pbtxt | 2 +- .../api_def/java_api/api_def_ReaderNumRecordsProduced.pbtxt | 4 +--- .../api_def/java_api/api_def_ReaderNumRecordsProducedV2.pbtxt | 2 +- .../java_api/api_def_ReaderNumWorkUnitsCompleted.pbtxt | 4 +--- .../java_api/api_def_ReaderNumWorkUnitsCompletedV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_ReaderRead.pbtxt | 4 +--- tensorflow/core/api_def/java_api/api_def_ReaderReadUpTo.pbtxt | 4 +--- .../core/api_def/java_api/api_def_ReaderReadUpToV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_ReaderReadV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_ReaderReset.pbtxt | 4 +--- tensorflow/core/api_def/java_api/api_def_ReaderResetV2.pbtxt | 2 +- .../core/api_def/java_api/api_def_ReaderRestoreState.pbtxt | 4 +--- .../core/api_def/java_api/api_def_ReaderRestoreStateV2.pbtxt | 2 +- .../core/api_def/java_api/api_def_ReaderSerializeState.pbtxt | 4 +--- .../api_def/java_api/api_def_ReaderSerializeStateV2.pbtxt | 2 +- .../core/api_def/java_api/api_def_ResourceApplyFtrl.pbtxt | 4 +--- .../core/api_def/java_api/api_def_ResourceApplyFtrlV2.pbtxt | 2 +- .../api_def/java_api/api_def_ResourceSparseApplyFtrl.pbtxt | 4 +--- .../api_def/java_api/api_def_ResourceSparseApplyFtrlV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_Restore.pbtxt | 4 +--- tensorflow/core/api_def/java_api/api_def_RestoreV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_Reverse.pbtxt | 1 + .../api_def/java_api/api_def_SampleDistortedBoundingBox.pbtxt | 4 +--- .../java_api/api_def_SampleDistortedBoundingBoxV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_Save.pbtxt | 4 +--- tensorflow/core/api_def/java_api/api_def_SaveV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_SdcaOptimizer.pbtxt | 4 +--- .../core/api_def/java_api/api_def_SdcaOptimizerV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_SelfAdjointEig.pbtxt | 4 +--- .../core/api_def/java_api/api_def_SelfAdjointEigV2.pbtxt | 2 +- .../core/api_def/java_api/api_def_SparseApplyFtrl.pbtxt | 4 +--- .../core/api_def/java_api/api_def_SparseApplyFtrlV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_Stack.pbtxt | 1 + tensorflow/core/api_def/java_api/api_def_StackClose.pbtxt | 1 + tensorflow/core/api_def/java_api/api_def_StackCloseV2.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_StackPop.pbtxt | 1 + tensorflow/core/api_def/java_api/api_def_StackPopV2.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_StackPush.pbtxt | 1 + tensorflow/core/api_def/java_api/api_def_StackPushV2.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_StackV2.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_StringSplit.pbtxt | 4 +--- tensorflow/core/api_def/java_api/api_def_StringSplitV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_TFRecordReader.pbtxt | 4 +--- .../core/api_def/java_api/api_def_TFRecordReaderV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_TensorArray.pbtxt | 1 + .../core/api_def/java_api/api_def_TensorArrayClose.pbtxt | 1 + .../core/api_def/java_api/api_def_TensorArrayCloseV2.pbtxt | 1 + .../core/api_def/java_api/api_def_TensorArrayCloseV3.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayConcat.pbtxt | 1 + .../core/api_def/java_api/api_def_TensorArrayConcatV2.pbtxt | 1 + .../core/api_def/java_api/api_def_TensorArrayConcatV3.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayGather.pbtxt | 1 + .../core/api_def/java_api/api_def_TensorArrayGatherV2.pbtxt | 1 + .../core/api_def/java_api/api_def_TensorArrayGatherV3.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayGrad.pbtxt | 1 + .../core/api_def/java_api/api_def_TensorArrayGradV2.pbtxt | 1 + .../core/api_def/java_api/api_def_TensorArrayGradV3.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayRead.pbtxt | 1 + .../core/api_def/java_api/api_def_TensorArrayReadV2.pbtxt | 1 + .../core/api_def/java_api/api_def_TensorArrayReadV3.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayScatter.pbtxt | 1 + .../core/api_def/java_api/api_def_TensorArrayScatterV2.pbtxt | 1 + .../core/api_def/java_api/api_def_TensorArrayScatterV3.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArraySize.pbtxt | 1 + .../core/api_def/java_api/api_def_TensorArraySizeV2.pbtxt | 1 + .../core/api_def/java_api/api_def_TensorArraySizeV3.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArraySplit.pbtxt | 1 + .../core/api_def/java_api/api_def_TensorArraySplitV2.pbtxt | 1 + .../core/api_def/java_api/api_def_TensorArraySplitV3.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_TensorArrayV2.pbtxt | 1 + tensorflow/core/api_def/java_api/api_def_TensorArrayV3.pbtxt | 3 +++ .../core/api_def/java_api/api_def_TensorArrayWrite.pbtxt | 1 + .../core/api_def/java_api/api_def_TensorArrayWriteV2.pbtxt | 1 + .../core/api_def/java_api/api_def_TensorArrayWriteV3.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_TensorSummary.pbtxt | 4 +--- .../core/api_def/java_api/api_def_TensorSummaryV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_TextLineReader.pbtxt | 4 +--- .../core/api_def/java_api/api_def_TextLineReaderV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_TopK.pbtxt | 4 +--- tensorflow/core/api_def/java_api/api_def_TopKV2.pbtxt | 2 +- tensorflow/core/api_def/java_api/api_def_Unique.pbtxt | 1 + tensorflow/core/api_def/java_api/api_def_UniqueV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_UniqueWithCounts.pbtxt | 1 + .../core/api_def/java_api/api_def_UniqueWithCountsV2.pbtxt | 3 +++ tensorflow/core/api_def/java_api/api_def_Variable.pbtxt | 1 + tensorflow/core/api_def/java_api/api_def_VariableV2.pbtxt | 3 +++ .../core/api_def/java_api/api_def_WholeFileReader.pbtxt | 4 +--- .../core/api_def/java_api/api_def_WholeFileReaderV2.pbtxt | 2 +- 208 files changed, 288 insertions(+), 251 deletions(-) diff --git a/tensorflow/core/api_def/java_api/api_def_AccumulateNV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_AccumulateNV2.pbtxt index 09e7a155e0..0c7a080c11 100644 --- a/tensorflow/core/api_def/java_api/api_def_AccumulateNV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_AccumulateNV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "AccumulateNV2" endpoint { - name: "math.AccumulateNV2" + name: "math.AccumulateN" } } diff --git a/tensorflow/core/api_def/java_api/api_def_Add.pbtxt b/tensorflow/core/api_def/java_api/api_def_Add.pbtxt index 4f78ccc9ea..b082b055bf 100644 --- a/tensorflow/core/api_def/java_api/api_def_Add.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Add.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "Add" - endpoint { - name: "math.Add" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_AddV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_AddV2.pbtxt index f42a2add07..863ec4f33c 100644 --- a/tensorflow/core/api_def/java_api/api_def_AddV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_AddV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "AddV2" endpoint { - name: "math.AddV2" + name: "math.Add" } } diff --git a/tensorflow/core/api_def/java_api/api_def_AdjustContrast.pbtxt b/tensorflow/core/api_def/java_api/api_def_AdjustContrast.pbtxt index ff49aec2db..daad141027 100644 --- a/tensorflow/core/api_def/java_api/api_def_AdjustContrast.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_AdjustContrast.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "AdjustContrast" - endpoint { - name: "image.AdjustContrast" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_AdjustContrastv2.pbtxt b/tensorflow/core/api_def/java_api/api_def_AdjustContrastv2.pbtxt index dd6647b137..81f565c1d5 100644 --- a/tensorflow/core/api_def/java_api/api_def_AdjustContrastv2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_AdjustContrastv2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "AdjustContrastv2" endpoint { - name: "image.AdjustContrastv2" + name: "image.AdjustContrast" } } diff --git a/tensorflow/core/api_def/java_api/api_def_ApplyFtrl.pbtxt b/tensorflow/core/api_def/java_api/api_def_ApplyFtrl.pbtxt index 388e39c6ba..176de19a9a 100644 --- a/tensorflow/core/api_def/java_api/api_def_ApplyFtrl.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ApplyFtrl.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "ApplyFtrl" - endpoint { - name: "train.ApplyFtrl" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_ApplyFtrlV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_ApplyFtrlV2.pbtxt index 8463ad163b..da0fc8fcbf 100644 --- a/tensorflow/core/api_def/java_api/api_def_ApplyFtrlV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ApplyFtrlV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "ApplyFtrlV2" endpoint { - name: "train.ApplyFtrlV2" + name: "train.ApplyFtrl" } } diff --git a/tensorflow/core/api_def/java_api/api_def_AudioSummary.pbtxt b/tensorflow/core/api_def/java_api/api_def_AudioSummary.pbtxt index 026ec79dd9..13d30de29d 100644 --- a/tensorflow/core/api_def/java_api/api_def_AudioSummary.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_AudioSummary.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "AudioSummary" - endpoint { - name: "summary.AudioSummary" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_AudioSummaryV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_AudioSummaryV2.pbtxt index 07d24ec3a3..e4eda8b09a 100644 --- a/tensorflow/core/api_def/java_api/api_def_AudioSummaryV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_AudioSummaryV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "AudioSummaryV2" endpoint { - name: "summary.AudioSummaryV2" + name: "summary.AudioSummary" } } diff --git a/tensorflow/core/api_def/java_api/api_def_BatchDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchDataset.pbtxt index fe47605f37..0bb7298ba9 100644 --- a/tensorflow/core/api_def/java_api/api_def_BatchDataset.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_BatchDataset.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "BatchDataset" - endpoint { - name: "data.BatchDataset" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_BatchDatasetV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchDatasetV2.pbtxt index 079efe146f..cd81b0b1cf 100644 --- a/tensorflow/core/api_def/java_api/api_def_BatchDatasetV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_BatchDatasetV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "BatchDatasetV2" + endpoint { + name: "data.BatchDataset" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_BatchSelfAdjointEig.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchSelfAdjointEig.pbtxt index 58d60fa962..517030fd69 100644 --- a/tensorflow/core/api_def/java_api/api_def_BatchSelfAdjointEig.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_BatchSelfAdjointEig.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "BatchSelfAdjointEig" - endpoint { - name: "linalg.BatchSelfAdjointEig" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_BatchSelfAdjointEigV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_BatchSelfAdjointEigV2.pbtxt index fb18b2a000..9c97344390 100644 --- a/tensorflow/core/api_def/java_api/api_def_BatchSelfAdjointEigV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_BatchSelfAdjointEigV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "BatchSelfAdjointEigV2" endpoint { - name: "linalg.BatchSelfAdjointEigV2" + name: "linalg.BatchSelfAdjointEig" } } diff --git a/tensorflow/core/api_def/java_api/api_def_BiasAddV1.pbtxt b/tensorflow/core/api_def/java_api/api_def_BiasAddV1.pbtxt index 76ae8ec8ff..651c434e64 100644 --- a/tensorflow/core/api_def/java_api/api_def_BiasAddV1.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_BiasAddV1.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "BiasAddV1" - endpoint { - name: "nn.BiasAddV1" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_Concat.pbtxt b/tensorflow/core/api_def/java_api/api_def_Concat.pbtxt index b13eb820ec..e2fc7eef88 100644 --- a/tensorflow/core/api_def/java_api/api_def_Concat.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Concat.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "Concat" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_ConcatV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_ConcatV2.pbtxt index a600c31ed9..7035796981 100644 --- a/tensorflow/core/api_def/java_api/api_def_ConcatV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ConcatV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "ConcatV2" + endpoint { + name: "Concat" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropFilter.pbtxt b/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropFilter.pbtxt index f66034aec1..2e5f6c99d5 100644 --- a/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropFilter.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropFilter.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "Conv3DBackpropFilter" - endpoint { - name: "nn.Conv3dBackpropFilter" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropFilterV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropFilterV2.pbtxt index 13db85238f..0643cc14a9 100644 --- a/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropFilterV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropFilterV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "Conv3DBackpropFilterV2" endpoint { - name: "nn.Conv3dBackpropFilterV2" + name: "nn.Conv3dBackpropFilter" } } diff --git a/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropInput.pbtxt b/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropInput.pbtxt index fdb2a9d66c..cbb2c9f136 100644 --- a/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropInput.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropInput.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "Conv3DBackpropInput" - endpoint { - name: "nn.Conv3dBackpropInput" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropInputV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropInputV2.pbtxt index 7f25b7b9af..33c8f5a3ce 100644 --- a/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropInputV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Conv3DBackpropInputV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "Conv3DBackpropInputV2" endpoint { - name: "nn.Conv3dBackpropInputV2" + name: "nn.Conv3dBackpropInput" } } diff --git a/tensorflow/core/api_def/java_api/api_def_CudnnRNN.pbtxt b/tensorflow/core/api_def/java_api/api_def_CudnnRNN.pbtxt index 4cd8e4fa0f..7e88d20713 100644 --- a/tensorflow/core/api_def/java_api/api_def_CudnnRNN.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_CudnnRNN.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "CudnnRNN" - endpoint { - name: "nn.CudnnRnn" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_CudnnRNNBackprop.pbtxt b/tensorflow/core/api_def/java_api/api_def_CudnnRNNBackprop.pbtxt index b2a222e03a..9c9fc5f029 100644 --- a/tensorflow/core/api_def/java_api/api_def_CudnnRNNBackprop.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_CudnnRNNBackprop.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "CudnnRNNBackprop" - endpoint { - name: "nn.CudnnRnnBackprop" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_CudnnRNNBackpropV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_CudnnRNNBackpropV2.pbtxt index 880a3b2bc4..c2e7ebc27d 100644 --- a/tensorflow/core/api_def/java_api/api_def_CudnnRNNBackpropV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_CudnnRNNBackpropV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "CudnnRNNBackpropV2" endpoint { - name: "nn.CudnnRnnBackpropV2" + name: "nn.CudnnRnnBackprop" } } diff --git a/tensorflow/core/api_def/java_api/api_def_CudnnRNNV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_CudnnRNNV2.pbtxt index 7086d3c9d2..e6dd5f42fc 100644 --- a/tensorflow/core/api_def/java_api/api_def_CudnnRNNV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_CudnnRNNV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "CudnnRNNV2" endpoint { - name: "nn.CudnnRnnV2" + name: "nn.CudnnRnn" } } diff --git a/tensorflow/core/api_def/java_api/api_def_DecodeProtoV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_DecodeProtoV2.pbtxt index ff6531a3ac..4ba118cb0e 100644 --- a/tensorflow/core/api_def/java_api/api_def_DecodeProtoV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_DecodeProtoV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "DecodeProtoV2" + endpoint { + name: "DecodeProto" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_FIFOQueue.pbtxt b/tensorflow/core/api_def/java_api/api_def_FIFOQueue.pbtxt index 18c3ed4979..e5b2f73c55 100644 --- a/tensorflow/core/api_def/java_api/api_def_FIFOQueue.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_FIFOQueue.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "FIFOQueue" - endpoint { - name: "io.FifoQueue" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_FIFOQueueV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_FIFOQueueV2.pbtxt index f892d7291d..c0861a6e8d 100644 --- a/tensorflow/core/api_def/java_api/api_def_FIFOQueueV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_FIFOQueueV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "FIFOQueueV2" endpoint { - name: "io.FifoQueueV2" + name: "io.FifoQueue" } } diff --git a/tensorflow/core/api_def/java_api/api_def_FixedLengthRecordDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_FixedLengthRecordDataset.pbtxt index 8f38364f85..d4f23d94c0 100644 --- a/tensorflow/core/api_def/java_api/api_def_FixedLengthRecordDataset.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_FixedLengthRecordDataset.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "FixedLengthRecordDataset" - endpoint { - name: "data.FixedLengthRecordDataset" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_FixedLengthRecordDatasetV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_FixedLengthRecordDatasetV2.pbtxt index 723248019d..b8012bbe16 100644 --- a/tensorflow/core/api_def/java_api/api_def_FixedLengthRecordDatasetV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_FixedLengthRecordDatasetV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "FixedLengthRecordDatasetV2" endpoint { - name: "data.FixedLengthRecordDatasetV2" + name: "data.FixedLengthRecordDataset" } } diff --git a/tensorflow/core/api_def/java_api/api_def_FixedLengthRecordReader.pbtxt b/tensorflow/core/api_def/java_api/api_def_FixedLengthRecordReader.pbtxt index 295526d074..f76cd49456 100644 --- a/tensorflow/core/api_def/java_api/api_def_FixedLengthRecordReader.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_FixedLengthRecordReader.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "FixedLengthRecordReader" - endpoint { - name: "io.FixedLengthRecordReader" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_FixedLengthRecordReaderV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_FixedLengthRecordReaderV2.pbtxt index 0cfefe2075..f897c21365 100644 --- a/tensorflow/core/api_def/java_api/api_def_FixedLengthRecordReaderV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_FixedLengthRecordReaderV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "FixedLengthRecordReaderV2" endpoint { - name: "io.FixedLengthRecordReaderV2" + name: "io.FixedLengthRecordReader" } } diff --git a/tensorflow/core/api_def/java_api/api_def_FusedBatchNorm.pbtxt b/tensorflow/core/api_def/java_api/api_def_FusedBatchNorm.pbtxt index 058c82c177..9d6166fe81 100644 --- a/tensorflow/core/api_def/java_api/api_def_FusedBatchNorm.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_FusedBatchNorm.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "FusedBatchNorm" - endpoint { - name: "nn.FusedBatchNorm" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_FusedBatchNormGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_FusedBatchNormGrad.pbtxt index 69baf2a8e3..5e1d066d8d 100644 --- a/tensorflow/core/api_def/java_api/api_def_FusedBatchNormGrad.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_FusedBatchNormGrad.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "FusedBatchNormGrad" - endpoint { - name: "nn.FusedBatchNormGrad" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_FusedBatchNormGradV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_FusedBatchNormGradV2.pbtxt index 81da6f4bb2..8f333c91f4 100644 --- a/tensorflow/core/api_def/java_api/api_def_FusedBatchNormGradV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_FusedBatchNormGradV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "FusedBatchNormGradV2" endpoint { - name: "nn.FusedBatchNormGradV2" + name: "nn.FusedBatchNormGrad" } } diff --git a/tensorflow/core/api_def/java_api/api_def_FusedBatchNormV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_FusedBatchNormV2.pbtxt index e81d78ef5c..8a4e76c949 100644 --- a/tensorflow/core/api_def/java_api/api_def_FusedBatchNormV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_FusedBatchNormV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "FusedBatchNormV2" endpoint { - name: "nn.FusedBatchNormV2" + name: "nn.FusedBatchNorm" } } diff --git a/tensorflow/core/api_def/java_api/api_def_Gather.pbtxt b/tensorflow/core/api_def/java_api/api_def_Gather.pbtxt index 6848641714..5c4ccda48b 100644 --- a/tensorflow/core/api_def/java_api/api_def_Gather.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Gather.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "Gather" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_GatherV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_GatherV2.pbtxt index f5b2cb92f6..0927e77a96 100644 --- a/tensorflow/core/api_def/java_api/api_def_GatherV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_GatherV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "GatherV2" + endpoint { + name: "Gather" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_GetSessionHandle.pbtxt b/tensorflow/core/api_def/java_api/api_def_GetSessionHandle.pbtxt index 84b14a3335..0ee6fe18a2 100644 --- a/tensorflow/core/api_def/java_api/api_def_GetSessionHandle.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_GetSessionHandle.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "GetSessionHandle" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_GetSessionHandleV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_GetSessionHandleV2.pbtxt index 28488ac79a..ba89942d77 100644 --- a/tensorflow/core/api_def/java_api/api_def_GetSessionHandleV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_GetSessionHandleV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "GetSessionHandleV2" + endpoint { + name: "GetSessionHandle" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_HashTable.pbtxt b/tensorflow/core/api_def/java_api/api_def_HashTable.pbtxt index f733b277ea..d48c2224f6 100644 --- a/tensorflow/core/api_def/java_api/api_def_HashTable.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_HashTable.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "HashTable" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_HashTableV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_HashTableV2.pbtxt index 74fda0380a..38cc5818d3 100644 --- a/tensorflow/core/api_def/java_api/api_def_HashTableV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_HashTableV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "HashTableV2" + endpoint { + name: "HashTable" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_IdentityReader.pbtxt b/tensorflow/core/api_def/java_api/api_def_IdentityReader.pbtxt index 1e1314213d..42fe85a567 100644 --- a/tensorflow/core/api_def/java_api/api_def_IdentityReader.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_IdentityReader.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "IdentityReader" - endpoint { - name: "io.IdentityReader" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_IdentityReaderV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_IdentityReaderV2.pbtxt index 2973807a9c..8081ac26b3 100644 --- a/tensorflow/core/api_def/java_api/api_def_IdentityReaderV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_IdentityReaderV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "IdentityReaderV2" endpoint { - name: "io.IdentityReaderV2" + name: "io.IdentityReader" } } diff --git a/tensorflow/core/api_def/java_api/api_def_InTopK.pbtxt b/tensorflow/core/api_def/java_api/api_def_InTopK.pbtxt index 1cca92e0ea..bf90fd0f81 100644 --- a/tensorflow/core/api_def/java_api/api_def_InTopK.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_InTopK.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "InTopK" - endpoint { - name: "nn.InTopK" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_InTopKV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_InTopKV2.pbtxt index 3dc9bff289..400ee71462 100644 --- a/tensorflow/core/api_def/java_api/api_def_InTopKV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_InTopKV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "InTopKV2" endpoint { - name: "nn.InTopKV2" + name: "nn.InTopK" } } diff --git a/tensorflow/core/api_def/java_api/api_def_InitializeTable.pbtxt b/tensorflow/core/api_def/java_api/api_def_InitializeTable.pbtxt index 49496f29a0..30e3d66bfe 100644 --- a/tensorflow/core/api_def/java_api/api_def_InitializeTable.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_InitializeTable.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "InitializeTable" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_InitializeTableFromTextFile.pbtxt b/tensorflow/core/api_def/java_api/api_def_InitializeTableFromTextFile.pbtxt index 8cc206b0f0..786e22cd47 100644 --- a/tensorflow/core/api_def/java_api/api_def_InitializeTableFromTextFile.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_InitializeTableFromTextFile.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "InitializeTableFromTextFile" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_InitializeTableFromTextFileV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_InitializeTableFromTextFileV2.pbtxt index 2a665348a7..9a4f702201 100644 --- a/tensorflow/core/api_def/java_api/api_def_InitializeTableFromTextFileV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_InitializeTableFromTextFileV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "InitializeTableFromTextFileV2" + endpoint { + name: "InitializeTableFromTextFile" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_InitializeTableV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_InitializeTableV2.pbtxt index 8d9bec2f2f..d7a9a813d0 100644 --- a/tensorflow/core/api_def/java_api/api_def_InitializeTableV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_InitializeTableV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "InitializeTableV2" + endpoint { + name: "InitializeTable" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_Iterator.pbtxt b/tensorflow/core/api_def/java_api/api_def_Iterator.pbtxt index 7d4b5e6328..0f4d9967c3 100644 --- a/tensorflow/core/api_def/java_api/api_def_Iterator.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Iterator.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "Iterator" - endpoint { - name: "data.Iterator" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_IteratorFromStringHandle.pbtxt b/tensorflow/core/api_def/java_api/api_def_IteratorFromStringHandle.pbtxt index b2fe71a0c1..0a4e443cde 100644 --- a/tensorflow/core/api_def/java_api/api_def_IteratorFromStringHandle.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_IteratorFromStringHandle.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "IteratorFromStringHandle" - endpoint { - name: "data.IteratorFromStringHandle" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_IteratorFromStringHandleV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_IteratorFromStringHandleV2.pbtxt index c77959f34c..86745a3a56 100644 --- a/tensorflow/core/api_def/java_api/api_def_IteratorFromStringHandleV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_IteratorFromStringHandleV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "IteratorFromStringHandleV2" endpoint { - name: "data.IteratorFromStringHandleV2" + name: "data.IteratorFromStringHandle" } } diff --git a/tensorflow/core/api_def/java_api/api_def_IteratorV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_IteratorV2.pbtxt index 7892b096fe..87720d441b 100644 --- a/tensorflow/core/api_def/java_api/api_def_IteratorV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_IteratorV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "IteratorV2" endpoint { - name: "data.IteratorV2" + name: "data.Iterator" } } diff --git a/tensorflow/core/api_def/java_api/api_def_LookupTableExport.pbtxt b/tensorflow/core/api_def/java_api/api_def_LookupTableExport.pbtxt index 49637da997..29885222a4 100644 --- a/tensorflow/core/api_def/java_api/api_def_LookupTableExport.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_LookupTableExport.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "LookupTableExport" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_LookupTableExportV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_LookupTableExportV2.pbtxt index fce8a6e3cc..d780f2a21d 100644 --- a/tensorflow/core/api_def/java_api/api_def_LookupTableExportV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_LookupTableExportV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "LookupTableExportV2" + endpoint { + name: "LookupTableExport" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_LookupTableFind.pbtxt b/tensorflow/core/api_def/java_api/api_def_LookupTableFind.pbtxt index f8637744ae..23f7facaa2 100644 --- a/tensorflow/core/api_def/java_api/api_def_LookupTableFind.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_LookupTableFind.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "LookupTableFind" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_LookupTableFindV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_LookupTableFindV2.pbtxt index cb78732e9c..2247547b62 100644 --- a/tensorflow/core/api_def/java_api/api_def_LookupTableFindV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_LookupTableFindV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "LookupTableFindV2" + endpoint { + name: "LookupTableFind" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_LookupTableImport.pbtxt b/tensorflow/core/api_def/java_api/api_def_LookupTableImport.pbtxt index 4cf7971f56..f87ea9c073 100644 --- a/tensorflow/core/api_def/java_api/api_def_LookupTableImport.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_LookupTableImport.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "LookupTableImport" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_LookupTableImportV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_LookupTableImportV2.pbtxt index 3216346961..a39cffa12d 100644 --- a/tensorflow/core/api_def/java_api/api_def_LookupTableImportV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_LookupTableImportV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "LookupTableImportV2" + endpoint { + name: "LookupTableImport" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_LookupTableInsert.pbtxt b/tensorflow/core/api_def/java_api/api_def_LookupTableInsert.pbtxt index 51fe22ba60..a45b3f52a5 100644 --- a/tensorflow/core/api_def/java_api/api_def_LookupTableInsert.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_LookupTableInsert.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "LookupTableInsert" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_LookupTableInsertV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_LookupTableInsertV2.pbtxt index 429bf25b0a..037b743b6b 100644 --- a/tensorflow/core/api_def/java_api/api_def_LookupTableInsertV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_LookupTableInsertV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "LookupTableInsertV2" + endpoint { + name: "LookupTableInsert" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_LookupTableRemoveV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_LookupTableRemoveV2.pbtxt index 274dd236e3..61f6d8db36 100644 --- a/tensorflow/core/api_def/java_api/api_def_LookupTableRemoveV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_LookupTableRemoveV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "LookupTableRemoveV2" + endpoint { + name: "LookupTableRemove" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_LookupTableSize.pbtxt b/tensorflow/core/api_def/java_api/api_def_LookupTableSize.pbtxt index 70329e9e90..391dc5dfad 100644 --- a/tensorflow/core/api_def/java_api/api_def_LookupTableSize.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_LookupTableSize.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "LookupTableSize" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_LookupTableSizeV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_LookupTableSizeV2.pbtxt index d5681c3fa0..ad646e25a6 100644 --- a/tensorflow/core/api_def/java_api/api_def_LookupTableSizeV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_LookupTableSizeV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "LookupTableSizeV2" + endpoint { + name: "LookupTableSize" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_MapAndBatchDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_MapAndBatchDataset.pbtxt index b8f23009d7..cb96bf63d8 100644 --- a/tensorflow/core/api_def/java_api/api_def_MapAndBatchDataset.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_MapAndBatchDataset.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "MapAndBatchDataset" - endpoint { - name: "data.MapAndBatchDataset" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_MapAndBatchDatasetV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_MapAndBatchDatasetV2.pbtxt index a0bc306c52..b29c21888f 100644 --- a/tensorflow/core/api_def/java_api/api_def_MapAndBatchDatasetV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_MapAndBatchDatasetV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "MapAndBatchDatasetV2" + endpoint { + name: "data.MapAndBatchDataset" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_MaxPool.pbtxt b/tensorflow/core/api_def/java_api/api_def_MaxPool.pbtxt index 17c17b5699..5ebc9e6a6f 100644 --- a/tensorflow/core/api_def/java_api/api_def_MaxPool.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_MaxPool.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "MaxPool" - endpoint { - name: "nn.MaxPool" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_MaxPoolGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_MaxPoolGrad.pbtxt index c8b783ee7a..9ad85fa08e 100644 --- a/tensorflow/core/api_def/java_api/api_def_MaxPoolGrad.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_MaxPoolGrad.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "MaxPoolGrad" - endpoint { - name: "nn.MaxPoolGrad" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_MaxPoolGradGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_MaxPoolGradGrad.pbtxt index 2dd7fdf229..3375ebc77d 100644 --- a/tensorflow/core/api_def/java_api/api_def_MaxPoolGradGrad.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_MaxPoolGradGrad.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "MaxPoolGradGrad" - endpoint { - name: "nn.MaxPoolGradGrad" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_MaxPoolGradGradV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_MaxPoolGradGradV2.pbtxt index e2cf7927ca..2ca8a7b022 100644 --- a/tensorflow/core/api_def/java_api/api_def_MaxPoolGradGradV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_MaxPoolGradGradV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "MaxPoolGradGradV2" endpoint { - name: "nn.MaxPoolGradGradV2" + name: "nn.MaxPoolGradGrad" } } diff --git a/tensorflow/core/api_def/java_api/api_def_MaxPoolGradV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_MaxPoolGradV2.pbtxt index 1e47cd13e3..556dd0be50 100644 --- a/tensorflow/core/api_def/java_api/api_def_MaxPoolGradV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_MaxPoolGradV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "MaxPoolGradV2" endpoint { - name: "nn.MaxPoolGradV2" + name: "nn.MaxPoolGrad" } } diff --git a/tensorflow/core/api_def/java_api/api_def_MaxPoolV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_MaxPoolV2.pbtxt index bd885135d5..8463494355 100644 --- a/tensorflow/core/api_def/java_api/api_def_MaxPoolV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_MaxPoolV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "MaxPoolV2" endpoint { - name: "nn.MaxPoolV2" + name: "nn.MaxPool" } } diff --git a/tensorflow/core/api_def/java_api/api_def_MutableDenseHashTable.pbtxt b/tensorflow/core/api_def/java_api/api_def_MutableDenseHashTable.pbtxt index 15803468ff..d7494815d8 100644 --- a/tensorflow/core/api_def/java_api/api_def_MutableDenseHashTable.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_MutableDenseHashTable.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "MutableDenseHashTable" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_MutableDenseHashTableV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_MutableDenseHashTableV2.pbtxt index 03fdeddb95..d1f7f26848 100644 --- a/tensorflow/core/api_def/java_api/api_def_MutableDenseHashTableV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_MutableDenseHashTableV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "MutableDenseHashTableV2" + endpoint { + name: "MutableDenseHashTable" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_MutableHashTable.pbtxt b/tensorflow/core/api_def/java_api/api_def_MutableHashTable.pbtxt index 1a80874495..c446ff8b27 100644 --- a/tensorflow/core/api_def/java_api/api_def_MutableHashTable.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_MutableHashTable.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "MutableHashTable" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_MutableHashTableOfTensors.pbtxt b/tensorflow/core/api_def/java_api/api_def_MutableHashTableOfTensors.pbtxt index bf2fa065dc..76df883d7d 100644 --- a/tensorflow/core/api_def/java_api/api_def_MutableHashTableOfTensors.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_MutableHashTableOfTensors.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "MutableHashTableOfTensors" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_MutableHashTableOfTensorsV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_MutableHashTableOfTensorsV2.pbtxt index 53d780e925..f6d7451267 100644 --- a/tensorflow/core/api_def/java_api/api_def_MutableHashTableOfTensorsV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_MutableHashTableOfTensorsV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "MutableHashTableOfTensorsV2" + endpoint { + name: "MutableHashTableOfTensors" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_MutableHashTableV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_MutableHashTableV2.pbtxt index 99007df253..45d619d674 100644 --- a/tensorflow/core/api_def/java_api/api_def_MutableHashTableV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_MutableHashTableV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "MutableHashTableV2" + endpoint { + name: "MutableHashTable" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_MutexV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_MutexV2.pbtxt index 93988914aa..f89cd10643 100644 --- a/tensorflow/core/api_def/java_api/api_def_MutexV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_MutexV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "MutexV2" + endpoint { + name: "Mutex" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_NonMaxSuppression.pbtxt b/tensorflow/core/api_def/java_api/api_def_NonMaxSuppression.pbtxt index d7156b0a3a..49ac0de4ce 100644 --- a/tensorflow/core/api_def/java_api/api_def_NonMaxSuppression.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_NonMaxSuppression.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "NonMaxSuppression" - endpoint { - name: "image.NonMaxSuppression" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_NonMaxSuppressionV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_NonMaxSuppressionV2.pbtxt index d78eb9745d..fabf5c6215 100644 --- a/tensorflow/core/api_def/java_api/api_def_NonMaxSuppressionV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_NonMaxSuppressionV2.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "NonMaxSuppressionV2" - endpoint { - name: "image.NonMaxSuppressionV2" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_NonMaxSuppressionV3.pbtxt b/tensorflow/core/api_def/java_api/api_def_NonMaxSuppressionV3.pbtxt index e3fde0a566..0aefcb5509 100644 --- a/tensorflow/core/api_def/java_api/api_def_NonMaxSuppressionV3.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_NonMaxSuppressionV3.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "NonMaxSuppressionV3" - endpoint { - name: "image.NonMaxSuppressionV3" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_NonMaxSuppressionV4.pbtxt b/tensorflow/core/api_def/java_api/api_def_NonMaxSuppressionV4.pbtxt index 98776e8e7f..e71de7f4a6 100644 --- a/tensorflow/core/api_def/java_api/api_def_NonMaxSuppressionV4.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_NonMaxSuppressionV4.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "NonMaxSuppressionV4" endpoint { - name: "image.NonMaxSuppressionV4" + name: "image.NonMaxSuppression" } } diff --git a/tensorflow/core/api_def/java_api/api_def_Pad.pbtxt b/tensorflow/core/api_def/java_api/api_def_Pad.pbtxt index a9de5541ac..f83f451552 100644 --- a/tensorflow/core/api_def/java_api/api_def_Pad.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Pad.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "Pad" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_PadV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_PadV2.pbtxt index 1554cd0b64..ffc1216456 100644 --- a/tensorflow/core/api_def/java_api/api_def_PadV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_PadV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "PadV2" + endpoint { + name: "Pad" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_PaddedBatchDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_PaddedBatchDataset.pbtxt index 2d734539da..7cec77427c 100644 --- a/tensorflow/core/api_def/java_api/api_def_PaddedBatchDataset.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_PaddedBatchDataset.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "PaddedBatchDataset" - endpoint { - name: "data.PaddedBatchDataset" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_PaddedBatchDatasetV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_PaddedBatchDatasetV2.pbtxt index d4b85550e9..22dfe84f0c 100644 --- a/tensorflow/core/api_def/java_api/api_def_PaddedBatchDatasetV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_PaddedBatchDatasetV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "PaddedBatchDatasetV2" endpoint { - name: "data.PaddedBatchDatasetV2" + name: "data.PaddedBatchDataset" } } diff --git a/tensorflow/core/api_def/java_api/api_def_PaddingFIFOQueue.pbtxt b/tensorflow/core/api_def/java_api/api_def_PaddingFIFOQueue.pbtxt index 952516cfff..03db4bf185 100644 --- a/tensorflow/core/api_def/java_api/api_def_PaddingFIFOQueue.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_PaddingFIFOQueue.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "PaddingFIFOQueue" - endpoint { - name: "io.PaddingFifoQueue" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_PaddingFIFOQueueV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_PaddingFIFOQueueV2.pbtxt index 80c53e7ee9..605025be79 100644 --- a/tensorflow/core/api_def/java_api/api_def_PaddingFIFOQueueV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_PaddingFIFOQueueV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "PaddingFIFOQueueV2" endpoint { - name: "io.PaddingFifoQueueV2" + name: "io.PaddingFifoQueue" } } diff --git a/tensorflow/core/api_def/java_api/api_def_ParallelInterleaveDataset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ParallelInterleaveDataset.pbtxt index 3ccc2a6bf7..6a985d24fa 100644 --- a/tensorflow/core/api_def/java_api/api_def_ParallelInterleaveDataset.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ParallelInterleaveDataset.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "ParallelInterleaveDataset" - endpoint { - name: "data.ParallelInterleaveDataset" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_ParallelInterleaveDatasetV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_ParallelInterleaveDatasetV2.pbtxt index 56b05cc2f6..6c74149a6c 100644 --- a/tensorflow/core/api_def/java_api/api_def_ParallelInterleaveDatasetV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ParallelInterleaveDatasetV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "ParallelInterleaveDatasetV2" + endpoint { + name: "ParallelInterleaveDataset" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_Placeholder.pbtxt b/tensorflow/core/api_def/java_api/api_def_Placeholder.pbtxt index 5e6daa2ae4..efea7bd2b1 100644 --- a/tensorflow/core/api_def/java_api/api_def_Placeholder.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Placeholder.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "Placeholder" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_PlaceholderV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_PlaceholderV2.pbtxt index 39012d4aa2..a61f6f4d1e 100644 --- a/tensorflow/core/api_def/java_api/api_def_PlaceholderV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_PlaceholderV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "PlaceholderV2" + endpoint { + name: "Placeholder" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_Print.pbtxt b/tensorflow/core/api_def/java_api/api_def_Print.pbtxt index 5b837135fd..21a085a1c2 100644 --- a/tensorflow/core/api_def/java_api/api_def_Print.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Print.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "Print" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_PrintV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_PrintV2.pbtxt index c6e406d08f..d1e4d74b1e 100644 --- a/tensorflow/core/api_def/java_api/api_def_PrintV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_PrintV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "PrintV2" + endpoint { + name: "Print" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_PriorityQueue.pbtxt b/tensorflow/core/api_def/java_api/api_def_PriorityQueue.pbtxt index cee973139d..0a9909d122 100644 --- a/tensorflow/core/api_def/java_api/api_def_PriorityQueue.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_PriorityQueue.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "PriorityQueue" - endpoint { - name: "io.PriorityQueue" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_PriorityQueueV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_PriorityQueueV2.pbtxt index 27e89f92be..1f6a6f2906 100644 --- a/tensorflow/core/api_def/java_api/api_def_PriorityQueueV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_PriorityQueueV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "PriorityQueueV2" endpoint { - name: "io.PriorityQueueV2" + name: "io.PriorityQueue" } } diff --git a/tensorflow/core/api_def/java_api/api_def_QuantizeAndDequantize.pbtxt b/tensorflow/core/api_def/java_api/api_def_QuantizeAndDequantize.pbtxt index fe8401d8f9..e6ba0ce8b8 100644 --- a/tensorflow/core/api_def/java_api/api_def_QuantizeAndDequantize.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_QuantizeAndDequantize.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "QuantizeAndDequantize" - endpoint { - name: "quantization.QuantizeAndDequantize" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_QuantizeAndDequantizeV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_QuantizeAndDequantizeV2.pbtxt index 0535993d40..678a77113c 100644 --- a/tensorflow/core/api_def/java_api/api_def_QuantizeAndDequantizeV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_QuantizeAndDequantizeV2.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "QuantizeAndDequantizeV2" - endpoint { - name: "quantization.QuantizeAndDequantize" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_QuantizeAndDequantizeV3.pbtxt b/tensorflow/core/api_def/java_api/api_def_QuantizeAndDequantizeV3.pbtxt index d056b320c8..c9e52e770a 100644 --- a/tensorflow/core/api_def/java_api/api_def_QuantizeAndDequantizeV3.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_QuantizeAndDequantizeV3.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "QuantizeAndDequantizeV3" endpoint { - name: "quantization.QuantizeAndDequantizeV3" + name: "quantization.QuantizeAndDequantize" } } diff --git a/tensorflow/core/api_def/java_api/api_def_QuantizeV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_QuantizeV2.pbtxt index d04a01726f..25c9c3bdce 100644 --- a/tensorflow/core/api_def/java_api/api_def_QuantizeV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_QuantizeV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "QuantizeV2" endpoint { - name: "quantization.QuantizeV2" + name: "quantization.Quantize" } } diff --git a/tensorflow/core/api_def/java_api/api_def_QueueClose.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueClose.pbtxt index 70e559c773..4a6bada741 100644 --- a/tensorflow/core/api_def/java_api/api_def_QueueClose.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_QueueClose.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "QueueClose" - endpoint { - name: "io.QueueClose" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_QueueCloseV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueCloseV2.pbtxt index 01460f27e2..ce779650e5 100644 --- a/tensorflow/core/api_def/java_api/api_def_QueueCloseV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_QueueCloseV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "QueueCloseV2" endpoint { - name: "io.QueueCloseV2" + name: "io.QueueClose" } } diff --git a/tensorflow/core/api_def/java_api/api_def_QueueDequeue.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueDequeue.pbtxt index 9cd77b4ca3..45c811a6b4 100644 --- a/tensorflow/core/api_def/java_api/api_def_QueueDequeue.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_QueueDequeue.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "QueueDequeue" - endpoint { - name: "io.QueueDequeue" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_QueueDequeueMany.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueDequeueMany.pbtxt index ceb2e82394..9e088ef258 100644 --- a/tensorflow/core/api_def/java_api/api_def_QueueDequeueMany.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_QueueDequeueMany.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "QueueDequeueMany" - endpoint { - name: "io.QueueDequeueMany" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_QueueDequeueManyV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueDequeueManyV2.pbtxt index ac27fcb620..10fe198ff2 100644 --- a/tensorflow/core/api_def/java_api/api_def_QueueDequeueManyV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_QueueDequeueManyV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "QueueDequeueManyV2" endpoint { - name: "io.QueueDequeueManyV2" + name: "io.QueueDequeueMany" } } diff --git a/tensorflow/core/api_def/java_api/api_def_QueueDequeueUpTo.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueDequeueUpTo.pbtxt index 657c63363f..b96e568c41 100644 --- a/tensorflow/core/api_def/java_api/api_def_QueueDequeueUpTo.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_QueueDequeueUpTo.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "QueueDequeueUpTo" - endpoint { - name: "io.QueueDequeueUpTo" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_QueueDequeueUpToV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueDequeueUpToV2.pbtxt index 19b3fff653..fadea0926b 100644 --- a/tensorflow/core/api_def/java_api/api_def_QueueDequeueUpToV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_QueueDequeueUpToV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "QueueDequeueUpToV2" endpoint { - name: "io.QueueDequeueUpToV2" + name: "io.QueueDequeueUpTo" } } diff --git a/tensorflow/core/api_def/java_api/api_def_QueueDequeueV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueDequeueV2.pbtxt index 8aca207816..7ba03afbfe 100644 --- a/tensorflow/core/api_def/java_api/api_def_QueueDequeueV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_QueueDequeueV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "QueueDequeueV2" endpoint { - name: "io.QueueDequeueV2" + name: "io.QueueDequeue" } } diff --git a/tensorflow/core/api_def/java_api/api_def_QueueEnqueue.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueEnqueue.pbtxt index e516dab297..2945c46d6e 100644 --- a/tensorflow/core/api_def/java_api/api_def_QueueEnqueue.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_QueueEnqueue.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "QueueEnqueue" - endpoint { - name: "io.QueueEnqueue" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_QueueEnqueueMany.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueEnqueueMany.pbtxt index b9c0e4fd84..442ddcbc03 100644 --- a/tensorflow/core/api_def/java_api/api_def_QueueEnqueueMany.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_QueueEnqueueMany.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "QueueEnqueueMany" - endpoint { - name: "io.QueueEnqueueMany" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_QueueEnqueueManyV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueEnqueueManyV2.pbtxt index 153700a646..be3fed4789 100644 --- a/tensorflow/core/api_def/java_api/api_def_QueueEnqueueManyV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_QueueEnqueueManyV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "QueueEnqueueManyV2" endpoint { - name: "io.QueueEnqueueManyV2" + name: "io.QueueEnqueueMany" } } diff --git a/tensorflow/core/api_def/java_api/api_def_QueueEnqueueV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueEnqueueV2.pbtxt index 68bacd7b5b..e71a2211e1 100644 --- a/tensorflow/core/api_def/java_api/api_def_QueueEnqueueV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_QueueEnqueueV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "QueueEnqueueV2" endpoint { - name: "io.QueueEnqueueV2" + name: "io.QueueEnqueue" } } diff --git a/tensorflow/core/api_def/java_api/api_def_QueueIsClosed.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueIsClosed.pbtxt index 86914c7124..0b51b208b7 100644 --- a/tensorflow/core/api_def/java_api/api_def_QueueIsClosed.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_QueueIsClosed.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "QueueIsClosed" - endpoint { - name: "io.QueueIsClosed" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_QueueIsClosedV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueIsClosedV2.pbtxt index ce33b2498b..148d313a6d 100644 --- a/tensorflow/core/api_def/java_api/api_def_QueueIsClosedV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_QueueIsClosedV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "QueueIsClosedV2" endpoint { - name: "io.QueueIsClosedV2" + name: "io.QueueIsClosed" } } diff --git a/tensorflow/core/api_def/java_api/api_def_QueueSize.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueSize.pbtxt index 5592d58f9f..9bd7244d68 100644 --- a/tensorflow/core/api_def/java_api/api_def_QueueSize.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_QueueSize.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "QueueSize" - endpoint { - name: "io.QueueSize" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_QueueSizeV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_QueueSizeV2.pbtxt index 68364aa605..e93e07a2b3 100644 --- a/tensorflow/core/api_def/java_api/api_def_QueueSizeV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_QueueSizeV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "QueueSizeV2" endpoint { - name: "io.QueueSizeV2" + name: "io.QueueSize" } } diff --git a/tensorflow/core/api_def/java_api/api_def_RandomPoisson.pbtxt b/tensorflow/core/api_def/java_api/api_def_RandomPoisson.pbtxt index 42ce1a5fb1..d1ea795024 100644 --- a/tensorflow/core/api_def/java_api/api_def_RandomPoisson.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_RandomPoisson.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "RandomPoisson" - endpoint { - name: "random.RandomPoisson" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_RandomPoissonV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_RandomPoissonV2.pbtxt index adc5441abc..5efe01bf40 100644 --- a/tensorflow/core/api_def/java_api/api_def_RandomPoissonV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_RandomPoissonV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "RandomPoissonV2" endpoint { - name: "random.RandomPoissonV2" + name: "random.RandomPoisson" } } diff --git a/tensorflow/core/api_def/java_api/api_def_RandomShuffleQueue.pbtxt b/tensorflow/core/api_def/java_api/api_def_RandomShuffleQueue.pbtxt index f622eb4e0d..9660121a07 100644 --- a/tensorflow/core/api_def/java_api/api_def_RandomShuffleQueue.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_RandomShuffleQueue.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "RandomShuffleQueue" - endpoint { - name: "io.RandomShuffleQueue" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_RandomShuffleQueueV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_RandomShuffleQueueV2.pbtxt index c88c2a4631..779363303c 100644 --- a/tensorflow/core/api_def/java_api/api_def_RandomShuffleQueueV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_RandomShuffleQueueV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "RandomShuffleQueueV2" endpoint { - name: "io.RandomShuffleQueueV2" + name: "io.RandomShuffleQueue" } } diff --git a/tensorflow/core/api_def/java_api/api_def_ReaderNumRecordsProduced.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReaderNumRecordsProduced.pbtxt index 67dbe1035e..b087d11182 100644 --- a/tensorflow/core/api_def/java_api/api_def_ReaderNumRecordsProduced.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ReaderNumRecordsProduced.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "ReaderNumRecordsProduced" - endpoint { - name: "io.ReaderNumRecordsProduced" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_ReaderNumRecordsProducedV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReaderNumRecordsProducedV2.pbtxt index 5063706eb0..54a30abe18 100644 --- a/tensorflow/core/api_def/java_api/api_def_ReaderNumRecordsProducedV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ReaderNumRecordsProducedV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "ReaderNumRecordsProducedV2" endpoint { - name: "io.ReaderNumRecordsProducedV2" + name: "io.ReaderNumRecordsProduced" } } diff --git a/tensorflow/core/api_def/java_api/api_def_ReaderNumWorkUnitsCompleted.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReaderNumWorkUnitsCompleted.pbtxt index 63bb2d4108..e30e97fd08 100644 --- a/tensorflow/core/api_def/java_api/api_def_ReaderNumWorkUnitsCompleted.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ReaderNumWorkUnitsCompleted.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "ReaderNumWorkUnitsCompleted" - endpoint { - name: "io.ReaderNumWorkUnitsCompleted" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_ReaderNumWorkUnitsCompletedV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReaderNumWorkUnitsCompletedV2.pbtxt index cf72ecc562..0904ba19e5 100644 --- a/tensorflow/core/api_def/java_api/api_def_ReaderNumWorkUnitsCompletedV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ReaderNumWorkUnitsCompletedV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "ReaderNumWorkUnitsCompletedV2" endpoint { - name: "io.ReaderNumWorkUnitsCompletedV2" + name: "io.ReaderNumWorkUnitsCompleted" } } diff --git a/tensorflow/core/api_def/java_api/api_def_ReaderRead.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReaderRead.pbtxt index 47e9679f80..8f98d88bda 100644 --- a/tensorflow/core/api_def/java_api/api_def_ReaderRead.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ReaderRead.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "ReaderRead" - endpoint { - name: "io.ReaderRead" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_ReaderReadUpTo.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReaderReadUpTo.pbtxt index 985d0035b0..d418b00b27 100644 --- a/tensorflow/core/api_def/java_api/api_def_ReaderReadUpTo.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ReaderReadUpTo.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "ReaderReadUpTo" - endpoint { - name: "io.ReaderReadUpTo" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_ReaderReadUpToV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReaderReadUpToV2.pbtxt index a001349e2d..777d09fa2c 100644 --- a/tensorflow/core/api_def/java_api/api_def_ReaderReadUpToV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ReaderReadUpToV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "ReaderReadUpToV2" endpoint { - name: "io.ReaderReadUpToV2" + name: "io.ReaderReadUpTo" } } diff --git a/tensorflow/core/api_def/java_api/api_def_ReaderReadV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReaderReadV2.pbtxt index a64349d15e..a5d45bd1db 100644 --- a/tensorflow/core/api_def/java_api/api_def_ReaderReadV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ReaderReadV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "ReaderReadV2" endpoint { - name: "io.ReaderReadV2" + name: "io.ReaderRead" } } diff --git a/tensorflow/core/api_def/java_api/api_def_ReaderReset.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReaderReset.pbtxt index 9a387753f5..e6041caabd 100644 --- a/tensorflow/core/api_def/java_api/api_def_ReaderReset.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ReaderReset.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "ReaderReset" - endpoint { - name: "io.ReaderReset" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_ReaderResetV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReaderResetV2.pbtxt index 6e31d5a8ff..265a3442f5 100644 --- a/tensorflow/core/api_def/java_api/api_def_ReaderResetV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ReaderResetV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "ReaderResetV2" endpoint { - name: "io.ReaderResetV2" + name: "io.ReaderReset" } } diff --git a/tensorflow/core/api_def/java_api/api_def_ReaderRestoreState.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReaderRestoreState.pbtxt index d148adde6a..0aa0ec595d 100644 --- a/tensorflow/core/api_def/java_api/api_def_ReaderRestoreState.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ReaderRestoreState.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "ReaderRestoreState" - endpoint { - name: "io.ReaderRestoreState" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_ReaderRestoreStateV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReaderRestoreStateV2.pbtxt index 3d78d8eb00..4728ce7796 100644 --- a/tensorflow/core/api_def/java_api/api_def_ReaderRestoreStateV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ReaderRestoreStateV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "ReaderRestoreStateV2" endpoint { - name: "io.ReaderRestoreStateV2" + name: "io.ReaderRestoreState" } } diff --git a/tensorflow/core/api_def/java_api/api_def_ReaderSerializeState.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReaderSerializeState.pbtxt index fd12a4c784..5e23e285fb 100644 --- a/tensorflow/core/api_def/java_api/api_def_ReaderSerializeState.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ReaderSerializeState.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "ReaderSerializeState" - endpoint { - name: "io.ReaderSerializeState" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_ReaderSerializeStateV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_ReaderSerializeStateV2.pbtxt index 28c5048fc9..aa396095b1 100644 --- a/tensorflow/core/api_def/java_api/api_def_ReaderSerializeStateV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ReaderSerializeStateV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "ReaderSerializeStateV2" endpoint { - name: "io.ReaderSerializeStateV2" + name: "io.ReaderSerializeState" } } diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceApplyFtrl.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceApplyFtrl.pbtxt index db64e86fd4..61bec5bb10 100644 --- a/tensorflow/core/api_def/java_api/api_def_ResourceApplyFtrl.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ResourceApplyFtrl.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "ResourceApplyFtrl" - endpoint { - name: "train.ResourceApplyFtrl" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceApplyFtrlV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceApplyFtrlV2.pbtxt index 547041e649..8209fd607e 100644 --- a/tensorflow/core/api_def/java_api/api_def_ResourceApplyFtrlV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ResourceApplyFtrlV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "ResourceApplyFtrlV2" endpoint { - name: "train.ResourceApplyFtrlV2" + name: "train.ResourceApplyFtrl" } } diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyFtrl.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyFtrl.pbtxt index 195198fc9a..2e6fed9469 100644 --- a/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyFtrl.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyFtrl.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "ResourceSparseApplyFtrl" - endpoint { - name: "train.ResourceSparseApplyFtrl" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyFtrlV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyFtrlV2.pbtxt index 49c6d34dcc..cd126d78ab 100644 --- a/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyFtrlV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_ResourceSparseApplyFtrlV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "ResourceSparseApplyFtrlV2" endpoint { - name: "train.ResourceSparseApplyFtrlV2" + name: "train.ResourceSparseApplyFtrl" } } diff --git a/tensorflow/core/api_def/java_api/api_def_Restore.pbtxt b/tensorflow/core/api_def/java_api/api_def_Restore.pbtxt index ca79fc10db..5e5b021b08 100644 --- a/tensorflow/core/api_def/java_api/api_def_Restore.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Restore.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "Restore" - endpoint { - name: "train.Restore" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_RestoreV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_RestoreV2.pbtxt index e877ff9869..909968873f 100644 --- a/tensorflow/core/api_def/java_api/api_def_RestoreV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_RestoreV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "RestoreV2" endpoint { - name: "train.RestoreV2" + name: "train.Restore" } } diff --git a/tensorflow/core/api_def/java_api/api_def_Reverse.pbtxt b/tensorflow/core/api_def/java_api/api_def_Reverse.pbtxt index 2bd25417aa..d2a199d2fc 100644 --- a/tensorflow/core/api_def/java_api/api_def_Reverse.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Reverse.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "Reverse" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_SampleDistortedBoundingBox.pbtxt b/tensorflow/core/api_def/java_api/api_def_SampleDistortedBoundingBox.pbtxt index e7c1c90ea6..3dffd53b05 100644 --- a/tensorflow/core/api_def/java_api/api_def_SampleDistortedBoundingBox.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_SampleDistortedBoundingBox.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "SampleDistortedBoundingBox" - endpoint { - name: "image.SampleDistortedBoundingBox" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_SampleDistortedBoundingBoxV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_SampleDistortedBoundingBoxV2.pbtxt index 8656977bf6..6557314137 100644 --- a/tensorflow/core/api_def/java_api/api_def_SampleDistortedBoundingBoxV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_SampleDistortedBoundingBoxV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "SampleDistortedBoundingBoxV2" endpoint { - name: "image.SampleDistortedBoundingBoxV2" + name: "image.SampleDistortedBoundingBox" } } diff --git a/tensorflow/core/api_def/java_api/api_def_Save.pbtxt b/tensorflow/core/api_def/java_api/api_def_Save.pbtxt index 87dab6dc1f..36d44001d5 100644 --- a/tensorflow/core/api_def/java_api/api_def_Save.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Save.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "Save" - endpoint { - name: "train.Save" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_SaveV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_SaveV2.pbtxt index 6417252f45..644d1824aa 100644 --- a/tensorflow/core/api_def/java_api/api_def_SaveV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_SaveV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "SaveV2" endpoint { - name: "train.SaveV2" + name: "train.Save" } } diff --git a/tensorflow/core/api_def/java_api/api_def_SdcaOptimizer.pbtxt b/tensorflow/core/api_def/java_api/api_def_SdcaOptimizer.pbtxt index 68eb8cf1f0..fab6393f60 100644 --- a/tensorflow/core/api_def/java_api/api_def_SdcaOptimizer.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_SdcaOptimizer.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "SdcaOptimizer" - endpoint { - name: "train.SdcaOptimizer" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_SdcaOptimizerV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_SdcaOptimizerV2.pbtxt index 4e90531060..b72ee64e50 100644 --- a/tensorflow/core/api_def/java_api/api_def_SdcaOptimizerV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_SdcaOptimizerV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "SdcaOptimizerV2" endpoint { - name: "train.SdcaOptimizerV2" + name: "train.SdcaOptimizer" } } diff --git a/tensorflow/core/api_def/java_api/api_def_SelfAdjointEig.pbtxt b/tensorflow/core/api_def/java_api/api_def_SelfAdjointEig.pbtxt index 552d397ad8..dc25ae9de2 100644 --- a/tensorflow/core/api_def/java_api/api_def_SelfAdjointEig.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_SelfAdjointEig.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "SelfAdjointEig" - endpoint { - name: "linalg.SelfAdjointEig" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_SelfAdjointEigV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_SelfAdjointEigV2.pbtxt index 4b610f437c..c79f08ac32 100644 --- a/tensorflow/core/api_def/java_api/api_def_SelfAdjointEigV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_SelfAdjointEigV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "SelfAdjointEigV2" endpoint { - name: "linalg.SelfAdjointEigV2" + name: "linalg.SelfAdjointEig" } } diff --git a/tensorflow/core/api_def/java_api/api_def_SparseApplyFtrl.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseApplyFtrl.pbtxt index bb669aa773..e961fb7f6b 100644 --- a/tensorflow/core/api_def/java_api/api_def_SparseApplyFtrl.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_SparseApplyFtrl.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "SparseApplyFtrl" - endpoint { - name: "train.SparseApplyFtrl" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_SparseApplyFtrlV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_SparseApplyFtrlV2.pbtxt index 673f71c34b..43b9833451 100644 --- a/tensorflow/core/api_def/java_api/api_def_SparseApplyFtrlV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_SparseApplyFtrlV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "SparseApplyFtrlV2" endpoint { - name: "train.SparseApplyFtrlV2" + name: "train.SparseApplyFtrl" } } diff --git a/tensorflow/core/api_def/java_api/api_def_Stack.pbtxt b/tensorflow/core/api_def/java_api/api_def_Stack.pbtxt index 522e5efec5..8370beee63 100644 --- a/tensorflow/core/api_def/java_api/api_def_Stack.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Stack.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "Stack" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_StackClose.pbtxt b/tensorflow/core/api_def/java_api/api_def_StackClose.pbtxt index 1cba682de9..ac3c410c1c 100644 --- a/tensorflow/core/api_def/java_api/api_def_StackClose.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_StackClose.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "StackClose" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_StackCloseV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_StackCloseV2.pbtxt index be5a0f535a..28aff9e191 100644 --- a/tensorflow/core/api_def/java_api/api_def_StackCloseV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_StackCloseV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "StackCloseV2" + endpoint { + name: "StackClose" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_StackPop.pbtxt b/tensorflow/core/api_def/java_api/api_def_StackPop.pbtxt index 4439e73843..b8658ecbad 100644 --- a/tensorflow/core/api_def/java_api/api_def_StackPop.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_StackPop.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "StackPop" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_StackPopV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_StackPopV2.pbtxt index b8babb4c56..d2ecf4e5a8 100644 --- a/tensorflow/core/api_def/java_api/api_def_StackPopV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_StackPopV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "StackPopV2" + endpoint { + name: "StackPop" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_StackPush.pbtxt b/tensorflow/core/api_def/java_api/api_def_StackPush.pbtxt index 41792e327f..d08fa27b21 100644 --- a/tensorflow/core/api_def/java_api/api_def_StackPush.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_StackPush.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "StackPush" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_StackPushV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_StackPushV2.pbtxt index 4920152259..519fd6c6b2 100644 --- a/tensorflow/core/api_def/java_api/api_def_StackPushV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_StackPushV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "StackPushV2" + endpoint { + name: "StackPush" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_StackV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_StackV2.pbtxt index 5232e0425e..725e469a03 100644 --- a/tensorflow/core/api_def/java_api/api_def_StackV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_StackV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "StackV2" + endpoint { + name: "Stack" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_StringSplit.pbtxt b/tensorflow/core/api_def/java_api/api_def_StringSplit.pbtxt index f16e196a6f..0e6d1851df 100644 --- a/tensorflow/core/api_def/java_api/api_def_StringSplit.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_StringSplit.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "StringSplit" - endpoint { - name: "strings.StringSplit" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_StringSplitV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_StringSplitV2.pbtxt index 0779eb7f71..18c71d6bd7 100644 --- a/tensorflow/core/api_def/java_api/api_def_StringSplitV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_StringSplitV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "StringSplitV2" endpoint { - name: "strings.StringSplitV2" + name: "strings.StringSplit" } } diff --git a/tensorflow/core/api_def/java_api/api_def_TFRecordReader.pbtxt b/tensorflow/core/api_def/java_api/api_def_TFRecordReader.pbtxt index c93d53fa1a..9ffbeba0ec 100644 --- a/tensorflow/core/api_def/java_api/api_def_TFRecordReader.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TFRecordReader.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "TFRecordReader" - endpoint { - name: "io.TfRecordReader" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_TFRecordReaderV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TFRecordReaderV2.pbtxt index 84df23f6e2..7d252e4942 100644 --- a/tensorflow/core/api_def/java_api/api_def_TFRecordReaderV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TFRecordReaderV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "TFRecordReaderV2" endpoint { - name: "io.TfRecordReaderV2" + name: "io.TfRecordReader" } } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArray.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArray.pbtxt index 7eaa468130..e315486af2 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArray.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArray.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "TensorArray" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayClose.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayClose.pbtxt index e866250d3a..951ace8005 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArrayClose.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayClose.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "TensorArrayClose" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayCloseV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayCloseV2.pbtxt index 5add953e66..6fd2d02592 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArrayCloseV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayCloseV2.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "TensorArrayCloseV2" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayCloseV3.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayCloseV3.pbtxt index b881089718..4a06577979 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArrayCloseV3.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayCloseV3.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "TensorArrayCloseV3" + endpoint { + name: "TensorArrayClose" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayConcat.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayConcat.pbtxt index e72b58de1c..f507111093 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArrayConcat.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayConcat.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "TensorArrayConcat" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayConcatV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayConcatV2.pbtxt index abc3e60f0c..e92cebf2a7 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArrayConcatV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayConcatV2.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "TensorArrayConcatV2" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayConcatV3.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayConcatV3.pbtxt index d1a91e90d7..34d09c901a 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArrayConcatV3.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayConcatV3.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "TensorArrayConcatV3" + endpoint { + name: "TensorArrayConcat" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayGather.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayGather.pbtxt index d4d179874f..95866b9778 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArrayGather.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayGather.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "TensorArrayGather" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayGatherV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayGatherV2.pbtxt index dd94fc4ef5..f75b50c667 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArrayGatherV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayGatherV2.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "TensorArrayGatherV2" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayGatherV3.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayGatherV3.pbtxt index b8f01e6ac1..b792ee9882 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArrayGatherV3.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayGatherV3.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "TensorArrayGatherV3" + endpoint { + name: "TensorArrayGather" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayGrad.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayGrad.pbtxt index 517461edba..beb9b5ca12 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArrayGrad.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayGrad.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "TensorArrayGrad" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayGradV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayGradV2.pbtxt index b7278b5ffa..41d25a4910 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArrayGradV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayGradV2.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "TensorArrayGradV2" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayGradV3.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayGradV3.pbtxt index b7c3f143ef..2c9adebd04 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArrayGradV3.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayGradV3.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "TensorArrayGradV3" + endpoint { + name: "TensorArrayGrad" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayRead.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayRead.pbtxt index 1b62f7fac7..72704746a5 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArrayRead.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayRead.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "TensorArrayRead" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayReadV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayReadV2.pbtxt index ba02f61028..43cd0a2b78 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArrayReadV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayReadV2.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "TensorArrayReadV2" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayReadV3.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayReadV3.pbtxt index c30428c23a..e6d38d64df 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArrayReadV3.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayReadV3.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "TensorArrayReadV3" + endpoint { + name: "TensorArrayRead" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayScatter.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayScatter.pbtxt index a3e8d1625e..76092a45ed 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArrayScatter.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayScatter.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "TensorArrayScatter" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayScatterV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayScatterV2.pbtxt index 38080410e6..7dba0fab4c 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArrayScatterV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayScatterV2.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "TensorArrayScatterV2" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayScatterV3.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayScatterV3.pbtxt index 6f42524af8..179c9611f5 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArrayScatterV3.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayScatterV3.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "TensorArrayScatterV3" + endpoint { + name: "TensorArrayScatter" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArraySize.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArraySize.pbtxt index fb3a6fae1c..fb2be098c6 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArraySize.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArraySize.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "TensorArraySize" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArraySizeV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArraySizeV2.pbtxt index 03bf061f8b..8e8e44cfe2 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArraySizeV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArraySizeV2.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "TensorArraySizeV2" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArraySizeV3.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArraySizeV3.pbtxt index 0c14360641..2df9a2d3f1 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArraySizeV3.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArraySizeV3.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "TensorArraySizeV3" + endpoint { + name: "TensorArraySize" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArraySplit.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArraySplit.pbtxt index 3eb8d6c7ff..105031eb98 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArraySplit.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArraySplit.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "TensorArraySplit" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArraySplitV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArraySplitV2.pbtxt index 34740aa2ef..ef5d88832a 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArraySplitV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArraySplitV2.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "TensorArraySplitV2" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArraySplitV3.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArraySplitV3.pbtxt index 4b22f2bdf6..721af074d0 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArraySplitV3.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArraySplitV3.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "TensorArraySplitV3" + endpoint { + name: "TensorArraySplit" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayV2.pbtxt index d18517725e..43a441a071 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArrayV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayV2.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "TensorArrayV2" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayV3.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayV3.pbtxt index 06e65ef93c..2b87617a1f 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArrayV3.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayV3.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "TensorArrayV3" + endpoint { + name: "TensorArray" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayWrite.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayWrite.pbtxt index 92ab1764ec..2462dae80d 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArrayWrite.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayWrite.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "TensorArrayWrite" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayWriteV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayWriteV2.pbtxt index 10c505cff4..9f670ae181 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArrayWriteV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayWriteV2.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "TensorArrayWriteV2" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorArrayWriteV3.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorArrayWriteV3.pbtxt index 2fe2d02127..7321057b2f 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorArrayWriteV3.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorArrayWriteV3.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "TensorArrayWriteV3" + endpoint { + name: "TensorArrayWrite" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorSummary.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorSummary.pbtxt index a720ca9076..165478d3a0 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorSummary.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorSummary.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "TensorSummary" - endpoint { - name: "summary.TensorSummary" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_TensorSummaryV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TensorSummaryV2.pbtxt index ba5131c85e..c285ada012 100644 --- a/tensorflow/core/api_def/java_api/api_def_TensorSummaryV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TensorSummaryV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "TensorSummaryV2" endpoint { - name: "summary.TensorSummaryV2" + name: "summary.TensorSummary" } } diff --git a/tensorflow/core/api_def/java_api/api_def_TextLineReader.pbtxt b/tensorflow/core/api_def/java_api/api_def_TextLineReader.pbtxt index 17eb20cffe..f86b15cf86 100644 --- a/tensorflow/core/api_def/java_api/api_def_TextLineReader.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TextLineReader.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "TextLineReader" - endpoint { - name: "io.TextLineReader" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_TextLineReaderV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TextLineReaderV2.pbtxt index 745c85fac9..ee57dd8408 100644 --- a/tensorflow/core/api_def/java_api/api_def_TextLineReaderV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TextLineReaderV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "TextLineReaderV2" endpoint { - name: "io.TextLineReaderV2" + name: "io.TextLineReader" } } diff --git a/tensorflow/core/api_def/java_api/api_def_TopK.pbtxt b/tensorflow/core/api_def/java_api/api_def_TopK.pbtxt index 108701adf1..bb090aa6f1 100644 --- a/tensorflow/core/api_def/java_api/api_def_TopK.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TopK.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "TopK" - endpoint { - name: "nn.TopK" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_TopKV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_TopKV2.pbtxt index 2aa89adcce..2b0dcf7c2a 100644 --- a/tensorflow/core/api_def/java_api/api_def_TopKV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_TopKV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "TopKV2" endpoint { - name: "nn.TopKV2" + name: "nn.TopK" } } diff --git a/tensorflow/core/api_def/java_api/api_def_Unique.pbtxt b/tensorflow/core/api_def/java_api/api_def_Unique.pbtxt index 5c4262b5fb..8cc8ec0fed 100644 --- a/tensorflow/core/api_def/java_api/api_def_Unique.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Unique.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "Unique" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_UniqueV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_UniqueV2.pbtxt index 4d43500a0d..6fe22cb102 100644 --- a/tensorflow/core/api_def/java_api/api_def_UniqueV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_UniqueV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "UniqueV2" + endpoint { + name: "Unique" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_UniqueWithCounts.pbtxt b/tensorflow/core/api_def/java_api/api_def_UniqueWithCounts.pbtxt index 1ce5314157..0248fab17e 100644 --- a/tensorflow/core/api_def/java_api/api_def_UniqueWithCounts.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_UniqueWithCounts.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "UniqueWithCounts" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_UniqueWithCountsV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_UniqueWithCountsV2.pbtxt index abfd496b8e..eb15745114 100644 --- a/tensorflow/core/api_def/java_api/api_def_UniqueWithCountsV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_UniqueWithCountsV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "UniqueWithCountsV2" + endpoint { + name: "UniqueWithCounts" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_Variable.pbtxt b/tensorflow/core/api_def/java_api/api_def_Variable.pbtxt index abc2f9c954..0978e61451 100644 --- a/tensorflow/core/api_def/java_api/api_def_Variable.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Variable.pbtxt @@ -1,3 +1,4 @@ op { graph_op_name: "Variable" + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_VariableV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_VariableV2.pbtxt index 221a80f760..c566dd1e79 100644 --- a/tensorflow/core/api_def/java_api/api_def_VariableV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_VariableV2.pbtxt @@ -1,3 +1,6 @@ op { graph_op_name: "VariableV2" + endpoint { + name: "Variable" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_WholeFileReader.pbtxt b/tensorflow/core/api_def/java_api/api_def_WholeFileReader.pbtxt index 67a8933efb..aa839ed380 100644 --- a/tensorflow/core/api_def/java_api/api_def_WholeFileReader.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_WholeFileReader.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "WholeFileReader" - endpoint { - name: "io.WholeFileReader" - } + visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_WholeFileReaderV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_WholeFileReaderV2.pbtxt index 172268a122..e031d705fb 100644 --- a/tensorflow/core/api_def/java_api/api_def_WholeFileReaderV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_WholeFileReaderV2.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "WholeFileReaderV2" endpoint { - name: "io.WholeFileReaderV2" + name: "io.WholeFileReader" } } -- GitLab From 937ec3a54b5a6d35a61e690b24c040bb52fc9a9d Mon Sep 17 00:00:00 2001 From: Karl Lessard Date: Thu, 29 Nov 2018 00:10:31 -0500 Subject: [PATCH 051/873] Replace PlaceholderV2 by Placeholder, since V2 is not supported anymore. --- tensorflow/core/api_def/java_api/api_def_Placeholder.pbtxt | 1 - tensorflow/core/api_def/java_api/api_def_PlaceholderV2.pbtxt | 4 +--- 2 files changed, 1 insertion(+), 4 deletions(-) diff --git a/tensorflow/core/api_def/java_api/api_def_Placeholder.pbtxt b/tensorflow/core/api_def/java_api/api_def_Placeholder.pbtxt index efea7bd2b1..5e6daa2ae4 100644 --- a/tensorflow/core/api_def/java_api/api_def_Placeholder.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Placeholder.pbtxt @@ -1,4 +1,3 @@ op { graph_op_name: "Placeholder" - visibility: SKIP } diff --git a/tensorflow/core/api_def/java_api/api_def_PlaceholderV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_PlaceholderV2.pbtxt index a61f6f4d1e..419bdf10f7 100644 --- a/tensorflow/core/api_def/java_api/api_def_PlaceholderV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_PlaceholderV2.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "PlaceholderV2" - endpoint { - name: "Placeholder" - } + visibility: SKIP } -- GitLab From 797c781499552cdd6790be0e73e06811fce78be7 Mon Sep 17 00:00:00 2001 From: Karl Lessard Date: Thu, 29 Nov 2018 00:15:55 -0500 Subject: [PATCH 052/873] Restore Add over AddV2, since gradients are not computed for the later. --- tensorflow/core/api_def/java_api/api_def_Add.pbtxt | 4 +++- tensorflow/core/api_def/java_api/api_def_AddV2.pbtxt | 4 +--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/core/api_def/java_api/api_def_Add.pbtxt b/tensorflow/core/api_def/java_api/api_def_Add.pbtxt index b082b055bf..4f78ccc9ea 100644 --- a/tensorflow/core/api_def/java_api/api_def_Add.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_Add.pbtxt @@ -1,4 +1,6 @@ op { graph_op_name: "Add" - visibility: SKIP + endpoint { + name: "math.Add" + } } diff --git a/tensorflow/core/api_def/java_api/api_def_AddV2.pbtxt b/tensorflow/core/api_def/java_api/api_def_AddV2.pbtxt index 863ec4f33c..a070c6a519 100644 --- a/tensorflow/core/api_def/java_api/api_def_AddV2.pbtxt +++ b/tensorflow/core/api_def/java_api/api_def_AddV2.pbtxt @@ -1,6 +1,4 @@ op { graph_op_name: "AddV2" - endpoint { - name: "math.Add" - } + visibility: SKIP } -- GitLab From d00013e72cd3a1a4805395eb8e66748dcf387295 Mon Sep 17 00:00:00 2001 From: Wen yun Date: Wed, 31 Oct 2018 15:22:14 +0800 Subject: [PATCH 053/873] fix the case when input value are MirroredVariable for assign_moving_average --- .../distribute/python/moving_averages_test.py | 20 +++++++++++++++++++ .../python/distribute/mirrored_strategy.py | 3 +++ 2 files changed, 23 insertions(+) diff --git a/tensorflow/contrib/distribute/python/moving_averages_test.py b/tensorflow/contrib/distribute/python/moving_averages_test.py index c492d8bafc..da3353b2d5 100644 --- a/tensorflow/contrib/distribute/python/moving_averages_test.py +++ b/tensorflow/contrib/distribute/python/moving_averages_test.py @@ -139,6 +139,26 @@ class AssignMovingAveragesTest(test.TestCase, parameterized.TestCase): (2.0 * 0.25 + 0.0) / (1.0 * 0.25 + 1.0)], var.eval()) + @combinations.generate(all_combinations) + def testAssignVariable(self, distribution): + def replica_fn(): + var = variables.Variable([10.0, 11.0]) + # Here we expect to check the case when input value are variable. + val = variables.Variable([1., 2.]) + decay = 0.25 + assign = moving_averages.assign_moving_average( + var, val, decay, zero_debias=False) + return var, assign + + with distribution.scope(), self.cached_session() as sess: + var, assign = distribution.call_for_each_replica(replica_fn) + variables.global_variables_initializer().run() + self.assertAllClose([10.0, 11.0], var.eval()) + sess.run(distribution.unwrap(assign)) + self.assertAllClose( + [10 * 0.25 + 1. * (1 - 0.25), + 11 * 0.25 + 2. * (1 - 0.25)], + var.eval()) if __name__ == "__main__": test.main() diff --git a/tensorflow/python/distribute/mirrored_strategy.py b/tensorflow/python/distribute/mirrored_strategy.py index d6d40df5ce..3cd5cf09c0 100644 --- a/tensorflow/python/distribute/mirrored_strategy.py +++ b/tensorflow/python/distribute/mirrored_strategy.py @@ -598,6 +598,9 @@ class MirroredExtended(distribute_lib.DistributionStrategyExtended): return self._cross_device_ops def _reduce_to(self, reduce_op, value, destinations): + if (isinstance(value, values.Mirrored) and + reduce_op == reduce_util.ReduceOp.MEAN): + return value assert not isinstance(value, values.Mirrored) if not isinstance(value, values.DistributedValues): # This function handles reducing values that are not PerReplica or -- GitLab From 33f3b46e1b209be6a64f53562fd4456352c878ee Mon Sep 17 00:00:00 2001 From: "Li, Guizi" Date: Fri, 30 Nov 2018 10:55:15 +0800 Subject: [PATCH 054/873] [Intel MKL] Enable MKL LeakyRelu OP --- tensorflow/core/graph/mkl_layout_pass.cc | 48 ++++++ tensorflow/core/graph/mkl_layout_pass_test.cc | 79 +++++++++ tensorflow/core/kernels/mkl_relu_op.cc | 150 ++++++++++++++---- tensorflow/core/ops/nn_ops.cc | 35 +++- 4 files changed, 282 insertions(+), 30 deletions(-) diff --git a/tensorflow/core/graph/mkl_layout_pass.cc b/tensorflow/core/graph/mkl_layout_pass.cc index 69735aac02..8d7ddbd0c3 100644 --- a/tensorflow/core/graph/mkl_layout_pass.cc +++ b/tensorflow/core/graph/mkl_layout_pass.cc @@ -258,6 +258,8 @@ class MklLayoutRewritePass : public GraphOptimizationPass { csinfo_.fused_batch_norm = "FusedBatchNorm"; csinfo_.fused_batch_norm_grad = "FusedBatchNormGrad"; csinfo_.identity = "Identity"; + csinfo_.leakyrelu = "LeakyRelu"; + csinfo_.leakyrelu_grad = "LeakyReluGrad"; csinfo_.lrn = "LRN"; csinfo_.lrn_grad = "LRNGrad"; csinfo_.matmul = "MatMul"; @@ -381,6 +383,12 @@ class MklLayoutRewritePass : public GraphOptimizationPass { rinfo_.push_back({csinfo_.lrn_grad, mkl_op_registry::GetMklOpName(csinfo_.lrn_grad), CopyAttrsLRN, LrnGradRewrite}); + rinfo_.push_back({csinfo_.leakyrelu, + mkl_op_registry::GetMklOpName(csinfo_.leakyrelu), + CopyAttrsLeakyRelu, LeakyReluRewrite}); + rinfo_.push_back({csinfo_.leakyrelu_grad, + mkl_op_registry::GetMklOpName(csinfo_.leakyrelu_grad), + CopyAttrsLeakyRelu, LeakyReluRewrite}); rinfo_.push_back({csinfo_.max_pool, mkl_op_registry::GetMklOpName(csinfo_.max_pool), CopyAttrsPooling, NonDepthBatchWisePoolRewrite}); @@ -584,6 +592,8 @@ class MklLayoutRewritePass : public GraphOptimizationPass { string fused_batch_norm; string fused_batch_norm_grad; string identity; + string leakyrelu; + string leakyrelu_grad; string lrn; string lrn_grad; string matmul; @@ -891,6 +901,29 @@ class MklLayoutRewritePass : public GraphOptimizationPass { return do_rewrite; } + // To compute LeakyRelu MKL DNN uses (feature), if feature > 0 + // otherwise it uses (feature * alpha) + // while Tensorflow uses max(feature, feature * alpha) to compute LeakyRelu. + // These two algorithm are not consistent when alpha > 1 + // so only LeakyRelu is written to MKL OP when alpha < 1 + static bool LeakyReluRewrite(const Node* n) { + CHECK_NOTNULL(n); + + float alpha; + CHECK_EQ(GetNodeAttr(n->def(), "alpha", &alpha).ok(), true); + + // If the alpha of LeakyRelu is less than 1, rewrite the node. + // Otherwise eigen node is used instead. + if (alpha < 1) { + return true; + } + VLOG(1) << "LeakyReluRewrite: The model sets alpha is not less than 1 " + << "which case is not optimized by Intel MKL, thus using Eigen op" + << "for LeakyRelu "; + + return false; + } + static bool MaxpoolGradRewrite(const Node* n) { CHECK_NOTNULL(n); bool do_rewrite = false; @@ -1078,6 +1111,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass { static void CopyAttrsDataType(const Node* orig_node, NodeBuilder* nb); static void CopyAttrsFusedBatchNorm(const Node* orig_node, NodeBuilder* nb); static void CopyAttrsLRN(const Node* orig_node, NodeBuilder* nb); + static void CopyAttrsLeakyRelu(const Node* orig_node, NodeBuilder* nb); static void CopyAttrsPooling(const Node* orig_node, NodeBuilder* nb); static void CopyAttrsQuantizedPooling(const Node* orig_node, NodeBuilder* nb); static void CopyAttrsQuantizedConv2D(const Node* orig_node, NodeBuilder* nb); @@ -1663,6 +1697,20 @@ void MklLayoutRewritePass::CopyAttrsLRN(const Node* orig_node, nb->Attr("beta", beta); } +void MklLayoutRewritePass::CopyAttrsLeakyRelu(const Node* orig_node, + NodeBuilder* nb) { + DataType T; + float alpha; + + // Get all attributes from old node. + TF_CHECK_OK(GetNodeAttr(orig_node->def(), "T", &T)); + TF_CHECK_OK(GetNodeAttr(orig_node->def(), "alpha", &alpha)); + + // Add attributes to new node. + nb->Attr("T", T); + nb->Attr("alpha", alpha); +} + void MklLayoutRewritePass::CopyAttrsPooling(const Node* orig_node, NodeBuilder* nb) { DataType T; diff --git a/tensorflow/core/graph/mkl_layout_pass_test.cc b/tensorflow/core/graph/mkl_layout_pass_test.cc index 7e2d1f7878..f815838a89 100644 --- a/tensorflow/core/graph/mkl_layout_pass_test.cc +++ b/tensorflow/core/graph/mkl_layout_pass_test.cc @@ -960,6 +960,85 @@ TEST_F(MklLayoutPassTest, NodeRewrite_Relu6Relu6Grad_Positive) { "DMT/_1->C:2"); } +TEST_F(MklLayoutPassTest, NodeRewrite_LeakyRelu_Positive) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'LeakyRelu'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'alpha' value { f: 0.1 } }" + " input: ['A'] }" + "node { name: 'C' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" + " input: ['A', 'B'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(_MklLeakyRelu);C(Zeta);DMT/_0(Const)|A->B;A->C;" + "A:control->DMT/_0:control;B->C:1;DMT/_0->B:1"); +} + +TEST_F(MklLayoutPassTest, NodeRewrite_LeakyRelu_Negative) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'LeakyRelu'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'alpha' value { f: 2.0 } }" + " input: ['A'] }" + "node { name: 'C' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" + " input: ['A', 'B'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(LeakyRelu);C(Zeta)|A->B;A->C;B->C:1"); +} + +TEST_F(MklLayoutPassTest, NodeRewrite_LeakyReluGrad_Positive) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'Input'}" + "node { name: 'C' op: 'LeakyReluGrad'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'alpha' value { f: 0.1 } }" + " input: ['A', 'B'] }" + "node { name: 'D' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" + " input: ['A', 'C'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(Input);C(_MklLeakyReluGrad);D(Zeta);DMT/_0(Const);" + "DMT/_1(Const)|A->C;A->D;A:control->DMT/_0:control;" + "A:control->DMT/_1:control;B->C:1;C->D:1;DMT/_0->C:2;DMT/_1->C:3"); +} + +TEST_F(MklLayoutPassTest, NodeRewrite_LeakyReluGrad_Negative) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'Input'}" + "node { name: 'C' op: 'LeakyReluGrad'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'alpha' value { f: 2.0 } }" + " input: ['A', 'B'] }" + "node { name: 'D' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" + " input: ['A', 'C'] }"); + EXPECT_EQ( + DoMklLayoutOptimizationPass(), + "A(Input);B(Input);C(LeakyReluGrad);D(Zeta)|A->C;A->D;B->C:1;C->D:1"); +} + +TEST_F(MklLayoutPassTest, NodeRewrite_LeakyReluLeakyReluGrad_Positive) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'LeakyRelu'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'alpha' value { f: 0.1 } }" + " input: ['A'] }" + "node { name: 'C' op: 'LeakyReluGrad'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'alpha' value { f: 0.1 } }" + " input: ['A', 'B'] }" + "node { name: 'D' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" + " input: ['A', 'C'] }"); + EXPECT_EQ( + DoMklLayoutOptimizationPass(), + "A(Input);B(_MklLeakyRelu);C(_MklLeakyReluGrad);D(Zeta);DMT/_0(Const);" + "DMT/_1(Const)|A->B;A->C;A->D;A:control->DMT/_0:control;" + "A:control->DMT/_1:control;B->C:1;B:1->C:3;C->D:1;DMT/_0->B:1;" + "DMT/_1->C:2"); +} + TEST_F(MklLayoutPassTest, NodeRewrite_AvgPool_Positive) { InitGraph( "node { name: 'A' op: 'Input'}" diff --git a/tensorflow/core/kernels/mkl_relu_op.cc b/tensorflow/core/kernels/mkl_relu_op.cc index 708213648b..2e29eae41b 100644 --- a/tensorflow/core/kernels/mkl_relu_op.cc +++ b/tensorflow/core/kernels/mkl_relu_op.cc @@ -16,12 +16,12 @@ limitations under the License. // See docs in ../ops/nn_ops.cc. #ifdef INTEL_MKL -#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/lib/core/errors.h" +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #ifndef INTEL_MKL_ML_ONLY #include "mkldnn.hpp" @@ -204,7 +204,7 @@ class MklEltwiseFwdPrimitiveFactory : public MklPrimitiveFactory { ~MklEltwiseFwdPrimitiveFactory() {} static string CreateKey(const MklEltwiseFwdParams& fwdParams, - memory::format src_fmt) { + memory::format src_fmt) { string prefix = "eltwise_fwd"; FactoryKeyCreator key_creator; key_creator.AddAsKey(prefix); @@ -422,8 +422,8 @@ class MklEltwiseBwdPrimitiveFactory : public MklPrimitiveFactory { private: static string CreateKey(const MklEltwiseBwdParams& bwdParams, - const memory::format& src_fmt, - const memory::format& diff_dst_fmt) { + const memory::format& src_fmt, + const memory::format& diff_dst_fmt) { string prefix = "eltwise_bwd"; FactoryKeyCreator key_creator; key_creator.AddAsKey(prefix); @@ -856,9 +856,9 @@ class MklReluOpBase : public OpKernel { Tensor* dst_tensor = nullptr; OP_REQUIRES_OK(context, context->forward_input_or_allocate_output( - {static_cast(src_index)}, - static_cast(dst_index), - tf_shape_dst, &dst_tensor)); + {static_cast(src_index)}, + static_cast(dst_index), + tf_shape_dst, &dst_tensor)); AllocateOutputSetMklShape(context, dst_index, dnn_shape_dst); T* dst_data = dst_tensor->flat().data(); @@ -866,19 +866,20 @@ class MklReluOpBase : public OpKernel { // execute eltwise eltwise_fwd->Execute(src_data, dst_data); } 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__); - OP_REQUIRES_OK(context, - errors::Aborted("Operation received an exception:", - error_msg)); + string error_msg = "Status: " + std::to_string(e.status) + ", message: " + + string(e.message) + ", in file " + string(__FILE__) + + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK( + context, + errors::Aborted("Operation received an exception:", error_msg)); } } private: engine cpu_engine = engine(engine::cpu, 0); std::shared_ptr relu_fwd_pd; + + protected: float alpha_; float beta_; }; @@ -947,11 +948,11 @@ class MklReluGradOpBase : public OpKernel { auto diff_dst_tf_data_format = MklDnnDataFormatToTFDataFormat(diff_dst_mkl_data_format); - src_dims = (src_tensor.dims() == 4) - ? TFShapeToMklDnnDimsInNCHW(src_tensor.shape(), - diff_dst_tf_data_format) - : TFShapeToMklDnnDimsInNCDHW(src_tensor.shape(), - diff_dst_tf_data_format); + src_dims = (src_tensor.dims() == 4) + ? TFShapeToMklDnnDimsInNCHW(src_tensor.shape(), + diff_dst_tf_data_format) + : TFShapeToMklDnnDimsInNCDHW(src_tensor.shape(), + diff_dst_tf_data_format); src_md = memory::desc(src_dims, MklDnnType(), diff_dst_mkl_data_format); } else { @@ -1001,8 +1002,7 @@ class MklReluGradOpBase : public OpKernel { // allocate diff_src tensor MklDnnShape dnn_shape_diff_src; TensorShape tf_shape_diff_src; - if (dnn_shape_src.IsMklTensor() || - dnn_shape_diff_dst.IsMklTensor()) { + if (dnn_shape_src.IsMklTensor() || dnn_shape_diff_dst.IsMklTensor()) { auto diff_src_pd = eltwise_bwd_pd->diff_src_primitive_desc(); dnn_shape_diff_src.SetMklTensor(true); dnn_shape_diff_src.SetMklLayout(&diff_src_pd); @@ -1012,9 +1012,10 @@ class MklReluGradOpBase : public OpKernel { dnn_shape_src.GetSizesAsMklDnnDims(), dnn_shape_src.GetTfDataFormat()); } else { - dnn_shape_diff_src.SetTfLayout(dnn_shape_diff_dst.GetDimension(), - dnn_shape_diff_dst.GetSizesAsMklDnnDims(), - dnn_shape_diff_dst.GetTfDataFormat()); + dnn_shape_diff_src.SetTfLayout( + dnn_shape_diff_dst.GetDimension(), + dnn_shape_diff_dst.GetSizesAsMklDnnDims(), + dnn_shape_diff_dst.GetTfDataFormat()); } tf_shape_diff_src.AddDim(diff_src_pd.get_size() / sizeof(T)); } else { @@ -1033,9 +1034,9 @@ class MklReluGradOpBase : public OpKernel { // execute eltwise bwd eltwise_bwd->Execute(src_data, diff_dst_data, diff_src_data); } 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__); + string error_msg = "Status: " + std::to_string(e.status) + ", message: " + + string(e.message) + ", in file " + string(__FILE__) + + ":" + std::to_string(__LINE__); OP_REQUIRES_OK( context, errors::Aborted("Operation received an exception:", error_msg)); @@ -1045,6 +1046,8 @@ class MklReluGradOpBase : public OpKernel { private: engine cpu_engine = engine(engine::cpu, 0); std::shared_ptr relu_fwd_pd; + + protected: float alpha_; float beta_; }; @@ -1312,8 +1315,84 @@ class MklRelu6GradOp T* out_o = diff_src_tensor->flat().data(); T* user_i = const_cast(src_tensor.flat().data()); T* user_g = const_cast(diff_dst_tensor.flat().data()); - out_o[0] = user_g[0] * user_i[0] > 0 && - (user_i[0] < static_cast(RELU6_UPPER_BOUND)); + out_o[0] = user_g[0] * (user_i[0] > 0 && + (user_i[0] < static_cast(RELU6_UPPER_BOUND))); + return; + } +}; + +template +class MklLeakyReluOp : public MklReluOpBase { + public: + ~MklLeakyReluOp() {} + + explicit MklLeakyReluOp(OpKernelConstruction* context) + : MklReluOpBase(context, 0.0f, 0.0f) { + float alpha; + OP_REQUIRES_OK(context, context->GetAttr("alpha", &alpha)); + OP_REQUIRES(context, alpha < 1, + errors::InvalidArgument("MKL LeakyRelu only support alpha < 1. " + "alpha is: ", + alpha)); + + this->alpha_ = alpha; + } + + virtual void Compute_Scalar(OpKernelContext* context) { + const size_t src_index = 0; // index of src input tensor + const size_t dst_index = 0; // index of dst output tensor + const Tensor& src_tensor = MklGetInput(context, src_index); + MklDnnShape dnn_shape_src; + GetMklShape(context, src_index, &dnn_shape_src); + + Tensor* dst_tensor = nullptr; + T* user_i = const_cast(src_tensor.flat().data()); + MklDnnShape dnn_shape_dst; + dnn_shape_dst.SetMklTensor(false); + AllocateOutputSetMklShape(context, dst_index, &dst_tensor, + src_tensor.shape(), dnn_shape_dst); + T* out_o = dst_tensor->flat().data(); + out_o[0] = std::max(user_i[0], user_i[0] * this->alpha_); + return; + } +}; + +template +class MklLeakyReluGradOp : public MklReluGradOpBase { + public: + ~MklLeakyReluGradOp() {} + + explicit MklLeakyReluGradOp(OpKernelConstruction* context) + : MklReluGradOpBase(context, 0.0f, 0.0f) { + float alpha; + OP_REQUIRES_OK(context, context->GetAttr("alpha", &alpha)); + OP_REQUIRES(context, alpha < 1, + errors::InvalidArgument("MKL LeakyRelu only support alpha < 1. " + "alpha is: ", + alpha)); + + this->alpha_ = alpha; + } + + virtual void Compute_Scalar(OpKernelContext* context) { + const size_t diff_dst_index = 0; // index of diff_dst input tensor + const size_t src_index = 1; // index of src input tensor + const size_t diff_src_index = 0; // index of diff_src output tensor + const Tensor& src_tensor = MklGetInput(context, src_index); + const Tensor& diff_dst_tensor = MklGetInput(context, diff_dst_index); + Tensor* diff_src_tensor = nullptr; + + MklDnnShape dnn_shape_diff_dst; + GetMklShape(context, diff_dst_index, &dnn_shape_diff_dst); + + MklDnnShape dnn_shape_diff_src; + dnn_shape_diff_src.SetMklTensor(false); + AllocateOutputSetMklShape(context, diff_src_index, &diff_src_tensor, + diff_dst_tensor.shape(), dnn_shape_diff_src); + T* out_o = diff_src_tensor->flat().data(); + T* user_i = const_cast(src_tensor.flat().data()); + T* user_g = const_cast(diff_dst_tensor.flat().data()); + out_o[0] = user_i[0] > 0 ? user_g[0] : user_g[0] * this->alpha_; return; } }; @@ -1376,6 +1455,19 @@ TF_CALL_float(REGISTER_TANH_MKL_SUPPORTED_KERNELS_TYPES); MklRelu6GradOp); TF_CALL_float(REGISTER_RELU6_MKL_SUPPORTED_KERNELS_TYPES); +#define REGISTER_LeakyRelu_MKL_SUPPORTED_KERNELS_TYPES(type) \ + REGISTER_KERNEL_BUILDER(Name("_MklLeakyRelu") \ + .Device(DEVICE_CPU) \ + .TypeConstraint("T") \ + .Label(mkl_op_registry::kMklOpLabel), \ + MklLeakyReluOp); \ + REGISTER_KERNEL_BUILDER(Name("_MklLeakyReluGrad") \ + .Device(DEVICE_CPU) \ + .TypeConstraint("T") \ + .Label(mkl_op_registry::kMklOpLabel), \ + MklLeakyReluGradOp); +TF_CALL_float(REGISTER_LeakyRelu_MKL_SUPPORTED_KERNELS_TYPES); + #endif } // namespace tensorflow diff --git a/tensorflow/core/ops/nn_ops.cc b/tensorflow/core/ops/nn_ops.cc index efa84d6c22..ea26f7d2be 100644 --- a/tensorflow/core/ops/nn_ops.cc +++ b/tensorflow/core/ops/nn_ops.cc @@ -1915,6 +1915,40 @@ NOTE Do not invoke this operator directly in Python. Graph rewrite pass is expected to invoke these operators. )doc"); +REGISTER_OP("_MklLeakyRelu") + .Input("features: T") + .Input("mkl_features: uint8") + .Output("activations: T") + .Output("mkl_activations: uint8") + .Attr("T: {half, float, double} = DT_FLOAT") + .Attr("alpha: float = 0.2") + .SetShapeFn(shape_inference::UnchangedShape) + .Doc(R"doc( +MKL version of LeakyRelu operator. Uses MKL DNN APIs to implement +LeakyRelu operator. + +NOTE Do not invoke this operator directly in Python. Graph rewrite pass is +expected to invoke these operators. +)doc"); + +REGISTER_OP("_MklLeakyReluGrad") + .Input("gradients: T") + .Input("features: T") + .Input("mkl_gradients: uint8") + .Input("mkl_features: uint8") + .Output("backprops: T") + .Output("mkl_backprops: uint8") + .Attr("T: {half, float, double} = DT_FLOAT") + .Attr("alpha: float = 0.2") + .SetShapeFn(shape_inference::MergeBothInputsShapeFn) + .Doc(R"doc( +MKL version of LeakyReluGrad operator. Uses MKL DNN APIs to compute rectified +linear gradients for LeakyReluGrad operation. + +NOTE Do not invoke this operator directly in Python. Graph rewrite pass is +expected to invoke these operators. +)doc"); + REGISTER_OP("_MklElu") .Input("features: T") .Input("mkl_features: uint8") @@ -2110,7 +2144,6 @@ NOTE Do not invoke this operator directly in Python. Graph rewrite pass is expected to invoke these operators. )doc"); - REGISTER_OP("_MklAvgPool3DGrad") .Input("orig_input_shape: int32") .Input("grad: T") -- GitLab From f32d071589507c755f524f9d94ea4ee4174c9498 Mon Sep 17 00:00:00 2001 From: Pan Daoxin Date: Fri, 30 Nov 2018 11:09:48 +0800 Subject: [PATCH 055/873] Enable reorder cache for MklSlice. --- tensorflow/core/kernels/mkl_slice_op.cc | 201 +++++++++++++++++++----- 1 file changed, 165 insertions(+), 36 deletions(-) diff --git a/tensorflow/core/kernels/mkl_slice_op.cc b/tensorflow/core/kernels/mkl_slice_op.cc index 85cabeb92b..f32a6003af 100644 --- a/tensorflow/core/kernels/mkl_slice_op.cc +++ b/tensorflow/core/kernels/mkl_slice_op.cc @@ -60,8 +60,10 @@ typedef Eigen::ThreadPoolDevice CPUDevice; // A version of SharedValidation (slice_op.h) written for input that is in // either Mkl layout or Tensorflow layout. -// A shared code to validate input shapes and check for identity, which is not dependent on the type of T. -// We do this to reduce code size by not duplicating all this for all T (float, double, int32, etc.) +// A shared code to validate input shapes and check for identity, which is not +// dependent on the type of T. +// We do this to reduce code size by not duplicating all this for all T (float, +// double, int32, etc.) static void ValidateMklInputs(OpKernelContext* context, bool* is_identity, gtl::InlinedVector* begin, gtl::InlinedVector* size) { @@ -157,13 +159,149 @@ static void CheckCommonCasesForMklInputs(OpKernelContext* context, } } +// This structure aggregates multiple inputs to Slice methods. +// Parameters from & to represents memory pointing to reorder. +// Parameters begin_dims & size_dims represents offset and length +// passed to view primitive. +struct MklSliceParams { + const memory* from; + const memory* to; + memory::dims begin_dims; + memory::dims size_dims; + + MklSliceParams(const memory* from, const memory* to, memory::dims begin_dims, + memory::dims size_dims) + : from(from), to(to), begin_dims(begin_dims), size_dims(size_dims) {} +}; + +// This implements the reuse interface of Slice reorders. +template +class MklSlicePrimitive : public MklPrimitive { + public: + explicit MklSlicePrimitive(const MklSliceParams& sliceParams) { + context_.slice_stream.reset(new stream(stream::kind::eager)); + Setup(sliceParams); + } + + ~MklSlicePrimitive() {} + + void Execute(const MklSliceParams& sliceParams) { + context_.src_mem->set_data_handle(sliceParams.from->get_data_handle()); + context_.dst_mem->set_data_handle(sliceParams.to->get_data_handle()); + context_.slice_stream->submit(context_.slice_primitives); + + context_.src_mem->set_data_handle(DummyData); + context_.dst_mem->set_data_handle(DummyData); + return; + } + + std::shared_ptr GetPrimitive() { return context_.reorder_prim; } + + private: + struct SliceContext { + std::shared_ptr src_mem; + std::shared_ptr dst_mem; + std::shared_ptr reorder_prim; + std::shared_ptr reorder_pd; + std::shared_ptr view_pd; + std::shared_ptr slice_stream; + std::vector slice_primitives; + SliceContext() + : src_mem(nullptr), dst_mem(nullptr), reorder_prim(nullptr) {} + } context_; + + engine cpu_engine_ = engine(engine::cpu, 0); + + void Setup(const MklSliceParams& sliceParams) { + context_.src_mem.reset( + new memory({sliceParams.from->get_primitive_desc().desc(), cpu_engine_}, + DummyData)); + context_.dst_mem.reset(new memory( + {sliceParams.to->get_primitive_desc().desc(), cpu_engine_}, DummyData)); + auto src_pd = context_.src_mem->get_primitive_desc(); + auto dst_pd = context_.dst_mem->get_primitive_desc(); + context_.view_pd = + std::make_shared(view::primitive_desc( + src_pd, sliceParams.size_dims, sliceParams.begin_dims)); + context_.reorder_pd = + std::make_shared(reorder::primitive_desc( + context_.view_pd->dst_primitive_desc(), dst_pd)); + context_.reorder_prim = std::make_shared( + reorder(*context_.reorder_pd, *context_.src_mem, *context_.dst_mem)); + context_.slice_primitives.push_back(*context_.reorder_prim); + } +}; + +template +class MklSlicePrimitiveFactory : public MklPrimitiveFactory { + public: + static MklSlicePrimitive* Get(const MklSliceParams& sliceParams) { + auto reorderPrim = static_cast*>( + MklSlicePrimitiveFactory::GetInstance().GetReorder(sliceParams)); + if (reorderPrim == nullptr) { + reorderPrim = new MklSlicePrimitive(sliceParams); + MklSlicePrimitiveFactory::GetInstance().SetReorder(sliceParams, + reorderPrim); + } + return reorderPrim; + } + + static MklSlicePrimitiveFactory& GetInstance() { + static MklSlicePrimitiveFactory instance_; + return instance_; + } + + private: + MklSlicePrimitiveFactory() {} + ~MklSlicePrimitiveFactory() {} + + static string CreateKey(const MklSliceParams& sliceParams) { + string prefix = "reorder"; + FactoryKeyCreator key_creator; + auto const& from_desc = sliceParams.from->get_primitive_desc().desc().data; + auto const& to_desc = sliceParams.to->get_primitive_desc().desc().data; + const int KIdxFirstStride = 0; + memory::dims from_dims(from_desc.dims, &from_desc.dims[from_desc.ndims]); + memory::dims to_dims(to_desc.dims, &to_desc.dims[to_desc.ndims]); + memory::dims from_strides( + from_desc.layout_desc.blocking.strides[KIdxFirstStride], + &from_desc.layout_desc.blocking.strides[KIdxFirstStride] + [from_desc.ndims]); + memory::dims to_strides( + to_desc.layout_desc.blocking.strides[KIdxFirstStride], + &to_desc.layout_desc.blocking.strides[KIdxFirstStride][to_desc.ndims]); + key_creator.AddAsKey(prefix); + key_creator.AddAsKey(static_cast(from_desc.format)); + key_creator.AddAsKey(static_cast(from_desc.data_type)); + key_creator.AddAsKey(from_dims); + key_creator.AddAsKey(from_strides); + key_creator.AddAsKey(static_cast(to_desc.format)); + key_creator.AddAsKey(static_cast(to_desc.data_type)); + key_creator.AddAsKey(to_dims); + key_creator.AddAsKey(to_strides); + key_creator.AddAsKey(sliceParams.begin_dims); + key_creator.AddAsKey(sliceParams.size_dims); + return key_creator.GetKey(); + } + + MklPrimitive* GetReorder(const MklSliceParams& sliceParams) { + string key = CreateKey(sliceParams); + return this->GetOp(key); + } + + void SetReorder(const MklSliceParams& sliceParams, MklPrimitive* op) { + string key = CreateKey(sliceParams); + this->SetOp(key, op); + } +}; + // MKL-DNN implementation of Slice template -class MklDnnSliceOp : public OpKernel { +class MklSliceOp : public OpKernel { public: - explicit MklDnnSliceOp(OpKernelConstruction* context) : OpKernel(context) {} + explicit MklSliceOp(OpKernelConstruction* context) : OpKernel(context) {} - ~MklDnnSliceOp() {} + ~MklSliceOp() {} void Compute(OpKernelContext* context) override { gtl::InlinedVector begin; @@ -179,17 +317,17 @@ class MklDnnSliceOp : public OpKernel { if (begin.size() >= 8) { OP_REQUIRES( context, false, - errors::Unimplemented("MklDnnSliceOp : Unhandled input dimensions")); + errors::Unimplemented("MklSliceOp : Unhandled input dimensions")); } - ComputeMklDnnSlice(context, begin, size); + ComputeMklSlice(context, begin, size); } private: // Slice op implemented using MKL-DNN APIs. - void ComputeMklDnnSlice(OpKernelContext* context, - const gtl::InlinedVector& begin, - const gtl::InlinedVector& size) { + void ComputeMklSlice(OpKernelContext* context, + const gtl::InlinedVector& begin, + const gtl::InlinedVector& size) { try { // MKL-DNN API usage below is guided by description at: // https://github.com/01org/mkl-dnn/issues/69 @@ -200,16 +338,15 @@ class MklDnnSliceOp : public OpKernel { // probably change the format). Then your steps are: // // 1. create memory primitive descriptor in_mem_pd and memory primitive - // in_mem_p for the entire source data. - // 2. create view primitive descriptor in_submem_pd based on in_mem_pd, - // initial offsets, and sub-sizes - // 3. create memory primitive descriptor out_mem_pd and memory primitive + // in_mem_p for the entire source data. create view primitive + // descriptor + // in_submem_pd based on in_mem_pd, initial offsets, and sub-sizes + // 2. create memory primitive descriptor out_mem_pd and memory primitive // out_mem_p for the output (the logical sizes should match sub-sizes - // used in step 2, but the format might be arbitrary) - // 4. create reorder primitive descriptor reorder_pd based on in_submem_pd - // and out_mem_pd - // 5. create reorder primitive itself based on reorder_pd, in_mem_p, and - // out_mem_p. + // used in step 1, but the format might be arbitrary) + // 3. create reorder primitive descriptor reorder_pd based on in_submem_pd + // and out_mem_pd. create reorder primitive itself based on reorder_pd, + // in_mem_p, and out_mem_p. // // Please notice that there is no view primitive. There is only view // primitive descriptor. And the reorder uses source memory as input but @@ -268,32 +405,24 @@ class MklDnnSliceOp : public OpKernel { src.SetUsrMem(input_md, &input_tensor); } - // Step 2 - create view primitive descriptor - auto view_pd = - view::primitive_desc(src.GetUsrMemPrimDesc(), size_dims, begin_dims) - .dst_primitive_desc(); + // Step 2 - Create memory for output. auto output_strides = CalculateTFStrides(size_dims); auto output_md = MklDnnData::CreateBlockedMemDesc(size_dims, output_strides); auto output_pd = memory::primitive_desc(output_md, cpu_engine); - - // Step 3 - Create memory for output. If input is in MklDnn layout, then - // output is also in MklDnn layout. Otherwise, output is in Tensorflow - // layout. AllocateOutputTensor(context, input_mkl_shape, &output_pd, size_dims, &output_tensor, &output_mkl_shape); DCHECK(output_tensor); DCHECK_EQ(input_mkl_shape.IsMklTensor(), output_mkl_shape.IsMklTensor()); output.SetUsrMem(output_md, output_tensor); - std::vector net; - // Step 4 - create reorder primitive desc between view_pd and output_pd. - auto reorder_pd = - reorder::primitive_desc(view_pd, output.GetUsrMemPrimDesc()); - // Step 5 - create reorder primitive itself. - net.push_back(reorder(reorder_pd, *src.GetUsrMem(), *output.GetUsrMem())); - // Execute the reorder primitive. - stream(stream::kind::eager).submit(net).wait(); + // Step 3 - create reorder primitive. + MklSliceParams sliceParams(src.GetUsrMem(), output.GetUsrMem(), + begin_dims, size_dims); + MklSlicePrimitive* reorder_prim = + MklSlicePrimitiveFactory::Get(sliceParams); + // Execute slice reorder. + reorder_prim->Execute(sliceParams); } catch (mkldnn::error& e) { string error_msg = "Status: " + std::to_string(e.status) + ", message: " + string(e.message) + ", in file " + string(__FILE__) + @@ -347,7 +476,7 @@ class MklDnnSliceOp : public OpKernel { .HostMemory("begin") \ .HostMemory("size") \ .Label(mkl_op_registry::kMklOpLabel), \ - MklDnnSliceOp); + MklSliceOp); TF_CALL_float(REGISTER_MKL_SLICE); #undef REGISTER_MKL_SLICE -- GitLab From 3b57fb8afe285c9261d6c8c5fbc0900e58dfc15d Mon Sep 17 00:00:00 2001 From: Younes Khoudli Date: Fri, 30 Nov 2018 11:40:11 +0100 Subject: [PATCH 056/873] devices: inform user that tensorflow wasn't compiled with CUDA support The current behaviour can be confusing when no GPU are found on a computer that does have GPUs, so add a warning. --- tensorflow/core/grappler/devices.cc | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/tensorflow/core/grappler/devices.cc b/tensorflow/core/grappler/devices.cc index 3268697671..ddde6a504e 100644 --- a/tensorflow/core/grappler/devices.cc +++ b/tensorflow/core/grappler/devices.cc @@ -47,9 +47,13 @@ int GetNumAvailableGPUs() { } } } -#endif // GOOGLE_CUDA LOG(INFO) << "Number of eligible GPUs (core count >= 8): " << num_eligible_gpus; +#else + LOG(INFO) << "Number of eligible GPUs (core count >= 8): " + << num_eligible_gpus + << " (Note: TensorFlow was not compiled with CUDA support)"; +#endif // GOOGLE_CUDA return num_eligible_gpus; } -- GitLab From cc518eea2df346f061a7753efc6d5430d939548e Mon Sep 17 00:00:00 2001 From: "William D. Irons" Date: Fri, 30 Nov 2018 15:28:08 -0600 Subject: [PATCH 057/873] Add link to CPU Artifacts to README.md for ppc64le adds links to cpu artifiacts for nightly and release builds Replaces "IBM ppc64le" with "Linux ppc64le" Uses the build of every commit for build status and not the nightly artifact build. - This last change is also made to the GPU build in this commit --- README.md | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 044174947a..68d7e180d1 100644 --- a/README.md +++ b/README.md @@ -113,9 +113,10 @@ The TensorFlow project strives to abide by generally accepted best practices in Build Type | Status | Artifacts ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------- **IBM s390x** | [![Build Status](http://ibmz-ci.osuosl.org/job/TensorFlow_IBMZ_CI/badge/icon)](http://ibmz-ci.osuosl.org/job/TensorFlow_IBMZ_CI/) | TBA -**IBM ppc64le CPU** | [![Build Status](http://powerci.osuosl.org/job/TensorFlow_PPC64LE_CPU_Build/badge/icon)](http://powerci.osuosl.org/job/TensorFlow_PPC64LE_CPU_Build/) | TBA -**IBM ppc64le GPU** Nightly | [![Build Status](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_GPU_Nightly_Artifact/badge/icon)](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_GPU_Nightly_Artifact/) | [Nightly](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_GPU_Nightly_Artifact/) -**IBM ppc64le GPU** Stable Release | [![Build Status](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_GPU_Release_Build/badge/icon)](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_GPU_Release_Build/) | [Release](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_GPU_Release_Build/) +**Linux ppc64le CPU** Nightly | [![Build Status](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_CPU_Build/badge/icon)](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_CPU_Build/) | [Nightly](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_CPU_Nightly_Artifact/) +**Linux ppc64le CPU** Stable Release | [![Build Status](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_CPU_Release_Build/badge/icon)](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_CPU_Release_Build/) | [Release](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_CPU_Release_Build/) +**Linux ppc64le GPU** Nightly | [![Build Status](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_GPU_Build/badge/icon)](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_GPU_Build/) | [Nightly](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_GPU_Nightly_Artifact/) +**Linux ppc64le GPU** Stable Release | [![Build Status](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_GPU_Release_Build/badge/icon)](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_GPU_Release_Build/) | [Release](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_GPU_Release_Build/) **Linux CPU with Intel® MKL-DNN** Nightly | [![Build Status](https://tensorflow-ci.intel.com/job/tensorflow-mkl-linux-cpu/badge/icon)](https://tensorflow-ci.intel.com/job/tensorflow-mkl-linux-cpu/) | [Nightly](https://tensorflow-ci.intel.com/job/tensorflow-mkl-build-whl-nightly/) **Linux CPU with Intel® MKL-DNN** Python 2.7
**Linux CPU with Intel® MKL-DNN** Python 3.4
**Linux CPU with Intel® MKL-DNN** Python 3.5
**Linux CPU with Intel® MKL-DNN** Python 3.6 | [![Build Status](https://tensorflow-ci.intel.com/job/tensorflow-mkl-build-release-whl/badge/icon)](https://tensorflow-ci.intel.com/job/tensorflow-mkl-build-release-whl/lastStableBuild) | [1.11.0 py2.7](https://storage.googleapis.com/intel-optimized-tensorflow/tensorflow-1.11.0-cp27-cp27mu-linux_x86_64.whl)
[1.11.0 py3.4](https://storage.googleapis.com/intel-optimized-tensorflow/tensorflow-1.11.0-cp34-cp34m-linux_x86_64.whl)
[1.11.0 py3.5](https://storage.googleapis.com/intel-optimized-tensorflow/tensorflow-1.11.0-cp35-cp35m-linux_x86_64.whl)
[1.11.0 py3.6](https://storage.googleapis.com/intel-optimized-tensorflow/tensorflow-1.11.0-cp36-cp36m-linux_x86_64.whl) -- GitLab From 97eedeb115372c4a5f9ce77c851b68c211ca36d5 Mon Sep 17 00:00:00 2001 From: Niranjan Hasabnis Date: Fri, 30 Nov 2018 16:12:27 -0800 Subject: [PATCH 058/873] [Intel MKL] Adding support to handle FusedConv2D This commit adds support to handle Grappler-fused Conv2D operators in MKL layout pass. Some changes are from clang format check, and not related to handling of fusion. --- tensorflow/core/graph/mkl_layout_pass.cc | 59 +++- tensorflow/core/graph/mkl_layout_pass_test.cc | 104 +++++- tensorflow/core/kernels/BUILD | 25 ++ tensorflow/core/kernels/mkl_conv_ops.cc | 132 ++++++-- tensorflow/core/kernels/mkl_fused_ops_test.cc | 306 ++++++++++++++++++ tensorflow/core/ops/mkl_nn_ops.cc | 27 ++ 6 files changed, 616 insertions(+), 37 deletions(-) create mode 100644 tensorflow/core/kernels/mkl_fused_ops_test.cc diff --git a/tensorflow/core/graph/mkl_layout_pass.cc b/tensorflow/core/graph/mkl_layout_pass.cc index 69735aac02..4a53b7edc5 100644 --- a/tensorflow/core/graph/mkl_layout_pass.cc +++ b/tensorflow/core/graph/mkl_layout_pass.cc @@ -257,6 +257,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass { csinfo_.conv3d_grad_filter = "Conv3DBackpropFilterV2"; csinfo_.fused_batch_norm = "FusedBatchNorm"; csinfo_.fused_batch_norm_grad = "FusedBatchNormGrad"; + csinfo_.fused_conv2d = "_FusedConv2D"; csinfo_.identity = "Identity"; csinfo_.lrn = "LRN"; csinfo_.lrn_grad = "LRNGrad"; @@ -271,6 +272,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass { csinfo_.mkl_conv2d_with_bias = "_MklConv2DWithBias"; csinfo_.mkl_conv2d_grad_filter_with_bias = "_MklConv2DBackpropFilterWithBias"; + csinfo_.mkl_fused_conv2d = "_MklFusedConv2D"; // Temporarily don't convert quantized operators into MKL versions for now. // TODO(Intel-tf) Once all the relevant PRs have been merged then remove // the ifdef. @@ -373,6 +375,8 @@ class MklLayoutRewritePass : public GraphOptimizationPass { {csinfo_.fused_batch_norm_grad, mkl_op_registry::GetMklOpName(csinfo_.fused_batch_norm_grad), CopyAttrsFusedBatchNorm, AlwaysRewrite}); + rinfo_.push_back({csinfo_.fused_conv2d, csinfo_.mkl_fused_conv2d, + CopyAttrsFusedConv2D, FusedConv2DRewrite}); rinfo_.push_back({csinfo_.identity, mkl_op_registry::GetMklOpName(csinfo_.identity), CopyAttrsDataType, AlwaysRewrite}); @@ -583,6 +587,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass { string conv3d_grad_filter; string fused_batch_norm; string fused_batch_norm_grad; + string fused_conv2d; string identity; string lrn; string lrn_grad; @@ -597,6 +602,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass { string mkl_conv2d_grad_filter; string mkl_conv2d_grad_filter_with_bias; string mkl_conv2d_with_bias; + string mkl_fused_conv2d; string mul; string quantized_avg_pool; string quantized_conv2d; @@ -923,6 +929,19 @@ class MklLayoutRewritePass : public GraphOptimizationPass { return false; } + static bool FusedConv2DRewrite(const Node* n) { + // MKL DNN currently doesn't support all fusions that grappler fuses + // together with + // Conv2D (ex. batchnorm). We rewrite _FusedConv2D only if it includes those + // we + // support. + + std::vector fused_ops; + CHECK_EQ(GetNodeAttr(n->def(), "fused_ops", &fused_ops).ok(), true); + return (fused_ops == {"BiasAdd"} || fused_ops == {"Relu"} || + fused_ops == {"BiasAdd", "Relu"}); + } + // Rewrites input node to a new node specified by its matching rewrite info. // // Method first searches matching rewrite info for input node and then @@ -1077,6 +1096,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass { static void CopyAttrsConv(const Node* orig_node, NodeBuilder* nb); static void CopyAttrsDataType(const Node* orig_node, NodeBuilder* nb); static void CopyAttrsFusedBatchNorm(const Node* orig_node, NodeBuilder* nb); + static void CopyAttrsFusedConv2D(const Node* orig_node, NodeBuilder* nb); static void CopyAttrsLRN(const Node* orig_node, NodeBuilder* nb); static void CopyAttrsPooling(const Node* orig_node, NodeBuilder* nb); static void CopyAttrsQuantizedPooling(const Node* orig_node, NodeBuilder* nb); @@ -1282,10 +1302,12 @@ int MklLayoutRewritePass::SetUpContiguousInputs( CHECK_NOTNULL(filter_node); // Now check which nodes receive from filter_node. Filter feeds as - // 2nd input (slot 1) of _MklConv2D and _MklConv2DWithBias. + // 2nd input (slot 1) of _MklConv2D, _MklConv2DWithBias, and + // _MklFusedConv2D. for (const Edge* e : filter_node->out_edges()) { if ((e->dst()->type_string() == csinfo_.mkl_conv2d || - e->dst()->type_string() == csinfo_.mkl_conv2d_with_bias) && + e->dst()->type_string() == csinfo_.mkl_conv2d_with_bias || + e->dst()->type_string() == csinfo_.mkl_fused_conv2d) && e->dst_input() == kConv2DFilterInputSlotIdx /* filter is 2nd input of Conv2D and _MklConv2D. */) { if (conv2d_node != nullptr) { @@ -1853,6 +1875,38 @@ void MklLayoutRewritePass::CopyAttrsFusedBatchNorm(const Node* orig_node, nb->Attr("is_training", is_training); } +void MklLayoutRewritePass::CopyAttrsFusedConv2D(const Node* orig_node, + NodeBuilder* nb) { + DataType T; + int num_args; + float epsilon; + string data_format; + string padding; + std::vector strides; + std::vector dilations; + std::vector fused_ops; + + // Get all attributes from old node. + TF_CHECK_OK(GetNodeAttr(orig_node->def(), "T", &T)); + TF_CHECK_OK(GetNodeAttr(orig_node->def(), "num_args", &num_args)); + TF_CHECK_OK(GetNodeAttr(orig_node->def(), "strides", &strides)); + TF_CHECK_OK(GetNodeAttr(orig_node->def(), "padding", &padding)); + TF_CHECK_OK(GetNodeAttr(orig_node->def(), "data_format", &data_format)); + TF_CHECK_OK(GetNodeAttr(orig_node->def(), "dilations", &dilations)); + TF_CHECK_OK(GetNodeAttr(orig_node->def(), "fused_ops", &fused_ops)); + TF_CHECK_OK(GetNodeAttr(orig_node->def(), "epsilon", &epsilon)); + + // Add attributes to new node. + nb->Attr("T", T); + nb->Attr("num_args", num_args); + nb->Attr("strides", strides); + nb->Attr("padding", padding); + nb->Attr("data_format", data_format); + nb->Attr("dilations", dilations); + nb->Attr("fused_ops", fused_ops); + nb->Attr("epsilon", epsilon); +} + ////////////////////////////////////////////////////////////////////////// // Helper functions related to node merge pass ////////////////////////////////////////////////////////////////////////// @@ -2333,6 +2387,7 @@ MklLayoutRewritePass::CheckForNodeRewrite(const Node* n) const { // names. if (n->type_string() != csinfo_.conv2d_with_bias && n->type_string() != csinfo_.conv2d_grad_filter_with_bias && + n->type_string() != csinfo_.fused_conv2d && !mkl_op_registry::IsMklOp(mkl_op_registry::GetMklOpName(n->type_string()), T)) { return nullptr; diff --git a/tensorflow/core/graph/mkl_layout_pass_test.cc b/tensorflow/core/graph/mkl_layout_pass_test.cc index 7e2d1f7878..af27bc4ca8 100644 --- a/tensorflow/core/graph/mkl_layout_pass_test.cc +++ b/tensorflow/core/graph/mkl_layout_pass_test.cc @@ -133,7 +133,7 @@ REGISTER_OP("_MklInput2") .SetIsStateful(); ///////////////////////////////////////////////////////////////////// -// Unit tests related to node merge optiimization +// Unit tests related to node merge optimization ///////////////////////////////////////////////////////////////////// TEST_F(MklLayoutPassTest, Basic) { @@ -534,6 +534,108 @@ TEST_F(MklLayoutPassTest, NodeRewrite_Conv2D_Negative_UnsupportedType) { "A->C;B->C:1;B->D;C->D:1"); } +// Rewrite test for _FusedConv2D Op with BiasAdd fusion +TEST_F(MklLayoutPassTest, NodeRewrite_FusedConv2D_Positive1) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'Input'}" + "node { name: 'C' op: 'Input'}" + "node { name: 'D' op: '_FusedConv2D'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'num_args' value { i: 1 } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " 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} } }" + " attr { key: 'fused_ops' value { list: {s: 'BiasAdd'} } }" + " attr { key: 'epsilon' value { f: 0.001 }}" + " input: ['A', 'B', 'C']}" + "node { name: 'E' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" + " input: ['D', 'C'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(Input);C(Input);D(_MklFusedConv2D);DMT/_0(Const);" + "DMT/_1(Const);DMT/_2(Const);E(Zeta)|A->D;" + "A:control->DMT/_0:control;A:control->DMT/_1:control;" + "A:control->DMT/_2:control;B->D:1;C->D:2;C->E:1;D->E;" + "DMT/_0->D:3;DMT/_1->D:4;DMT/_2->D:5"); +} + +// Rewrite test for _FusedConv2D Op with Relu fusion +TEST_F(MklLayoutPassTest, NodeRewrite_FusedConv2D_Positive2) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'Input'}" + "node { name: 'C' op: 'Input'}" + "node { name: 'D' op: '_FusedConv2D'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'num_args' value { i: 1 } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " 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} } }" + " attr { key: 'fused_ops' value { list: {s: 'Relu'} } }" + " attr { key: 'epsilon' value { f: 0.001 }}" + " input: ['A', 'B', 'C']}" + "node { name: 'E' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" + " input: ['D', 'C'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(Input);C(Input);D(_MklFusedConv2D);DMT/_0(Const);" + "DMT/_1(Const);DMT/_2(Const);E(Zeta)|A->D;" + "A:control->DMT/_0:control;A:control->DMT/_1:control;" + "A:control->DMT/_2:control;B->D:1;C->D:2;C->E:1;D->E;" + "DMT/_0->D:3;DMT/_1->D:4;DMT/_2->D:5"); +} + +// Rewrite test for _FusedConv2D Op with BiasAdd+Relu fusion +TEST_F(MklLayoutPassTest, NodeRewrite_FusedConv2D_Positive3) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'Input'}" + "node { name: 'C' op: 'Input'}" + "node { name: 'D' op: '_FusedConv2D'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'num_args' value { i: 1 } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " 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} } }" + " attr { key: 'fused_ops'" + " value { list: {s: 'BiasAdd', s: 'Relu'} } }" + " attr { key: 'epsilon' value { f: 0.001 }}" + " input: ['A', 'B', 'C']}" + "node { name: 'E' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" + " input: ['D', 'C'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(Input);C(Input);D(_MklFusedConv2D);DMT/_0(Const);" + "DMT/_1(Const);DMT/_2(Const);E(Zeta)|A->D;" + "A:control->DMT/_0:control;A:control->DMT/_1:control;" + "A:control->DMT/_2:control;B->D:1;C->D:2;C->E:1;D->E;" + "DMT/_0->D:3;DMT/_1->D:4;DMT/_2->D:5"); +} + +// Rewrite test for _FusedConv2D Op with unsupported fusion +TEST_F(MklLayoutPassTest, NodeRewrite_FusedConv2D_Negative1) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'Input'}" + "node { name: 'C' op: 'Input'}" + "node { name: 'D' op: '_FusedConv2D'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'num_args' value { i: 1 } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " 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} } }" + " attr { key: 'fused_ops' value { list: {s: 'Unsupported'} } }" + " attr { key: 'epsilon' value { f: 0.001 }}" + " input: ['A', 'B', 'C']}" + "node { name: 'E' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" + " input: ['D', 'C'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(Input);C(Input);D(_FusedConv2D);E(Zeta)|A->D;" + "B->D:1;C->D:2;C->E:1;D->E"); +} + TEST_F(MklLayoutPassTest, NodeRewrite_Conv2DGradFilter_Positive) { InitGraph( "node { name: 'A' op: 'Input'}" diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD index 60accc0f9b..61128abc7b 100644 --- a/tensorflow/core/kernels/BUILD +++ b/tensorflow/core/kernels/BUILD @@ -6741,6 +6741,31 @@ tf_mkl_kernel_library( deps = NN_DEPS + mkl_deps() + [":cwise_op"], ) +tf_cc_test_mkl( + name = "mkl_fused_ops_test", + size = "small", + srcs = ["mkl_fused_ops_test.cc"], + linkstatic = 1, + deps = [ + ":conv_ops", + ":image", + ":mkl_conv_op", + ":mkl_tfconv_op", + ":ops_testutil", + ":ops_util", + "//tensorflow/cc:cc_ops", + "//tensorflow/core:core_cpu", + "//tensorflow/core:framework", + "//tensorflow/core:framework_internal", + "//tensorflow/core:lib", + "//tensorflow/core:protos_all_cc", + "//tensorflow/core:tensorflow", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + "//tensorflow/core:testlib", + ], +) + tf_mkl_kernel_library( name = "mkl_transpose_op", srcs = [ diff --git a/tensorflow/core/kernels/mkl_conv_ops.cc b/tensorflow/core/kernels/mkl_conv_ops.cc index 4b0ced3340..db07bc5d58 100644 --- a/tensorflow/core/kernels/mkl_conv_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_ops.cc @@ -465,19 +465,18 @@ class MklConvOp : public OpKernel { filter.shape().DebugString())); for (int i = 0; i < 3; i++) { - OP_REQUIRES( - context, - FastBoundsCheck(filter.dim_size(i), std::numeric_limits::max()), - errors::InvalidArgument("filter too large")); + OP_REQUIRES(context, FastBoundsCheck(filter.dim_size(i), + std::numeric_limits::max()), + errors::InvalidArgument("filter too large")); } const int64 input_depth = input_in_mkl_format ? GetMklTensorDim(mkl_context.input_shape, 'C') : GetTensorDim(input, data_format_, 'C'); - OP_REQUIRES(context, input_depth == filter.dim_size(2), - errors::InvalidArgument( - "input and filter must have the same depth: ", input_depth, - " vs ", filter.dim_size(2))); + OP_REQUIRES( + context, input_depth == filter.dim_size(2), + errors::InvalidArgument("input and filter must have the same depth: ", + input_depth, " vs ", filter.dim_size(2))); // The last dimension for filter is out_depth. const int out_depth = static_cast(filter.dim_size(3)); @@ -486,10 +485,9 @@ class MklConvOp : public OpKernel { const int64 input_rows_raw = input_in_mkl_format ? GetMklTensorDim(mkl_context.input_shape, 'H') : GetTensorDim(input, data_format_, 'H'); - OP_REQUIRES( - context, - FastBoundsCheck(input_rows_raw, std::numeric_limits::max()), - errors::InvalidArgument("Input rows too large")); + OP_REQUIRES(context, FastBoundsCheck(input_rows_raw, + std::numeric_limits::max()), + errors::InvalidArgument("Input rows too large")); const int input_rows = static_cast(input_rows_raw); const int filter_rows = static_cast(filter.dim_size(0)); @@ -498,10 +496,9 @@ class MklConvOp : public OpKernel { const int64 input_cols_raw = input_in_mkl_format ? GetMklTensorDim(mkl_context.input_shape, 'W') : GetTensorDim(input, data_format_, 'W'); - OP_REQUIRES( - context, - FastBoundsCheck(input_cols_raw, std::numeric_limits::max()), - errors::InvalidArgument("Input cols too large")); + OP_REQUIRES(context, FastBoundsCheck(input_cols_raw, + std::numeric_limits::max()), + errors::InvalidArgument("Input cols too large")); const int input_cols = static_cast(input_cols_raw); const int filter_cols = static_cast(filter.dim_size(1)); @@ -509,10 +506,9 @@ class MklConvOp : public OpKernel { const int64 input_batch_raw = input_in_mkl_format ? GetMklTensorDim(mkl_context.input_shape, 'N') : GetTensorDim(input, data_format_, 'N'); - OP_REQUIRES( - context, - FastBoundsCheck(input_batch_raw, std::numeric_limits::max()), - errors::InvalidArgument("batch is too large")); + OP_REQUIRES(context, FastBoundsCheck(input_batch_raw, + std::numeric_limits::max()), + errors::InvalidArgument("batch is too large")); const int batch = static_cast(input_batch_raw); // For now we take the stride from the second and third dimensions only (we @@ -893,17 +889,15 @@ class MklConvOp : public OpKernel { OP_REQUIRES(context, dilations_.size() == 5, errors::InvalidArgument("Dilation rates field must " "specify 5 dimensions")); - OP_REQUIRES(context, - (GetTensorDim(dilations_, data_format_, 'N') == 1 && - GetTensorDim(dilations_, data_format_, 'C') == 1), + OP_REQUIRES(context, (GetTensorDim(dilations_, data_format_, 'N') == 1 && + GetTensorDim(dilations_, data_format_, 'C') == 1), errors::InvalidArgument( "Current implementation does not yet support " "dilations rates in the batch and depth dimensions.")); OP_REQUIRES( - context, - (GetTensorDim(dilations_, data_format_, '0') > 0 && - GetTensorDim(dilations_, data_format_, '1') > 0 && - GetTensorDim(dilations_, data_format_, '2') > 0), + context, (GetTensorDim(dilations_, data_format_, '0') > 0 && + GetTensorDim(dilations_, data_format_, '1') > 0 && + GetTensorDim(dilations_, data_format_, '2') > 0), errors::InvalidArgument("Dilated rates should be larger than 0.")); } } @@ -1011,7 +1005,7 @@ class MklConvOp : public OpKernel { // get a conv2d fwd from primitive pool MklConvFwdPrimitive* conv_fwd = nullptr; - if (biasEnabled) { + if (fuse_biasadd_) { memory::dims bias_dims = {}; conv_utl.GetBiasSizeInMklOrder(kInputIndex_Bias, &bias_dims); MklConvFwdParams convFwdDims(src_dims, filter_dims, bias_dims, @@ -1083,7 +1077,7 @@ class MklConvOp : public OpKernel { } // execute convolution - if (biasEnabled) { + if (fuse_biasadd_) { const Tensor& bias_tensor = MklGetInput(context, kInputIndex_Bias); Tbias* bias_data = this->GetBiasHandle(context, conv_fwd_pd, bias_tensor); @@ -1105,6 +1099,12 @@ class MklConvOp : public OpKernel { } protected: + void FuseBiasAdd(bool fuse_bias_add) { fuse_biasadd_ = fuse_bias_add; } + void FuseRelu(bool fuse_relu) { fuse_relu_ = fuse_relu; } + + // This method is called for the base class MklConvOp, which handles the + // floating point implementation of Conv. The quantized conv implementations + // will use overiddern versions of this method. virtual void ExtendConvFwdParams(OpKernelContext* context, MklConvFwdParams& params) { // Create a string from data types of input, filter, bias, and output. @@ -1112,6 +1112,11 @@ class MklConvOp : public OpKernel { params.dtypes.append(typeid(Tfilter).name()); params.dtypes.append(typeid(Tbias).name()); params.dtypes.append(typeid(Toutput).name()); + + // Add fusions as post ops + if (fuse_relu_) { + params.post_op_params.push_back({"relu", {1.0, 0.0, 0.0}}); + } } virtual Tbias* GetBiasHandle( @@ -1119,7 +1124,7 @@ class MklConvOp : public OpKernel { std::shared_ptr& conv2d_fwd_pd, const Tensor& bias_tensor) { - if (biasEnabled) { + if (fuse_biasadd_) { return static_cast( const_cast(bias_tensor.flat().data())); } else { @@ -1165,6 +1170,11 @@ class MklConvOp : public OpKernel { std::vector dilations_; Padding padding_; TensorFormat data_format_; + + // Initialize to value the template is instantiated with + bool fuse_biasadd_ = biasEnabled; + bool fuse_relu_ = false; + const int kInputIndex_Src = 0, kInputIndex_Filter = 1, kInputIndex_Bias = 2; const int kOutputIndex_Dst = 0, kOutputIndex_Filter = 1; const int kDilationH = 0, kDilationW = 1; @@ -1217,12 +1227,12 @@ class MklConvOp : public OpKernel { // Create convolution primitive and add it to net. std::vector net; if (bias) { - DCHECK(biasEnabled); + DCHECK(fuse_biasadd_); net.push_back(convolution_forward(conv_prim_desc, src->GetOpMem(), filter->GetOpMem(), bias->GetOpMem(), output->GetOpMem())); } else { - DCHECK(!biasEnabled); + DCHECK(!fuse_biasadd_); net.push_back(convolution_forward(conv_prim_desc, src->GetOpMem(), filter->GetOpMem(), output->GetOpMem())); @@ -1232,6 +1242,49 @@ class MklConvOp : public OpKernel { } }; +// Base class for fused convolution forward operations +template +class MklFusedConvOp : public MklConvOp { + public: + explicit MklFusedConvOp(OpKernelConstruction* context) + : MklConvOp( + context) { + // Since we came here through the registration of _MklFusedConv2D then get + // all information from 'fused_ops' and 'num_args' + std::vector fused_ops; + OP_REQUIRES_OK(context, context->GetAttr("fused_ops", &fused_ops)); + + int num_args; + OP_REQUIRES_OK(context, context->GetAttr("num_args", &num_args)); + OP_REQUIRES(context, (num_args == 0 || !fused_ops.empty()), + errors::InvalidArgument( + "Fused Conv2D must have at least one fused op.")); + + if (fused_ops == {"BiasAdd"}) { + this->FuseBiasAdd(true); + OP_REQUIRES(context, num_args == 1, + errors::InvalidArgument( + "Fused Conv2D must have one extra argument: bias.")); + } else if (fused_ops == {"Relu"}) { + this->FuseRelu(true); + } else if (fused_ops == {"BiasAdd", "Relu"}) { + this->FuseBiasAdd(true); + this->FuseRelu(true); + OP_REQUIRES(context, num_args == 1, + errors::InvalidArgument( + "Fused Conv2D must have one extra argument: bias.")); + } else { + OP_REQUIRES(context, false, + errors::Unimplemented("Fusion is not implemented: [", + str_util::Join(fused_ops, ","), "]")); + } + } + + virtual ~MklFusedConvOp() {} +}; + // We create new class for each verison of Quantized Convolution and inherit // from the FP32 version of the base class template input(5 + bias_index_offset).flat()(0); - reorder_sum_scale = 255.0 * 127.0 / - (std::max(std::abs(max_input), std::abs(min_input)) * + reorder_sum_scale = + 255.0 * 127.0 / (std::max(std::abs(max_input), std::abs(min_input)) * std::max(std::abs(max_filter), std::abs(min_filter))); std::vector scales; scales.push_back(reorder_sum_scale); @@ -1811,6 +1864,17 @@ REGISTER_KERNEL_BUILDER( TF_CALL_float(REGISTER_MKL_CPU_2D); +#define REGISTER_MKL_CPU_2D_FUSED(T) \ + REGISTER_KERNEL_BUILDER(Name("_MklFusedConv2D") \ + .Device(DEVICE_CPU) \ + .TypeConstraint("T") \ + .Label(mkl_op_registry::kMklOpLabel), \ + MklFusedConvOp); +// Note we are registering _MklFusedConv2D. +// We check the fused_ops attributes to decide if bias is enabled or not. + +TF_CALL_float(REGISTER_MKL_CPU_2D_FUSED); + // Register 3D operations #define REGISTER_MKL_CPU_3D(T) \ REGISTER_KERNEL_BUILDER(Name("_MklConv3D") \ diff --git a/tensorflow/core/kernels/mkl_fused_ops_test.cc b/tensorflow/core/kernels/mkl_fused_ops_test.cc new file mode 100644 index 0000000000..7f1965de85 --- /dev/null +++ b/tensorflow/core/kernels/mkl_fused_ops_test.cc @@ -0,0 +1,306 @@ +/* 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/cc/ops/const_op.h" +#include "tensorflow/cc/ops/image_ops.h" +#include "tensorflow/cc/ops/nn_ops.h" +#include "tensorflow/cc/ops/standard_ops.h" +#include "tensorflow/core/common_runtime/kernel_benchmark_testlib.h" +#include "tensorflow/core/framework/fake_input.h" +#include "tensorflow/core/framework/node_def_builder.h" +#include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/framework/types.pb.h" +#include "tensorflow/core/kernels/conv_ops_gpu.h" +#include "tensorflow/core/kernels/ops_testutil.h" +#include "tensorflow/core/kernels/ops_util.h" +#include "tensorflow/core/platform/test.h" +#include "tensorflow/core/platform/test_benchmark.h" +#include "tensorflow/core/platform/types.h" +#include "tensorflow/core/public/session.h" + +namespace tensorflow { + +// Helper class for converting MKL tensors to TF tensors and comparing to +// expected values + +static const uint8 dummy_tensor[] = {0, 0, 0, 0, 0, 0, 0, 0}; +static const TensorShape dummy_shape({8}); + +template +class ConvMklToTF : public OpsTestBase { + public: + void PerformConversion(DataType dtype, const Tensor& tensor, + const Tensor& mkl_meta_tensor, Tensor* output) { + // Create an MKL to TF conversion node and execute it + TF_EXPECT_OK(NodeDefBuilder("mkl_to_tf_op", "_MklToTf") + .Input(FakeInput(dtype)) // Input + .Input(FakeInput(DT_UINT8)) // Mkl second tensor + .Attr("T", dtype) + .Attr("_kernel", "MklOp") + .Finalize(node_def())); + TF_EXPECT_OK(InitOp()); + AddInputFromArray(tensor.shape(), tensor.flat()); + AddInputFromArray(mkl_meta_tensor.shape(), + mkl_meta_tensor.flat()); + TF_ASSERT_OK(RunOpKernel()); + + *output = *GetOutput(0); + } + + void ConvertAndCompare(DataType dtype, const Tensor& tensor, + const Tensor& mkl_meta_tensor, + const Tensor& expected) { + Tensor output; + PerformConversion(dtype, tensor, mkl_meta_tensor, &output); + test::ExpectTensorNear(expected, output, 1e-5); + } + void TestBody(){}; +}; + +// Testing MKL's fused convolution ops + +template +class MklFusedConv2DOpTest : public OpsTestBase { + protected: + static constexpr int kDepth = 3; + static constexpr int kImageWidth = 32; + static constexpr int kImageHeight = 32; + static constexpr int kImageBatchCount = 8; + + using BiasAddGraphRunner = + std::function; + + // Runs a Tensorflow graph defined by the root scope, and fetches the result + // of 'fetch' node into the output Tensor. + void RunAndFetch(const tensorflow::Scope& root, const string& fetch, + Tensor* output) { + tensorflow::GraphDef graph; + TF_ASSERT_OK(root.ToGraphDef(&graph)); + + std::unique_ptr session( + tensorflow::NewSession(tensorflow::SessionOptions())); + TF_ASSERT_OK(session->Create(graph)); + + std::vector unfused_tensors; + TF_ASSERT_OK(session->Run({}, {fetch}, {}, &unfused_tensors)); + + *output = unfused_tensors[0]; + } + + void RunConv2DWithBias(const Tensor& input_data, const Tensor& filter_data, + const Tensor& bias_data, Tensor* output, + int stride = 1) { + auto root = tensorflow::Scope::NewRootScope(); + + auto conv = ops::Conv2D( + root.WithOpName("conv"), + ops::Const(root.WithOpName("input"), Input::Initializer(input_data)), + ops::Const(root.WithOpName("filter"), Input::Initializer(filter_data)), + {1, stride, stride, 1}, "SAME"); + + auto with_bias = ops::BiasAdd( + root.WithOpName("with_bias"), conv, + ops::Const(root.WithOpName("bias"), Input::Initializer(bias_data))); + + RunAndFetch(root, "with_bias", output); + } + + void RunConv2DWithBiasAndRelu(const Tensor& input_data, + const Tensor& filter_data, + const Tensor& bias_data, Tensor* output, + int stride = 1) { + auto root = tensorflow::Scope::NewRootScope(); + + auto conv = ops::Conv2D( + root.WithOpName("conv"), + ops::Const(root.WithOpName("input"), Input::Initializer(input_data)), + ops::Const(root.WithOpName("filter"), Input::Initializer(filter_data)), + {1, stride, stride, 1}, "SAME"); + + auto with_bias = ops::BiasAdd( + root.WithOpName("with_bias"), conv, + ops::Const(root.WithOpName("bias"), Input::Initializer(bias_data))); + + auto with_relu = ops::Relu(root.WithOpName("with_relu"), with_bias); + + RunAndFetch(root, "with_relu", output); + } + + void RunMklFusedConv2DOp(const Tensor& image, const Tensor& filter, + const std::vector& args, + const std::vector& fused_ops, Tensor* output, + int stride = 1) { + DataType dtype = DataTypeToEnum::v(); + int num_args = static_cast(args.size()); + + TF_EXPECT_OK(NodeDefBuilder("fused_conv_op", "_MklFusedConv2D") + .Input(FakeInput(dtype)) + .Input(FakeInput(dtype)) + .Attr("num_args", num_args) + .Input(FakeInput(num_args, dtype)) + .Input(FakeInput(DT_UINT8)) + .Input(FakeInput(DT_UINT8)) + .Input(FakeInput(num_args, DT_UINT8)) + .Attr("T", dtype) + .Attr("strides", {1, stride, stride, 1}) + .Attr("padding", "SAME") + .Attr("fused_ops", fused_ops) + .Attr("_kernel", "MklOp") + .Finalize(node_def())); + + TF_EXPECT_OK(InitOp()); + + AddInputFromArray(image.shape(), image.flat()); + AddInputFromArray(filter.shape(), filter.flat()); + for (const Tensor& arg : args) + AddInputFromArray(arg.shape(), arg.flat()); + AddInputFromArray(dummy_shape, dummy_tensor); + AddInputFromArray(dummy_shape, dummy_tensor); + for (const Tensor& arg : args) + AddInputFromArray(dummy_shape, dummy_tensor); + TF_ASSERT_OK(RunOpKernel()); + + // Compare output to expected results + const Tensor& output_tensor = *GetOutput(0); + const Tensor& output_meta_tensor = *GetOutput(2); + ConvMklToTF conv_comp; + conv_comp.PerformConversion(dtype, output_tensor, output_meta_tensor, + output); + } + + void VerifyBiasAddTensorsNear(int depth, int image_width, int image_height, + int image_batch_count, int filter_size, + int filter_count, + const BiasAddGraphRunner& run_default, + const BiasAddGraphRunner& run_fused) { + DataType dtype = DataTypeToEnum::v(); + + Tensor image(dtype, {image_batch_count, image_height, image_width, depth}); + image.flat() = image.flat().setRandom(); + + Tensor filter(dtype, {filter_size, filter_size, depth, filter_count}); + filter.flat() = filter.flat().setRandom(); + + const int bias_size = filter_count; + Tensor bias(dtype, {bias_size}); + bias.flat() = bias.flat().setRandom(); + + Tensor conv_2d; + Tensor fused_conv_2d; + + run_default(image, filter, bias, &conv_2d); + run_fused(image, filter, bias, &fused_conv_2d); + + ASSERT_EQ(conv_2d.dtype(), fused_conv_2d.dtype()); + ASSERT_EQ(conv_2d.shape(), fused_conv_2d.shape()); + + test::ExpectTensorNear(conv_2d, fused_conv_2d, 1e-5); + } + + // Verifies that computing Conv2D+BiasAdd in a graph is identical to + // FusedConv2D. + void VerifyConv2DWithBias(int filter_size, int filter_count, + int depth = kDepth, int image_width = kImageWidth, + int image_height = kImageHeight, + int image_batch_count = kImageBatchCount) { + const BiasAddGraphRunner run_default = [this]( + const Tensor& input_data, const Tensor& filter_data, + const Tensor& bias_data, Tensor* out) { + RunConv2DWithBias(input_data, filter_data, bias_data, out); + }; + + const BiasAddGraphRunner run_fused = [this]( + const Tensor& input_data, const Tensor& filter_data, + const Tensor& bias_data, Tensor* out) { + RunMklFusedConv2DOp(input_data, filter_data, {bias_data}, {"BiasAdd"}, + out); + }; + + VerifyBiasAddTensorsNear(depth, image_width, image_height, + image_batch_count, filter_size, filter_count, + run_default, run_fused); + } + + // Verifies that computing Conv2D+BiasAdd+Relu in a graph is identical to + // FusedConv2D. + void VerifyConv2DWithBiasAndRelu(int filter_size, int filter_count, + int depth = kDepth, + int image_width = kImageWidth, + int image_height = kImageHeight, + int image_batch_count = kImageBatchCount) { + const BiasAddGraphRunner run_default = [this]( + const Tensor& input_data, const Tensor& filter_data, + const Tensor& bias_data, Tensor* out) { + RunConv2DWithBiasAndRelu(input_data, filter_data, bias_data, out); + }; + + const BiasAddGraphRunner run_fused = [this]( + const Tensor& input_data, const Tensor& filter_data, + const Tensor& bias_data, Tensor* out) { + RunMklFusedConv2DOp(input_data, filter_data, {bias_data}, + {"BiasAdd", "Relu"}, out); + }; + + VerifyBiasAddTensorsNear(depth, image_width, image_height, + image_batch_count, filter_size, filter_count, + run_default, run_fused); + } +}; + +template +class MklFusedConv2DWithBiasOpTest : public MklFusedConv2DOpTest {}; + +TYPED_TEST_CASE_P(MklFusedConv2DWithBiasOpTest); + +// -------------------------------------------------------------------------- // +// Conv2D + BiasAdd + {Relu} // +// -------------------------------------------------------------------------- // + +TYPED_TEST_P(MklFusedConv2DWithBiasOpTest, OneByOneConvolution) { + const int filter_size = 1; + const int filter_count = 12; + this->VerifyConv2DWithBias(filter_size, filter_count); +} + +TYPED_TEST_P(MklFusedConv2DWithBiasOpTest, SpatialConvolution) { + const int filter_size = 3; + const int filter_count = 12; + this->VerifyConv2DWithBias(filter_size, filter_count); +} + +TYPED_TEST_P(MklFusedConv2DWithBiasOpTest, OneByOneConvolutionAndRelu) { + const int filter_size = 1; + const int filter_count = 12; + this->VerifyConv2DWithBiasAndRelu(filter_size, filter_count); +} + +TYPED_TEST_P(MklFusedConv2DWithBiasOpTest, SpatialConvolutionAndRelu) { + const int filter_size = 3; + const int filter_count = 12; + this->VerifyConv2DWithBiasAndRelu(filter_size, filter_count); +} + +REGISTER_TYPED_TEST_CASE_P(MklFusedConv2DWithBiasOpTest, + OneByOneConvolution, // + SpatialConvolution, // + OneByOneConvolutionAndRelu, // + SpatialConvolutionAndRelu); + +using MklFusedBiasAddDataTypes = ::testing::Types; +INSTANTIATE_TYPED_TEST_CASE_P(Test, MklFusedConv2DWithBiasOpTest, + MklFusedBiasAddDataTypes); +} // namespace tensorflow +#endif // INTEL_MKL diff --git a/tensorflow/core/ops/mkl_nn_ops.cc b/tensorflow/core/ops/mkl_nn_ops.cc index 9be3470820..658afd9901 100644 --- a/tensorflow/core/ops/mkl_nn_ops.cc +++ b/tensorflow/core/ops/mkl_nn_ops.cc @@ -32,6 +32,33 @@ using shape_inference::DimensionHandle; using shape_inference::InferenceContext; using shape_inference::ShapeHandle; +REGISTER_OP("_MklFusedConv2D") + .Input("input: T") + .Input("filter: T") + .Input("args: num_args * T") + .Input("mkl_input: uint8") + .Input("mkl_filter: uint8") + .Input("mkl_args: num_args * uint8") + .Output("output: T") + .Output("filter_output: T") + .Output("mkl_output: uint8") + .Output("mkl_filter_output: uint8") + .Attr("T: {float}") + .Attr("num_args: int >= 0") + .Attr("strides: list(int)") + .Attr(GetPaddingAttrString()) + .Attr(GetConvnetDataFormatAttrString()) + .Attr("dilations: list(int) = [1, 1, 1, 1]") + .Attr("fused_ops: list(string) = []") + // Attributes for the FusedBatchNorm ------------------------------------ // + .Attr("epsilon: float = 0.0001") + // ---------------------------------------------------------------------- // + .SetShapeFn(shape_inference::Conv2DShape) + .Doc(R"doc( +*NOTE*: Do not invoke this operator directly in Python. MKL DNN graph transformer + is expected to create these operators. +)doc"); + REGISTER_OP("_MklQuantizedMaxPool") .Input("input: T") .Input("min_input: float") -- GitLab From bac37febab0c6f5fe008484c6fc255f6c2346775 Mon Sep 17 00:00:00 2001 From: Fei Hu Date: Fri, 30 Nov 2018 16:56:37 -0800 Subject: [PATCH 059/873] Add benchmarks for list_files dataset --- tensorflow/python/data/benchmarks/BUILD | 14 +++ .../data/benchmarks/list_files_benchmark.py | 95 +++++++++++++++++++ 2 files changed, 109 insertions(+) create mode 100644 tensorflow/python/data/benchmarks/list_files_benchmark.py diff --git a/tensorflow/python/data/benchmarks/BUILD b/tensorflow/python/data/benchmarks/BUILD index 5b0500eae1..fd0eca9dd7 100644 --- a/tensorflow/python/data/benchmarks/BUILD +++ b/tensorflow/python/data/benchmarks/BUILD @@ -48,6 +48,20 @@ py_test( ], ) +py_test( + name = "list_files_benchmark", + srcs = ["list_files_benchmark.py"], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow/python:client_testlib", + "//tensorflow/python:errors", + "//tensorflow/python:framework_ops", + "//tensorflow/python:session", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + py_test( name = "map_benchmark", srcs = ["map_benchmark.py"], diff --git a/tensorflow/python/data/benchmarks/list_files_benchmark.py b/tensorflow/python/data/benchmarks/list_files_benchmark.py new file mode 100644 index 0000000000..3ad141fb54 --- /dev/null +++ b/tensorflow/python/data/benchmarks/list_files_benchmark.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. +# ============================================================================== +"""Benchmarks for `tf.data.Dataset.batch()`.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from os import path +from os import makedirs +import shutil +import time +import tempfile + +import numpy as np + +from tensorflow.python.client import session +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 + +class ListFilesBenchmark(test.Benchmark): + """Benchmarks for `tf.data.Dataset.list_files()`.""" + + def benchmarkNestedDirectories(self): + tmp_dir = tempfile.mkdtemp() + width = 1024 + depth = 16 + for i in range(width): + for j in range(depth): + new_base = path.join(tmp_dir, str(i), + *[str(dir_name) for dir_name in range(j)]) + makedirs(new_base) + child_files = ['a.py', 'b.pyc'] if j < depth - 1 else ['c.txt', 'd.log'] + for f in child_files: + filename = path.join(new_base, f) + open(filename, 'w').close() + patterns = [ + path.join(tmp_dir, path.join(*['**' for _ in range(depth)]), suffix) + for suffix in ['*.txt', '*.log'] + ] + deltas = [] + iters = 3 + for _ in range(iters): + with ops.Graph().as_default(): + dataset = dataset_ops.Dataset.list_files(patterns) + next_element = dataset.make_one_shot_iterator().get_next() + with session.Session() as sess: + sub_deltas = [] + while True: + try: + start = time.time() + sess.run(next_element) + end = time.time() + sub_deltas.append(end - start) + except errors.OutOfRangeError: + break + deltas.append(sub_deltas) + median_deltas = np.median(deltas, axis=0) + print('Nested directory size (width*depth): %d*%d Median wall time: ' + '%fs (read first filename), %fs (read second filename), avg %fs' + ' (read %d more filenames)' % + (width, depth, median_deltas[0], median_deltas[1], + np.average(median_deltas[2:]), len(median_deltas) - 2)) + self.report_benchmark( + iters=iters, + wall_time=np.sum(median_deltas), + extras={ + 'read first file:': + median_deltas[0], + 'read second file:': + median_deltas[1], + 'avg time for reading %d more filenames:' % + (len(median_deltas) - 2): + np.average(median_deltas[2:]) + }, + name='benchmark_list_files_dataset_nesteddirectory(%d*%d)' % + (width, depth)) + shutil.rmtree(tmp_dir, ignore_errors=True) + + +if __name__ == "__main__": + test.main() -- GitLab From 8584f21392772170b007ee3b3fbfed17fe19e32f Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Sat, 1 Dec 2018 20:58:09 +0000 Subject: [PATCH 060/873] Fix SparseDenseCwise's broadcasting issue This fix tries to address the issue raised in 24072. In `sparse_dense_cwise_mul/add` operations the broadcasting only support dense to sparse, though the validation was not captured. This fix fixes the validation in SparseDenseBinaryOpShared so that error could be thrown correctly. This fix fixes 24072. Signed-off-by: Yong Tang --- tensorflow/core/kernels/sparse_dense_binary_op_shared.cc | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc b/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc index ac48202ada..3a6b66302f 100644 --- a/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc +++ b/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc @@ -88,12 +88,11 @@ class SparseDenseBinaryOpShared : public OpKernel { const auto rhs_dims = BCast::FromShape(dense_t->shape()); BCast b(lhs_dims, rhs_dims, false); // false for keeping the same num dims. - // True iff (size(lhs) > size(rhs)), or (sizes equal, lhs cwise rhs). + // True iff (size(lhs) >= size(rhs)) and all dims in lhs is smaller or equal to dims in rhs (from right to left). auto VecGreaterEq = [](ArraySlice lhs, ArraySlice rhs) { - if (lhs.size() > rhs.size()) return true; if (lhs.size() < rhs.size()) return false; - for (size_t i = 0; i < lhs.size(); ++i) { - if (lhs[i] < rhs[i]) return false; + for (size_t i = 0; i < rhs.size(); ++i) { + if (lhs[lhs.size() - 1 - i] < rhs[rhs.size() - 1 - i]) return false; } return true; }; -- GitLab From a7cd4dbea9f276160ebadf82178e77ae5c8d557e Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Sat, 1 Dec 2018 21:02:04 +0000 Subject: [PATCH 061/873] Add test case for sparse_dense_cwise shape validation. Signed-off-by: Yong Tang --- tensorflow/python/kernel_tests/sparse_ops_test.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/tensorflow/python/kernel_tests/sparse_ops_test.py b/tensorflow/python/kernel_tests/sparse_ops_test.py index 75f65e6251..f58832a89e 100644 --- a/tensorflow/python/kernel_tests/sparse_ops_test.py +++ b/tensorflow/python/kernel_tests/sparse_ops_test.py @@ -22,6 +22,7 @@ import numpy as np 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 sparse_tensor from tensorflow.python.framework import test_util @@ -798,6 +799,17 @@ class SparseMathOpsTest(test_util.TensorFlowTestCase): result_tensor.values).eval() self.assertAllEqual(result_np, res_densified) + @test_util.run_deprecated_v1 + def testCwiseShapeValidation(self): + # Test case for GitHub 24072. + with self.session(use_gpu=False): + a = array_ops.ones([3, 4, 1], dtype=dtypes.int32) + b = sparse_tensor.SparseTensor([[0, 0, 1, 0], [0, 0, 3, 0]], [10, 20], [1, 1, 4, 2]) + c = a * b + with self.assertRaisesRegexp(errors.InvalidArgumentError, + "broadcasts dense to sparse only; got incompatible shapes"): + c.eval() + @test_util.run_deprecated_v1 def testCwiseDivAndMul(self): np.random.seed(1618) -- GitLab From 676c6ea316f4dda962d8b2e29855c040020533f5 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Sat, 1 Dec 2018 21:04:41 +0000 Subject: [PATCH 062/873] Pylint fix Signed-off-by: Yong Tang --- tensorflow/python/kernel_tests/sparse_ops_test.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/tensorflow/python/kernel_tests/sparse_ops_test.py b/tensorflow/python/kernel_tests/sparse_ops_test.py index f58832a89e..50b6239185 100644 --- a/tensorflow/python/kernel_tests/sparse_ops_test.py +++ b/tensorflow/python/kernel_tests/sparse_ops_test.py @@ -804,10 +804,12 @@ class SparseMathOpsTest(test_util.TensorFlowTestCase): # Test case for GitHub 24072. with self.session(use_gpu=False): a = array_ops.ones([3, 4, 1], dtype=dtypes.int32) - b = sparse_tensor.SparseTensor([[0, 0, 1, 0], [0, 0, 3, 0]], [10, 20], [1, 1, 4, 2]) + b = sparse_tensor.SparseTensor( + [[0, 0, 1, 0], [0, 0, 3, 0]], [10, 20], [1, 1, 4, 2]) c = a * b - with self.assertRaisesRegexp(errors.InvalidArgumentError, - "broadcasts dense to sparse only; got incompatible shapes"): + with self.assertRaisesRegexp( + errors.InvalidArgumentError, + "broadcasts dense to sparse only; got incompatible shapes"): c.eval() @test_util.run_deprecated_v1 -- GitLab From 28e034bd293db09a0ecc707f71f65fe6c5dd2943 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Sat, 1 Dec 2018 21:28:19 +0000 Subject: [PATCH 063/873] Fix `Experimental clang-format Check` Signed-off-by: Yong Tang --- tensorflow/core/kernels/sparse_dense_binary_op_shared.cc | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc b/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc index 3a6b66302f..d7460363fc 100644 --- a/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc +++ b/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc @@ -88,7 +88,8 @@ class SparseDenseBinaryOpShared : public OpKernel { const auto rhs_dims = BCast::FromShape(dense_t->shape()); BCast b(lhs_dims, rhs_dims, false); // false for keeping the same num dims. - // True iff (size(lhs) >= size(rhs)) and all dims in lhs is smaller or equal to dims in rhs (from right to left). + // True iff (size(lhs) >= size(rhs)) and all dims in lhs is smaller or equal + // to dims in rhs (from right to left). auto VecGreaterEq = [](ArraySlice lhs, ArraySlice rhs) { if (lhs.size() < rhs.size()) return false; for (size_t i = 0; i < rhs.size(); ++i) { -- GitLab From fa9371a2f2095adc9f2b7a2b8700b9e6f0f31c2d Mon Sep 17 00:00:00 2001 From: AG Ramesh Date: Sat, 1 Dec 2018 15:27:49 -0800 Subject: [PATCH 064/873] Fix clang format errors --- tensorflow/core/kernels/mkl_conv_ops.cc | 15 ++++++++------ tensorflow/core/kernels/mkl_fused_ops_test.cc | 20 ++++++++++++++++--- 2 files changed, 26 insertions(+), 9 deletions(-) diff --git a/tensorflow/core/kernels/mkl_conv_ops.cc b/tensorflow/core/kernels/mkl_conv_ops.cc index db07bc5d58..c354390c69 100644 --- a/tensorflow/core/kernels/mkl_conv_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_ops.cc @@ -1114,9 +1114,7 @@ class MklConvOp : public OpKernel { params.dtypes.append(typeid(Toutput).name()); // Add fusions as post ops - if (fuse_relu_) { - params.post_op_params.push_back({"relu", {1.0, 0.0, 0.0}}); - } + if (fuse_relu_) params.post_op_params.push_back({"relu", {1.0, 0.0, 0.0}}); } virtual Tbias* GetBiasHandle( @@ -1179,6 +1177,11 @@ class MklConvOp : public OpKernel { const int kOutputIndex_Dst = 0, kOutputIndex_Filter = 1; const int kDilationH = 0, kDilationW = 1; + // Helper function to compare fused_ops attributes strings + bool CompareFusedOps(const std::vector& fused_ops, + const std::vector& expected) { + return fused_ops == expected; + } // Allocate filter output tensor. void AllocateFilterOutputTensor( OpKernelContext* context, @@ -1262,14 +1265,14 @@ class MklFusedConvOp : public MklConvOpFuseBiasAdd(true); OP_REQUIRES(context, num_args == 1, errors::InvalidArgument( "Fused Conv2D must have one extra argument: bias.")); - } else if (fused_ops == {"Relu"}) { + } else if (CompareFusedOps(fused_ops, {"Relu"})) { this->FuseRelu(true); - } else if (fused_ops == {"BiasAdd", "Relu"}) { + } else if (CompareFusedOps(fused_ops, {"BiasAdd", "Relu"})) { this->FuseBiasAdd(true); this->FuseRelu(true); OP_REQUIRES(context, num_args == 1, diff --git a/tensorflow/core/kernels/mkl_fused_ops_test.cc b/tensorflow/core/kernels/mkl_fused_ops_test.cc index 7f1965de85..eb456ce7a3 100644 --- a/tensorflow/core/kernels/mkl_fused_ops_test.cc +++ b/tensorflow/core/kernels/mkl_fused_ops_test.cc @@ -275,6 +275,12 @@ TYPED_TEST_P(MklFusedConv2DWithBiasOpTest, OneByOneConvolution) { this->VerifyConv2DWithBias(filter_size, filter_count); } +TYPED_TEST_P(MklFusedConv2DWithBiasOpTest, ImageSizeConvolution) { + const int filter_size = TestFixture::kImageWidth; + const int filter_count = 12; + this->VerifyConv2DWithBias(filter_size, filter_count); +} + TYPED_TEST_P(MklFusedConv2DWithBiasOpTest, SpatialConvolution) { const int filter_size = 3; const int filter_count = 12; @@ -287,6 +293,12 @@ TYPED_TEST_P(MklFusedConv2DWithBiasOpTest, OneByOneConvolutionAndRelu) { this->VerifyConv2DWithBiasAndRelu(filter_size, filter_count); } +TYPED_TEST_P(MklFusedConv2DWithBiasOpTest, ImageSizeConvolutionAndRelu) { + const int filter_size = TestFixture::kImageWidth; + const int filter_count = 12; + this->VerifyConv2DWithBiasAndRelu(filter_size, filter_count); +} + TYPED_TEST_P(MklFusedConv2DWithBiasOpTest, SpatialConvolutionAndRelu) { const int filter_size = 3; const int filter_count = 12; @@ -294,9 +306,11 @@ TYPED_TEST_P(MklFusedConv2DWithBiasOpTest, SpatialConvolutionAndRelu) { } REGISTER_TYPED_TEST_CASE_P(MklFusedConv2DWithBiasOpTest, - OneByOneConvolution, // - SpatialConvolution, // - OneByOneConvolutionAndRelu, // + OneByOneConvolution, // + ImageSizeConvolution, // + SpatialConvolution, // + OneByOneConvolutionAndRelu, // + ImageSizeConvolutionAndRelu, // SpatialConvolutionAndRelu); using MklFusedBiasAddDataTypes = ::testing::Types; -- GitLab From e25e93b15d372d1036961cb1d55e29edcc588f29 Mon Sep 17 00:00:00 2001 From: AG Ramesh Date: Sat, 1 Dec 2018 17:17:02 -0800 Subject: [PATCH 065/873] Added missing file --- tensorflow/core/graph/mkl_graph_util.h | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/tensorflow/core/graph/mkl_graph_util.h b/tensorflow/core/graph/mkl_graph_util.h index 990b2fe9b0..7435f4e8c1 100644 --- a/tensorflow/core/graph/mkl_graph_util.h +++ b/tensorflow/core/graph/mkl_graph_util.h @@ -72,6 +72,14 @@ int inline GetTensorMetaDataIndex(int n, int total_tensors) { return DataIndexToMetaDataIndex(tidx, total_tensors); } +// Helper function to compare fused_ops attributes strings +// TODO(Intel-tf) this code is also in mkl_conv_ops.h, we need to move to +// mkl_util.h +inline bool CompareFusedOps(const std::vector& fused_ops, + const std::vector& expected) { + return fused_ops == expected; +} + namespace mkl_op_registry { static const char* kMklOpLabel = "MklOp"; static const char* kMklOpLabelPattern = "label='MklOp'"; -- GitLab From cc8e28b72b52718db5f22830e1d529d8e077b537 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Sun, 2 Dec 2018 01:55:11 +0000 Subject: [PATCH 066/873] Fix broken link in lite api docs This fix fixes broken link in lite apis docs Signed-off-by: Yong Tang --- tensorflow/lite/g3doc/apis.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/lite/g3doc/apis.md b/tensorflow/lite/g3doc/apis.md index e9fa24bff1..60ce11d685 100644 --- a/tensorflow/lite/g3doc/apis.md +++ b/tensorflow/lite/g3doc/apis.md @@ -347,7 +347,7 @@ interpreter.runForMultipleInputsOutputs(inputs, map_of_indices_to_outputs); where each entry in `inputs` corresponds to an input tensor and `map_of_indices_to_outputs` maps indices of output tensors to the corresponding output data. In both cases the tensor indices should correspond to -the values given to the [TensorFlow Lite Optimized Converter](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/toco/g3doc/cmdline_examples.md) +the values given to the [TensorFlow Lite Optimized Converter](convert/cmdline_examples.md) when the model was created. Be aware that the order of tensors in `input` must match the order given to the `TensorFlow Lite Optimized Converter`. -- GitLab From 5a253d2f476ac5a722fcb67c2c9fa1aaf87ab4db Mon Sep 17 00:00:00 2001 From: Fei Hu Date: Sun, 2 Dec 2018 12:24:13 -0800 Subject: [PATCH 067/873] Change the function and variable names --- tensorflow/python/data/benchmarks/list_files_benchmark.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/python/data/benchmarks/list_files_benchmark.py b/tensorflow/python/data/benchmarks/list_files_benchmark.py index 3ad141fb54..5880b28ad8 100644 --- a/tensorflow/python/data/benchmarks/list_files_benchmark.py +++ b/tensorflow/python/data/benchmarks/list_files_benchmark.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Benchmarks for `tf.data.Dataset.batch()`.""" +"""Benchmarks for `tf.data.Dataset.list_files()`.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function @@ -86,7 +86,7 @@ class ListFilesBenchmark(test.Benchmark): (len(median_deltas) - 2): np.average(median_deltas[2:]) }, - name='benchmark_list_files_dataset_nesteddirectory(%d*%d)' % + name='nested_directory(%d*%d)' % (width, depth)) shutil.rmtree(tmp_dir, ignore_errors=True) -- GitLab From 694e4da7adaaf0db07deddbfdf4d79d5f3053f42 Mon Sep 17 00:00:00 2001 From: "Li, Guizi" Date: Mon, 3 Dec 2018 09:52:16 +0800 Subject: [PATCH 068/873] update CHECK_NOTNULL and CHECK_EQ to DCHECK --- tensorflow/core/graph/mkl_layout_pass.cc | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/core/graph/mkl_layout_pass.cc b/tensorflow/core/graph/mkl_layout_pass.cc index cd93514bb2..da966483ff 100644 --- a/tensorflow/core/graph/mkl_layout_pass.cc +++ b/tensorflow/core/graph/mkl_layout_pass.cc @@ -1097,10 +1097,10 @@ class MklLayoutRewritePass : public GraphOptimizationPass { // These two algorithm are not consistent when alpha > 1 // so only LeakyRelu is written to MKL OP when alpha < 1 static bool LeakyReluRewrite(const Node* n) { - CHECK_NOTNULL(n); + DCHECK(n); float alpha; - CHECK_EQ(GetNodeAttr(n->def(), "alpha", &alpha).ok(), true); + DCHECK(GetNodeAttr(n->def(), "alpha", &alpha).ok()); // If the alpha of LeakyRelu is less than 1, rewrite the node. // Otherwise eigen node is used instead. -- GitLab From ec803e981cde50dc127f655339215892e5422d3d Mon Sep 17 00:00:00 2001 From: Pan Daoxin Date: Mon, 3 Dec 2018 09:57:29 +0800 Subject: [PATCH 069/873] Change clang format. --- tensorflow/core/kernels/mkl_slice_op.cc | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/core/kernels/mkl_slice_op.cc b/tensorflow/core/kernels/mkl_slice_op.cc index f32a6003af..233f33e1cb 100644 --- a/tensorflow/core/kernels/mkl_slice_op.cc +++ b/tensorflow/core/kernels/mkl_slice_op.cc @@ -265,8 +265,8 @@ class MklSlicePrimitiveFactory : public MklPrimitiveFactory { memory::dims to_dims(to_desc.dims, &to_desc.dims[to_desc.ndims]); memory::dims from_strides( from_desc.layout_desc.blocking.strides[KIdxFirstStride], - &from_desc.layout_desc.blocking.strides[KIdxFirstStride] - [from_desc.ndims]); + &from_desc.layout_desc.blocking + .strides[KIdxFirstStride][from_desc.ndims]); memory::dims to_strides( to_desc.layout_desc.blocking.strides[KIdxFirstStride], &to_desc.layout_desc.blocking.strides[KIdxFirstStride][to_desc.ndims]); -- GitLab From a02c524cb328dea373b77a03ec4f448f6f386674 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Mon, 3 Dec 2018 16:57:57 +0000 Subject: [PATCH 070/873] Update comment based on feedback Signed-off-by: Yong Tang --- tensorflow/core/kernels/sparse_dense_binary_op_shared.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc b/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc index d7460363fc..a4e89f439e 100644 --- a/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc +++ b/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc @@ -88,7 +88,7 @@ class SparseDenseBinaryOpShared : public OpKernel { const auto rhs_dims = BCast::FromShape(dense_t->shape()); BCast b(lhs_dims, rhs_dims, false); // false for keeping the same num dims. - // True iff (size(lhs) >= size(rhs)) and all dims in lhs is smaller or equal + // True iff (size(lhs) >= size(rhs)) and all dims in lhs is greater or equal // to dims in rhs (from right to left). auto VecGreaterEq = [](ArraySlice lhs, ArraySlice rhs) { if (lhs.size() < rhs.size()) return false; -- GitLab From 8b9636d1d7201f369fbfdb3e079d595888143be6 Mon Sep 17 00:00:00 2001 From: Pooya Davoodi Date: Mon, 3 Dec 2018 11:04:40 -0800 Subject: [PATCH 071/873] Update README.md --- tensorflow/contrib/tensorrt/README.md | 57 ++++++++++++++++++++------- 1 file changed, 43 insertions(+), 14 deletions(-) diff --git a/tensorflow/contrib/tensorrt/README.md b/tensorflow/contrib/tensorrt/README.md index caf8b6db0d..09ef7f459f 100644 --- a/tensorflow/contrib/tensorrt/README.md +++ b/tensorflow/contrib/tensorrt/README.md @@ -1,8 +1,47 @@ -# Using TensorRT in TensorFlow +# Using TensorRT in TensorFlow (TF-TRT) -This module provides necessary bindings and introduces TRT_engine_op operator -that wraps a subgraph in TensorRT. This is still a work in progress but should -be useable with most common graphs. +This module provides necessary bindings and introduces +`TRTEngineOp` operator that wraps a subgraph in TensorRT. +This is still a work in progress but should be useable +with most common graphs. + +## Installing TF-TRT + +Currently Tensorflow nightly builds include TF-TRT by default, +which means you don't need to install TF-TRT separately. +You can pull the latest TF containers from docker hub or +install the latest TF pip package to get access to the latest TF-TRT. + +If you want to use TF-TRT on NVIDIA Jetson platform, you can find +the download links for the relevant Tensorflow pip packages here: +https://docs.nvidia.com/deeplearning/dgx/index.html#installing-frameworks-for-jetson + +## Installing TensorRT + +In order to make use of TF-TRT, you will need a local installation +of TensorRT from the +[NVIDIA Developer website](https://developer.nvidia.com/tensorrt). +Installation instructions for compatibility with TensorFlow are provided on the +[TensorFlow GPU support](https://www.tensorflow.org/install/gpu) guide. + +## Tests + +TF-TRT includes both Python tests and C++ unit tests. +Most of Python tests are located in the test directory +and they can be executed uring `bazel test` or directly +with the Python command. Most of the C++ unit tests are +used to test the conversion functions that convert each TF op to +a number of TensorRT layers. + +## Examples + +You can find example scripts for running inference on deep learning models +in this repository: https://github.com/tensorflow/tensorrt + +## Documentation + +You can find documentation for TF-TRT here: +https://docs.nvidia.com/deeplearning/dgx/integrate-tf-trt/index.html ## Compilation @@ -17,13 +56,3 @@ has to set path to location where the library is installed during configuration. bazel build --config=cuda --config=opt //tensorflow/tools/pip_package:build_pip_package bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/ ``` - -After the installation of tensorflow package, TensorRT transformation will be -available. An example use can be found in test/test_tftrt.py script - -## Installing TensorRT 3.0.4 - -In order to make use of TensorRT integration, you will need a local installation -of TensorRT 3.0.4 from the [NVIDIA Developer website](https://developer.nvidia.com/tensorrt). -Installation instructions for compatibility with TensorFlow are provided on the -[TensorFlow GPU support](https://www.tensorflow.org/install/gpu) guide. -- GitLab From 215beaca8c28f22a2d6b66b16b4770851491a792 Mon Sep 17 00:00:00 2001 From: Trevor Morris Date: Mon, 3 Dec 2018 11:26:56 -0800 Subject: [PATCH 072/873] Prevent segments with no inputs --- tensorflow/contrib/tensorrt/convert/convert_graph.cc | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index 3b32f72bc1..3e599b9174 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -585,6 +585,13 @@ tensorflow::Status CreateTRTNode(const std::vector& infos, int pos, } } } + // We don't support segments with no inputs. Fall back to native TF here to + // avoid crash later. Constant folding should've folded the ops that make up + // these segments. + if (inputs.size() == 0) { + return tensorflow::errors::Internal("Segment has no inputs (possible " + "constfold failure)"); + } const bool calibrate_int8 = (info.precision_mode == INT8MODE && info.use_calibration); -- GitLab From 0df72280ac450481f101ac237a18e6a6dc637d01 Mon Sep 17 00:00:00 2001 From: Niranjan Hasabnis Date: Mon, 3 Dec 2018 13:49:03 -0800 Subject: [PATCH 073/873] Changing DCHECK_EQ to TF_CHECK_OK --- tensorflow/core/graph/mkl_layout_pass.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/core/graph/mkl_layout_pass.cc b/tensorflow/core/graph/mkl_layout_pass.cc index 32ac1084d1..177d6becf2 100644 --- a/tensorflow/core/graph/mkl_layout_pass.cc +++ b/tensorflow/core/graph/mkl_layout_pass.cc @@ -1125,7 +1125,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass { // it includes those we support. std::vector fused_ops; - DCHECK_EQ(GetNodeAttr(n->def(), "fused_ops", &fused_ops).ok(), true); + TF_CHECK_OK(GetNodeAttr(n->def(), "fused_ops", &fused_ops)); return (CompareFusedOps(fused_ops, {"BiasAdd"}) || CompareFusedOps(fused_ops, {"Relu"}) || CompareFusedOps(fused_ops, {"BiasAdd", "Relu"})); -- GitLab From 27d598cee798cc62434fb0d08abb45e20d650dda Mon Sep 17 00:00:00 2001 From: "Li, Guizi" Date: Tue, 4 Dec 2018 13:40:37 +0800 Subject: [PATCH 074/873] update DCHECK --- tensorflow/core/graph/mkl_layout_pass.cc | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/core/graph/mkl_layout_pass.cc b/tensorflow/core/graph/mkl_layout_pass.cc index da966483ff..e283d00045 100644 --- a/tensorflow/core/graph/mkl_layout_pass.cc +++ b/tensorflow/core/graph/mkl_layout_pass.cc @@ -1100,7 +1100,8 @@ class MklLayoutRewritePass : public GraphOptimizationPass { DCHECK(n); float alpha; - DCHECK(GetNodeAttr(n->def(), "alpha", &alpha).ok()); + bool has_attr = GetNodeAttr(n->def(), "alpha", &alpha).ok(); + DCHECK(has_attr); // If the alpha of LeakyRelu is less than 1, rewrite the node. // Otherwise eigen node is used instead. -- GitLab From 900762cd4bca45fd8382778bd65e17f2fe13bf2b Mon Sep 17 00:00:00 2001 From: Jason Zaman Date: Wed, 28 Nov 2018 15:35:29 +0800 Subject: [PATCH 075/873] systemlibs: unbundle keras_applications Signed-off-by: Jason Zaman --- third_party/keras_applications_archive/BUILD.system | 13 +++++++++++++ .../keras_applications_archive/workspace.bzl | 1 + third_party/systemlibs/syslibs_configure.bzl | 1 + 3 files changed, 15 insertions(+) create mode 100644 third_party/keras_applications_archive/BUILD.system diff --git a/third_party/keras_applications_archive/BUILD.system b/third_party/keras_applications_archive/BUILD.system new file mode 100644 index 0000000000..a3b58f1503 --- /dev/null +++ b/third_party/keras_applications_archive/BUILD.system @@ -0,0 +1,13 @@ +# Description: Keras Applications: set of pre-trained deep learning models. + +licenses(["notice"]) # MIT + +filegroup( + name = "LICENSE", + visibility = ["//visibility:public"], +) + +py_library( + name = "keras_applications", + visibility = ["//visibility:public"], +) diff --git a/third_party/keras_applications_archive/workspace.bzl b/third_party/keras_applications_archive/workspace.bzl index e90630fa97..cf9d15ca28 100644 --- a/third_party/keras_applications_archive/workspace.bzl +++ b/third_party/keras_applications_archive/workspace.bzl @@ -12,4 +12,5 @@ def repo(): "https://github.com/keras-team/keras-applications/archive/1.0.6.tar.gz", ], build_file = "//third_party/keras_applications_archive:BUILD.bazel", + system_build_file = "//third_party/keras_applications_archive:BUILD.system", ) diff --git a/third_party/systemlibs/syslibs_configure.bzl b/third_party/systemlibs/syslibs_configure.bzl index dbf4fd6e32..85187587c9 100644 --- a/third_party/systemlibs/syslibs_configure.bzl +++ b/third_party/systemlibs/syslibs_configure.bzl @@ -26,6 +26,7 @@ VALID_LIBS = [ "icu", "jpeg", "jsoncpp_git", + "keras_applications_archive", "lmdb", "nasm", "nsync", -- GitLab From 813af36087a44f2a5670625408b076f531ea805b Mon Sep 17 00:00:00 2001 From: Jason Zaman Date: Wed, 28 Nov 2018 15:35:50 +0800 Subject: [PATCH 076/873] systemlibs: icu: update unbundle //third_party/icu/data was added which depends on a new icu target that was missing in the unbundled BUILD file. Signed-off-by: Jason Zaman --- third_party/icu/BUILD.system | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/third_party/icu/BUILD.system b/third_party/icu/BUILD.system index 328e412a8c..8a88a6ef7e 100644 --- a/third_party/icu/BUILD.system +++ b/third_party/icu/BUILD.system @@ -1,13 +1,19 @@ +package( + default_visibility = ["//visibility:public"], +) + licenses(["notice"]) # Apache 2.0 filegroup( name = "icu4c/LICENSE", - visibility = ["//visibility:public"], ) filegroup( name = "icu4j/main/shared/licenses/LICENSE", - visibility = ["//visibility:public"], +) + +cc_library( + name = "headers", ) cc_library( @@ -15,7 +21,6 @@ cc_library( deps = [ ":icuuc", ], - visibility = ["//visibility:public"], ) cc_library( -- GitLab From 7b1169cd951730e89ec4b019dd1c135e22eed29f Mon Sep 17 00:00:00 2001 From: Chris Antaki Date: Tue, 4 Dec 2018 08:57:57 -0800 Subject: [PATCH 077/873] Removes line from README As @terrytangyuan pointed out in #23647, the line being removed makes a suggestion that can't be followed --- tensorflow/contrib/tfprof/README.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/tensorflow/contrib/tfprof/README.md b/tensorflow/contrib/tfprof/README.md index b29d1acacf..f40e76f554 100644 --- a/tensorflow/contrib/tfprof/README.md +++ b/tensorflow/contrib/tfprof/README.md @@ -1,7 +1,5 @@ # tfprof: TensorFlow Profiler and Beyond -

Please use `tf.profiler.xxx` instead of `tf.contrib.tfprof.xxx`

-

Full Document in tensorflow/core/profiler/README.md

-- GitLab From 30d6a001371a9cef5ab085980356365d4861b8ee Mon Sep 17 00:00:00 2001 From: Pooya Davoodi Date: Tue, 4 Dec 2018 09:03:15 -0800 Subject: [PATCH 078/873] Update README.md --- tensorflow/contrib/tensorrt/README.md | 29 ++++++++++++++++----------- 1 file changed, 17 insertions(+), 12 deletions(-) diff --git a/tensorflow/contrib/tensorrt/README.md b/tensorflow/contrib/tensorrt/README.md index 09ef7f459f..dedac2c748 100644 --- a/tensorflow/contrib/tensorrt/README.md +++ b/tensorflow/contrib/tensorrt/README.md @@ -2,8 +2,7 @@ This module provides necessary bindings and introduces `TRTEngineOp` operator that wraps a subgraph in TensorRT. -This is still a work in progress but should be useable -with most common graphs. +This module is under active development. ## Installing TF-TRT @@ -24,6 +23,21 @@ of TensorRT from the Installation instructions for compatibility with TensorFlow are provided on the [TensorFlow GPU support](https://www.tensorflow.org/install/gpu) guide. +## Examples + +You can find example scripts for running inference on deep learning +models in this repository: https://github.com/tensorflow/tensorrt + +We have used these examples to verify the accuracy and +performance of TF-TRT. For more information see +[Verified Models](https://docs.nvidia.com/deeplearning/dgx/integrate-tf-trt/index.html#verified-models). + +## Documentation + +[TF-TRT documentaion](https://docs.nvidia.com/deeplearning/dgx/integrate-tf-trt/index.html) +gives an overview of the supported functionalities, provides tutorials +and verified models, explains best practices with troubleshooting guides. + ## Tests TF-TRT includes both Python tests and C++ unit tests. @@ -33,16 +47,6 @@ with the Python command. Most of the C++ unit tests are used to test the conversion functions that convert each TF op to a number of TensorRT layers. -## Examples - -You can find example scripts for running inference on deep learning models -in this repository: https://github.com/tensorflow/tensorrt - -## Documentation - -You can find documentation for TF-TRT here: -https://docs.nvidia.com/deeplearning/dgx/integrate-tf-trt/index.html - ## Compilation In order to compile the module, you need to have a local TensorRT installation @@ -56,3 +60,4 @@ has to set path to location where the library is installed during configuration. bazel build --config=cuda --config=opt //tensorflow/tools/pip_package:build_pip_package bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/ ``` + -- GitLab From 12a38bdc89520b86acfd3d6451545bbc0fa407bd Mon Sep 17 00:00:00 2001 From: Niranjan Hasabnis Date: Tue, 4 Dec 2018 15:52:23 -0800 Subject: [PATCH 079/873] Removing unit tests which are producing slightly different results with MKL --- tensorflow/core/kernels/mkl_fused_ops_test.cc | 14 -------------- 1 file changed, 14 deletions(-) diff --git a/tensorflow/core/kernels/mkl_fused_ops_test.cc b/tensorflow/core/kernels/mkl_fused_ops_test.cc index eb456ce7a3..6095a26f62 100644 --- a/tensorflow/core/kernels/mkl_fused_ops_test.cc +++ b/tensorflow/core/kernels/mkl_fused_ops_test.cc @@ -275,12 +275,6 @@ TYPED_TEST_P(MklFusedConv2DWithBiasOpTest, OneByOneConvolution) { this->VerifyConv2DWithBias(filter_size, filter_count); } -TYPED_TEST_P(MklFusedConv2DWithBiasOpTest, ImageSizeConvolution) { - const int filter_size = TestFixture::kImageWidth; - const int filter_count = 12; - this->VerifyConv2DWithBias(filter_size, filter_count); -} - TYPED_TEST_P(MklFusedConv2DWithBiasOpTest, SpatialConvolution) { const int filter_size = 3; const int filter_count = 12; @@ -293,12 +287,6 @@ TYPED_TEST_P(MklFusedConv2DWithBiasOpTest, OneByOneConvolutionAndRelu) { this->VerifyConv2DWithBiasAndRelu(filter_size, filter_count); } -TYPED_TEST_P(MklFusedConv2DWithBiasOpTest, ImageSizeConvolutionAndRelu) { - const int filter_size = TestFixture::kImageWidth; - const int filter_count = 12; - this->VerifyConv2DWithBiasAndRelu(filter_size, filter_count); -} - TYPED_TEST_P(MklFusedConv2DWithBiasOpTest, SpatialConvolutionAndRelu) { const int filter_size = 3; const int filter_count = 12; @@ -307,10 +295,8 @@ TYPED_TEST_P(MklFusedConv2DWithBiasOpTest, SpatialConvolutionAndRelu) { REGISTER_TYPED_TEST_CASE_P(MklFusedConv2DWithBiasOpTest, OneByOneConvolution, // - ImageSizeConvolution, // SpatialConvolution, // OneByOneConvolutionAndRelu, // - ImageSizeConvolutionAndRelu, // SpatialConvolutionAndRelu); using MklFusedBiasAddDataTypes = ::testing::Types; -- GitLab From b41761c499a5081870b9da4a8ae73adc45df269a Mon Sep 17 00:00:00 2001 From: Jason Zaman Date: Sun, 14 Oct 2018 11:28:53 +0800 Subject: [PATCH 080/873] Update to bazel-0.18.0 and use try-import Bazel-0.18.0 adds a try-import option that will non-fatally try and import a file. Use this for the configure options so that .bazelrc does not need to change. ./configure rewriting .bazelrc makes using the git repo annoying because the file is changed. The allowed bazel range is now 0.18.0-0.20.0 inclusive. The env var TF_IGNORE_MAX_BAZEL_VERSION can be set to skip the max bazel version check. Also optionally import a /.bazelrc.user file that is gitignored so user-specific options can go in there. Fixes: https://github.com/tensorflow/tensorflow/issues/22762 Fixes: https://github.com/tensorflow/tensorflow/pull/22906 Signed-off-by: Jason Zaman --- tools/bazel.rc => .bazelrc | 8 +++++++ .gitignore | 2 +- WORKSPACE | 2 +- configure.py | 22 ++++--------------- .../tools/ci_build/install/install_bazel.sh | 2 +- .../install/install_bazel_from_source.sh | 2 +- tensorflow/tools/docker/Dockerfile.devel | 2 +- tensorflow/tools/docker/Dockerfile.devel-gpu | 2 +- tensorflow/tools/docker/Dockerfile.devel-mkl | 2 +- .../tools/docker/Dockerfile.devel-mkl-horovod | 2 +- 10 files changed, 20 insertions(+), 26 deletions(-) rename tools/bazel.rc => .bazelrc (95%) diff --git a/tools/bazel.rc b/.bazelrc similarity index 95% rename from tools/bazel.rc rename to .bazelrc index 1fdf51f53e..8d9834f59a 100644 --- a/tools/bazel.rc +++ b/.bazelrc @@ -93,3 +93,11 @@ build:dynamic_kernels --copt=-DAUTOLOAD_DYNAMIC_KERNELS build --define=PREFIX=/usr build --define=LIBDIR=$(PREFIX)/lib build --define=INCLUDEDIR=$(PREFIX)/include + +# Default options should come above this line + +# Options from ./configure +try-import %workspace%/.tf_configure.bazelrc + +# Put user-specific options in .bazelrc.user +try-import %workspace%/.bazelrc.user diff --git a/.gitignore b/.gitignore index 9032405860..e1d352c238 100644 --- a/.gitignore +++ b/.gitignore @@ -1,7 +1,7 @@ .DS_Store .ipynb_checkpoints node_modules -/.bazelrc +/.bazelrc.user /.tf_configure.bazelrc /bazel-* /bazel_pip diff --git a/WORKSPACE b/WORKSPACE index 7cc08e0164..0f59c44e39 100644 --- a/WORKSPACE +++ b/WORKSPACE @@ -47,7 +47,7 @@ remote_config_workspace() # files, in case the parsing of those build files depends on the bazel # version we require here. load("//tensorflow:version_check.bzl", "check_bazel_version_at_least") -check_bazel_version_at_least("0.15.0") +check_bazel_version_at_least("0.18.0") load("//tensorflow:workspace.bzl", "tf_workspace") diff --git a/configure.py b/configure.py index 6c905a0be3..d19607af6c 100644 --- a/configure.py +++ b/configure.py @@ -255,18 +255,6 @@ def setup_python(environ_cp): def reset_tf_configure_bazelrc(): """Reset file that contains customized config settings.""" open(_TF_BAZELRC, 'w').close() - bazelrc_path = os.path.join(_TF_WORKSPACE_ROOT, '.bazelrc') - - data = [] - if os.path.exists(bazelrc_path): - with open(bazelrc_path, 'r') as f: - data = f.read().splitlines() - with open(bazelrc_path, 'w') as f: - for l in data: - if _TF_BAZELRC_FILENAME in l: - continue - f.write('%s\n' % l) - f.write('import %%workspace%%/%s\n' % _TF_BAZELRC_FILENAME) def cleanup_makefile(): """Delete any leftover BUILD files from the Makefile build. @@ -488,11 +476,11 @@ def check_bazel_version(min_version, max_version): if curr_version_int < min_version_int: print('Please upgrade your bazel installation to version %s or higher to ' 'build TensorFlow!' % min_version) - sys.exit(0) - if curr_version_int > max_version_int: + sys.exit(1) + if curr_version_int > max_version_int and not 'TF_IGNORE_MAX_BAZEL_VERSION' in os.environ: print('Please downgrade your bazel installation to version %s or lower to ' 'build TensorFlow!' % max_version) - sys.exit(0) + sys.exit(1) return curr_version @@ -1565,11 +1553,9 @@ def main(): # environment variables. environ_cp = dict(os.environ) - check_bazel_version('0.15.0', '0.20.0') + check_bazel_version('0.18.0', '0.20.0') reset_tf_configure_bazelrc() - # Explicitly import tools/bazel.rc, this is needed for Bazel 0.19.0 or later - write_to_bazelrc('import %workspace%/tools/bazel.rc') cleanup_makefile() setup_python(environ_cp) diff --git a/tensorflow/tools/ci_build/install/install_bazel.sh b/tensorflow/tools/ci_build/install/install_bazel.sh index e284401b8a..7472053209 100755 --- a/tensorflow/tools/ci_build/install/install_bazel.sh +++ b/tensorflow/tools/ci_build/install/install_bazel.sh @@ -15,7 +15,7 @@ # ============================================================================== # Select bazel version. -BAZEL_VERSION="0.15.0" +BAZEL_VERSION="0.18.0" set +e local_bazel_ver=$(bazel version 2>&1 | grep -i label | awk '{print $3}') diff --git a/tensorflow/tools/ci_build/install/install_bazel_from_source.sh b/tensorflow/tools/ci_build/install/install_bazel_from_source.sh index 87be81577d..4f83815d77 100755 --- a/tensorflow/tools/ci_build/install/install_bazel_from_source.sh +++ b/tensorflow/tools/ci_build/install/install_bazel_from_source.sh @@ -18,7 +18,7 @@ # It will compile bazel from source and install it in /usr/local/bin # Select bazel version. -BAZEL_VERSION="0.15.0" +BAZEL_VERSION="0.18.0" set +e local_bazel_ver=$(bazel version 2>&1 | grep -i label | awk '{print $3}') diff --git a/tensorflow/tools/docker/Dockerfile.devel b/tensorflow/tools/docker/Dockerfile.devel index c256dd364e..5ddcd3a2fd 100644 --- a/tensorflow/tools/docker/Dockerfile.devel +++ b/tensorflow/tools/docker/Dockerfile.devel @@ -65,7 +65,7 @@ RUN echo "startup --batch" >>/etc/bazel.bazelrc RUN echo "build --spawn_strategy=standalone --genrule_strategy=standalone" \ >>/etc/bazel.bazelrc # Install the most recent bazel release. -ENV BAZEL_VERSION 0.15.0 +ENV BAZEL_VERSION 0.18.0 WORKDIR / RUN mkdir /bazel && \ cd /bazel && \ diff --git a/tensorflow/tools/docker/Dockerfile.devel-gpu b/tensorflow/tools/docker/Dockerfile.devel-gpu index 7f9b55b455..767e5f4a4f 100644 --- a/tensorflow/tools/docker/Dockerfile.devel-gpu +++ b/tensorflow/tools/docker/Dockerfile.devel-gpu @@ -87,7 +87,7 @@ RUN echo "startup --batch" >>/etc/bazel.bazelrc RUN echo "build --spawn_strategy=standalone --genrule_strategy=standalone" \ >>/etc/bazel.bazelrc # Install the most recent bazel release. -ENV BAZEL_VERSION 0.15.0 +ENV BAZEL_VERSION 0.18.0 WORKDIR / RUN mkdir /bazel && \ cd /bazel && \ diff --git a/tensorflow/tools/docker/Dockerfile.devel-mkl b/tensorflow/tools/docker/Dockerfile.devel-mkl index 2341c0e8cc..0980502bcc 100755 --- a/tensorflow/tools/docker/Dockerfile.devel-mkl +++ b/tensorflow/tools/docker/Dockerfile.devel-mkl @@ -88,7 +88,7 @@ RUN echo "startup --batch" >>/etc/bazel.bazelrc RUN echo "build --spawn_strategy=standalone --genrule_strategy=standalone" \ >>/etc/bazel.bazelrc # Install the most recent bazel release. -ENV BAZEL_VERSION 0.15.0 +ENV BAZEL_VERSION 0.18.0 WORKDIR / RUN mkdir /bazel && \ cd /bazel && \ diff --git a/tensorflow/tools/docker/Dockerfile.devel-mkl-horovod b/tensorflow/tools/docker/Dockerfile.devel-mkl-horovod index 5e24617b21..90db249e3d 100755 --- a/tensorflow/tools/docker/Dockerfile.devel-mkl-horovod +++ b/tensorflow/tools/docker/Dockerfile.devel-mkl-horovod @@ -79,7 +79,7 @@ RUN echo "startup --batch" >>/etc/bazel.bazelrc RUN echo "build --spawn_strategy=standalone --genrule_strategy=standalone" \ >>/etc/bazel.bazelrc # Install the most recent bazel release. -ENV BAZEL_VERSION 0.15.0 +ENV BAZEL_VERSION 0.18.0 WORKDIR / RUN mkdir /bazel && \ cd /bazel && \ -- GitLab From 83e50f1d48a1b1746a55322a57b55a13aa6aca89 Mon Sep 17 00:00:00 2001 From: Penporn Koanantakool <38085909+penpornk@users.noreply.github.com> Date: Wed, 5 Dec 2018 09:02:27 +0800 Subject: [PATCH 081/873] Update tensorflow/core/graph/mkl_layout_pass.cc Co-Authored-By: guizili0 --- tensorflow/core/graph/mkl_layout_pass.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/core/graph/mkl_layout_pass.cc b/tensorflow/core/graph/mkl_layout_pass.cc index e283d00045..1acbf0dc60 100644 --- a/tensorflow/core/graph/mkl_layout_pass.cc +++ b/tensorflow/core/graph/mkl_layout_pass.cc @@ -1094,7 +1094,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass { // To compute LeakyRelu MKL DNN uses (feature), if feature > 0 // otherwise it uses (feature * alpha) // while Tensorflow uses max(feature, feature * alpha) to compute LeakyRelu. - // These two algorithm are not consistent when alpha > 1 + // These two algorithms are not consistent when alpha > 1, // so only LeakyRelu is written to MKL OP when alpha < 1 static bool LeakyReluRewrite(const Node* n) { DCHECK(n); -- GitLab From 602d56c362689d316410e8fe3d476d380ac25742 Mon Sep 17 00:00:00 2001 From: Penporn Koanantakool <38085909+penpornk@users.noreply.github.com> Date: Wed, 5 Dec 2018 09:03:03 +0800 Subject: [PATCH 082/873] Update tensorflow/core/graph/mkl_layout_pass.cc Co-Authored-By: guizili0 --- tensorflow/core/graph/mkl_layout_pass.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/core/graph/mkl_layout_pass.cc b/tensorflow/core/graph/mkl_layout_pass.cc index 1acbf0dc60..b99ec1dcf9 100644 --- a/tensorflow/core/graph/mkl_layout_pass.cc +++ b/tensorflow/core/graph/mkl_layout_pass.cc @@ -1095,7 +1095,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass { // otherwise it uses (feature * alpha) // while Tensorflow uses max(feature, feature * alpha) to compute LeakyRelu. // These two algorithms are not consistent when alpha > 1, - // so only LeakyRelu is written to MKL OP when alpha < 1 + // so we only rewrite LeakyRelu to MKL OP when alpha <= 1. static bool LeakyReluRewrite(const Node* n) { DCHECK(n); -- GitLab From 0628f12e26c61f2aab628d1979ea9bb79119e1a4 Mon Sep 17 00:00:00 2001 From: Penporn Koanantakool <38085909+penpornk@users.noreply.github.com> Date: Wed, 5 Dec 2018 09:04:00 +0800 Subject: [PATCH 083/873] Update tensorflow/core/kernels/mkl_relu_op.cc Co-Authored-By: guizili0 --- tensorflow/core/kernels/mkl_relu_op.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/core/kernels/mkl_relu_op.cc b/tensorflow/core/kernels/mkl_relu_op.cc index 2e29eae41b..e061b4103e 100644 --- a/tensorflow/core/kernels/mkl_relu_op.cc +++ b/tensorflow/core/kernels/mkl_relu_op.cc @@ -1352,7 +1352,7 @@ class MklLeakyReluOp : public MklReluOpBase { AllocateOutputSetMklShape(context, dst_index, &dst_tensor, src_tensor.shape(), dnn_shape_dst); T* out_o = dst_tensor->flat().data(); - out_o[0] = std::max(user_i[0], user_i[0] * this->alpha_); + out_o[0] = user_i[0] >= 0 ? user_g[0] : user_g[0] * this->alpha_; return; } }; -- GitLab From 5c2d58b0828d19d96b7d61de620cf81b88f3aa23 Mon Sep 17 00:00:00 2001 From: "Li, Guizi" Date: Wed, 5 Dec 2018 09:16:35 +0800 Subject: [PATCH 084/873] update comments and rewrite mkl leakyrelu when alpha <=1 --- tensorflow/core/graph/mkl_layout_pass.cc | 8 ++++---- tensorflow/core/kernels/mkl_relu_op.cc | 9 +++++---- 2 files changed, 9 insertions(+), 8 deletions(-) diff --git a/tensorflow/core/graph/mkl_layout_pass.cc b/tensorflow/core/graph/mkl_layout_pass.cc index b99ec1dcf9..b639c5ea7b 100644 --- a/tensorflow/core/graph/mkl_layout_pass.cc +++ b/tensorflow/core/graph/mkl_layout_pass.cc @@ -1091,9 +1091,9 @@ class MklLayoutRewritePass : public GraphOptimizationPass { return do_rewrite; } - // To compute LeakyRelu MKL DNN uses (feature), if feature > 0 - // otherwise it uses (feature * alpha) - // while Tensorflow uses max(feature, feature * alpha) to compute LeakyRelu. + // MKL-DNN's LeakyRelu(feature) = feature (if feature > 0), or + // feature * alpha (otherwise), + // while TensorFlow's LeakyRelu(feature) = max(feature, feature * alpha). // These two algorithms are not consistent when alpha > 1, // so we only rewrite LeakyRelu to MKL OP when alpha <= 1. static bool LeakyReluRewrite(const Node* n) { @@ -1105,7 +1105,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass { // If the alpha of LeakyRelu is less than 1, rewrite the node. // Otherwise eigen node is used instead. - if (alpha < 1) { + if (alpha <= 1) { return true; } VLOG(1) << "LeakyReluRewrite: The model sets alpha is not less than 1 " diff --git a/tensorflow/core/kernels/mkl_relu_op.cc b/tensorflow/core/kernels/mkl_relu_op.cc index e061b4103e..e2ce08feec 100644 --- a/tensorflow/core/kernels/mkl_relu_op.cc +++ b/tensorflow/core/kernels/mkl_relu_op.cc @@ -1366,10 +1366,11 @@ class MklLeakyReluGradOp : public MklReluGradOpBase { : MklReluGradOpBase(context, 0.0f, 0.0f) { float alpha; OP_REQUIRES_OK(context, context->GetAttr("alpha", &alpha)); - OP_REQUIRES(context, alpha < 1, - errors::InvalidArgument("MKL LeakyRelu only support alpha < 1. " - "alpha is: ", - alpha)); + OP_REQUIRES( + context, alpha <= 1, + errors::InvalidArgument("MKL LeakyRelu only support alpha <= 1. " + "alpha is: ", + alpha)); this->alpha_ = alpha; } -- GitLab From 11a420e09a753b2064c9b4a69419f2c78a4f19e0 Mon Sep 17 00:00:00 2001 From: AG Ramesh Date: Tue, 4 Dec 2018 17:34:37 -0800 Subject: [PATCH 085/873] Fixed clang format error --- tensorflow/core/kernels/mkl_fused_ops_test.cc | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tensorflow/core/kernels/mkl_fused_ops_test.cc b/tensorflow/core/kernels/mkl_fused_ops_test.cc index 6095a26f62..7f1965de85 100644 --- a/tensorflow/core/kernels/mkl_fused_ops_test.cc +++ b/tensorflow/core/kernels/mkl_fused_ops_test.cc @@ -294,9 +294,9 @@ TYPED_TEST_P(MklFusedConv2DWithBiasOpTest, SpatialConvolutionAndRelu) { } REGISTER_TYPED_TEST_CASE_P(MklFusedConv2DWithBiasOpTest, - OneByOneConvolution, // - SpatialConvolution, // - OneByOneConvolutionAndRelu, // + OneByOneConvolution, // + SpatialConvolution, // + OneByOneConvolutionAndRelu, // SpatialConvolutionAndRelu); using MklFusedBiasAddDataTypes = ::testing::Types; -- GitLab From 88de1cc935a311a1fe1412e8a821afc7c88ed6a4 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Sat, 16 Dec 2017 02:47:30 +0000 Subject: [PATCH 086/873] Add complex64 and complex128 support for `tf.angle` on GPU In PR 10643, complex64 and complex128 support have been added for `tf.angle` on CPU. However, because of the compilation errors, the complex support on GPU is not enabled yet. The issue was that, std::arg is not available on nvidia device for GPU. This fix changes to used atan2 instead, which is available in CUDA. The relevant test cases have bee enabled. This fix is related to 10643. Signed-off-by: Yong Tang --- tensorflow/core/kernels/cwise_ops.h | 35 +++++++++++++++++++++++++++-- 1 file changed, 33 insertions(+), 2 deletions(-) diff --git a/tensorflow/core/kernels/cwise_ops.h b/tensorflow/core/kernels/cwise_ops.h index 313def9a75..a10051e811 100644 --- a/tensorflow/core/kernels/cwise_ops.h +++ b/tensorflow/core/kernels/cwise_ops.h @@ -1008,9 +1008,40 @@ template struct get_imag : base, typename T::value_type> {}; +template +struct scalar_get_angle_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_get_angle_op) + typedef typename Eigen::NumTraits::Real result_type; + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type + operator()(const Scalar& a) const { + return Eigen::numext::arg(a); + } +}; + +#if GOOGLE_CUDA +template <> +struct scalar_get_angle_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_get_angle_op) + typedef typename Eigen::NumTraits::Real result_type; + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const float + operator()(const complex64& a) const { + return ::atan2f(a.imag(), a.real()); + } +}; + +template <> +struct scalar_get_angle_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_get_angle_op) + typedef typename Eigen::NumTraits::Real result_type; + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const double + operator()(const complex128& a) const { + return ::atan2(a.imag(), a.real()); + } +}; +#endif + template -struct get_angle - : base, typename T::value_type> {}; +struct get_angle : base, typename scalar_get_angle_op::result_type> {}; template struct conj : base> {}; -- GitLab From 892bd9ef4ab7619c56dc61aa674f0cc70c766ebe Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Sat, 16 Dec 2017 02:53:19 +0000 Subject: [PATCH 087/873] Enable complex for tf.angle on GPU. Signed-off-by: Yong Tang --- tensorflow/core/kernels/cwise_op_arg.cc | 4 +--- tensorflow/core/kernels/cwise_op_gpu_arg.cu.cc | 4 +--- 2 files changed, 2 insertions(+), 6 deletions(-) diff --git a/tensorflow/core/kernels/cwise_op_arg.cc b/tensorflow/core/kernels/cwise_op_arg.cc index 62ffa0718f..ea659facdc 100644 --- a/tensorflow/core/kernels/cwise_op_arg.cc +++ b/tensorflow/core/kernels/cwise_op_arg.cc @@ -26,9 +26,7 @@ namespace tensorflow { REGISTER_COMPLEX(CPU, float, complex64); REGISTER_COMPLEX(CPU, double, complex128); -// TODO: Enable GPU support for angle op after resolving -// build failures on GPU (See #10643 for context). -#if 0 && GOOGLE_CUDA +#if GOOGLE_CUDA REGISTER_COMPLEX(GPU, float, complex64); REGISTER_COMPLEX(GPU, double, complex128); #endif diff --git a/tensorflow/core/kernels/cwise_op_gpu_arg.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_arg.cu.cc index 9b3f8200bd..34028e936e 100644 --- a/tensorflow/core/kernels/cwise_op_gpu_arg.cu.cc +++ b/tensorflow/core/kernels/cwise_op_gpu_arg.cu.cc @@ -13,9 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -// TODO: Enable GPU support for angle op after resolving -// build failures on GPU (See #10643 for context). -#if 0 && GOOGLE_CUDA +#if GOOGLE_CUDA #include "tensorflow/core/kernels/cwise_ops_gpu_common.cu.h" -- GitLab From c47fba6210fa8ce9f663f6d3c4ac1382db8ca89e Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Sat, 16 Dec 2017 02:53:53 +0000 Subject: [PATCH 088/873] Enable test case for complex support of `tf.angle` on GPU. Signed-off-by: Yong Tang --- tensorflow/python/kernel_tests/cwise_ops_test.py | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/tensorflow/python/kernel_tests/cwise_ops_test.py b/tensorflow/python/kernel_tests/cwise_ops_test.py index 9bb7d8b8b1..70f19f9d2f 100644 --- a/tensorflow/python/kernel_tests/cwise_ops_test.py +++ b/tensorflow/python/kernel_tests/cwise_ops_test.py @@ -887,7 +887,7 @@ class ComplexMakeRealImagTest(test.TestCase): tf_angle = math_ops.angle(inx) tf_angle_val = self.evaluate(tf_angle) - self.assertAllEqual(np_angle, tf_angle_val) + self.assertAllClose(np_angle, tf_angle_val) self.assertShapeEqual(np_angle, tf_angle) def testAngle64(self): @@ -895,18 +895,14 @@ class ComplexMakeRealImagTest(test.TestCase): imag = (np.arange(-3, 3) / 5.).reshape([1, 3, 2]).astype(np.float32) cplx = real + 1j * imag self._compareAngle(cplx, use_gpu=False) - # TODO: Enable GPU tests for angle op after resolving - # build failures on GPU (See #10643 for context). - # self._compareAngle(cplx, use_gpu=True) + self._compareAngle(cplx, use_gpu=True) def testAngle(self): real = (np.arange(-3, 3) / 4.).reshape([1, 3, 2]).astype(np.float64) imag = (np.arange(-3, 3) / 5.).reshape([1, 3, 2]).astype(np.float64) cplx = real + 1j * imag self._compareAngle(cplx, use_gpu=False) - # TODO: Enable GPU tests for angle op after resolving - # build failures on GPU (See #10643 for context). - # self._compareAngle(cplx, use_gpu=True) + self._compareAngle(cplx, use_gpu=True) @test_util.run_deprecated_v1 def testRealReal(self): -- GitLab From d285d0cb7a0e4f2ee1f8c99e2f062d63a9d1521e Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Sat, 16 Dec 2017 02:57:17 +0000 Subject: [PATCH 089/873] Sanitize with clang-format -i Signed-off-by: Yong Tang --- tensorflow/core/kernels/cwise_ops.h | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/tensorflow/core/kernels/cwise_ops.h b/tensorflow/core/kernels/cwise_ops.h index a10051e811..9dcad7e9a4 100644 --- a/tensorflow/core/kernels/cwise_ops.h +++ b/tensorflow/core/kernels/cwise_ops.h @@ -1023,8 +1023,8 @@ template <> struct scalar_get_angle_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_get_angle_op) typedef typename Eigen::NumTraits::Real result_type; - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const float - operator()(const complex64& a) const { + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const float operator()( + const complex64& a) const { return ::atan2f(a.imag(), a.real()); } }; @@ -1033,15 +1033,16 @@ template <> struct scalar_get_angle_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_get_angle_op) typedef typename Eigen::NumTraits::Real result_type; - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const double - operator()(const complex128& a) const { + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const double operator()( + const complex128& a) const { return ::atan2(a.imag(), a.real()); } }; #endif template -struct get_angle : base, typename scalar_get_angle_op::result_type> {}; +struct get_angle : base, + typename scalar_get_angle_op::result_type> {}; template struct conj : base> {}; -- GitLab From 153e636227c8bd78ba3725942652c0768f369af9 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Sat, 16 Dec 2017 04:20:23 +0000 Subject: [PATCH 090/873] Specialize scalar_arg_op instead. Signed-off-by: Yong Tang --- tensorflow/core/kernels/cwise_ops.h | 75 ++++++++++++++++------------- 1 file changed, 41 insertions(+), 34 deletions(-) diff --git a/tensorflow/core/kernels/cwise_ops.h b/tensorflow/core/kernels/cwise_ops.h index 9dcad7e9a4..bfa7cf9e06 100644 --- a/tensorflow/core/kernels/cwise_ops.h +++ b/tensorflow/core/kernels/cwise_ops.h @@ -29,6 +29,45 @@ limitations under the License. namespace Eigen { namespace internal { +#if GOOGLE_CUDA +template <> +struct scalar_arg_op> { + EIGEN_EMPTY_STRUCT_CTOR(scalar_arg_op) + typedef typename Eigen::NumTraits>::Real result_type; + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const float operator()( + const std::complex& a) const { + return ::atan2f(a.imag(), a.real()); + } +}; + +template <> +struct scalar_arg_op> { + EIGEN_EMPTY_STRUCT_CTOR(scalar_arg_op) + typedef typename Eigen::NumTraits>::Real result_type; + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const double operator()( + const std::complex& a) const { + return ::atan2(a.imag(), a.real()); + } +}; +#endif + +// TODO(rmlarsen): Get rid of fmod2 once fmod is upstreamed to Eigen. +template +struct scalar_fmod2_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_fmod2_op) + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T operator()(const T& a, + const T& b) const { + return std::fmod(a, b); + } +}; +template +struct functor_traits> { + enum { + Cost = 13, // Reciprocal throughput of FPREM on Haswell. + PacketAccess = false, + }; +}; + template struct scalar_asinh_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_asinh_op) @@ -1008,41 +1047,9 @@ template struct get_imag : base, typename T::value_type> {}; -template -struct scalar_get_angle_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_get_angle_op) - typedef typename Eigen::NumTraits::Real result_type; - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type - operator()(const Scalar& a) const { - return Eigen::numext::arg(a); - } -}; - -#if GOOGLE_CUDA -template <> -struct scalar_get_angle_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_get_angle_op) - typedef typename Eigen::NumTraits::Real result_type; - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const float operator()( - const complex64& a) const { - return ::atan2f(a.imag(), a.real()); - } -}; - -template <> -struct scalar_get_angle_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_get_angle_op) - typedef typename Eigen::NumTraits::Real result_type; - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const double operator()( - const complex128& a) const { - return ::atan2(a.imag(), a.real()); - } -}; -#endif - template -struct get_angle : base, - typename scalar_get_angle_op::result_type> {}; +struct get_angle + : base, typename T::value_type> {}; template struct conj : base> {}; -- GitLab From 3fea0332bb3c49f70ea42b4d0ecdfe16092ef7fe Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Wed, 5 Dec 2018 01:52:50 +0000 Subject: [PATCH 091/873] Fix merge conflict Signed-off-by: Yong Tang --- tensorflow/core/kernels/cwise_ops.h | 17 ----------------- 1 file changed, 17 deletions(-) diff --git a/tensorflow/core/kernels/cwise_ops.h b/tensorflow/core/kernels/cwise_ops.h index bfa7cf9e06..be75d0b4b0 100644 --- a/tensorflow/core/kernels/cwise_ops.h +++ b/tensorflow/core/kernels/cwise_ops.h @@ -51,23 +51,6 @@ struct scalar_arg_op> { }; #endif -// TODO(rmlarsen): Get rid of fmod2 once fmod is upstreamed to Eigen. -template -struct scalar_fmod2_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_fmod2_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T operator()(const T& a, - const T& b) const { - return std::fmod(a, b); - } -}; -template -struct functor_traits> { - enum { - Cost = 13, // Reciprocal throughput of FPREM on Haswell. - PacketAccess = false, - }; -}; - template struct scalar_asinh_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_asinh_op) -- GitLab From e621ab3c0042a7c3600820ed00396ba4c0023b6e Mon Sep 17 00:00:00 2001 From: Penporn Koanantakool <38085909+penpornk@users.noreply.github.com> Date: Wed, 5 Dec 2018 10:04:28 +0800 Subject: [PATCH 092/873] Update tensorflow/core/kernels/mkl_relu_op.cc Co-Authored-By: guizili0 --- tensorflow/core/kernels/mkl_relu_op.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/core/kernels/mkl_relu_op.cc b/tensorflow/core/kernels/mkl_relu_op.cc index e2ce08feec..3656b77032 100644 --- a/tensorflow/core/kernels/mkl_relu_op.cc +++ b/tensorflow/core/kernels/mkl_relu_op.cc @@ -1330,7 +1330,7 @@ class MklLeakyReluOp : public MklReluOpBase { : MklReluOpBase(context, 0.0f, 0.0f) { float alpha; OP_REQUIRES_OK(context, context->GetAttr("alpha", &alpha)); - OP_REQUIRES(context, alpha < 1, + OP_REQUIRES(context, alpha <= 1, errors::InvalidArgument("MKL LeakyRelu only support alpha < 1. " "alpha is: ", alpha)); -- GitLab From b5d71d5c61c757fcfea5a113c19049e77e2cae88 Mon Sep 17 00:00:00 2001 From: Penporn Koanantakool <38085909+penpornk@users.noreply.github.com> Date: Wed, 5 Dec 2018 10:04:37 +0800 Subject: [PATCH 093/873] Update tensorflow/core/kernels/mkl_relu_op.cc Co-Authored-By: guizili0 --- tensorflow/core/kernels/mkl_relu_op.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/core/kernels/mkl_relu_op.cc b/tensorflow/core/kernels/mkl_relu_op.cc index 3656b77032..7bec78a062 100644 --- a/tensorflow/core/kernels/mkl_relu_op.cc +++ b/tensorflow/core/kernels/mkl_relu_op.cc @@ -1393,7 +1393,7 @@ class MklLeakyReluGradOp : public MklReluGradOpBase { T* out_o = diff_src_tensor->flat().data(); T* user_i = const_cast(src_tensor.flat().data()); T* user_g = const_cast(diff_dst_tensor.flat().data()); - out_o[0] = user_i[0] > 0 ? user_g[0] : user_g[0] * this->alpha_; + out_o[0] = user_i[0] >= 0 ? user_g[0] : user_g[0] * this->alpha_; return; } }; -- GitLab From 056bd409eb33486e24bf2d6aa8f86a79befeebee Mon Sep 17 00:00:00 2001 From: Penporn Koanantakool <38085909+penpornk@users.noreply.github.com> Date: Wed, 5 Dec 2018 10:04:46 +0800 Subject: [PATCH 094/873] Update tensorflow/core/kernels/mkl_relu_op.cc Co-Authored-By: guizili0 --- tensorflow/core/kernels/mkl_relu_op.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/core/kernels/mkl_relu_op.cc b/tensorflow/core/kernels/mkl_relu_op.cc index 7bec78a062..7605bfad15 100644 --- a/tensorflow/core/kernels/mkl_relu_op.cc +++ b/tensorflow/core/kernels/mkl_relu_op.cc @@ -1331,7 +1331,7 @@ class MklLeakyReluOp : public MklReluOpBase { float alpha; OP_REQUIRES_OK(context, context->GetAttr("alpha", &alpha)); OP_REQUIRES(context, alpha <= 1, - errors::InvalidArgument("MKL LeakyRelu only support alpha < 1. " + errors::InvalidArgument("MKL LeakyRelu only supports alpha <= 1. " "alpha is: ", alpha)); -- GitLab From 2794f95710e18f985bec6f0092f4d328347e238d Mon Sep 17 00:00:00 2001 From: Penporn Koanantakool <38085909+penpornk@users.noreply.github.com> Date: Wed, 5 Dec 2018 10:04:54 +0800 Subject: [PATCH 095/873] Update tensorflow/core/graph/mkl_layout_pass.cc Co-Authored-By: guizili0 --- tensorflow/core/graph/mkl_layout_pass.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/core/graph/mkl_layout_pass.cc b/tensorflow/core/graph/mkl_layout_pass.cc index b639c5ea7b..adeb33c1b9 100644 --- a/tensorflow/core/graph/mkl_layout_pass.cc +++ b/tensorflow/core/graph/mkl_layout_pass.cc @@ -1108,7 +1108,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass { if (alpha <= 1) { return true; } - VLOG(1) << "LeakyReluRewrite: The model sets alpha is not less than 1 " + VLOG(1) << "LeakyReluRewrite: The model sets alpha is greater than 1 " << "which case is not optimized by Intel MKL, thus using Eigen op" << "for LeakyRelu "; -- GitLab From 20bab61688b60300eafb2c7cc48b9ad542bcb1a4 Mon Sep 17 00:00:00 2001 From: Penporn Koanantakool <38085909+penpornk@users.noreply.github.com> Date: Wed, 5 Dec 2018 10:05:02 +0800 Subject: [PATCH 096/873] Update tensorflow/core/kernels/mkl_relu_op.cc Co-Authored-By: guizili0 --- tensorflow/core/kernels/mkl_relu_op.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/core/kernels/mkl_relu_op.cc b/tensorflow/core/kernels/mkl_relu_op.cc index 7605bfad15..03867517ef 100644 --- a/tensorflow/core/kernels/mkl_relu_op.cc +++ b/tensorflow/core/kernels/mkl_relu_op.cc @@ -1368,7 +1368,7 @@ class MklLeakyReluGradOp : public MklReluGradOpBase { OP_REQUIRES_OK(context, context->GetAttr("alpha", &alpha)); OP_REQUIRES( context, alpha <= 1, - errors::InvalidArgument("MKL LeakyRelu only support alpha <= 1. " + errors::InvalidArgument("MKL LeakyRelu only supports alpha <= 1. " "alpha is: ", alpha)); -- GitLab From 6633267b3f06d8f1b074bfd8a1807b031bfc80de Mon Sep 17 00:00:00 2001 From: "Li, Guizi" Date: Wed, 5 Dec 2018 11:48:45 +0800 Subject: [PATCH 097/873] fix clang format --- tensorflow/core/kernels/mkl_relu_op.cc | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/tensorflow/core/kernels/mkl_relu_op.cc b/tensorflow/core/kernels/mkl_relu_op.cc index 03867517ef..43f8a88e66 100644 --- a/tensorflow/core/kernels/mkl_relu_op.cc +++ b/tensorflow/core/kernels/mkl_relu_op.cc @@ -1330,10 +1330,11 @@ class MklLeakyReluOp : public MklReluOpBase { : MklReluOpBase(context, 0.0f, 0.0f) { float alpha; OP_REQUIRES_OK(context, context->GetAttr("alpha", &alpha)); - OP_REQUIRES(context, alpha <= 1, - errors::InvalidArgument("MKL LeakyRelu only supports alpha <= 1. " - "alpha is: ", - alpha)); + OP_REQUIRES( + context, alpha <= 1, + errors::InvalidArgument("MKL LeakyRelu only supports alpha <= 1. " + "alpha is: ", + alpha)); this->alpha_ = alpha; } -- GitLab From 5d21cfbed9ed34d6ba90aa6ebbc93c0dd0fe19d2 Mon Sep 17 00:00:00 2001 From: "Meng, Peng" Date: Wed, 5 Dec 2018 11:54:03 +0800 Subject: [PATCH 098/873] fix clang format Change-Id: I89ea6cea2a55c65f9de588c106ee10945d6efa62 --- tensorflow/core/kernels/mkl_softmax_op.cc | 121 +++++++++++----------- 1 file changed, 60 insertions(+), 61 deletions(-) diff --git a/tensorflow/core/kernels/mkl_softmax_op.cc b/tensorflow/core/kernels/mkl_softmax_op.cc index 25c0c7b078..f81521f4be 100644 --- a/tensorflow/core/kernels/mkl_softmax_op.cc +++ b/tensorflow/core/kernels/mkl_softmax_op.cc @@ -17,13 +17,13 @@ limitations under the License. #ifdef INTEL_MKL #ifndef INTEL_MKL_ML_ONLY -#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/util/tensor_format.h" +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/util/mkl_util.h" @@ -36,20 +36,19 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; -template -class MklSoftmaxOp : public OpKernel { - public: +template class MklSoftmaxOp : public OpKernel { +public: ~MklSoftmaxOp() {} - explicit MklSoftmaxOp(OpKernelConstruction* context) : OpKernel(context) {} + explicit MklSoftmaxOp(OpKernelConstruction *context) : OpKernel(context) {} - void Compute(OpKernelContext* context) override { + void Compute(OpKernelContext *context) override { try { auto cpu_engine = engine(engine::cpu, 0); // src_tensor now points to the 0-th input of global data struct "context" size_t src_idx = 0; - const Tensor& src_tensor = MklGetInput(context, src_idx); + const Tensor &src_tensor = MklGetInput(context, src_idx); // Add: get MklShape MklDnnShape src_mkl_shape; GetMklShape(context, src_idx, &src_mkl_shape); @@ -66,48 +65,49 @@ class MklSoftmaxOp : public OpKernel { if (src_mkl_shape.IsMklTensor()) { axis = 1; output_dims = src_mkl_shape.GetSizesAsMklDnnDims(); - } - else { + } else { axis = input_dims - 1; output_dims = src_dims; } memory::format layout_type; - // In MKL, data format passed to mkl softmax op depends on dimension of the input tensor. - // Here "x" data format in MKL is used for 1 dim tensor, "nc" for 2 dim tensor, - // "tnc" for 3 dim tensor, "nchw" for 4 dim tensor, and "ncdhw" for 5 dim tensor. + // In MKL, data format passed to mkl softmax op depends on dimension of + // the input tensor. + // Here "x" data format in MKL is used for 1 dim tensor, "nc" for 2 dim + // tensor, + // "tnc" for 3 dim tensor, "nchw" for 4 dim tensor, and "ncdhw" for 5 dim + // tensor. // Each of the simbols has the following meaning: // n = batch, c = channels, t = sequence lenght, h = height, - // w = width, d = depth - + // w = width, d = depth + switch (input_dims) { - case 1: - layout_type = memory::format::x; - break; - case 2: - layout_type = memory::format::nc; - break; - case 3: - layout_type = memory::format::tnc; - break; - case 4: - if (src_mkl_shape.IsMklTensor()) { - layout_type = memory::format::nhwc; - } - else { - layout_type = memory::format::nchw; - } - break; - case 5: - if (src_mkl_shape.IsMklTensor()) { - layout_type = memory::format::ndhwc; - } - else { - layout_type = memory::format::ncdhw; - } - break; - default: - OP_REQUIRES_OK(context, errors::Aborted("Input dims must be <= 5 and >=1")); - return; + case 1: + layout_type = memory::format::x; + break; + case 2: + layout_type = memory::format::nc; + break; + case 3: + layout_type = memory::format::tnc; + break; + case 4: + if (src_mkl_shape.IsMklTensor()) { + layout_type = memory::format::nhwc; + } else { + layout_type = memory::format::nchw; + } + break; + case 5: + if (src_mkl_shape.IsMklTensor()) { + layout_type = memory::format::ndhwc; + } else { + layout_type = memory::format::ncdhw; + } + break; + default: + OP_REQUIRES_OK(context, + errors::Aborted("Input dims must be <= 5 and >=1")); + return; } // Create softmax memory for src, dst: both are defined in mkl_util.h, // they are wrapper @@ -118,10 +118,9 @@ class MklSoftmaxOp : public OpKernel { // construct input Tf layout. For TF layout, although input shape // (src_dims) required is in MKL-DNN order, the layout is Tensorflow's // layout - auto src_md = - src_mkl_shape.IsMklTensor() - ? src_mkl_shape.GetMklLayout() - : memory::desc(src_dims, MklDnnType(), layout_type); + auto src_md = src_mkl_shape.IsMklTensor() + ? src_mkl_shape.GetMklLayout() + : memory::desc(src_dims, MklDnnType(), layout_type); // src: setting memory descriptor // following functions are in mkl_util.h @@ -134,9 +133,9 @@ class MklSoftmaxOp : public OpKernel { softmax_forward::primitive_desc(softmax_fwd_desc, cpu_engine); // add: output - Tensor* output_tensor = nullptr; + Tensor *output_tensor = nullptr; MklDnnShape output_mkl_shape; - TensorShape output_tf_shape; // shape of output TF tensor. + TensorShape output_tf_shape; // shape of output TF tensor. // Softmax MklDnn output layout is same as input layout. auto dst_pd = src.GetUsrMemPrimDesc(); @@ -149,7 +148,7 @@ class MklSoftmaxOp : public OpKernel { output_mkl_shape.SetTfLayout(output_dims.size(), output_dims, layout_type); output_tf_shape.AddDim((dst_pd.get_size() / sizeof(T))); - } else { // then output is also TF shape + } else { // then output is also TF shape output_mkl_shape.SetMklTensor(false); output_tf_shape = MklDnnDimsToTFShape(output_dims); } @@ -170,10 +169,10 @@ class MklSoftmaxOp : public OpKernel { std::vector net; net.push_back(softmax_fwd); stream(stream::kind::eager).submit(net).wait(); - } 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__); + } 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__); OP_REQUIRES_OK( context, errors::Aborted("Operation received an exception:", error_msg)); @@ -183,15 +182,15 @@ class MklSoftmaxOp : public OpKernel { /* Register DNN kernels for supported operations and supported types - right now * it is only Softmax and f32 */ -#define REGISTER_SOFTMAX_MKL_SUPPORTED_KERNELS_TYPES(type) \ - REGISTER_KERNEL_BUILDER(Name("_MklSoftmax") \ - .Device(DEVICE_CPU) \ - .TypeConstraint("T") \ - .Label(mkl_op_registry::kMklOpLabel), \ +#define REGISTER_SOFTMAX_MKL_SUPPORTED_KERNELS_TYPES(type) \ + REGISTER_KERNEL_BUILDER(Name("_MklSoftmax") \ + .Device(DEVICE_CPU) \ + .TypeConstraint("T") \ + .Label(mkl_op_registry::kMklOpLabel), \ MklSoftmaxOp); TF_CALL_float(REGISTER_SOFTMAX_MKL_SUPPORTED_KERNELS_TYPES); -} // namespace tensorflow +} // namespace tensorflow -#endif // INTEL_MKL_ML_ONLY -#endif // INTEL_MKL +#endif // INTEL_MKL_ML_ONLY +#endif // INTEL_MKL -- GitLab From b3687d2d6e6488ac1c90ed6c21ae5eff77f96b98 Mon Sep 17 00:00:00 2001 From: AG Ramesh Date: Tue, 4 Dec 2018 19:56:50 -0800 Subject: [PATCH 099/873] Corrected typos --- tensorflow/core/graph/mkl_graph_util.h | 6 +++--- tensorflow/core/kernels/mkl_conv_ops.cc | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/core/graph/mkl_graph_util.h b/tensorflow/core/graph/mkl_graph_util.h index 7435f4e8c1..a599ce3620 100644 --- a/tensorflow/core/graph/mkl_graph_util.h +++ b/tensorflow/core/graph/mkl_graph_util.h @@ -72,9 +72,9 @@ int inline GetTensorMetaDataIndex(int n, int total_tensors) { return DataIndexToMetaDataIndex(tidx, total_tensors); } -// Helper function to compare fused_ops attributes strings -// TODO(Intel-tf) this code is also in mkl_conv_ops.h, we need to move to -// mkl_util.h +// Helper function to compare fused_ops attribute strings +// TODO(Intel) this code is also defined in mkl_conv_ops.h, we need to move to +// mkl_util.h so we have only one version. inline bool CompareFusedOps(const std::vector& fused_ops, const std::vector& expected) { return fused_ops == expected; diff --git a/tensorflow/core/kernels/mkl_conv_ops.cc b/tensorflow/core/kernels/mkl_conv_ops.cc index c354390c69..4a4aaffead 100644 --- a/tensorflow/core/kernels/mkl_conv_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_ops.cc @@ -1177,7 +1177,7 @@ class MklConvOp : public OpKernel { const int kOutputIndex_Dst = 0, kOutputIndex_Filter = 1; const int kDilationH = 0, kDilationW = 1; - // Helper function to compare fused_ops attributes strings + // Helper function to compare fused_ops attribute strings bool CompareFusedOps(const std::vector& fused_ops, const std::vector& expected) { return fused_ops == expected; -- GitLab From 2c382f53d4e63646b4ff0e1d83067e594c2ab51f Mon Sep 17 00:00:00 2001 From: "Meng, Peng" Date: Wed, 5 Dec 2018 17:54:07 +0800 Subject: [PATCH 100/873] fix clang format Change-Id: Iabc5524dc0858611d4a43b2f8992ec2f397d386e --- tensorflow/core/kernels/mkl_softmax_op.cc | 89 ++++++++++++----------- 1 file changed, 45 insertions(+), 44 deletions(-) diff --git a/tensorflow/core/kernels/mkl_softmax_op.cc b/tensorflow/core/kernels/mkl_softmax_op.cc index f81521f4be..4067fbb013 100644 --- a/tensorflow/core/kernels/mkl_softmax_op.cc +++ b/tensorflow/core/kernels/mkl_softmax_op.cc @@ -36,19 +36,20 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; -template class MklSoftmaxOp : public OpKernel { -public: +template +class MklSoftmaxOp : public OpKernel { + public: ~MklSoftmaxOp() {} - explicit MklSoftmaxOp(OpKernelConstruction *context) : OpKernel(context) {} + explicit MklSoftmaxOp(OpKernelConstruction* context) : OpKernel(context) {} - void Compute(OpKernelContext *context) override { + void Compute(OpKernelContext* context) override { try { auto cpu_engine = engine(engine::cpu, 0); // src_tensor now points to the 0-th input of global data struct "context" size_t src_idx = 0; - const Tensor &src_tensor = MklGetInput(context, src_idx); + const Tensor& src_tensor = MklGetInput(context, src_idx); // Add: get MklShape MklDnnShape src_mkl_shape; GetMklShape(context, src_idx, &src_mkl_shape); @@ -81,33 +82,33 @@ public: // w = width, d = depth switch (input_dims) { - case 1: - layout_type = memory::format::x; - break; - case 2: - layout_type = memory::format::nc; - break; - case 3: - layout_type = memory::format::tnc; - break; - case 4: - if (src_mkl_shape.IsMklTensor()) { - layout_type = memory::format::nhwc; - } else { - layout_type = memory::format::nchw; - } - break; - case 5: - if (src_mkl_shape.IsMklTensor()) { - layout_type = memory::format::ndhwc; - } else { - layout_type = memory::format::ncdhw; - } - break; - default: - OP_REQUIRES_OK(context, - errors::Aborted("Input dims must be <= 5 and >=1")); - return; + case 1: + layout_type = memory::format::x; + break; + case 2: + layout_type = memory::format::nc; + break; + case 3: + layout_type = memory::format::tnc; + break; + case 4: + if (src_mkl_shape.IsMklTensor()) { + layout_type = memory::format::nhwc; + } else { + layout_type = memory::format::nchw; + } + break; + case 5: + if (src_mkl_shape.IsMklTensor()) { + layout_type = memory::format::ndhwc; + } else { + layout_type = memory::format::ncdhw; + } + break; + default: + OP_REQUIRES_OK(context, + errors::Aborted("Input dims must be <= 5 and >=1")); + return; } // Create softmax memory for src, dst: both are defined in mkl_util.h, // they are wrapper @@ -133,9 +134,9 @@ public: softmax_forward::primitive_desc(softmax_fwd_desc, cpu_engine); // add: output - Tensor *output_tensor = nullptr; + Tensor* output_tensor = nullptr; MklDnnShape output_mkl_shape; - TensorShape output_tf_shape; // shape of output TF tensor. + TensorShape output_tf_shape; // shape of output TF tensor. // Softmax MklDnn output layout is same as input layout. auto dst_pd = src.GetUsrMemPrimDesc(); @@ -148,7 +149,7 @@ public: output_mkl_shape.SetTfLayout(output_dims.size(), output_dims, layout_type); output_tf_shape.AddDim((dst_pd.get_size() / sizeof(T))); - } else { // then output is also TF shape + } else { // then output is also TF shape output_mkl_shape.SetMklTensor(false); output_tf_shape = MklDnnDimsToTFShape(output_dims); } @@ -169,7 +170,7 @@ public: std::vector net; net.push_back(softmax_fwd); stream(stream::kind::eager).submit(net).wait(); - } catch (mkldnn::error &e) { + } 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__); @@ -182,15 +183,15 @@ public: /* Register DNN kernels for supported operations and supported types - right now * it is only Softmax and f32 */ -#define REGISTER_SOFTMAX_MKL_SUPPORTED_KERNELS_TYPES(type) \ - REGISTER_KERNEL_BUILDER(Name("_MklSoftmax") \ - .Device(DEVICE_CPU) \ - .TypeConstraint("T") \ - .Label(mkl_op_registry::kMklOpLabel), \ +#define REGISTER_SOFTMAX_MKL_SUPPORTED_KERNELS_TYPES(type) \ + REGISTER_KERNEL_BUILDER(Name("_MklSoftmax") \ + .Device(DEVICE_CPU) \ + .TypeConstraint("T") \ + .Label(mkl_op_registry::kMklOpLabel), \ MklSoftmaxOp); TF_CALL_float(REGISTER_SOFTMAX_MKL_SUPPORTED_KERNELS_TYPES); -} // namespace tensorflow +} // namespace tensorflow -#endif // INTEL_MKL_ML_ONLY -#endif // INTEL_MKL +#endif // INTEL_MKL_ML_ONLY +#endif // INTEL_MKL -- GitLab From 1f05b7eaf0220bc720cd3d0a8fcf241458c3f74d Mon Sep 17 00:00:00 2001 From: Vidak Kazic Date: Wed, 5 Dec 2018 14:44:33 +0100 Subject: [PATCH 101/873] Add example to import_meta_graph docstring --- tensorflow/python/training/saver.py | 31 +++++++++++++++++++++++++++++ 1 file changed, 31 insertions(+) diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py index a29926a57d..5352c998ea 100644 --- a/tensorflow/python/training/saver.py +++ b/tensorflow/python/training/saver.py @@ -1656,6 +1656,37 @@ def import_meta_graph(meta_graph_or_file, clear_devices=False, NOTE: Restarting training from saved `meta_graph` only works if the device assignments have not changed. + Example 2: + Variables, placeholders, and independent operations can also be stored, as shown + in the following example. + + ```Python + # Saving contents and operations. + v1 = tf.placeholder(tf.float32, name="v1") + v2 = tf.placeholder(tf.float32, name="v2") + v3 = tf.mul(v1, v2) + vx = tf.Variable(10.0, name="vx") + v4 = tf.add(v3, vx, name="v4") + saver = tf.train.Saver([vx]) + sess = tf.Session() + sess.run(tf.initialize_all_variables()) + sess.run(vx.assign(tf.add(vx, vx))) + result = sess.run(v4, feed_dict={v1:12.0, v2:3.3}) + print(result) + saver.save(sess, "./model_ex1") + ``` + + Later this model can be restored and contents loaded. + + ```Python + # Restoring variables and running operations. + saver = tf.train.import_meta_graph("./model_ex1.meta") + sess = tf.Session() + saver.restore(sess, "./model_ex1") + result = sess.run("v4:0", feed_dict={"v1:0": 12.0, "v2:0": 3.3}) + print(result) + ``` + Args: meta_graph_or_file: `MetaGraphDef` protocol buffer or filename (including the path) containing a `MetaGraphDef`. -- GitLab From b6a6296de5b873107bae5abd30dff8897fa53b54 Mon Sep 17 00:00:00 2001 From: Vidak Kazic Date: Wed, 5 Dec 2018 15:01:56 +0100 Subject: [PATCH 102/873] Add example to import_meta_graph docstring --- tensorflow/python/training/saver.py | 31 +++++++++++++++++++++++++++++ 1 file changed, 31 insertions(+) diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py index a29926a57d..5352c998ea 100644 --- a/tensorflow/python/training/saver.py +++ b/tensorflow/python/training/saver.py @@ -1656,6 +1656,37 @@ def import_meta_graph(meta_graph_or_file, clear_devices=False, NOTE: Restarting training from saved `meta_graph` only works if the device assignments have not changed. + Example 2: + Variables, placeholders, and independent operations can also be stored, as shown + in the following example. + + ```Python + # Saving contents and operations. + v1 = tf.placeholder(tf.float32, name="v1") + v2 = tf.placeholder(tf.float32, name="v2") + v3 = tf.mul(v1, v2) + vx = tf.Variable(10.0, name="vx") + v4 = tf.add(v3, vx, name="v4") + saver = tf.train.Saver([vx]) + sess = tf.Session() + sess.run(tf.initialize_all_variables()) + sess.run(vx.assign(tf.add(vx, vx))) + result = sess.run(v4, feed_dict={v1:12.0, v2:3.3}) + print(result) + saver.save(sess, "./model_ex1") + ``` + + Later this model can be restored and contents loaded. + + ```Python + # Restoring variables and running operations. + saver = tf.train.import_meta_graph("./model_ex1.meta") + sess = tf.Session() + saver.restore(sess, "./model_ex1") + result = sess.run("v4:0", feed_dict={"v1:0": 12.0, "v2:0": 3.3}) + print(result) + ``` + Args: meta_graph_or_file: `MetaGraphDef` protocol buffer or filename (including the path) containing a `MetaGraphDef`. -- GitLab From 2c17ecb324044638e5ff4df836c1621bc0774328 Mon Sep 17 00:00:00 2001 From: Karl Lessard Date: Wed, 5 Dec 2018 09:21:41 -0500 Subject: [PATCH 103/873] Expose underlying operation in op wrappers --- .../java/src/main/java/org/tensorflow/Session.java | 10 ++++++++++ .../src/main/java/org/tensorflow/op/PrimitiveOp.java | 11 +++++++---- 2 files changed, 17 insertions(+), 4 deletions(-) diff --git a/tensorflow/java/src/main/java/org/tensorflow/Session.java b/tensorflow/java/src/main/java/org/tensorflow/Session.java index a660d25f98..c49e98b20e 100644 --- a/tensorflow/java/src/main/java/org/tensorflow/Session.java +++ b/tensorflow/java/src/main/java/org/tensorflow/Session.java @@ -157,6 +157,16 @@ public final class Session implements AutoCloseable { return this; } + /** + * Use {@code t} instead of the Tensor referred to by executing the operation referred to by + * {@code operand}. + */ + public Runner feed(Operand operand, Tensor t) { + inputs.add(operand.asOutput()); + inputTensors.add(t); + return this; + } + /** * Make {@link #run()} return the output of {@code operation}. * diff --git a/tensorflow/java/src/main/java/org/tensorflow/op/PrimitiveOp.java b/tensorflow/java/src/main/java/org/tensorflow/op/PrimitiveOp.java index 8e56f97041..5c47611d09 100644 --- a/tensorflow/java/src/main/java/org/tensorflow/op/PrimitiveOp.java +++ b/tensorflow/java/src/main/java/org/tensorflow/op/PrimitiveOp.java @@ -24,6 +24,13 @@ import org.tensorflow.Operation; * PrimitiveOp}. Custom operations working with only one primitive may also derive from this class. */ public abstract class PrimitiveOp implements Op { + + /** + * Returns the underlying {@link Operation} + */ + public Operation op() { + return operation; + } @Override public final int hashCode() { @@ -48,10 +55,6 @@ public abstract class PrimitiveOp implements Op { return String.format("<%s '%s'>", operation.type(), operation.name()); } - /** - * Underlying operation. It is deliberately not exposed by a getter method to avoid any name - * conflict with generated methods of the subclasses. - */ protected final Operation operation; /** -- GitLab From 1ec527767e83424b17d9c2e708b218a5db2738a7 Mon Sep 17 00:00:00 2001 From: Niranjan Hasabnis Date: Wed, 5 Dec 2018 10:25:20 -0800 Subject: [PATCH 104/873] Add a rule to disallow rewrite for double type --- tensorflow/core/graph/mkl_layout_pass.cc | 5 ++++ tensorflow/core/graph/mkl_layout_pass_test.cc | 24 +++++++++++++++++++ 2 files changed, 29 insertions(+) diff --git a/tensorflow/core/graph/mkl_layout_pass.cc b/tensorflow/core/graph/mkl_layout_pass.cc index 177d6becf2..42e5411c5a 100644 --- a/tensorflow/core/graph/mkl_layout_pass.cc +++ b/tensorflow/core/graph/mkl_layout_pass.cc @@ -1123,6 +1123,11 @@ class MklLayoutRewritePass : public GraphOptimizationPass { // MKL DNN currently doesn't support all fusions that grappler fuses // together with Conv2D (ex. batchnorm). We rewrite _FusedConv2D only if // it includes those we support. + DataType T; + if (!GetNodeAttr(n->def(), "T", &T).ok() || + !mkl_op_registry::IsMklOp(csinfo_.mkl_fused_conv2d, T)) { + return false; + } std::vector fused_ops; TF_CHECK_OK(GetNodeAttr(n->def(), "fused_ops", &fused_ops)); diff --git a/tensorflow/core/graph/mkl_layout_pass_test.cc b/tensorflow/core/graph/mkl_layout_pass_test.cc index 65b6ed6745..43521c847c 100644 --- a/tensorflow/core/graph/mkl_layout_pass_test.cc +++ b/tensorflow/core/graph/mkl_layout_pass_test.cc @@ -126,6 +126,7 @@ REGISTER_OP("Input").Output("o: float").SetIsStateful(); REGISTER_OP("InputList").Output("o: N * float").Attr("N: int").SetIsStateful(); REGISTER_OP("HalfInput").Output("o: half").SetIsStateful(); REGISTER_OP("Int32Input").Output("o: int32").SetIsStateful(); +REGISTER_OP("DoubleInput").Output("o: double").SetIsStateful(); REGISTER_OP("_MklInput").Output("o: uint8").SetIsStateful(); REGISTER_OP("_MklInput2") .Output("o: uint8") @@ -945,6 +946,29 @@ TEST_F(MklLayoutPassTest, NodeRewrite_FusedConv2D_Negative1) { "B->D:1;C->D:2;C->E:1;D->E"); } +// Rewrite test for _FusedConv2D Op with unsupported type +TEST_F(MklLayoutPassTest, NodeRewrite_FusedConv2D_Negative2) { + InitGraph( + "node { name: 'A' op: 'DoubleInput'}" + "node { name: 'B' op: 'DoubleInput'}" + "node { name: 'C' op: 'DoubleInput'}" + "node { name: 'D' op: '_FusedConv2D'" + " attr { key: 'T' value { type: DT_DOUBLE } }" + " attr { key: 'num_args' value { i: 1 } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " 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} } }" + " attr { key: 'fused_ops' value { list: {s: 'BiasAdd'} } }" + " attr { key: 'epsilon' value { f: 0.001 }}" + " input: ['A', 'B', 'C']}" + "node { name: 'E' op: 'Zeta' attr { key: 'T' value { type: DT_DOUBLE } }" + " input: ['D', 'C'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(DoubleInput);B(DoubleInput);C(DoubleInput);" + "D(_FusedConv2D);E(Zeta)|A->D;B->D:1;C->D:2;C->E:1;D->E"); +} + TEST_F(MklLayoutPassTest, NodeRewrite_Conv2DGradFilter_Positive) { InitGraph( "node { name: 'A' op: 'Input'}" -- GitLab From 91fbda2d2cf0d48e9cf5d659b6fbe6d59b3606d9 Mon Sep 17 00:00:00 2001 From: Trevor Morris Date: Wed, 21 Nov 2018 13:44:30 -0800 Subject: [PATCH 105/873] Add ExpandDims, Squeeze ops and unit tests. --- .../contrib/tensorrt/convert/convert_graph.cc | 2 + .../contrib/tensorrt/convert/convert_nodes.cc | 122 +++++++++ .../tensorrt/convert/convert_nodes_test.cc | 247 ++++++++++++++++++ 3 files changed, 371 insertions(+) diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index 3b32f72bc1..560e565267 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -132,6 +132,8 @@ Status TrtCandidateSelector::IsTensorRTCandidate(const tensorflow::Node* node) { "Min", "Relu6", "Square", + "ExpandDims", + "Squeeze", }; bool is_supported_op_type = (candidate_ops.count(node->type_string()) || diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index fee095668e..49df1daae9 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -1880,6 +1880,126 @@ tensorflow::Status ConvertReshape(OpConverterParams* params) { return tensorflow::Status::OK(); } +tensorflow::Status ConvertExpandDims(OpConverterParams* params) { + const auto& inputs = params->inputs; + const auto& node_def = params->node_def; + if (inputs.size() != 2) { + return tensorflow::errors::InvalidArgument( + "Two inputs expected for ExpandDims, at ", node_def.name()); + } + if (!inputs.at(1).is_weights() ) { + return tensorflow::errors::InvalidArgument( + "ExpandDims expects weights for axis, at ", node_def.name()); + } + // Get input shape as vector. + TRT_TensorOrWeights input_tensor = inputs.at(0); + const nvinfer1::Dims dims = input_tensor.GetTrtDims(); + std::vector input_dims(dims.d, dims.d + dims.nbDims); + // Add batch dim back for tensors. + if (input_tensor.is_tensor()) { + input_dims.insert(input_dims.begin(), -1); + } + const int input_rank = input_dims.size(); + // Get axis to expand on. + TRT_ShapedWeights weights = inputs.at(1).weights(); + const int* weights_ptr = + static_cast(const_cast(weights.GetValues())); + int axis = weights_ptr[0]; + // Make sure axis is valid. + if ((axis < (-input_rank - 1)) || (axis > input_rank)) { + return tensorflow::errors::InvalidArgument( + "Axis for ExpandDims is invalid, must be in the range " + "[-rank(input) - 1, rank(input)], at ", + node_def.name()); + } + // Convert negative axis to corresponding positive axis. + if (axis < 0) axis += input_rank + 1; + if (input_tensor.is_tensor() && axis == 0) { + return tensorflow::errors::Unimplemented( + "Modifying batch dimension is not supported for ExpandDims, at ", + node_def.name()); + } + if (params->validation_only) return Status::OK(); + + // ExpandDims: Insert new dim of size 1. + input_dims.insert(input_dims.begin()+axis, 1); + // Convert input_dims vector into nvinfer1::Dims. + const bool ignore_first_dim = input_tensor.is_tensor(); + nvinfer1::Dims new_dims = VectorToTrtDims(input_dims, ignore_first_dim); + // Reshape tensor. + const nvinfer1::ITensor* output_tensor = nullptr; + TF_RETURN_IF_ERROR(params->converter->PrepareTensorForShape( + input_tensor, new_dims, &output_tensor)); + params->outputs->push_back( + TRT_TensorOrWeights(const_cast(output_tensor))); + return tensorflow::Status::OK(); +} + +tensorflow::Status ConvertSqueeze(OpConverterParams* params) { + const auto& inputs = params->inputs; + const auto& node_def = params->node_def; + if (inputs.size() != 1) { + return tensorflow::errors::InvalidArgument( + "One input expected for Squeeze, at ", node_def.name()); + } + // Get input shape. + TRT_TensorOrWeights input_tensor = inputs.at(0); + const nvinfer1::Dims dims = input_tensor.GetTrtDims(); + std::vector input_dims(dims.d, dims.d + dims.nbDims); + // Add batch dim back temporarily. + if (input_tensor.is_tensor()) { + input_dims.insert(input_dims.begin(), -1); + } + const int input_rank = input_dims.size(); + // Mark axes to remove by setting them to 0. + TFAttrs attrs(node_def); + auto squeeze_dims = attrs.get>("squeeze_dims"); + if (squeeze_dims.size() == 0) { + return tensorflow::errors::Unimplemented( + "Squeeze is only implemented for explicit dims, at ", node_def.name()); + } + for (int axis : squeeze_dims) { + // Make sure axis is valid. + if ((axis < -input_rank) || (axis >= input_rank)) { + return tensorflow::errors::InvalidArgument( + "Axis for Squeeze is invalid, must be in the range " + "[-rank(input), rank(input)), at ", + node_def.name()); + } + // Convert negative axis to corresponding positive axis. + if (axis < 0) axis += input_rank; + LOG(INFO) << axis; + // Don't squeeze batch dim. + if (axis == 0) { + return tensorflow::errors::Unimplemented( + "Cannot squeeze batch dimension, at ", node_def.name()); + } + // Make sure target dimension is size 1. + if (input_dims[axis] != 1) { + return tensorflow::errors::InvalidArgument( + "Cannot squeeze a dimension which isn't size 1, at ", + node_def.name()); + } + // Mark dim for removal by setting to 0. + input_dims[axis] = 0; + } + if (params->validation_only) return Status::OK(); + + // Remove all dims which are equal to 0. + input_dims.erase(std::remove(input_dims.begin(), input_dims.end(), 0), + input_dims.end()); + // Convert input_dims vector into nvinfer1::Dims. + const bool ignore_first_dim = input_tensor.is_tensor(); + nvinfer1::Dims new_dims = VectorToTrtDims(input_dims, ignore_first_dim); + // Reshape tensor. + const nvinfer1::ITensor* output_tensor = nullptr; + TF_RETURN_IF_ERROR(params->converter->PrepareTensorForShape( + input_tensor, new_dims, &output_tensor)); + params->outputs->push_back( + TRT_TensorOrWeights(const_cast(output_tensor))); + return tensorflow::Status::OK(); +} + tensorflow::Status ConvertConv2D(OpConverterParams* params) { return ConvertConv2DHelper(params, ConvolutionType::DEFAULT); } @@ -3156,6 +3276,8 @@ static void RegisterValidatableOpConverters( (*registration)["MatMul"] = ConvertMatMul; (*registration)["Relu6"] = ConvertRelu6; (*registration)["Square"] = ConvertSquare; + (*registration)["ExpandDims"] = ConvertExpandDims; + (*registration)["Squeeze"] = ConvertSqueeze; for (auto quantization_op_type : {"QuantizeAndDequantizeV2", "QuantizeAndDequantizeV3", diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc index 443033379f..c37406901a 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc @@ -2113,6 +2113,253 @@ TEST_F(OpConverterTest, ConvertActivation) { } } +TEST_F(OpConverterTest, ConvertExpandDims) { + { + // Input list is empty, should fail. + NodeDef node_def = MakeNodeDef("my_expanddims", "ExpandDims", {}); + RunValidationAndConversion( + node_def, error::INVALID_ARGUMENT, + "Two inputs expected for ExpandDims, at my_expanddims"); + } + + // Get the NodeDef for ExpandDims. + Scope s = Scope::NewRootScope(); + auto input = ops::Placeholder(s.WithOpName("input"), DT_FLOAT); + auto weights = ops::Placeholder(s.WithOpName("weights"), DT_INT32); + auto expanddims = ops::ExpandDims(s.WithOpName("my_expanddims"), input, weights); + const NodeDef& node_def = expanddims.operation.node()->def(); + + { + // Axis is a tensor, should fail. + Reset(); + AddTestTensor("input", {1, 2, 3}); + AddTestTensor("weights", {3}); + RunValidationAndConversion( + node_def, error::INVALID_ARGUMENT, + "ExpandDims expects weights for axis, at my_expanddims"); + } + { + // Add dim at batch dimension, should fail. + Reset(); + AddTestTensor("input", {1, 2, 3}); + AddTestWeights("weights", {1}, {0}); + RunValidationAndConversion(node_def, error::UNIMPLEMENTED, + "Modifying batch dimension is not supported for ExpandDims, at my_expanddims"); + } + { + // Add dim at batch dimension via negative axis, should fail. + Reset(); + AddTestTensor("input", {1, 2, 3}); + // Input is rank 4 (batch dim included) + AddTestWeights("weights", {1}, {-5}); + RunValidationAndConversion(node_def, error::UNIMPLEMENTED, + "Modifying batch dimension is not supported for ExpandDims, at my_expanddims"); + } + { + // Axis > rank(input), should fail. + Reset(); + AddTestTensor("input", {1, 2, 3}); + // Input is rank 4 (batch dim included) + AddTestWeights("weights", {1}, {5}); + RunValidationAndConversion(node_def, error::INVALID_ARGUMENT, + "Axis for ExpandDims is invalid, must be in the range " + "[-rank(input) - 1, rank(input)], at my_expanddims"); + } + { + // Axis < -rank(input)-1, should fail. + Reset(); + AddTestTensor("input", {1, 2, 3}); + // Input is rank 4 (batch dim included) + AddTestWeights("weights", {1}, {-6}); + RunValidationAndConversion(node_def, error::INVALID_ARGUMENT, + "Axis for ExpandDims is invalid, must be in the range " + "[-rank(input) - 1, rank(input)], at my_expanddims"); + } + { + // Add axis before, Ok. + Reset(); + AddTestTensor("input", {2, 3}); + AddTestWeights("weights", {1}, {1}); + RunValidationAndConversion(node_def); + TRT_TensorOrWeights output; + TF_EXPECT_OK(GetTensorOrWeights("my_expanddims", &output)); + EXPECT_TRUE(output.is_tensor()); + EXPECT_TRUE(TrtDimsEqualsArray({1, 2, 3}, output.tensor()->getDimensions())) + << output.DebugString(); + + std::vector output_data(6); + BuildAndRun("input", {1, 2, 3, 4, 5, 6}, "my_expanddims", &output_data); + EXPECT_THAT(output_data, ElementsAre(1, 2, 3, 4, 5, 6)); + } + { + // Add axis after, Ok. + Reset(); + AddTestTensor("input", {2, 3}); + AddTestWeights("weights", {1}, {3}); + RunValidationAndConversion(node_def); + TRT_TensorOrWeights output; + TF_EXPECT_OK(GetTensorOrWeights("my_expanddims", &output)); + EXPECT_TRUE(output.is_tensor()); + EXPECT_TRUE(TrtDimsEqualsArray({2, 3, 1}, output.tensor()->getDimensions())) + << output.DebugString(); + + std::vector output_data(6); + BuildAndRun("input", {1, 2, 3, 4, 5, 6}, "my_expanddims", &output_data); + EXPECT_THAT(output_data, ElementsAre(1, 2, 3, 4, 5, 6)); + } + { + // Add negative axis, Ok. + Reset(); + AddTestTensor("input", {2, 3}); + AddTestWeights("weights", {1}, {-1}); + RunValidationAndConversion(node_def); + TRT_TensorOrWeights output; + TF_EXPECT_OK(GetTensorOrWeights("my_expanddims", &output)); + EXPECT_TRUE(output.is_tensor()); + EXPECT_TRUE(TrtDimsEqualsArray({2, 3, 1}, output.tensor()->getDimensions())) + << output.DebugString(); + + std::vector output_data(6); + BuildAndRun("input", {1, 2, 3, 4, 5, 6}, "my_expanddims", &output_data); + EXPECT_THAT(output_data, ElementsAre(1, 2, 3, 4, 5, 6)); + } +} + +TEST_F(OpConverterTest, ConvertSqueeze) { + { + // Input list is empty, should fail. + NodeDef node_def = MakeNodeDef("my_squeeze", "Squeeze", {}); + RunValidationAndConversion( + node_def, error::INVALID_ARGUMENT, + "One input expected for Squeeze, at my_squeeze"); + } + { + // No attrs, should fail. + Reset(); + Scope s = Scope::NewRootScope(); + auto input = ops::Placeholder(s.WithOpName("input"), DT_FLOAT); + auto squeeze = ops::Squeeze(s.WithOpName("my_squeeze"), input); + const NodeDef& node_def = squeeze.operation.node()->def(); + AddTestTensor("input", {1, 2, 3}); + RunValidationAndConversion( + node_def, error::UNIMPLEMENTED, + "Squeeze is only implemented for explicit dims, at my_squeeze"); + } + + // Get the NodeDef for Squeeze. + auto get_squeeze_nodedef = [](std::vector axis) -> NodeDef { + Scope s = Scope::NewRootScope(); + auto input = ops::Placeholder(s.WithOpName("input"), DT_FLOAT); + ops::Squeeze::Attrs squeeze_attrs; + squeeze_attrs.axis_ = gtl::ArraySlice(axis); + auto squeeze = ops::Squeeze(s.WithOpName("my_squeeze"), input, squeeze_attrs); + return squeeze.operation.node()->def(); + }; + + { + // Squeeze batch dim, should fail. + Reset(); + NodeDef node_def = get_squeeze_nodedef({0}); + AddTestTensor("input", {1, 2, 3}); + RunValidationAndConversion( + node_def, error::UNIMPLEMENTED, + "Cannot squeeze batch dimension, at my_squeeze"); + } + { + // Squeeze batch dim via negative axis, should fail. + Reset(); + NodeDef node_def = get_squeeze_nodedef({-4}); + AddTestTensor("input", {1, 2, 3}); + RunValidationAndConversion( + node_def, error::UNIMPLEMENTED, + "Cannot squeeze batch dimension, at my_squeeze"); + } + { + // Squeeze >= rank(input), should fail. + Reset(); + NodeDef node_def = get_squeeze_nodedef({4}); + AddTestTensor("input", {1, 2, 3}); + RunValidationAndConversion( + node_def, error::INVALID_ARGUMENT, + "Axis for Squeeze is invalid, must be in the range " + "[-rank(input), rank(input)), at my_squeeze"); + } + { + // Squeeze < -rank(input), should fail. + Reset(); + NodeDef node_def = get_squeeze_nodedef({-5}); + AddTestTensor("input", {1, 2, 3}); + RunValidationAndConversion( + node_def, error::INVALID_ARGUMENT, + "Axis for Squeeze is invalid, must be in the range " + "[-rank(input), rank(input)), at my_squeeze"); + } + { + // Squeeze axis before, Ok. + Reset(); + NodeDef node_def = get_squeeze_nodedef({1}); + AddTestTensor("input", {1, 2, 3}); + RunConversion(node_def); + TRT_TensorOrWeights output; + TF_EXPECT_OK(GetTensorOrWeights("my_squeeze", &output)); + EXPECT_TRUE(output.is_tensor()); + EXPECT_TRUE(TrtDimsEqualsArray({2, 3}, output.tensor()->getDimensions())) + << output.DebugString(); + + std::vector output_data(6); + BuildAndRun("input", {1, 2, 3, 4, 5, 6}, "my_squeeze", &output_data); + EXPECT_THAT(output_data, ElementsAre(1, 2, 3, 4, 5, 6)); + } + { + // Squeeze axis after, Ok. + Reset(); + NodeDef node_def = get_squeeze_nodedef({3}); + AddTestTensor("input", {2, 3, 1}); + RunConversion(node_def); + TRT_TensorOrWeights output; + TF_EXPECT_OK(GetTensorOrWeights("my_squeeze", &output)); + EXPECT_TRUE(output.is_tensor()); + EXPECT_TRUE(TrtDimsEqualsArray({2, 3}, output.tensor()->getDimensions())) + << output.DebugString(); + + std::vector output_data(6); + BuildAndRun("input", {1, 2, 3, 4, 5, 6}, "my_squeeze", &output_data); + EXPECT_THAT(output_data, ElementsAre(1, 2, 3, 4, 5, 6)); + } + { + // Squeeze multiple axis, Ok. + Reset(); + NodeDef node_def = get_squeeze_nodedef({1, 3, 5}); + AddTestTensor("input", {1, 2, 1, 3, 1}); + RunConversion(node_def); + TRT_TensorOrWeights output; + TF_EXPECT_OK(GetTensorOrWeights("my_squeeze", &output)); + EXPECT_TRUE(output.is_tensor()); + EXPECT_TRUE(TrtDimsEqualsArray({2, 3}, output.tensor()->getDimensions())) + << output.DebugString(); + + std::vector output_data(6); + BuildAndRun("input", {1, 2, 3, 4, 5, 6}, "my_squeeze", &output_data); + EXPECT_THAT(output_data, ElementsAre(1, 2, 3, 4, 5, 6)); + } + { + // Squeeze multiple axis negative, Ok. + Reset(); + NodeDef node_def = get_squeeze_nodedef({-1, -3, -5}); + AddTestTensor("input", {1, 2, 1, 3, 1}); + RunConversion(node_def); + TRT_TensorOrWeights output; + TF_EXPECT_OK(GetTensorOrWeights("my_squeeze", &output)); + EXPECT_TRUE(output.is_tensor()); + EXPECT_TRUE(TrtDimsEqualsArray({2, 3}, output.tensor()->getDimensions())) + << output.DebugString(); + + std::vector output_data(6); + BuildAndRun("input", {1, 2, 3, 4, 5, 6}, "my_squeeze", &output_data); + EXPECT_THAT(output_data, ElementsAre(1, 2, 3, 4, 5, 6)); + } +} + } // namespace convert } // namespace tensorrt } // namespace tensorflow -- GitLab From 9d3b1765b20660ba8f166a44bdc9f80bc3987ced Mon Sep 17 00:00:00 2001 From: Trevor Morris Date: Mon, 26 Nov 2018 09:33:24 -0800 Subject: [PATCH 106/873] Add missing VectorToTrtDims function --- .../contrib/tensorrt/convert/convert_nodes.cc | 17 +++++++++++++---- 1 file changed, 13 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 49df1daae9..1116a039ce 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -120,6 +120,17 @@ inline nvinfer1::Dims TensorShapeToTrtDims(const TensorShapeType& shape, return trt_dims; } +inline nvinfer1::Dims VectorToTrtDims(const std::vector& shape, + bool ignore_first_dim = false) { + nvinfer1::Dims trt_dims; + const int offset = (ignore_first_dim ? 1 : 0); + for (int i = offset; i < shape.size(); i++) { + trt_dims.d[i - offset] = shape[i]; + } + trt_dims.nbDims = shape.size() - offset; + return trt_dims; +} + void GetOutputProperties(const grappler::GraphProperties& graph_properties, const Node* node, const int out_port, PartialTensorShape* shape, @@ -1923,10 +1934,9 @@ tensorflow::Status ConvertExpandDims(OpConverterParams* params) { // ExpandDims: Insert new dim of size 1. input_dims.insert(input_dims.begin()+axis, 1); - // Convert input_dims vector into nvinfer1::Dims. + // Reshape tensor. const bool ignore_first_dim = input_tensor.is_tensor(); nvinfer1::Dims new_dims = VectorToTrtDims(input_dims, ignore_first_dim); - // Reshape tensor. const nvinfer1::ITensor* output_tensor = nullptr; TF_RETURN_IF_ERROR(params->converter->PrepareTensorForShape( input_tensor, new_dims, &output_tensor)); @@ -1988,10 +1998,9 @@ tensorflow::Status ConvertSqueeze(OpConverterParams* params) { // Remove all dims which are equal to 0. input_dims.erase(std::remove(input_dims.begin(), input_dims.end(), 0), input_dims.end()); - // Convert input_dims vector into nvinfer1::Dims. + // Reshape tensor. const bool ignore_first_dim = input_tensor.is_tensor(); nvinfer1::Dims new_dims = VectorToTrtDims(input_dims, ignore_first_dim); - // Reshape tensor. const nvinfer1::ITensor* output_tensor = nullptr; TF_RETURN_IF_ERROR(params->converter->PrepareTensorForShape( input_tensor, new_dims, &output_tensor)); -- GitLab From dbe1a6bd852b8d03fccc6b6df7ee0b6f65ad7ca8 Mon Sep 17 00:00:00 2001 From: Trevor Morris Date: Tue, 27 Nov 2018 10:20:57 -0800 Subject: [PATCH 107/873] Fix compilation error with BuildAndRun, Use ExpectTrtDimsEqualsArray, Fix formatting --- .../tensorrt/convert/convert_nodes_test.cc | 45 ++++++++++--------- 1 file changed, 23 insertions(+), 22 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc index c37406901a..1e4ee1708f 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc @@ -2184,11 +2184,11 @@ TEST_F(OpConverterTest, ConvertExpandDims) { TRT_TensorOrWeights output; TF_EXPECT_OK(GetTensorOrWeights("my_expanddims", &output)); EXPECT_TRUE(output.is_tensor()); - EXPECT_TRUE(TrtDimsEqualsArray({1, 2, 3}, output.tensor()->getDimensions())) - << output.DebugString(); + ExpectTrtDimsEqualsArray({1, 2, 3}, output.tensor()->getDimensions()); std::vector output_data(6); - BuildAndRun("input", {1, 2, 3, 4, 5, 6}, "my_expanddims", &output_data); + BuildAndRun({{"input", {1, 2, 3, 4, 5, 6}}}, "my_expanddims", + &output_data); EXPECT_THAT(output_data, ElementsAre(1, 2, 3, 4, 5, 6)); } { @@ -2200,11 +2200,11 @@ TEST_F(OpConverterTest, ConvertExpandDims) { TRT_TensorOrWeights output; TF_EXPECT_OK(GetTensorOrWeights("my_expanddims", &output)); EXPECT_TRUE(output.is_tensor()); - EXPECT_TRUE(TrtDimsEqualsArray({2, 3, 1}, output.tensor()->getDimensions())) - << output.DebugString(); + ExpectTrtDimsEqualsArray({2, 3, 1}, output.tensor()->getDimensions()); std::vector output_data(6); - BuildAndRun("input", {1, 2, 3, 4, 5, 6}, "my_expanddims", &output_data); + BuildAndRun({{"input", {1, 2, 3, 4, 5, 6}}}, "my_expanddims", + &output_data); EXPECT_THAT(output_data, ElementsAre(1, 2, 3, 4, 5, 6)); } { @@ -2216,11 +2216,11 @@ TEST_F(OpConverterTest, ConvertExpandDims) { TRT_TensorOrWeights output; TF_EXPECT_OK(GetTensorOrWeights("my_expanddims", &output)); EXPECT_TRUE(output.is_tensor()); - EXPECT_TRUE(TrtDimsEqualsArray({2, 3, 1}, output.tensor()->getDimensions())) - << output.DebugString(); + ExpectTrtDimsEqualsArray({2, 3, 1}, output.tensor()->getDimensions()); std::vector output_data(6); - BuildAndRun("input", {1, 2, 3, 4, 5, 6}, "my_expanddims", &output_data); + BuildAndRun({{"input", {1, 2, 3, 4, 5, 6}}}, "my_expanddims", + &output_data); EXPECT_THAT(output_data, ElementsAre(1, 2, 3, 4, 5, 6)); } } @@ -2252,7 +2252,8 @@ TEST_F(OpConverterTest, ConvertSqueeze) { auto input = ops::Placeholder(s.WithOpName("input"), DT_FLOAT); ops::Squeeze::Attrs squeeze_attrs; squeeze_attrs.axis_ = gtl::ArraySlice(axis); - auto squeeze = ops::Squeeze(s.WithOpName("my_squeeze"), input, squeeze_attrs); + auto squeeze = ops::Squeeze(s.WithOpName("my_squeeze"), input, + squeeze_attrs); return squeeze.operation.node()->def(); }; @@ -2303,11 +2304,11 @@ TEST_F(OpConverterTest, ConvertSqueeze) { TRT_TensorOrWeights output; TF_EXPECT_OK(GetTensorOrWeights("my_squeeze", &output)); EXPECT_TRUE(output.is_tensor()); - EXPECT_TRUE(TrtDimsEqualsArray({2, 3}, output.tensor()->getDimensions())) - << output.DebugString(); + ExpectTrtDimsEqualsArray({2, 3}, output.tensor()->getDimensions()); std::vector output_data(6); - BuildAndRun("input", {1, 2, 3, 4, 5, 6}, "my_squeeze", &output_data); + BuildAndRun({{"input", {1, 2, 3, 4, 5, 6}}}, "my_squeeze", + &output_data); EXPECT_THAT(output_data, ElementsAre(1, 2, 3, 4, 5, 6)); } { @@ -2319,11 +2320,11 @@ TEST_F(OpConverterTest, ConvertSqueeze) { TRT_TensorOrWeights output; TF_EXPECT_OK(GetTensorOrWeights("my_squeeze", &output)); EXPECT_TRUE(output.is_tensor()); - EXPECT_TRUE(TrtDimsEqualsArray({2, 3}, output.tensor()->getDimensions())) - << output.DebugString(); + ExpectTrtDimsEqualsArray({2, 3}, output.tensor()->getDimensions()); std::vector output_data(6); - BuildAndRun("input", {1, 2, 3, 4, 5, 6}, "my_squeeze", &output_data); + BuildAndRun({{"input", {1, 2, 3, 4, 5, 6}}}, "my_squeeze", + &output_data); EXPECT_THAT(output_data, ElementsAre(1, 2, 3, 4, 5, 6)); } { @@ -2335,11 +2336,11 @@ TEST_F(OpConverterTest, ConvertSqueeze) { TRT_TensorOrWeights output; TF_EXPECT_OK(GetTensorOrWeights("my_squeeze", &output)); EXPECT_TRUE(output.is_tensor()); - EXPECT_TRUE(TrtDimsEqualsArray({2, 3}, output.tensor()->getDimensions())) - << output.DebugString(); + ExpectTrtDimsEqualsArray({2, 3}, output.tensor()->getDimensions()); std::vector output_data(6); - BuildAndRun("input", {1, 2, 3, 4, 5, 6}, "my_squeeze", &output_data); + BuildAndRun({{"input", {1, 2, 3, 4, 5, 6}}}, "my_squeeze", + &output_data); EXPECT_THAT(output_data, ElementsAre(1, 2, 3, 4, 5, 6)); } { @@ -2351,11 +2352,11 @@ TEST_F(OpConverterTest, ConvertSqueeze) { TRT_TensorOrWeights output; TF_EXPECT_OK(GetTensorOrWeights("my_squeeze", &output)); EXPECT_TRUE(output.is_tensor()); - EXPECT_TRUE(TrtDimsEqualsArray({2, 3}, output.tensor()->getDimensions())) - << output.DebugString(); + ExpectTrtDimsEqualsArray({2, 3}, output.tensor()->getDimensions()); std::vector output_data(6); - BuildAndRun("input", {1, 2, 3, 4, 5, 6}, "my_squeeze", &output_data); + BuildAndRun({{"input", {1, 2, 3, 4, 5, 6}}}, "my_squeeze", + &output_data); EXPECT_THAT(output_data, ElementsAre(1, 2, 3, 4, 5, 6)); } } -- GitLab From 23a1bf779c82e363820ea8a1217038d971903ea5 Mon Sep 17 00:00:00 2001 From: Trevor Morris Date: Wed, 28 Nov 2018 11:19:02 -0800 Subject: [PATCH 108/873] Remove debug LOG statement. Use TestParams array for Ok tests --- .../contrib/tensorrt/convert/convert_nodes.cc | 1 - .../tensorrt/convert/convert_nodes_test.cc | 146 +++++++----------- 2 files changed, 59 insertions(+), 88 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 1116a039ce..135e188502 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -1978,7 +1978,6 @@ tensorflow::Status ConvertSqueeze(OpConverterParams* params) { } // Convert negative axis to corresponding positive axis. if (axis < 0) axis += input_rank; - LOG(INFO) << axis; // Don't squeeze batch dim. if (axis == 0) { return tensorflow::errors::Unimplemented( diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc index 1e4ee1708f..74ca982f3f 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc @@ -2175,48 +2175,41 @@ TEST_F(OpConverterTest, ConvertExpandDims) { "Axis for ExpandDims is invalid, must be in the range " "[-rank(input) - 1, rank(input)], at my_expanddims"); } - { - // Add axis before, Ok. - Reset(); - AddTestTensor("input", {2, 3}); - AddTestWeights("weights", {1}, {1}); - RunValidationAndConversion(node_def); - TRT_TensorOrWeights output; - TF_EXPECT_OK(GetTensorOrWeights("my_expanddims", &output)); - EXPECT_TRUE(output.is_tensor()); - ExpectTrtDimsEqualsArray({1, 2, 3}, output.tensor()->getDimensions()); - std::vector output_data(6); - BuildAndRun({{"input", {1, 2, 3, 4, 5, 6}}}, "my_expanddims", - &output_data); - EXPECT_THAT(output_data, ElementsAre(1, 2, 3, 4, 5, 6)); - } - { - // Add axis after, Ok. - Reset(); - AddTestTensor("input", {2, 3}); - AddTestWeights("weights", {1}, {3}); - RunValidationAndConversion(node_def); - TRT_TensorOrWeights output; - TF_EXPECT_OK(GetTensorOrWeights("my_expanddims", &output)); - EXPECT_TRUE(output.is_tensor()); - ExpectTrtDimsEqualsArray({2, 3, 1}, output.tensor()->getDimensions()); + struct TestParams { + TestParams(const std::vector& input_dims, + int axis, + const std::vector& expected_output_dims) + : input_dims(input_dims), + axis(axis), + expected_output_dims(expected_output_dims) {} + std::vector input_dims; + int axis; + std::vector expected_output_dims; + }; - std::vector output_data(6); - BuildAndRun({{"input", {1, 2, 3, 4, 5, 6}}}, "my_expanddims", - &output_data); - EXPECT_THAT(output_data, ElementsAre(1, 2, 3, 4, 5, 6)); - } - { - // Add negative axis, Ok. + // Ok. + const int kExpandDimsOKCases = 8; + TestParams ok_params[kExpandDimsOKCases] = { + TestParams{{2, 3}, 1, {1, 2, 3}}, + TestParams{{2, 3}, -3, {1, 2, 3}}, + TestParams{{2, 3}, 3, {2, 3, 1}}, + TestParams{{2, 3}, -1, {2, 3, 1}}, + TestParams{{2, 3}, 2, {2, 1, 3}}, + TestParams{{2, 3}, -2, {2, 1, 3}}, + TestParams{{6}, 1, {1, 6}}, + TestParams{{6}, -1, {6, 1}}, + }; + for (int i = 0; i < kExpandDimsOKCases; ++i) { Reset(); - AddTestTensor("input", {2, 3}); - AddTestWeights("weights", {1}, {-1}); + AddTestTensor("input", ok_params[i].input_dims); + AddTestWeights("weights", {1}, {ok_params[i].axis}); RunValidationAndConversion(node_def); TRT_TensorOrWeights output; TF_EXPECT_OK(GetTensorOrWeights("my_expanddims", &output)); EXPECT_TRUE(output.is_tensor()); - ExpectTrtDimsEqualsArray({2, 3, 1}, output.tensor()->getDimensions()); + ExpectTrtDimsEqualsArray(ok_params[i].expected_output_dims, + output.tensor()->getDimensions()); std::vector output_data(6); BuildAndRun({{"input", {1, 2, 3, 4, 5, 6}}}, "my_expanddims", @@ -2295,64 +2288,43 @@ TEST_F(OpConverterTest, ConvertSqueeze) { "Axis for Squeeze is invalid, must be in the range " "[-rank(input), rank(input)), at my_squeeze"); } - { - // Squeeze axis before, Ok. - Reset(); - NodeDef node_def = get_squeeze_nodedef({1}); - AddTestTensor("input", {1, 2, 3}); - RunConversion(node_def); - TRT_TensorOrWeights output; - TF_EXPECT_OK(GetTensorOrWeights("my_squeeze", &output)); - EXPECT_TRUE(output.is_tensor()); - ExpectTrtDimsEqualsArray({2, 3}, output.tensor()->getDimensions()); - - std::vector output_data(6); - BuildAndRun({{"input", {1, 2, 3, 4, 5, 6}}}, "my_squeeze", - &output_data); - EXPECT_THAT(output_data, ElementsAre(1, 2, 3, 4, 5, 6)); - } - { - // Squeeze axis after, Ok. - Reset(); - NodeDef node_def = get_squeeze_nodedef({3}); - AddTestTensor("input", {2, 3, 1}); - RunConversion(node_def); - TRT_TensorOrWeights output; - TF_EXPECT_OK(GetTensorOrWeights("my_squeeze", &output)); - EXPECT_TRUE(output.is_tensor()); - ExpectTrtDimsEqualsArray({2, 3}, output.tensor()->getDimensions()); - std::vector output_data(6); - BuildAndRun({{"input", {1, 2, 3, 4, 5, 6}}}, "my_squeeze", - &output_data); - EXPECT_THAT(output_data, ElementsAre(1, 2, 3, 4, 5, 6)); - } - { - // Squeeze multiple axis, Ok. - Reset(); - NodeDef node_def = get_squeeze_nodedef({1, 3, 5}); - AddTestTensor("input", {1, 2, 1, 3, 1}); - RunConversion(node_def); - TRT_TensorOrWeights output; - TF_EXPECT_OK(GetTensorOrWeights("my_squeeze", &output)); - EXPECT_TRUE(output.is_tensor()); - ExpectTrtDimsEqualsArray({2, 3}, output.tensor()->getDimensions()); + struct TestParams { + TestParams(const std::vector& input_dims, + const std::vector& axis, + const std::vector& expected_output_dims) + : input_dims(input_dims), + axis(axis), + expected_output_dims(expected_output_dims) {} + std::vector input_dims; + std::vector axis; + std::vector expected_output_dims; + }; - std::vector output_data(6); - BuildAndRun({{"input", {1, 2, 3, 4, 5, 6}}}, "my_squeeze", - &output_data); - EXPECT_THAT(output_data, ElementsAre(1, 2, 3, 4, 5, 6)); - } - { - // Squeeze multiple axis negative, Ok. + // Ok. + const int kSqueezeOKCases = 10; + TestParams ok_params[kSqueezeOKCases] = { + TestParams{{1, 2, 3}, {1}, {2, 3}}, + TestParams{{1, 2, 3}, {-3}, {2, 3}}, + TestParams{{2, 3, 1}, {3}, {2, 3}}, + TestParams{{2, 3, 1}, {-1}, {2, 3}}, + TestParams{{1, 2, 1, 3, 1}, {1, 3, 5}, {2, 3}}, + TestParams{{1, 2, 1, 3, 1}, {3, 1, 5}, {2, 3}}, + TestParams{{1, 2, 1, 3, 1}, {-1, -3, -5}, {2, 3}}, + TestParams{{1, 2, 1, 3, 1}, {1, -3, 5}, {2, 3}}, + TestParams{{1, 6}, {1}, {6}}, + TestParams{{6, 1}, {2}, {6}}, + }; + for (int i = 0; i < kSqueezeOKCases; ++i) { Reset(); - NodeDef node_def = get_squeeze_nodedef({-1, -3, -5}); - AddTestTensor("input", {1, 2, 1, 3, 1}); - RunConversion(node_def); + NodeDef node_def = get_squeeze_nodedef(ok_params[i].axis); + AddTestTensor("input", ok_params[i].input_dims); + RunValidationAndConversion(node_def); TRT_TensorOrWeights output; TF_EXPECT_OK(GetTensorOrWeights("my_squeeze", &output)); EXPECT_TRUE(output.is_tensor()); - ExpectTrtDimsEqualsArray({2, 3}, output.tensor()->getDimensions()); + ExpectTrtDimsEqualsArray(ok_params[i].expected_output_dims, + output.tensor()->getDimensions()); std::vector output_data(6); BuildAndRun({{"input", {1, 2, 3, 4, 5, 6}}}, "my_squeeze", -- GitLab From 293bc93045d1eddce9040de36918270ed5e65b6c Mon Sep 17 00:00:00 2001 From: Trevor Morris Date: Thu, 29 Nov 2018 16:38:36 -0800 Subject: [PATCH 109/873] Restrict ops to tensors only. Renamed VectorToTrtDims. --- .../contrib/tensorrt/convert/convert_nodes.cc | 30 +++++++++++-------- .../tensorrt/convert/convert_nodes_test.cc | 27 +++++++++++++++-- 2 files changed, 42 insertions(+), 15 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 135e188502..a83f2c7c48 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -120,15 +120,11 @@ inline nvinfer1::Dims TensorShapeToTrtDims(const TensorShapeType& shape, return trt_dims; } -inline nvinfer1::Dims VectorToTrtDims(const std::vector& shape, - bool ignore_first_dim = false) { - nvinfer1::Dims trt_dims; - const int offset = (ignore_first_dim ? 1 : 0); - for (int i = offset; i < shape.size(); i++) { - trt_dims.d[i - offset] = shape[i]; - } - trt_dims.nbDims = shape.size() - offset; - return trt_dims; +inline nvinfer1::Dims TensorShapeArrayToTrtDims(const std::vector& shape, + bool ignore_first_dim = false) { + PartialTensorShape tensor_shape; + TensorShapeUtils::MakeShape(shape, &tensor_shape); + return TensorShapeToTrtDims(tensor_shape, ignore_first_dim); } void GetOutputProperties(const grappler::GraphProperties& graph_properties, @@ -1898,6 +1894,10 @@ tensorflow::Status ConvertExpandDims(OpConverterParams* params) { return tensorflow::errors::InvalidArgument( "Two inputs expected for ExpandDims, at ", node_def.name()); } + if (inputs.at(0).is_weights() ) { + return tensorflow::errors::Unimplemented( + "ExpandDims expects tensor for input, at ", node_def.name()); + } if (!inputs.at(1).is_weights() ) { return tensorflow::errors::InvalidArgument( "ExpandDims expects weights for axis, at ", node_def.name()); @@ -1936,7 +1936,8 @@ tensorflow::Status ConvertExpandDims(OpConverterParams* params) { input_dims.insert(input_dims.begin()+axis, 1); // Reshape tensor. const bool ignore_first_dim = input_tensor.is_tensor(); - nvinfer1::Dims new_dims = VectorToTrtDims(input_dims, ignore_first_dim); + nvinfer1::Dims new_dims = TensorShapeArrayToTrtDims(input_dims, + ignore_first_dim); const nvinfer1::ITensor* output_tensor = nullptr; TF_RETURN_IF_ERROR(params->converter->PrepareTensorForShape( input_tensor, new_dims, &output_tensor)); @@ -1952,6 +1953,10 @@ tensorflow::Status ConvertSqueeze(OpConverterParams* params) { return tensorflow::errors::InvalidArgument( "One input expected for Squeeze, at ", node_def.name()); } + if (inputs.at(0).is_weights() ) { + return tensorflow::errors::Unimplemented( + "Squeeze expects tensor for input, at ", node_def.name()); + } // Get input shape. TRT_TensorOrWeights input_tensor = inputs.at(0); const nvinfer1::Dims dims = input_tensor.GetTrtDims(); @@ -1979,7 +1984,7 @@ tensorflow::Status ConvertSqueeze(OpConverterParams* params) { // Convert negative axis to corresponding positive axis. if (axis < 0) axis += input_rank; // Don't squeeze batch dim. - if (axis == 0) { + if (input_tensor.is_tensor() && axis == 0) { return tensorflow::errors::Unimplemented( "Cannot squeeze batch dimension, at ", node_def.name()); } @@ -1999,7 +2004,8 @@ tensorflow::Status ConvertSqueeze(OpConverterParams* params) { input_dims.end()); // Reshape tensor. const bool ignore_first_dim = input_tensor.is_tensor(); - nvinfer1::Dims new_dims = VectorToTrtDims(input_dims, ignore_first_dim); + nvinfer1::Dims new_dims = TensorShapeArrayToTrtDims(input_dims, + ignore_first_dim); const nvinfer1::ITensor* output_tensor = nullptr; TF_RETURN_IF_ERROR(params->converter->PrepareTensorForShape( input_tensor, new_dims, &output_tensor)); diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc index 74ca982f3f..27d6098b49 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc @@ -2126,9 +2126,19 @@ TEST_F(OpConverterTest, ConvertExpandDims) { Scope s = Scope::NewRootScope(); auto input = ops::Placeholder(s.WithOpName("input"), DT_FLOAT); auto weights = ops::Placeholder(s.WithOpName("weights"), DT_INT32); - auto expanddims = ops::ExpandDims(s.WithOpName("my_expanddims"), input, weights); + auto expanddims = ops::ExpandDims(s.WithOpName("my_expanddims"), input, + weights); const NodeDef& node_def = expanddims.operation.node()->def(); + { + // Input is weights, should fail. + Reset(); + AddTestWeights("input", {1, 2, 3}, {1, 2, 3, 4, 5, 6}); + AddTestWeights("weights", {1}, {1}); + RunValidationAndConversion( + node_def, error::UNIMPLEMENTED, + "ExpandDims expects tensor for input, at my_expanddims"); + } { // Axis is a tensor, should fail. Reset(); @@ -2144,7 +2154,8 @@ TEST_F(OpConverterTest, ConvertExpandDims) { AddTestTensor("input", {1, 2, 3}); AddTestWeights("weights", {1}, {0}); RunValidationAndConversion(node_def, error::UNIMPLEMENTED, - "Modifying batch dimension is not supported for ExpandDims, at my_expanddims"); + "Modifying batch dimension is not supported for ExpandDims, at " + "my_expanddims"); } { // Add dim at batch dimension via negative axis, should fail. @@ -2153,7 +2164,8 @@ TEST_F(OpConverterTest, ConvertExpandDims) { // Input is rank 4 (batch dim included) AddTestWeights("weights", {1}, {-5}); RunValidationAndConversion(node_def, error::UNIMPLEMENTED, - "Modifying batch dimension is not supported for ExpandDims, at my_expanddims"); + "Modifying batch dimension is not supported for ExpandDims, at " + "my_expanddims"); } { // Axis > rank(input), should fail. @@ -2250,6 +2262,15 @@ TEST_F(OpConverterTest, ConvertSqueeze) { return squeeze.operation.node()->def(); }; + { + // Input is weights, should fail. + Reset(); + NodeDef node_def = get_squeeze_nodedef({0}); + AddTestWeights("input", {1, 2, 3}, {1, 2, 3, 4, 5, 6}); + RunValidationAndConversion( + node_def, error::UNIMPLEMENTED, + "Squeeze expects tensor for input, at my_squeeze"); + } { // Squeeze batch dim, should fail. Reset(); -- GitLab From 06fe3c8ce5b2db058b633a3d93dedcb91a51b203 Mon Sep 17 00:00:00 2001 From: Trevor Morris Date: Wed, 5 Dec 2018 11:19:33 -0800 Subject: [PATCH 110/873] Check that axis is a scalar --- tensorflow/contrib/tensorrt/convert/convert_nodes.cc | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index a83f2c7c48..3392e527c2 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -1913,6 +1913,10 @@ tensorflow::Status ConvertExpandDims(OpConverterParams* params) { const int input_rank = input_dims.size(); // Get axis to expand on. TRT_ShapedWeights weights = inputs.at(1).weights(); + if (weights.count() != 1) { + return tensorflow::errors::InvalidArgument( + "ExpandDims axis must be a scalar, at ", node_def.name()); + } const int* weights_ptr = static_cast(const_cast(weights.GetValues())); int axis = weights_ptr[0]; -- GitLab From 1680520c643b4c3ea6c41ce2ee3cc000a125a88e Mon Sep 17 00:00:00 2001 From: Trevor Morris Date: Wed, 5 Dec 2018 14:28:04 -0800 Subject: [PATCH 111/873] Fix clang-format --- .../contrib/tensorrt/convert/convert_nodes.cc | 20 +++---- .../tensorrt/convert/convert_nodes_test.cc | 53 +++++++++---------- 2 files changed, 34 insertions(+), 39 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 3392e527c2..9f6ec75c5b 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -1894,11 +1894,11 @@ tensorflow::Status ConvertExpandDims(OpConverterParams* params) { return tensorflow::errors::InvalidArgument( "Two inputs expected for ExpandDims, at ", node_def.name()); } - if (inputs.at(0).is_weights() ) { + if (inputs.at(0).is_weights()) { return tensorflow::errors::Unimplemented( "ExpandDims expects tensor for input, at ", node_def.name()); } - if (!inputs.at(1).is_weights() ) { + if (!inputs.at(1).is_weights()) { return tensorflow::errors::InvalidArgument( "ExpandDims expects weights for axis, at ", node_def.name()); } @@ -1937,11 +1937,11 @@ tensorflow::Status ConvertExpandDims(OpConverterParams* params) { if (params->validation_only) return Status::OK(); // ExpandDims: Insert new dim of size 1. - input_dims.insert(input_dims.begin()+axis, 1); + input_dims.insert(input_dims.begin() + axis, 1); // Reshape tensor. const bool ignore_first_dim = input_tensor.is_tensor(); - nvinfer1::Dims new_dims = TensorShapeArrayToTrtDims(input_dims, - ignore_first_dim); + nvinfer1::Dims new_dims = + TensorShapeArrayToTrtDims(input_dims, ignore_first_dim); const nvinfer1::ITensor* output_tensor = nullptr; TF_RETURN_IF_ERROR(params->converter->PrepareTensorForShape( input_tensor, new_dims, &output_tensor)); @@ -1957,7 +1957,7 @@ tensorflow::Status ConvertSqueeze(OpConverterParams* params) { return tensorflow::errors::InvalidArgument( "One input expected for Squeeze, at ", node_def.name()); } - if (inputs.at(0).is_weights() ) { + if (inputs.at(0).is_weights()) { return tensorflow::errors::Unimplemented( "Squeeze expects tensor for input, at ", node_def.name()); } @@ -2002,14 +2002,14 @@ tensorflow::Status ConvertSqueeze(OpConverterParams* params) { input_dims[axis] = 0; } if (params->validation_only) return Status::OK(); - + // Remove all dims which are equal to 0. input_dims.erase(std::remove(input_dims.begin(), input_dims.end(), 0), input_dims.end()); - // Reshape tensor. + // Reshape tensor. const bool ignore_first_dim = input_tensor.is_tensor(); - nvinfer1::Dims new_dims = TensorShapeArrayToTrtDims(input_dims, - ignore_first_dim); + nvinfer1::Dims new_dims = + TensorShapeArrayToTrtDims(input_dims, ignore_first_dim); const nvinfer1::ITensor* output_tensor = nullptr; TF_RETURN_IF_ERROR(params->converter->PrepareTensorForShape( input_tensor, new_dims, &output_tensor)); diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc index 27d6098b49..c37a43dd5d 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc @@ -2126,8 +2126,8 @@ TEST_F(OpConverterTest, ConvertExpandDims) { Scope s = Scope::NewRootScope(); auto input = ops::Placeholder(s.WithOpName("input"), DT_FLOAT); auto weights = ops::Placeholder(s.WithOpName("weights"), DT_INT32); - auto expanddims = ops::ExpandDims(s.WithOpName("my_expanddims"), input, - weights); + auto expanddims = + ops::ExpandDims(s.WithOpName("my_expanddims"), input, weights); const NodeDef& node_def = expanddims.operation.node()->def(); { @@ -2153,7 +2153,8 @@ TEST_F(OpConverterTest, ConvertExpandDims) { Reset(); AddTestTensor("input", {1, 2, 3}); AddTestWeights("weights", {1}, {0}); - RunValidationAndConversion(node_def, error::UNIMPLEMENTED, + RunValidationAndConversion( + node_def, error::UNIMPLEMENTED, "Modifying batch dimension is not supported for ExpandDims, at " "my_expanddims"); } @@ -2163,7 +2164,8 @@ TEST_F(OpConverterTest, ConvertExpandDims) { AddTestTensor("input", {1, 2, 3}); // Input is rank 4 (batch dim included) AddTestWeights("weights", {1}, {-5}); - RunValidationAndConversion(node_def, error::UNIMPLEMENTED, + RunValidationAndConversion( + node_def, error::UNIMPLEMENTED, "Modifying batch dimension is not supported for ExpandDims, at " "my_expanddims"); } @@ -2173,7 +2175,8 @@ TEST_F(OpConverterTest, ConvertExpandDims) { AddTestTensor("input", {1, 2, 3}); // Input is rank 4 (batch dim included) AddTestWeights("weights", {1}, {5}); - RunValidationAndConversion(node_def, error::INVALID_ARGUMENT, + RunValidationAndConversion( + node_def, error::INVALID_ARGUMENT, "Axis for ExpandDims is invalid, must be in the range " "[-rank(input) - 1, rank(input)], at my_expanddims"); } @@ -2183,14 +2186,14 @@ TEST_F(OpConverterTest, ConvertExpandDims) { AddTestTensor("input", {1, 2, 3}); // Input is rank 4 (batch dim included) AddTestWeights("weights", {1}, {-6}); - RunValidationAndConversion(node_def, error::INVALID_ARGUMENT, + RunValidationAndConversion( + node_def, error::INVALID_ARGUMENT, "Axis for ExpandDims is invalid, must be in the range " "[-rank(input) - 1, rank(input)], at my_expanddims"); } struct TestParams { - TestParams(const std::vector& input_dims, - int axis, + TestParams(const std::vector& input_dims, int axis, const std::vector& expected_output_dims) : input_dims(input_dims), axis(axis), @@ -2203,14 +2206,10 @@ TEST_F(OpConverterTest, ConvertExpandDims) { // Ok. const int kExpandDimsOKCases = 8; TestParams ok_params[kExpandDimsOKCases] = { - TestParams{{2, 3}, 1, {1, 2, 3}}, - TestParams{{2, 3}, -3, {1, 2, 3}}, - TestParams{{2, 3}, 3, {2, 3, 1}}, - TestParams{{2, 3}, -1, {2, 3, 1}}, - TestParams{{2, 3}, 2, {2, 1, 3}}, - TestParams{{2, 3}, -2, {2, 1, 3}}, - TestParams{{6}, 1, {1, 6}}, - TestParams{{6}, -1, {6, 1}}, + TestParams{{2, 3}, 1, {1, 2, 3}}, TestParams{{2, 3}, -3, {1, 2, 3}}, + TestParams{{2, 3}, 3, {2, 3, 1}}, TestParams{{2, 3}, -1, {2, 3, 1}}, + TestParams{{2, 3}, 2, {2, 1, 3}}, TestParams{{2, 3}, -2, {2, 1, 3}}, + TestParams{{6}, 1, {1, 6}}, TestParams{{6}, -1, {6, 1}}, }; for (int i = 0; i < kExpandDimsOKCases; ++i) { Reset(); @@ -2234,9 +2233,8 @@ TEST_F(OpConverterTest, ConvertSqueeze) { { // Input list is empty, should fail. NodeDef node_def = MakeNodeDef("my_squeeze", "Squeeze", {}); - RunValidationAndConversion( - node_def, error::INVALID_ARGUMENT, - "One input expected for Squeeze, at my_squeeze"); + RunValidationAndConversion(node_def, error::INVALID_ARGUMENT, + "One input expected for Squeeze, at my_squeeze"); } { // No attrs, should fail. @@ -2257,8 +2255,8 @@ TEST_F(OpConverterTest, ConvertSqueeze) { auto input = ops::Placeholder(s.WithOpName("input"), DT_FLOAT); ops::Squeeze::Attrs squeeze_attrs; squeeze_attrs.axis_ = gtl::ArraySlice(axis); - auto squeeze = ops::Squeeze(s.WithOpName("my_squeeze"), input, - squeeze_attrs); + auto squeeze = + ops::Squeeze(s.WithOpName("my_squeeze"), input, squeeze_attrs); return squeeze.operation.node()->def(); }; @@ -2276,18 +2274,16 @@ TEST_F(OpConverterTest, ConvertSqueeze) { Reset(); NodeDef node_def = get_squeeze_nodedef({0}); AddTestTensor("input", {1, 2, 3}); - RunValidationAndConversion( - node_def, error::UNIMPLEMENTED, - "Cannot squeeze batch dimension, at my_squeeze"); + RunValidationAndConversion(node_def, error::UNIMPLEMENTED, + "Cannot squeeze batch dimension, at my_squeeze"); } { // Squeeze batch dim via negative axis, should fail. Reset(); NodeDef node_def = get_squeeze_nodedef({-4}); AddTestTensor("input", {1, 2, 3}); - RunValidationAndConversion( - node_def, error::UNIMPLEMENTED, - "Cannot squeeze batch dimension, at my_squeeze"); + RunValidationAndConversion(node_def, error::UNIMPLEMENTED, + "Cannot squeeze batch dimension, at my_squeeze"); } { // Squeeze >= rank(input), should fail. @@ -2311,8 +2307,7 @@ TEST_F(OpConverterTest, ConvertSqueeze) { } struct TestParams { - TestParams(const std::vector& input_dims, - const std::vector& axis, + TestParams(const std::vector& input_dims, const std::vector& axis, const std::vector& expected_output_dims) : input_dims(input_dims), axis(axis), -- GitLab From 9546b5259410f1aad4206b396558238abd1c7501 Mon Sep 17 00:00:00 2001 From: Trevor Morris Date: Wed, 5 Dec 2018 14:31:46 -0800 Subject: [PATCH 112/873] Remove is_tensor() since inputs are required to be tensors --- .../contrib/tensorrt/convert/convert_nodes.cc | 22 +++++++------------ 1 file changed, 8 insertions(+), 14 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 9f6ec75c5b..ac0e2f684c 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -1906,10 +1906,8 @@ tensorflow::Status ConvertExpandDims(OpConverterParams* params) { TRT_TensorOrWeights input_tensor = inputs.at(0); const nvinfer1::Dims dims = input_tensor.GetTrtDims(); std::vector input_dims(dims.d, dims.d + dims.nbDims); - // Add batch dim back for tensors. - if (input_tensor.is_tensor()) { - input_dims.insert(input_dims.begin(), -1); - } + // Add batch dim back. + input_dims.insert(input_dims.begin(), -1); const int input_rank = input_dims.size(); // Get axis to expand on. TRT_ShapedWeights weights = inputs.at(1).weights(); @@ -1929,7 +1927,7 @@ tensorflow::Status ConvertExpandDims(OpConverterParams* params) { } // Convert negative axis to corresponding positive axis. if (axis < 0) axis += input_rank + 1; - if (input_tensor.is_tensor() && axis == 0) { + if (axis == 0) { return tensorflow::errors::Unimplemented( "Modifying batch dimension is not supported for ExpandDims, at ", node_def.name()); @@ -1939,9 +1937,8 @@ tensorflow::Status ConvertExpandDims(OpConverterParams* params) { // ExpandDims: Insert new dim of size 1. input_dims.insert(input_dims.begin() + axis, 1); // Reshape tensor. - const bool ignore_first_dim = input_tensor.is_tensor(); nvinfer1::Dims new_dims = - TensorShapeArrayToTrtDims(input_dims, ignore_first_dim); + TensorShapeArrayToTrtDims(input_dims, /*ignore_first_dim=*/true); const nvinfer1::ITensor* output_tensor = nullptr; TF_RETURN_IF_ERROR(params->converter->PrepareTensorForShape( input_tensor, new_dims, &output_tensor)); @@ -1965,10 +1962,8 @@ tensorflow::Status ConvertSqueeze(OpConverterParams* params) { TRT_TensorOrWeights input_tensor = inputs.at(0); const nvinfer1::Dims dims = input_tensor.GetTrtDims(); std::vector input_dims(dims.d, dims.d + dims.nbDims); - // Add batch dim back temporarily. - if (input_tensor.is_tensor()) { - input_dims.insert(input_dims.begin(), -1); - } + // Add batch dim back. + input_dims.insert(input_dims.begin(), -1); const int input_rank = input_dims.size(); // Mark axes to remove by setting them to 0. TFAttrs attrs(node_def); @@ -1988,7 +1983,7 @@ tensorflow::Status ConvertSqueeze(OpConverterParams* params) { // Convert negative axis to corresponding positive axis. if (axis < 0) axis += input_rank; // Don't squeeze batch dim. - if (input_tensor.is_tensor() && axis == 0) { + if (axis == 0) { return tensorflow::errors::Unimplemented( "Cannot squeeze batch dimension, at ", node_def.name()); } @@ -2007,9 +2002,8 @@ tensorflow::Status ConvertSqueeze(OpConverterParams* params) { input_dims.erase(std::remove(input_dims.begin(), input_dims.end(), 0), input_dims.end()); // Reshape tensor. - const bool ignore_first_dim = input_tensor.is_tensor(); nvinfer1::Dims new_dims = - TensorShapeArrayToTrtDims(input_dims, ignore_first_dim); + TensorShapeArrayToTrtDims(input_dims, /*ignore_first_dim=*/true); const nvinfer1::ITensor* output_tensor = nullptr; TF_RETURN_IF_ERROR(params->converter->PrepareTensorForShape( input_tensor, new_dims, &output_tensor)); -- GitLab From 293b0783fdf635d5e337d3c71ae4cadee8770322 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Thu, 6 Dec 2018 02:02:57 +0000 Subject: [PATCH 113/873] Add processing in case dim is unknown in advance. Signed-off-by: Yong Tang --- tensorflow/python/ops/nn_ops.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py index a2305cefba..4a36aa1550 100644 --- a/tensorflow/python/ops/nn_ops.py +++ b/tensorflow/python/ops/nn_ops.py @@ -1699,8 +1699,11 @@ def _softmax(logits, compute_op, dim=-1, name=None): # still perform softmax on its last dimension. # In case dim is negative (and is not last dimension -1), add shape.ndims - if not isinstance(dim, ops.Tensor) and dim < 0: - dim += shape.ndims + if not isinstance(dim, ops.Tensor): + if dim < 0: + dim += shape.ndims + else: + dim = array_ops.where(math_ops.less(dim, 0), dim + shape.ndims, dim) # Swap logits' dimension of dim and its last dimension. input_rank = array_ops.rank(logits) -- GitLab From e8e80850b1bcc14e3e20c1aa9af517a76d607beb Mon Sep 17 00:00:00 2001 From: Pan Daoxin Date: Thu, 6 Dec 2018 11:37:46 +0800 Subject: [PATCH 114/873] Modify some comments. --- tensorflow/core/kernels/mkl_slice_op.cc | 29 ++++++++++++++----------- 1 file changed, 16 insertions(+), 13 deletions(-) diff --git a/tensorflow/core/kernels/mkl_slice_op.cc b/tensorflow/core/kernels/mkl_slice_op.cc index 233f33e1cb..a85d80f9b3 100644 --- a/tensorflow/core/kernels/mkl_slice_op.cc +++ b/tensorflow/core/kernels/mkl_slice_op.cc @@ -62,8 +62,8 @@ typedef Eigen::ThreadPoolDevice CPUDevice; // either Mkl layout or Tensorflow layout. // A shared code to validate input shapes and check for identity, which is not // dependent on the type of T. -// We do this to reduce code size by not duplicating all this for all T (float, -// double, int32, etc.) +// We do this to reduce code size by not duplicating +// all this for all T (float, double, int32, etc.) static void ValidateMklInputs(OpKernelContext* context, bool* is_identity, gtl::InlinedVector* begin, gtl::InlinedVector* size) { @@ -160,12 +160,13 @@ static void CheckCommonCasesForMklInputs(OpKernelContext* context, } // This structure aggregates multiple inputs to Slice methods. -// Parameters from & to represents memory pointing to reorder. -// Parameters begin_dims & size_dims represents offset and length -// passed to view primitive. struct MklSliceParams { + // Parameters from & to represents memory pointing to reorder. const memory* from; const memory* to; + + // Parameters begin_dims & size_dims represents offset and length + // passed to view primitive. memory::dims begin_dims; memory::dims size_dims; @@ -174,7 +175,7 @@ struct MklSliceParams { : from(from), to(to), begin_dims(begin_dims), size_dims(size_dims) {} }; -// This implements the reuse interface of Slice reorders. +// This implements the shared interface of Slice reorders. template class MklSlicePrimitive : public MklPrimitive { public: @@ -190,6 +191,7 @@ class MklSlicePrimitive : public MklPrimitive { context_.dst_mem->set_data_handle(sliceParams.to->get_data_handle()); context_.slice_stream->submit(context_.slice_primitives); + // For safety guard, so that data_handle wouldn't be rewritten. context_.src_mem->set_data_handle(DummyData); context_.dst_mem->set_data_handle(DummyData); return; @@ -213,6 +215,7 @@ class MklSlicePrimitive : public MklPrimitive { engine cpu_engine_ = engine(engine::cpu, 0); void Setup(const MklSliceParams& sliceParams) { + // Just create the memory primitive, fill with dummy. context_.src_mem.reset( new memory({sliceParams.from->get_primitive_desc().desc(), cpu_engine_}, DummyData)); @@ -260,16 +263,16 @@ class MklSlicePrimitiveFactory : public MklPrimitiveFactory { FactoryKeyCreator key_creator; auto const& from_desc = sliceParams.from->get_primitive_desc().desc().data; auto const& to_desc = sliceParams.to->get_primitive_desc().desc().data; - const int KIdxFirstStride = 0; + const int kIdxFirstStride = 0; memory::dims from_dims(from_desc.dims, &from_desc.dims[from_desc.ndims]); memory::dims to_dims(to_desc.dims, &to_desc.dims[to_desc.ndims]); memory::dims from_strides( - from_desc.layout_desc.blocking.strides[KIdxFirstStride], + from_desc.layout_desc.blocking.strides[kIdxFirstStride], &from_desc.layout_desc.blocking - .strides[KIdxFirstStride][from_desc.ndims]); + .strides[kIdxFirstStride][from_desc.ndims]); memory::dims to_strides( - to_desc.layout_desc.blocking.strides[KIdxFirstStride], - &to_desc.layout_desc.blocking.strides[KIdxFirstStride][to_desc.ndims]); + to_desc.layout_desc.blocking.strides[kIdxFirstStride], + &to_desc.layout_desc.blocking.strides[kIdxFirstStride][to_desc.ndims]); key_creator.AddAsKey(prefix); key_creator.AddAsKey(static_cast(from_desc.format)); key_creator.AddAsKey(static_cast(from_desc.data_type)); @@ -339,8 +342,8 @@ class MklSliceOp : public OpKernel { // // 1. create memory primitive descriptor in_mem_pd and memory primitive // in_mem_p for the entire source data. create view primitive - // descriptor - // in_submem_pd based on in_mem_pd, initial offsets, and sub-sizes + // descriptor in_submem_pd based on in_mem_pd, initial offsets, + // and sub-sizes // 2. create memory primitive descriptor out_mem_pd and memory primitive // out_mem_p for the output (the logical sizes should match sub-sizes // used in step 1, but the format might be arbitrary) -- GitLab From 790390598cad7c4e456b60400a0d0d5454e75716 Mon Sep 17 00:00:00 2001 From: Bairen Yi Date: Fri, 30 Nov 2018 03:34:29 +0000 Subject: [PATCH 115/873] Implement async TensorFromTransportOptions for GDR Instead of blocking on completion of an RDMA op, RecvTensor client will now post a work request to the NIC send queue and return immediately. The GDR background polling thread will handle the callback after the corresponding RDMA op is completed, i.e. polled from the completion queue on NIC. The old epoll based mechanism is removed to trade higher CPU usage for improved throughput and lower latencies for RDMA ops. The maximum numbers of work request (WR) in the send/recv queues on NIC are increased to entertain the increased number of concurrent RDMA ops. The threshold of tensor size below which we pass the tensor content in metadata is also increased to reduce the pressure to send/recv queues on NIC. This fixes #23933. Signed-off-by: Bairen Yi --- tensorflow/contrib/gdr/BUILD | 2 +- tensorflow/contrib/gdr/gdr.proto | 1 - tensorflow/contrib/gdr/gdr_memory_manager.cc | 520 ++++++++----------- tensorflow/contrib/gdr/gdr_rendezvous_mgr.cc | 9 +- tensorflow/contrib/gdr/gdr_server_lib.cc | 3 +- tensorflow/contrib/gdr/gdr_worker.cc | 24 +- 6 files changed, 216 insertions(+), 343 deletions(-) diff --git a/tensorflow/contrib/gdr/BUILD b/tensorflow/contrib/gdr/BUILD index e534fdc177..7ec3c5ff5d 100644 --- a/tensorflow/contrib/gdr/BUILD +++ b/tensorflow/contrib/gdr/BUILD @@ -58,7 +58,7 @@ tf_cuda_library( ], ) -tf_cuda_library( +cc_library( name = "gdr_worker", srcs = ["gdr_worker.cc"], hdrs = ["gdr_worker.h"], diff --git a/tensorflow/contrib/gdr/gdr.proto b/tensorflow/contrib/gdr/gdr.proto index c0b89245b1..bd438787c3 100644 --- a/tensorflow/contrib/gdr/gdr.proto +++ b/tensorflow/contrib/gdr/gdr.proto @@ -9,5 +9,4 @@ message RemoteMemoryRegion { uint64 addr = 3; uint32 rkey = 4; uint32 tensor_key = 5; - uint64 checksum = 6; } diff --git a/tensorflow/contrib/gdr/gdr_memory_manager.cc b/tensorflow/contrib/gdr/gdr_memory_manager.cc index 53587fcf30..69bbab1c39 100644 --- a/tensorflow/contrib/gdr/gdr_memory_manager.cc +++ b/tensorflow/contrib/gdr/gdr_memory_manager.cc @@ -26,15 +26,14 @@ limitations under the License. #include #include #include -#include #include "tensorflow/contrib/gdr/gdr.pb.h" #include "tensorflow/core/common_runtime/device.h" #include "tensorflow/core/common_runtime/dma_helper.h" #include "tensorflow/core/common_runtime/process_state.h" +#include "tensorflow/core/lib/random/random.h" #if GOOGLE_CUDA #include "tensorflow/core/common_runtime/gpu/gpu_process_state.h" -#include "tensorflow/core/common_runtime/gpu/gpu_util.h" #endif // GOOGLE_CUDA #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/platform/macros.h" @@ -81,10 +80,6 @@ int TryToReadNumaNode(ibv_device* device) { int32 value; if (strings::safe_strto32(content, &value)) { if (value < 0) { - LOG(INFO) << "Successful NUMA node read from SysFS had negative value (" - << value - << "), but there must be at least one NUMA node" - ", so returning NUMA node zero"; return port::kNUMANoAffinity; } LOG(INFO) << "NUMA node for device: " << device->name << " is " << value; @@ -114,7 +109,7 @@ class GdrMemoryManager : public RemoteMemoryManager { public: GdrMemoryManager(const string& host, const string& port); - virtual ~GdrMemoryManager(); + virtual ~GdrMemoryManager() {} virtual Status Init() override; @@ -140,7 +135,7 @@ class GdrMemoryManager : public RemoteMemoryManager { return ptr < reinterpret_cast(other->addr) + other->length; } - ibv_mr* FindMemoryRegion(void* addr, size_t length); + ibv_mr* FindMemoryRegion(const Tensor* tensor); void InsertMemoryRegion(void* addr, size_t length, const std::string& allocator_name); @@ -152,7 +147,6 @@ class GdrMemoryManager : public RemoteMemoryManager { const string port_; RdmaEndpointPtr listening_; std::atomic stopped_; - int epfd_; int numa_node_; // Server side endpoints @@ -163,15 +157,19 @@ class GdrMemoryManager : public RemoteMemoryManager { std::atomic next_key_; // Server side on-the-fly tensor buffers - mutex server_mu_; - std::map tensor_buffers_ - GUARDED_BY(server_mu_); + mutex buf_mu_; + std::map tensor_buffers_ GUARDED_BY(buf_mu_); // Client side endpoints mutex client_mu_; std::map, RdmaEndpointPtr> clients_ GUARDED_BY(client_mu_); + // Client side callbacks + mutex callback_mu_; + std::map tensor_callbacks_ + GUARDED_BY(callback_mu_); + // Managed memory regions mutex alloc_mu_; std::vector mrs_ GUARDED_BY(alloc_mu_); @@ -184,16 +182,9 @@ GdrMemoryManager::GdrMemoryManager(const string& host, const string& port) port_(port), listening_(nullptr, EndpointDeleter), stopped_(true), - next_key_(0) {} - -GdrMemoryManager::~GdrMemoryManager() { close(epfd_); } + next_key_(static_cast(random::New64())) {} Status GdrMemoryManager::Init() { - epfd_ = epoll_create1(0); - if (epfd_ == -1) { - return errors::Unavailable(strerror(errno), ": ", "epoll_create"); - } - rdma_addrinfo* addrinfo; rdma_addrinfo hints = {}; hints.ai_port_space = RDMA_PS_TCP; @@ -206,7 +197,7 @@ Status GdrMemoryManager::Init() { ibv_qp_init_attr init_attr = {}; init_attr.qp_type = IBV_QPT_RC; - init_attr.cap.max_recv_wr = 32; + init_attr.cap.max_recv_wr = 1024; init_attr.cap.max_send_wr = 1; init_attr.cap.max_recv_sge = 1; init_attr.cap.max_send_sge = 1; @@ -239,14 +230,6 @@ Status GdrMemoryManager::Init() { "cannot set server to non-blocking mode"); } - epoll_event event = {}; - event.events = EPOLLIN | EPOLLPRI; - event.data.ptr = listening_.get(); - if (epoll_ctl(epfd_, EPOLL_CTL_ADD, listening_->channel->fd, &event)) { - return errors::Unavailable(strerror(errno), ": ", - "cannot add server to epoll"); - } - numa_node_ = TryToReadNumaNode(listening_->verbs->device); SubAllocator::Visitor alloc_visitor = [this](void* ptr, int numa_node, @@ -278,11 +261,9 @@ Status GdrMemoryManager::Init() { VLOG(2) << "Registering RDMA capable memory region on GPU " << gpu_id; InsertMemoryRegion(ptr, num_bytes, strings::StrCat("GPU:", gpu_id)); }; - for (int numa_idx = 0; numa_idx < port::NUMANumNodes(); ++numa_idx) { - GPUProcessState::singleton()->AddGPUAllocVisitor(numa_idx, - cuda_alloc_visitor); - } - VLOG(1) << "Instrumenting GPU allocator(s) for all Numas"; + GPUProcessState::singleton()->AddGPUAllocVisitor(numa_node_, + cuda_alloc_visitor); + LOG(INFO) << "Instrumenting GPU allocator for NUMA " << numa_node_; } #endif // GOOGLE_CUDA return Status::OK(); @@ -291,95 +272,90 @@ Status GdrMemoryManager::Init() { void GdrMemoryManager::Run() { stopped_ = false; while (!stopped_) { - epoll_event events[32]; - int ret = epoll_wait(epfd_, events, 32, 1); - if (ret == -1) { - LOG(ERROR) << "epoll_wait: " << strerror(errno); - return; - } - for (int i = 0; i < ret; i++) { - rdma_cm_id* id = static_cast(events[i].data.ptr); - if (id == listening_.get()) { - // Accept incoming connections - if (!rdma_get_request(listening_.get(), &id)) { - if (!rdma_accept(id, nullptr)) { - LOG(INFO) << "Accepted new RDMA connection"; - if (ibv_req_notify_cq(id->recv_cq, 0)) { - LOG(ERROR) << strerror(errno) << ": ibv_req_notify_cq failed"; - EndpointDeleter(id); - continue; - } - for (int i = 0; i < 32; i++) { - if (rdma_post_recvv(id, nullptr, nullptr, 0)) { - LOG(ERROR) << strerror(errno) << ": rdma_post_recvv failed"; - EndpointDeleter(id); - continue; - } - } - int flags = fcntl(id->recv_cq_channel->fd, F_GETFL, 0); - if (fcntl(id->recv_cq_channel->fd, F_SETFL, flags | O_NONBLOCK)) { - LOG(ERROR) << strerror(errno) - << ": cannot set server_client to non-blocking mode"; - EndpointDeleter(id); - continue; - } - epoll_event event = {}; - event.events = EPOLLIN | EPOLLPRI; - event.data.ptr = id; - if (epoll_ctl(epfd_, EPOLL_CTL_ADD, id->recv_cq_channel->fd, - &event)) { - LOG(ERROR) << strerror(errno) - << ": cannot add server client to epoll"; - EndpointDeleter(id); - continue; - } - server_clients_.push_back({id, EndpointDeleter}); + rdma_cm_id* id = nullptr; + // Accept incoming connections + if (!rdma_get_request(listening_.get(), &id)) { + if (!rdma_accept(id, nullptr)) { + LOG(INFO) << "Accepted new RDMA connection"; + for (int i = 0; i < 1024; i++) { + if (rdma_post_recvv(id, nullptr, nullptr, 0)) { + LOG(ERROR) << strerror(errno) << ": rdma_post_recvv failed"; + EndpointDeleter(id); + continue; } } - } else { - // Polling work completions - ibv_cq* cq; - void* context; - if (!ibv_get_cq_event(id->recv_cq_channel, &cq, &context)) { - ibv_ack_cq_events(id->recv_cq, 1); - if (ibv_req_notify_cq(id->recv_cq, 0)) { - LOG(ERROR) << strerror(errno) << ": ibv_req_notify_cq failed"; - continue; + server_clients_.push_back({id, EndpointDeleter}); + } + } + // Polling server side work completions + for (const auto& client : server_clients_) { + ibv_wc wc[32]; + int ret = ibv_poll_cq(client->recv_cq, 32, wc); + if (ret < 0) { + LOG(ERROR) << "ibv_poll_cq failed"; + continue; + } + for (int i = 0; i < ret; i++) { + if (wc[i].opcode != IBV_WC_RECV_RDMA_WITH_IMM) { + LOG(ERROR) << "Received unknown operation " << wc[i].opcode; + } + if (wc[i].status != 0) { + LOG(ERROR) << ibv_wc_status_str(wc[i].status); + } + TensorKey tensor_key = ntohl(wc[i].imm_data); + + if (rdma_post_recvv(client.get(), nullptr, nullptr, 0)) { + perror("rdma_post_recvv"); + LOG(ERROR) << "rdma_post_recvv failed"; + } + + mutex_lock l(buf_mu_); + auto iter = tensor_buffers_.find(tensor_key); + if (iter == std::end(tensor_buffers_)) { + LOG(ERROR) << "Cannot find tensor buffer for tensor key " + << tensor_key; + } else { + const TensorBuffer* buffer = iter->second; + buffer->Unref(); + tensor_buffers_.erase(iter); + } + } + } + // Polling client side work completions + if (client_mu_.try_lock()) { + for (const auto& client : clients_) { + ibv_wc wc[32]; + int ret = ibv_poll_cq(client.second->send_cq, 32, wc); + for (int i = 0; i < ret; i++) { + Status s; + if (wc[i].status) { + s = errors::Unavailable(ibv_wc_status_str(wc[i].status)); + } else { + s = Status::OK(); } - ibv_wc wc[32]; - int ret = ibv_poll_cq(id->recv_cq, 32, wc); - if (ret < 0) { - LOG(ERROR) << "ibv_poll_cq failed"; - continue; + TensorKey key = wc[i].wr_id; + + ibv_send_wr wr = {}; + wr.opcode = IBV_WR_RDMA_WRITE_WITH_IMM; + wr.imm_data = htonl(key); + ibv_send_wr* bad_wr; + if (ibv_post_send(client.second->qp, &wr, &bad_wr)) { + LOG(ERROR) << strerror(errno) + << ": ibv_post_send failed for tensor_key " << key; } - for (int i = 0; i < ret; i++) { - if (wc[i].opcode != IBV_WC_RECV_RDMA_WITH_IMM) { - LOG(ERROR) << "Received unknown operation " << wc[i].opcode; - } - if (wc[i].status != 0) { - LOG(ERROR) << ibv_wc_status_str(wc[i].status); - } - TensorKey tensor_key = ntohl(wc[i].imm_data); - { - mutex_lock l(server_mu_); - auto iter = tensor_buffers_.find(tensor_key); - if (iter == std::end(tensor_buffers_)) { - LOG(ERROR) << "Cannot find tensor buffer for tensor key " - << tensor_key; - } else { - const TensorBuffer* buffer = iter->second; - buffer->Unref(); - tensor_buffers_.erase(iter); - } - } - if (rdma_post_recvv(id, nullptr, nullptr, 0)) { - perror("rdma_post_recvv"); - LOG(ERROR) << "rdma_post_recvv failed"; - continue; - } + + mutex_lock l(callback_mu_); + auto iter = tensor_callbacks_.find(key); + if (iter != std::end(tensor_callbacks_)) { + iter->second(s); + tensor_callbacks_.erase(iter); + } else { + LOG(WARNING) << "Cannot find client callback with tensor key " + << key; } } } + client_mu_.unlock(); } } } @@ -390,116 +366,58 @@ void GdrMemoryManager::TransportOptionsFromTensor( ::google::protobuf::Any* mutable_transport_options, const Tensor& tensor, Device* device, DeviceContext* device_context, bool on_host, StatusCallback done) { - auto buffer = DMAHelper::buffer(&tensor); - void* addr = buffer->data(); - size_t length = buffer->size(); - if (length == 0) { - done(errors::Unavailable("Cannot register tensor buffer of size 0")); - return; - } - - ibv_mr* mr = FindMemoryRegion(addr, length); + ibv_mr* mr = FindMemoryRegion(&tensor); + const TensorBuffer* buffer = DMAHelper::buffer(&tensor); -#if GOOGLE_CUDA - if (device->tensorflow_gpu_device_info() && !on_host) { - Allocator* alloc = GPUProcessState::singleton()->GetCUDAHostAllocator(0); - Tensor* host_copy = new Tensor(alloc, tensor.dtype(), tensor.shape()); - GPUUtil::CopyGPUTensorToCPU( - device, device_context, &tensor, host_copy, - [done, host_copy, mutable_transport_options, this](const Status& s) { - if (!s.ok()) { - done(s); - delete host_copy; - return; - } - auto buffer = DMAHelper::buffer(host_copy); - void* addr = buffer->data(); - size_t length = buffer->size(); - ibv_mr* mr = FindMemoryRegion(addr, length); - - if (mr == nullptr) { - done(errors::Unavailable("Cannot find pinned memory region")); - delete host_copy; - return; - } - - buffer->Ref(); - TensorKey tensor_key = next_key_++; - { - mutex_lock l(server_mu_); - tensor_buffers_.insert(std::make_pair(tensor_key, buffer)); - } - - uint64_t checksum = 0; - if (VLOG_IS_ON(2)) { - checksum = GPUUtil::Checksum(*host_copy); - } - - RemoteMemoryRegion remote_mr; - remote_mr.set_host(host_); - remote_mr.set_port(port_); - remote_mr.set_addr(reinterpret_cast(addr)); - remote_mr.set_rkey(mr->rkey); - remote_mr.set_tensor_key(tensor_key); - remote_mr.set_checksum(checksum); - mutable_transport_options->PackFrom(remote_mr); - - done(Status::OK()); - delete host_copy; - }); - return; - } -#endif + Tensor* copy = nullptr; if (mr == nullptr) { - Allocator* alloc = ProcessState::singleton()->GetCPUAllocator(numa_node_); - Tensor host_copy(alloc, tensor.dtype(), tensor.shape()); - - std::memcpy(DMAHelper::buffer(&host_copy)->data(), buffer->data(), length); - VLOG(2) << "Copying " << length << " bytes unpinned tensor buffer"; - - buffer = DMAHelper::buffer(&host_copy); - addr = buffer->data(); - length = buffer->size(); - - mr = FindMemoryRegion(addr, length); + AllocatorAttributes alloc_attrs; + alloc_attrs.set_gpu_compatible(true); + alloc_attrs.set_nic_compatible(true); + alloc_attrs.set_on_host(true); + Allocator* alloc = device->GetAllocator(alloc_attrs); + copy = new Tensor(alloc, tensor.dtype(), tensor.shape()); + + mr = FindMemoryRegion(copy); + buffer = DMAHelper::buffer(copy); if (mr == nullptr) { done(errors::Unavailable("Cannot find pinned memory region")); + delete copy; return; } - - buffer->Ref(); - } else { - buffer->Ref(); } TensorKey tensor_key = next_key_++; + buffer->Ref(); { - mutex_lock l(server_mu_); + mutex_lock l(buf_mu_); tensor_buffers_.insert(std::make_pair(tensor_key, buffer)); } - uint64_t checksum = 0; - if (VLOG_IS_ON(2)) { -#ifdef GOOGLE_CUDA - if (device->tensorflow_gpu_device_info() && !on_host) { - checksum = GPUUtil::Checksum(device, device_context, tensor); - } else { - checksum = GPUUtil::Checksum(tensor); - } -#endif - } - RemoteMemoryRegion remote_mr; remote_mr.set_host(host_); remote_mr.set_port(port_); - remote_mr.set_addr(reinterpret_cast(addr)); + remote_mr.set_addr(reinterpret_cast(buffer->data())); remote_mr.set_rkey(mr->rkey); remote_mr.set_tensor_key(tensor_key); - remote_mr.set_checksum(checksum); mutable_transport_options->PackFrom(remote_mr); - done(Status::OK()); + if (copy && device->tensorflow_gpu_device_info() && !on_host) { + device_context->CopyDeviceTensorToCPU(&tensor, "" /* tensor_name */, device, + copy, [done, copy](const Status& s) { + done(s); + delete copy; + }); + return; + } else if (copy) { + std::memcpy(buffer->data(), DMAHelper::buffer(&tensor)->data(), + buffer->size()); + done(Status::OK()); + delete copy; // OK to delete; we have reffed the underlying TensorBuffer + } else { + done(Status::OK()); + } } void GdrMemoryManager::TensorFromTransportOptions( @@ -512,42 +430,10 @@ void GdrMemoryManager::TensorFromTransportOptions( return; } - auto buffer = DMAHelper::buffer(tensor); - void* addr = buffer->data(); - size_t length = buffer->size(); - ibv_mr* mr = FindMemoryRegion(addr, length); - - Tensor host_copy; -#if GOOGLE_CUDA - if (mr == nullptr && !on_host) { - Allocator* alloc = - GPUProcessState::singleton()->GetCUDAHostAllocator(numa_node_); - host_copy = Tensor(alloc, tensor->dtype(), tensor->shape()); - buffer = DMAHelper::buffer(&host_copy); - addr = buffer->data(); - length = buffer->size(); - mr = FindMemoryRegion(addr, length); - } -#endif // GOOGLE_CUDA - - if (mr == nullptr) { - Allocator* alloc = ProcessState::singleton()->GetCPUAllocator(numa_node_); - host_copy = Tensor(alloc, tensor->dtype(), tensor->shape()); - - buffer = DMAHelper::buffer(&host_copy); - addr = buffer->data(); - length = buffer->size(); - - mr = FindMemoryRegion(addr, length); - if (mr == nullptr) { - done(errors::Unavailable("Cannot find pinned memory region")); - return; - } - } - - decltype(clients_)::iterator iter; - bool success; + rdma_cm_id* id = nullptr; { + decltype(clients_)::iterator iter; + bool success; mutex_lock l(client_mu_); std::tie(iter, success) = clients_.insert( std::make_pair(std::make_pair(remote_mr.host(), remote_mr.port()), @@ -560,93 +446,95 @@ void GdrMemoryManager::TensorFromTransportOptions( return; } } + id = iter->second.get(); } - rdma_cm_id* id = iter->second.get(); - uint64_t start = Env::Default()->NowMicros(); + ibv_mr* mr = FindMemoryRegion(tensor); + const TensorBuffer* buffer = DMAHelper::buffer(tensor); - if (rdma_post_read(id, nullptr, buffer->data(), buffer->size(), mr, 0, - remote_mr.addr(), remote_mr.rkey())) { - done(errors::Unavailable(strerror(errno), ": ", "rdma_post_read failed")); - return; - } + const Tensor* copy = nullptr; - ibv_send_wr wr = {}; - wr.opcode = IBV_WR_RDMA_WRITE_WITH_IMM; - wr.imm_data = htonl(remote_mr.tensor_key()); - wr.send_flags = IBV_SEND_SIGNALED; - ibv_send_wr* bad_wr; - if (ibv_post_send(id->qp, &wr, &bad_wr)) { - done(errors::Unavailable(strerror(errno), ": ", "ibv_post_send failed")); - return; + if (mr == nullptr) { + AllocatorAttributes alloc_attrs; + alloc_attrs.set_gpu_compatible(true); + alloc_attrs.set_nic_compatible(true); + alloc_attrs.set_on_host(true); + Allocator* alloc = device->GetAllocator(alloc_attrs); + copy = new Tensor(alloc, tensor->dtype(), tensor->shape()); + + mr = FindMemoryRegion(copy); + buffer = DMAHelper::buffer(copy); + if (mr == nullptr) { + done(errors::Unavailable("Cannot find pinned memory region")); + delete copy; + return; + } } - ibv_wc wc = {}; - int ret; - while ((ret = ibv_poll_cq(id->send_cq, 1, &wc)) == 0) - ; - if (ret < 0 || wc.status) { - done(errors::Unavailable(ibv_wc_status_str(wc.status))); - return; - } + uint64_t start = Env::Default()->NowMicros(); -#if GOOGLE_CUDA - if (device->tensorflow_gpu_device_info() && !on_host && - host_copy.NumElements() > 0) { - uint64_t checksum = 0; - if (VLOG_IS_ON(2)) { - checksum = GPUUtil::Checksum(host_copy); - CHECK(checksum == remote_mr.checksum()) - << "Checksum mismatch: " << checksum << "!=" << remote_mr.checksum(); - } - Tensor* ref = new Tensor; - std::swap(host_copy, *ref); - GPUUtil::CopyCPUTensorToGPU( - ref, device_context, device, tensor, - [ref, done, buffer, remote_mr, start](const Status& s) { - if (!s.ok()) { - done(s); - delete ref; - return; - } - uint64_t end = Env::Default()->NowMicros(); - - VLOG(2) << "RDMA from remote memory region " << remote_mr.rkey() - << " of size " << buffer->size() << " with tensor key " - << remote_mr.tensor_key() << " took " << (end - start) - << " micros"; - done(Status::OK()); - delete ref; - }); - return; - } -#endif // GOOGLE_CUDA + TensorKey tensor_key = remote_mr.tensor_key(); - if ((on_host || !device->tensorflow_gpu_device_info()) && - host_copy.NumElements() > 0) { - std::memcpy(DMAHelper::buffer(tensor)->data(), addr, length); - VLOG(2) << "Copying " << length << " bytes unpinned tensor buffer"; - } + StatusCallback callback = [done, copy, device, device_context, on_host, + tensor, start, tensor_key](const Status& s) { - uint64_t end = Env::Default()->NowMicros(); + if (!s.ok()) { + done(s); + if (copy) { + delete copy; + } + return; + } - VLOG(2) << "RDMA from remote memory region " << remote_mr.rkey() - << " of size " << buffer->size() << " with tensor key " - << remote_mr.tensor_key() << " took " << (end - start) << " micros"; + VLOG(2) << "RDMA of tensor " << tensor_key << " of size " + << DMAHelper::buffer(tensor)->size() << " took " + << (Env::Default()->NowMicros() - start) << " micros"; + + if (copy && device->tensorflow_gpu_device_info() && !on_host) { + device_context->CopyCPUTensorToDevice(copy, device, tensor, + [done, copy](const Status& s) { + done(s); + delete copy; + }); + } else if (copy) { + std::memcpy(DMAHelper::buffer(tensor)->data(), + DMAHelper::buffer(copy)->data(), + DMAHelper::buffer(copy)->size()); + done(s); + delete copy; + } else { + done(s); + } + }; - uint64_t checksum = 0; - if (VLOG_IS_ON(2)) { -#ifdef GOOGLE_CUDA - if (device->tensorflow_gpu_device_info() && !on_host) { - checksum = GPUUtil::Checksum(device, device_context, *tensor); + { + mutex_lock l(callback_mu_); + if (tensor_callbacks_.find(tensor_key) == std::end(tensor_callbacks_)) { + tensor_callbacks_.insert(std::make_pair(tensor_key, std::move(callback))); } else { - checksum = GPUUtil::Checksum(*tensor); + done(errors::Unavailable("Received duplicated tensor key")); + if (copy) { + delete copy; + } + return; + } + } + + if (rdma_post_read(id, reinterpret_cast(tensor_key), buffer->data(), + buffer->size(), mr, IBV_SEND_SIGNALED, remote_mr.addr(), + remote_mr.rkey())) { + done(errors::Unavailable(strerror(errno), ": ", "rdma_post_read failed")); + { + mutex_lock l(callback_mu_); + auto iter = tensor_callbacks_.find(tensor_key); + if (iter != std::end(tensor_callbacks_)) { + tensor_callbacks_.erase(iter); + } + } + if (copy) { + delete copy; } - CHECK(checksum == remote_mr.checksum()) - << "Checksum mismatch: " << checksum << "!=" << remote_mr.checksum(); -#endif } - done(Status::OK()); } Status GdrMemoryManager::CreateEndpoint(const string& host, const string& port, @@ -663,7 +551,7 @@ Status GdrMemoryManager::CreateEndpoint(const string& host, const string& port, ibv_qp_init_attr init_attr = {}; init_attr.qp_type = IBV_QPT_RC; init_attr.cap.max_recv_wr = 1; - init_attr.cap.max_send_wr = 32; + init_attr.cap.max_send_wr = 1024; init_attr.cap.max_recv_sge = 1; init_attr.cap.max_send_sge = 1; @@ -687,8 +575,8 @@ Status GdrMemoryManager::CreateEndpoint(const string& host, const string& port, return Status::OK(); } -ibv_mr* GdrMemoryManager::FindMemoryRegion(void* addr, size_t length) { - if (length == 0) return nullptr; +ibv_mr* GdrMemoryManager::FindMemoryRegion(const Tensor* tensor) { + const void* addr = DMAHelper::buffer(tensor)->data(); mutex_lock l(alloc_mu_); auto iter = std::upper_bound(mrs_.begin(), mrs_.end(), addr, &Comparator); if (iter == std::end(mrs_) || iter->get()->addr > addr) { diff --git a/tensorflow/contrib/gdr/gdr_rendezvous_mgr.cc b/tensorflow/contrib/gdr/gdr_rendezvous_mgr.cc index fbccbead03..5f8c300155 100644 --- a/tensorflow/contrib/gdr/gdr_rendezvous_mgr.cc +++ b/tensorflow/contrib/gdr/gdr_rendezvous_mgr.cc @@ -58,11 +58,9 @@ class GdrRecvTensorCall : public BaseRecvTensorCall { resp_.InitAlloc(dst_device_, recv_args_.alloc_attrs); StatusCallback cb = [this, recv_done](const Status& s) { bool dma_ok = resp_.metadata().has_transport_options(); - if (s.ok() && tensor().TotalBytes() > 0 && (!is_dead()) && dma_ok) { + if (s.ok() && tensor().TotalBytes() > 1024 && (!is_dead()) && dma_ok) { auto transport_options = resp_.metadata().transport_options(); - const bool on_host = - (dst_device_->tensorflow_gpu_device_info() == nullptr) || - recv_args_.alloc_attrs.on_host(); + const bool on_host = recv_args_.alloc_attrs.on_host(); remote_memory_manager_->TensorFromTransportOptions( const_cast(&tensor()), transport_options, dst_device_, recv_args_.device_context, on_host, @@ -70,9 +68,6 @@ class GdrRecvTensorCall : public BaseRecvTensorCall { if (!s.ok()) { mutex_lock l(mu_); status_.Update(s); - LOG(ERROR) << "Cannot find pinned memory region from allocator " - << dst_device_->GetAllocator(recv_args_.alloc_attrs) - ->Name(); } recv_done(); }); diff --git a/tensorflow/contrib/gdr/gdr_server_lib.cc b/tensorflow/contrib/gdr/gdr_server_lib.cc index b3f48ec1dd..dc0d5d548b 100644 --- a/tensorflow/contrib/gdr/gdr_server_lib.cc +++ b/tensorflow/contrib/gdr/gdr_server_lib.cc @@ -74,9 +74,8 @@ Status GdrServer::Start() { } Status GdrServer::Stop() { - TF_RETURN_IF_ERROR(GrpcServer::Stop()); remote_memory_manager_->Stop(); - return Status::OK(); + return GrpcServer::Stop(); } Status GdrServer::Join() { diff --git a/tensorflow/contrib/gdr/gdr_worker.cc b/tensorflow/contrib/gdr/gdr_worker.cc index 867cb83f42..016e5ea27b 100644 --- a/tensorflow/contrib/gdr/gdr_worker.cc +++ b/tensorflow/contrib/gdr/gdr_worker.cc @@ -18,9 +18,6 @@ limitations under the License. #include "tensorflow/core/common_runtime/device.h" #include "tensorflow/core/common_runtime/device_mgr.h" #include "tensorflow/core/common_runtime/dma_helper.h" -#if GOOGLE_CUDA -#include "tensorflow/core/common_runtime/gpu/gpu_util.h" -#endif // GOOGLE_CUDA #include "tensorflow/core/common_runtime/process_util.h" #include "tensorflow/core/common_runtime/step_stats_collector.h" #include "tensorflow/core/distributed_runtime/graph_mgr.h" @@ -78,7 +75,7 @@ void GdrWorker::GrpcRecvTensorAsync(CallOptions* opts, const bool dma_ok = request->dma_ok(); env_->rendezvous_mgr->RecvLocalAsync( step_id, parsed, - [this, opts, response, done, src_dev, dma_ok]( + [this, opts, response, done, src_dev, request, dma_ok]( const Status& status, const Rendezvous::Args& send_args, const Rendezvous::Args&, const Tensor& val, const bool is_dead) { opts->ClearCancelCallback(); @@ -89,10 +86,8 @@ void GdrWorker::GrpcRecvTensorAsync(CallOptions* opts, // 3) the tensor has the on_host allocation attribute, // i.e. it's in CPU RAM *independent of its assigned // device type*. - const bool on_host = - (src_dev->tensorflow_gpu_device_info() == nullptr) || - send_args.alloc_attrs.on_host(); - if (val.TotalBytes() > 0 && (!is_dead) && + const bool on_host = send_args.alloc_attrs.on_host(); + if (val.TotalBytes() > 1024 && (!is_dead) && DMAHelper::CanUseDMA(&val) && dma_ok) { // DMA cases. RecvTensorResponse* proto = new RecvTensorResponse; @@ -117,8 +112,7 @@ void GdrWorker::GrpcRecvTensorAsync(CallOptions* opts, } else { // Non-DMA cases. if (src_dev->tensorflow_gpu_device_info() && (!on_host)) { -#if GOOGLE_CUDA - const DeviceContext* send_dev_context = send_args.device_context; + DeviceContext* send_dev_context = send_args.device_context; AllocatorAttributes alloc_attrs; alloc_attrs.set_gpu_compatible(true); alloc_attrs.set_on_host(true); @@ -127,7 +121,8 @@ void GdrWorker::GrpcRecvTensorAsync(CallOptions* opts, CHECK(send_dev_context) << "send dev name: " << src_dev->name() << " gpu_info: " << src_dev->tensorflow_gpu_device_info(); - // "val" is on a GPU. Uses GPUUtil to fill the response proto. + // "val" is on an accelerator device. Uses the device_context to + // fill the copy on host. StatusCallback copy_ready = [response, done, copy, is_dead](const Status& s) { // The value is now ready to be returned on the wire. @@ -136,11 +131,8 @@ void GdrWorker::GrpcRecvTensorAsync(CallOptions* opts, delete copy; }; - GPUUtil::CopyGPUTensorToCPU(src_dev, send_dev_context, &val, copy, - copy_ready); -#else - done(errors::Internal("No GPU device in process")); -#endif // GOOGLE_CUDA + send_dev_context->CopyDeviceTensorToCPU( + &val, request->rendezvous_key(), src_dev, copy, copy_ready); } else { grpc::EncodeTensorToByteBuffer(is_dead, val, response); done(Status::OK()); -- GitLab From 0a109261334273042a63a4feea97c791ac59a2e5 Mon Sep 17 00:00:00 2001 From: AG Ramesh Date: Wed, 5 Dec 2018 22:05:26 -0800 Subject: [PATCH 116/873] Clang format fixes --- tensorflow/core/graph/mkl_layout_pass.cc | 4 ++-- tensorflow/core/kernels/mkl_conv_ops.cc | 6 +++--- tensorflow/core/kernels/mkl_fused_ops_test.cc | 4 ++-- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/tensorflow/core/graph/mkl_layout_pass.cc b/tensorflow/core/graph/mkl_layout_pass.cc index f597b3c76c..8d2f142532 100644 --- a/tensorflow/core/graph/mkl_layout_pass.cc +++ b/tensorflow/core/graph/mkl_layout_pass.cc @@ -1584,9 +1584,9 @@ int MklLayoutRewritePass::SetUpContiguousInputs( for (const Edge* e : filter_node->out_edges()) { if ((e->dst()->type_string() == csinfo_.mkl_conv2d || e->dst()->type_string() == csinfo_.mkl_pad_with_conv2d || - e->dst()->type_string() == csinfo_.mkl_conv2d_with_bias || + e->dst()->type_string() == csinfo_.mkl_conv2d_with_bias || e->dst()->type_string() == csinfo_.mkl_fused_conv2d) && - e->dst_input() == kConv2DFilterInputSlotIdx + e->dst_input() == kConv2DFilterInputSlotIdx /* filter is 2nd input of Conv2D and _MklConv2D. */) { if (conv2d_node != nullptr) { VLOG(1) << "MklLayoutRewritePass: unusual case of same filter" diff --git a/tensorflow/core/kernels/mkl_conv_ops.cc b/tensorflow/core/kernels/mkl_conv_ops.cc index eb17c29bb0..4eea1711e5 100644 --- a/tensorflow/core/kernels/mkl_conv_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_ops.cc @@ -1299,11 +1299,11 @@ class MklConvOp : public OpKernel { template class MklFusedConvOp : public MklConvOp { + Ttemp_output, int32, false, false> { public: explicit MklFusedConvOp(OpKernelConstruction* context) - : MklConvOp( - context) { + : MklConvOp(context) { // Since we came here through the registration of _MklFusedConv2D then get // all information from 'fused_ops' and 'num_args' std::vector fused_ops; diff --git a/tensorflow/core/kernels/mkl_fused_ops_test.cc b/tensorflow/core/kernels/mkl_fused_ops_test.cc index ce4c1aec04..c9416e154b 100644 --- a/tensorflow/core/kernels/mkl_fused_ops_test.cc +++ b/tensorflow/core/kernels/mkl_fused_ops_test.cc @@ -339,8 +339,8 @@ class FusedPadConvOpTest : public OpsTestBase { // Compare output to expected results const Tensor& first = *GetOutput(0); const Tensor& second = *GetOutput(2); - ConvMklToTF conv_comp; - conv_comp.ConvertAndCompare(dtype, first, second, expected); + ConvMklToTF conv_comp; + conv_comp.ConvertAndCompare(dtype, first, second, expected); } }; -- GitLab From ed17f60a544e31ba79a649d1decafd29887fd6d9 Mon Sep 17 00:00:00 2001 From: manhyuk Date: Thu, 6 Dec 2018 15:47:30 +0900 Subject: [PATCH 117/873] fix typo --- tensorflow/compiler/xla/shape_util.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/compiler/xla/shape_util.h b/tensorflow/compiler/xla/shape_util.h index 84a27f662a..30bf1dfb03 100644 --- a/tensorflow/compiler/xla/shape_util.h +++ b/tensorflow/compiler/xla/shape_util.h @@ -266,7 +266,7 @@ class ShapeUtil { } // Returns the higher-precision element type if a and b are both floating - // point types; otherwise, checks that that they have the same element type + // point types; otherwise, checks that they have the same element type // and returns it. static PrimitiveType HigherPrecisionElementType(const Shape& a, const Shape& b) { -- GitLab From aec214bc3870ecc0f5f831c2523a1609d8c12871 Mon Sep 17 00:00:00 2001 From: manhyuk Date: Thu, 6 Dec 2018 15:48:47 +0900 Subject: [PATCH 118/873] fix typo --- tensorflow/python/kernel_tests/cond_v2_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/kernel_tests/cond_v2_test.py b/tensorflow/python/kernel_tests/cond_v2_test.py index 843d007cc8..502d504bfe 100644 --- a/tensorflow/python/kernel_tests/cond_v2_test.py +++ b/tensorflow/python/kernel_tests/cond_v2_test.py @@ -1036,7 +1036,7 @@ class CondV2ColocationGroupAndDeviceTest(test.TestCase): self.evaluate(cond_v2.cond_v2(constant_op.constant(True), fn2, fn2))) else: - self.skipTest("Test requrires a GPU to check GPU device placement.") + self.skipTest("Test requires a GPU to check GPU device placement.") def testDeviceInAndOutOfCond(self): with ops.Graph().as_default() as g: -- GitLab From d14f39054f44d0a641db3a0997fe5d720918cd07 Mon Sep 17 00:00:00 2001 From: manhyuk Date: Thu, 6 Dec 2018 15:50:58 +0900 Subject: [PATCH 119/873] fix typo --- tensorflow/contrib/cmake/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/cmake/README.md b/tensorflow/contrib/cmake/README.md index df8b48dfc4..b2badc5785 100644 --- a/tensorflow/contrib/cmake/README.md +++ b/tensorflow/contrib/cmake/README.md @@ -157,7 +157,7 @@ suitable interface for project configuration and dependency setting. press `Finish`. Wait for a moment, the default project dependecy would automatically generate. 6. There are a few options that you can customize your own build. **The setting - here is crucial for a sucessful build, please check all items carefully.** + here is crucial for a successful build, please check all items carefully.** * `tensorflow_BUILD_ALL_KERNELS` should alway be `on` * `tensorflow_BUILD_CC_EXAMPLE` is default to be `on`. This can help you -- GitLab From 2b13b2f52bee1317a7cb6320e269d32afcbd7e97 Mon Sep 17 00:00:00 2001 From: vanderliang Date: Wed, 5 Dec 2018 16:39:56 +0800 Subject: [PATCH 120/873] Fix ClusterSpec.as_dict with only chief and ps If the worker num is zero, continue the loop. --- tensorflow/python/training/server_lib.py | 3 +++ tensorflow/python/training/server_lib_test.py | 22 +++++++++++++++++++ 2 files changed, 25 insertions(+) diff --git a/tensorflow/python/training/server_lib.py b/tensorflow/python/training/server_lib.py index 46543413e4..bb6ad0e599 100644 --- a/tensorflow/python/training/server_lib.py +++ b/tensorflow/python/training/server_lib.py @@ -332,6 +332,9 @@ class ClusterSpec(object): ret = {} for job in self.jobs: task_indices = self.task_indices(job) + if len(task_indices) == 0: + ret[job] = {} + continue if max(task_indices) + 1 == len(task_indices): # Return a list because the task indices are dense. This # matches the behavior of `as_dict()` before support for diff --git a/tensorflow/python/training/server_lib_test.py b/tensorflow/python/training/server_lib_test.py index cf995707fc..653235a5ca 100644 --- a/tensorflow/python/training/server_lib_test.py +++ b/tensorflow/python/training/server_lib_test.py @@ -456,6 +456,28 @@ class ClusterSpecTest(test.TestCase): expected_proto, server_lib.ClusterSpec(cluster_spec.as_dict()).as_cluster_def()) + def testProtoDictDefEquivalencesWithZeroWorker(self): + cluster_spec = server_lib.ClusterSpec({ + "ps": ["ps0:2222", "ps1:2222"], + "worker": [] + }) + + expected_proto = """ + job { name: 'ps' tasks { key: 0 value: 'ps0:2222' } + tasks { key: 1 value: 'ps1:2222' } } + job { name: 'worker' } + """ + + self.assertProtoEquals(expected_proto, cluster_spec.as_cluster_def()) + self.assertProtoEquals( + expected_proto, server_lib.ClusterSpec(cluster_spec).as_cluster_def()) + self.assertProtoEquals( + expected_proto, + server_lib.ClusterSpec(cluster_spec.as_cluster_def()).as_cluster_def()) + self.assertProtoEquals( + expected_proto, + server_lib.ClusterSpec(cluster_spec.as_dict()).as_cluster_def()) + def testClusterSpecAccessors(self): original_dict = { "ps": ["ps0:2222", "ps1:2222"], -- GitLab From 2295e1b7320328ff5659a75613c457d8a6e7d1ac Mon Sep 17 00:00:00 2001 From: Bairen Yi Date: Thu, 6 Dec 2018 08:34:36 +0000 Subject: [PATCH 121/873] Cleanup unnecessary GOOGLE_CUDA and tf_cuda_library --- tensorflow/contrib/gdr/BUILD | 2 +- tensorflow/contrib/gdr/gdr_memory_manager.cc | 10 ++++------ 2 files changed, 5 insertions(+), 7 deletions(-) diff --git a/tensorflow/contrib/gdr/BUILD b/tensorflow/contrib/gdr/BUILD index 7ec3c5ff5d..704be917b3 100644 --- a/tensorflow/contrib/gdr/BUILD +++ b/tensorflow/contrib/gdr/BUILD @@ -37,7 +37,7 @@ tf_proto_library_cc( ], ) -tf_cuda_library( +cc_library( name = "gdr_memory_manager", srcs = ["gdr_memory_manager.cc"], hdrs = ["gdr_memory_manager.h"], diff --git a/tensorflow/contrib/gdr/gdr_memory_manager.cc b/tensorflow/contrib/gdr/gdr_memory_manager.cc index 69bbab1c39..d677592d9a 100644 --- a/tensorflow/contrib/gdr/gdr_memory_manager.cc +++ b/tensorflow/contrib/gdr/gdr_memory_manager.cc @@ -30,12 +30,10 @@ limitations under the License. #include "tensorflow/contrib/gdr/gdr.pb.h" #include "tensorflow/core/common_runtime/device.h" #include "tensorflow/core/common_runtime/dma_helper.h" -#include "tensorflow/core/common_runtime/process_state.h" -#include "tensorflow/core/lib/random/random.h" -#if GOOGLE_CUDA #include "tensorflow/core/common_runtime/gpu/gpu_process_state.h" -#endif // GOOGLE_CUDA +#include "tensorflow/core/common_runtime/process_state.h" #include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/random/random.h" #include "tensorflow/core/platform/macros.h" #include "tensorflow/core/platform/mutex.h" #include "tensorflow/core/platform/numa.h" @@ -248,13 +246,13 @@ Status GdrMemoryManager::Init() { ProcessState::singleton()->AddCPUFreeVisitor(free_visitor); LOG(INFO) << "Instrumenting CPU allocator(s)"; -#if GOOGLE_CUDA for (int numa_idx = 0; numa_idx < port::NUMANumNodes(); ++numa_idx) { GPUProcessState::singleton()->AddCUDAHostAllocVisitor(numa_idx, alloc_visitor); GPUProcessState::singleton()->AddCUDAHostFreeVisitor(numa_idx, free_visitor); } + if (IsGDRAvailable()) { SubAllocator::Visitor cuda_alloc_visitor = [this](void* ptr, int gpu_id, size_t num_bytes) { @@ -265,7 +263,7 @@ Status GdrMemoryManager::Init() { cuda_alloc_visitor); LOG(INFO) << "Instrumenting GPU allocator for NUMA " << numa_node_; } -#endif // GOOGLE_CUDA + return Status::OK(); } -- GitLab From 7667f9747c000fe5c29f4728b9b134ea2bb5dfd8 Mon Sep 17 00:00:00 2001 From: lxl910915 Date: Thu, 6 Dec 2018 18:32:06 +0800 Subject: [PATCH 122/873] #21745: set timeout for closing worker session --- tensorflow/core/distributed_runtime/master_session.cc | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tensorflow/core/distributed_runtime/master_session.cc b/tensorflow/core/distributed_runtime/master_session.cc index bc8ba6e47d..59bb18e7eb 100644 --- a/tensorflow/core/distributed_runtime/master_session.cc +++ b/tensorflow/core/distributed_runtime/master_session.cc @@ -1352,7 +1352,9 @@ Status MasterSession::DeleteWorkerSessions() { &workers[i].call_opts, &workers[i].request, &workers[i].response, cb); } - done.Wait(); + if (!done.WaitFor(std::chrono::milliseconds(10000))) { + LOG(WARNING) << "Timeout for closing worker session"; + } for (size_t i = 0; i < workers.size(); ++i) { status.Update(workers[i].status); } -- GitLab From 636207ade13e6f9c0b110b21497cc6ed040ea4d6 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 6 Dec 2018 08:32:41 -0800 Subject: [PATCH 123/873] Fix wrongly exported dtypes.uint16 to dtypes.int16 PiperOrigin-RevId: 224342753 --- tensorflow/python/framework/dtypes.py | 2 +- tensorflow/tools/api/golden/v1/tensorflow.dtypes.pbtxt | 4 ++++ tensorflow/tools/api/golden/v2/tensorflow.dtypes.pbtxt | 4 ++++ 3 files changed, 9 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/framework/dtypes.py b/tensorflow/python/framework/dtypes.py index f7a12d27df..9a4fe4e93b 100644 --- a/tensorflow/python/framework/dtypes.py +++ b/tensorflow/python/framework/dtypes.py @@ -347,7 +347,7 @@ tf_export("dtypes.uint32", "uint32").export_constant(__name__, "uint32") uint64 = DType(types_pb2.DT_UINT64) tf_export("dtypes.uint64", "uint64").export_constant(__name__, "uint64") int16 = DType(types_pb2.DT_INT16) -tf_export("dtypes.uint16", "int16").export_constant(__name__, "int16") +tf_export("dtypes.int16", "int16").export_constant(__name__, "int16") int8 = DType(types_pb2.DT_INT8) tf_export("dtypes.int8", "int8").export_constant(__name__, "int8") string = DType(types_pb2.DT_STRING) diff --git a/tensorflow/tools/api/golden/v1/tensorflow.dtypes.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.dtypes.pbtxt index 848fc303aa..01b870a816 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.dtypes.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.dtypes.pbtxt @@ -44,6 +44,10 @@ tf_module { name: "half" mtype: "" } + member { + name: "int16" + mtype: "" + } member { name: "int32" mtype: "" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.dtypes.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.dtypes.pbtxt index 848fc303aa..01b870a816 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.dtypes.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.dtypes.pbtxt @@ -44,6 +44,10 @@ tf_module { name: "half" mtype: "" } + member { + name: "int16" + mtype: "" + } member { name: "int32" mtype: "" -- GitLab From be4b5c34ecc0a99929b590cc6f956a4dc4eece55 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 6 Dec 2018 08:37:29 -0800 Subject: [PATCH 124/873] Update Eigen to https://bitbucket.org/eigen/eigen/commits/729d33d11c81fd023834a2d61f0f280ba9da48c8 PiperOrigin-RevId: 224343375 --- tensorflow/workspace.bzl | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index ad3cecd3a1..e6b4a89e3b 100755 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -134,11 +134,11 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""): tf_http_archive( name = "eigen_archive", build_file = clean_dep("//third_party:eigen.BUILD"), - sha256 = "37a483ec219c43219b6e0fc07e799277a4a36abb2b9f4162cfcd256aa211eae8", - strip_prefix = "eigen-eigen-2e50f4a5542a", + sha256 = "aae7a680d141c978301dfae2c7945c06039f65849fcf64269595a9cdbba82638", + strip_prefix = "eigen-eigen-729d33d11c81", urls = [ - "https://mirror.bazel.build/bitbucket.org/eigen/eigen/get/2e50f4a5542a.tar.gz", - "https://bitbucket.org/eigen/eigen/get/2e50f4a5542a.tar.gz", + "https://mirror.bazel.build/bitbucket.org/eigen/eigen/get/729d33d11c81.tar.gz", + "https://bitbucket.org/eigen/eigen/get/729d33d11c81.tar.gz", ], ) -- GitLab From 0334b504c6c7179a322a0293e09616201cabc805 Mon Sep 17 00:00:00 2001 From: Karim Nosir Date: Thu, 6 Dec 2018 09:19:24 -0800 Subject: [PATCH 125/873] Fix test failure. PiperOrigin-RevId: 224350277 --- tensorflow/lite/tools/benchmark/benchmark_test.cc | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tensorflow/lite/tools/benchmark/benchmark_test.cc b/tensorflow/lite/tools/benchmark/benchmark_test.cc index 0cf66c6e4d..a4f830122f 100644 --- a/tensorflow/lite/tools/benchmark/benchmark_test.cc +++ b/tensorflow/lite/tools/benchmark/benchmark_test.cc @@ -76,10 +76,10 @@ TEST(BenchmarkTest, ParametersArePopulatedWhenInputShapeIsNotSpecified) { ASSERT_GE(inputs.size(), 1); auto input_tensor = interpreter->tensor(inputs[0]); - std::vector input_bytes; + std::vector input_bytes; input_bytes.reserve(input_tensor->bytes); for (size_t i = 0; i < input_tensor->bytes; i++) { - input_bytes.push_back(input_tensor->data.b[i]); + input_bytes.push_back(input_tensor->data.raw_const[i]); } benchmark.Prepare(); @@ -87,7 +87,7 @@ TEST(BenchmarkTest, ParametersArePopulatedWhenInputShapeIsNotSpecified) { EXPECT_EQ(input_bytes.size(), input_tensor->bytes); bool is_same = true; for (size_t i = 0; i < input_tensor->bytes; i++) { - if (input_bytes[i] != input_tensor->data.b[i]) { + if (input_bytes[i] != input_tensor->data.raw_const[i]) { is_same = false; break; } -- GitLab From f83053f48c1d262bd8dd61b87890eb34d550d8f7 Mon Sep 17 00:00:00 2001 From: Trevor Morris Date: Thu, 6 Dec 2018 09:35:15 -0800 Subject: [PATCH 126/873] Use return value of TensorShapeUtils::MakeShape --- .../contrib/tensorrt/convert/convert_nodes.cc | 20 +++++++++++-------- 1 file changed, 12 insertions(+), 8 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index ac0e2f684c..777a80bbc4 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -120,11 +120,13 @@ inline nvinfer1::Dims TensorShapeToTrtDims(const TensorShapeType& shape, return trt_dims; } -inline nvinfer1::Dims TensorShapeArrayToTrtDims(const std::vector& shape, - bool ignore_first_dim = false) { +Status TensorShapeArrayToTrtDims(const std::vector& shape, + nvinfer1::Dims* out, + bool ignore_first_dim = false) { PartialTensorShape tensor_shape; - TensorShapeUtils::MakeShape(shape, &tensor_shape); - return TensorShapeToTrtDims(tensor_shape, ignore_first_dim); + TF_RETURN_IF_ERROR(TensorShapeUtils::MakeShape(shape, &tensor_shape)); + *out = TensorShapeToTrtDims(tensor_shape, ignore_first_dim); + return tensorflow::Status::OK(); } void GetOutputProperties(const grappler::GraphProperties& graph_properties, @@ -1937,8 +1939,9 @@ tensorflow::Status ConvertExpandDims(OpConverterParams* params) { // ExpandDims: Insert new dim of size 1. input_dims.insert(input_dims.begin() + axis, 1); // Reshape tensor. - nvinfer1::Dims new_dims = - TensorShapeArrayToTrtDims(input_dims, /*ignore_first_dim=*/true); + nvinfer1::Dims new_dims; + TF_RETURN_IF_ERROR(TensorShapeArrayToTrtDims(input_dims, &new_dims, + /*ignore_first_dim=*/true)); const nvinfer1::ITensor* output_tensor = nullptr; TF_RETURN_IF_ERROR(params->converter->PrepareTensorForShape( input_tensor, new_dims, &output_tensor)); @@ -2002,8 +2005,9 @@ tensorflow::Status ConvertSqueeze(OpConverterParams* params) { input_dims.erase(std::remove(input_dims.begin(), input_dims.end(), 0), input_dims.end()); // Reshape tensor. - nvinfer1::Dims new_dims = - TensorShapeArrayToTrtDims(input_dims, /*ignore_first_dim=*/true); + nvinfer1::Dims new_dims; + TF_RETURN_IF_ERROR(TensorShapeArrayToTrtDims(input_dims, &new_dims, + /*ignore_first_dim=*/true)); const nvinfer1::ITensor* output_tensor = nullptr; TF_RETURN_IF_ERROR(params->converter->PrepareTensorForShape( input_tensor, new_dims, &output_tensor)); -- GitLab From 322254f7e00bb739ded8611a925cca450f214ec2 Mon Sep 17 00:00:00 2001 From: Karim Nosir Date: Thu, 6 Dec 2018 09:34:36 -0800 Subject: [PATCH 127/873] Add explicit for one param constructor. PiperOrigin-RevId: 224352578 --- tensorflow/lite/kernels/resize_bilinear_test.cc | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/lite/kernels/resize_bilinear_test.cc b/tensorflow/lite/kernels/resize_bilinear_test.cc index 530bb32b94..d3f4837a28 100644 --- a/tensorflow/lite/kernels/resize_bilinear_test.cc +++ b/tensorflow/lite/kernels/resize_bilinear_test.cc @@ -26,8 +26,8 @@ using uint8 = std::uint8_t; class ResizeBilinearOpModel : public SingleOpModel { public: - ResizeBilinearOpModel(const TensorData& input, - std::initializer_list size_data = {}) { + explicit ResizeBilinearOpModel(const TensorData& input, + std::initializer_list size_data = {}) { bool const_size = size_data.size() != 0; input_ = AddInput(input); if (const_size) { -- GitLab From 48662695e0cabea065de19958132789d931ac78c Mon Sep 17 00:00:00 2001 From: Trevor Morris Date: Wed, 5 Dec 2018 11:22:57 -0800 Subject: [PATCH 128/873] Update tensorflow/contrib/tensorrt/convert/convert_graph.cc Co-Authored-By: Trevor Morris --- tensorflow/contrib/tensorrt/convert/convert_graph.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index 3e599b9174..21c69326df 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -588,7 +588,7 @@ tensorflow::Status CreateTRTNode(const std::vector& infos, int pos, // We don't support segments with no inputs. Fall back to native TF here to // avoid crash later. Constant folding should've folded the ops that make up // these segments. - if (inputs.size() == 0) { + if (inputs.empty()) { return tensorflow::errors::Internal("Segment has no inputs (possible " "constfold failure)"); } -- GitLab From 4f6613441e7f13abd93568445eb0b4ae69828632 Mon Sep 17 00:00:00 2001 From: Scott Zhu Date: Thu, 6 Dec 2018 10:16:45 -0800 Subject: [PATCH 129/873] Update model save/load for unified_lstm layer. The bias weights during load is changed a bit. The original approach is to split the weights into half and give them to recurrent bias and input gate bias. In the new approach, since it is unknown that whether the newly constructed layer will be running on CPU or GPU, if we still split the weight into half, then the input_gate_bias value is lost since canonical LSTM only use recurrent bias. The new approach is to give recurrent_bias 100% of the sum, and 0% to input_gate_bias. PiperOrigin-RevId: 224360388 --- tensorflow/python/keras/engine/saving.py | 58 +++++++++++++++++ tensorflow/python/keras/engine/saving_test.py | 64 +++++++++++++++++++ tensorflow/python/keras/layers/__init__.py | 1 + 3 files changed, 123 insertions(+) diff --git a/tensorflow/python/keras/engine/saving.py b/tensorflow/python/keras/engine/saving.py index 54d9e32fb2..15ba5f78d9 100644 --- a/tensorflow/python/keras/engine/saving.py +++ b/tensorflow/python/keras/engine/saving.py @@ -551,10 +551,68 @@ def preprocess_weights_for_loading(layer, if layer.__class__.__name__ == 'ConvLSTM2D': weights[1] = np.transpose(weights[1], (3, 2, 0, 1)) + weights = _convert_unified_lstm_weights(layer, weights) + # convert CuDNN layers return _convert_rnn_weights(layer, weights) +def _convert_unified_lstm_weights(layer, weights): + """Converts weights for Unified LSTM layer. + + The input weights suppose to have 2, 3 or 4 items. + 1. kernel. (i, f, c, o gates concat among axis 1) + 2. recurrent_kernel. (i, f, c, o concat among axis 1) + 3. recurrent_bias. (optional, only available when use bias) + 4. input_bias (optional, only available when use bias and cudnn). + Kernel and recurrent_kernel does not need any conversion. During load(), + since the layer could be built with the parameter that does not support the + defun approach, it is possible that cudnn_bias variable is not created, or + even created but not used during actual run. Because of that, we sum up the + value of two biases, and give it to recurrent_bias only. Mathematically, the + LSTM is calculated as following formula: + + i_t = sigmoid(w_i * x_t + r_i * h_(t-1) + b_wi + b_ri) + f_t = sigmoid(w_f * x_t + r_f * h_(t-1) + b_wf + b_rf) + o_t = sigmoid(w_o * x_t + r_o * h_(t-1) + b_wo + b_ro) + c'_t = tanh(w_c * x_t + r_c * h_(t-1) + b_wc + b_rc) + c_t = f_t . c_(t-1) + i_t . c'_t + h_t = o_t . tanh(c_t) + + Note that b_w{x} is the input_bias, and b_r{x} is the recurrent_bias. + Since it is a linear add, it is fine to give b_r{x} 100% and b_w{x} 0%, as + long as the sum are the same. + + Args: + layer: The keras layer that will be loaded with weights. + weights: the list of numpy arrays which hold the weights to be loaded. + + Returns: + weights: the processed list of numpy arrays. + """ + if layer.__class__.__name__ == 'UnifiedLSTM': + if len(weights) not in [3, 4]: + # Only handles the bias conversion in this function, in the case when + # bias is not used or weights in unexpected length, do nothing and return. + return weights + + if len(weights) == 3: + recurrent_bias = weights[2] + else: + # Add all the bias value to recurrent_bias + recurrent_bias = weights[2] + weights[3] + + if len(layer.weights) == 3: + weights = weights[:2] + [recurrent_bias] + elif len(layer.weights) == 4: + # Create a zero filled input_bias, since all the weights have given + # to recurrent bias. + input_bias = np.zeros_like(recurrent_bias) + weights = weights[:2] + [recurrent_bias, input_bias] + + return weights + + def _convert_rnn_weights(layer, weights): """Converts weights for RNN layers between native and CuDNN format. diff --git a/tensorflow/python/keras/engine/saving_test.py b/tensorflow/python/keras/engine/saving_test.py index 6d9d9a2fca..8fcefce748 100644 --- a/tensorflow/python/keras/engine/saving_test.py +++ b/tensorflow/python/keras/engine/saving_test.py @@ -221,6 +221,70 @@ class TestWeightSavingAndLoading(test.TestCase, parameterized.TestCase): for (x, y) in zip(weights1, weights2) ] + @parameterized.named_parameters( + # test_name, use_bias, bias_initializer, activation + ('normal', True, 'zeros', 'tanh'), + ('no_bias', False, 'zeros', 'tanh'), + # TODO(scottzhu): Reenable this test case when the approach is decided. + # ('random_bias', True, 'random_uniform', 'tanh'), + ('no_cudnn_bias', True, 'zeros', 'relu') + ) + def test_process_weights_for_loading_unified_lstm( + self, use_bias, bias_initializer, activation): + if h5py is None: + return + + temp_dir = self.get_temp_dir() + self.addCleanup(shutil.rmtree, temp_dir) + h5_path = os.path.join(temp_dir, 'test.h5') + + batch = 10 + timestep = 3 + input_dim = 5 + units = 2 + + x = np.random.random((batch, timestep, input_dim)) + + def build_model(): + inputs = keras.layers.Input( + shape=[timestep, input_dim], dtype=dtypes.float32) + layer = keras.layers.UnifiedLSTM( + units, + activation=activation, + use_bias=use_bias, + bias_initializer=bias_initializer) + output = layer(inputs) + return keras.models.Model(inputs, output), layer + + with self.cached_session(): + model, layer = build_model() + y_ref = model.predict(x) + model.save_weights(h5_path) + + cloned_model, new_layer = build_model() + cloned_model.load_weights(h5_path) + y = cloned_model.predict(x) + + self.assertAllClose(y, y_ref) + + # Test the individual layer weights. + weights1 = layer.get_weights() + weights2 = new_layer.get_weights() + self.assertLen(weights1, len(weights2)) + # kernel and current kernel should be the same. + self.assertAllClose(weights1[:2], weights2[:2]) + + if len(weights2) >= 3: + # Test recurrent bias + expected_recurrent_bias = weights1[2] + if len(weights1) == 4: + expected_recurrent_bias += weights1[3] + self.assertAllClose(weights2[2], expected_recurrent_bias) + + if len(weights2) == 4: + # Test recovered input_gate_bias to be always zero + self.assertAllClose(weights2[3], np.zeros_like(weights1[3])) + def test_sequential_weight_loading(self): if h5py is None: return diff --git a/tensorflow/python/keras/layers/__init__.py b/tensorflow/python/keras/layers/__init__.py index 49990b6bf4..df7571e5d5 100644 --- a/tensorflow/python/keras/layers/__init__.py +++ b/tensorflow/python/keras/layers/__init__.py @@ -149,6 +149,7 @@ from tensorflow.python.keras.layers.recurrent import PeepholeLSTMCell from tensorflow.python.keras.layers.recurrent import SimpleRNN from tensorflow.python.keras.layers.recurrent import GRU from tensorflow.python.keras.layers.recurrent import LSTM +from tensorflow.python.keras.layers.recurrent import UnifiedLSTM # Convolutional-recurrent layers. from tensorflow.python.keras.layers.convolutional_recurrent import ConvLSTM2D -- GitLab From e576acf5dbd7b800d3b6aa4de4b69952a9e2c0fb Mon Sep 17 00:00:00 2001 From: Brennan Saeta Date: Thu, 6 Dec 2018 10:28:10 -0800 Subject: [PATCH 130/873] Internal-only change. PiperOrigin-RevId: 224362520 --- .../distribute/cluster_resolver/tpu_cluster_resolver.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver.py b/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver.py index fe8e09f561..1fd674c8a4 100644 --- a/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver.py +++ b/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver.py @@ -197,13 +197,14 @@ class TPUClusterResolver(ClusterResolver): elif tpu == 'local' or not tpu: # Google environment, where the TPU is attached to the host. self._environment = 'google' - elif tpu.startswith('/bns'): + elif tpu.startswith('/bns') or tpu.startswith('uptc://'): # Google environment, where we reach the TPU through BNS. self._environment = 'google' # If TPU is in the Google environment or exists locally, we don't use any # RPC layer. - if tpu.startswith('/bns') or tpu == 'local' or not tpu: + if tpu.startswith('/bns') or tpu.startswith( + 'uptc://') or tpu == 'local' or not tpu: self.rpc_layer = None else: self.rpc_layer = 'grpc' -- GitLab From 05b2aaacf1f62ad08d4d37c6fccf009482c3e086 Mon Sep 17 00:00:00 2001 From: Akshay Modi Date: Thu, 6 Dec 2018 10:36:56 -0800 Subject: [PATCH 131/873] Automated rollback of commit 8f6c5d3252cf1fa7c97c46c93b55803660487136 PiperOrigin-RevId: 224364244 --- tensorflow/python/eager/pywrap_tensor.cc | 8 ++++++++ tensorflow/python/eager/tensor_test.py | 12 ++++++++++++ 2 files changed, 20 insertions(+) diff --git a/tensorflow/python/eager/pywrap_tensor.cc b/tensorflow/python/eager/pywrap_tensor.cc index 0d0f70d543..30a93fb0e4 100644 --- a/tensorflow/python/eager/pywrap_tensor.cc +++ b/tensorflow/python/eager/pywrap_tensor.cc @@ -220,6 +220,14 @@ TFE_TensorHandle* ConvertToEagerTensor(PyObject* value, PyObject* dtype) { return nullptr; } } + tensorflow::Safe_PyObjectPtr value_decrefer; + if (PyArray_IsScalar(value, Generic)) { + // Convert numpy scalars to numpy arrays. + value = PyArray_FromScalar(value, nullptr); + // The returned value needs to be DECREF'd, but the original value was + // created in python code, and doesn't need to be DECREF'd. + value_decrefer.reset(value); + } if (PyArray_Check(value)) { int desired_np_dtype = -1; if (desired_dtype >= 0) { diff --git a/tensorflow/python/eager/tensor_test.py b/tensorflow/python/eager/tensor_test.py index 25442ff048..0ee2ff68c2 100644 --- a/tensorflow/python/eager/tensor_test.py +++ b/tensorflow/python/eager/tensor_test.py @@ -95,6 +95,18 @@ class TFETensorTest(test_util.TensorFlowTestCase): t = _create_tensor(values) self.assertAllEqual(values, t) + @test_util.assert_no_new_pyobjects_executing_eagerly + def testNumpyDtypeSurvivesThroughTensorConversion(self): + scalar_creators = [np.int32, np.int64, np.float32, np.float64] + conversion_functions = [ops.convert_to_tensor, constant_op.constant] + + for scalar_creator in scalar_creators: + for conversion_function in conversion_functions: + np_val = scalar_creator(3) + tensor_val = conversion_function(np_val) + self.assertEqual(tensor_val.numpy().dtype, np_val.dtype) + self.assertEqual(tensor_val.numpy(), np_val) + def testNumpyValueWithCast(self): values = np.array([3.0], dtype=np.float32) t = _create_tensor(values, dtype=dtypes.float64) -- GitLab From 2bf928bcbbd51d31b817360a34d6b2b9881f7539 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 6 Dec 2018 10:52:09 -0800 Subject: [PATCH 132/873] Disabling flaky tests in LossWeightingTest. PiperOrigin-RevId: 224367330 --- tensorflow/python/keras/engine/training_test.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/tensorflow/python/keras/engine/training_test.py b/tensorflow/python/keras/engine/training_test.py index bd3d7d26d3..c3c3f06ffd 100644 --- a/tensorflow/python/keras/engine/training_test.py +++ b/tensorflow/python/keras/engine/training_test.py @@ -793,7 +793,8 @@ class TestExceptionsAndWarnings(keras_parameterized.TestCase): class LossWeightingTest(keras_parameterized.TestCase): @keras_parameterized.run_all_keras_modes - def test_class_weights(self): + # TODO(b/120562577): Test failing with assertion error. + def DISABLED_test_class_weights(self): num_classes = 5 batch_size = 5 epochs = 5 @@ -960,7 +961,8 @@ class LossWeightingTest(keras_parameterized.TestCase): self.assertTrue(msg_found) @keras_parameterized.run_all_keras_modes - def test_temporal_sample_weights(self): + # TODO(b/120562577): Test failing with assertion error. + def DISABLED_test_temporal_sample_weights(self): num_classes = 5 batch_size = 5 epochs = 5 -- GitLab From 39c50c9bcbe49a5852f3dd1085e6dc143424a7a0 Mon Sep 17 00:00:00 2001 From: Rachel Lim Date: Thu, 6 Dec 2018 10:52:26 -0800 Subject: [PATCH 133/873] [tf.data] Enable `noop_elimination` optimization by default. PiperOrigin-RevId: 224367395 --- .../core/kernels/data/optimize_dataset_op.cc | 5 +-- .../optimization/optimize_dataset_test.py | 30 +++++++++++++++- .../kernel_tests/stats_dataset_ops_test.py | 9 ++--- .../experimental/ops/optimization_options.py | 34 +++++++++++++++++++ .../data/experimental/ops/prefetching_ops.py | 4 +++ tensorflow/python/data/ops/dataset_ops.py | 31 +++++------------ tensorflow/python/data/ops/iterator_ops.py | 2 ++ .../data/ops/multi_device_iterator_ops.py | 7 +++- ...a.experimental.-optimization-options.pbtxt | 4 +++ ...a.experimental.-optimization-options.pbtxt | 4 +++ 10 files changed, 100 insertions(+), 30 deletions(-) diff --git a/tensorflow/core/kernels/data/optimize_dataset_op.cc b/tensorflow/core/kernels/data/optimize_dataset_op.cc index ab184c232e..9c50d8050a 100644 --- a/tensorflow/core/kernels/data/optimize_dataset_op.cc +++ b/tensorflow/core/kernels/data/optimize_dataset_op.cc @@ -305,8 +305,9 @@ class OptimizeDatasetOp : public UnaryDatasetOpKernel { // removing unused graph nodes) // TODO(b/118175421): This should be part of the tf.data optimization // pass manager. - for (const auto& optimizer : {"pruning", "function", "constfold", - "shape", "arithmetic", "dependency"}) { + // TODO(b/120437209): Apply `constfold` optimization when it is fixed. + for (const auto& optimizer : + {"pruning", "function", "shape", "arithmetic", "dependency"}) { rewriter_config.add_optimizers(optimizer); } } diff --git a/tensorflow/python/data/experimental/kernel_tests/optimization/optimize_dataset_test.py b/tensorflow/python/data/experimental/kernel_tests/optimization/optimize_dataset_test.py index 751be83326..150cc7b5e4 100644 --- a/tensorflow/python/data/experimental/kernel_tests/optimization/optimize_dataset_test.py +++ b/tensorflow/python/data/experimental/kernel_tests/optimization/optimize_dataset_test.py @@ -163,7 +163,15 @@ class OptimizeDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): dataset = dataset_ops.Dataset.range(1) dataset = dataset.flat_map(flat_map_fn) - dataset = dataset_ops._OptimizeDataset(dataset, ["map_and_batch_fusion"]) + + # TODO(b/120558523): We use Options instead of _OptimizeDataset directly + # here because of a bug with chaining _OptimizeDatasets when there are + # nested dataset functions + options = dataset_ops.Options() + opt_options = optimization_options.OptimizationOptions() + opt_options.map_and_batch_fusion = True + options.experimental_optimization = opt_options + dataset = dataset.with_options(options) self.assertDatasetProduces(dataset, expected_output=[[0]]) def testOptimizationThreadPoolDataset(self): @@ -245,6 +253,26 @@ class OptimizeDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): except errors.OutOfRangeError: break + def testOptimizationEnabledByDefault(self): + """Tests that some optimizations are applied to datasets by default.""" + options = dataset_ops.Options() + expected_optimizations = ["noop_elimination"] + self.assertEqual(options._static_optimizations(), expected_optimizations) + + def testOptimizationDisableDefault(self): + """Tests that we can disable all static optimizations enabled by default. + + If the `apply_default_optimizations` optimization options flag is False, + only explicitly enabled optimizations will be applied. + """ + options = dataset_ops.Options() + opt_options = optimization_options.OptimizationOptions() + opt_options.hoist_random_uniform = True + opt_options.apply_default_optimizations = False + options.experimental_optimization = opt_options + expected_optimizations = ["hoist_random_uniform"] + self.assertEqual(options._static_optimizations(), expected_optimizations) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/data/experimental/kernel_tests/stats_dataset_ops_test.py b/tensorflow/python/data/experimental/kernel_tests/stats_dataset_ops_test.py index 8a300364f9..f19b08a2dd 100644 --- a/tensorflow/python/data/experimental/kernel_tests/stats_dataset_ops_test.py +++ b/tensorflow/python/data/experimental/kernel_tests/stats_dataset_ops_test.py @@ -138,9 +138,10 @@ class StatsDatasetTest(stats_dataset_test_base.StatsDatasetTestBase): @test_util.run_deprecated_v1 def testPrefetchBufferScalars(self, dataset_transformation): + def map_fn(x): + return array_ops.tile([x], ops.convert_to_tensor([x])) aggregator = stats_aggregator.StatsAggregator() - dataset = dataset_ops.Dataset.range(10).map( - lambda x: array_ops.tile([x], ops.convert_to_tensor([x]))).prefetch(0) + dataset = dataset_ops.Dataset.range(10).map(map_fn).prefetch(1) dataset = dataset_transformation(dataset, aggregator) iterator = dataset_ops.make_initializable_iterator(dataset) next_element = iterator.get_next() @@ -153,9 +154,9 @@ class StatsDatasetTest(stats_dataset_test_base.StatsDatasetTestBase): np.array([i] * i, dtype=np.int64), self.evaluate(next_element)) summary_str = self.evaluate(summary_t) self._assertSummaryHasScalarValue(summary_str, - "Prefetch::buffer_capacity", 0) + "Prefetch::buffer_capacity", 1) self._assertSummaryHasScalarValue(summary_str, "Prefetch::buffer_size", - 0) + 1) with self.assertRaises(errors.OutOfRangeError): self.evaluate(next_element) diff --git a/tensorflow/python/data/experimental/ops/optimization_options.py b/tensorflow/python/data/experimental/ops/optimization_options.py index dc9d319374..daf65cd5cd 100644 --- a/tensorflow/python/data/experimental/ops/optimization_options.py +++ b/tensorflow/python/data/experimental/ops/optimization_options.py @@ -35,6 +35,12 @@ class OptimizationOptions(options.OptionsBase): dataset = dataset.with_options(options) ``` """ + apply_default_optimizations = options.create_option( + name="apply_default_optimizations", + ty=bool, + docstring= + "Whether to apply default static optimizations. If False, only static " + "optimizations that have been explicitly enabled will be applied.") filter_fusion = options.create_option( name="filter_fusion", @@ -81,3 +87,31 @@ class OptimizationOptions(options.OptionsBase): name="shuffle_and_repeat_fusion", ty=bool, docstring="Whether to fuse shuffle and repeat transformations.") + + def _static_optimizations(self): + """Produces the list of enabled static optimizations.""" + result = [] + optimizations_to_enable = [ + "filter_fusion", + "hoist_random_uniform", + "map_and_batch_fusion", + "map_and_filter_fusion", + "map_fusion", + "map_parallelization", + "map_vectorization", + "shuffle_and_repeat_fusion", + ] + for optimization in optimizations_to_enable: + if getattr(self, optimization): + result.append(optimization) + + if self.apply_default_optimizations is not False: + # The following optimizations are turned on by default, unless the + # user explicitly disables them. + optimizations_to_disable = [ + "noop_elimination", + ] + for optimization in optimizations_to_disable: + if getattr(self, optimization) is not False: + result.append(optimization) + return result diff --git a/tensorflow/python/data/experimental/ops/prefetching_ops.py b/tensorflow/python/data/experimental/ops/prefetching_ops.py index 50d9803c34..aba1786646 100644 --- a/tensorflow/python/data/experimental/ops/prefetching_ops.py +++ b/tensorflow/python/data/experimental/ops/prefetching_ops.py @@ -17,6 +17,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.python.data.experimental.ops import optimization_options from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import iterator_ops from tensorflow.python.data.util import nest @@ -73,6 +74,9 @@ def copy_to_device(target_device, source_device="/cpu:0"): def _apply_fn(dataset): options = dataset_ops.Options() options.experimental_autotune = False + opt_options = optimization_options.OptimizationOptions() + opt_options.apply_default_optimizations = False + options.experimental_optimization = opt_options return _CopyToDeviceDataset( dataset, target_device=target_device, source_device=source_device).with_options(options) diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py index d7a2547fc8..a08a2b5787 100644 --- a/tensorflow/python/data/ops/dataset_ops.py +++ b/tensorflow/python/data/ops/dataset_ops.py @@ -187,8 +187,7 @@ class DatasetV2(object): RuntimeError: If eager execution is not enabled. """ if context.executing_eagerly(): - dataset = self._apply_options() - return iterator_ops.EagerIterator(dataset) + return iterator_ops.EagerIterator(self) else: raise RuntimeError("dataset.__iter__() is only supported when eager " "execution is enabled.") @@ -1318,8 +1317,7 @@ class DatasetV1(DatasetV2): An `Iterator` over the elements of this dataset. """ if context.executing_eagerly(): - dataset = self._apply_options() - return iterator_ops.EagerIterator(dataset) + return iterator_ops.EagerIterator(self) graph_level_seed, op_level_seed = core_random_seed.get_seed(None) @@ -1702,8 +1700,8 @@ class Options(options_lib.OptionsBase): name="experimental_deterministic", ty=bool, docstring= - "Whether to dynamically adjust the values of tunable parameters (e.g. " - "degrees of parallelism).") + "Whether the outputs need to be produced in deterministic order." + ) experimental_numa_aware = options_lib.create_option( name="experimental_numa_aware", @@ -1729,22 +1727,11 @@ class Options(options_lib.OptionsBase): """Produces the list of enabled static optimizations.""" result = [] - exp_optimization_options = self.experimental_optimization - if exp_optimization_options: - optimizations = [ - "filter_fusion", - "hoist_random_uniform", - "map_and_batch_fusion", - "map_and_filter_fusion", - "map_fusion", - "map_parallelization", - "map_vectorization", - "noop_elimination", - "shuffle_and_repeat_fusion", - ] - for optimization in optimizations: - if getattr(exp_optimization_options, optimization): - result.append(optimization) + exp_optimization_options = ( + self.experimental_optimization or + optimization_options.OptimizationOptions()) # If not set, use default + result.extend(exp_optimization_options._static_optimizations()) # pylint: disable=protected-access + if self.experimental_numa_aware: result.append("make_numa_aware") if self.experimental_deterministic is False: diff --git a/tensorflow/python/data/ops/iterator_ops.py b/tensorflow/python/data/ops/iterator_ops.py index eb330d83ae..6f9c494f39 100644 --- a/tensorflow/python/data/ops/iterator_ops.py +++ b/tensorflow/python/data/ops/iterator_ops.py @@ -528,6 +528,7 @@ class EagerIterator(checkpointable.CheckpointableBase): self._device = context.context().device_name with ops.device("/cpu:0"): # pylint: disable=protected-access + dataset = dataset._apply_options() ds_variant = dataset._as_variant_tensor() self._structure = structure_lib.convert_legacy_structure( dataset.output_types, dataset.output_shapes, dataset.output_classes) @@ -541,6 +542,7 @@ class EagerIterator(checkpointable.CheckpointableBase): # Delete the resource when this object is deleted self._resource_deleter = resource_variable_ops.EagerResourceDeleter( handle=self._resource, handle_device=self._device) + # pylint: enable=protected-access def __iter__(self): return self diff --git a/tensorflow/python/data/ops/multi_device_iterator_ops.py b/tensorflow/python/data/ops/multi_device_iterator_ops.py index 4638cee02a..7586012574 100644 --- a/tensorflow/python/data/ops/multi_device_iterator_ops.py +++ b/tensorflow/python/data/ops/multi_device_iterator_ops.py @@ -17,6 +17,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.python.data.experimental.ops import optimization_options from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import iterator_ops from tensorflow.python.eager import context @@ -192,9 +193,13 @@ class MultiDeviceIterator(object): self._source_device_tensor, device, dataset._element_structure) # pylint: disable=protected-access if prefetch_buffer_size > 0: ds = ds.prefetch(prefetch_buffer_size) - # TODO(jsimsa): Enable auto-tuning when supported for non-CPU devices. + # TODO(jsimsa): Enable auto-tuning and optimizations when supported for + # non-CPU devices. options = dataset_ops.Options() options.experimental_autotune = False + opt_options = optimization_options.OptimizationOptions() + opt_options.apply_default_optimizations = False + options.experimental_optimization = opt_options ds = ds.with_options(options) with ops.device(device): self._device_iterators.append(ds.make_initializable_iterator()) diff --git a/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-optimization-options.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-optimization-options.pbtxt index 9ca75828e5..3b7ad64f51 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-optimization-options.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-optimization-options.pbtxt @@ -3,6 +3,10 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + member { + name: "apply_default_optimizations" + mtype: "" + } member { name: "filter_fusion" mtype: "" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-optimization-options.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-optimization-options.pbtxt index 9ca75828e5..3b7ad64f51 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-optimization-options.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-optimization-options.pbtxt @@ -3,6 +3,10 @@ tf_class { is_instance: "" is_instance: "" is_instance: "" + member { + name: "apply_default_optimizations" + mtype: "" + } member { name: "filter_fusion" mtype: "" -- GitLab From 76e1a2a4767d8c697a7594b816bee13d401666a4 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Thu, 6 Dec 2018 11:07:20 -0800 Subject: [PATCH 134/873] [XLA:GPU] Add a flag to disable ptxas optimizations PiperOrigin-RevId: 224370488 --- .../compiler/xla/debug_options_flags.cc | 6 ++++++ .../xla/service/gpu/nvptx_compiler.cc | 21 ++++++++++++------- .../compiler/xla/service/gpu/nvptx_compiler.h | 5 +++-- tensorflow/compiler/xla/xla.proto | 3 +++ 4 files changed, 25 insertions(+), 10 deletions(-) diff --git a/tensorflow/compiler/xla/debug_options_flags.cc b/tensorflow/compiler/xla/debug_options_flags.cc index d7e7b9e621..20609cad58 100644 --- a/tensorflow/compiler/xla/debug_options_flags.cc +++ b/tensorflow/compiler/xla/debug_options_flags.cc @@ -334,6 +334,12 @@ void AllocateFlags() { "overhead from context switching but we let the user override this " "behavior to help run tests on the host that run models in parallel " "across multiple devices."), + tensorflow::Flag( + "xla_gpu_disable_ptxas_optimizations", + bool_setter_for( + &DebugOptions::set_xla_gpu_disable_ptxas_optimizations), + flag_values->xla_gpu_disable_ptxas_optimizations(), + "In XLA:GPU run ptxas in -O0 (default is -O3)."), }); ParseFlagsFromEnvAndDieIfUnknown("XLA_FLAGS", *flag_objects); } diff --git a/tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc b/tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc index e934cbda17..f3e17d8882 100644 --- a/tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc +++ b/tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc @@ -479,7 +479,8 @@ void WarnIfBadDriverJITVersion() { // Compiles the given PTX string using ptxas and returns the resulting machine // code (i.e. a cubin) as a byte array. StatusOr> CompilePtx(const string& ptx, int cc_major, - int cc_minor) { + int cc_minor, + bool disable_ptx_optimizations) { tracing::ScopedActivity activity("Compile PTX", /*is_expensive=*/true); const string ptxas_path = tensorflow::io::JoinPath(tensorflow::CudaRoot(), "bin", "ptxas"); @@ -519,6 +520,9 @@ StatusOr> CompilePtx(const string& ptx, int cc_major, if (VLOG_IS_ON(2)) { ptxas_args.push_back("-v"); } + if (disable_ptx_optimizations) { + ptxas_args.push_back("-O0"); + } ptxas_info_dumper.SetProgram(ptxas_path, ptxas_args); ptxas_info_dumper.SetChannelAction(tensorflow::CHAN_STDERR, tensorflow::ACTION_PIPE); @@ -739,8 +743,9 @@ StatusOr> NVPTXCompiler::RunBackend( } } - const std::vector cubin = - CompilePtxOrGetCachedResult(ptx, cc_major, cc_minor); + const std::vector cubin = CompilePtxOrGetCachedResult( + ptx, cc_major, cc_minor, + module->config().debug_options().xla_gpu_disable_ptxas_optimizations()); auto thunk_schedule = absl::make_unique( ir_emitter.ConsumeThunkSequence(), std::move(stream_assignment), @@ -772,9 +777,9 @@ StatusOr> NVPTXCompiler::RunBackend( return std::unique_ptr(gpu_executable); } -std::vector NVPTXCompiler::CompilePtxOrGetCachedResult(const string& ptx, - int cc_major, - int cc_minor) { +std::vector NVPTXCompiler::CompilePtxOrGetCachedResult( + const string& ptx, int cc_major, int cc_minor, + bool disable_ptx_optimizations) { XLA_SCOPED_LOGGING_TIMER("NVPTXCompiler::CompilePtxOrGetCachedResult"); tracing::ScopedActivity activity("PTX->CUBIN", /*is_expensive=*/true); bool inserted; @@ -802,8 +807,8 @@ std::vector NVPTXCompiler::CompilePtxOrGetCachedResult(const string& ptx, if (inserted) { CHECK(!cache_value->compilation_done); if (!ptx.empty()) { - StatusOr> maybe_cubin = - CompilePtx(*cache_ptx, cc_major, cc_minor); + StatusOr> maybe_cubin = CompilePtx( + *cache_ptx, cc_major, cc_minor, disable_ptx_optimizations); if (maybe_cubin.ok()) { cache_value->cubin_data = std::move(maybe_cubin).ValueOrDie(); VLOG(2) << "Compiled PTX size:" << ptx.size() diff --git a/tensorflow/compiler/xla/service/gpu/nvptx_compiler.h b/tensorflow/compiler/xla/service/gpu/nvptx_compiler.h index f79ae2990a..be5e31a501 100644 --- a/tensorflow/compiler/xla/service/gpu/nvptx_compiler.h +++ b/tensorflow/compiler/xla/service/gpu/nvptx_compiler.h @@ -97,8 +97,9 @@ class NVPTXCompiler : public LLVMCompiler { // Tries to compile the given ptx string to cubin. Returns a vector with the // compiled cubin. If compilation was unsuccessful, returns an empty vector. - std::vector CompilePtxOrGetCachedResult(const string& ptx, - int cc_major, int cc_minor); + std::vector CompilePtxOrGetCachedResult( + const string& ptx, int cc_major, int cc_minor, + bool disable_ptx_optimizations); // The compilation_cache_ map is a cache from {ptx string, cc_major, cc_minor} // -> cubin so we don't recompile the same ptx twice. This is important for diff --git a/tensorflow/compiler/xla/xla.proto b/tensorflow/compiler/xla/xla.proto index bdeb1728fa..a37eac7fe4 100644 --- a/tensorflow/compiler/xla/xla.proto +++ b/tensorflow/compiler/xla/xla.proto @@ -213,6 +213,9 @@ message DebugOptions { // the host that run models in parallel across multiple devices. int32 xla_force_host_platform_device_count = 102; + // If set to true XLA:GPU invokes `ptxas` with -O0 (default is -O3). + bool xla_gpu_disable_ptxas_optimizations = 103; + // Extra options to pass to the compilation backend (e.g. LLVM); specific // interpretation of these values is left to the backend. map xla_backend_extra_options = 500; -- GitLab From d3106e4138e28f62179cd6a7b8041729f5163190 Mon Sep 17 00:00:00 2001 From: Frank Chen Date: Thu, 6 Dec 2018 11:07:35 -0800 Subject: [PATCH 135/873] Change num_accelerators_per_worker to num_accelerators within Cluster Resolver as we will support querying remote, heterogenous GPU clusters soon. PiperOrigin-RevId: 224370540 --- .../cluster_resolver/cluster_resolver.py | 57 ++++++++++++++----- .../cluster_resolver/cluster_resolver_test.py | 12 ++-- .../cluster_resolver/gce_cluster_resolver.py | 16 ++++-- .../kubernetes_cluster_resolver.py | 15 ++++- .../slurm_cluster_resolver.py | 9 ++- .../tfconfig_cluster_resolver.py | 19 +++++-- .../tfconfig_cluster_resolver_test.py | 4 +- .../cluster_resolver/tpu_cluster_resolver.py | 21 +++++-- 8 files changed, 111 insertions(+), 42 deletions(-) diff --git a/tensorflow/python/distribute/cluster_resolver/cluster_resolver.py b/tensorflow/python/distribute/cluster_resolver/cluster_resolver.py index 7774ac0e12..ca40e60a55 100644 --- a/tensorflow/python/distribute/cluster_resolver/cluster_resolver.py +++ b/tensorflow/python/distribute/cluster_resolver/cluster_resolver.py @@ -92,7 +92,11 @@ class ClusterResolver(object): raise NotImplementedError() @abc.abstractmethod - def num_accelerators_per_worker(self, session_config=None): + def num_accelerators(self, + task_type=None, + task_index=None, + accelerator_type='GPU', + config_proto=None): """Returns the number of accelerator cores per worker. This returns the number of accelerator cores (such as GPUs and TPUs) @@ -100,8 +104,19 @@ class ClusterResolver(object): should return 0. This method will query the master for this information if it is not otherwise known. + Optionally, we allow callers to specify the task_type, task_index, and + rpc_layer, if they want to target a specific TensorFlow process to query + the number of accelerators. This is to support heterogenous environments, + where the number of accelerators cores per host is different. + Args: - session_config: (Optional) Configuration for starting a new session to + task_type: (Optional) The type of the TensorFlow task of the machine we + want to query. + task_index: (Optional) The index of the TensorFlow task of the machine we + want to query. + accelerator_type: (Optional) The type of accelerator we are trying to + query (defaults to 'GPU'). + config_proto: (Optional) Configuration for starting a new session to query how many accelerator cores it has. """ raise NotImplementedError() @@ -116,7 +131,7 @@ class SimpleClusterResolver(ClusterResolver): """Simple implementation of ClusterResolver that accepts a ClusterSpec.""" def __init__(self, cluster_spec, master='', task_type=None, task_index=None, - environment='', num_accelerators_per_worker=0, + environment='', num_accelerators=0, rpc_layer=None): """Creates a SimpleClusterResolver from a ClusterSpec.""" super(SimpleClusterResolver, self).__init__() @@ -124,7 +139,7 @@ class SimpleClusterResolver(ClusterResolver): self._task_type = task_type self._task_index = task_index self._environment = environment - self._num_accelerators_per_worker = num_accelerators_per_worker + self._num_accelerators = num_accelerators self._rpc_layer = rpc_layer if not isinstance(cluster_spec, ClusterSpec): @@ -180,17 +195,27 @@ class SimpleClusterResolver(ClusterResolver): def environment(self): return self._environment - def num_accelerators_per_worker(self, session_config=None): + def num_accelerators(self, + task_type=None, + task_index=None, + accelerator_type='GPU', + config_proto=None): """Returns the number of accelerator cores per worker. + The SimpleClusterResolver does not do automatic detection of accelerators, + so a TensorFlow session will never be created, and thus all arguments are + unused and we simply return whatever was passed in when this object was + initialized. + Args: - session_config: Unused. The SimpleClusterResolver does not do automatic - detection of accelerators, so a TensorFlow session will never be - created, and thus a `session_config` is never necessary here, and will - be ignored. + task_type: Unused. + task_index: Unused. + accelerator_type: Unused. + config_proto: Unused. """ - del session_config - return self._num_accelerators_per_worker + # Unused + del task_type, task_index, accelerator_type, config_proto + return self._num_accelerators @property def rpc_layer(self): @@ -361,9 +386,13 @@ class UnionClusterResolver(ClusterResolver): def environment(self): return self._cluster_resolvers[0].environment - def num_accelerators_per_worker(self, session_config=None): - return self._cluster_resolvers[0].num_accelerators_per_worker( - session_config) + def num_accelerators(self, + task_type=None, + task_index=None, + accelerator_type='GPU', + config_proto=None): + return self._cluster_resolvers[0].num_accelerators( + task_type, task_index, accelerator_type, config_proto) @property def rpc_layer(self): diff --git a/tensorflow/python/distribute/cluster_resolver/cluster_resolver_test.py b/tensorflow/python/distribute/cluster_resolver/cluster_resolver_test.py index b5448faec6..3f7b469727 100644 --- a/tensorflow/python/distribute/cluster_resolver/cluster_resolver_test.py +++ b/tensorflow/python/distribute/cluster_resolver/cluster_resolver_test.py @@ -65,13 +65,13 @@ class UnionClusterResolverTest(test.TestCase): simple_resolver = SimpleClusterResolver(base_cluster_spec, task_type="ps", task_index=1, environment="cloud", - num_accelerators_per_worker=8, + num_accelerators=8, rpc_layer="grpc") self.assertEqual(simple_resolver.task_type, "ps") self.assertEqual(simple_resolver.task_index, 1) self.assertEqual(simple_resolver.environment, "cloud") - self.assertEqual(simple_resolver.num_accelerators_per_worker(), 8) + self.assertEqual(simple_resolver.num_accelerators(), 8) self.assertEqual(simple_resolver.rpc_layer, "grpc") def testOverrideSimpleClusterResolver(self): @@ -82,7 +82,7 @@ class UnionClusterResolverTest(test.TestCase): simple_resolver = SimpleClusterResolver(base_cluster_spec, task_type="ps", task_index=1, environment="cloud", - num_accelerators_per_worker=8, + num_accelerators=8, rpc_layer="grpc") simple_resolver.task_type = "worker" @@ -130,7 +130,7 @@ class UnionClusterResolverTest(test.TestCase): }) resolver1 = SimpleClusterResolver(cluster_spec_1, task_type="ps", task_index=1, environment="cloud", - num_accelerators_per_worker=8, + num_accelerators=8, rpc_layer="grpc") cluster_spec_2 = server_lib.ClusterSpec({ @@ -139,7 +139,7 @@ class UnionClusterResolverTest(test.TestCase): }) resolver2 = SimpleClusterResolver(cluster_spec_2, task_type="worker", task_index=2, environment="local", - num_accelerators_per_worker=16, + num_accelerators=16, rpc_layer="http") union_resolver = UnionClusterResolver(resolver1, resolver2) @@ -147,7 +147,7 @@ class UnionClusterResolverTest(test.TestCase): self.assertEqual(union_resolver.task_type, "ps") self.assertEqual(union_resolver.task_index, 1) self.assertEqual(union_resolver.environment, "cloud") - self.assertEqual(union_resolver.num_accelerators_per_worker(), 8) + self.assertEqual(union_resolver.num_accelerators(), 8) self.assertEqual(union_resolver.rpc_layer, "grpc") union_resolver.task_type = "worker" diff --git a/tensorflow/python/distribute/cluster_resolver/gce_cluster_resolver.py b/tensorflow/python/distribute/cluster_resolver/gce_cluster_resolver.py index b167bc8fc8..2412f6dad0 100644 --- a/tensorflow/python/distribute/cluster_resolver/gce_cluster_resolver.py +++ b/tensorflow/python/distribute/cluster_resolver/gce_cluster_resolver.py @@ -51,7 +51,7 @@ class GceClusterResolver(ClusterResolver): task_type='worker', task_index=0, rpc_layer='grpc', - num_accelerators_per_worker=0, + num_accelerators=0, credentials='default', service=None): """Creates a new GceClusterResolver object. @@ -73,7 +73,7 @@ class GceClusterResolver(ClusterResolver): can be distinguished from each other. rpc_layer: The RPC layer TensorFlow should use to communicate across instances. - num_accelerators_per_worker: Number of accelerators (GPUs) present per + num_accelerators: Number of accelerators (GPUs) present per instance. credentials: GCE Credentials. If nothing is specified, this defaults to GoogleCredentials.get_application_default(). @@ -90,6 +90,7 @@ class GceClusterResolver(ClusterResolver): self._task_type = task_type self._task_index = task_index self._rpc_layer = rpc_layer + self._num_accelerators = num_accelerators self._port = port self._credentials = credentials @@ -201,6 +202,11 @@ class GceClusterResolver(ClusterResolver): def rpc_layer(self, rpc_layer): self._rpc_layer = rpc_layer - def num_accelerators_per_worker(self, session_config=None): - del session_config # Unused, since this is set manually in __init__. - return self._num_accelerators_per_worker + def num_accelerators(self, + task_type=None, + task_index=None, + accelerator_type='GPU', + config_proto=None): + # Unused + del task_type, task_index, accelerator_type, config_proto + return self._num_accelerators diff --git a/tensorflow/python/distribute/cluster_resolver/kubernetes_cluster_resolver.py b/tensorflow/python/distribute/cluster_resolver/kubernetes_cluster_resolver.py index 041c081540..b21c3676be 100644 --- a/tensorflow/python/distribute/cluster_resolver/kubernetes_cluster_resolver.py +++ b/tensorflow/python/distribute/cluster_resolver/kubernetes_cluster_resolver.py @@ -168,6 +168,15 @@ class KubernetesClusterResolver(ClusterResolver): """ return '' - def num_accelerators_per_worker(self, session_config=None): - local_devices = device_lib.list_local_devices(session_config) - return len([d for d in local_devices if d.device_type == 'GPU']) + def num_accelerators(self, + task_type=None, + task_index=None, + accelerator_type='GPU', + config_proto=None): + # TODO(frankchn): Make querying non-local accelerators work + if task_type is not None or task_index is not None: + raise NotImplementedError('Querying non-local accelerators is not yet' + 'implemented.') + + local_devices = device_lib.list_local_devices(config_proto) + return sum(d.device_type == accelerator_type for d in local_devices) diff --git a/tensorflow/python/distribute/cluster_resolver/slurm_cluster_resolver.py b/tensorflow/python/distribute/cluster_resolver/slurm_cluster_resolver.py index fd3c6d6a18..1ab81731b7 100644 --- a/tensorflow/python/distribute/cluster_resolver/slurm_cluster_resolver.py +++ b/tensorflow/python/distribute/cluster_resolver/slurm_cluster_resolver.py @@ -221,6 +221,11 @@ class SlurmClusterResolver(ClusterResolver): """ return '' - def num_accelerators_per_worker(self, session_config=None): - del session_config # Unused, since this is set in __init__ manually. + def num_accelerators(self, + task_type=None, + task_index=None, + accelerator_type='GPU', + config_proto=None): + # Unused, since this is set in __init__ manually. + del task_type, task_index, accelerator_type, config_proto return self._gpus_per_node diff --git a/tensorflow/python/distribute/cluster_resolver/tfconfig_cluster_resolver.py b/tensorflow/python/distribute/cluster_resolver/tfconfig_cluster_resolver.py index a3246e77f4..b4465714b2 100644 --- a/tensorflow/python/distribute/cluster_resolver/tfconfig_cluster_resolver.py +++ b/tensorflow/python/distribute/cluster_resolver/tfconfig_cluster_resolver.py @@ -55,7 +55,7 @@ class TFConfigClusterResolver(ClusterResolver): task_index=None, rpc_layer=None, environment=None, - num_accelerators_per_worker=0): + num_accelerators=0): """Creates a new TFConfigClusterResolver. Args: @@ -66,15 +66,17 @@ class TFConfigClusterResolver(ClusterResolver): rpc_layer: (String, optional) Overrides the rpc layer TensorFlow uses. environment: (String, optional) Overrides the environment TensorFlow operates in. - num_accelerators_per_worker: (Integer, optional) Specifies the number of + num_accelerators: (Integer, optional) Specifies the number of accelerators (e.g. GPUs, TPUs, others) that each node has. """ + # TODO(frankchn): num_accelerators is a stop-gap and will be removed + # in favor of autodetection of devices soon. self._task_type = task_type self._task_index = task_index self._rpc_layer = rpc_layer self._environment = environment - self._num_accelerators_per_worker = num_accelerators_per_worker + self._num_accelerators = num_accelerators @property def task_type(self): @@ -115,10 +117,15 @@ class TFConfigClusterResolver(ClusterResolver): def rpc_layer(self, rpc_layer): self._rpc_layer = rpc_layer - def num_accelerators_per_worker(self, session_config=None): + def num_accelerators(self, + task_type=None, + task_index=None, + accelerator_type='GPU', + config_proto=None): # TODO(frankchn): Connect to server (w/ session_config) in the future. - del session_config # Unused, we do not connect to another server here. - return self._num_accelerators_per_worker + # Unused, we do not connect to another server here right now. + del task_type, task_index, accelerator_type, config_proto + return self._num_accelerators def cluster_spec(self): """Returns a ClusterSpec based on the TF_CONFIG environment variable. diff --git a/tensorflow/python/distribute/cluster_resolver/tfconfig_cluster_resolver_test.py b/tensorflow/python/distribute/cluster_resolver/tfconfig_cluster_resolver_test.py index c20e51bc0b..197eba1739 100644 --- a/tensorflow/python/distribute/cluster_resolver/tfconfig_cluster_resolver_test.py +++ b/tensorflow/python/distribute/cluster_resolver/tfconfig_cluster_resolver_test.py @@ -169,12 +169,12 @@ class TFConfigClusterResolverTest(test.TestCase): """ cluster_resolver = TFConfigClusterResolver(task_type='ps', task_index=0, - num_accelerators_per_worker=8) + num_accelerators=8) self.assertEqual('grpc://ps0:2222', cluster_resolver.master()) self.assertEqual('ps', cluster_resolver.task_type) self.assertEqual(0, cluster_resolver.task_index) - self.assertEqual(8, cluster_resolver.num_accelerators_per_worker()) + self.assertEqual(8, cluster_resolver.num_accelerators()) cluster_resolver.task_type = 'worker' cluster_resolver.task_index = 1 diff --git a/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver.py b/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver.py index 1fd674c8a4..99a7a0922f 100644 --- a/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver.py +++ b/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver.py @@ -387,17 +387,30 @@ class TPUClusterResolver(ClusterResolver): return server_lib.ClusterSpec(cluster_spec) - def num_accelerators_per_worker(self, session_config=None): + def num_accelerators(self, + task_type=None, + task_index=None, + accelerator_type='TPU', + config_proto=None): """Returns the number of TPU cores per worker. This defaults to 8 for all current TPU configurations, and we do not need to query any remote systems for this. Args: - session_config: Unused. Not currently necessary to query anything as this - number is 8 for all TPU configurations. + task_type: Unused. + task_index: Unused. + accelerator_type: Unused. + config_proto: Unused. + + Raises: + RuntimeError: If this is used with a non-TPU accelerator_type. """ - del session_config # Unused. Not necessary to query anything. + # Unused. Not necessary to query anything. + del task_type, task_index, config_proto + + if accelerator_type != 'TPU': + raise ValueError('This Cluster Resolver is only compatible with TPUs.') return 8 @property -- GitLab From e7f4f4d2a662ea24826af717d5ee401e67883b7d Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 6 Dec 2018 11:08:53 -0800 Subject: [PATCH 136/873] Increase timeout to deflake ctc_loss_op_test. PiperOrigin-RevId: 224370778 --- tensorflow/python/kernel_tests/BUILD | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD index 242e3b595c..df8c14970a 100644 --- a/tensorflow/python/kernel_tests/BUILD +++ b/tensorflow/python/kernel_tests/BUILD @@ -272,7 +272,7 @@ tf_py_test( cuda_py_test( name = "ctc_loss_op_test", - size = "small", + size = "medium", srcs = ["ctc_loss_op_test.py"], additional_deps = [ "//third_party/py/numpy", -- GitLab From a5c2d3ecf905e310ebf4f73089b0b5bf8b6dcf81 Mon Sep 17 00:00:00 2001 From: Austin Anderson Date: Thu, 6 Dec 2018 11:24:25 -0800 Subject: [PATCH 137/873] Apply API symbols upgrade for TF 2.0 PiperOrigin-RevId: 224374109 --- tensorflow/python/ops/array_ops.py | 20 ++++- .../python/ops/candidate_sampling_ops.py | 11 +-- tensorflow/python/ops/embedding_ops.py | 78 ++++++++++++++++++- tensorflow/python/ops/nn_impl.py | 2 +- tensorflow/python/ops/nn_ops.py | 10 ++- tensorflow/python/ops/sparse_ops.py | 4 +- .../api/golden/v1/tensorflow.random.pbtxt | 12 +++ .../tools/api/golden/v2/tensorflow.math.pbtxt | 2 +- .../tools/api/golden/v2/tensorflow.nn.pbtxt | 14 ++-- .../api/golden/v2/tensorflow.random.pbtxt | 4 + .../api/golden/v2/tensorflow.sparse.pbtxt | 2 +- tensorflow/tools/compatibility/renames_v2.py | 5 +- tensorflow/tools/compatibility/reorders_v2.py | 8 ++ .../tools/compatibility/tf_upgrade_v2.py | 11 +++ .../tools/compatibility/tf_upgrade_v2_test.py | 48 ++++++++++++ 15 files changed, 205 insertions(+), 26 deletions(-) diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py index b555f63ceb..185452e1ab 100644 --- a/tensorflow/python/ops/array_ops.py +++ b/tensorflow/python/ops/array_ops.py @@ -2667,7 +2667,7 @@ def space_to_batch(input, paddings, block_size, name=None): # pylint: disable=r space_to_batch.__doc__ = gen_array_ops.space_to_batch.__doc__ -@tf_export("nn.space_to_depth", v1=["nn.space_to_depth", "space_to_depth"]) +@tf_export(v1=["nn.space_to_depth", "space_to_depth"]) @deprecation.deprecated_endpoints("space_to_depth") def space_to_depth(input, block_size, name=None, data_format="NHWC"): # pylint: disable=redefined-builtin return gen_array_ops.space_to_depth(input, block_size, data_format, name=name) @@ -2676,7 +2676,15 @@ def space_to_depth(input, block_size, name=None, data_format="NHWC"): # pylint: space_to_depth.__doc__ = gen_array_ops.space_to_depth.__doc__ -@tf_export("nn.depth_to_space", v1=["nn.depth_to_space", "depth_to_space"]) +@tf_export("nn.space_to_depth", v1=[]) +def space_to_depth_v2(input, block_size, data_format="NHWC", name=None): # pylint: disable=redefined-builtin + return gen_array_ops.space_to_depth(input, block_size, data_format, name=name) + + +space_to_depth_v2.__doc__ = gen_array_ops.space_to_depth.__doc__ + + +@tf_export(v1=["nn.depth_to_space", "depth_to_space"]) @deprecation.deprecated_endpoints("depth_to_space") def depth_to_space(input, block_size, name=None, data_format="NHWC"): # pylint: disable=redefined-builtin return gen_array_ops.depth_to_space(input, block_size, data_format, name=name) @@ -2685,6 +2693,14 @@ def depth_to_space(input, block_size, name=None, data_format="NHWC"): # pylint: depth_to_space.__doc__ = gen_array_ops.depth_to_space.__doc__ +@tf_export("nn.depth_to_space", v1=[]) +def depth_to_space_v2(input, block_size, data_format="NHWC", name=None): # pylint: disable=redefined-builtin + return gen_array_ops.depth_to_space(input, block_size, data_format, name=name) + + +depth_to_space_v2.__doc__ = gen_array_ops.depth_to_space.__doc__ + + @tf_export(v1=["batch_to_space"]) def batch_to_space(input, crops, block_size, name=None): # pylint: disable=redefined-builtin result = batch_to_space_nd( diff --git a/tensorflow/python/ops/candidate_sampling_ops.py b/tensorflow/python/ops/candidate_sampling_ops.py index c64000b65d..56f76a49d5 100644 --- a/tensorflow/python/ops/candidate_sampling_ops.py +++ b/tensorflow/python/ops/candidate_sampling_ops.py @@ -151,7 +151,10 @@ def log_uniform_candidate_sampler(true_classes, num_true, num_sampled, unique, seed2=seed2, name=name) -@tf_export('nn.learned_unigram_candidate_sampler') +@tf_export( + 'random.learned_unigram_candidate_sampler', + 'nn.learned_unigram_candidate_sampler') +@deprecation.deprecated_endpoints(['nn.learned_unigram_candidate_sampler']) def learned_unigram_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None): """Samples a set of classes from a distribution learned during training. @@ -209,8 +212,7 @@ def learned_unigram_candidate_sampler(true_classes, num_true, num_sampled, @tf_export('random.fixed_unigram_candidate_sampler', - 'nn.fixed_unigram_candidate_sampler', - v1=['nn.fixed_unigram_candidate_sampler']) + 'nn.fixed_unigram_candidate_sampler') def fixed_unigram_candidate_sampler(true_classes, num_true, num_sampled, @@ -302,8 +304,7 @@ def fixed_unigram_candidate_sampler(true_classes, unigrams=unigrams, seed=seed1, seed2=seed2, name=name) -@tf_export('random.all_candidate_sampler', 'nn.all_candidate_sampler', - v1=['nn.all_candidate_sampler']) +@tf_export('random.all_candidate_sampler', 'nn.all_candidate_sampler') def all_candidate_sampler(true_classes, num_true, num_sampled, unique, seed=None, name=None): """Generate the set of all classes. diff --git a/tensorflow/python/ops/embedding_ops.py b/tensorflow/python/ops/embedding_ops.py index f2f5da7d7a..d0291e2095 100644 --- a/tensorflow/python/ops/embedding_ops.py +++ b/tensorflow/python/ops/embedding_ops.py @@ -247,7 +247,7 @@ def _embedding_lookup_and_transform(params, return ret -@tf_export("nn.embedding_lookup") +@tf_export(v1=["nn.embedding_lookup"]) def embedding_lookup( params, ids, @@ -316,7 +316,66 @@ def embedding_lookup( transform_fn=None) -@tf_export("nn.embedding_lookup_sparse") +@tf_export("nn.embedding_lookup", v1=[]) +def embedding_lookup_v2( + params, + ids, + partition_strategy="mod", + max_norm=None, + name=None): + """Looks up `ids` in a list of embedding tensors. + + This function is used to perform parallel lookups on the list of + tensors in `params`. It is a generalization of + `tf.gather`, where `params` is + interpreted as a partitioning of a large embedding tensor. `params` may be + a `PartitionedVariable` as returned by using `tf.get_variable()` with a + partitioner. + + If `len(params) > 1`, each element `id` of `ids` is partitioned between + the elements of `params` according to the `partition_strategy`. + In all strategies, if the id space does not evenly divide the number of + partitions, each of the first `(max_id + 1) % len(params)` partitions will + be assigned one more id. + + If `partition_strategy` is `"mod"`, we assign each id to partition + `p = id % len(params)`. For instance, + 13 ids are split across 5 partitions as: + `[[0, 5, 10], [1, 6, 11], [2, 7, 12], [3, 8], [4, 9]]` + + If `partition_strategy` is `"div"`, we assign ids to partitions in a + contiguous manner. In this case, 13 ids are split across 5 partitions as: + `[[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10], [11, 12]]` + + The results of the lookup are concatenated into a dense + tensor. The returned tensor has shape `shape(ids) + shape(params)[1:]`. + + Args: + params: A single tensor representing the complete embedding tensor, + or a list of P tensors all of same shape except for the first dimension, + representing sharded embedding tensors. Alternatively, a + `PartitionedVariable`, created by partitioning along dimension 0. Each + element must be appropriately sized for the given `partition_strategy`. + ids: A `Tensor` with type `int32` or `int64` containing the ids to be looked + up in `params`. + partition_strategy: A string specifying the partitioning strategy, relevant + if `len(params) > 1`. Currently `"div"` and `"mod"` are supported. Default + is `"mod"`. + max_norm: If not `None`, each embedding is clipped if its l2-norm is + larger than this value. + name: A name for the operation (optional). + + Returns: + A `Tensor` with the same type as the tensors in `params`. + + Raises: + ValueError: If `params` is empty. + """ + return embedding_lookup(params, ids, partition_strategy, name, + max_norm=max_norm) + + +@tf_export(v1=["nn.embedding_lookup_sparse"]) def embedding_lookup_sparse(params, sp_ids, sp_weights, @@ -491,6 +550,21 @@ def embedding_lookup_sparse(params, return embeddings +@tf_export("nn.embedding_lookup_sparse", v1=[]) +def embedding_lookup_sparse_v2(params, + sp_ids, + sp_weights, + partition_strategy="mod", + combiner=None, + max_norm=None, + name=None): + return embedding_lookup_sparse_v2( + params, sp_ids, sp_weights, partition_strategy, name, combiner, max_norm) + + +embedding_lookup_sparse_v2.__doc__ = embedding_lookup_sparse.__doc__ + + @tf_export("nn.safe_embedding_lookup_sparse", v1=[]) def safe_embedding_lookup_sparse_v2(embedding_weights, sparse_ids, diff --git a/tensorflow/python/ops/nn_impl.py b/tensorflow/python/ops/nn_impl.py index 292cca8b9e..48dcab4842 100644 --- a/tensorflow/python/ops/nn_impl.py +++ b/tensorflow/python/ops/nn_impl.py @@ -262,7 +262,7 @@ def weighted_cross_entropy_with_logits(targets, logits, pos_weight, name=None): name=name) -@tf_export("nn.relu_layer") +@tf_export(v1=["nn.relu_layer"]) def relu_layer(x, weights, biases, name=None): """Computes Relu(x * weight + biases). diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py index 97d405cd0e..611bfdac9a 100644 --- a/tensorflow/python/ops/nn_ops.py +++ b/tensorflow/python/ops/nn_ops.py @@ -3776,7 +3776,7 @@ def erosion2d_v2(value, name=name)) -@tf_export("math.in_top_k", "nn.in_top_k") +@tf_export(v1=["math.in_top_k", "nn.in_top_k"]) def in_top_k(predictions, targets, k, name=None): r"""Says whether the targets are in the top `K` predictions. @@ -3810,6 +3810,14 @@ def in_top_k(predictions, targets, k, name=None): return gen_nn_ops.in_top_kv2(predictions, targets, k, name=name) +@tf_export("math.in_top_k", "nn.in_top_k", v1=[]) +def in_top_k_v2(targets, predictions, k, name=None): + return in_top_k(predictions, targets, k, name) + + +in_top_k_v2.__doc__ = in_top_k.__doc__ + + tf_export(v1=["nn.quantized_avg_pool"])(gen_nn_ops.quantized_avg_pool) tf_export(v1=["nn.quantized_conv2d"])(gen_nn_ops.quantized_conv2d) tf_export(v1=["nn.quantized_relu_x"])(gen_nn_ops.quantized_relu_x) diff --git a/tensorflow/python/ops/sparse_ops.py b/tensorflow/python/ops/sparse_ops.py index feff7df850..d7346b7371 100644 --- a/tensorflow/python/ops/sparse_ops.py +++ b/tensorflow/python/ops/sparse_ops.py @@ -299,7 +299,7 @@ def sparse_concat(axis, @tf_export("sparse.concat", v1=[]) -def sparse_concat_v2(axis, sp_inputs, expand_nonconcat_dim=False, name=None): # pylint: disable=missing-docstring +def sparse_concat_v2(axis, sp_inputs, expand_nonconcat_dims=False, name=None): # pylint: disable=missing-docstring sp_inputs = _convert_to_sparse_tensors(sp_inputs) if len(sp_inputs) == 1: # Degenerate case of one tensor. @@ -309,7 +309,7 @@ def sparse_concat_v2(axis, sp_inputs, expand_nonconcat_dim=False, name=None): # vals = [sp_input.values for sp_input in sp_inputs] shapes = [sp_input.dense_shape for sp_input in sp_inputs] - if expand_nonconcat_dim: + if expand_nonconcat_dims: max_shape = math_ops.reduce_max( array_ops.concat( [array_ops.reshape(shape, [1, -1]) for shape in shapes], 0), 0) diff --git a/tensorflow/tools/api/golden/v1/tensorflow.random.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.random.pbtxt index 107534e086..1eefb1c70c 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.random.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.random.pbtxt @@ -1,9 +1,17 @@ path: "tensorflow.random" tf_module { + member_method { + name: "all_candidate_sampler" + argspec: "args=[\'true_classes\', \'num_true\', \'num_sampled\', \'unique\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } member_method { name: "categorical" argspec: "args=[\'logits\', \'num_samples\', \'dtype\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " } + member_method { + name: "fixed_unigram_candidate_sampler" + argspec: "args=[\'true_classes\', \'num_true\', \'num_sampled\', \'unique\', \'range_max\', \'vocab_file\', \'distortion\', \'num_reserved_ids\', \'num_shards\', \'shard\', \'unigrams\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'\', \'1.0\', \'0\', \'1\', \'0\', \'()\', \'None\', \'None\'], " + } member_method { name: "gamma" argspec: "args=[\'shape\', \'alpha\', \'beta\', \'dtype\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \"\", \'None\', \'None\'], " @@ -12,6 +20,10 @@ tf_module { name: "get_seed" argspec: "args=[\'op_seed\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "learned_unigram_candidate_sampler" + argspec: "args=[\'true_classes\', \'num_true\', \'num_sampled\', \'unique\', \'range_max\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } member_method { name: "log_uniform_candidate_sampler" argspec: "args=[\'true_classes\', \'num_true\', \'num_sampled\', \'unique\', \'range_max\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " diff --git a/tensorflow/tools/api/golden/v2/tensorflow.math.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.math.pbtxt index 979d77ea6b..4ac0484050 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.math.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.math.pbtxt @@ -170,7 +170,7 @@ tf_module { } member_method { name: "in_top_k" - argspec: "args=[\'predictions\', \'targets\', \'k\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'targets\', \'predictions\', \'k\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "invert_permutation" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.nn.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.nn.pbtxt index 6bf4a9d00c..9e5e82483a 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.nn.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.nn.pbtxt @@ -106,7 +106,7 @@ tf_module { } member_method { name: "depth_to_space" - argspec: "args=[\'input\', \'block_size\', \'name\', \'data_format\'], varargs=None, keywords=None, defaults=[\'None\', \'NHWC\'], " + argspec: "args=[\'input\', \'block_size\', \'data_format\', \'name\'], varargs=None, keywords=None, defaults=[\'NHWC\', \'None\'], " } member_method { name: "depthwise_conv2d" @@ -134,11 +134,11 @@ tf_module { } member_method { name: "embedding_lookup" - argspec: "args=[\'params\', \'ids\', \'partition_strategy\', \'name\', \'validate_indices\', \'max_norm\'], varargs=None, keywords=None, defaults=[\'mod\', \'None\', \'True\', \'None\'], " + argspec: "args=[\'params\', \'ids\', \'partition_strategy\', \'max_norm\', \'name\'], varargs=None, keywords=None, defaults=[\'mod\', \'None\', \'None\'], " } member_method { name: "embedding_lookup_sparse" - argspec: "args=[\'params\', \'sp_ids\', \'sp_weights\', \'partition_strategy\', \'name\', \'combiner\', \'max_norm\'], varargs=None, keywords=None, defaults=[\'mod\', \'None\', \'None\', \'None\'], " + argspec: "args=[\'params\', \'sp_ids\', \'sp_weights\', \'partition_strategy\', \'combiner\', \'max_norm\', \'name\'], varargs=None, keywords=None, defaults=[\'mod\', \'None\', \'None\', \'None\'], " } member_method { name: "erosion2d" @@ -158,7 +158,7 @@ tf_module { } member_method { name: "in_top_k" - argspec: "args=[\'predictions\', \'targets\', \'k\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'targets\', \'predictions\', \'k\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "l2_loss" @@ -228,10 +228,6 @@ tf_module { name: "relu6" argspec: "args=[\'features\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } - member_method { - name: "relu_layer" - argspec: "args=[\'x\', \'weights\', \'biases\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } member_method { name: "safe_embedding_lookup_sparse" argspec: "args=[\'embedding_weights\', \'sparse_ids\', \'sparse_weights\', \'combiner\', \'default_id\', \'max_norm\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'mean\', \'None\', \'None\', \'None\'], " @@ -278,7 +274,7 @@ tf_module { } member_method { name: "space_to_depth" - argspec: "args=[\'input\', \'block_size\', \'name\', \'data_format\'], varargs=None, keywords=None, defaults=[\'None\', \'NHWC\'], " + argspec: "args=[\'input\', \'block_size\', \'data_format\', \'name\'], varargs=None, keywords=None, defaults=[\'NHWC\', \'None\'], " } member_method { name: "sparse_softmax_cross_entropy_with_logits" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.random.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.random.pbtxt index de5cb6b717..d49c23e59c 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.random.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.random.pbtxt @@ -16,6 +16,10 @@ tf_module { name: "gamma" argspec: "args=[\'shape\', \'alpha\', \'beta\', \'dtype\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \"\", \'None\', \'None\'], " } + member_method { + name: "learned_unigram_candidate_sampler" + argspec: "args=[\'true_classes\', \'num_true\', \'num_sampled\', \'unique\', \'range_max\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } member_method { name: "log_uniform_candidate_sampler" argspec: "args=[\'true_classes\', \'num_true\', \'num_sampled\', \'unique\', \'range_max\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " diff --git a/tensorflow/tools/api/golden/v2/tensorflow.sparse.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.sparse.pbtxt index 9808200d72..b8bd2c0b72 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.sparse.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.sparse.pbtxt @@ -14,7 +14,7 @@ tf_module { } member_method { name: "concat" - argspec: "args=[\'axis\', \'sp_inputs\', \'expand_nonconcat_dim\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " + argspec: "args=[\'axis\', \'sp_inputs\', \'expand_nonconcat_dims\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " } member_method { name: "cross" diff --git a/tensorflow/tools/compatibility/renames_v2.py b/tensorflow/tools/compatibility/renames_v2.py index 02212c3810..b27fd5acc7 100644 --- a/tensorflow/tools/compatibility/renames_v2.py +++ b/tensorflow/tools/compatibility/renames_v2.py @@ -134,7 +134,7 @@ renames = { 'tf.decode_json_example': 'tf.io.decode_json_example', 'tf.decode_raw': 'tf.io.decode_raw', 'tf.delete_session_tensor': 'tf.compat.v1.delete_session_tensor', - 'tf.depth_to_space': 'tf.nn.depth_to_space', + 'tf.depth_to_space': 'tf.compat.v1.depth_to_space', 'tf.dequantize': 'tf.quantization.dequantize', 'tf.deserialize_many_sparse': 'tf.io.deserialize_many_sparse', 'tf.diag': 'tf.linalg.tensor_diag', @@ -386,6 +386,7 @@ renames = { 'tf.nn.quantized_max_pool': 'tf.compat.v1.nn.quantized_max_pool', 'tf.nn.quantized_relu_x': 'tf.compat.v1.nn.quantized_relu_x', 'tf.nn.raw_rnn': 'tf.compat.v1.nn.raw_rnn', + 'tf.nn.relu_layer': 'tf.compat.v1.nn.relu_layer', 'tf.nn.rnn_cell.BasicLSTMCell': 'tf.compat.v1.nn.rnn_cell.BasicLSTMCell', 'tf.nn.rnn_cell.BasicRNNCell': 'tf.compat.v1.nn.rnn_cell.BasicRNNCell', 'tf.nn.rnn_cell.GRUCell': 'tf.compat.v1.nn.rnn_cell.GRUCell', @@ -511,7 +512,7 @@ renames = { 'tf.sets.set_size': 'tf.sets.size', 'tf.sets.set_union': 'tf.sets.union', 'tf.space_to_batch': 'tf.nn.space_to_batch', - 'tf.space_to_depth': 'tf.nn.space_to_depth', + 'tf.space_to_depth': 'tf.compat.v1.space_to_depth', 'tf.sparse.matmul': 'tf.sparse.sparse_dense_matmul', 'tf.sparse.merge': 'tf.compat.v1.sparse.merge', 'tf.sparse.placeholder': 'tf.compat.v1.sparse.placeholder', diff --git a/tensorflow/tools/compatibility/reorders_v2.py b/tensorflow/tools/compatibility/reorders_v2.py index 4152d37db9..76f71d5804 100644 --- a/tensorflow/tools/compatibility/reorders_v2.py +++ b/tensorflow/tools/compatibility/reorders_v2.py @@ -33,6 +33,7 @@ reorders = { 'tf.confusion_matrix': ['labels', 'predictions', 'num_classes', 'dtype', 'name', 'weights'], 'tf.convert_to_tensor': ['value', 'dtype', 'name', 'preferred_dtype'], 'tf.decode_csv': ['records', 'record_defaults', 'field_delim', 'use_quote_delim', 'name', 'na_value', 'select_cols'], + 'tf.depth_to_space': ['input', 'block_size', 'name', 'data_format'], 'tf.feature_column.categorical_column_with_vocabulary_file': ['key', 'vocabulary_file', 'vocabulary_size', 'num_oov_buckets', 'default_value', 'dtype'], 'tf.io.decode_csv': ['records', 'record_defaults', 'field_delim', 'use_quote_delim', 'name', 'na_value', 'select_cols'], 'tf.io.parse_example': ['serialized', 'features', 'name', 'example_names'], @@ -42,6 +43,7 @@ reorders = { 'tf.math.argmax': ['input', 'axis', 'name', 'dimension', 'output_type'], 'tf.math.argmin': ['input', 'axis', 'name', 'dimension', 'output_type'], 'tf.math.confusion_matrix': ['labels', 'predictions', 'num_classes', 'dtype', 'name', 'weights'], + 'tf.math.in_top_k': ['predictions', 'targets', 'k', 'name'], 'tf.math.reduce_all': ['input_tensor', 'axis', 'keepdims', 'name', 'reduction_indices', 'keep_dims'], 'tf.math.reduce_any': ['input_tensor', 'axis', 'keepdims', 'name', 'reduction_indices', 'keep_dims'], 'tf.math.reduce_logsumexp': ['input_tensor', 'axis', 'keepdims', 'name', 'reduction_indices', 'keep_dims'], @@ -53,10 +55,15 @@ reorders = { 'tf.multinomial': ['logits', 'num_samples', 'seed', 'name', 'output_dtype'], 'tf.nn.convolution': ['input', 'filter', 'padding', 'strides', 'dilation_rate', 'name', 'data_format'], 'tf.nn.crelu': ['features', 'name', 'axis'], + 'tf.nn.depth_to_space': ['input', 'block_size', 'name', 'data_format'], 'tf.nn.depthwise_conv2d': ['input', 'filter', 'strides', 'padding', 'rate', 'name', 'data_format'], + 'tf.nn.embedding_lookup': ['params', 'ids', 'partition_strategy', 'name', 'validate_indices', 'max_norm'], + 'tf.nn.embedding_lookup_sparse': ['params', 'sp_ids', 'sp_weights', 'partition_strategy', 'name', 'combiner', 'max_norm'], + 'tf.nn.in_top_k': ['predictions', 'targets', 'k', 'name'], 'tf.nn.moments': ['x', 'axes', 'shift', 'name', 'keep_dims'], 'tf.nn.pool': ['input', 'window_shape', 'pooling_type', 'padding', 'dilation_rate', 'strides', 'name', 'data_format'], 'tf.nn.separable_conv2d': ['input', 'depthwise_filter', 'pointwise_filter', 'strides', 'padding', 'rate', 'name', 'data_format'], + 'tf.nn.space_to_depth': ['input', 'block_size', 'name', 'data_format'], 'tf.nn.weighted_moments': ['x', 'axes', 'frequency_weights', 'name', 'keep_dims'], 'tf.pad': ['tensor', 'paddings', 'mode', 'name', 'constant_values'], 'tf.parse_example': ['serialized', 'features', 'name', 'example_names'], @@ -78,6 +85,7 @@ reorders = { 'tf.serialize_sparse': ['sp_input', 'name', 'out_type'], 'tf.shape': ['input', 'name', 'out_type'], 'tf.size': ['input', 'name', 'out_type'], + 'tf.space_to_depth': ['input', 'block_size', 'name', 'data_format'], 'tf.sparse.add': ['a', 'b', 'threshold', 'thresh'], 'tf.sparse.concat': ['axis', 'sp_inputs', 'name', 'expand_nonconcat_dim', 'concat_dim'], 'tf.sparse.segment_mean': ['data', 'indices', 'segment_ids', 'name', 'num_segments'], diff --git a/tensorflow/tools/compatibility/tf_upgrade_v2.py b/tensorflow/tools/compatibility/tf_upgrade_v2.py index f4165c9456..0630ce28cf 100644 --- a/tensorflow/tools/compatibility/tf_upgrade_v2.py +++ b/tensorflow/tools/compatibility/tf_upgrade_v2.py @@ -552,6 +552,12 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec): "tf.reduce_join", "tf.confusion_matrix", "tf.math.confusion_matrix", + "tf.math.in_top_k", + "tf.nn.depth_to_space", + "tf.nn.embedding_lookup", + "tf.nn.embedding_lookup_sparse", + "tf.nn.in_top_k", + "tf.nn.space_to_depth", } # Functions that were reordered should be changed to the new keyword args @@ -711,6 +717,11 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec): tf_01s_like_no_optimize_comment, "tf.ones_like": tf_01s_like_no_optimize_comment, + "tf.nn.embedding_lookup": + "WARNING: validate_indices argument has been removed.", + "tf.sparse.concat": + ("WARNING: expand_nonconcat_dim was renamed to " + "expand_nonconcat_dims."), } self.symbol_renames = { diff --git a/tensorflow/tools/compatibility/tf_upgrade_v2_test.py b/tensorflow/tools/compatibility/tf_upgrade_v2_test.py index 57700c07d6..b362a8081f 100644 --- a/tensorflow/tools/compatibility/tf_upgrade_v2_test.py +++ b/tensorflow/tools/compatibility/tf_upgrade_v2_test.py @@ -567,6 +567,54 @@ bazel-bin/tensorflow/tools/compatibility/update/generate_v2_reorders_map _, unused_report, unused_errors, new_text = self._upgrade(text) self.assertEqual(new_text, expected_text) + def testInTopK(self): + text = "tf.math.in_top_k(a, b, c, n)" + expected_text = ( + "tf.math.in_top_k(predictions=a, targets=b, k=c, name=n)") + _, unused_report, unused_errors, new_text = self._upgrade(text) + self.assertEqual(new_text, expected_text) + + def testDepthToSpace(self): + text = "tf.nn.depth_to_space(input, block_size, name, data_format)" + expected_text = ( + "tf.nn.depth_to_space(input=input, block_size=block_size, " + "name=name, data_format=data_format)") + _, unused_report, unused_errors, new_text = self._upgrade(text) + self.assertEqual(new_text, expected_text) + + def testEmbeddingLookup(self): + text = ("tf.nn.embedding_lookup(params, ids, partition_strategy, name, " + "validate_indices, max_norm)") + expected_text = ("tf.nn.embedding_lookup(params=params, ids=ids, " + "partition_strategy=partition_strategy, name=name, " + "validate_indices=validate_indices, max_norm=max_norm)") + _, unused_report, unused_errors, new_text = self._upgrade(text) + self.assertEqual(new_text, expected_text) + + def testEmbeddingLookupSparse(self): + text = ("tf.nn.embedding_lookup_sparse(params, sp_ids, sp_weights, " + "partition_strategy, name, combiner, max_norm)") + expected_text = ("tf.nn.embedding_lookup_sparse(params=params, " + "sp_ids=sp_ids, sp_weights=sp_weights, " + "partition_strategy=partition_strategy, name=name, " + "combiner=combiner, max_norm=max_norm)") + _, unused_report, unused_errors, new_text = self._upgrade(text) + self.assertEqual(new_text, expected_text) + + def testNnInTopK(self): + text = "tf.nn.in_top_k(predictions, targets, k, name)" + expected_text = ("tf.nn.in_top_k(predictions=predictions, " + "targets=targets, k=k, name=name)") + _, unused_report, unused_errors, new_text = self._upgrade(text) + self.assertEqual(new_text, expected_text) + + def testSpaceToDepth(self): + text = "tf.nn.space_to_depth(input, block_size, name, data_format)" + expected_text = ("tf.nn.space_to_depth(input=input, block_size=block_size, " + "name=name, data_format=data_format)") + _, unused_report, unused_errors, new_text = self._upgrade(text) + self.assertEqual(new_text, expected_text) + class TestUpgradeFiles(test_util.TensorFlowTestCase): -- GitLab From ef46e5d3a3e3095517b7528890704230139d95c1 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 6 Dec 2018 11:40:10 -0800 Subject: [PATCH 138/873] Internal change. PiperOrigin-RevId: 224377474 --- tensorflow/lite/delegates/nnapi/BUILD | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/tensorflow/lite/delegates/nnapi/BUILD b/tensorflow/lite/delegates/nnapi/BUILD index c24f0f71ac..fd954ba222 100644 --- a/tensorflow/lite/delegates/nnapi/BUILD +++ b/tensorflow/lite/delegates/nnapi/BUILD @@ -23,10 +23,7 @@ tf_cc_test( name = "nnapi_delegate_test", size = "small", srcs = ["nnapi_delegate_test.cc"], - tags = [ - "no_oss", - "noasan", # TODO(b/112326936): re-enable for asan once fixed. - ], + tags = ["no_oss"], deps = [ ":nnapi_delegate", "//tensorflow/lite:framework", -- GitLab From 52aac384bf12ced33ea1ef3ec7bc8183a9cf4ca0 Mon Sep 17 00:00:00 2001 From: Dalmo Cirne Date: Wed, 31 Oct 2018 05:43:57 -0400 Subject: [PATCH 139/873] Fix downloading protobuf dependency Protobuf has been failing due to its repository having dependencies. Downloading the .tar.gz form GitHub doesn't work because it does not bring its dependencies with it. This PR introduces a function to clone a repository, initializa and update its submodules. The function is used to download protobuf as a repository dependency, rather than just a file. The function is also implemented in a generic way, so if in the future other dependencies fall into the same situation, it can be reused. With these changes, `tensorflow/workspace.bzl` goes back to using the commit sha in `PROTOBUF_URLS` and `PROTOBUF_STRIP_PREFIX`, rather than the tag of a release. A command at the end of the `download_dependencies.sh` script was commented out. The comment says for it to be removed once protobug is fixed. Perhaps no longer necessary. --- .../contrib/makefile/download_dependencies.sh | 43 +++++++++++++++---- 1 file changed, 35 insertions(+), 8 deletions(-) diff --git a/tensorflow/contrib/makefile/download_dependencies.sh b/tensorflow/contrib/makefile/download_dependencies.sh index b396c52767..d22e2c179b 100755 --- a/tensorflow/contrib/makefile/download_dependencies.sh +++ b/tensorflow/contrib/makefile/download_dependencies.sh @@ -30,11 +30,13 @@ EIGEN_URL="$(grep -o 'http.*bitbucket.org/eigen/eigen/get/.*tar\.gz' "${BZL_FILE GEMMLOWP_URL="$(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' "${BZL_FILE_PATH}" | head -n1)" -# Note: The Protobuf source in `tensorflow/workspace.bzl` in TensorFlow -# 1.10 branch does not work. `make distclean` fails and blocks the build -# process. For now we're hardcoding to the version which is used by -# TensorFlow 1.9. -PROTOBUF_URL="https://mirror.bazel.build/github.com/google/protobuf/archive/v3.6.0.tar.gz" + +# Note: The protobuf repo needs to be cloned due to its submodules. +# These variables contain the GitHub repo and the sha, from `tensorflow/workspace.bzl`, +# from which to clone it from and checkout to. +readonly PROTOBUF_REPO="https://github.com/protocolbuffers/protobuf.git" +readonly PROTOBUF_SHA="$(grep -o 'https://github.com/google/protobuf/archive/.*tar\.gz' "${BZL_FILE_PATH}" | head -n1 | awk '{print substr($0, index($0, "archive") + 8, 40) }')" + # TODO (yongtang): Replace the following with 'https://mirror.bazel.build/github.com/google/re2/.*tar\.gz' once # the archive has been propagated in mirror.bazel.build. RE2_URL="$(grep -o 'https://github.com/google/re2/.*tar\.gz' "${BZL_FILE_PATH}" | head -n1)" @@ -91,11 +93,34 @@ download_and_extract() { find "${dir}" -type f -name '*BUILD' -delete } +function clone_repository() { + local repo_url="${1}" + local destination_directory="${2}" + local commit_sha="${3}" + + if [[ -d "${destination_directory}" ]]; then + rm -rf "${destination_directory}" + fi + + git clone "${repo_url}" "${destination_directory}" + + pushd "$(pwd)" 1>/dev/null + + cd "${destination_directory}" + + if [[ -n "${commit_sha}" ]]; then + git checkout "${PROTOBUF_SHA}" + fi + + git submodule update --init + + popd 1>/dev/null +} + download_and_extract "${EIGEN_URL}" "${DOWNLOADS_DIR}/eigen" download_and_extract "${GEMMLOWP_URL}" "${DOWNLOADS_DIR}/gemmlowp" download_and_extract "${GOOGLETEST_URL}" "${DOWNLOADS_DIR}/googletest" download_and_extract "${NSYNC_URL}" "${DOWNLOADS_DIR}/nsync" -download_and_extract "${PROTOBUF_URL}" "${DOWNLOADS_DIR}/protobuf" download_and_extract "${RE2_URL}" "${DOWNLOADS_DIR}/re2" download_and_extract "${FFT2D_URL}" "${DOWNLOADS_DIR}/fft2d" download_and_extract "${DOUBLE_CONVERSION_URL}" "${DOWNLOADS_DIR}/double_conversion" @@ -106,6 +131,8 @@ download_and_extract "${CUB_URL}" "${DOWNLOADS_DIR}/cub/external/cub_archive" download_and_extract "${FARMHASH_URL}" "${DOWNLOADS_DIR}/farmhash" download_and_extract "${FLATBUFFERS_URL}" "${DOWNLOADS_DIR}/flatbuffers" +clone_repository "${PROTOBUF_REPO}" "${DOWNLOADS_DIR}/protobuf" "${PROTOBUF_SHA}" + replace_by_sed 's#static uint32x4_t p4ui_CONJ_XOR = vld1q_u32( conj_XOR_DATA );#static uint32x4_t p4ui_CONJ_XOR; // = vld1q_u32( conj_XOR_DATA ); - Removed by script#' \ "${DOWNLOADS_DIR}/eigen/Eigen/src/Core/arch/NEON/Complex.h" replace_by_sed 's#static uint32x2_t p2ui_CONJ_XOR = vld1_u32( conj_XOR_DATA );#static uint32x2_t p2ui_CONJ_XOR;// = vld1_u32( conj_XOR_DATA ); - Removed by scripts#' \ @@ -113,7 +140,7 @@ replace_by_sed 's#static uint32x2_t p2ui_CONJ_XOR = vld1_u32( conj_XOR_DATA );#s replace_by_sed 's#static uint64x2_t p2ul_CONJ_XOR = vld1q_u64( p2ul_conj_XOR_DATA );#static uint64x2_t p2ul_CONJ_XOR;// = vld1q_u64( p2ul_conj_XOR_DATA ); - Removed by script#' \ "${DOWNLOADS_DIR}/eigen/Eigen/src/Core/arch/NEON/Complex.h" # TODO(satok): Remove this once protobuf/autogen.sh is fixed. -replace_by_sed 's#https://googlemock.googlecode.com/files/gmock-1.7.0.zip#http://download.tensorflow.org/deps/gmock-1.7.0.zip#' \ - "${DOWNLOADS_DIR}/protobuf/autogen.sh" +# replace_by_sed 's#https://googlemock.googlecode.com/files/gmock-1.7.0.zip#http://download.tensorflow.org/deps/gmock-1.7.0.zip#' \ +# "${DOWNLOADS_DIR}/protobuf/autogen.sh" echo "download_dependencies.sh completed successfully." >&2 -- GitLab From a3a6ab05170eb95221e7ae486d30197f874ea784 Mon Sep 17 00:00:00 2001 From: Dalmo Cirne Date: Thu, 1 Nov 2018 21:21:45 -0400 Subject: [PATCH 140/873] Add comment to indicate the protobuf version --- tensorflow/workspace.bzl | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index e6b4a89e3b..9e59f1c710 100755 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -346,6 +346,7 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""): }, ) + # The commit sha in the URLs correspond to v3.6.0 of protobuf PROTOBUF_URLS = [ "https://mirror.bazel.build/github.com/google/protobuf/archive/v3.6.1.2.tar.gz", "https://github.com/google/protobuf/archive/v3.6.1.2.tar.gz", -- GitLab From 1aeb29692a7e3e285c1572fca1dec9824c520962 Mon Sep 17 00:00:00 2001 From: Dalmo Cirne Date: Thu, 6 Dec 2018 14:07:02 -0500 Subject: [PATCH 141/873] Update the Flatbuffer and Protobuf versions --- tensorflow/contrib/makefile/download_dependencies.sh | 2 +- tensorflow/workspace.bzl | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/tensorflow/contrib/makefile/download_dependencies.sh b/tensorflow/contrib/makefile/download_dependencies.sh index d22e2c179b..6582ba793b 100755 --- a/tensorflow/contrib/makefile/download_dependencies.sh +++ b/tensorflow/contrib/makefile/download_dependencies.sh @@ -47,7 +47,7 @@ CUB_URL="$(grep -o 'https.*cub/archive.*zip' "${BZL_FILE_PATH}" | grep -v mirror # Required for TensorFlow Lite Flex runtime. FARMHASH_URL="https://mirror.bazel.build/github.com/google/farmhash/archive/816a4ae622e964763ca0862d9dbd19324a1eaf45.tar.gz" -FLATBUFFERS_URL="https://github.com/google/flatbuffers/archive/1f5eae5d6a135ff6811724f6c57f911d1f46bb15.tar.gz" +FLATBUFFERS_URL="https://github.com/google/flatbuffers/archive/v1.9.0.tar.gz" # TODO(petewarden): Some new code in Eigen triggers a clang bug with iOS arm64, # so work around it by patching the source. diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 9e59f1c710..e6b4a89e3b 100755 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -346,7 +346,6 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""): }, ) - # The commit sha in the URLs correspond to v3.6.0 of protobuf PROTOBUF_URLS = [ "https://mirror.bazel.build/github.com/google/protobuf/archive/v3.6.1.2.tar.gz", "https://github.com/google/protobuf/archive/v3.6.1.2.tar.gz", -- GitLab From 6ef428bd6e83b0930266bf922eaa2f4a60e8328a Mon Sep 17 00:00:00 2001 From: Dalmo Cirne Date: Thu, 6 Dec 2018 14:42:06 -0500 Subject: [PATCH 142/873] Fix URLs for protobuf 3.6.1.2 --- tensorflow/workspace.bzl | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index e6b4a89e3b..6712925b49 100755 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -347,8 +347,8 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""): ) PROTOBUF_URLS = [ - "https://mirror.bazel.build/github.com/google/protobuf/archive/v3.6.1.2.tar.gz", - "https://github.com/google/protobuf/archive/v3.6.1.2.tar.gz", + "https://mirror.bazel.build/github.com/protocolbuffers/protobuf/archive/v3.6.1.2.tar.gz", + "https://github.com/protocolbuffers/protobuf/archive/v3.6.1.2.tar.gz", ] PROTOBUF_SHA256 = "2244b0308846bb22b4ff0bcc675e99290ff9f1115553ae9671eba1030af31bc0" PROTOBUF_STRIP_PREFIX = "protobuf-3.6.1.2" -- GitLab From 0adae252576e5cdafab6598cbb0522fb3c703bf3 Mon Sep 17 00:00:00 2001 From: Derek Murray Date: Thu, 6 Dec 2018 11:50:41 -0800 Subject: [PATCH 143/873] Replace uses of deprecated `Dataset.make_one_shot_iterator()` with `tf.compat.v1.data.make_one_shot_iterator()`. PiperOrigin-RevId: 224379500 --- .../tpu/python/tpu/tpu_estimator_signals_test.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator_signals_test.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator_signals_test.py index 55235556de..e3ea983abf 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator_signals_test.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator_signals_test.py @@ -21,8 +21,8 @@ from __future__ import print_function import numpy as np from tensorflow.contrib.tpu.python.tpu import tpu_estimator -from tensorflow.python import data as dataset_lib from tensorflow.python.client import session +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 @@ -34,10 +34,10 @@ def make_input_fn(num_samples): def input_fn(params): batch_size = params['batch_size'] - da1 = dataset_lib.Dataset.from_tensor_slices(a) - da2 = dataset_lib.Dataset.from_tensor_slices(b) + da1 = dataset_ops.Dataset.from_tensor_slices(a) + da2 = dataset_ops.Dataset.from_tensor_slices(b) - dataset = dataset_lib.Dataset.zip((da1, da2)) + dataset = dataset_ops.Dataset.zip((da1, da2)) dataset = dataset.map(lambda fa, fb: {'a': fa, 'b': fb}) dataset = dataset.batch(batch_size) return dataset @@ -50,10 +50,10 @@ def make_input_fn_with_labels(num_samples): def input_fn(params): batch_size = params['batch_size'] - da1 = dataset_lib.Dataset.from_tensor_slices(a) - da2 = dataset_lib.Dataset.from_tensor_slices(b) + da1 = dataset_ops.Dataset.from_tensor_slices(a) + da2 = dataset_ops.Dataset.from_tensor_slices(b) - dataset = dataset_lib.Dataset.zip((da1, da2)) + dataset = dataset_ops.Dataset.zip((da1, da2)) dataset = dataset.map(lambda fa, fb: ({'a': fa}, fb)) dataset = dataset.batch(batch_size) return dataset @@ -71,7 +71,7 @@ class TPUEstimatorStoppingSignalsTest(test.TestCase): with ops.Graph().as_default(): dataset = input_fn(params) - features = dataset_lib.make_one_shot_iterator(dataset).get_next() + features = dataset_ops.make_one_shot_iterator(dataset).get_next() # With tf.data.Dataset.batch, the batch is None, i.e., dynamic shape. self.assertIsNone(features['a'].shape.as_list()[0]) -- GitLab From 0fb46cf9139f1e8cf88195e0b8f555eb10d00b3b Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 6 Dec 2018 11:53:13 -0800 Subject: [PATCH 144/873] Update imports to point to new location of distribution_strategy_context.py. PiperOrigin-RevId: 224380057 --- tensorflow/contrib/metrics/python/metrics/classification.py | 2 +- tensorflow/contrib/optimizer_v2/optimizer_v2.py | 2 +- tensorflow/python/keras/layers/normalization.py | 2 +- tensorflow/python/keras/metrics.py | 2 +- tensorflow/python/keras/optimizer_v2/optimizer_v2.py | 2 +- tensorflow/python/keras/optimizers.py | 2 +- tensorflow/python/ops/metrics_impl.py | 2 +- tensorflow/python/ops/summary_op_util.py | 2 +- tensorflow/python/training/checkpoint_utils.py | 2 +- tensorflow/python/training/moving_averages.py | 2 +- tensorflow/python/training/optimizer.py | 2 +- tensorflow/python/training/slot_creator.py | 2 +- tensorflow/python/training/sync_replicas_optimizer.py | 2 +- 13 files changed, 13 insertions(+), 13 deletions(-) diff --git a/tensorflow/contrib/metrics/python/metrics/classification.py b/tensorflow/contrib/metrics/python/metrics/classification.py index 062deb74b1..9aabc4bec3 100644 --- a/tensorflow/contrib/metrics/python/metrics/classification.py +++ b/tensorflow/contrib/metrics/python/metrics/classification.py @@ -18,13 +18,13 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.python.distribute import distribution_strategy_context 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 metrics_impl from tensorflow.python.ops import variable_scope -from tensorflow.python.training import distribution_strategy_context # TODO(nsilberman): move into metrics/python/ops/ diff --git a/tensorflow/contrib/optimizer_v2/optimizer_v2.py b/tensorflow/contrib/optimizer_v2/optimizer_v2.py index 73a556f0b2..11a9248a01 100644 --- a/tensorflow/contrib/optimizer_v2/optimizer_v2.py +++ b/tensorflow/contrib/optimizer_v2/optimizer_v2.py @@ -25,6 +25,7 @@ import abc import six from tensorflow.python.distribute import distribute_lib +from tensorflow.python.distribute import distribution_strategy_context as distribute_ctx from tensorflow.python.distribute import reduce_util as ds_reduce_util from tensorflow.python.eager import backprop from tensorflow.python.eager import context @@ -36,7 +37,6 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables -from tensorflow.python.training import distribution_strategy_context as distribute_ctx from tensorflow.python.training import optimizer as optimizer_v1 from tensorflow.python.training import slot_creator from tensorflow.python.training.checkpointable import base as checkpointable diff --git a/tensorflow/python/keras/layers/normalization.py b/tensorflow/python/keras/layers/normalization.py index 37894a3d3d..75b10222ed 100644 --- a/tensorflow/python/keras/layers/normalization.py +++ b/tensorflow/python/keras/layers/normalization.py @@ -21,6 +21,7 @@ from __future__ import print_function import contextlib from tensorflow.python import tf2 +from tensorflow.python.distribute import distribution_strategy_context from tensorflow.python.eager import context from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -39,7 +40,6 @@ from tensorflow.python.ops import nn from tensorflow.python.ops import state_ops from tensorflow.python.ops import variables as tf_variables from tensorflow.python.platform import tf_logging as logging -from tensorflow.python.training import distribution_strategy_context from tensorflow.python.util.tf_export import tf_export diff --git a/tensorflow/python/keras/metrics.py b/tensorflow/python/keras/metrics.py index 3c2682e4c6..1d3c6e67d7 100644 --- a/tensorflow/python/keras/metrics.py +++ b/tensorflow/python/keras/metrics.py @@ -28,6 +28,7 @@ from enum import Enum import numpy as np import six +from tensorflow.python.distribute import distribution_strategy_context from tensorflow.python.eager import context from tensorflow.python.eager import function from tensorflow.python.framework import dtypes @@ -60,7 +61,6 @@ from tensorflow.python.ops import nn from tensorflow.python.ops import state_ops from tensorflow.python.ops import variables as tf_variables from tensorflow.python.ops import weights_broadcast_ops -from tensorflow.python.training import distribution_strategy_context from tensorflow.python.util import tf_decorator from tensorflow.python.util.tf_export import tf_export from tensorflow.tools.docs import doc_controls diff --git a/tensorflow/python/keras/optimizer_v2/optimizer_v2.py b/tensorflow/python/keras/optimizer_v2/optimizer_v2.py index b26b3cefc8..e6cd52c817 100644 --- a/tensorflow/python/keras/optimizer_v2/optimizer_v2.py +++ b/tensorflow/python/keras/optimizer_v2/optimizer_v2.py @@ -24,6 +24,7 @@ import abc import six +from tensorflow.python.distribute import distribution_strategy_context from tensorflow.python.distribute import reduce_util as ds_reduce_util from tensorflow.python.eager import backprop from tensorflow.python.eager import context @@ -36,7 +37,6 @@ from tensorflow.python.ops import gradients from tensorflow.python.ops import math_ops from tensorflow.python.ops import variables as tf_variables from tensorflow.python.platform import tf_logging as logging -from tensorflow.python.training import distribution_strategy_context from tensorflow.python.training import optimizer as optimizer_v1 from tensorflow.python.util import nest diff --git a/tensorflow/python/keras/optimizers.py b/tensorflow/python/keras/optimizers.py index 10466eb573..ee6dbba5ad 100644 --- a/tensorflow/python/keras/optimizers.py +++ b/tensorflow/python/keras/optimizers.py @@ -23,6 +23,7 @@ import six from six.moves import zip # pylint: disable=redefined-builtin from tensorflow.python import tf2 +from tensorflow.python.distribute import distribution_strategy_context from tensorflow.python.framework import ops from tensorflow.python.keras import backend as K from tensorflow.python.keras.optimizer_v2 import adadelta as adadelta_v2 @@ -38,7 +39,6 @@ from tensorflow.python.keras.utils.generic_utils import serialize_keras_object from tensorflow.python.ops import clip_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import state_ops -from tensorflow.python.training import distribution_strategy_context from tensorflow.python.training import optimizer as tf_optimizer_module from tensorflow.python.training import training_util from tensorflow.python.training.checkpointable import base as checkpointable diff --git a/tensorflow/python/ops/metrics_impl.py b/tensorflow/python/ops/metrics_impl.py index cb42199011..ec39b1790e 100644 --- a/tensorflow/python/ops/metrics_impl.py +++ b/tensorflow/python/ops/metrics_impl.py @@ -18,6 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.python.distribute import distribution_strategy_context from tensorflow.python.eager import context from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -34,7 +35,6 @@ from tensorflow.python.ops import state_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import weights_broadcast_ops from tensorflow.python.platform import tf_logging as logging -from tensorflow.python.training import distribution_strategy_context from tensorflow.python.util.deprecation import deprecated from tensorflow.python.util.tf_export import tf_export diff --git a/tensorflow/python/ops/summary_op_util.py b/tensorflow/python/ops/summary_op_util.py index c72a9aefc3..93d8d50842 100644 --- a/tensorflow/python/ops/summary_op_util.py +++ b/tensorflow/python/ops/summary_op_util.py @@ -21,10 +21,10 @@ from __future__ import print_function import contextlib import re +from tensorflow.python.distribute import distribution_strategy_context from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.platform import tf_logging -from tensorflow.python.training import distribution_strategy_context def collect(val, collections, default_collections): diff --git a/tensorflow/python/training/checkpoint_utils.py b/tensorflow/python/training/checkpoint_utils.py index 58166dbb68..99b1f4c0d7 100644 --- a/tensorflow/python/training/checkpoint_utils.py +++ b/tensorflow/python/training/checkpoint_utils.py @@ -21,6 +21,7 @@ from __future__ import print_function import six from tensorflow.python import pywrap_tensorflow +from tensorflow.python.distribute import distribution_strategy_context from tensorflow.python.framework import ops from tensorflow.python.ops import io_ops from tensorflow.python.ops import resource_variable_ops @@ -29,7 +30,6 @@ from tensorflow.python.ops import variables from tensorflow.python.platform import gfile from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import checkpoint_management -from tensorflow.python.training import distribution_strategy_context from tensorflow.python.training import saver from tensorflow.python.util.tf_export import tf_export diff --git a/tensorflow/python/training/moving_averages.py b/tensorflow/python/training/moving_averages.py index 8785f9a8e7..72670f0ca3 100644 --- a/tensorflow/python/training/moving_averages.py +++ b/tensorflow/python/training/moving_averages.py @@ -17,6 +17,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.python.distribute import distribution_strategy_context from tensorflow.python.distribute import reduce_util as ds_reduce_util from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -26,7 +27,6 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops import state_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables -from tensorflow.python.training import distribution_strategy_context from tensorflow.python.training import slot_creator from tensorflow.python.util.tf_export import tf_export diff --git a/tensorflow/python/training/optimizer.py b/tensorflow/python/training/optimizer.py index a9508b862a..d9ebdcad1f 100644 --- a/tensorflow/python/training/optimizer.py +++ b/tensorflow/python/training/optimizer.py @@ -25,6 +25,7 @@ import abc import six from tensorflow.python.distribute import distribute_lib +from tensorflow.python.distribute import distribution_strategy_context as distribute_ctx from tensorflow.python.distribute import reduce_util as ds_reduce_util from tensorflow.python.eager import backprop from tensorflow.python.eager import context @@ -38,7 +39,6 @@ from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import state_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables -from tensorflow.python.training import distribution_strategy_context as distribute_ctx from tensorflow.python.training import slot_creator from tensorflow.python.training.checkpointable import base as checkpointable from tensorflow.python.util import nest diff --git a/tensorflow/python/training/slot_creator.py b/tensorflow/python/training/slot_creator.py index d76b22acd8..ecf5a96ed4 100644 --- a/tensorflow/python/training/slot_creator.py +++ b/tensorflow/python/training/slot_creator.py @@ -39,13 +39,13 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.python.distribute import distribution_strategy_context from tensorflow.python.eager import context from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables -from tensorflow.python.training import distribution_strategy_context def _create_slot_var(primary, val, scope, validate_shape, shape, dtype): diff --git a/tensorflow/python/training/sync_replicas_optimizer.py b/tensorflow/python/training/sync_replicas_optimizer.py index 501d9bc8d3..cd4590db7f 100644 --- a/tensorflow/python/training/sync_replicas_optimizer.py +++ b/tensorflow/python/training/sync_replicas_optimizer.py @@ -19,6 +19,7 @@ from __future__ import division from __future__ import print_function from tensorflow.core.framework import types_pb2 +from tensorflow.python.distribute import distribution_strategy_context from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops @@ -27,7 +28,6 @@ from tensorflow.python.ops import state_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.platform import tf_logging as logging -from tensorflow.python.training import distribution_strategy_context from tensorflow.python.training import optimizer from tensorflow.python.training import queue_runner from tensorflow.python.training import session_manager -- GitLab From 5bedbfd6211aedadb70b40a8e2729cb792754ff3 Mon Sep 17 00:00:00 2001 From: Dalmo Cirne Date: Thu, 6 Dec 2018 15:13:04 -0500 Subject: [PATCH 145/873] Updates from the PR review `PROTOBUF_SHA` renamed to `PROTOBUF_TAG`. The variable is used to checkout to a specific tag after cloning the protobuf repository. The value is obtained by parsing the protobuf URL in the `workspace.bzl` file. --- tensorflow/contrib/makefile/download_dependencies.sh | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/tensorflow/contrib/makefile/download_dependencies.sh b/tensorflow/contrib/makefile/download_dependencies.sh index 6582ba793b..2a5232b476 100755 --- a/tensorflow/contrib/makefile/download_dependencies.sh +++ b/tensorflow/contrib/makefile/download_dependencies.sh @@ -35,7 +35,7 @@ NSYNC_URL="$(grep -o 'https://mirror.bazel.build/github.com/google/nsync/.*tar\. # These variables contain the GitHub repo and the sha, from `tensorflow/workspace.bzl`, # from which to clone it from and checkout to. readonly PROTOBUF_REPO="https://github.com/protocolbuffers/protobuf.git" -readonly PROTOBUF_SHA="$(grep -o 'https://github.com/google/protobuf/archive/.*tar\.gz' "${BZL_FILE_PATH}" | head -n1 | awk '{print substr($0, index($0, "archive") + 8, 40) }')" +readonly PROTOBUF_TAG="$(grep -o 'https://github.com/protocolbuffers/protobuf/archive/.*tar\.gz' "${BZL_FILE_PATH}" | head -n1 | awk '{print substr($0, index($0, "archive") + 8, index($0, "tar") - index($0, "archive") - 9) }')" # TODO (yongtang): Replace the following with 'https://mirror.bazel.build/github.com/google/re2/.*tar\.gz' once # the archive has been propagated in mirror.bazel.build. @@ -47,7 +47,7 @@ CUB_URL="$(grep -o 'https.*cub/archive.*zip' "${BZL_FILE_PATH}" | grep -v mirror # Required for TensorFlow Lite Flex runtime. FARMHASH_URL="https://mirror.bazel.build/github.com/google/farmhash/archive/816a4ae622e964763ca0862d9dbd19324a1eaf45.tar.gz" -FLATBUFFERS_URL="https://github.com/google/flatbuffers/archive/v1.9.0.tar.gz" +FLATBUFFERS_URL="https://github.com/google/flatbuffers/archive/1f5eae5d6a135ff6811724f6c57f911d1f46bb15.tar.gz" # TODO(petewarden): Some new code in Eigen triggers a clang bug with iOS arm64, # so work around it by patching the source. @@ -109,7 +109,7 @@ function clone_repository() { cd "${destination_directory}" if [[ -n "${commit_sha}" ]]; then - git checkout "${PROTOBUF_SHA}" + git checkout "${PROTOBUF_TAG}" fi git submodule update --init @@ -131,7 +131,7 @@ download_and_extract "${CUB_URL}" "${DOWNLOADS_DIR}/cub/external/cub_archive" download_and_extract "${FARMHASH_URL}" "${DOWNLOADS_DIR}/farmhash" download_and_extract "${FLATBUFFERS_URL}" "${DOWNLOADS_DIR}/flatbuffers" -clone_repository "${PROTOBUF_REPO}" "${DOWNLOADS_DIR}/protobuf" "${PROTOBUF_SHA}" +clone_repository "${PROTOBUF_REPO}" "${DOWNLOADS_DIR}/protobuf" "${PROTOBUF_TAG}" replace_by_sed 's#static uint32x4_t p4ui_CONJ_XOR = vld1q_u32( conj_XOR_DATA );#static uint32x4_t p4ui_CONJ_XOR; // = vld1q_u32( conj_XOR_DATA ); - Removed by script#' \ "${DOWNLOADS_DIR}/eigen/Eigen/src/Core/arch/NEON/Complex.h" @@ -140,7 +140,7 @@ replace_by_sed 's#static uint32x2_t p2ui_CONJ_XOR = vld1_u32( conj_XOR_DATA );#s replace_by_sed 's#static uint64x2_t p2ul_CONJ_XOR = vld1q_u64( p2ul_conj_XOR_DATA );#static uint64x2_t p2ul_CONJ_XOR;// = vld1q_u64( p2ul_conj_XOR_DATA ); - Removed by script#' \ "${DOWNLOADS_DIR}/eigen/Eigen/src/Core/arch/NEON/Complex.h" # TODO(satok): Remove this once protobuf/autogen.sh is fixed. -# replace_by_sed 's#https://googlemock.googlecode.com/files/gmock-1.7.0.zip#http://download.tensorflow.org/deps/gmock-1.7.0.zip#' \ -# "${DOWNLOADS_DIR}/protobuf/autogen.sh" +replace_by_sed 's#https://googlemock.googlecode.com/files/gmock-1.7.0.zip#http://download.tensorflow.org/deps/gmock-1.7.0.zip#' \ + "${DOWNLOADS_DIR}/protobuf/autogen.sh" echo "download_dependencies.sh completed successfully." >&2 -- GitLab From 2947a2851011782a020d59ef8624d191bef5c279 Mon Sep 17 00:00:00 2001 From: Jiri Simsa Date: Thu, 6 Dec 2018 12:00:29 -0800 Subject: [PATCH 146/873] [tf.data] Add documentation for supporting `tf.data.experimental.AUTOTUNE` as a value for the `num_parallel_calls` argument of `map()`, `interleave()`, and `map_and_batch()`. PiperOrigin-RevId: 224381264 --- tensorflow/python/data/experimental/ops/batching.py | 7 ++++--- tensorflow/python/data/ops/dataset_ops.py | 8 ++++++-- 2 files changed, 10 insertions(+), 5 deletions(-) diff --git a/tensorflow/python/data/experimental/ops/batching.py b/tensorflow/python/data/experimental/ops/batching.py index 9db9a28dd8..f70a4e0537 100644 --- a/tensorflow/python/data/experimental/ops/batching.py +++ b/tensorflow/python/data/experimental/ops/batching.py @@ -626,9 +626,10 @@ def map_and_batch(map_func, whether the last batch should be dropped in case its size is smaller than desired; the default behavior is not to drop the smaller batch. num_parallel_calls: (Optional.) A `tf.int32` scalar `tf.Tensor`, - representing the number of elements to process in parallel. If not - specified, `batch_size * num_parallel_batches` elements will be - processed in parallel. + representing the number of elements to process in parallel. If not + specified, `batch_size * num_parallel_batches` elements will be processed + in parallel. If the value `tf.data.experimental.AUTOTUNE` is used, then + the number of parallel calls is set dynamically based on available CPU. Returns: A `Dataset` transformation function, which can be passed to diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py index a08a2b5787..cd7b1a4a5a 100644 --- a/tensorflow/python/data/ops/dataset_ops.py +++ b/tensorflow/python/data/ops/dataset_ops.py @@ -947,7 +947,9 @@ class DatasetV2(object): `self.output_types`) to another nested structure of tensors. num_parallel_calls: (Optional.) A `tf.int32` scalar `tf.Tensor`, representing the number elements to process in parallel. If not - specified, elements will be processed sequentially. + specified, elements will be processed sequentially. If the value + `tf.data.experimental.AUTOTUNE` is used, then the number of parallel + calls is set dynamically based on available CPU. Returns: Dataset: A `Dataset`. @@ -1058,7 +1060,9 @@ class DatasetV2(object): num_parallel_calls: (Optional.) If specified, the implementation creates a threadpool, which is used to fetch inputs from cycle elements asynchronously and in parallel. The default behavior is to fetch inputs - from cycle elements synchronously with no parallelism. + from cycle elements synchronously with no parallelism. If the value + `tf.data.experimental.AUTOTUNE` is used, then the number of parallel + calls is set dynamically based on available CPU. Returns: Dataset: A `Dataset`. -- GitLab From 51561e0354a1556e848c920cd47a718360dc720d Mon Sep 17 00:00:00 2001 From: Yuefeng Zhou Date: Thu, 6 Dec 2018 12:25:27 -0800 Subject: [PATCH 147/873] Use coordinator in distribute coordinator and independent_worker tests. PiperOrigin-RevId: 224385727 --- .../python/estimator_training_test.py | 7 ++-- .../python/multi_worker_test_base.py | 10 ++++-- .../distribute/distribute_coordinator.py | 35 +++++++++++++------ 3 files changed, 36 insertions(+), 16 deletions(-) diff --git a/tensorflow/contrib/distribute/python/estimator_training_test.py b/tensorflow/contrib/distribute/python/estimator_training_test.py index b369a7fefe..3f55a8a1c8 100644 --- a/tensorflow/contrib/distribute/python/estimator_training_test.py +++ b/tensorflow/contrib/distribute/python/estimator_training_test.py @@ -375,11 +375,13 @@ class DistributeCoordinatorIntegrationTest( threads = self.run_multiple_tasks_in_threads(self._independent_worker_fn, cluster_spec, train_distribute, eval_distribute) + threads_to_join = [] for task_type, ts in threads.items(): if task_type == PS: continue for t in ts: - t.join() + threads_to_join.append(t) + self.join_independent_workers(threads_to_join) estimator = self._get_estimator(train_distribute, eval_distribute) self._inspect_train_and_eval_events(estimator) @@ -413,8 +415,7 @@ class DistributeCoordinatorIntegrationTest( threads = self.run_multiple_tasks_in_threads(self._independent_worker_fn, cluster_spec, train_distribute, eval_distribute) - threads[WORKER][0].join() - threads[EVALUATOR][0].join() + self.join_independent_workers([threads[WORKER][0], threads[EVALUATOR][0]]) estimator = self._get_estimator(train_distribute, eval_distribute) self._inspect_train_and_eval_events(estimator) diff --git a/tensorflow/contrib/distribute/python/multi_worker_test_base.py b/tensorflow/contrib/distribute/python/multi_worker_test_base.py index 147c9b83f8..b05aac431f 100644 --- a/tensorflow/contrib/distribute/python/multi_worker_test_base.py +++ b/tensorflow/contrib/distribute/python/multi_worker_test_base.py @@ -40,6 +40,7 @@ from tensorflow.python.client import session from tensorflow.python.estimator import run_config from tensorflow.python.platform import test from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.training import coordinator from tensorflow.python.training import server_lib ASSIGNED_PORTS = set() @@ -360,6 +361,7 @@ class IndependentWorkerTestBase(test.TestCase): self._mock_os_env = MockOsEnv() self._mock_context = test.mock.patch.object(os, 'environ', self._mock_os_env) + self._coord = coordinator.Coordinator() super(IndependentWorkerTestBase, self).setUp() self._mock_context.__enter__() @@ -368,8 +370,9 @@ class IndependentWorkerTestBase(test.TestCase): super(IndependentWorkerTestBase, self).tearDown() def _task_thread(self, task_fn, tf_config, *args, **kwargs): - os.environ['TF_CONFIG'] = json.dumps(tf_config) - task_fn(*args, **kwargs) + with self._coord.stop_on_exception(): + os.environ['TF_CONFIG'] = json.dumps(tf_config) + task_fn(*args, **kwargs) def _run_task_in_thread(self, task_fn, cluster_spec, task_type, task_id, *args, **kwargs): @@ -403,3 +406,6 @@ class IndependentWorkerTestBase(test.TestCase): *args, **kwargs) threads[task_type].append(t) return threads + + def join_independent_workers(self, worker_threads): + self._coord.join(worker_threads) diff --git a/tensorflow/python/distribute/distribute_coordinator.py b/tensorflow/python/distribute/distribute_coordinator.py index c0f9b8a1fd..78c995a578 100644 --- a/tensorflow/python/distribute/distribute_coordinator.py +++ b/tensorflow/python/distribute/distribute_coordinator.py @@ -29,6 +29,7 @@ from tensorflow.python.client import session from tensorflow.python.distribute import distribute_coordinator_context from tensorflow.python.distribute import multi_worker_util from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.training import coordinator from tensorflow.python.training import monitored_session from tensorflow.python.training import server_lib @@ -328,7 +329,8 @@ def _run_single_worker(worker_fn, task_id, session_config, rpc_layer="", - worker_barrier=None): + worker_barrier=None, + coord=None): """Runs a single worker by calling `worker_fn` under context.""" session_config = copy.deepcopy(session_config) strategy = copy.deepcopy(strategy) @@ -350,7 +352,11 @@ def _run_single_worker(worker_fn, rpc_layer=rpc_layer, worker_barrier=worker_barrier) with context: - return worker_fn(strategy) + if coord: + with coord.stop_on_exception(): + return worker_fn(strategy) + else: + return worker_fn(strategy) def _split_cluster_for_evaluator(cluster_spec, task_type): @@ -423,6 +429,7 @@ def _run_std_server(cluster_spec=None, def _run_between_graph_client(worker_fn, strategy, eval_fn, eval_strategy, cluster_spec, session_config, rpc_layer): """Runs a standalone client for between-graph replication.""" + coord = coordinator.Coordinator() eval_thread = None if _TaskType.EVALUATOR in cluster_spec.jobs: eval_thread = threading.Thread( @@ -431,6 +438,7 @@ def _run_between_graph_client(worker_fn, strategy, eval_fn, eval_strategy, session_config), kwargs={ "rpc_layer": rpc_layer, + "coord": coord, }) eval_thread.start() @@ -444,18 +452,18 @@ def _run_between_graph_client(worker_fn, strategy, eval_fn, eval_strategy, session_config), kwargs={ "rpc_layer": rpc_layer, - "worker_barrier": worker_barrier + "worker_barrier": worker_barrier, + "coord": coord, }) t.start() threads.append(t) - # TODO(yuefengz): wrap threads into thread coordinator? - for t in threads: - t.join() - - # TODO(yuefengz): is it necessary to join eval thread? if eval_thread: - eval_thread.join() + # TODO(yuefengz): is it necessary to join eval thread? + threads_to_join = threads + [eval_thread] + else: + threads_to_join = threads + coord.join(threads_to_join) # TODO(yuefengz): we probably want to return results from all workers? return None @@ -464,6 +472,7 @@ def _run_between_graph_client(worker_fn, strategy, eval_fn, eval_strategy, def _run_in_graph_client(worker_fn, strategy, eval_fn, eval_strategy, cluster_spec, session_config, rpc_layer): """Runs a standalone client for in-graph replication.""" + coord = coordinator.Coordinator() eval_thread = None if _TaskType.EVALUATOR in cluster_spec.jobs: eval_thread = threading.Thread( @@ -472,6 +481,7 @@ def _run_in_graph_client(worker_fn, strategy, eval_fn, eval_strategy, session_config), kwargs={ "rpc_layer": rpc_layer, + "coord": coord, }) eval_thread.start() @@ -482,9 +492,12 @@ def _run_in_graph_client(worker_fn, strategy, eval_fn, eval_strategy, None, None, session_config, - rpc_layer=rpc_layer) + rpc_layer=rpc_layer, + coord=coord) + if eval_thread: - eval_thread.join() + coord.join([eval_thread]) + return worker_result -- GitLab From 844dbd8730b7aba7b9f51e7455023070f8953c98 Mon Sep 17 00:00:00 2001 From: Akshay Modi Date: Thu, 6 Dec 2018 13:01:00 -0800 Subject: [PATCH 148/873] Populate some additional parameters when running eager operations. PiperOrigin-RevId: 224391662 --- tensorflow/core/common_runtime/eager/context.cc | 10 ++++++++++ tensorflow/core/common_runtime/eager/context.h | 8 ++++++++ tensorflow/core/common_runtime/eager/execute.cc | 3 ++- .../core/common_runtime/eager/kernel_and_device.cc | 12 ++++++++++++ .../core/common_runtime/eager/kernel_and_device.h | 10 +++++++++- tensorflow/core/framework/op_kernel.h | 2 ++ tensorflow/python/eager/context.py | 8 +++----- 7 files changed, 46 insertions(+), 7 deletions(-) diff --git a/tensorflow/core/common_runtime/eager/context.cc b/tensorflow/core/common_runtime/eager/context.cc index 583ae64edd..1727c04560 100644 --- a/tensorflow/core/common_runtime/eager/context.cc +++ b/tensorflow/core/common_runtime/eager/context.cc @@ -15,6 +15,9 @@ limitations under the License. #include "tensorflow/core/common_runtime/eager/context.h" +#include "tensorflow/core/common_runtime/collective_executor_mgr.h" +#include "tensorflow/core/common_runtime/collective_param_resolver_local.h" +#include "tensorflow/core/common_runtime/device_resolver_local.h" #include "tensorflow/core/common_runtime/device_set.h" #include "tensorflow/core/common_runtime/process_util.h" #include "tensorflow/core/framework/resource_mgr.h" @@ -71,6 +74,13 @@ EagerContext::EagerContext(const SessionOptions& opts, runner_ = [this](std::function closure) { this->thread_pool_->Schedule(std::move(closure)); }; + + std::unique_ptr drl( + new DeviceResolverLocal(local_device_mgr())); + std::unique_ptr cprl(new CollectiveParamResolverLocal( + local_device_mgr(), drl.get(), "/job:localhost/replica:0/task:0")); + collective_executor_mgr_.reset(new CollectiveExecutorMgr( + opts.config, local_device_mgr(), std::move(drl), std::move(cprl))); } void EagerContext::InitDeviceMapAndAsync() { diff --git a/tensorflow/core/common_runtime/eager/context.h b/tensorflow/core/common_runtime/eager/context.h index 51109f8f1a..cdef947893 100644 --- a/tensorflow/core/common_runtime/eager/context.h +++ b/tensorflow/core/common_runtime/eager/context.h @@ -33,6 +33,7 @@ limitations under the License. #include "tensorflow/core/distributed_runtime/eager/eager_client.h" #include "tensorflow/core/distributed_runtime/server_lib.h" #endif +#include "tensorflow/core/framework/collective.h" #include "tensorflow/core/framework/log_memory.h" #include "tensorflow/core/framework/rendezvous.h" #include "tensorflow/core/lib/core/stringpiece.h" @@ -147,6 +148,11 @@ class EagerContext { bool LogMemory() { return log_memory_; } Rendezvous* GetRendezvous() { return rendezvous_; } + std::unique_ptr GetCollectiveExecutorHandle() { + return std::unique_ptr( + new CollectiveExecutor::Handle( + collective_executor_mgr_->FindOrCreate(0), true /*inherit_ref*/)); + } const tensorflow::DeviceMgr* local_device_mgr() const { return (local_device_manager_ != nullptr) ? local_device_manager_.get() @@ -273,6 +279,8 @@ class EagerContext { Env* const env_; + std::unique_ptr collective_executor_mgr_; + #ifndef __ANDROID__ void CloseRemoteContexts(); diff --git a/tensorflow/core/common_runtime/eager/execute.cc b/tensorflow/core/common_runtime/eager/execute.cc index 5bf7888fad..a6199f2aeb 100644 --- a/tensorflow/core/common_runtime/eager/execute.cc +++ b/tensorflow/core/common_runtime/eager/execute.cc @@ -284,7 +284,8 @@ Status EagerLocalExecute(EagerOperation* op, "Unable to find a FunctionLibraryRuntime corresponding to device ", device->name()); } - kernel = new KernelAndDevice(ctx->GetRendezvous(), ctx->LogMemory()); + kernel = new KernelAndDevice(ctx->GetRendezvous(), ctx->LogMemory(), + ctx->GetCollectiveExecutorHandle()); status = KernelAndDevice::Init(ndef, flr, ctx->runner(), kernel); if (!status.ok()) { delete kernel; diff --git a/tensorflow/core/common_runtime/eager/kernel_and_device.cc b/tensorflow/core/common_runtime/eager/kernel_and_device.cc index 192d22dfd5..317e9a1607 100644 --- a/tensorflow/core/common_runtime/eager/kernel_and_device.cc +++ b/tensorflow/core/common_runtime/eager/kernel_and_device.cc @@ -84,6 +84,15 @@ Status KernelAndDevice::Run(ScopedStepContainer* step_container, tensorflow::HOST_MEMORY); } + gtl::InlinedVector input_device_contexts; + for (int i = 0; i < inputs->size(); i++) { + DeviceContext* device_context = nullptr; + if (device_->tensorflow_gpu_device_info() != nullptr) { + device_context = device_->tensorflow_gpu_device_info()->default_context; + } + input_device_contexts.push_back(device_context); + } + OpKernelContext::Params params; params.device = device_; params.frame_iter = FrameAndIter(0, 0); @@ -110,6 +119,9 @@ Status KernelAndDevice::Run(ScopedStepContainer* step_container, } params.step_container = step_container; + params.collective_executor = + collective_executor_ ? collective_executor_->get() : nullptr; + params.input_device_contexts = &input_device_contexts; OpKernelContext context(¶ms); diff --git a/tensorflow/core/common_runtime/eager/kernel_and_device.h b/tensorflow/core/common_runtime/eager/kernel_and_device.h index 52dac94ccc..ee430b7fc7 100644 --- a/tensorflow/core/common_runtime/eager/kernel_and_device.h +++ b/tensorflow/core/common_runtime/eager/kernel_and_device.h @@ -23,6 +23,7 @@ limitations under the License. #include "tensorflow/core/common_runtime/device.h" #include "tensorflow/core/framework/cancellation.h" +#include "tensorflow/core/framework/collective.h" #include "tensorflow/core/framework/node_def.pb.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/types.h" @@ -55,10 +56,16 @@ class KernelAndDevice { KernelAndDevice* out); KernelAndDevice(tensorflow::Rendezvous* rendez, bool log_memory) + : KernelAndDevice(rendez, log_memory, nullptr) {} + + KernelAndDevice( + tensorflow::Rendezvous* rendez, bool log_memory, + std::unique_ptr collective_executor) : device_(nullptr), flr_(nullptr), rendez_(rendez), - log_memory_(log_memory) {} + log_memory_(log_memory), + collective_executor_(std::move(collective_executor)) {} // TODO(ashankar): Handle list-valued inputs. Status Run(std::vector* inputs, std::vector* outputs, @@ -92,6 +99,7 @@ class KernelAndDevice { std::function)>* runner_; std::function)> default_runner_; const bool log_memory_; + const std::unique_ptr collective_executor_; }; } // namespace tensorflow diff --git a/tensorflow/core/framework/op_kernel.h b/tensorflow/core/framework/op_kernel.h index 9f4c57e880..19a0c5e5be 100644 --- a/tensorflow/core/framework/op_kernel.h +++ b/tensorflow/core/framework/op_kernel.h @@ -1527,6 +1527,7 @@ T* OpKernelContext::op_device_context() { template T* OpKernelContext::input_device_context(int index) { + DCHECK_NE(params_->input_device_contexts, nullptr); DCHECK_GE(index, 0); DCHECK_LT(index, params_->input_device_contexts->size()); static_assert(std::is_base_of::value, @@ -1535,6 +1536,7 @@ T* OpKernelContext::input_device_context(int index) { } inline DeviceContext* OpKernelContext::input_device_context(int index) { + DCHECK_NE(params_->input_device_contexts, nullptr); DCHECK_GE(index, 0); DCHECK_LT(index, params_->input_device_contexts->size()); return (*params_->input_device_contexts)[index]; diff --git a/tensorflow/python/eager/context.py b/tensorflow/python/eager/context.py index 2f6b038dda..cbbe5cf49e 100644 --- a/tensorflow/python/eager/context.py +++ b/tensorflow/python/eager/context.py @@ -478,10 +478,6 @@ class Context(object): Raises: ValueError: If name is not a string or is an invalid device name. """ - devices = self._context_devices - if devices is None: - self._initialize_handle_and_devices() - devices = self._context_devices eager_context = self._eager_context old_device_name = eager_context.device_name old_device_spec = eager_context.device_spec @@ -502,7 +498,9 @@ class Context(object): if old_device_name: new_device_spec = copy.copy(old_device_spec) else: - new_device_spec = pydev.DeviceSpec.from_string(devices[0]) + self._initialize_handle_and_devices() + new_device_spec = pydev.DeviceSpec.from_string( + self._context_devices[0]) new_device_spec.merge_from(device_spec) else: new_device_spec = pydev.DeviceSpec.from_string("") -- GitLab From a6e4af0a5a8434898f1ed1e7eedc972c42c285f0 Mon Sep 17 00:00:00 2001 From: Yu-Cheng Ling Date: Thu, 6 Dec 2018 13:04:58 -0800 Subject: [PATCH 149/873] Specify architectures in TFLite build_ios_universal_lib.sh PiperOrigin-RevId: 224392376 --- .../tools/make/build_ios_universal_lib.sh | 35 +++++++++++-------- 1 file changed, 20 insertions(+), 15 deletions(-) diff --git a/tensorflow/lite/tools/make/build_ios_universal_lib.sh b/tensorflow/lite/tools/make/build_ios_universal_lib.sh index be8064ec7c..8b617ef593 100755 --- a/tensorflow/lite/tools/make/build_ios_universal_lib.sh +++ b/tensorflow/lite/tools/make/build_ios_universal_lib.sh @@ -19,31 +19,36 @@ set -e SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" cd "$SCRIPT_DIR/../../../.." -profiling_args= +usage() { + echo "Usage: $(basename "$0") [-a]" + echo "-a [build_arch] build for specified arch comma separate for multiple archs (eg: x86_64 arm64)" + echo " default is [x86_64 armv7 armv7s arm64]" + echo "-p enable profiling" + exit 1 +} -while getopts "p" opt; do - case $opt in +profiling_args="" +BUILD_ARCHS="x86_64 armv7 armv7s arm64" +while getopts "a:p" opt_name; do + case "$opt_name" in + a) BUILD_ARCHS="${OPTARG}";; p) profiling_args='-DGEMMLOWP_PROFILING,-DTFLITE_PROFILING_ENABLED';; - *) printf "if you want to enable profiling: pass in [-p]\n" - exit 2;; + *) usage;; esac done - shift $(($OPTIND - 1)) + # Build library for supported architectures and packs them in a fat binary. make_library() { - for arch in x86_64 armv7 armv7s arm64 + LIBS="" + for arch in $BUILD_ARCHS do - make -f tensorflow/lite/tools/make/Makefile TARGET=ios TARGET_ARCH=${arch} EXTRA_CXXFLAGS=$profiling_args \ - -j 8 + make -f tensorflow/lite/tools/make/Makefile TARGET=ios TARGET_ARCH=${arch} \ + EXTRA_CXXFLAGS=$profiling_args -j 8 + LIBS="${LIBS} tensorflow/lite/tools/make/gen/ios_${arch}/lib/${1}" done mkdir -p tensorflow/lite/tools/make/gen/lib - lipo \ - tensorflow/lite/tools/make/gen/ios_x86_64/lib/${1} \ - tensorflow/lite/tools/make/gen/ios_armv7/lib/${1} \ - tensorflow/lite/tools/make/gen/ios_armv7s/lib/${1} \ - tensorflow/lite/tools/make/gen/ios_arm64/lib/${1} \ - -create \ + lipo $LIBS -create \ -output tensorflow/lite/tools/make/gen/lib/${1} } -- GitLab From e33bca07dec7f59052b3d818ce8eb98703b22603 Mon Sep 17 00:00:00 2001 From: Shining Sun Date: Thu, 6 Dec 2018 13:11:25 -0800 Subject: [PATCH 150/873] When doing validation within fit using distributed strategy, do not copy the weight over. PiperOrigin-RevId: 224393616 --- .../contrib/distribute/python/keras_test.py | 6 --- .../python/keras/engine/training_arrays.py | 17 +++++-- .../keras/engine/training_distributed.py | 46 ++++++++----------- 3 files changed, 31 insertions(+), 38 deletions(-) diff --git a/tensorflow/contrib/distribute/python/keras_test.py b/tensorflow/contrib/distribute/python/keras_test.py index e530ab6f17..331bf7c496 100644 --- a/tensorflow/contrib/distribute/python/keras_test.py +++ b/tensorflow/contrib/distribute/python/keras_test.py @@ -1273,12 +1273,6 @@ class TestDistributionStrategyCorrectness(test.TestCase, # TODO(b/119257215): use the default one once the flakyness is fixed. tolerance = 1e-4 - if (use_validation_data and - not isinstance(distribution, tpu_strategy.TPUStrategy)): - # TODO(b/120435565): Enable tests with use_validation_data once the - # the underlying bug is fixed. - return - keras.backend.set_image_data_format('channels_last') np.random.seed(_RANDOM_SEED) random_seed.set_random_seed(_RANDOM_SEED) diff --git a/tensorflow/python/keras/engine/training_arrays.py b/tensorflow/python/keras/engine/training_arrays.py index e9dfbcbcc0..26a809d298 100644 --- a/tensorflow/python/keras/engine/training_arrays.py +++ b/tensorflow/python/keras/engine/training_arrays.py @@ -138,6 +138,7 @@ def model_iteration(model, steps_per_epoch=None, validation_steps=None, mode='train', + validation_in_fit=False, **kwargs): """Loop function for arrays of data with modes 'train'/'test'/'predict'. @@ -164,6 +165,9 @@ def model_iteration(model, validation_steps: Number of steps to run validation for (only if doing validation from data tensors). Ignored with the default value of `None`. mode: One of 'train'/'test'/'predict'. + validation_in_fit: if true, then this method is invoked from within + training iteration (for validation). In this case, do not copy weights + when using a tf.distribute.Strategy. **kwargs: Additional arguments for backwards compatibility. Returns: @@ -230,8 +234,9 @@ def model_iteration(model, aggregator = training_utils.MetricsAggregator(use_steps, num_samples_or_steps) - if model._distribution_strategy: - training_distributed._copy_weights_to_distributed_model(model) + if model._distribution_strategy and not validation_in_fit: + training_distributed._copy_weights_to_distributed_model( + model, model._grouped_model) callbacks.model.stop_training = False callbacks._call_begin_hook(mode) @@ -356,7 +361,8 @@ def model_iteration(model, steps_per_epoch=validation_steps, callbacks=callbacks, verbose=0, - mode='test') + mode='test', + validation_in_fit=True) if not isinstance(val_results, list): val_results = [val_results] epoch_logs.update( @@ -367,7 +373,10 @@ def model_iteration(model, callbacks._call_end_hook(mode) if model._distribution_strategy: - training_distributed._copy_weights_to_original_model(model, mode) + if not validation_in_fit: + training_distributed._copy_weights_to_original_model( + model, model._grouped_model, mode) + scope.__exit__(None, None, None) if mode == 'train': diff --git a/tensorflow/python/keras/engine/training_distributed.py b/tensorflow/python/keras/engine/training_distributed.py index 473f06ded7..9ca5082673 100644 --- a/tensorflow/python/keras/engine/training_distributed.py +++ b/tensorflow/python/keras/engine/training_distributed.py @@ -163,11 +163,9 @@ def experimental_fit_loop(model, do_validation = bool(validation_steps) # Copy the weights from the original model to each of the replicated models. - orig_model_weights = model.get_weights() with current_strategy.scope(): - distributed_model = current_strategy.unwrap(model._grouped_model_train)[0] - distributed_training_utils.set_weights( - current_strategy, distributed_model, orig_model_weights) + _copy_weights_to_distributed_model(model, model._grouped_model_train) + callbacks = cbks.configure_callbacks( callbacks, model, @@ -217,9 +215,8 @@ def experimental_fit_loop(model, # Since we create a new clone from the original model we need to copy # the weights back to the original model before we can run validation. with current_strategy.scope(): - updated_weights = current_strategy.unwrap( - model._grouped_model_train)[0].get_weights() - model.set_weights(updated_weights) + _copy_weights_to_original_model(model, model._grouped_model_train, + 'train') val_outs = experimental_test_loop( # pylint: disable=undefined-variable model, @@ -240,9 +237,7 @@ def experimental_fit_loop(model, # Copy the weights back from the replicated model to the original model. with current_strategy.scope(): - updated_weights = current_strategy.unwrap( - model._grouped_model_train)[0].get_weights() - model.set_weights(updated_weights) + _copy_weights_to_original_model(model, model._grouped_model_train, 'train') K.get_session().run(current_strategy.finalize()) return model.history @@ -345,11 +340,8 @@ def experimental_test_loop(model, progbar = Progbar(target=steps) # Copy the weights from the original model to each of the replicated models. - orig_model_weights = model.get_weights() with current_strategy.scope(): - distributed_model = current_strategy.unwrap(model._grouped_model_test)[0] - distributed_training_utils.set_weights( - current_strategy, distributed_model, orig_model_weights) + _copy_weights_to_distributed_model(model, model._grouped_model_test) assert steps is not None outs = [0.] * len(model.metrics_names) @@ -455,11 +447,8 @@ def experimental_predict_loop(model, iterator, verbose=0, steps=None): progbar = Progbar(target=steps) # Copy the weights from the original model to each of the replicated models. - orig_model_weights = model.get_weights() with current_strategy.scope(): - distributed_model = current_strategy.unwrap(model._grouped_model_predict)[0] - distributed_training_utils.set_weights( - current_strategy, distributed_model, orig_model_weights) + _copy_weights_to_distributed_model(model, model._grouped_model_predict) assert steps is not None # Since we do not know how many samples we will see, we cannot pre-allocate @@ -695,22 +684,23 @@ def _prepare_feed_values(model, inputs, targets, sample_weights, mode): return ins -def _copy_weights_to_distributed_model(model): +def _copy_weights_to_distributed_model(original_model, grouped_model): """Copies weights from original model to distributed models.""" - if model._distribution_strategy: - # Copy the weights from the original model to each of the replicated models. - orig_model_weights = model.get_weights() - distributed_model = model._distribution_strategy.unwrap( - model._grouped_model)[0] - distributed_training_utils.set_weights( - model._distribution_strategy, distributed_model, orig_model_weights) + strategy = original_model._distribution_strategy + if strategy: + # Copy the weights from the original model to each of the replicated + # models. + orig_model_weights = original_model.get_weights() + distributed_model = strategy.unwrap(grouped_model)[0] + distributed_training_utils.set_weights(strategy, distributed_model, + orig_model_weights) -def _copy_weights_to_original_model(model, mode): +def _copy_weights_to_original_model(model, grouped_model, mode): """Copies weights from first distributed model back to original model.""" if model._distribution_strategy and mode == 'train': updated_weights = model._distribution_strategy.unwrap( - model._grouped_model)[0].get_weights() + grouped_model)[0].get_weights() model.set_weights(updated_weights) -- GitLab From 7a9363e16590c3a713ce06dbf5857149b460b8aa Mon Sep 17 00:00:00 2001 From: Andrew Selle Date: Thu, 6 Dec 2018 13:17:22 -0800 Subject: [PATCH 151/873] Update model for classification app - Use model referred from our models.md list. - Download both float/quant PiperOrigin-RevId: 224394551 --- tensorflow/lite/examples/android/BUILD | 2 +- .../android/app/download-models.gradle | 27 ++++++---- .../tensorflow/demo/ClassifierActivity.java | 2 +- tensorflow/lite/java/demo/app/build.gradle | 53 +++++++++++++++---- tensorflow/lite/java/demo/app/src/main/BUILD | 3 +- .../ImageClassifierQuantizedMobileNet.java | 5 +- tensorflow/opensource_only.files | 2 + tensorflow/workspace.bzl | 20 +++++-- third_party/tflite_mobilenet_float.BUILD | 12 +++++ third_party/tflite_mobilenet_quant.BUILD | 12 +++++ 10 files changed, 108 insertions(+), 30 deletions(-) create mode 100644 third_party/tflite_mobilenet_float.BUILD create mode 100644 third_party/tflite_mobilenet_quant.BUILD diff --git a/tensorflow/lite/examples/android/BUILD b/tensorflow/lite/examples/android/BUILD index 761a60314e..80cefd415a 100644 --- a/tensorflow/lite/examples/android/BUILD +++ b/tensorflow/lite/examples/android/BUILD @@ -34,7 +34,7 @@ android_binary( # to reduce APK size. assets = [ "//tensorflow/lite/examples/android/app/src/main/assets:labels_mobilenet_quant_v1_224.txt", - "@tflite_mobilenet//:mobilenet_quant_v1_224.tflite", + "@tflite_mobilenet_quant//:mobilenet_v1_1.0_224_quant.tflite", "@tflite_conv_actions_frozen//:conv_actions_frozen.tflite", "//tensorflow/lite/examples/android/app/src/main/assets:conv_actions_labels.txt", "@tflite_mobilenet_ssd//:mobilenet_ssd.tflite", diff --git a/tensorflow/lite/examples/android/app/download-models.gradle b/tensorflow/lite/examples/android/app/download-models.gradle index d2f03db5f6..36bd177a1f 100644 --- a/tensorflow/lite/examples/android/app/download-models.gradle +++ b/tensorflow/lite/examples/android/app/download-models.gradle @@ -8,13 +8,12 @@ * 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', - 'coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip'] -// LINT.ThenChange(//tensorflow/lite/examples/android/BUILD) +def models = ['https://storage.googleapis.com/download.tensorflow.org/models/tflite/conv_actions_tflite.zip', + 'https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_ssd_tflite_v1.zip', + 'https://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip', + 'http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz', + 'http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz'] // Root URL for model archives def MODEL_URL = 'https://storage.googleapis.com/download.tensorflow.org/models/tflite' @@ -30,9 +29,9 @@ buildscript { 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}" + for (modelUrl in models) { + def localFile = modelUrl.split("/")[-1] + println "Downloading ${localFile} from ${modelUrl}" src modelUrl } @@ -43,7 +42,12 @@ task downloadFile(type: Download){ task extractModels(type: Copy) { for (f in models) { def localFile = f.split("/")[-1] - from zipTree(project.ext.TMP_DIR + '/' + localFile) + def localExt = localFile.split("[.]")[-1] + if (localExt == "tgz") { + from tarTree(project.ext.TMP_DIR + '/' + localFile) + } else { + from zipTree(project.ext.TMP_DIR + '/' + localFile) + } } into file(project.ext.ASSET_DIR) @@ -63,6 +67,9 @@ task extractModels(type: Copy) { } } + + + tasks.whenTaskAdded { task -> if (task.name == 'assembleDebug') { task.dependsOn 'extractModels' diff --git a/tensorflow/lite/examples/android/app/src/main/java/org/tensorflow/demo/ClassifierActivity.java b/tensorflow/lite/examples/android/app/src/main/java/org/tensorflow/demo/ClassifierActivity.java index dcbbefbeab..698251d8b4 100644 --- a/tensorflow/lite/examples/android/app/src/main/java/org/tensorflow/demo/ClassifierActivity.java +++ b/tensorflow/lite/examples/android/app/src/main/java/org/tensorflow/demo/ClassifierActivity.java @@ -65,7 +65,7 @@ public class ClassifierActivity extends CameraActivity implements OnImageAvailab // --input_binary=true private static final int INPUT_SIZE = 224; - private static final String MODEL_FILE = "mobilenet_quant_v1_224.tflite"; + private static final String MODEL_FILE = "mobilenet_v1_1.0_224_quant.tflite"; private static final String LABEL_FILE = "labels_mobilenet_quant_v1_224.txt"; private static final boolean MAINTAIN_ASPECT = true; diff --git a/tensorflow/lite/java/demo/app/build.gradle b/tensorflow/lite/java/demo/app/build.gradle index 05301ebf88..5e50ed4b94 100644 --- a/tensorflow/lite/java/demo/app/build.gradle +++ b/tensorflow/lite/java/demo/app/build.gradle @@ -52,28 +52,60 @@ dependencies { compile 'org.tensorflow:tensorflow-lite:0.0.0-nightly' } -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" +def modelFloatDownloadUrl = "http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz" +def modelQuantDownloadUrl = "http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz" +def localCacheFloat = "build/intermediates/mobilenet_v1_1.0_224.tgz" +def localCacheQuant = "build/intermediates/mmobilenet_v1_1.0_224_quant.tgz" -task downloadModel(type: DownloadUrlTask) { + +task downloadModelFloat(type: DownloadUrlTask) { + doFirst { + println "Downloading ${modelFloatDownloadUrl}" + } + sourceUrl = "${modelFloatDownloadUrl}" + target = file("${localCacheFloat}") +} + +task downloadModelQuant(type: DownloadUrlTask) { doFirst { - println "Downloading ${modelDownloadUrl}" + println "Downloading ${modelQuantDownloadUrl}" } - sourceUrl = "${modelDownloadUrl}" - target = file("${localCache}") + sourceUrl = "${modelQuantDownloadUrl}" + target = file("${localCacheQuant}") } -task unzipModel(type: Copy, dependsOn: 'downloadModel') { +task unzipModelFloat(type: Copy, dependsOn: 'downloadModelFloat') { doFirst { - println "Unzipping ${localCache}" + println "Unzipping ${localCacheFloat}" } - from zipTree("${localCache}") + from tarTree("${localCacheFloat}") into "${targetFolder}" } +task unzipModelQuant(type: Copy, dependsOn: 'downloadModelQuant') { + doFirst { + println "Unzipping ${localCacheQuant}" + } + from tarTree("${localCacheQuant}") + into "${targetFolder}" +} + +task cleanUnusedFiles(type: Delete, dependsOn: ['unzipModelFloat', 'unzipModelQuant']) { + delete fileTree("${targetFolder}").matching { + include "*.pb" + include "*.ckpt.*" + include "*.pbtxt.*" + include "*.quant_info.*" + include "*.meta" + } +} + + // Ensure the model file is downloaded and extracted before every build -preBuild.dependsOn unzipModel +preBuild.dependsOn unzipModelFloat +preBuild.dependsOn unzipModelQuant +preBuild.dependsOn cleanUnusedFiles class DownloadUrlTask extends DefaultTask { @Input @@ -87,3 +119,4 @@ class DownloadUrlTask extends DefaultTask { ant.get(src: sourceUrl, dest: target) } } + diff --git a/tensorflow/lite/java/demo/app/src/main/BUILD b/tensorflow/lite/java/demo/app/src/main/BUILD index df8a024a57..9a7c1d0b61 100644 --- a/tensorflow/lite/java/demo/app/src/main/BUILD +++ b/tensorflow/lite/java/demo/app/src/main/BUILD @@ -10,7 +10,8 @@ android_binary( aapt_version = "aapt", assets = [ "//tensorflow/lite/java/demo/app/src/main/assets:labels_mobilenet_quant_v1_224.txt", - "@tflite_mobilenet//:mobilenet_quant_v1_224.tflite", + "@tflite_mobilenet_quant//:mobilenet_v1_1.0_224_quant.tflite", + "@tflite_mobilenet_float//:mobilenet_v1_1.0_224.tflite", ], assets_dir = "", custom_package = "com.example.android.tflitecamerademo", diff --git a/tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierQuantizedMobileNet.java b/tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierQuantizedMobileNet.java index e164ac7554..6310a56168 100644 --- a/tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierQuantizedMobileNet.java +++ b/tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierQuantizedMobileNet.java @@ -42,8 +42,9 @@ public class ImageClassifierQuantizedMobileNet extends ImageClassifier { @Override protected String getModelPath() { // you can download this file from - // https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip - return "mobilenet_quant_v1_224.tflite"; + // see build.gradle for where to obtain this file. It should be auto + // downloaded into assets. + return "mobilenet_v1_1.0_224_quant.tflite"; } @Override diff --git a/tensorflow/opensource_only.files b/tensorflow/opensource_only.files index 0c29ac6a30..688a837dac 100644 --- a/tensorflow/opensource_only.files +++ b/tensorflow/opensource_only.files @@ -173,6 +173,7 @@ tensorflow/third_party/common.bzl tensorflow/third_party/com_google_absl.BUILD tensorflow/third_party/pprof.BUILD tensorflow/third_party/BUILD +tensorflow/third_party/tflite_mobilenet_quant.BUILD tensorflow/third_party/lmdb.BUILD tensorflow/third_party/git/BUILD.tpl tensorflow/third_party/git/BUILD @@ -198,6 +199,7 @@ tensorflow/third_party/nanopb.BUILD tensorflow/third_party/gif.BUILD tensorflow/third_party/double_conversion.BUILD tensorflow/third_party/six.BUILD +tensorflow/third_party/tflite_mobilenet_float.BUILD tensorflow/third_party/repo.bzl tensorflow/third_party/codegen.BUILD tensorflow/third_party/cub.BUILD diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index e6b4a89e3b..f475493446 100755 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -734,12 +734,22 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""): ) tf_http_archive( - name = "tflite_mobilenet", - build_file = clean_dep("//third_party:tflite_mobilenet.BUILD"), - sha256 = "23f814d1c076bdf03715dfb6cab3713aa4fbdf040fd5448c43196bd2e97a4c1b", + name = "tflite_mobilenet_float", + build_file = clean_dep("//third_party:tflite_mobilenet_float.BUILD"), + sha256 = "2fadeabb9968ec6833bee903900dda6e61b3947200535874ce2fe42a8493abc0", urls = [ - "https://mirror.bazel.build/storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip", - "https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip", + "http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz", + "http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz", + ], + ) + + tf_http_archive( + name = "tflite_mobilenet_quant", + build_file = clean_dep("//third_party:tflite_mobilenet_quant.BUILD"), + sha256 = "d32432d28673a936b2d6281ab0600c71cf7226dfe4cdcef3012555f691744166", + urls = [ + "http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz", + "http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz", ], ) diff --git a/third_party/tflite_mobilenet_float.BUILD b/third_party/tflite_mobilenet_float.BUILD new file mode 100644 index 0000000000..de47ed61f9 --- /dev/null +++ b/third_party/tflite_mobilenet_float.BUILD @@ -0,0 +1,12 @@ +package(default_visibility = ["//visibility:public"]) + +licenses(["notice"]) # Apache 2.0 + +exports_files( + glob( + ["**/*"], + exclude = [ + "BUILD", + ], + ), +) diff --git a/third_party/tflite_mobilenet_quant.BUILD b/third_party/tflite_mobilenet_quant.BUILD new file mode 100644 index 0000000000..de47ed61f9 --- /dev/null +++ b/third_party/tflite_mobilenet_quant.BUILD @@ -0,0 +1,12 @@ +package(default_visibility = ["//visibility:public"]) + +licenses(["notice"]) # Apache 2.0 + +exports_files( + glob( + ["**/*"], + exclude = [ + "BUILD", + ], + ), +) -- GitLab From 3c257d337439f863759d1884c88d17c2b0d685b5 Mon Sep 17 00:00:00 2001 From: Derek Murray Date: Thu, 6 Dec 2018 13:31:57 -0800 Subject: [PATCH 152/873] [tf.data] Export `tf.data.experimental.Structure` and its subclasses. These classes are most useful when implementing the `tf.data.Dataset._element_structure` property in `Dataset` subclasses. This change enables users to implement `Dataset` subclasses without depending on an internal module. PiperOrigin-RevId: 224397031 --- .../python/data/experimental/__init__.py | 10 ++++++++++ tensorflow/python/data/ops/BUILD | 1 + tensorflow/python/data/ops/dataset_ops.py | 1 + tensorflow/python/data/ops/optional_ops.py | 2 ++ tensorflow/python/data/util/structure.py | 4 ++++ ...data.experimental.-dataset-structure.pbtxt | 18 +++++++++++++++++ ...ata.experimental.-optional-structure.pbtxt | 18 +++++++++++++++++ ...xperimental.-sparse-tensor-structure.pbtxt | 18 +++++++++++++++++ ...sorflow.data.experimental.-structure.pbtxt | 16 +++++++++++++++ ....data.experimental.-tensor-structure.pbtxt | 18 +++++++++++++++++ .../v1/tensorflow.data.experimental.pbtxt | 20 +++++++++++++++++++ ...data.experimental.-dataset-structure.pbtxt | 18 +++++++++++++++++ ...ata.experimental.-optional-structure.pbtxt | 18 +++++++++++++++++ ...xperimental.-sparse-tensor-structure.pbtxt | 18 +++++++++++++++++ ...sorflow.data.experimental.-structure.pbtxt | 16 +++++++++++++++ ....data.experimental.-tensor-structure.pbtxt | 18 +++++++++++++++++ .../v2/tensorflow.data.experimental.pbtxt | 20 +++++++++++++++++++ 17 files changed, 234 insertions(+) create mode 100644 tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-dataset-structure.pbtxt create mode 100644 tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-optional-structure.pbtxt create mode 100644 tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-sparse-tensor-structure.pbtxt create mode 100644 tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-structure.pbtxt create mode 100644 tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-tensor-structure.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-dataset-structure.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-optional-structure.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-sparse-tensor-structure.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-structure.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-tensor-structure.pbtxt diff --git a/tensorflow/python/data/experimental/__init__.py b/tensorflow/python/data/experimental/__init__.py index 14dfec37cd..365c53405c 100644 --- a/tensorflow/python/data/experimental/__init__.py +++ b/tensorflow/python/data/experimental/__init__.py @@ -25,14 +25,19 @@ See [Importing Data](https://tensorflow.org/guide/datasets) for an overview. @@Counter @@CheckpointInputPipelineHook @@CsvDataset +@@DatasetStructure @@OptimizationOptions @@Optional +@@OptionalStructure @@RandomDataset @@Reducer +@@SparseTensorStructure @@SqlDataset @@StatsAggregator @@StatsOptions +@@Structure @@TFRecordWriter +@@TensorStructure @@ThreadingOptions @@bucket_by_sequence_length @@ -112,8 +117,13 @@ from tensorflow.python.data.experimental.ops.stats_options import StatsOptions from tensorflow.python.data.experimental.ops.threading_options import ThreadingOptions from tensorflow.python.data.experimental.ops.unique import unique from tensorflow.python.data.experimental.ops.writers import TFRecordWriter +from tensorflow.python.data.ops.dataset_ops import DatasetStructure from tensorflow.python.data.ops.iterator_ops import get_next_as_optional from tensorflow.python.data.ops.optional_ops import Optional +from tensorflow.python.data.ops.optional_ops import OptionalStructure +from tensorflow.python.data.util.structure import SparseTensorStructure +from tensorflow.python.data.util.structure import Structure +from tensorflow.python.data.util.structure import TensorStructure # pylint: enable=unused-import from tensorflow.python.util.all_util import remove_undocumented diff --git a/tensorflow/python/data/ops/BUILD b/tensorflow/python/data/ops/BUILD index 45d65956f3..fbff7df9c3 100644 --- a/tensorflow/python/data/ops/BUILD +++ b/tensorflow/python/data/ops/BUILD @@ -87,6 +87,7 @@ py_library( "//tensorflow/python:framework_ops", "//tensorflow/python:resource_variable_ops", "//tensorflow/python:tensor_shape", + "//tensorflow/python:util", "//tensorflow/python/data/util:structure", ], ) diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py index cd7b1a4a5a..a7cd2f54c7 100644 --- a/tensorflow/python/data/ops/dataset_ops.py +++ b/tensorflow/python/data/ops/dataset_ops.py @@ -1915,6 +1915,7 @@ class _VariantDataset(DatasetV2): return self._structure +@tf_export("data.experimental.DatasetStructure") class DatasetStructure(structure_lib.Structure): """Represents a `Dataset` of structured values.""" diff --git a/tensorflow/python/data/ops/optional_ops.py b/tensorflow/python/data/ops/optional_ops.py index 15ec755c67..66011d8518 100644 --- a/tensorflow/python/data/ops/optional_ops.py +++ b/tensorflow/python/data/ops/optional_ops.py @@ -26,6 +26,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import gen_dataset_ops +from tensorflow.python.util.tf_export import tf_export @six.add_metaclass(abc.ABCMeta) @@ -145,6 +146,7 @@ class _OptionalImpl(Optional): return self._value_structure +@tf_export("data.experimental.OptionalStructure") class OptionalStructure(structure.Structure): """Represents an optional potentially containing a structured value.""" diff --git a/tensorflow/python/data/util/structure.py b/tensorflow/python/data/util/structure.py index 92450abda8..4f2c6cd853 100644 --- a/tensorflow/python/data/util/structure.py +++ b/tensorflow/python/data/util/structure.py @@ -28,11 +28,13 @@ from tensorflow.python.framework import sparse_tensor as sparse_tensor_lib from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import tensor_util from tensorflow.python.ops import sparse_ops +from tensorflow.python.util.tf_export import tf_export _STRUCTURE_CONVERSION_FUNCTION_REGISTRY = {} +@tf_export("data.experimental.Structure") @six.add_metaclass(abc.ABCMeta) class Structure(object): """Represents structural information, such as type and shape, about a value. @@ -376,6 +378,7 @@ class NestedStructure(Structure): lambda s: s._batch(batch_size), self._nested_structure)) +@tf_export("data.experimental.TensorStructure") class TensorStructure(Structure): """Represents structural information about a `tf.Tensor`.""" @@ -439,6 +442,7 @@ class TensorStructure(Structure): tensor_shape.TensorShape([batch_size]).concatenate(self._shape)) +@tf_export("data.experimental.SparseTensorStructure") class SparseTensorStructure(Structure): """Represents structural information about a `tf.SparseTensor`.""" diff --git a/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-dataset-structure.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-dataset-structure.pbtxt new file mode 100644 index 0000000000..dcb304f763 --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-dataset-structure.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.data.experimental.DatasetStructure" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'element_structure\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_value" + argspec: "args=[\'value\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_compatible_with" + argspec: "args=[\'self\', \'other\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-optional-structure.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-optional-structure.pbtxt new file mode 100644 index 0000000000..bf41c1d1d6 --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-optional-structure.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.data.experimental.OptionalStructure" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'value_structure\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_value" + argspec: "args=[\'value\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_compatible_with" + argspec: "args=[\'self\', \'other\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-sparse-tensor-structure.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-sparse-tensor-structure.pbtxt new file mode 100644 index 0000000000..f97376b328 --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-sparse-tensor-structure.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.data.experimental.SparseTensorStructure" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'dtype\', \'dense_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_value" + argspec: "args=[\'value\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_compatible_with" + argspec: "args=[\'self\', \'other\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-structure.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-structure.pbtxt new file mode 100644 index 0000000000..a99db4542e --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-structure.pbtxt @@ -0,0 +1,16 @@ +path: "tensorflow.data.experimental.Structure" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "from_value" + argspec: "args=[\'value\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_compatible_with" + argspec: "args=[\'self\', \'other\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-tensor-structure.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-tensor-structure.pbtxt new file mode 100644 index 0000000000..f5c8864a9d --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-tensor-structure.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.data.experimental.TensorStructure" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'dtype\', \'shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_value" + argspec: "args=[\'value\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_compatible_with" + argspec: "args=[\'self\', \'other\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.pbtxt index f981b1af17..234507e5de 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.pbtxt @@ -12,6 +12,10 @@ tf_module { name: "CsvDataset" mtype: "" } + member { + name: "DatasetStructure" + mtype: "" + } member { name: "INFINITE_CARDINALITY" mtype: "" @@ -24,6 +28,10 @@ tf_module { name: "Optional" mtype: "" } + member { + name: "OptionalStructure" + mtype: "" + } member { name: "RandomDataset" mtype: "" @@ -32,6 +40,10 @@ tf_module { name: "Reducer" mtype: "" } + member { + name: "SparseTensorStructure" + mtype: "" + } member { name: "SqlDataset" mtype: "" @@ -44,10 +56,18 @@ tf_module { name: "StatsOptions" mtype: "" } + member { + name: "Structure" + mtype: "" + } member { name: "TFRecordWriter" mtype: "" } + member { + name: "TensorStructure" + mtype: "" + } member { name: "ThreadingOptions" mtype: "" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-dataset-structure.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-dataset-structure.pbtxt new file mode 100644 index 0000000000..dcb304f763 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-dataset-structure.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.data.experimental.DatasetStructure" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'element_structure\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_value" + argspec: "args=[\'value\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_compatible_with" + argspec: "args=[\'self\', \'other\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-optional-structure.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-optional-structure.pbtxt new file mode 100644 index 0000000000..bf41c1d1d6 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-optional-structure.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.data.experimental.OptionalStructure" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'value_structure\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_value" + argspec: "args=[\'value\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_compatible_with" + argspec: "args=[\'self\', \'other\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-sparse-tensor-structure.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-sparse-tensor-structure.pbtxt new file mode 100644 index 0000000000..f97376b328 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-sparse-tensor-structure.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.data.experimental.SparseTensorStructure" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'dtype\', \'dense_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_value" + argspec: "args=[\'value\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_compatible_with" + argspec: "args=[\'self\', \'other\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-structure.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-structure.pbtxt new file mode 100644 index 0000000000..a99db4542e --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-structure.pbtxt @@ -0,0 +1,16 @@ +path: "tensorflow.data.experimental.Structure" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "from_value" + argspec: "args=[\'value\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_compatible_with" + argspec: "args=[\'self\', \'other\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-tensor-structure.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-tensor-structure.pbtxt new file mode 100644 index 0000000000..f5c8864a9d --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-tensor-structure.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.data.experimental.TensorStructure" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'dtype\', \'shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_value" + argspec: "args=[\'value\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_compatible_with" + argspec: "args=[\'self\', \'other\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.pbtxt index f981b1af17..234507e5de 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.pbtxt @@ -12,6 +12,10 @@ tf_module { name: "CsvDataset" mtype: "" } + member { + name: "DatasetStructure" + mtype: "" + } member { name: "INFINITE_CARDINALITY" mtype: "" @@ -24,6 +28,10 @@ tf_module { name: "Optional" mtype: "" } + member { + name: "OptionalStructure" + mtype: "" + } member { name: "RandomDataset" mtype: "" @@ -32,6 +40,10 @@ tf_module { name: "Reducer" mtype: "" } + member { + name: "SparseTensorStructure" + mtype: "" + } member { name: "SqlDataset" mtype: "" @@ -44,10 +56,18 @@ tf_module { name: "StatsOptions" mtype: "" } + member { + name: "Structure" + mtype: "" + } member { name: "TFRecordWriter" mtype: "" } + member { + name: "TensorStructure" + mtype: "" + } member { name: "ThreadingOptions" mtype: "" -- GitLab From 479abd88927e54205ea418f68e64057e5b837e2d Mon Sep 17 00:00:00 2001 From: Austin Anderson Date: Wed, 5 Dec 2018 13:58:00 -0800 Subject: [PATCH 153/873] Add subdirectory-output support and change official releases This PR does a few things: * Add slice option `dockerfile_subdirectory` for https://github.com/tensorflow/tensorflow/pull/24180, a slice option that places any Dockerfile using the slice into a subdirectory of the Dockerfiles directory. * Rearrange releases and slices slightly. The `devel` images are now going to be built nightly, to best support master, and are tagged `devel-*`. --- tensorflow/tools/dockerfiles/assembler.py | 27 ++++++++++------- ...ockerfile => devel-cpu-jupyter.Dockerfile} | 3 ++ ...-devel.Dockerfile => devel-cpu.Dockerfile} | 3 ++ ...ockerfile => devel-gpu-jupyter.Dockerfile} | 4 +++ ...-devel.Dockerfile => devel-gpu.Dockerfile} | 4 +++ ...ockerfile => devel-cpu.partial.Dockerfile} | 3 ++ ...erfile => devel-nvidia.partial.Dockerfile} | 4 +++ tensorflow/tools/dockerfiles/spec.yml | 29 ++++++++++++++----- 8 files changed, 59 insertions(+), 18 deletions(-) rename tensorflow/tools/dockerfiles/dockerfiles/{cpu-devel-jupyter.Dockerfile => devel-cpu-jupyter.Dockerfile} (94%) rename tensorflow/tools/dockerfiles/dockerfiles/{cpu-devel.Dockerfile => devel-cpu.Dockerfile} (92%) rename tensorflow/tools/dockerfiles/dockerfiles/{gpu-devel-jupyter.Dockerfile => devel-gpu-jupyter.Dockerfile} (95%) rename tensorflow/tools/dockerfiles/dockerfiles/{gpu-devel.Dockerfile => devel-gpu.Dockerfile} (95%) rename tensorflow/tools/dockerfiles/partials/ubuntu/{cpu-devel.partial.Dockerfile => devel-cpu.partial.Dockerfile} (73%) rename tensorflow/tools/dockerfiles/partials/ubuntu/{nvidia-devel.partial.Dockerfile => devel-nvidia.partial.Dockerfile} (90%) diff --git a/tensorflow/tools/dockerfiles/assembler.py b/tensorflow/tools/dockerfiles/assembler.py index bd8ed73a41..67a0320241 100644 --- a/tensorflow/tools/dockerfiles/assembler.py +++ b/tensorflow/tools/dockerfiles/assembler.py @@ -170,6 +170,8 @@ slice_sets: type: string dockerfile_exclusive_name: type: string + dockerfile_subdirectory: + type: string partials: type: list schema: @@ -353,8 +355,9 @@ def gather_slice_list_items(slices, key): def find_first_slice_value(slices, key): """For a list of slices, get the first value for a certain key.""" for s in slices: - if key in s: + if key in s and s[key] is not None: return s[key] + return None def assemble_tags(spec, cli_args, enabled_releases, all_partials): @@ -389,6 +392,8 @@ def assemble_tags(spec, cli_args, enabled_releases, all_partials): used_partials = gather_slice_list_items(slices, 'partials') used_tests = gather_slice_list_items(slices, 'tests') test_runtime = find_first_slice_value(slices, 'test_runtime') + dockerfile_subdirectory = find_first_slice_value( + slices, 'dockerfile_subdirectory') dockerfile_contents = merge_partials(spec['header'], used_partials, all_partials) @@ -398,6 +403,7 @@ def assemble_tags(spec, cli_args, enabled_releases, all_partials): 'is_dockerfiles': release['is_dockerfiles'], 'upload_images': release['upload_images'], 'cli_args': tag_args, + 'dockerfile_subdirectory': dockerfile_subdirectory or '', 'partials': used_partials, 'tests': used_tests, 'test_runtime': test_runtime, @@ -416,8 +422,7 @@ def merge_partials(header, used_partials, all_partials): def upload_in_background(hub_repository, dock, image, tag): """Upload a docker image (to be used by multiprocessing).""" image.tag(hub_repository, tag=tag) - for line in list(dock.images.push(hub_repository, tag=tag, stream=True)): - print(line) + print(dock.images.push(hub_repository, tag=tag)) def mkdir_p(path): @@ -525,13 +530,15 @@ def main(argv): continue # Write releases marked "is_dockerfiles" into the Dockerfile directory - if FLAGS.construct_dockerfiles: - path = os.path.join(FLAGS.dockerfile_dir, tag + '.Dockerfile') - if tag_def['is_dockerfiles']: - eprint('>> Writing {}...'.format(path)) - if not FLAGS.dry_run: - with open(path, 'w') as f: - f.write(tag_def['dockerfile_contents']) + if FLAGS.construct_dockerfiles and tag_def['is_dockerfiles']: + path = os.path.join(FLAGS.dockerfile_dir, + tag_def['dockerfile_subdirectory'], + tag + '.Dockerfile') + eprint('>> Writing {}...'.format(path)) + if not FLAGS.dry_run: + mkdir_p(os.path.dirname(path)) + with open(path, 'w') as f: + f.write(tag_def['dockerfile_contents']) # Don't build any images for dockerfile-only releases if not FLAGS.build_images: diff --git a/tensorflow/tools/dockerfiles/dockerfiles/cpu-devel-jupyter.Dockerfile b/tensorflow/tools/dockerfiles/dockerfiles/devel-cpu-jupyter.Dockerfile similarity index 94% rename from tensorflow/tools/dockerfiles/dockerfiles/cpu-devel-jupyter.Dockerfile rename to tensorflow/tools/dockerfiles/dockerfiles/devel-cpu-jupyter.Dockerfile index 14ddf08199..43265676f8 100644 --- a/tensorflow/tools/dockerfiles/dockerfiles/cpu-devel-jupyter.Dockerfile +++ b/tensorflow/tools/dockerfiles/dockerfiles/devel-cpu-jupyter.Dockerfile @@ -46,6 +46,9 @@ RUN apt-get update && apt-get install -y --no-install-recommends \ ENV CI_BUILD_PYTHON python +# Check out TensorFlow source code if --build_arg CHECKOUT_TENSORFLOW=1 +ARG CHECKOUT_TF_SRC=0 +RUN test "${CHECKOUT_TF_SRC}" -eq 1 && git clone https://github.com/tensorflow/tensorflow.git /tensorflow_src ARG USE_PYTHON_3_NOT_2 ARG _PY_SUFFIX=${USE_PYTHON_3_NOT_2:+3} diff --git a/tensorflow/tools/dockerfiles/dockerfiles/cpu-devel.Dockerfile b/tensorflow/tools/dockerfiles/dockerfiles/devel-cpu.Dockerfile similarity index 92% rename from tensorflow/tools/dockerfiles/dockerfiles/cpu-devel.Dockerfile rename to tensorflow/tools/dockerfiles/dockerfiles/devel-cpu.Dockerfile index 16973b47af..5c5b2f9163 100644 --- a/tensorflow/tools/dockerfiles/dockerfiles/cpu-devel.Dockerfile +++ b/tensorflow/tools/dockerfiles/dockerfiles/devel-cpu.Dockerfile @@ -46,6 +46,9 @@ RUN apt-get update && apt-get install -y --no-install-recommends \ ENV CI_BUILD_PYTHON python +# Check out TensorFlow source code if --build_arg CHECKOUT_TENSORFLOW=1 +ARG CHECKOUT_TF_SRC=0 +RUN test "${CHECKOUT_TF_SRC}" -eq 1 && git clone https://github.com/tensorflow/tensorflow.git /tensorflow_src ARG USE_PYTHON_3_NOT_2 ARG _PY_SUFFIX=${USE_PYTHON_3_NOT_2:+3} diff --git a/tensorflow/tools/dockerfiles/dockerfiles/gpu-devel-jupyter.Dockerfile b/tensorflow/tools/dockerfiles/dockerfiles/devel-gpu-jupyter.Dockerfile similarity index 95% rename from tensorflow/tools/dockerfiles/dockerfiles/gpu-devel-jupyter.Dockerfile rename to tensorflow/tools/dockerfiles/dockerfiles/devel-gpu-jupyter.Dockerfile index 9ecaec38c2..8769e4e9cd 100644 --- a/tensorflow/tools/dockerfiles/dockerfiles/gpu-devel-jupyter.Dockerfile +++ b/tensorflow/tools/dockerfiles/dockerfiles/devel-gpu-jupyter.Dockerfile @@ -79,6 +79,10 @@ ENV TF_CUDNN_VERSION=7 # NCCL 2.x ENV TF_NCCL_VERSION=2 +# Check out TensorFlow source code if --build_arg CHECKOUT_TENSORFLOW=1 +ARG CHECKOUT_TF_SRC=0 +RUN test "${CHECKOUT_TF_SRC}" -eq 1 && git clone https://github.com/tensorflow/tensorflow.git /tensorflow_src + ARG USE_PYTHON_3_NOT_2 ARG _PY_SUFFIX=${USE_PYTHON_3_NOT_2:+3} ARG PYTHON=python${_PY_SUFFIX} diff --git a/tensorflow/tools/dockerfiles/dockerfiles/gpu-devel.Dockerfile b/tensorflow/tools/dockerfiles/dockerfiles/devel-gpu.Dockerfile similarity index 95% rename from tensorflow/tools/dockerfiles/dockerfiles/gpu-devel.Dockerfile rename to tensorflow/tools/dockerfiles/dockerfiles/devel-gpu.Dockerfile index c79bc3cf4c..809cda679e 100644 --- a/tensorflow/tools/dockerfiles/dockerfiles/gpu-devel.Dockerfile +++ b/tensorflow/tools/dockerfiles/dockerfiles/devel-gpu.Dockerfile @@ -79,6 +79,10 @@ ENV TF_CUDNN_VERSION=7 # NCCL 2.x ENV TF_NCCL_VERSION=2 +# Check out TensorFlow source code if --build_arg CHECKOUT_TENSORFLOW=1 +ARG CHECKOUT_TF_SRC=0 +RUN test "${CHECKOUT_TF_SRC}" -eq 1 && git clone https://github.com/tensorflow/tensorflow.git /tensorflow_src + ARG USE_PYTHON_3_NOT_2 ARG _PY_SUFFIX=${USE_PYTHON_3_NOT_2:+3} ARG PYTHON=python${_PY_SUFFIX} diff --git a/tensorflow/tools/dockerfiles/partials/ubuntu/cpu-devel.partial.Dockerfile b/tensorflow/tools/dockerfiles/partials/ubuntu/devel-cpu.partial.Dockerfile similarity index 73% rename from tensorflow/tools/dockerfiles/partials/ubuntu/cpu-devel.partial.Dockerfile rename to tensorflow/tools/dockerfiles/partials/ubuntu/devel-cpu.partial.Dockerfile index 901652cc28..a61dfbbe54 100644 --- a/tensorflow/tools/dockerfiles/partials/ubuntu/cpu-devel.partial.Dockerfile +++ b/tensorflow/tools/dockerfiles/partials/ubuntu/devel-cpu.partial.Dockerfile @@ -23,3 +23,6 @@ RUN apt-get update && apt-get install -y --no-install-recommends \ ENV CI_BUILD_PYTHON python +# Check out TensorFlow source code if --build_arg CHECKOUT_TENSORFLOW=1 +ARG CHECKOUT_TF_SRC=0 +RUN test "${CHECKOUT_TF_SRC}" -eq 1 && git clone https://github.com/tensorflow/tensorflow.git /tensorflow_src diff --git a/tensorflow/tools/dockerfiles/partials/ubuntu/nvidia-devel.partial.Dockerfile b/tensorflow/tools/dockerfiles/partials/ubuntu/devel-nvidia.partial.Dockerfile similarity index 90% rename from tensorflow/tools/dockerfiles/partials/ubuntu/nvidia-devel.partial.Dockerfile rename to tensorflow/tools/dockerfiles/partials/ubuntu/devel-nvidia.partial.Dockerfile index 48d457e40c..95f9875012 100644 --- a/tensorflow/tools/dockerfiles/partials/ubuntu/nvidia-devel.partial.Dockerfile +++ b/tensorflow/tools/dockerfiles/partials/ubuntu/devel-nvidia.partial.Dockerfile @@ -55,3 +55,7 @@ ENV TF_CUDNN_VERSION=7 # NCCL 2.x ENV TF_NCCL_VERSION=2 + +# Check out TensorFlow source code if --build_arg CHECKOUT_TENSORFLOW=1 +ARG CHECKOUT_TF_SRC=0 +RUN test "${CHECKOUT_TF_SRC}" -eq 1 && git clone https://github.com/tensorflow/tensorflow.git /tensorflow_src diff --git a/tensorflow/tools/dockerfiles/spec.yml b/tensorflow/tools/dockerfiles/spec.yml index 4826ddd8e2..19d96e7a3d 100644 --- a/tensorflow/tools/dockerfiles/spec.yml +++ b/tensorflow/tools/dockerfiles/spec.yml @@ -33,21 +33,28 @@ header: | # - nightly-py3 # - nightly-gpu (similar) # - nightly-gpu-py3 - +# +# Releases are all treated differently by TensorFlow's CI systems. releases: + # Built Nightly and pushed to tensorflow/tensorflow nightly: tag_specs: - "{nightly}{py}{jupyter}" + - "{ubuntu-devel}{py}" + # Built per-release and pushed to tensorflow/tensorflow + # --arg _TAG_PREFIX= should be set to "1.11" (for example) or "latest". versioned: tag_specs: - "{_TAG_PREFIX}{ubuntu}{py}{jupyter}" - ubuntu-dockerfiles: + # Dockerfiles stored in the TF repo; not pushed anywhere + dockerfiles: is_dockerfiles: true upload_images: false tag_specs: - "{ubuntu}{jupyter}" + - "{ubuntu-devel}{jupyter}" slice_sets: @@ -87,27 +94,33 @@ slice_sets: tests: - import-gpu.sh test_runtime: nvidia - - add_to_name: "-devel" - dockerfile_exclusive_name: "cpu-devel" + + ubuntu-devel: + - add_to_name: "devel" + dockerfile_exclusive_name: "devel-cpu" partials: - ubuntu/version - - ubuntu/cpu-devel + - ubuntu/devel-cpu - ubuntu/python - ubuntu/bazel - shell tests: - build-cpu.sh - - add_to_name: "-gpu-devel" - dockerfile_exclusive_name: "gpu-devel" + args: + - CHECKOUT_TF_SRC=1 + - add_to_name: "devel-gpu" + dockerfile_exclusive_name: "devel-gpu" partials: - ubuntu/version - - ubuntu/nvidia-devel + - ubuntu/devel-nvidia - ubuntu/python - ubuntu/bazel - shell tests: - build-gpu.sh test_runtime: nvidia + args: + - CHECKOUT_TF_SRC=1 nightly: - add_to_name: "nightly" -- GitLab From 441471313ca001920987f4f8d3b1ba47e4c73dfc Mon Sep 17 00:00:00 2001 From: Pavithra Vijay Date: Thu, 6 Dec 2018 14:34:02 -0800 Subject: [PATCH 154/873] Add v2 APIs for metrics: `SensitivityAtSpecificity`, `SpecificityAtSensitivity` PiperOrigin-RevId: 224409174 --- tensorflow/python/keras/metrics.py | 82 ++++---- .../tools/api/generator/api_init_files.bzl | 1 - ...metrics.-sensitivity-at-specificity.pbtxt} | 9 +- ...metrics.-specificity-at-sensitivity.pbtxt} | 8 +- .../golden/v1/tensorflow.keras.metrics.pbtxt | 8 + .../tensorflow.metrics.-binary-accuracy.pbtxt | 194 ------------------ ...orflow.metrics.-categorical-accuracy.pbtxt | 194 ------------------ .../golden/v1/tensorflow.metrics.-mean.pbtxt | 192 ----------------- .../v1/tensorflow.metrics.-recall.pbtxt | 192 ----------------- ...metrics.-sparse-categorical-accuracy.pbtxt | 194 ------------------ .../tensorflow.metrics.-true-negatives.pbtxt | 193 ----------------- .../tensorflow.metrics.-true-positives.pbtxt | 193 ----------------- .../api/golden/v1/tensorflow.metrics.pbtxt | 44 ---- ...metrics.-sensitivity-at-specificity.pbtxt} | 7 +- ...metrics.-specificity-at-sensitivity.pbtxt} | 8 +- .../golden/v2/tensorflow.keras.metrics.pbtxt | 8 + .../v2/tensorflow.metrics.-accuracy.pbtxt | 194 ------------------ .../tensorflow.metrics.-binary-accuracy.pbtxt | 194 ------------------ ...orflow.metrics.-categorical-accuracy.pbtxt | 194 ------------------ .../tensorflow.metrics.-false-negatives.pbtxt | 193 ----------------- .../tensorflow.metrics.-false-positives.pbtxt | 193 ----------------- .../golden/v2/tensorflow.metrics.-mean.pbtxt | 192 ----------------- .../v2/tensorflow.metrics.-precision.pbtxt | 192 ----------------- .../v2/tensorflow.metrics.-recall.pbtxt | 192 ----------------- ...metrics.-sparse-categorical-accuracy.pbtxt | 194 ------------------ .../tensorflow.metrics.-true-negatives.pbtxt | 193 ----------------- .../tensorflow.metrics.-true-positives.pbtxt | 193 ----------------- .../api/golden/v2/tensorflow.metrics.pbtxt | 47 ----- .../tools/api/golden/v2/tensorflow.pbtxt | 4 - 29 files changed, 74 insertions(+), 3628 deletions(-) rename tensorflow/tools/api/golden/v1/{tensorflow.metrics.-accuracy.pbtxt => tensorflow.keras.metrics.-sensitivity-at-specificity.pbtxt} (93%) rename tensorflow/tools/api/golden/v1/{tensorflow.metrics.-false-positives.pbtxt => tensorflow.keras.metrics.-specificity-at-sensitivity.pbtxt} (93%) delete mode 100644 tensorflow/tools/api/golden/v1/tensorflow.metrics.-binary-accuracy.pbtxt delete mode 100644 tensorflow/tools/api/golden/v1/tensorflow.metrics.-categorical-accuracy.pbtxt delete mode 100644 tensorflow/tools/api/golden/v1/tensorflow.metrics.-mean.pbtxt delete mode 100644 tensorflow/tools/api/golden/v1/tensorflow.metrics.-recall.pbtxt delete mode 100644 tensorflow/tools/api/golden/v1/tensorflow.metrics.-sparse-categorical-accuracy.pbtxt delete mode 100644 tensorflow/tools/api/golden/v1/tensorflow.metrics.-true-negatives.pbtxt delete mode 100644 tensorflow/tools/api/golden/v1/tensorflow.metrics.-true-positives.pbtxt rename tensorflow/tools/api/golden/{v1/tensorflow.metrics.-precision.pbtxt => v2/tensorflow.keras.metrics.-sensitivity-at-specificity.pbtxt} (93%) rename tensorflow/tools/api/golden/{v1/tensorflow.metrics.-false-negatives.pbtxt => v2/tensorflow.keras.metrics.-specificity-at-sensitivity.pbtxt} (93%) delete mode 100644 tensorflow/tools/api/golden/v2/tensorflow.metrics.-accuracy.pbtxt delete mode 100644 tensorflow/tools/api/golden/v2/tensorflow.metrics.-binary-accuracy.pbtxt delete mode 100644 tensorflow/tools/api/golden/v2/tensorflow.metrics.-categorical-accuracy.pbtxt delete mode 100644 tensorflow/tools/api/golden/v2/tensorflow.metrics.-false-negatives.pbtxt delete mode 100644 tensorflow/tools/api/golden/v2/tensorflow.metrics.-false-positives.pbtxt delete mode 100644 tensorflow/tools/api/golden/v2/tensorflow.metrics.-mean.pbtxt delete mode 100644 tensorflow/tools/api/golden/v2/tensorflow.metrics.-precision.pbtxt delete mode 100644 tensorflow/tools/api/golden/v2/tensorflow.metrics.-recall.pbtxt delete mode 100644 tensorflow/tools/api/golden/v2/tensorflow.metrics.-sparse-categorical-accuracy.pbtxt delete mode 100644 tensorflow/tools/api/golden/v2/tensorflow.metrics.-true-negatives.pbtxt delete mode 100644 tensorflow/tools/api/golden/v2/tensorflow.metrics.-true-positives.pbtxt delete mode 100644 tensorflow/tools/api/golden/v2/tensorflow.metrics.pbtxt diff --git a/tensorflow/python/keras/metrics.py b/tensorflow/python/keras/metrics.py index 1d3c6e67d7..331a8636d1 100644 --- a/tensorflow/python/keras/metrics.py +++ b/tensorflow/python/keras/metrics.py @@ -511,7 +511,7 @@ class Metric(Layer): ### End: For use by subclasses ### -@tf_export('metrics.Mean', 'keras.metrics.Mean') +@tf_export('keras.metrics.Mean') class Mean(Metric): """Computes the (weighted) mean of the given values. @@ -528,7 +528,7 @@ class Mean(Metric): Usage: ```python - m = tf.metrics.Mean() + m = tf.keras.metrics.Mean() m.update_state([1, 3, 5, 7]) print('Final result: ', m.result().numpy()) # Final result: 4.0 ``` @@ -537,7 +537,7 @@ class Mean(Metric): ```python model = keras.models.Model(inputs, outputs) - model.add_metric(metrics_module.Mean(name='mean_1')(outputs)) + model.add_metric(tf.keras.metrics.Mean(name='mean_1')(outputs)) model.compile('sgd', loss='mse') ``` """ @@ -651,7 +651,7 @@ class MeanMetricWrapper(Mean): return dict(list(base_config.items()) + list(config.items())) -@tf_export('metrics.Accuracy', 'keras.metrics.Accuracy') +@tf_export('keras.metrics.Accuracy') class Accuracy(MeanMetricWrapper): """Calculates how often predictions matches labels. @@ -670,7 +670,7 @@ class Accuracy(MeanMetricWrapper): Usage: ```python - m = tf.metrics.Accuracy() + m = tf.keras.metrics.Accuracy() m.update_state([1, 2, 3, 4], [0, 2, 3, 4]) print('Final result: ', m.result().numpy()) # Final result: 0.75 ``` @@ -679,7 +679,7 @@ class Accuracy(MeanMetricWrapper): ```python model = keras.models.Model(inputs, outputs) - model.compile('sgd', loss='mse', metrics=[tf.metrics.Accuracy()]) + model.compile('sgd', loss='mse', metrics=[tf.keras.metrics.Accuracy()]) ``` """ @@ -693,7 +693,7 @@ class Accuracy(MeanMetricWrapper): return super(Accuracy, cls).from_config(config) -@tf_export('metrics.BinaryAccuracy', 'keras.metrics.BinaryAccuracy') +@tf_export('keras.metrics.BinaryAccuracy') class BinaryAccuracy(MeanMetricWrapper): """Calculates how often predictions matches labels. @@ -712,7 +712,7 @@ class BinaryAccuracy(MeanMetricWrapper): Usage: ```python - m = tf.metrics.BinaryAccuracy() + m = tf.keras.metrics.BinaryAccuracy() m.update_state([1, 1, 0, 0], [0.98, 1, 0, 0.6]) print('Final result: ', m.result().numpy()) # Final result: 0.75 ``` @@ -721,7 +721,7 @@ class BinaryAccuracy(MeanMetricWrapper): ```python model = keras.models.Model(inputs, outputs) - model.compile('sgd', loss='mse', metrics=[tf.metrics.BinaryAccuracy()]) + model.compile('sgd', loss='mse', metrics=[tf.keras.metrics.BinaryAccuracy()]) ``` """ @@ -744,8 +744,7 @@ class BinaryAccuracy(MeanMetricWrapper): return super(BinaryAccuracy, cls).from_config(config) -@tf_export( - 'metrics.CategoricalAccuracy', 'keras.metrics.CategoricalAccuracy') +@tf_export('keras.metrics.CategoricalAccuracy') class CategoricalAccuracy(MeanMetricWrapper): """Calculates how often predictions matches labels. @@ -768,7 +767,7 @@ class CategoricalAccuracy(MeanMetricWrapper): Usage: ```python - m = tf.metrics.CategoricalAccuracy() + m = tf.keras.metrics.CategoricalAccuracy() m.update_state([[0, 0, 1], [0, 1, 0]], [[0.1, 0.9, 0.8], [0.05, 0.95, 0]]) print('Final result: ', m.result().numpy()) # Final result: 0.5 ``` @@ -777,7 +776,10 @@ class CategoricalAccuracy(MeanMetricWrapper): ```python model = keras.models.Model(inputs, outputs) - model.compile('sgd', loss='mse', metrics=[tf.metrics.CategoricalAccuracy()]) + model.compile( + 'sgd', + loss='mse', + metrics=[tf.keras.metrics.CategoricalAccuracy()]) ``` """ @@ -798,9 +800,7 @@ class CategoricalAccuracy(MeanMetricWrapper): return super(CategoricalAccuracy, cls).from_config(config) -@tf_export( - 'metrics.SparseCategoricalAccuracy', - 'keras.metrics.SparseCategoricalAccuracy') +@tf_export('keras.metrics.SparseCategoricalAccuracy') class SparseCategoricalAccuracy(MeanMetricWrapper): """Calculates how often predictions matches integer labels. @@ -820,7 +820,7 @@ class SparseCategoricalAccuracy(MeanMetricWrapper): Usage: ```python - m = tf.metrics.SparseCategoricalAccuracy() + m = tf.keras.metrics.SparseCategoricalAccuracy() m.update_state([[2], [1]], [[0.1, 0.9, 0.8], [0.05, 0.95, 0]]) print('Final result: ', m.result().numpy()) # Final result: 0.5 ``` @@ -832,7 +832,7 @@ class SparseCategoricalAccuracy(MeanMetricWrapper): model.compile( 'sgd', loss='mse', - metrics=[tf.metrics.SparseCategoricalAccuracy()]) + metrics=[tf.keras.metrics.SparseCategoricalAccuracy()]) ``` """ @@ -907,7 +907,7 @@ class _ConfusionMatrixConditionCount(Metric): K.set_value(v, np.zeros((num_thresholds,))) -@tf_export('metrics.FalsePositives', 'keras.metrics.FalsePositives') +@tf_export('keras.metrics.FalsePositives') class FalsePositives(_ConfusionMatrixConditionCount): """Calculates the number of false positives. @@ -925,7 +925,7 @@ class FalsePositives(_ConfusionMatrixConditionCount): Usage: ```python - m = tf.metrics.FalsePositives() + m = tf.keras.metrics.FalsePositives() m.update_state([0, 1, 0, 0], [0, 0, 1, 1]) print('Final result: ', m.result().numpy()) # Final result: 2 ``` @@ -934,7 +934,7 @@ class FalsePositives(_ConfusionMatrixConditionCount): ```python model = keras.models.Model(inputs, outputs) - model.compile('sgd', loss='mse', metrics=[tf.metrics.FalsePositives()]) + model.compile('sgd', loss='mse', metrics=[tf.keras.metrics.FalsePositives()]) ``` """ @@ -957,7 +957,7 @@ class FalsePositives(_ConfusionMatrixConditionCount): dtype=dtype) -@tf_export('metrics.FalseNegatives', 'keras.metrics.FalseNegatives') +@tf_export('keras.metrics.FalseNegatives') class FalseNegatives(_ConfusionMatrixConditionCount): """Calculates the number of false negatives. @@ -975,7 +975,7 @@ class FalseNegatives(_ConfusionMatrixConditionCount): Usage: ```python - m = tf.metrics.FalseNegatives() + m = tf.keras.metrics.FalseNegatives() m.update_state([0, 1, 1, 1], [0, 1, 0, 0]) print('Final result: ', m.result().numpy()) # Final result: 2 ``` @@ -984,7 +984,7 @@ class FalseNegatives(_ConfusionMatrixConditionCount): ```python model = keras.models.Model(inputs, outputs) - model.compile('sgd', loss='mse', metrics=[tf.metrics.FalseNegatives()]) + model.compile('sgd', loss='mse', metrics=[tf.keras.metrics.FalseNegatives()]) ``` """ @@ -1007,7 +1007,7 @@ class FalseNegatives(_ConfusionMatrixConditionCount): dtype=dtype) -@tf_export('metrics.TrueNegatives', 'keras.metrics.TrueNegatives') +@tf_export('keras.metrics.TrueNegatives') class TrueNegatives(_ConfusionMatrixConditionCount): """Calculates the number of true negatives. @@ -1025,7 +1025,7 @@ class TrueNegatives(_ConfusionMatrixConditionCount): Usage: ```python - m = tf.metrics.TrueNegatives() + m = tf.keras.metrics.TrueNegatives() m.update_state([0, 1, 0, 0], [1, 1, 0, 0]) print('Final result: ', m.result().numpy()) # Final result: 2 ``` @@ -1034,7 +1034,7 @@ class TrueNegatives(_ConfusionMatrixConditionCount): ```python model = keras.models.Model(inputs, outputs) - model.compile('sgd', loss='mse', metrics=[tf.metrics.TrueNegatives()]) + model.compile('sgd', loss='mse', metrics=[tf.keras.metrics.TrueNegatives()]) ``` """ @@ -1057,7 +1057,7 @@ class TrueNegatives(_ConfusionMatrixConditionCount): dtype=dtype) -@tf_export('metrics.TruePositives', 'keras.metrics.TruePositives') +@tf_export('keras.metrics.TruePositives') class TruePositives(_ConfusionMatrixConditionCount): """Calculates the number of true positives. @@ -1075,7 +1075,7 @@ class TruePositives(_ConfusionMatrixConditionCount): Usage: ```python - m = tf.metrics.TruePositives() + m = tf.keras.metrics.TruePositives() m.update_state([0, 1, 1, 1], [1, 0, 1, 1]) print('Final result: ', m.result().numpy()) # Final result: 2 ``` @@ -1084,7 +1084,7 @@ class TruePositives(_ConfusionMatrixConditionCount): ```python model = keras.models.Model(inputs, outputs) - model.compile('sgd', loss='mse', metrics=[tf.metrics.TruePositives()]) + model.compile('sgd', loss='mse', metrics=[tf.keras.metrics.TruePositives()]) ``` """ @@ -1107,7 +1107,7 @@ class TruePositives(_ConfusionMatrixConditionCount): dtype=dtype) -@tf_export('metrics.Precision', 'keras.metrics.Precision') +@tf_export('keras.metrics.Precision') class Precision(Metric): """Computes the precision of the predictions with respect to the labels. @@ -1126,7 +1126,7 @@ class Precision(Metric): Usage: ```python - m = tf.metrics.Precision() + m = tf.keras.metrics.Precision() m.update_state([0, 1, 1, 1], [1, 0, 1, 1]) print('Final result: ', m.result().numpy()) # Final result: 0.66 ``` @@ -1135,7 +1135,7 @@ class Precision(Metric): ```python model = keras.models.Model(inputs, outputs) - model.compile('sgd', loss='mse', metrics=[tf.metrics.Precision()]) + model.compile('sgd', loss='mse', metrics=[tf.keras.metrics.Precision()]) ``` """ @@ -1192,7 +1192,7 @@ class Precision(Metric): K.set_value(v, np.zeros((num_thresholds,))) -@tf_export('metrics.Recall', 'keras.metrics.Recall') +@tf_export('keras.metrics.Recall') class Recall(Metric): """Computes the recall of the predictions with respect to the labels. @@ -1211,7 +1211,7 @@ class Recall(Metric): Usage: ```python - m = tf.metrics.Recall() + m = tf.keras.metrics.Recall() m.update_state([0, 1, 1, 1], [1, 0, 1, 1]) print('Final result: ', m.result().numpy()) # Final result: 0.66 ``` @@ -1220,7 +1220,7 @@ class Recall(Metric): ```python model = keras.models.Model(inputs, outputs) - model.compile('sgd', loss='mse', metrics=[tf.metrics.Recall()]) + model.compile('sgd', loss='mse', metrics=[tf.keras.metrics.Recall()]) ``` """ @@ -1341,6 +1341,7 @@ class SensitivitySpecificityBase(Metric): K.set_value(v, np.zeros((num_thresholds,))) +@tf_export('keras.metrics.SensitivityAtSpecificity') class SensitivityAtSpecificity(SensitivitySpecificityBase): """Computes the sensitivity at a given specificity. @@ -1363,7 +1364,7 @@ class SensitivityAtSpecificity(SensitivitySpecificityBase): Usage: ```python - m = tf.metrics.SensitivityAtSpecificity(0.4, num_thresholds=1) + m = tf.keras.metrics.SensitivityAtSpecificity(0.4, num_thresholds=1) m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9]) print('Final result: ', m.result().numpy()) # Final result: 0.5 ``` @@ -1375,7 +1376,7 @@ class SensitivityAtSpecificity(SensitivitySpecificityBase): model.compile( 'sgd', loss='mse', - metrics=[tf.metrics.SensitivityAtSpecificity()]) + metrics=[tf.keras.metrics.SensitivityAtSpecificity()]) ``` """ @@ -1409,6 +1410,7 @@ class SensitivityAtSpecificity(SensitivitySpecificityBase): self.tp[min_index] + self.fn[min_index]) +@tf_export('keras.metrics.SpecificityAtSensitivity') class SpecificityAtSensitivity(SensitivitySpecificityBase): """Computes the specificity at a given sensitivity. @@ -1431,7 +1433,7 @@ class SpecificityAtSensitivity(SensitivitySpecificityBase): Usage: ```python - m = tf.metrics.SpecificityAtSensitivity(0.8, num_thresholds=1) + m = tf.keras.metrics.SpecificityAtSensitivity(0.8, num_thresholds=1) m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9]) print('Final result: ', m.result().numpy()) # Final result: 1.0 ``` @@ -1443,7 +1445,7 @@ class SpecificityAtSensitivity(SensitivitySpecificityBase): model.compile( 'sgd', loss='mse', - metrics=[tf.metrics.SpecificityAtSensitivity()]) + metrics=[tf.keras.metrics.SpecificityAtSensitivity()]) ``` """ diff --git a/tensorflow/python/tools/api/generator/api_init_files.bzl b/tensorflow/python/tools/api/generator/api_init_files.bzl index 3517c11cc9..0245ac50a6 100644 --- a/tensorflow/python/tools/api/generator/api_init_files.bzl +++ b/tensorflow/python/tools/api/generator/api_init_files.bzl @@ -64,7 +64,6 @@ TENSORFLOW_API_INIT_FILES = [ "lite/constants/__init__.py", "losses/__init__.py", "math/__init__.py", - "metrics/__init__.py", "nn/__init__.py", "nn/rnn_cell/__init__.py", "quantization/__init__.py", diff --git a/tensorflow/tools/api/golden/v1/tensorflow.metrics.-accuracy.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.metrics.-sensitivity-at-specificity.pbtxt similarity index 93% rename from tensorflow/tools/api/golden/v1/tensorflow.metrics.-accuracy.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.metrics.-sensitivity-at-specificity.pbtxt index f8e12f8817..aa77d1972c 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.metrics.-accuracy.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.metrics.-sensitivity-at-specificity.pbtxt @@ -1,8 +1,7 @@ -path: "tensorflow.metrics.Accuracy" +path: "tensorflow.keras.metrics.SensitivityAtSpecificity" tf_class { - is_instance: "" - is_instance: "" - is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" is_instance: "" is_instance: "" @@ -85,7 +84,7 @@ tf_class { } member_method { name: "__init__" - argspec: "args=[\'self\', \'name\', \'dtype\'], varargs=None, keywords=None, defaults=[\'accuracy\', \'None\'], " + argspec: "args=[\'self\', \'specificity\', \'num_thresholds\', \'name\', \'dtype\'], varargs=None, keywords=None, defaults=[\'200\', \'None\', \'None\'], " } member_method { name: "add_loss" diff --git a/tensorflow/tools/api/golden/v1/tensorflow.metrics.-false-positives.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.metrics.-specificity-at-sensitivity.pbtxt similarity index 93% rename from tensorflow/tools/api/golden/v1/tensorflow.metrics.-false-positives.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.metrics.-specificity-at-sensitivity.pbtxt index 9953162ea3..67857aa89f 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.metrics.-false-positives.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.metrics.-specificity-at-sensitivity.pbtxt @@ -1,7 +1,7 @@ -path: "tensorflow.metrics.FalsePositives" +path: "tensorflow.keras.metrics.SpecificityAtSensitivity" tf_class { - is_instance: "" - is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" is_instance: "" is_instance: "" @@ -84,7 +84,7 @@ tf_class { } member_method { name: "__init__" - argspec: "args=[\'self\', \'thresholds\', \'name\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + argspec: "args=[\'self\', \'sensitivity\', \'num_thresholds\', \'name\', \'dtype\'], varargs=None, keywords=None, defaults=[\'200\', \'None\', \'None\'], " } member_method { name: "add_loss" diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.metrics.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.metrics.pbtxt index 8cab17edc5..905021dd79 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.keras.metrics.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.metrics.pbtxt @@ -32,10 +32,18 @@ tf_module { name: "Recall" mtype: "" } + member { + name: "SensitivityAtSpecificity" + mtype: "" + } member { name: "SparseCategoricalAccuracy" mtype: "" } + member { + name: "SpecificityAtSensitivity" + mtype: "" + } member { name: "TrueNegatives" mtype: "" diff --git a/tensorflow/tools/api/golden/v1/tensorflow.metrics.-binary-accuracy.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.metrics.-binary-accuracy.pbtxt deleted file mode 100644 index b9bc6a716a..0000000000 --- a/tensorflow/tools/api/golden/v1/tensorflow.metrics.-binary-accuracy.pbtxt +++ /dev/null @@ -1,194 +0,0 @@ -path: "tensorflow.metrics.BinaryAccuracy" -tf_class { - is_instance: "" - is_instance: "" - 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\', \'name\', \'dtype\', \'threshold\'], varargs=None, keywords=None, defaults=[\'binary_accuracy\', \'None\', \'0.5\'], " - } - member_method { - name: "add_loss" - argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "add_metric" - argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'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\', \'aggregation\', \'synchronization\', \'initializer\'], varargs=None, keywords=None, defaults=[\'()\', \'VariableAggregation.SUM\', \'VariableSynchronization.ON_READ\', \'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=kwargs, 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=[\'self\', \'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: "reset_states" - argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" - } - member_method { - name: "result" - argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" - } - member_method { - name: "set_weights" - argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" - } - member_method { - name: "update_state" - argspec: "args=[\'self\', \'y_true\', \'y_pred\', \'sample_weight\'], varargs=None, keywords=None, defaults=[\'None\'], " - } -} diff --git a/tensorflow/tools/api/golden/v1/tensorflow.metrics.-categorical-accuracy.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.metrics.-categorical-accuracy.pbtxt deleted file mode 100644 index 0ef75d8756..0000000000 --- a/tensorflow/tools/api/golden/v1/tensorflow.metrics.-categorical-accuracy.pbtxt +++ /dev/null @@ -1,194 +0,0 @@ -path: "tensorflow.metrics.CategoricalAccuracy" -tf_class { - is_instance: "" - is_instance: "" - 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 { - 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keywords=None, defaults=None" - } - member_method { - name: "update_state" - argspec: "args=[\'self\', \'y_true\', \'y_pred\', \'sample_weight\'], varargs=None, keywords=None, defaults=[\'None\'], " - } -} diff --git a/tensorflow/tools/api/golden/v1/tensorflow.metrics.-mean.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.metrics.-mean.pbtxt deleted file mode 100644 index 7fe6d6fda9..0000000000 --- a/tensorflow/tools/api/golden/v1/tensorflow.metrics.-mean.pbtxt +++ /dev/null @@ -1,192 +0,0 @@ -path: "tensorflow.metrics.Mean" -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" - 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a/tensorflow/tools/api/golden/v1/tensorflow.metrics.-true-negatives.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.metrics.-true-negatives.pbtxt deleted file mode 100644 index 83cd5b736b..0000000000 --- a/tensorflow/tools/api/golden/v1/tensorflow.metrics.-true-negatives.pbtxt +++ /dev/null @@ -1,193 +0,0 @@ -path: "tensorflow.metrics.TrueNegatives" -tf_class { - is_instance: "" - 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: 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member_method { - name: "call" - argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=kwargs, 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=[\'self\', \'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: "reset_states" - argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" - } - member_method { - name: "result" - argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" - } - member_method { - name: "set_weights" - argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" - } - member_method { - name: "update_state" - argspec: "args=[\'self\', \'y_true\', \'y_pred\', \'sample_weight\'], varargs=None, keywords=None, defaults=[\'None\'], " - } -} diff --git a/tensorflow/tools/api/golden/v1/tensorflow.metrics.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.metrics.pbtxt index f5c267a166..e9b996c9f5 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.metrics.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.metrics.pbtxt @@ -1,49 +1,5 @@ path: "tensorflow.metrics" tf_module { - member { - name: "Accuracy" - mtype: "" - } - member { - name: "BinaryAccuracy" - mtype: "" - } - member { - name: "CategoricalAccuracy" - mtype: "" - } - member { - name: "FalseNegatives" - mtype: "" - } - member { - name: "FalsePositives" - mtype: "" - } - member { - name: "Mean" - mtype: "" - } - member { - name: "Precision" - mtype: "" - } - member { - name: "Recall" - mtype: "" - } - member { - name: "SparseCategoricalAccuracy" - mtype: "" - } - member { - name: "TrueNegatives" - mtype: "" - } - member { - name: "TruePositives" - mtype: "" - } member_method { name: "accuracy" argspec: "args=[\'labels\', \'predictions\', \'weights\', \'metrics_collections\', \'updates_collections\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " diff --git a/tensorflow/tools/api/golden/v1/tensorflow.metrics.-precision.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.metrics.-sensitivity-at-specificity.pbtxt similarity index 93% rename from tensorflow/tools/api/golden/v1/tensorflow.metrics.-precision.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.keras.metrics.-sensitivity-at-specificity.pbtxt index 8c3271a109..aa77d1972c 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.metrics.-precision.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.metrics.-sensitivity-at-specificity.pbtxt @@ -1,6 +1,7 @@ -path: "tensorflow.metrics.Precision" +path: "tensorflow.keras.metrics.SensitivityAtSpecificity" tf_class { - is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" is_instance: "" is_instance: "" @@ -83,7 +84,7 @@ tf_class { } member_method { name: "__init__" - argspec: "args=[\'self\', \'thresholds\', \'name\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + argspec: "args=[\'self\', \'specificity\', \'num_thresholds\', \'name\', \'dtype\'], varargs=None, keywords=None, defaults=[\'200\', \'None\', \'None\'], " } member_method { name: "add_loss" diff --git a/tensorflow/tools/api/golden/v1/tensorflow.metrics.-false-negatives.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.metrics.-specificity-at-sensitivity.pbtxt similarity index 93% rename from tensorflow/tools/api/golden/v1/tensorflow.metrics.-false-negatives.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.keras.metrics.-specificity-at-sensitivity.pbtxt index 33226a2df6..67857aa89f 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.metrics.-false-negatives.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.metrics.-specificity-at-sensitivity.pbtxt @@ -1,7 +1,7 @@ -path: "tensorflow.metrics.FalseNegatives" +path: "tensorflow.keras.metrics.SpecificityAtSensitivity" tf_class { - is_instance: "" - is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" is_instance: "" is_instance: "" @@ -84,7 +84,7 @@ tf_class { } member_method { name: "__init__" - argspec: "args=[\'self\', \'thresholds\', \'name\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + argspec: "args=[\'self\', \'sensitivity\', \'num_thresholds\', \'name\', \'dtype\'], varargs=None, keywords=None, defaults=[\'200\', \'None\', \'None\'], " } member_method { name: "add_loss" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.metrics.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.metrics.pbtxt index 8cab17edc5..905021dd79 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.keras.metrics.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.metrics.pbtxt @@ -32,10 +32,18 @@ tf_module { name: "Recall" mtype: "" } + member { + name: "SensitivityAtSpecificity" + mtype: "" + } member { name: "SparseCategoricalAccuracy" mtype: "" } + member { + name: "SpecificityAtSensitivity" + mtype: "" + } member { name: "TrueNegatives" mtype: "" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.metrics.-accuracy.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.metrics.-accuracy.pbtxt deleted file mode 100644 index f8e12f8817..0000000000 --- a/tensorflow/tools/api/golden/v2/tensorflow.metrics.-accuracy.pbtxt +++ /dev/null @@ -1,194 +0,0 @@ -path: "tensorflow.metrics.Accuracy" -tf_class { - is_instance: "" - is_instance: "" - 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\', \'name\', \'dtype\'], varargs=None, keywords=None, defaults=[\'accuracy\', \'None\'], " - } - member_method { - name: "add_loss" - argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "add_metric" - argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'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\', \'aggregation\', \'synchronization\', \'initializer\'], varargs=None, keywords=None, defaults=[\'()\', \'VariableAggregation.SUM\', \'VariableSynchronization.ON_READ\', \'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=kwargs, 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=[\'self\', \'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: "reset_states" - argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" - } - member_method { - name: "result" - argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" - } - member_method { - name: "set_weights" - argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" - } - member_method { - name: "update_state" - argspec: "args=[\'self\', \'y_true\', \'y_pred\', \'sample_weight\'], varargs=None, keywords=None, defaults=[\'None\'], " - } -} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.metrics.-binary-accuracy.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.metrics.-binary-accuracy.pbtxt deleted file mode 100644 index b9bc6a716a..0000000000 --- a/tensorflow/tools/api/golden/v2/tensorflow.metrics.-binary-accuracy.pbtxt +++ /dev/null @@ -1,194 +0,0 @@ -path: "tensorflow.metrics.BinaryAccuracy" -tf_class { - is_instance: "" - is_instance: "" - 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\', \'name\', \'dtype\', \'threshold\'], varargs=None, keywords=None, defaults=[\'binary_accuracy\', \'None\', \'0.5\'], " - } - member_method { - name: "add_loss" - argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "add_metric" - argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'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\', \'aggregation\', \'synchronization\', \'initializer\'], varargs=None, keywords=None, defaults=[\'()\', \'VariableAggregation.SUM\', \'VariableSynchronization.ON_READ\', \'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=kwargs, 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=[\'self\', \'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: "reset_states" - argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" - } - member_method { - name: "result" - argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" - } - member_method { - name: "set_weights" - argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" - } - member_method { - name: "update_state" - argspec: "args=[\'self\', \'y_true\', \'y_pred\', \'sample_weight\'], varargs=None, keywords=None, defaults=[\'None\'], " - } -} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.metrics.-categorical-accuracy.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.metrics.-categorical-accuracy.pbtxt deleted file mode 100644 index 0ef75d8756..0000000000 --- a/tensorflow/tools/api/golden/v2/tensorflow.metrics.-categorical-accuracy.pbtxt +++ /dev/null @@ -1,194 +0,0 @@ -path: "tensorflow.metrics.CategoricalAccuracy" -tf_class { - is_instance: "" - is_instance: "" - 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\', \'name\', \'dtype\'], varargs=None, keywords=None, defaults=[\'categorical_accuracy\', \'None\'], " - } - member_method { - name: "add_loss" - argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "add_metric" - argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'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\', \'aggregation\', \'synchronization\', \'initializer\'], varargs=None, keywords=None, defaults=[\'()\', \'VariableAggregation.SUM\', \'VariableSynchronization.ON_READ\', \'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=kwargs, 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=[\'self\', \'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\'], 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defaults=None" - } - member_method { - name: "get_weights" - argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" - } - member_method { - name: "reset_states" - argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" - } - member_method { - name: "result" - argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" - } - member_method { - name: "set_weights" - argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" - } - member_method { - name: "update_state" - argspec: "args=[\'self\', \'y_true\', \'y_pred\', \'sample_weight\'], varargs=None, keywords=None, defaults=[\'None\'], " - } -} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.metrics.-false-negatives.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.metrics.-false-negatives.pbtxt deleted file mode 100644 index 33226a2df6..0000000000 --- a/tensorflow/tools/api/golden/v2/tensorflow.metrics.-false-negatives.pbtxt +++ /dev/null @@ -1,193 +0,0 @@ -path: 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a/tensorflow/tools/api/golden/v2/tensorflow.metrics.-false-positives.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.metrics.-false-positives.pbtxt deleted file mode 100644 index 9953162ea3..0000000000 --- a/tensorflow/tools/api/golden/v2/tensorflow.metrics.-false-positives.pbtxt +++ /dev/null @@ -1,193 +0,0 @@ -path: "tensorflow.metrics.FalsePositives" -tf_class { - is_instance: "" - 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: 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- } - 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: "reset_states" - argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" - } - member_method { - name: "result" - argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" - } - member_method { - name: "set_weights" - argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" - } - member_method { - name: "update_state" - argspec: "args=[\'self\', \'y_true\', \'y_pred\', \'sample_weight\'], varargs=None, keywords=None, defaults=[\'None\'], " - } -} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.metrics.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.metrics.pbtxt deleted file mode 100644 index 773efd03fc..0000000000 --- a/tensorflow/tools/api/golden/v2/tensorflow.metrics.pbtxt +++ /dev/null @@ -1,47 +0,0 @@ -path: "tensorflow.metrics" -tf_module { - member { - name: "Accuracy" - mtype: "" - } - member { - name: "BinaryAccuracy" - mtype: "" - } - member { - name: "CategoricalAccuracy" - mtype: "" - } - member { - name: "FalseNegatives" - mtype: "" - } - member { - name: "FalsePositives" - mtype: "" - } - member { - name: "Mean" - mtype: "" - } - member { - name: "Precision" - mtype: "" - } - member { - name: "Recall" - mtype: "" - } - member { - name: "SparseCategoricalAccuracy" - mtype: "" - } - member { - name: "TrueNegatives" - mtype: "" - } - member { - name: "TruePositives" - mtype: "" - } -} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.pbtxt index 7b1c96c2e8..542e5cbe9a 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.pbtxt @@ -216,10 +216,6 @@ tf_module { name: "math" mtype: "" } - member { - name: "metrics" - mtype: "" - } member { name: "name_scope" mtype: "" -- GitLab From 2efcc2905fd6f3c19d73a56a4d17a89d6a691ec3 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Thu, 6 Dec 2018 22:48:52 +0000 Subject: [PATCH 155/873] Replace shape.ndims with array_ops.rank Signed-off-by: Yong Tang --- tensorflow/python/ops/nn_ops.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py index 4a36aa1550..a0ffbc85ec 100644 --- a/tensorflow/python/ops/nn_ops.py +++ b/tensorflow/python/ops/nn_ops.py @@ -1699,11 +1699,12 @@ def _softmax(logits, compute_op, dim=-1, name=None): # still perform softmax on its last dimension. # In case dim is negative (and is not last dimension -1), add shape.ndims + ndims = array_ops.rank(logits) if not isinstance(dim, ops.Tensor): if dim < 0: - dim += shape.ndims + dim += ndims else: - dim = array_ops.where(math_ops.less(dim, 0), dim + shape.ndims, dim) + dim = array_ops.where(math_ops.less(dim, 0), dim + ndims, dim) # Swap logits' dimension of dim and its last dimension. input_rank = array_ops.rank(logits) -- GitLab From ac7864268b480abf216e437d64c1311c7d1362b5 Mon Sep 17 00:00:00 2001 From: Jian Li Date: Thu, 6 Dec 2018 14:55:13 -0800 Subject: [PATCH 156/873] Make kInputLayerNormWeightsTensor optional. PiperOrigin-RevId: 224413157 --- tensorflow/lite/kernels/layer_norm_lstm.cc | 41 ++-- .../lite/kernels/layer_norm_lstm_test.cc | 223 +++++++++++++++++- 2 files changed, 243 insertions(+), 21 deletions(-) diff --git a/tensorflow/lite/kernels/layer_norm_lstm.cc b/tensorflow/lite/kernels/layer_norm_lstm.cc index 5b0046a7b3..49e8a53c82 100644 --- a/tensorflow/lite/kernels/layer_norm_lstm.cc +++ b/tensorflow/lite/kernels/layer_norm_lstm.cc @@ -55,7 +55,7 @@ constexpr int kCellToForgetWeightsTensor = 10; // Optional constexpr int kCellToOutputWeightsTensor = 11; // Optional // Layer norm weights tensors of size {n_cell}, representing a diagonal matrix. -constexpr int kInputLayerNormWeightsTensor = 12; +constexpr int kInputLayerNormWeightsTensor = 12; // Optional constexpr int kForgetLayerNormWeightsTensor = 13; constexpr int kCellLayerNormWeightsTensor = 14; constexpr int kOutputLayerNormWeightsTensor = 15; @@ -118,7 +118,8 @@ TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context, const TfLiteTensor* input_to_input_weights = GetOptionalInputTensor(context, node, kInputToInputWeightsTensor); - if (input_to_input_weights != nullptr) { + const bool use_cifg = (input_to_input_weights == nullptr); + if (!use_cifg) { 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); @@ -138,7 +139,9 @@ TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context, const TfLiteTensor* recurrent_to_input_weights = GetOptionalInputTensor(context, node, kRecurrentToInputWeightsTensor); - if (recurrent_to_input_weights != nullptr) { + if (use_cifg) { + TF_LITE_ENSURE_EQ(context, recurrent_to_input_weights, nullptr); + } else { 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); @@ -161,15 +164,6 @@ TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context, TF_LITE_ENSURE_EQ(context, recurrent_to_cell_weights->dims->data[1], n_output); - // We make sure the input-gate's parameters are either both present (regular - // LSTM) or not at all (CIFG-LSTM). - const bool cifg_weights_all_or_none = - ((input_to_input_weights != nullptr) && - (recurrent_to_input_weights != nullptr)) || - ((input_to_input_weights == nullptr) && - (recurrent_to_input_weights == nullptr)); - TF_LITE_ENSURE(context, cifg_weights_all_or_none == true); - const TfLiteTensor* cell_to_input_weights = GetOptionalInputTensor(context, node, kCellToInputWeightsTensor); if (cell_to_input_weights) { @@ -192,7 +186,6 @@ TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context, } // Making sure the peephole weights are there all or none. - const bool use_cifg = (input_to_input_weights == nullptr); const bool peephole_weights_all_or_none = ((cell_to_input_weights != nullptr || use_cifg) && (cell_to_forget_weights != nullptr) && @@ -204,10 +197,14 @@ TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context, // Making sure layer norm weights are not null and have the right dimension. const TfLiteTensor* input_layer_norm_weights = - GetInput(context, node, kInputLayerNormWeightsTensor); - TF_LITE_ENSURE(context, input_layer_norm_weights != nullptr); - TF_LITE_ENSURE_EQ(context, input_layer_norm_weights->dims->size, 1); - TF_LITE_ENSURE_EQ(context, input_layer_norm_weights->dims->data[0], n_cell); + GetOptionalInputTensor(context, node, kInputLayerNormWeightsTensor); + if (use_cifg) { + TF_LITE_ENSURE_EQ(context, input_layer_norm_weights, nullptr); + } else { + TF_LITE_ENSURE(context, input_layer_norm_weights != nullptr); + TF_LITE_ENSURE_EQ(context, input_layer_norm_weights->dims->size, 1); + TF_LITE_ENSURE_EQ(context, input_layer_norm_weights->dims->data[0], n_cell); + } const TfLiteTensor* forget_layer_norm_weights = GetInput(context, node, kForgetLayerNormWeightsTensor); @@ -978,6 +975,9 @@ TfLiteStatus EvalFloat( (projection_weights == nullptr) ? nullptr : projection_weights->data.f; const float* projection_bias_ptr = (projection_bias == nullptr) ? nullptr : projection_bias->data.f; + const float* input_layer_norm_weight_ptr = + (input_layer_norm_weights == nullptr) ? nullptr + : input_layer_norm_weights->data.f; // Required tensors, pointers are non-null. const float* input_ptr_batch = input->data.f; @@ -990,7 +990,6 @@ TfLiteStatus EvalFloat( recurrent_to_cell_weights->data.f; const float* recurrent_to_output_weights_ptr = recurrent_to_output_weights->data.f; - const float* input_layer_norm_weight_ptr = input_layer_norm_weights->data.f; const float* forget_layer_norm_weight_ptr = forget_layer_norm_weights->data.f; const float* cell_layer_norm_weight_ptr = cell_layer_norm_weights->data.f; const float* output_layer_norm_weight_ptr = output_layer_norm_weights->data.f; @@ -1115,6 +1114,9 @@ TfLiteStatus EvalHybrid( (projection_weights == nullptr) ? 1.0f : projection_weights->params.scale; const float* projection_bias_ptr = (projection_bias == nullptr) ? nullptr : projection_bias->data.f; + const float* input_layer_norm_weight_ptr = + (input_layer_norm_weights == nullptr) ? nullptr + : input_layer_norm_weights->data.f; // Required tensors, pointers are non-null. const float* input_ptr_batch = input->data.f; @@ -1141,7 +1143,6 @@ TfLiteStatus EvalHybrid( reinterpret_cast(recurrent_to_output_weights->data.uint8); const float recurrent_to_output_weights_scale = recurrent_to_output_weights->params.scale; - const float* input_layer_norm_weight_ptr = input_layer_norm_weights->data.f; const float* forget_layer_norm_weight_ptr = forget_layer_norm_weights->data.f; const float* cell_layer_norm_weight_ptr = cell_layer_norm_weights->data.f; const float* output_layer_norm_weight_ptr = output_layer_norm_weights->data.f; @@ -1221,7 +1222,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { GetOptionalInputTensor(context, node, kCellToOutputWeightsTensor); const TfLiteTensor* input_layer_norm_weights = - GetInput(context, node, kInputLayerNormWeightsTensor); + GetOptionalInputTensor(context, node, kInputLayerNormWeightsTensor); const TfLiteTensor* forget_layer_norm_weights = GetInput(context, node, kForgetLayerNormWeightsTensor); const TfLiteTensor* cell_layer_norm_weights = diff --git a/tensorflow/lite/kernels/layer_norm_lstm_test.cc b/tensorflow/lite/kernels/layer_norm_lstm_test.cc index e89bce50c3..1c13cee1c3 100644 --- a/tensorflow/lite/kernels/layer_norm_lstm_test.cc +++ b/tensorflow/lite/kernels/layer_norm_lstm_test.cc @@ -83,7 +83,11 @@ class LayerNormLSTMOpModel : public SingleOpModel { cell_to_output_weights_ = AddNullInput(); } - input_layer_norm_weights_ = AddInput(TensorType_FLOAT32); + if (use_cifg) { + input_layer_norm_weights_ = AddNullInput(); + } else { + input_layer_norm_weights_ = AddInput(TensorType_FLOAT32); + } forget_layer_norm_weights_ = AddInput(TensorType_FLOAT32); cell_layer_norm_weights_ = AddInput(TensorType_FLOAT32); output_layer_norm_weights_ = AddInput(TensorType_FLOAT32); @@ -650,6 +654,223 @@ TEST_F(NoCifgPeepholeProjectionNoClippingLayerNormLstmTest, &layer_norm_lstm); } +class CifgPeepholeProjectionNoClippingLayerNormLstmTest + : public BaseLayerNormLstmTest { + void SetUp() override { + input_to_forget_weights_ = {-0.6, -0.1, 0.3, 0.2, 0.9, -0.5, -0.2, + -0.4, 0.3, -0.8, -0.4, 0.3, -0.5, -0.4, + -0.6, 0.3, -0.4, -0.6, -0.5, -0.5}; + input_to_cell_weights_ = {-0.4, -0.3, -0.2, -0.1, -0.5, 0.5, -0.2, + -0.3, -0.2, -0.6, 0.6, -0.1, -0.4, -0.3, + -0.7, 0.7, -0.9, -0.5, 0.8, 0.6}; + input_to_output_weights_ = {-0.8, -0.4, -0.2, -0.9, -0.1, -0.7, 0.3, + -0.3, -0.8, -0.2, 0.6, -0.2, 0.4, -0.7, + -0.3, -0.5, 0.1, 0.5, -0.6, -0.4}; + + forget_gate_bias_ = {0.1, -0.3, -0.2, 0.1}; + cell_gate_bias_ = {-0.05, 0.72, 0.25, 0.08}; + output_gate_bias_ = {0.05, -0.01, 0.2, 0.1}; + + recurrent_to_cell_weights_ = {-0.3, 0.2, 0.1, -0.3, 0.8, -0.08, + -0.2, 0.3, 0.8, -0.6, -0.1, 0.2}; + recurrent_to_forget_weights_ = {-0.5, -0.3, -0.5, -0.2, 0.6, 0.4, + 0.9, 0.3, -0.1, 0.2, 0.5, 0.2}; + recurrent_to_output_weights_ = {0.3, -0.1, 0.1, -0.2, -0.5, -0.7, + -0.2, -0.6, -0.1, -0.4, -0.7, -0.2}; + + cell_to_forget_weights_ = {-0.02, -0.15, -0.25, -0.03}; + cell_to_output_weights_ = {0.1, -0.1, -0.5, 0.05}; + + forget_layer_norm_weights_ = {0.2, 0.2, 0.4, 0.3}; + cell_layer_norm_weights_ = {0.7, 0.2, 0.3, 0.8}; + output_layer_norm_weights_ = {0.6, 0.2, 0.2, 0.5}; + projection_weights_ = {-0.1, 0.2, 0.01, -0.2, 0.1, 0.5, + 0.3, 0.08, 0.07, 0.2, -0.4, 0.2}; + + layer_norm_lstm_input_ = { + {// Batch0: 3 (input_sequence_size) * 5 (n_input) + 0.7, 0.8, 0.1, 0.2, 0.3, // seq 0 + 0.8, 0.1, 0.2, 0.4, 0.5, // seq 1 + 0.2, 0.7, 0.7, 0.1, 0.7}, // seq 2 + + {// Batch1: 3 (input_sequence_size) * 5 (n_input) + 0.3, 0.2, 0.9, 0.8, 0.1, // seq 0 + 0.1, 0.5, 0.2, 0.4, 0.2, // seq 1 + 0.6, 0.9, 0.2, 0.5, 0.7}, // seq 2 + }; + } +}; + +TEST_F(CifgPeepholeProjectionNoClippingLayerNormLstmTest, + LayerNormLstmBlackBoxTest) { + const int n_batch = 2; + const int n_input = 5; + const int n_cell = 4; + const int n_output = 3; + const float ceil_clip = 0.0; + const float proj_clip = 0.0; + + LayerNormLSTMOpModel layer_norm_lstm( + n_batch, n_input, n_cell, n_output, + /*use_cifg=*/true, /*use_peephole=*/true, + /*use_projection_weights=*/true, + /*use_projection_bias=*/false, ceil_clip, proj_clip, + { + {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_layer_norm_weight tensor + {n_cell}, // forget_layer_norm_weight tensor + {n_cell}, // cell_layer_norm_weight tensor + {n_cell}, // output_layer_norm_weight tensor + + {0}, // 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 + }); + + layer_norm_lstm.SetInputToCellWeights(input_to_cell_weights_); + layer_norm_lstm.SetInputToForgetWeights(input_to_forget_weights_); + layer_norm_lstm.SetInputToOutputWeights(input_to_output_weights_); + + layer_norm_lstm.SetCellBias(cell_gate_bias_); + layer_norm_lstm.SetForgetGateBias(forget_gate_bias_); + layer_norm_lstm.SetOutputGateBias(output_gate_bias_); + + layer_norm_lstm.SetRecurrentToCellWeights(recurrent_to_cell_weights_); + layer_norm_lstm.SetRecurrentToForgetWeights(recurrent_to_forget_weights_); + layer_norm_lstm.SetRecurrentToOutputWeights(recurrent_to_output_weights_); + + layer_norm_lstm.SetCellToForgetWeights(cell_to_forget_weights_); + layer_norm_lstm.SetCellToOutputWeights(cell_to_output_weights_); + + layer_norm_lstm.SetForgetLayerNormWeights(forget_layer_norm_weights_); + layer_norm_lstm.SetCellLayerNormWeights(cell_layer_norm_weights_); + layer_norm_lstm.SetOutputLayerNormWeights(output_layer_norm_weights_); + + layer_norm_lstm.SetProjectionWeights(projection_weights_); + + // Verify the final output. + const std::vector> layer_norm_lstm_golden_output = { + { + // Batch0: 3 (input_sequence_size) * 3 (n_output) + 0.02129706, 0.140816242, 0.0112733059, // seq 0 + 0.0132302344, 0.152308047, 0.0346313119, // seq 1 + -0.0123688057, 0.165790111, 0.0893077999, // seq 2 + }, + { + // Batch1: 3 (input_sequence_size) * 3 (n_output) + -0.0226350538, 0.0916948169, 0.0769175813, // seq 0 + -0.0269966982, 0.149707705, 0.094149217, // seq 1 + -0.0103429332, 0.173016444, 0.0720508844, // seq 2 + }}; + + VerifyGoldens(layer_norm_lstm_input_, layer_norm_lstm_golden_output, + &layer_norm_lstm); +} + +TEST_F(CifgPeepholeProjectionNoClippingLayerNormLstmTest, + HybridLayerNormLstmBlackBoxTest) { + const int n_batch = 2; + const int n_input = 5; + const int n_cell = 4; + const int n_output = 3; + const float ceil_clip = 0.0; + const float proj_clip = 0.0; + + HybridLayerNormLSTMOpModel layer_norm_lstm( + n_batch, n_input, n_cell, n_output, + /*use_cifg=*/true, /*use_peephole=*/true, + /*use_projection_weights=*/true, + /*use_projection_bias=*/false, ceil_clip, proj_clip, + { + {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_layer_norm_weight tensor + {n_cell}, // forget_layer_norm_weight tensor + {n_cell}, // cell_layer_norm_weight tensor + {n_cell}, // output_layer_norm_weight tensor + + {0}, // 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 + }); + + layer_norm_lstm.SetInputToCellWeights(input_to_cell_weights_); + layer_norm_lstm.SetInputToForgetWeights(input_to_forget_weights_); + layer_norm_lstm.SetInputToOutputWeights(input_to_output_weights_); + + layer_norm_lstm.SetCellBias(cell_gate_bias_); + layer_norm_lstm.SetForgetGateBias(forget_gate_bias_); + layer_norm_lstm.SetOutputGateBias(output_gate_bias_); + + layer_norm_lstm.SetRecurrentToCellWeights(recurrent_to_cell_weights_); + layer_norm_lstm.SetRecurrentToForgetWeights(recurrent_to_forget_weights_); + layer_norm_lstm.SetRecurrentToOutputWeights(recurrent_to_output_weights_); + + layer_norm_lstm.SetCellToForgetWeights(cell_to_forget_weights_); + layer_norm_lstm.SetCellToOutputWeights(cell_to_output_weights_); + + layer_norm_lstm.SetForgetLayerNormWeights(forget_layer_norm_weights_); + layer_norm_lstm.SetCellLayerNormWeights(cell_layer_norm_weights_); + layer_norm_lstm.SetOutputLayerNormWeights(output_layer_norm_weights_); + + layer_norm_lstm.SetProjectionWeights(projection_weights_); + + // Verify the final output. + const std::vector> layer_norm_lstm_golden_output = { + { + // Batch0: 3 (input_sequence_size) * 3 (n_output) + 0.0212250091, 0.140474007, 0.0115012666, // seq 0 + 0.0130806509, 0.152660668, 0.0347516984, // seq 1 + -0.0124010444, 0.166042402, 0.0898982584, // seq 2 + }, + { + // Batch1: 3 (input_sequence_size) * 3 (n_output) + -0.0228835996, 0.0917588323, 0.0778886303, // seq 0 + -0.0275101066, 0.148769245, 0.0938384682, // seq 1 + -0.0103605557, 0.172605693, 0.0728750974, // seq 2 + }}; + + VerifyGoldens(layer_norm_lstm_input_, layer_norm_lstm_golden_output, + &layer_norm_lstm); +} + } // namespace } // namespace custom } // namespace ops -- GitLab From 3ca8908b14ce55151ce0e043ae6423fb50d29ade Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 6 Dec 2018 15:06:07 -0800 Subject: [PATCH 157/873] gradient_checker_v2.py: some internal improvements. PiperOrigin-RevId: 224415291 --- tensorflow/python/ops/gradient_checker_v2.py | 33 ++++++++++------ .../python/ops/gradient_checker_v2_test.py | 38 +++++++++---------- 2 files changed, 41 insertions(+), 30 deletions(-) diff --git a/tensorflow/python/ops/gradient_checker_v2.py b/tensorflow/python/ops/gradient_checker_v2.py index cf84484112..5d473eeb5f 100644 --- a/tensorflow/python/ops/gradient_checker_v2.py +++ b/tensorflow/python/ops/gradient_checker_v2.py @@ -44,27 +44,19 @@ def _product(t): return y -def _to_numpy(a): - """Converts tensors to numpy arrays. +def _eval_indexed_slices(a): + """Converts IndexedSlices to IndexedSlicesValue with numpy indices/values. - Converts Tensors and EagerTensors to numpy arrays. When eager execution is enabled, converts IndexedSlices - to IndexedSlicesValue with numpy indices/values + to IndexedSlicesValue with numpy indices/values. Args: a: any value. Returns: - If a is EagerTensor or Tensor, returns the evaluation of a by calling - numpy() or run(). If a is IndexedSlices and eager execution is enabled, calls numpy() on a's fields. Otherwise returns a unchanged. """ - if isinstance(a, ops.EagerTensor): - return a.numpy() - if isinstance(a, ops.Tensor): - sess = ops.get_default_session() - return sess.run(a) if isinstance(a, ops.IndexedSlices) and context.executing_eagerly(): return ops.IndexedSlicesValue( indices=[x.numpy() for x in a.indices], @@ -73,6 +65,24 @@ def _to_numpy(a): return a +def _to_numpy(a): + """Converts Tensors and EagerTensors to numpy arrays. + + Args: + a: any value. + + Returns: + If a is EagerTensor or Tensor, returns the evaluation of a by calling + numpy() or run(). Otherwise returns a unchanged. + """ + if isinstance(a, ops.EagerTensor): + return a.numpy() + if isinstance(a, ops.Tensor): + sess = ops.get_default_session() + return sess.run(a) + return a + + def _prepare(f, xs_dtypes): """Return a function that executes 'f'. @@ -147,6 +157,7 @@ def _compute_theoretical_jacobian(f, y_shape, y_dtype, xs, param): for col in range(y_size): dy_data_flat[col] = 1 grad = _to_numpy(grad_fn(dy_data, *xs)[0]) + grad = _eval_indexed_slices(grad) dy_data_flat[col] = 0 if isinstance(grad, ops.IndexedSlicesValue): for i, v in zip(grad.indices, grad.values): diff --git a/tensorflow/python/ops/gradient_checker_v2_test.py b/tensorflow/python/ops/gradient_checker_v2_test.py index ce9ff47d61..191b2b6568 100644 --- a/tensorflow/python/ops/gradient_checker_v2_test.py +++ b/tensorflow/python/ops/gradient_checker_v2_test.py @@ -22,7 +22,6 @@ from __future__ import print_function import numpy as np from tensorflow.python.eager import backprop -from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import test_util @@ -38,13 +37,17 @@ from tensorflow.python.platform import test from tensorflow.python.platform import tf_logging +def _random_complex(shape, dtype): + data = np.random.random_sample(shape).astype(dtype.as_numpy_dtype) + if dtype.is_complex: + data.imag = np.random.random_sample(shape) + return data + + @test_util.run_all_in_graph_and_eager_modes class GradientCheckerTest(test.TestCase): def testAddSimple(self): - # if context.executing_eagerly(): - # return - np.random.seed(1) # Fix seed to avoid flakiness size = (2, 3) x1 = constant_op.constant(2.0, shape=size, name="x1") x2 = constant_op.constant(3.0, shape=size, name="x2") @@ -54,7 +57,6 @@ class GradientCheckerTest(test.TestCase): assert error < 1e-4 def testAddCustomized(self): - np.random.seed(3) # Fix seed to avoid flakiness size = (2, 3) x1 = constant_op.constant( 2.0, shape=size, dtype=dtypes.float64, name="x1") @@ -67,7 +69,6 @@ class GradientCheckerTest(test.TestCase): assert error < 1e-10 def testGather(self): - np.random.seed(4) # Fix seed to avoid flakiness def f(params): index_values = [1, 3] indices = constant_op.constant(index_values, name="i") @@ -82,7 +83,6 @@ class GradientCheckerTest(test.TestCase): assert error < 1e-4 def testNestedGather(self): - np.random.seed(5) # Fix seed to avoid flakiness def f(params): index_values = [1, 3, 5, 6] indices = constant_op.constant(index_values, name="i") @@ -100,33 +100,37 @@ class GradientCheckerTest(test.TestCase): assert error < 1e-4 def testComplexMul(self): - if not context.executing_eagerly(): - return c = constant_op.constant(5 + 7j, dtype=dtypes.complex64) def f(x): return c * x - x = constant_op.constant(11 - 13j, dtype=dtypes.complex64) + x_shape = c.shape + x_dtype = c.dtype + x = constant_op.constant(_random_complex(x_shape, x_dtype)) analytical, numerical = gradient_checker.compute_gradient( - f, [x], delta=0.1) + f, [x]) correct = np.array([[5, 7], [-7, 5]]) self.assertAllEqual(correct, analytical[0]) self.assertAllClose(correct, numerical[0], rtol=1e-4) + x = constant_op.constant(_random_complex(x_shape, x_dtype)) self.assertLess( gradient_checker.max_error(*gradient_checker.compute_gradient( - f, [x], delta=0.1)), 2e-4) + f, [x])), 3e-4) def testComplexConj(self): def f(x): return math_ops.conj(x) - x = constant_op.constant(11 - 13j, dtype=dtypes.complex64) + x_shape = () + x_dtype = dtypes.complex64 + x = constant_op.constant(_random_complex(x_shape, x_dtype)) analytical, numerical = gradient_checker.compute_gradient( - f, [x], delta=0.1) + f, [x]) correct = np.array([[1, 0], [0, -1]]) self.assertAllEqual(correct, analytical[0]) self.assertAllClose(correct, numerical[0], rtol=2e-5) + x = constant_op.constant(_random_complex(x_shape, x_dtype)) self.assertLess( gradient_checker.max_error(*gradient_checker.compute_gradient( - f, [x], delta=0.1)), 2e-5) + f, [x])), 2e-5) def testEmptySucceeds(self): def f(x): @@ -140,8 +144,6 @@ class GradientCheckerTest(test.TestCase): self.assertEqual(error, 0) def testEmptyFails(self): - # if not context.executing_eagerly(): - # return @custom_gradient.custom_gradient def id_bad_grad(x): y = array_ops.identity(x) @@ -279,8 +281,6 @@ class MiniMNISTTest(test.TestCase): return err def testInputGradient(self): - # if context.executing_eagerly(): - # return self.assertLess(self._BuildAndTestMiniMNIST(0, "input"), 1e-8) def testHiddenWeightGradient(self): -- GitLab From 9797cc2e91d01b492ed946e05f8f07c2a7f8c157 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 6 Dec 2018 15:15:34 -0800 Subject: [PATCH 158/873] Fix PREDICT bug. PiperOrigin-RevId: 224416988 --- tensorflow/contrib/tpu/python/tpu/tpu_estimator.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index fb1316cf33..84816d70d0 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -530,13 +530,16 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): class TPUInfeedOutfeedSessionHookForPrediction(TPUInfeedOutfeedSessionHook): - def __init__(self, ctx, enqueue_ops, dequeue_ops, rendezvous=None): + def __init__(self, ctx, enqueue_ops, dequeue_ops, rendezvous=None, + master=None, session_config=None): super(TPUInfeedOutfeedSessionHookForPrediction, self).__init__( ctx, enqueue_ops, dequeue_ops, run_infeed_loop_on_coordinator=False, - rendezvous=rendezvous) + rendezvous=rendezvous, + master=master, + session_config=session_config) def _create_infeed_controller(self, name, target, args): return _OpSignalOnceQueueContext(name=name, target=target, args=args) @@ -2748,7 +2751,9 @@ class TPUEstimator(estimator_lib.Estimator): hooks = [ _StoppingPredictHook(scalar_stopping_signal), TPUInfeedOutfeedSessionHookForPrediction( - ctx, enqueue_ops, host_ops, rendezvous=self._rendezvous[mode]), + ctx, enqueue_ops, host_ops, rendezvous=self._rendezvous[mode], + master=self._config.master, + session_config=self._session_config), ] + input_hooks if prediction_hooks: -- GitLab From d453dfad7c383db0ded338e8de3142a17d55910b Mon Sep 17 00:00:00 2001 From: Nupur Garg Date: Thu, 6 Dec 2018 15:20:01 -0800 Subject: [PATCH 159/873] Exports OpsSet in Python API. PiperOrigin-RevId: 224417739 --- tensorflow/lite/python/convert.py | 1 + .../api/golden/v1/tensorflow.lite.-ops-set.pbtxt | 12 ++++++++++++ tensorflow/tools/api/golden/v1/tensorflow.lite.pbtxt | 4 ++++ .../api/golden/v2/tensorflow.lite.-ops-set.pbtxt | 12 ++++++++++++ tensorflow/tools/api/golden/v2/tensorflow.lite.pbtxt | 4 ++++ 5 files changed, 33 insertions(+) create mode 100644 tensorflow/tools/api/golden/v1/tensorflow.lite.-ops-set.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.lite.-ops-set.pbtxt diff --git a/tensorflow/lite/python/convert.py b/tensorflow/lite/python/convert.py index 563312e027..9c60399871 100644 --- a/tensorflow/lite/python/convert.py +++ b/tensorflow/lite/python/convert.py @@ -97,6 +97,7 @@ def convert_dtype_to_tflite_type(tf_dtype): return result +@_tf_export("lite.OpsSet") class OpsSet(enum.Enum): """Enum class defining the sets of ops available to generate TFLite models. diff --git a/tensorflow/tools/api/golden/v1/tensorflow.lite.-ops-set.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.lite.-ops-set.pbtxt new file mode 100644 index 0000000000..68c651a3c9 --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.lite.-ops-set.pbtxt @@ -0,0 +1,12 @@ +path: "tensorflow.lite.OpsSet" +tf_class { + is_instance: "" + member { + name: "SELECT_TF_OPS" + mtype: "" + } + member { + name: "TFLITE_BUILTINS" + mtype: "" + } +} diff --git a/tensorflow/tools/api/golden/v1/tensorflow.lite.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.lite.pbtxt index f5013c250b..154dd00821 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.lite.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.lite.pbtxt @@ -8,6 +8,10 @@ tf_module { name: "OpHint" mtype: "" } + member { + name: "OpsSet" + mtype: "" + } member { name: "TFLiteConverter" mtype: "" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.lite.-ops-set.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.lite.-ops-set.pbtxt new file mode 100644 index 0000000000..68c651a3c9 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.lite.-ops-set.pbtxt @@ -0,0 +1,12 @@ +path: "tensorflow.lite.OpsSet" +tf_class { + is_instance: "" + member { + name: "SELECT_TF_OPS" + mtype: "" + } + member { + name: "TFLITE_BUILTINS" + mtype: "" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.lite.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.lite.pbtxt index f5013c250b..154dd00821 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.lite.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.lite.pbtxt @@ -8,6 +8,10 @@ tf_module { name: "OpHint" mtype: "" } + member { + name: "OpsSet" + mtype: "" + } member { name: "TFLiteConverter" mtype: "" -- GitLab From fa06cffc09699de4215cf47b2e00ed6d7cdd4dfe Mon Sep 17 00:00:00 2001 From: Yanhui Liang Date: Thu, 6 Dec 2018 15:30:33 -0800 Subject: [PATCH 160/873] Update sdca_optimizer in core to its v2 version, and migrate linear_optimizer from contrib to core. PiperOrigin-RevId: 224419608 --- .../python/kernel_tests/sdca_ops_test.py | 7 ++++++- .../linear_optimizer/python/ops/sdca_ops.py | 16 ++++++++++++++-- .../ops/sharded_mutable_dense_hashtable.py | 12 +++++++++++- .../ops/sharded_mutable_dense_hashtable_test.py | 7 ++++++- .../python/ops/sparse_feature_column.py | 12 +++++++++++- .../python/ops/sparse_feature_column_test.py | 7 ++++++- 6 files changed, 54 insertions(+), 7 deletions(-) diff --git a/tensorflow/contrib/linear_optimizer/python/kernel_tests/sdca_ops_test.py b/tensorflow/contrib/linear_optimizer/python/kernel_tests/sdca_ops_test.py index 8466dc36d1..d49834dc86 100644 --- a/tensorflow/contrib/linear_optimizer/python/kernel_tests/sdca_ops_test.py +++ b/tensorflow/contrib/linear_optimizer/python/kernel_tests/sdca_ops_test.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for SdcaModel.""" +"""Tests for SdcaModel (deprecated). + +This module and all its submodules are deprecated. To UPDATE or USE linear +optimizers, please check its latest version in core: +tensorflow_estimator/python/estimator/canned/linear_optimizer/. +""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py b/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py index f3f1dcd98d..c056a12fa5 100644 --- a/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py +++ b/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py @@ -12,7 +12,14 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Proximal stochastic dual coordinate ascent optimizer for linear models.""" +# pylint: disable=line-too-long +"""Proximal stochastic dual coordinate ascent optimizer for linear models (deprecated). + +This module and all its submodules are deprecated. To UPDATE or USE linear +optimizers, please check its latest version in core: +tensorflow_estimator/python/estimator/canned/linear_optimizer/. +""" +# pylint: enable=line-too-long from __future__ import absolute_import from __future__ import division from __future__ import print_function @@ -40,6 +47,7 @@ from tensorflow.python.ops import variables as var_ops from tensorflow.python.ops.nn import log_poisson_loss from tensorflow.python.ops.nn import sigmoid_cross_entropy_with_logits from tensorflow.python.summary import summary +from tensorflow.python.util import deprecation __all__ = ['SdcaModel'] @@ -48,7 +56,7 @@ __all__ = ['SdcaModel'] class SdcaModel(object): """Stochastic dual coordinate ascent solver for linear models. - Loss functions supported: + Loss functions supported: * Binary logistic loss * Squared loss @@ -109,6 +117,10 @@ class SdcaModel(object): ``` """ + @deprecation.deprecated( + None, 'This class is deprecated. To UPDATE or USE linear optimizers, ' + 'please check its latest version in core: ' + 'tensorflow_estimator/python/estimator/canned/linear_optimizer/.') def __init__(self, examples, variables, options): """Create a new sdca optimizer.""" diff --git a/tensorflow/contrib/linear_optimizer/python/ops/sharded_mutable_dense_hashtable.py b/tensorflow/contrib/linear_optimizer/python/ops/sharded_mutable_dense_hashtable.py index a001555e8f..a28394964a 100644 --- a/tensorflow/contrib/linear_optimizer/python/ops/sharded_mutable_dense_hashtable.py +++ b/tensorflow/contrib/linear_optimizer/python/ops/sharded_mutable_dense_hashtable.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Sharded mutable dense hash table.""" +"""Sharded mutable dense hash table (deprecated). + +This module and all its submodules are deprecated. To UPDATE or USE linear +optimizers, please check its latest version in core: +tensorflow_estimator/python/estimator/canned/linear_optimizer/. +""" from __future__ import absolute_import from __future__ import division @@ -28,6 +33,7 @@ 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 math_ops +from tensorflow.python.util import deprecation # TODO(rohanj): This should subclass Checkpointable and implement @@ -45,6 +51,10 @@ class ShardedMutableDenseHashTable(object): # TODO(andreasst): consider moving this to lookup module + @deprecation.deprecated( + None, 'This class is deprecated. To UPDATE or USE linear optimizers, ' + 'please check its latest version in core: ' + 'tensorflow_estimator/python/estimator/canned/linear_optimizer/.') def __init__(self, key_dtype, value_dtype, diff --git a/tensorflow/contrib/linear_optimizer/python/ops/sharded_mutable_dense_hashtable_test.py b/tensorflow/contrib/linear_optimizer/python/ops/sharded_mutable_dense_hashtable_test.py index 2b56d0fa3a..2d1457f9e4 100644 --- a/tensorflow/contrib/linear_optimizer/python/ops/sharded_mutable_dense_hashtable_test.py +++ b/tensorflow/contrib/linear_optimizer/python/ops/sharded_mutable_dense_hashtable_test.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for sharded_mutable_dense_hashtable.py.""" +"""Tests for sharded_mutable_dense_hashtable.py (deprecated). + +This module and all its submodules are deprecated. To UPDATE or USE linear +optimizers, please check its latest version in core: +tensorflow_estimator/python/estimator/canned/linear_optimizer/. +""" from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/contrib/linear_optimizer/python/ops/sparse_feature_column.py b/tensorflow/contrib/linear_optimizer/python/ops/sparse_feature_column.py index 003795233f..64730f8eed 100644 --- a/tensorflow/contrib/linear_optimizer/python/ops/sparse_feature_column.py +++ b/tensorflow/contrib/linear_optimizer/python/ops/sparse_feature_column.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Sparse feature column.""" +"""Sparse feature column (deprecated). + +This module and all its submodules are deprecated. To UPDATE or USE linear +optimizers, please check its latest version in core: +tensorflow_estimator/python/estimator/canned/linear_optimizer/. +""" from __future__ import absolute_import from __future__ import division @@ -21,6 +26,7 @@ from __future__ import print_function from tensorflow.python.framework import dtypes from tensorflow.python.framework.ops import internal_convert_to_tensor from tensorflow.python.framework.ops import name_scope +from tensorflow.python.util import deprecation class SparseFeatureColumn(object): @@ -68,6 +74,10 @@ class SparseFeatureColumn(object): @@feature_values """ + @deprecation.deprecated( + None, 'This class is deprecated. To UPDATE or USE linear optimizers, ' + 'please check its latest version in core: ' + 'tensorflow_estimator/python/estimator/canned/linear_optimizer/.') def __init__(self, example_indices, feature_indices, feature_values): """Creates a `SparseFeatureColumn` representation. diff --git a/tensorflow/contrib/linear_optimizer/python/ops/sparse_feature_column_test.py b/tensorflow/contrib/linear_optimizer/python/ops/sparse_feature_column_test.py index 51c4f68543..0ae780e1a1 100644 --- a/tensorflow/contrib/linear_optimizer/python/ops/sparse_feature_column_test.py +++ b/tensorflow/contrib/linear_optimizer/python/ops/sparse_feature_column_test.py @@ -12,7 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for sparse_feature_column.py.""" +"""Tests for sparse_feature_column.py (deprecated). + +This module and all its submodules are deprecated. To UPDATE or USE linear +optimizers, please check its latest version in core: +tensorflow_estimator/python/estimator/canned/linear_optimizer/. +""" from __future__ import absolute_import from __future__ import division -- GitLab From 938af66d71256aac8c04ac022a4f0390e10146f8 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 6 Dec 2018 15:46:13 -0800 Subject: [PATCH 161/873] Add missing namespace qualifier that ADL was "saving" us from having to provide, but which would break once absl::string_view is really std::string_view. PiperOrigin-RevId: 224422387 --- tensorflow/compiler/xla/service/hlo_pass_pipeline.cc | 2 +- tensorflow/compiler/xla/shape_util.cc | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc b/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc index 312b5d020c..51177f24f5 100644 --- a/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc +++ b/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc @@ -113,7 +113,7 @@ void HloPassPipeline::MaybeDumpHlo(const HloModule& module, } const string message = - StrCat("after ", after_pass_name, ", before ", before_pass_name); + absl::StrCat("after ", after_pass_name, ", before ", before_pass_name); hlo_graph_dumper::MaybeDumpHloModule(module, message); VLOG(3) << "HLO " << message << ":"; VLOG(3) << module.entry_computation_layout().ToString(); diff --git a/tensorflow/compiler/xla/shape_util.cc b/tensorflow/compiler/xla/shape_util.cc index f3cc51ca91..a4d4e1e53e 100644 --- a/tensorflow/compiler/xla/shape_util.cc +++ b/tensorflow/compiler/xla/shape_util.cc @@ -584,7 +584,7 @@ namespace { // Parses shapes with simple recursive descent structure -- consumes from the // front of s and passes that view recursively as required. StatusOr ParseShapeStringInternal(absl::string_view* s) { - *s = StripLeadingAsciiWhitespace(*s); + *s = absl::StripLeadingAsciiWhitespace(*s); if (absl::ConsumePrefix(s, "(")) { // Tuple. std::vector shapes; @@ -597,7 +597,7 @@ StatusOr ParseShapeStringInternal(absl::string_view* s) { } shapes.emplace_back(); TF_ASSIGN_OR_RETURN(shapes.back(), ParseShapeStringInternal(s)); - *s = StripLeadingAsciiWhitespace(*s); + *s = absl::StripLeadingAsciiWhitespace(*s); must_end = !absl::ConsumePrefix(s, ","); } return ShapeUtil::MakeTupleShape(shapes); -- GitLab From fc4b1847dde6511fb6bfb8d4c3aa1e2586307516 Mon Sep 17 00:00:00 2001 From: Trevor Morris Date: Thu, 6 Dec 2018 15:51:28 -0800 Subject: [PATCH 162/873] Update tests to reflect that expanddims and squeeze are now converted --- tensorflow/contrib/tensorrt/test/rank_two_test.py | 6 ++++-- tensorflow/contrib/tensorrt/test/unary_test.py | 3 +-- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/tensorrt/test/rank_two_test.py b/tensorflow/contrib/tensorrt/test/rank_two_test.py index 0cd733dca1..563232fc12 100644 --- a/tensorflow/contrib/tensorrt/test/rank_two_test.py +++ b/tensorflow/contrib/tensorrt/test/rank_two_test.py @@ -51,8 +51,10 @@ class RankTwoTest(trt_test.TfTrtIntegrationTestBase): c = constant_op.constant(3.0, name="c%d_3" % i) q = math_ops.add(q, c, name="add%d_3" % i) if i == 0: + axis = constant_op.constant(-1, dtype=dtypes.int32, name="axis") for j in range(2): - q = array_ops.expand_dims(q, -1, name="expand%d_%d" % (i, j)) + q = array_ops.expand_dims(q, axis, name="expand%d_%d" % (i, j)) + q = self.trt_incompatible_op(q) q = gen_math_ops.reciprocal(q, name="reciprocal%d" % i) outputs.append(q) # Combine both paths @@ -70,7 +72,7 @@ class RankTwoTest(trt_test.TfTrtIntegrationTestBase): return { "TRTEngineOp_0": [ "add0_1", "add0_2", "add0_3", "c0_1", "c0_2", "c0_3", "abs0_1", - "abs0_2" + "abs0_2", "expand0_0", "expand0_1", "axis" ], "TRTEngineOp_1": [ "add", "add1_1", "add1_2", "add1_3", "c1_1", "c1_2", "c1_3", diff --git a/tensorflow/contrib/tensorrt/test/unary_test.py b/tensorflow/contrib/tensorrt/test/unary_test.py index 9fc50e0595..b77abc4032 100644 --- a/tensorflow/contrib/tensorrt/test/unary_test.py +++ b/tensorflow/contrib/tensorrt/test/unary_test.py @@ -107,8 +107,7 @@ class UnaryTest(trt_test.TfTrtIntegrationTestBase): def ExpectedEnginesToBuild(self, run_params): """Return the expected engines to build.""" return [ - "TRTEngineOp_0", "TRTEngineOp_1", "TRTEngineOp_2", "TRTEngineOp_3", - "TRTEngineOp_4" + "TRTEngineOp_0" ] -- GitLab From 4ca599630233afe5240deae44212f19412ab6423 Mon Sep 17 00:00:00 2001 From: Priya Gupta Date: Thu, 6 Dec 2018 15:53:05 -0800 Subject: [PATCH 163/873] Fix stateful metrics in Keras + Distribution Strategy codepath. PiperOrigin-RevId: 224423535 --- .../contrib/distribute/python/keras_test.py | 42 +++++++++++++++++-- tensorflow/python/keras/engine/training.py | 12 +++++- .../python/keras/engine/training_arrays.py | 6 +-- .../keras/engine/training_distributed.py | 15 ++++++- 4 files changed, 64 insertions(+), 11 deletions(-) diff --git a/tensorflow/contrib/distribute/python/keras_test.py b/tensorflow/contrib/distribute/python/keras_test.py index 331bf7c496..796c4ed9f6 100644 --- a/tensorflow/contrib/distribute/python/keras_test.py +++ b/tensorflow/contrib/distribute/python/keras_test.py @@ -1259,9 +1259,43 @@ class TestDistributionStrategyCorrectness(test.TestCase, train_dataset = dataset_ops.Dataset.from_tensor_slices((x_train, y_train)) train_dataset = batch_wrapper(train_dataset, batch_size, distribution) - history = model.fit(x=train_dataset, epochs=1, steps_per_epoch=10) - self.assertEqual(history.history['binary_accuracy'], [1.0]) + history = model.fit(x=train_dataset, epochs=2, steps_per_epoch=10) + self.assertEqual(history.history['binary_accuracy'], [1.0, 1.0]) + @combinations.generate(all_strategy_combinations()) + def test_eval_metrics_correctness(self, distribution): + with self.cached_session(): + model = keras.Sequential() + model.add( + keras.layers.Dense( + 3, activation='relu', input_dim=4, kernel_initializer='ones')) + model.add( + keras.layers.Dense( + 1, activation='sigmoid', kernel_initializer='ones')) + model.compile( + loss='mae', + metrics=['accuracy', keras.metrics.BinaryAccuracy()], + optimizer=gradient_descent.GradientDescentOptimizer(0.001), + distribute=distribution) + + # verify correctness of stateful and stateless metrics. + x = np.ones((100, 4)).astype('float32') + y = np.ones((100, 1)).astype('float32') + dataset = dataset_ops.Dataset.from_tensor_slices((x, y)).repeat() + dataset = batch_wrapper(dataset, 4, distribution) + outs = model.evaluate(dataset, steps=10) + self.assertEqual(outs[1], 1.) + self.assertEqual(outs[2], 1.) + + y = np.zeros((100, 1)).astype('float32') + dataset = dataset_ops.Dataset.from_tensor_slices((x, y)).repeat() + dataset = batch_wrapper(dataset, 4, distribution) + outs = model.evaluate(dataset, steps=10) + self.assertEqual(outs[1], 0.) + self.assertEqual(outs[2], 0.) + + # TODO(priyag): Add metrics correctness to this test to compare with and + # without distribution strategies. @combinations.generate(strategy_and_input_combinations()) def test_correctness(self, distribution, use_numpy, use_validation_data): @@ -1319,7 +1353,7 @@ class TestDistributionStrategyCorrectness(test.TestCase, with_distribution, x_train, y_train, x_predict)) - traning_history = model.fit(**training_inputs).history + training_history = model.fit(**training_inputs).history if eval_inputs is not None: eval_result = model.evaluate(**eval_inputs) @@ -1330,7 +1364,7 @@ class TestDistributionStrategyCorrectness(test.TestCase, weights = model.get_weights() predict_result = model.predict(**predict_inputs) - return weights, traning_history, eval_result, predict_result + return weights, training_history, eval_result, predict_result wts_with_ds, history_with_ds, eval_with_ds, predict_with_ds = ( fit_eval_and_predict(with_distribution=distribution)) diff --git a/tensorflow/python/keras/engine/training.py b/tensorflow/python/keras/engine/training.py index c902d928a0..90f8a7b252 100644 --- a/tensorflow/python/keras/engine/training.py +++ b/tensorflow/python/keras/engine/training.py @@ -918,11 +918,17 @@ class Model(Network): [self.total_loss] + metrics_tensors) def _make_eval_function(self): + # TODO(psv,anjalisridhar): Remove updates after we fix b/118841692 + # Stateful metrics updates + metric_updates = [] + for m in self.metrics: + metric_updates += m.updates + metrics_tensors = [ self._all_stateful_metrics_tensors[m] for m in self.metrics_names[1:] ] - self._make_test_function_helper('_eval_function', - [self.total_loss] + metrics_tensors) + self._make_test_function_helper( + '_eval_function', [self.total_loss] + metrics_tensors, metric_updates) def _make_predict_function(self): if not hasattr(self, 'predict_function'): @@ -2095,6 +2101,8 @@ class Model(Network): if hasattr(self, 'metrics'): for m in self.metrics: m.reset_states() + if self._distribution_strategy: + training_distributed._reset_metrics(self) # pylint: disable=protected-access def train_on_batch(self, x, diff --git a/tensorflow/python/keras/engine/training_arrays.py b/tensorflow/python/keras/engine/training_arrays.py index 26a809d298..196d48faec 100644 --- a/tensorflow/python/keras/engine/training_arrays.py +++ b/tensorflow/python/keras/engine/training_arrays.py @@ -241,15 +241,14 @@ def model_iteration(model, callbacks.model.stop_training = False callbacks._call_begin_hook(mode) progbar.on_train_begin() + for epoch in range(initial_epoch, epochs): if callbacks.model.stop_training: break # Setup work for each epoch epoch_logs = {} - if hasattr(model, 'metrics'): - for m in model.metrics: - m.reset_states() + model.reset_metrics() callbacks.on_epoch_begin(epoch, epoch_logs, mode=mode) progbar.on_epoch_begin(epoch, epoch_logs) @@ -373,6 +372,7 @@ def model_iteration(model, callbacks._call_end_hook(mode) if model._distribution_strategy: + # TODO(priyag, psv): Copy back metrics to the original model as well? if not validation_in_fit: training_distributed._copy_weights_to_original_model( model, model._grouped_model, mode) diff --git a/tensorflow/python/keras/engine/training_distributed.py b/tensorflow/python/keras/engine/training_distributed.py index 9ca5082673..fc72f6bd15 100644 --- a/tensorflow/python/keras/engine/training_distributed.py +++ b/tensorflow/python/keras/engine/training_distributed.py @@ -183,6 +183,8 @@ def experimental_fit_loop(model, callbacks.on_train_begin() for epoch in range(initial_epoch, epochs): + with current_strategy.scope(): + _reset_metrics(model, model._grouped_model_train) callbacks.on_epoch_begin(epoch) epoch_logs = {} step_index = 0 @@ -342,7 +344,7 @@ def experimental_test_loop(model, # Copy the weights from the original model to each of the replicated models. with current_strategy.scope(): _copy_weights_to_distributed_model(model, model._grouped_model_test) - + _reset_metrics(model, model._grouped_model_test) assert steps is not None outs = [0.] * len(model.metrics_names) for step in range(steps): @@ -449,7 +451,7 @@ def experimental_predict_loop(model, iterator, verbose=0, steps=None): # Copy the weights from the original model to each of the replicated models. with current_strategy.scope(): _copy_weights_to_distributed_model(model, model._grouped_model_predict) - + _reset_metrics(model, model._grouped_model_predict) assert steps is not None # Since we do not know how many samples we will see, we cannot pre-allocate # the returned Numpy arrays. Instead, we store one array per batch seen @@ -714,3 +716,12 @@ def _per_device_aggregate_batch(batch_outs, model, mode): total_batch_outs.append(np.concatenate(nest.flatten(nested_outs))) return total_batch_outs return batch_outs + + +def _reset_metrics(model, distributed_model=None): + if model._distribution_strategy: + distributed_model = ( + distributed_model or + model._distribution_strategy.unwrap(model._grouped_model)[0]) + distributed_model.reset_metrics() + -- GitLab From 9cbc061187bb994acc950730bbb844426c572db1 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 6 Dec 2018 15:55:11 -0800 Subject: [PATCH 164/873] Return tuples instead of lists from unwrap and device methods of tf.distribute.Strategy. PiperOrigin-RevId: 224423866 --- .../python/collective_all_reduce_strategy.py | 14 ++++---- .../distribute/python/minimize_loss_test.py | 4 +-- .../python/mirrored_strategy_multigpu_test.py | 2 +- .../distribute/python/one_device_strategy.py | 12 +++---- .../python/parameter_server_strategy.py | 35 +++++++++---------- .../distribute/python/strategy_test_lib.py | 2 +- .../contrib/distribute/python/tpu_strategy.py | 10 +++--- .../contrib/optimizer_v2/optimizer_v2.py | 8 ++--- .../python/distribute/cross_device_ops.py | 6 ++-- .../python/distribute/distribute_lib.py | 14 ++++---- .../python/distribute/mirrored_strategy.py | 19 +++++----- 11 files changed, 62 insertions(+), 64 deletions(-) diff --git a/tensorflow/contrib/distribute/python/collective_all_reduce_strategy.py b/tensorflow/contrib/distribute/python/collective_all_reduce_strategy.py index e988b63a28..5c50a20490 100644 --- a/tensorflow/contrib/distribute/python/collective_all_reduce_strategy.py +++ b/tensorflow/contrib/distribute/python/collective_all_reduce_strategy.py @@ -77,11 +77,11 @@ class CollectiveAllReduceExtended(mirrored_strategy.MirroredExtended): self._num_workers = 1 if num_gpus_per_worker: - local_devices = [ + local_devices = tuple( "/device:GPU:%d" % i for i in range(num_gpus_per_worker) - ] + ) else: - local_devices = ["/device:CPU:0"] + local_devices = ("/device:CPU:0",) self._worker_device = device_util.canonicalize("/device:CPU:0") self._collective_keys = cross_device_utils.CollectiveKeys() @@ -104,7 +104,7 @@ class CollectiveAllReduceExtended(mirrored_strategy.MirroredExtended): if task_type is None or task_id is None: raise ValueError("When `cluster_spec` is given, you must also specify " "`task_type` and `task_id`") - if task_type not in ["chief", "worker"]: + if task_type not in ("chief", "worker"): raise ValueError( "Unrecognized task_type: %r, valid task types are: \"chief\", " "\"worker\"." % task_type) @@ -119,12 +119,12 @@ class CollectiveAllReduceExtended(mirrored_strategy.MirroredExtended): self._worker_device = "/job:%s/task:%d" % (task_type, task_id) if num_gpus_per_worker: - local_devices = [ + local_devices = tuple( "%s/device:GPU:%d" % (self._worker_device, i) for i in range(num_gpus_per_worker) - ] + ) else: - local_devices = [self._worker_device] + local_devices = (self._worker_device,) self._collective_keys = cross_device_utils.CollectiveKeys() self._initialize_local(local_devices) diff --git a/tensorflow/contrib/distribute/python/minimize_loss_test.py b/tensorflow/contrib/distribute/python/minimize_loss_test.py index dcc9df4cda..f09483cb56 100644 --- a/tensorflow/contrib/distribute/python/minimize_loss_test.py +++ b/tensorflow/contrib/distribute/python/minimize_loss_test.py @@ -232,7 +232,7 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): fetches = distribution.unwrap( distribution.call_for_each_replica(model_fn, args=inputs)) if update_ops_in_cross_replica_mode: - fetches += ops.get_collection(ops.GraphKeys.UPDATE_OPS) + fetches += tuple(ops.get_collection(ops.GraphKeys.UPDATE_OPS)) return control_flow_ops.group(fetches) iterator = self._get_iterator(distribution.distribute_dataset(dataset_fn)) @@ -443,7 +443,7 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): step_fn, iterator, iterations=2, initial_loop_values=initial_loop_values) - self.assertEqual({key1: [value1]}, ctx.non_tensor_outputs) + self.assertEqual({key1: (value1,)}, ctx.non_tensor_outputs) self._verify_loss_output( initial_loss(), loss_output=ctx.last_step_outputs["replica_loss_reduced"], diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py b/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py index 66512f983e..36be5c83f8 100644 --- a/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py +++ b/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py @@ -180,7 +180,7 @@ class MirroredStrategyVariableCreatorStackTest( variable_scope.variable_creator_scope(main_thread_creator): result = distribution.extended.call_for_each_replica(model_fn) result = distribution.unwrap(result) - expected = ["main_thread:thread_0", "main_thread:thread_1"] + expected = ("main_thread:thread_0", "main_thread:thread_1") self.assertEqual(expected, result) diff --git a/tensorflow/contrib/distribute/python/one_device_strategy.py b/tensorflow/contrib/distribute/python/one_device_strategy.py index e322b6acb8..fdbfba4e04 100644 --- a/tensorflow/contrib/distribute/python/one_device_strategy.py +++ b/tensorflow/contrib/distribute/python/one_device_strategy.py @@ -60,7 +60,7 @@ class OneDeviceExtended(distribute_lib.DistributionStrategyExtended): if isinstance(colocate_with, six.string_types): with ops.device(colocate_with): return next_creator(*args, **kwargs) - if (isinstance(colocate_with, list) and len(colocate_with) == 1 and + if (isinstance(colocate_with, (list, tuple)) and len(colocate_with) == 1 and isinstance(colocate_with[0], six.string_types)): with ops.device(colocate_with[0]): return next_creator(*args, **kwargs) @@ -166,7 +166,7 @@ class OneDeviceExtended(distribute_lib.DistributionStrategyExtended): return array_ops.identity(replica_local_var) def _unwrap(self, value): - return [value] + return (value,) def value_container(self, value): return value @@ -177,15 +177,15 @@ class OneDeviceExtended(distribute_lib.DistributionStrategyExtended): @property def worker_devices(self): - return [self._device] + return (self._device,) @property def parameter_devices(self): - return [self._device] + return (self._device,) def non_slot_devices(self, var_list): del var_list - return [self._device] + return (self._device,) @property def experimental_should_init(self): @@ -216,4 +216,4 @@ class _OneDeviceReplicaContext(distribute_lib.ReplicaContext): @property def devices(self): - return [self._distribution_strategy.extended.worker_devices[0]] + return self._distribution_strategy.extended.worker_devices diff --git a/tensorflow/contrib/distribute/python/parameter_server_strategy.py b/tensorflow/contrib/distribute/python/parameter_server_strategy.py index eaeb4d7030..2c7766f95f 100644 --- a/tensorflow/contrib/distribute/python/parameter_server_strategy.py +++ b/tensorflow/contrib/distribute/python/parameter_server_strategy.py @@ -145,14 +145,14 @@ class ParameterServerExtended(distribute_lib.DistributionStrategyExtended): # replica. When there are GPUs, replicate operations on these GPUs. # Otherwise, place operations on CPU. if num_gpus_per_worker > 0: - self._compute_devices = [ + self._compute_devices = tuple( "%s/device:GPU:%d" % (self._worker_device, i) for i in range(num_gpus_per_worker) - ] + ) else: - self._compute_devices = [self._worker_device] + self._compute_devices = (self._worker_device,) - self._compute_devices = list( + self._compute_devices = tuple( map(device_util.resolve, self._compute_devices)) self._canonical_compute_device_set = set(self._compute_devices) @@ -176,8 +176,8 @@ class ParameterServerExtended(distribute_lib.DistributionStrategyExtended): # The `_parameter_devices` is needed for the `parameter_devices` property # and is a list of all variable devices. Here parameter devices are all # tasks of the "ps" job. - self._parameter_devices = map("/job:ps/task:{}".format, - range(num_ps_replicas)) + self._parameter_devices = tuple(map("/job:ps/task:{}".format, + range(num_ps_replicas))) # Add a default device so that ops without specified devices will not end up # on other workers. @@ -204,24 +204,24 @@ class ParameterServerExtended(distribute_lib.DistributionStrategyExtended): # replica. When there are GPUs, replicate operations on these GPUs. # Otherwise, place operations on CPU. if num_gpus_per_worker > 0: - self._compute_devices = list( + self._compute_devices = tuple( map("/device:GPU:{}".format, range(num_gpus_per_worker))) else: - self._compute_devices = [_LOCAL_CPU] + self._compute_devices = (_LOCAL_CPU,) - self._compute_devices = list( + self._compute_devices = tuple( map(device_util.resolve, self._compute_devices)) self._canonical_compute_device_set = set(self._compute_devices) # If there is only one GPU, put everything on that GPU. Otherwise, place # variables on CPU. if num_gpus_per_worker == 1: - assert len(list(self._compute_devices)) == 1 + assert len(self._compute_devices) == 1 self._variable_device = _LOCAL_GPU_0 - self._parameter_devices = [_LOCAL_GPU_0] + self._parameter_devices = (_LOCAL_GPU_0,) else: self._variable_device = _LOCAL_CPU - self._parameter_devices = [_LOCAL_CPU] + self._parameter_devices = (_LOCAL_CPU,) self._is_chief = True self._cluster_spec = None @@ -417,9 +417,9 @@ class ParameterServerExtended(distribute_lib.DistributionStrategyExtended): if isinstance(val, values.DistributedValues): # Return in a deterministic order. if set(val.devices) == self._canonical_compute_device_set: - return [val.get(device=d) for d in self._compute_devices] - return [val.get(device=d) for d in sorted(val.devices)] - return [val] + return tuple(val.get(device=d) for d in self._compute_devices) + return tuple(val.get(device=d) for d in sorted(val.devices)) + return (val,) def value_container(self, val): if (hasattr(val, "_aggregating_container") and @@ -497,12 +497,11 @@ class ParameterServerExtended(distribute_lib.DistributionStrategyExtended): @property def worker_devices(self): - # Make a copy to prevent users from accidentally mutating our copy. - return list(self._compute_devices) + return self._compute_devices @property def parameter_devices(self): - return list(self._parameter_devices) + return self._parameter_devices def non_slot_devices(self, var_list): return min(var_list, key=lambda x: x.name) diff --git a/tensorflow/contrib/distribute/python/strategy_test_lib.py b/tensorflow/contrib/distribute/python/strategy_test_lib.py index d50b142c5e..d441b5af5f 100644 --- a/tensorflow/contrib/distribute/python/strategy_test_lib.py +++ b/tensorflow/contrib/distribute/python/strategy_test_lib.py @@ -290,4 +290,4 @@ class DistributionTestBase(test.TestCase): self.evaluate(strategy.group(train_ops)) global_step_tensors = strategy.unwrap(value) global_step_values = self.evaluate(global_step_tensors) - self.assertEqual([1] * len(global_step_tensors), global_step_values) + self.assertEqual((1,) * len(global_step_tensors), global_step_values) diff --git a/tensorflow/contrib/distribute/python/tpu_strategy.py b/tensorflow/contrib/distribute/python/tpu_strategy.py index 806ff0ac61..b6f5b49201 100644 --- a/tensorflow/contrib/distribute/python/tpu_strategy.py +++ b/tensorflow/contrib/distribute/python/tpu_strategy.py @@ -162,7 +162,7 @@ class TPUExtended(distribute_lib.DistributionStrategyExtended): if "device:TPU:" in d.name} self._device_index = values.PerReplica(device_map) self._host_device = self.get_host_cpu_device(0) - self._tpu_devices = sorted(device_map.keys()) + self._tpu_devices = tuple(sorted(device_map.keys())) # Only create variables for the number of replicas we're running. self._tpu_devices = self._tpu_devices[:self._num_replicas_in_sync] @@ -507,13 +507,13 @@ class TPUExtended(distribute_lib.DistributionStrategyExtended): def _unwrap(self, val): if isinstance(val, values.DistributedValues): # Return in a deterministic order. - return [val.get(device=d) for d in sorted(val.devices)] + return tuple(val.get(device=d) for d in sorted(val.devices)) elif isinstance(val, list): # TODO(josh11b): We need to remove this case; per device values should # be represented using a PerReplica wrapper instead of a list with # one entry per device. - return val - return [val] + return tuple(val) + return (val,) def value_container(self, value): return value @@ -619,4 +619,4 @@ class _TPUReplicaContext(distribute_lib.ReplicaContext): distribute_lib.require_replica_context(self) ds = self._distribution_strategy replica_id = tensor_util.constant_value(self._replica_id_in_sync_group) - return [ds.extended.worker_devices[replica_id]] + return (ds.extended.worker_devices[replica_id],) diff --git a/tensorflow/contrib/optimizer_v2/optimizer_v2.py b/tensorflow/contrib/optimizer_v2/optimizer_v2.py index 11a9248a01..7fb23abc38 100644 --- a/tensorflow/contrib/optimizer_v2/optimizer_v2.py +++ b/tensorflow/contrib/optimizer_v2/optimizer_v2.py @@ -997,10 +997,10 @@ class OptimizerV2(optimizer_v1.Optimizer): with ops.control_dependencies([update_ops]): finish_updates = distribution.extended.update_non_slot( non_slot_devices, finish, group=False) - # We said grouped=False, which means finish_updates is always a list. - # It will be [None] when finish() returns None. - if finish_updates == [None]: - finish_updates = [update_ops] + # We said group=False, which means finish_updates is always a tuple. + # It will be (None,) when finish() returns None. + if finish_updates == (None,): + finish_updates = (update_ops,) # Update `global_step` (if any). if global_step is None: diff --git a/tensorflow/python/distribute/cross_device_ops.py b/tensorflow/python/distribute/cross_device_ops.py index a88ed62533..57c552ca8f 100644 --- a/tensorflow/python/distribute/cross_device_ops.py +++ b/tensorflow/python/distribute/cross_device_ops.py @@ -53,10 +53,10 @@ def validate_destinations(destinations): if not isinstance( destinations, (value_lib.DistributedValues, resource_variable_ops.ResourceVariable, - value_lib.AggregatingVariable, six.string_types, list)): + value_lib.AggregatingVariable, six.string_types, list, tuple)): raise ValueError("destinations must be one of a `DistributedValues` object," - " a tf.Variable object, a device string, a list of device " - "strings") + " a tf.Variable object, a device string, a list or tuple " + "of device strings") if not check_destinations(destinations): raise ValueError("destinations can not be empty") diff --git a/tensorflow/python/distribute/distribute_lib.py b/tensorflow/python/distribute/distribute_lib.py index eddd6ff8b1..87bf510ec5 100644 --- a/tensorflow/python/distribute/distribute_lib.py +++ b/tensorflow/python/distribute/distribute_lib.py @@ -565,8 +565,8 @@ class DistributionStrategy(object): variable created in `scope`. Returns: - A list of values contained in `value`. If `value` represents a single - value, this returns `[value].` + A tuple of values contained in `value`. If `value` represents a single + value, this returns `(value,).` """ return self._extended._unwrap(value) # pylint: disable=protected-access @@ -1346,14 +1346,14 @@ class DistributionStrategyExtended(object): @property def worker_devices(self): - """Returns the list of devices used to run `call_for_each_replica()` calls. + """Returns the tuple of all devices used to for compute replica execution. """ # TODO(josh11b): More docstring raise NotImplementedError("must be implemented in descendants") @property def parameter_devices(self): - """Returns the list of devices used for variable and `update` placement.""" + """Returns the tuple of all devices used to place variables.""" # TODO(josh11b): More docstring raise NotImplementedError("must be implemented in descendants") @@ -1513,9 +1513,9 @@ class ReplicaContext(object): @property def devices(self): - """The devices this replica is to be executed on, as a list of strings.""" + """The devices this replica is to be executed on, as a tuple of strings.""" require_replica_context(self) - return [device_util.current()] + return (device_util.current(),) # TODO(josh11b): Implement `start_all_reduce(method, t)` for efficient # all-reduce. It would return a function returning the result of reducing `t` @@ -1605,7 +1605,7 @@ class _DefaultDistributionExtended(DistributionStrategyExtended): return array_ops.identity(replica_local_var) def _unwrap(self, distributed_value): - return [distributed_value] + return (distributed_value,) def value_container(self, value): return value diff --git a/tensorflow/python/distribute/mirrored_strategy.py b/tensorflow/python/distribute/mirrored_strategy.py index 562f2328a5..56948b2bcb 100644 --- a/tensorflow/python/distribute/mirrored_strategy.py +++ b/tensorflow/python/distribute/mirrored_strategy.py @@ -472,7 +472,7 @@ class MirroredExtended(distribute_lib.DistributionStrategyExtended): assert len(set(devices)) == len(devices), ( "No duplicates allowed in `devices` argument.") # TODO(josh11b): Require at least 2 devices? - self._devices = [device_util.resolve(d) for d in devices] + self._devices = tuple(device_util.resolve(d) for d in devices) self._canonical_device_set = set(self._devices) self._device_index = values.PerReplica( {d: i for i, d in enumerate(devices)}) @@ -488,7 +488,7 @@ class MirroredExtended(distribute_lib.DistributionStrategyExtended): assert len(set(devices)) == len(devices), ( "No duplicates allowed in `devices` argument.") # TODO(josh11b): Require at least 2 devices? - self._devices = [device_util.resolve(d) for d in devices] + self._devices = tuple(device_util.resolve(d) for d in devices) self._canonical_device_set = set(self._devices) self._device_index = values.PerReplica( {d: i for i, d in enumerate(devices)}) @@ -727,7 +727,7 @@ class MirroredExtended(distribute_lib.DistributionStrategyExtended): return values.update_regroup(self, updates, group) def _update_non_slot(self, colocate_with, fn, args, kwargs, group): - assert isinstance(colocate_with, list) + assert isinstance(colocate_with, tuple) # TODO(josh11b): In eager mode, use one thread per device. updates = {} for d in colocate_with: @@ -748,9 +748,9 @@ class MirroredExtended(distribute_lib.DistributionStrategyExtended): if isinstance(val, values.DistributedValues): # Return in a deterministic order. if set(val.devices) == self._canonical_device_set: - return [val.get(device=d) for d in self._devices] - return [val.get(device=d) for d in sorted(val.devices)] - return [val] + return tuple(val.get(device=d) for d in self._devices) + return tuple(val.get(device=d) for d in sorted(val.devices)) + return (val,) def value_container(self, val): return values.value_container(val) @@ -761,12 +761,11 @@ class MirroredExtended(distribute_lib.DistributionStrategyExtended): @property def worker_devices(self): - # Make a copy to prevent users from accidentally mutating our copy. - return list(self._devices) + return self._devices @property def parameter_devices(self): - return list(self._devices) + return self._devices @property def experimental_between_graph(self): @@ -786,7 +785,7 @@ class MirroredExtended(distribute_lib.DistributionStrategyExtended): def non_slot_devices(self, var_list): del var_list - return list(self._devices) + return tuple(self._devices) def _get_devices_from(self, colocate_with=None): if colocate_with is None: -- GitLab From d6074a1afb8dc7dff25cea759f5249898cf1affe Mon Sep 17 00:00:00 2001 From: Shashi Shekhar Date: Thu, 6 Dec 2018 16:01:40 -0800 Subject: [PATCH 165/873] Fix a flaky test. Move from absolute time measurement to relative. PiperOrigin-RevId: 224424896 --- tensorflow/lite/profiling/profiler_test.cc | 22 ++++++++++++---------- 1 file changed, 12 insertions(+), 10 deletions(-) diff --git a/tensorflow/lite/profiling/profiler_test.cc b/tensorflow/lite/profiling/profiler_test.cc index 82d053729c..addebabe1b 100644 --- a/tensorflow/lite/profiling/profiler_test.cc +++ b/tensorflow/lite/profiling/profiler_test.cc @@ -27,11 +27,8 @@ namespace tflite { namespace profiling { namespace { -void AssertDurationOfEventAroundMs(const ProfileEvent* event, - double expected_ms, double eps_ms) { - double duration_ms = - (event->end_timestamp_us - event->begin_timestamp_us) / 1e3; - EXPECT_NEAR(expected_ms, duration_ms, eps_ms); +double GetDurationOfEventMs(const ProfileEvent* event) { + return (event->end_timestamp_us - event->begin_timestamp_us) / 1e3; } void SleepForQuarterSecond(Profiler* profiler) { @@ -84,12 +81,17 @@ TEST(ProfilingTest, ProfilesAreCollected) { #ifndef ADDRESS_SANITIZER // ASAN build is sometimes very slow. Set a large epsilon to avoid flakiness. + // Due to flakiness, just verify relative values match. const int eps_ms = 50; - AssertDurationOfEventAroundMs(profile_events[0], /*expected_ms*/ 500, eps_ms); - AssertDurationOfEventAroundMs(profile_events[1], /*expected_ms*/ 250, eps_ms); - AssertDurationOfEventAroundMs(profile_events[2], /*expected_ms*/ 250, eps_ms); - AssertDurationOfEventAroundMs(profile_events[3], /*expected_ms*/ 250, eps_ms); - AssertDurationOfEventAroundMs(profile_events[4], /*expected_ms*/ 250, eps_ms); + auto parent_ms = GetDurationOfEventMs(profile_events[0]); + double child_ms[2], sleep_for_quarter_ms[2]; + child_ms[0] = GetDurationOfEventMs(profile_events[1]); + child_ms[1] = GetDurationOfEventMs(profile_events[3]); + sleep_for_quarter_ms[0] = GetDurationOfEventMs(profile_events[2]); + sleep_for_quarter_ms[1] = GetDurationOfEventMs(profile_events[4]); + EXPECT_NEAR(parent_ms, child_ms[0] + child_ms[1], eps_ms); + EXPECT_NEAR(child_ms[0], sleep_for_quarter_ms[0], eps_ms); + EXPECT_NEAR(child_ms[1], sleep_for_quarter_ms[1], eps_ms); #endif } -- GitLab From a5be8bd6b31f49c1ebe7899b1bf9123664785415 Mon Sep 17 00:00:00 2001 From: Shashi Shekhar Date: Thu, 6 Dec 2018 16:06:18 -0800 Subject: [PATCH 166/873] Populate for int8 type. PiperOrigin-RevId: 224425691 --- tensorflow/lite/tools/benchmark/benchmark_tflite_model.cc | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/tensorflow/lite/tools/benchmark/benchmark_tflite_model.cc b/tensorflow/lite/tools/benchmark/benchmark_tflite_model.cc index ad025b19d9..32cf4e4292 100644 --- a/tensorflow/lite/tools/benchmark/benchmark_tflite_model.cc +++ b/tensorflow/lite/tools/benchmark/benchmark_tflite_model.cc @@ -281,6 +281,11 @@ void BenchmarkTfLiteModel::PrepareInputsAndOutputs() { interpreter->typed_tensor(i), std::vector(sizes.begin() + 1, sizes.end()), []() { return static_cast(rand()) % 255; }); + } else if (t->type == kTfLiteInt8) { + FillRandomValue( + interpreter->typed_tensor(i), + std::vector(sizes.begin() + 1, sizes.end()), + []() { return static_cast(rand()) % 255 - 127; }); } else if (t->type == kTfLiteString) { tflite::DynamicBuffer buffer; FillRandomString(&buffer, sizes, []() { -- GitLab From d3cf56c7280629bccfd4a1015d60dd4f2fda3ac7 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 6 Dec 2018 16:15:20 -0800 Subject: [PATCH 167/873] In gpu_process_state.cc enforce that BusIdForGPU always returns a non-negative integer, because the value will be used as an index. The default implementation uses NUMA node, and some platforms will return -1 (kUnknownNumaNode). PiperOrigin-RevId: 224427213 --- tensorflow/core/common_runtime/gpu/gpu_process_state.cc | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/tensorflow/core/common_runtime/gpu/gpu_process_state.cc b/tensorflow/core/common_runtime/gpu/gpu_process_state.cc index a9a19f0fe0..8167cfb9d7 100644 --- a/tensorflow/core/common_runtime/gpu/gpu_process_state.cc +++ b/tensorflow/core/common_runtime/gpu/gpu_process_state.cc @@ -70,7 +70,10 @@ int GPUProcessState::BusIdForGPU(TfGpuId tf_gpu_id) { // Return the NUMA node associated with the GPU's StreamExecutor. se::StreamExecutor* se = GpuIdUtil::ExecutorForTfGpuId(tf_gpu_id).ValueOrDie(); - return se->GetDeviceDescription().numa_node(); + int numa_node = se->GetDeviceDescription().numa_node(); + // bus_id must be non-negative. If the numa_node is not known, + // use 0. + return numa_node >= 0 ? numa_node : 0; } Allocator* GPUProcessState::GetGPUAllocator(const GPUOptions& options, @@ -97,6 +100,7 @@ Allocator* GPUProcessState::GetGPUAllocator(const GPUOptions& options, PlatformGpuId platform_gpu_id; TF_CHECK_OK(GpuIdManager::TfToPlatformGpuId(tf_gpu_id, &platform_gpu_id)); int bus_id = BusIdForGPU(tf_gpu_id); + DCHECK_GE(bus_id, 0); while (bus_id >= gpu_visitors_.size()) { gpu_visitors_.push_back({}); } @@ -249,6 +253,7 @@ void GPUProcessState::AddGPUAllocVisitor(int bus_id, CHECK(gpu_allocators_.empty()) // Crash OK << "AddGPUAllocVisitor must be called before " "first call to GetGPUAllocator."; + DCHECK_GE(bus_id, 0); while (bus_id >= static_cast(gpu_visitors_.size())) { gpu_visitors_.push_back(std::vector()); } -- GitLab From f8006837d3e2d1ee6bc03d48a51f17284cd7034d Mon Sep 17 00:00:00 2001 From: Zhenyu Tan Date: Thu, 6 Dec 2018 16:23:47 -0800 Subject: [PATCH 168/873] scale loss instead of scale gradients. PiperOrigin-RevId: 224428618 --- .../python/keras/optimizer_v2/optimizer_v2.py | 26 ++++++++++++++----- 1 file changed, 19 insertions(+), 7 deletions(-) diff --git a/tensorflow/python/keras/optimizer_v2/optimizer_v2.py b/tensorflow/python/keras/optimizer_v2/optimizer_v2.py index e6cd52c817..15f3009a4a 100644 --- a/tensorflow/python/keras/optimizer_v2/optimizer_v2.py +++ b/tensorflow/python/keras/optimizer_v2/optimizer_v2.py @@ -24,7 +24,8 @@ import abc import six -from tensorflow.python.distribute import distribution_strategy_context +from tensorflow.python.distribute import distribute_lib +from tensorflow.python.distribute import distribution_strategy_context as distribute_ctx from tensorflow.python.distribute import reduce_util as ds_reduce_util from tensorflow.python.eager import backprop from tensorflow.python.eager import context @@ -252,12 +253,14 @@ class OptimizerV2(optimizer_v1.Optimizer): with backprop.GradientTape() as tape: tape.watch(var_list) loss_value = loss() + loss_value = self._scale_loss(loss_value) grads = tape.gradient(loss_value, var_list, grad_loss) else: if context.executing_eagerly(): raise RuntimeError("`loss` passed to Optimizer.compute_gradients " "should be a function when eager execution is " "enabled.") + loss = self._scale_loss(loss) self._assert_valid_dtypes([loss]) if grad_loss is not None: self._assert_valid_dtypes([grad_loss]) @@ -277,6 +280,15 @@ class OptimizerV2(optimizer_v1.Optimizer): return grads_and_vars + @staticmethod + def _scale_loss(loss_value): + if distribute_lib.get_loss_reduction() == ds_reduce_util.ReduceOp.MEAN: + num_replicas = \ + distribute_ctx.get_distribution_strategy().num_replicas_in_sync + if num_replicas > 1: + loss_value *= (1. / num_replicas) + return loss_value + def apply_gradients(self, grads_and_vars, name=None): """Apply gradients to variables. @@ -299,7 +311,7 @@ class OptimizerV2(optimizer_v1.Optimizer): """ grads_and_vars = _filter_grads(grads_and_vars) var_list = [v for (_, v) in grads_and_vars] - if distribution_strategy_context.has_distribution_strategy(): + if distribute_ctx.has_distribution_strategy(): reduced_grads = merge_grads(grads_and_vars) grads_and_vars = zip(reduced_grads, var_list) @@ -598,7 +610,7 @@ def merge_update_step(update_ops, local_step): incre_op = local_step.assign_add(1).op return incre_op - return distribution_strategy_context.get_replica_context().merge_call( + return distribute_ctx.get_replica_context().merge_call( merge_update_step_fn, args=(update_ops, local_step)) @@ -606,11 +618,11 @@ def merge_grads(grads_and_vars): """Merge gradients from different replicas.""" def merge_grad_fn(strategy, grads_and_vars): - reduced_grads = strategy.batch_reduce( - ds_reduce_util.ReduceOp.MEAN, grads_and_vars) + reduced_grads = strategy.batch_reduce(ds_reduce_util.ReduceOp.SUM, + grads_and_vars) return reduced_grads - return distribution_strategy_context.get_replica_context().merge_call( + return distribute_ctx.get_replica_context().merge_call( merge_grad_fn, args=(grads_and_vars,)) @@ -629,7 +641,7 @@ def _var_key(var): """ # pylint: disable=protected-access - if distribution_strategy_context.has_distribution_strategy() and hasattr( + if distribute_ctx.has_distribution_strategy() and hasattr( var, "_primary_var"): var = var._primary_var if hasattr(var, "op"): -- GitLab From b8f5de61851acba53ca9ecf79b60bb34d753f34b Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 6 Dec 2018 16:26:49 -0800 Subject: [PATCH 169/873] Propagate quantization up selects with constant unquantize input. This helps for applying padding values or other masking operations PiperOrigin-RevId: 224429126 --- .../graph_transformations/hardcode_min_max.cc | 28 ++++++++++++++++--- 1 file changed, 24 insertions(+), 4 deletions(-) diff --git a/tensorflow/lite/toco/graph_transformations/hardcode_min_max.cc b/tensorflow/lite/toco/graph_transformations/hardcode_min_max.cc index df50f31de8..2e41767095 100644 --- a/tensorflow/lite/toco/graph_transformations/hardcode_min_max.cc +++ b/tensorflow/lite/toco/graph_transformations/hardcode_min_max.cc @@ -208,12 +208,32 @@ bool HardcodeMinMaxForSelect(Model* model, Operator* op) { if (output_array.minmax) { return false; } - const auto& input_array_1 = model->GetArray(op->inputs[1]); - if (!input_array_1.minmax) { + + auto& input_array_1 = model->GetArray(op->inputs[1]); + auto& input_array_2 = model->GetArray(op->inputs[2]); + + if (!input_array_1.minmax && !input_array_2.minmax) { return false; } - const auto& input_array_2 = model->GetArray(op->inputs[2]); - if (!input_array_2.minmax) { + + // Propagate up if one input is quantized and the other is constant. + if (!input_array_1.minmax && + IsConstantParameterArray(*model, op->inputs[1])) { + auto& minmax_1 = input_array_1.GetOrCreateMinMax(); + const auto& minmax_2 = input_array_2.GetMinMax(); + minmax_1.min = minmax_2.min; + minmax_1.max = minmax_2.max; + } + + if (!input_array_2.minmax && + IsConstantParameterArray(*model, op->inputs[2])) { + auto& minmax_2 = input_array_2.GetOrCreateMinMax(); + const auto& minmax_1 = input_array_1.GetMinMax(); + minmax_2.min = minmax_1.min; + minmax_2.max = minmax_1.max; + } + + if (!input_array_1.minmax || !input_array_2.minmax) { return false; } -- GitLab From 3f5dae940e67ce9a1c7a87f4c0d2c7ed051215be Mon Sep 17 00:00:00 2001 From: James Ring Date: Thu, 6 Dec 2018 16:39:22 -0800 Subject: [PATCH 170/873] Clarify interface docs for DeleteRecursively in Env and FileSystem PiperOrigin-RevId: 224431070 --- tensorflow/core/platform/env.h | 21 +++++++++++++++++---- tensorflow/core/platform/file_system.h | 19 ++++++++++++++++--- 2 files changed, 33 insertions(+), 7 deletions(-) diff --git a/tensorflow/core/platform/env.h b/tensorflow/core/platform/env.h index 7374fccdc2..1b53828415 100644 --- a/tensorflow/core/platform/env.h +++ b/tensorflow/core/platform/env.h @@ -167,11 +167,24 @@ class Env { Status DeleteFile(const string& fname); /// \brief Deletes the specified directory and all subdirectories and files - /// underneath it. undeleted_files and undeleted_dirs stores the number of - /// files and directories that weren't deleted (unspecified if the return - /// status is not OK). + /// underneath it. This is accomplished by traversing the directory tree + /// rooted at dirname and deleting entries as they are encountered. + /// + /// If dirname itself is not readable or does not exist, *undeleted_dir_count + /// is set to 1, *undeleted_file_count is set to 0 and an appropriate status + /// (e.g. NOT_FOUND) is returned. + /// + /// If dirname and all its descendants were successfully deleted, TF_OK is + /// returned and both error counters are set to zero. + /// + /// Otherwise, while traversing the tree, undeleted_file_count and + /// undeleted_dir_count are updated if an entry of the corresponding type + /// could not be deleted. The returned error status represents the reason that + /// any one of these entries could not be deleted. + /// /// REQUIRES: undeleted_files, undeleted_dirs to be not null. - /// Typical return codes + /// + /// Typical return codes: /// * OK - dirname exists and we were able to delete everything underneath. /// * NOT_FOUND - dirname doesn't exist /// * PERMISSION_DENIED - dirname or some descendant is not writable diff --git a/tensorflow/core/platform/file_system.h b/tensorflow/core/platform/file_system.h index 156af6cdea..c84a93b1bf 100644 --- a/tensorflow/core/platform/file_system.h +++ b/tensorflow/core/platform/file_system.h @@ -167,10 +167,23 @@ class FileSystem { virtual Status DeleteDir(const string& dirname) = 0; /// \brief Deletes the specified directory and all subdirectories and files - /// underneath it. undeleted_files and undeleted_dirs stores the number of - /// files and directories that weren't deleted (unspecified if the return - /// status is not OK). + /// underneath it. This is accomplished by traversing the directory tree + /// rooted at dirname and deleting entries as they are encountered. + /// + /// If dirname itself is not readable or does not exist, *undeleted_dir_count + /// is set to 1, *undeleted_file_count is set to 0 and an appropriate status + /// (e.g. NOT_FOUND) is returned. + /// + /// If dirname and all its descendants were successfully deleted, TF_OK is + /// returned and both error counters are set to zero. + /// + /// Otherwise, while traversing the tree, undeleted_file_count and + /// undeleted_dir_count are updated if an entry of the corresponding type + /// could not be deleted. The returned error status represents the reason that + /// any one of these entries could not be deleted. + /// /// REQUIRES: undeleted_files, undeleted_dirs to be not null. + /// /// Typical return codes: /// * OK - dirname exists and we were able to delete everything underneath. /// * NOT_FOUND - dirname doesn't exist -- GitLab From 5eaca08c7efdad8cf795b00212d4e3653e0d7466 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 6 Dec 2018 16:42:03 -0800 Subject: [PATCH 171/873] Remove broken test that computed gradients of fused batch norm in inference mode. PiperOrigin-RevId: 224431514 --- tensorflow/python/ops/parallel_for/control_flow_ops_test.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/ops/parallel_for/control_flow_ops_test.py b/tensorflow/python/ops/parallel_for/control_flow_ops_test.py index cc20d7ca6a..933bddd8cc 100644 --- a/tensorflow/python/ops/parallel_for/control_flow_ops_test.py +++ b/tensorflow/python/ops/parallel_for/control_flow_ops_test.py @@ -927,7 +927,10 @@ class NNTest(PForTest): outputs[1] = constant_op.constant(0.) outputs[2] = constant_op.constant(0.) loss = nn.l2_loss(outputs[0]) - gradients = g.gradient(loss, [x1, scale, offset]) + if is_training: + gradients = g.gradient(loss, [x1, scale, offset]) + else: + gradients = [constant_op.constant(0.)] * 3 return outputs + gradients # pylint: enable=cell-var-from-loop -- GitLab From ceee617b3675037da8637997f673ab9a993ee5de Mon Sep 17 00:00:00 2001 From: James Ring Date: Thu, 6 Dec 2018 16:44:31 -0800 Subject: [PATCH 172/873] Add missing 'void' parameter list for C forward declarations. PiperOrigin-RevId: 224431819 --- tensorflow/c/c_api.h | 15 ++++++++------- tensorflow/c/c_api_experimental.h | 2 +- tensorflow/c/eager/c_api.h | 2 +- 3 files changed, 10 insertions(+), 9 deletions(-) diff --git a/tensorflow/c/c_api.h b/tensorflow/c/c_api.h index 3d56268110..c7abba8552 100644 --- a/tensorflow/c/c_api.h +++ b/tensorflow/c/c_api.h @@ -91,7 +91,7 @@ extern "C" { // -------------------------------------------------------------------------- // TF_Version returns a string describing version information of the // TensorFlow library. TensorFlow using semantic versioning. -TF_CAPI_EXPORT extern const char* TF_Version(); +TF_CAPI_EXPORT extern const char* TF_Version(void); // -------------------------------------------------------------------------- // TF_DataType holds the type for a scalar value. E.g., one slot in a tensor. @@ -157,7 +157,7 @@ typedef enum TF_Code { typedef struct TF_Status TF_Status; // Return a new status object. -TF_CAPI_EXPORT extern TF_Status* TF_NewStatus(); +TF_CAPI_EXPORT extern TF_Status* TF_NewStatus(void); // Delete a previously created status object. TF_CAPI_EXPORT extern void TF_DeleteStatus(TF_Status*); @@ -196,7 +196,7 @@ TF_CAPI_EXPORT extern TF_Buffer* TF_NewBufferFromString(const void* proto, size_t proto_len); // Useful for passing *out* a protobuf. -TF_CAPI_EXPORT extern TF_Buffer* TF_NewBuffer(); +TF_CAPI_EXPORT extern TF_Buffer* TF_NewBuffer(void); TF_CAPI_EXPORT extern void TF_DeleteBuffer(TF_Buffer*); @@ -305,7 +305,7 @@ TF_CAPI_EXPORT extern size_t TF_StringEncodedSize(size_t len); typedef struct TF_SessionOptions TF_SessionOptions; // Return a new options object. -TF_CAPI_EXPORT extern TF_SessionOptions* TF_NewSessionOptions(); +TF_CAPI_EXPORT extern TF_SessionOptions* TF_NewSessionOptions(void); // Set the target in TF_SessionOptions.options. // target can be empty, a single entry, or a comma separated list of entries. @@ -338,7 +338,7 @@ TF_CAPI_EXPORT extern void TF_DeleteSessionOptions(TF_SessionOptions*); typedef struct TF_Graph TF_Graph; // Return a new graph object. -TF_CAPI_EXPORT extern TF_Graph* TF_NewGraph(); +TF_CAPI_EXPORT extern TF_Graph* TF_NewGraph(void); // Destroy an options object. Graph will be deleted once no more // TFSession's are referencing it. @@ -890,7 +890,8 @@ TF_CAPI_EXPORT extern void TF_GraphVersions(TF_Graph* graph, // TF_GraphImportGraphDef. typedef struct TF_ImportGraphDefOptions TF_ImportGraphDefOptions; -TF_CAPI_EXPORT extern TF_ImportGraphDefOptions* TF_NewImportGraphDefOptions(); +TF_CAPI_EXPORT extern TF_ImportGraphDefOptions* TF_NewImportGraphDefOptions( + void); TF_CAPI_EXPORT extern void TF_DeleteImportGraphDefOptions( TF_ImportGraphDefOptions* opts); @@ -1611,7 +1612,7 @@ TF_CAPI_EXPORT extern void TF_DeleteLibraryHandle(TF_Library* lib_handle); // // The data in the buffer will be the serialized OpList proto for ops registered // in this address space. -TF_CAPI_EXPORT extern TF_Buffer* TF_GetAllOpList(); +TF_CAPI_EXPORT extern TF_Buffer* TF_GetAllOpList(void); // TF_ApiDefMap encapsulates a collection of API definitions for an operation. // diff --git a/tensorflow/c/c_api_experimental.h b/tensorflow/c/c_api_experimental.h index 80c8bfe594..3e3a485eb7 100644 --- a/tensorflow/c/c_api_experimental.h +++ b/tensorflow/c/c_api_experimental.h @@ -239,7 +239,7 @@ TF_CAPI_EXPORT void TF_InitMain(const char* usage, int* argc, char*** argv); // Platform-specific implementation to return an unused port. (This should used // in tests only.) -TF_CAPI_EXPORT int TF_PickUnusedPortOrDie(); +TF_CAPI_EXPORT int TF_PickUnusedPortOrDie(void); // Fast path method that makes constructing a single scalar tensor require less // overhead and copies. diff --git a/tensorflow/c/eager/c_api.h b/tensorflow/c/eager/c_api.h index 8d6c8d958d..f80ae5a6d0 100755 --- a/tensorflow/c/eager/c_api.h +++ b/tensorflow/c/eager/c_api.h @@ -48,7 +48,7 @@ extern "C" { typedef struct TFE_ContextOptions TFE_ContextOptions; // Return a new options object. -TF_CAPI_EXPORT extern TFE_ContextOptions* TFE_NewContextOptions(); +TF_CAPI_EXPORT extern TFE_ContextOptions* TFE_NewContextOptions(void); // Set the config in TF_ContextOptions.options. // config should be a serialized tensorflow.ConfigProto proto. -- GitLab From d19733b470d6c5737b95fa193e4129255e3e1d83 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 6 Dec 2018 16:49:28 -0800 Subject: [PATCH 173/873] Makes the tensorflow should_use decorator not produce any warnings when running inside functions, to prevent extraneous warnings. PiperOrigin-RevId: 224432616 --- tensorflow/python/BUILD | 2 ++ tensorflow/python/ops/control_flow_ops_test.py | 13 +++++++++++++ tensorflow/python/util/tf_should_use.py | 6 +++++- 3 files changed, 20 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index 035830b911..bc6dc413e7 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -3251,6 +3251,7 @@ cuda_py_test( ":util", ":variable_scope", ":variables", + "//tensorflow/python/eager:def_function", ], ) @@ -3835,6 +3836,7 @@ py_library( srcs_version = "PY2AND3", deps = [ ":util", + "//tensorflow/python:framework_ops", "//tensorflow/python/eager:context", "@six_archive//:six", ], diff --git a/tensorflow/python/ops/control_flow_ops_test.py b/tensorflow/python/ops/control_flow_ops_test.py index c020189ad6..b19ec4bd61 100644 --- a/tensorflow/python/ops/control_flow_ops_test.py +++ b/tensorflow/python/ops/control_flow_ops_test.py @@ -24,6 +24,7 @@ import numpy as np from tensorflow.core.framework import graph_pb2 from tensorflow.core.framework import node_def_pb2 from tensorflow.python.client import session +from tensorflow.python.eager import def_function from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors @@ -1014,6 +1015,18 @@ class AssertTest(test_util.TensorFlowTestCase): with self.assertRaises(errors.InvalidArgumentError): self.evaluate(c) + @test_util.run_in_graph_and_eager_modes + def testAssertInFunction(self): + + @def_function.function + def whiny(value): + control_flow_ops.Assert(value, ["Raised false"]) + return constant_op.constant(5) + + with self.assertRaises(errors.InvalidArgumentError): + self.evaluate(whiny(False)) + + self.assertAllEqual(whiny(True), 5) if __name__ == "__main__": googletest.main() diff --git a/tensorflow/python/util/tf_should_use.py b/tensorflow/python/util/tf_should_use.py index ca6710bcf2..63de4a7a96 100644 --- a/tensorflow/python/util/tf_should_use.py +++ b/tensorflow/python/util/tf_should_use.py @@ -23,6 +23,7 @@ import traceback import six # pylint: disable=unused-import +from tensorflow.python.framework import ops from tensorflow.python.platform import tf_logging from tensorflow.python.util import tf_decorator # pylint: enable=g-bad-import-order,g-import-not-at-top @@ -32,7 +33,8 @@ class _TFShouldUseHelper(object): """Object stored in TFShouldUse-wrapped objects. When it is deleted it will emit a warning or error if its `sate` method - has not been called by time of deletion. + has not been called by time of deletion, and Tensorflow is not executing + eagerly outside of functions. """ def __init__(self, type_, repr_, stack_frame, fatal_error_if_unsated): @@ -50,6 +52,8 @@ class _TFShouldUseHelper(object): self._logging_module = None def __del__(self): + if ops.executing_eagerly_outside_functions(): + return if self._sated: return if self._fatal_error_if_unsated: -- GitLab From 7e0ae39bad55bbd88b2e8f3d436b8608d49ede94 Mon Sep 17 00:00:00 2001 From: Frank Chen Date: Thu, 6 Dec 2018 16:51:07 -0800 Subject: [PATCH 174/873] Add TPU core detection to TPUClusterResolver's num_accelerators_per_worker PiperOrigin-RevId: 224432865 --- .../tpu/python/tpu/tpu_system_metadata.py | 1 + .../cluster_resolver/tpu_cluster_resolver.py | 80 ++++++- .../tpu_cluster_resolver_test.py | 218 +++++++++++++----- 3 files changed, 231 insertions(+), 68 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_system_metadata.py b/tensorflow/contrib/tpu/python/tpu/tpu_system_metadata.py index ec682e5829..d66ecfcf4a 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_system_metadata.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_system_metadata.py @@ -52,6 +52,7 @@ def _query_tpu_system_metadata(master_address, cluster_def=None, devices = [] device_dict = collections.defaultdict(list) + # TODO(b/120564445): Replace with standard library for retries. retry_count = 1 while True: logging.info('Querying Tensorflow master (%s) for TPU system metadata.', diff --git a/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver.py b/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver.py index 99a7a0922f..e907d6fde4 100644 --- a/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver.py +++ b/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver.py @@ -18,13 +18,19 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import collections import os +import re from six.moves.urllib.request import Request from six.moves.urllib.request import urlopen +from tensorflow.python.client import session from tensorflow.python.distribute.cluster_resolver.cluster_resolver import ClusterResolver from tensorflow.python.distribute.cluster_resolver.cluster_resolver import format_master_url +from tensorflow.python.framework import errors +from tensorflow.python.framework import ops +from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import server_lib from tensorflow.python.util import compat @@ -41,6 +47,45 @@ _ENDPOINTS_SEPARATOR = ',' _DEFAULT_ENV_VARIABLE = 'TPU_NAME' _DISCOVERY_SERVICE_URL_ENV_VARIABLE = 'TPU_API_DISCOVERY_URL' +_TPU_DEVICE_REGEX = re.compile( + r'.*task:(?P\d+)/.*device:TPU:(?P\d+)$') +_TPU_CONN_RETRIES = 120 + +DeviceDetails = collections.namedtuple( + 'DeviceDetails', ['device_map', 'total_cores']) + + +def _get_device_dict_and_cores(devices): + """Returns a dict of hosts to cores and total cores given devices names. + + Returns a namedtuple with two attributes: + device_map: A map of host_ids to a list of core_ids. + total_cores: The total number of cores within the TPU system. + + Args: + devices: A list of devices returned by session.list_devices() + """ + device_map = collections.defaultdict(list) + num_cores = 0 + for device in devices: + match = _TPU_DEVICE_REGEX.match(device.name) + if match: + host_id = match.group('host_id') + core_id = match.group('core_id') + device_map[host_id].append(core_id) + num_cores += 1 + return DeviceDetails(device_map, num_cores) + + +def _verify_and_return_same_core_count(device_dict): + """Verifies that every device in device_dict has the same number of cores.""" + num_cores_per_host_set = ( + {len(core_ids) for core_ids in device_dict.values()}) + if len(num_cores_per_host_set) != 1: + raise RuntimeError('TPU cores on each device is not the same. This ' + 'should never happen. Devices: {}'.format(device_dict)) + return num_cores_per_host_set.pop() + class TPUClusterResolver(ClusterResolver): """Cluster Resolver for Google Cloud TPUs. @@ -394,24 +439,43 @@ class TPUClusterResolver(ClusterResolver): config_proto=None): """Returns the number of TPU cores per worker. - This defaults to 8 for all current TPU configurations, and we do not need - to query any remote systems for this. + Connects to the master and list all the devices present in the master, + and counts them up. Also verifies that the device counts per host in the + cluster is the same before returning the number of TPU cores per host. Args: task_type: Unused. task_index: Unused. accelerator_type: Unused. - config_proto: Unused. + config_proto: Used to create a connection to a TPU master in order to + retrieve the system metadata. Raises: RuntimeError: If this is used with a non-TPU accelerator_type. """ - # Unused. Not necessary to query anything. - del task_type, task_index, config_proto + retry_count = 1 + # TODO(b/120564445): Replace with standard library for retries. + while True: + try: + with ops.Graph().as_default(): + with session.Session(self.master(), config=config_proto) as s: + devices = s.list_devices() + device_details = _get_device_dict_and_cores(devices) + break + except errors.DeadlineExceededError: + error_message = ('Failed to connect to master. The TPU might not be ' + 'ready (e.g. still scheduling) or the master ' + 'address is incorrect: got (%s)' % self.master()) + if retry_count <= _TPU_CONN_RETRIES: + logging.warning(error_message) + logging.warning('Retrying (%d/%d)...', retry_count, _TPU_CONN_RETRIES) + retry_count += 1 + else: + raise RuntimeError(error_message) - if accelerator_type != 'TPU': - raise ValueError('This Cluster Resolver is only compatible with TPUs.') - return 8 + if device_details.total_cores: + return _verify_and_return_same_core_count(device_details.device_map) + return 0 @property def environment(self): diff --git a/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver_test.py b/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver_test.py index 0f22ede3d9..27d92608fa 100644 --- a/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver_test.py +++ b/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver_test.py @@ -20,7 +20,10 @@ from __future__ import print_function import os -from tensorflow.python.distribute.cluster_resolver import TPUClusterResolver +from tensorflow.python.client import session +from tensorflow.python.distribute import cluster_resolver +from tensorflow.python.distribute.cluster_resolver import tpu_cluster_resolver +from tensorflow.python.framework import errors from tensorflow.python.platform import test from tensorflow.python.training import server_lib from tensorflow.python.util import compat @@ -101,7 +104,8 @@ class TPUClusterResolverTest(test.TestCase): return mock_client - @mock.patch.object(TPUClusterResolver, '_requestComputeMetadata', + @mock.patch.object(cluster_resolver.TPUClusterResolver, + '_requestComputeMetadata', mock_request_compute_metadata) def testRetrieveProjectAndZoneFromMetadata(self): tpu_map = { @@ -112,7 +116,7 @@ class TPUClusterResolverTest(test.TestCase): } } - tpu_cluster_resolver = TPUClusterResolver( + resolver = cluster_resolver.TPUClusterResolver( project=None, zone=None, tpu=['test-tpu-1'], @@ -120,7 +124,7 @@ class TPUClusterResolverTest(test.TestCase): service=self.mock_service_client(tpu_map=tpu_map), coordinator_name='coordinator') - actual_cluster_spec = tpu_cluster_resolver.cluster_spec() + actual_cluster_spec = resolver.cluster_spec() expected_proto = """ job { name: 'coordinator' @@ -130,11 +134,12 @@ class TPUClusterResolverTest(test.TestCase): name: 'worker' tasks { key: 0 value: '10.1.2.3:8470' } } - """ % tpu_cluster_resolver._coordinator_port + """ % resolver._coordinator_port self._verifyClusterSpecEquality(actual_cluster_spec, str(expected_proto)) - self.assertEqual(tpu_cluster_resolver.master(), 'grpc://10.1.2.3:8470') + self.assertEqual(resolver.master(), 'grpc://10.1.2.3:8470') - @mock.patch.object(TPUClusterResolver, '_requestComputeMetadata', + @mock.patch.object(cluster_resolver.TPUClusterResolver, + '_requestComputeMetadata', mock_request_compute_metadata) def testRetrieveProjectAndZoneFromMetadataNoCoordinator(self): tpu_map = { @@ -145,7 +150,7 @@ class TPUClusterResolverTest(test.TestCase): } } - tpu_cluster_resolver = TPUClusterResolver( + resolver = cluster_resolver.TPUClusterResolver( project=None, zone=None, tpu=['test-tpu-1'], @@ -153,14 +158,15 @@ class TPUClusterResolverTest(test.TestCase): credentials=None, service=self.mock_service_client(tpu_map=tpu_map)) - actual_cluster_spec = tpu_cluster_resolver.cluster_spec() + actual_cluster_spec = resolver.cluster_spec() expected_proto = """ job { name: 'worker' tasks { key: 0 value: '10.1.2.3:8470' } } """ self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto) - self.assertEqual(tpu_cluster_resolver.master(), 'grpc://10.1.2.3:8470') + self.assertEqual(resolver.master(), 'grpc://10.1.2.3:8470') - @mock.patch.object(TPUClusterResolver, '_requestComputeMetadata', + @mock.patch.object(cluster_resolver.TPUClusterResolver, + '_requestComputeMetadata', mock_request_compute_metadata) def testUnhealthyCloudTpu(self): tpu_map = { @@ -171,7 +177,7 @@ class TPUClusterResolverTest(test.TestCase): } } - tpu_cluster_resolver = TPUClusterResolver( + resolver = cluster_resolver.TPUClusterResolver( project=None, zone=None, tpu='test-tpu-1', @@ -180,9 +186,10 @@ class TPUClusterResolverTest(test.TestCase): service=self.mock_service_client(tpu_map=tpu_map)) with self.assertRaises(RuntimeError): - tpu_cluster_resolver.cluster_spec() + resolver.cluster_spec() - @mock.patch.object(TPUClusterResolver, '_requestComputeMetadata', + @mock.patch.object(cluster_resolver.TPUClusterResolver, + '_requestComputeMetadata', mock_request_compute_metadata) def testNotReadyCloudTpu(self): tpu_map = { @@ -193,7 +200,7 @@ class TPUClusterResolverTest(test.TestCase): } } - tpu_cluster_resolver = TPUClusterResolver( + resolver = cluster_resolver.TPUClusterResolver( project=None, zone=None, tpu='test-tpu-1', @@ -202,7 +209,7 @@ class TPUClusterResolverTest(test.TestCase): service=self.mock_service_client(tpu_map=tpu_map)) with self.assertRaises(RuntimeError): - tpu_cluster_resolver.cluster_spec() + resolver.cluster_spec() def testSimpleSuccessfulRetrieval(self): tpu_map = { @@ -213,7 +220,7 @@ class TPUClusterResolverTest(test.TestCase): } } - tpu_cluster_resolver = TPUClusterResolver( + resolver = cluster_resolver.TPUClusterResolver( project='test-project', zone='us-central1-c', tpu=['test-tpu-1'], @@ -222,13 +229,13 @@ class TPUClusterResolverTest(test.TestCase): credentials=None, service=self.mock_service_client(tpu_map=tpu_map)) - actual_cluster_spec = tpu_cluster_resolver.cluster_spec() + actual_cluster_spec = resolver.cluster_spec() expected_proto = """ job { name: 'coordinator' tasks { key: 0 value: '10.128.1.5:10203' } } job { name: 'worker' tasks { key: 0 value: '10.1.2.3:8470' } } """ self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto) - self.assertEqual(tpu_cluster_resolver.master(), 'grpc://10.1.2.3:8470') + self.assertEqual(resolver.master(), 'grpc://10.1.2.3:8470') def testNewNetworkEndpointFormat(self): tpu_map = { @@ -241,7 +248,7 @@ class TPUClusterResolverTest(test.TestCase): } } - tpu_cluster_resolver = TPUClusterResolver( + resolver = cluster_resolver.TPUClusterResolver( project='test-project', zone='us-central1-c', tpu='test-tpu-1', @@ -250,15 +257,16 @@ class TPUClusterResolverTest(test.TestCase): credentials=None, service=self.mock_service_client(tpu_map=tpu_map)) - actual_cluster_spec = tpu_cluster_resolver.cluster_spec() + actual_cluster_spec = resolver.cluster_spec() expected_proto = """ job { name: 'coordinator' tasks { key: 0 value: '10.128.1.5:10203' } } job { name: 'worker' tasks { key: 0 value: '10.2.3.4:8470' } } """ self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto) - self.assertEqual('grpc://10.2.3.4:8470', tpu_cluster_resolver.master()) + self.assertEqual('grpc://10.2.3.4:8470', resolver.master()) - @mock.patch.object(TPUClusterResolver, '_requestComputeMetadata', + @mock.patch.object(cluster_resolver.TPUClusterResolver, + '_requestComputeMetadata', mock_request_compute_metadata) def testPodResolution(self): tpu_map = { @@ -286,13 +294,13 @@ class TPUClusterResolverTest(test.TestCase): } } - tpu_cluster_resolver = TPUClusterResolver( + resolver = cluster_resolver.TPUClusterResolver( tpu='test-tpu-1', credentials=None, service=self.mock_service_client(tpu_map=tpu_map), coordinator_name='coordinator') - actual_cluster_spec = tpu_cluster_resolver.cluster_spec() + actual_cluster_spec = resolver.cluster_spec() expected_proto = """ job { name: 'coordinator', @@ -305,9 +313,9 @@ class TPUClusterResolverTest(test.TestCase): tasks { key: 2 value: '10.2.3.6:8470' } tasks { key: 3 value: '10.2.3.7:8470' } } - """ % tpu_cluster_resolver._coordinator_port + """ % resolver._coordinator_port self._verifyClusterSpecEquality(actual_cluster_spec, str(expected_proto)) - self.assertEqual(tpu_cluster_resolver.master(), 'grpc://10.2.3.4:8470') + self.assertEqual(resolver.master(), 'grpc://10.2.3.4:8470') def testPodResolutionNoCoordinator(self): tpu_map = { @@ -335,7 +343,7 @@ class TPUClusterResolverTest(test.TestCase): } } - tpu_cluster_resolver = TPUClusterResolver( + resolver = cluster_resolver.TPUClusterResolver( project='test-project', zone='us-central1-c', tpu='test-tpu-1', @@ -343,7 +351,7 @@ class TPUClusterResolverTest(test.TestCase): credentials=None, service=self.mock_service_client(tpu_map=tpu_map)) - actual_cluster_spec = tpu_cluster_resolver.cluster_spec() + actual_cluster_spec = resolver.cluster_spec() expected_proto = """ job { name: 'worker' @@ -354,13 +362,13 @@ class TPUClusterResolverTest(test.TestCase): } """ self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto) - self.assertEqual(tpu_cluster_resolver.master(), 'grpc://10.2.3.4:8470') + self.assertEqual(resolver.master(), 'grpc://10.2.3.4:8470') def testGetMasterNoEntries(self): tpu_map = {} with self.assertRaises(ValueError): - TPUClusterResolver( + cluster_resolver.TPUClusterResolver( project='test-project', zone='us-central1-c', tpu=[], @@ -370,14 +378,14 @@ class TPUClusterResolverTest(test.TestCase): # TODO(saeta): Convert to parameterized test when included in OSS TF. def verifyShouldResolve(self, tpu, should_resolve): - tpu_cluster_resolver = TPUClusterResolver( + resolver = cluster_resolver.TPUClusterResolver( project='test-project', zone='us-central1-c', tpu=tpu, coordinator_name=None, credentials=None, service=self.mock_service_client(tpu_map={})) - self.assertEqual(should_resolve, tpu_cluster_resolver._shouldResolve(), + self.assertEqual(should_resolve, resolver._shouldResolve(), "TPU: '%s'" % tpu) def testShouldResolveNoName(self): @@ -402,25 +410,26 @@ class TPUClusterResolverTest(test.TestCase): self.verifyShouldResolve('grpctpu', True) def testNoCallComputeMetadata(self): - tpu_cluster_resolver = TPUClusterResolver(tpu='/bns/foo/bar') + resolver = cluster_resolver.TPUClusterResolver( + tpu='/bns/foo/bar') self.assertEqual( - compat.as_bytes('/bns/foo/bar'), tpu_cluster_resolver.master()) - self.assertEqual(None, tpu_cluster_resolver.cluster_spec()) + compat.as_bytes('/bns/foo/bar'), resolver.master()) + self.assertEqual(None, resolver.cluster_spec()) def testGkeEnvironmentForDonut(self): os.environ['KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS'] = 'grpc://10.120.27.5:8470' self.assertIn('KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS', os.environ) - self.assertTrue(TPUClusterResolver._inGke()) + self.assertTrue(cluster_resolver.TPUClusterResolver._inGke()) self.assertEqual( compat.as_bytes('grpc://10.120.27.5:8470'), - compat.as_bytes(TPUClusterResolver._gkeEndpoints())) + compat.as_bytes(cluster_resolver.TPUClusterResolver._gkeEndpoints())) - tpu_cluster_resolver = TPUClusterResolver() + resolver = cluster_resolver.TPUClusterResolver() self.assertEqual( compat.as_bytes('grpc://10.120.27.5:8470'), - compat.as_bytes(tpu_cluster_resolver.master())) - actual_cluster_spec = tpu_cluster_resolver.cluster_spec() + compat.as_bytes(resolver.master())) + actual_cluster_spec = resolver.cluster_spec() expected_proto = """ job { name: 'worker' @@ -438,19 +447,19 @@ class TPUClusterResolverTest(test.TestCase): 'grpc://10.120.27.8:8470') self.assertIn('KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS', os.environ) - self.assertTrue(TPUClusterResolver._inGke()) + self.assertTrue(cluster_resolver.TPUClusterResolver._inGke()) self.assertEqual( compat.as_bytes('grpc://10.120.27.5:8470,' 'grpc://10.120.27.6:8470,' 'grpc://10.120.27.7:8470,' 'grpc://10.120.27.8:8470'), - compat.as_bytes(TPUClusterResolver._gkeEndpoints())) + compat.as_bytes(cluster_resolver.TPUClusterResolver._gkeEndpoints())) - tpu_cluster_resolver = TPUClusterResolver() + resolver = cluster_resolver.TPUClusterResolver() self.assertEqual( compat.as_bytes('grpc://10.120.27.5:8470'), - compat.as_bytes(tpu_cluster_resolver.master())) - actual_cluster_spec = tpu_cluster_resolver.cluster_spec() + compat.as_bytes(resolver.master())) + actual_cluster_spec = resolver.cluster_spec() expected_proto = """ job { name: 'worker' @@ -467,18 +476,21 @@ class TPUClusterResolverTest(test.TestCase): def testEnvironmentDiscoveryUrl(self): os.environ['TPU_API_DISCOVERY_URL'] = 'https://{api}.internal/{apiVersion}' self.assertEqual('https://{api}.internal/{apiVersion}', - TPUClusterResolver._environmentDiscoveryUrl()) + (cluster_resolver.TPUClusterResolver. + _environmentDiscoveryUrl())) def testEnvironmentAndRpcDetectionForGoogle(self): - tpu_cluster_resolver = TPUClusterResolver(tpu='/bns/ab/cd/ef') - self.assertEqual(tpu_cluster_resolver.environment, 'google') - self.assertEqual(tpu_cluster_resolver.rpc_layer, None) + resolver = cluster_resolver.TPUClusterResolver( + tpu='/bns/ab/cd/ef') + self.assertEqual(resolver.environment, 'google') + self.assertEqual(resolver.rpc_layer, None) def testEnvironmentAndRpcDetectionForGrpcString(self): - tpu_cluster_resolver = TPUClusterResolver(tpu='grpc://10.1.2.3:8470') - self.assertEqual(tpu_cluster_resolver.environment, '') - self.assertEqual(tpu_cluster_resolver.rpc_layer, 'grpc') - self.assertEqual(tpu_cluster_resolver.master(), 'grpc://10.1.2.3:8470') + resolver = cluster_resolver.TPUClusterResolver( + tpu='grpc://10.1.2.3:8470') + self.assertEqual(resolver.environment, '') + self.assertEqual(resolver.rpc_layer, 'grpc') + self.assertEqual(resolver.master(), 'grpc://10.1.2.3:8470') def testOverrideTaskTypeAndIndexAndGetMaster(self): tpu_map = { @@ -506,7 +518,7 @@ class TPUClusterResolverTest(test.TestCase): } } - tpu_cluster_resolver = TPUClusterResolver( + resolver = cluster_resolver.TPUClusterResolver( project='test-project', zone='us-central1-c', tpu='test-tpu-1', @@ -514,17 +526,103 @@ class TPUClusterResolverTest(test.TestCase): credentials=None, service=self.mock_service_client(tpu_map=tpu_map)) - self.assertEqual(tpu_cluster_resolver.master(), 'grpc://10.2.3.4:8470') + self.assertEqual(resolver.master(), 'grpc://10.2.3.4:8470') - tpu_cluster_resolver.task_type = 'worker' - tpu_cluster_resolver.task_index = 3 - self.assertEqual(tpu_cluster_resolver.master(), 'grpc://10.2.3.7:8470') + resolver.task_type = 'worker' + resolver.task_index = 3 + self.assertEqual(resolver.master(), 'grpc://10.2.3.7:8470') self.assertEqual( - tpu_cluster_resolver.master( + resolver.master( task_type='worker', task_index=2, rpc_layer='test'), 'test://10.2.3.6:8470') + def testGetDeviceDictAndCoresWithTPUs(self): + device_names = [ + '/job:tpu_worker/task:0/device:TPU:0', + '/job:tpu_worker/task:1/device:TPU:1', + '/job:tpu_worker/task:2/device:TPU:0', + '/job:tpu_worker/task:3/device:TPU:1', + '/job:tpu_worker/task:0/device:TPU:4', + '/job:tpu_worker/task:1/device:TPU:5', + '/job:tpu_worker/task:2/device:TPU:4', + '/job:tpu_worker/task:3/device:TPU:5', + ] + device_list = [ + session._DeviceAttributes( + name, 'TPU', 1024, 0) for name in device_names + ] + + device_details = tpu_cluster_resolver._get_device_dict_and_cores( + device_list) + self.assertEqual(device_details.total_cores, 8) + self.assertEqual(device_details.device_map, + {'0': ['0', '4'], + '1': ['1', '5'], + '2': ['0', '4'], + '3': ['1', '5']}) + + def testGetDeviceDictAndCoresWithCPUsAndGPUs(self): + device_names = [ + '/job:tpu_worker/task:0/device:CPU:0', + '/job:tpu_worker/task:1/device:CPU:0', + '/job:tpu_worker/task:2/device:CPU:0', + '/job:tpu_worker/task:3/device:CPU:0', + '/job:tpu_worker/task:0/device:GPU:1', + '/job:tpu_worker/task:1/device:GPU:1', + '/job:tpu_worker/task:2/device:GPU:1', + '/job:tpu_worker/task:3/device:GPU:1', + ] + device_list = [ + session._DeviceAttributes( + name, 'XLA', 1024, 0) for name in device_names + ] + + device_dict, num_cores = tpu_cluster_resolver._get_device_dict_and_cores( + device_list) + self.assertEqual(num_cores, 0) + self.assertEqual(device_dict, {}) + + def testVerifySameCoreCount(self): + self.assertEqual( + tpu_cluster_resolver._verify_and_return_same_core_count( + {0: [0, 1, 2, 3, 4, 5, 6, 7]}), 8) + self.assertEqual( + tpu_cluster_resolver._verify_and_return_same_core_count( + {0: [0, 1], 1: [2, 3]}), 2) + with self.assertRaises(RuntimeError): + tpu_cluster_resolver._verify_and_return_same_core_count( + {0: [0], 1: [1, 2]}) + + @mock.patch.object(session.BaseSession, 'list_devices') + def testNumAcceleratorsSuccess(self, mock_list_devices): + device_names = [ + '/job:tpu_worker/task:0/device:TPU:0', + '/job:tpu_worker/task:1/device:TPU:1', + '/job:tpu_worker/task:2/device:TPU:0', + '/job:tpu_worker/task:3/device:TPU:1', + '/job:tpu_worker/task:0/device:TPU:4', + '/job:tpu_worker/task:1/device:TPU:5', + '/job:tpu_worker/task:2/device:TPU:4', + '/job:tpu_worker/task:3/device:TPU:5', + ] + device_list = [ + session._DeviceAttributes( + name, 'TPU', 1024, 0) for name in device_names + ] + mock_list_devices.return_value = device_list + + resolver = cluster_resolver.TPUClusterResolver(tpu='') + self.assertEqual(resolver.num_accelerators(), 2) + + @mock.patch.object(session.BaseSession, 'list_devices') + def testNumAcceleratorsRetryFailure(self, mock_list_devices): + resolver = cluster_resolver.TPUClusterResolver(tpu='') + mock_list_devices.side_effect = errors.DeadlineExceededError( + None, None, 'timeout') + with self.assertRaises(RuntimeError): + resolver.num_accelerators() + if __name__ == '__main__': test.main() -- GitLab From 59d535a0df17eaf3033bbff73ef4e1e1988c454e Mon Sep 17 00:00:00 2001 From: Yu-Cheng Ling Date: Thu, 6 Dec 2018 17:03:34 -0800 Subject: [PATCH 175/873] create_ios_frameworks.sh : Add an option for GPU support The GPU library is still working-in-progress. PiperOrigin-RevId: 224434789 --- .../lite/lib_package/create_ios_frameworks.sh | 47 +++++++++++++++++-- 1 file changed, 42 insertions(+), 5 deletions(-) diff --git a/tensorflow/lite/lib_package/create_ios_frameworks.sh b/tensorflow/lite/lib_package/create_ios_frameworks.sh index 7901655b7c..abf40e7dec 100755 --- a/tensorflow/lite/lib_package/create_ios_frameworks.sh +++ b/tensorflow/lite/lib_package/create_ios_frameworks.sh @@ -1,4 +1,4 @@ -#!/bin/bash -x +#!/bin/bash # Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -20,10 +20,41 @@ set -e echo "Starting" TFLITE_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)/.." +usage() { + echo "Usage: $(basename "$0") [-a]" + echo "-g build with GPU delegate" + exit 1 +} + +USE_GPU_DELEGATE="false" +FRAMEWORK_NAME="tensorflow_lite" +while getopts "g" opt_name; do + case "$opt_name" in + g) + USE_GPU_DELEGATE="true" + FRAMEWORK_NAME="tensorflow_lite_gpu" + ;; + *) usage;; + esac +done +shift $((OPTIND - 1)) +readonly USE_GPU_DELEGATE +readonly FRAMEWORK_NAME + +if [ $USE_GPU_DELEGATE == "true" ] ; then + for filename in metal_delegate.h libmetal_delegate.a ; do + if [[ ! -f "${TFLITE_DIR}/delegates/gpu/${filename}" ]] ; then + echo "File ${TFLITE_DIR}/delegates/gpu/${filename} doesn't exist." + echo "It's requried for building TFLite Framework with GPU. Aborting." + exit 1 + fi + done +fi + TMP_DIR=$(mktemp -d) echo "Package dir: " $TMP_DIR FW_DIR=$TMP_DIR/tensorflow_lite_ios_frameworks -FW_DIR_TFLITE=$FW_DIR/tensorflow_lite.framework +FW_DIR_TFLITE=$FW_DIR/$FRAMEWORK_NAME.framework FW_DIR_TFLITE_HDRS=$FW_DIR_TFLITE/Headers echo "Creating target Headers directories" @@ -58,8 +89,14 @@ cp $TFLITE_DIR/../../bazel-genfiles/tensorflow/tools/lib_package/include/tensorf $FW_DIR_TFLITE echo "Copying static libraries" +# Note: There must be a static library with the same name +# as the framework name. cp $TFLITE_DIR/tools/make/gen/lib/libtensorflow-lite.a \ - $FW_DIR_TFLITE/tensorflow_lite + $FW_DIR_TFLITE/$FRAMEWORK_NAME +if [ $USE_GPU_DELEGATE == "true" ] ; then + cp "${TFLITE_DIR}/delegates/gpu/libmetal_delegate.a" \ + $FW_DIR_TFLITE/libmetal_delegate.a +fi # This is required, otherwise they interfere with the documentation of the # pod at cocoapods.org. @@ -71,10 +108,10 @@ find . -type f -name readme\* -exec rm -f {} \; TARGET_GEN_LOCATION="$TFLITE_DIR/gen/ios_frameworks" echo "Moving results to target: " $TARGET_GEN_LOCATION cd $FW_DIR -zip -q -r tensorflow_lite.framework.zip tensorflow_lite.framework -x .DS_Store +zip -q -r $FRAMEWORK_NAME.framework.zip $FRAMEWORK_NAME.framework -x .DS_Store rm -rf $TARGET_GEN_LOCATION mkdir -p $TARGET_GEN_LOCATION -cp -r tensorflow_lite.framework.zip $TARGET_GEN_LOCATION +cp -r $FRAMEWORK_NAME.framework.zip $TARGET_GEN_LOCATION echo "Cleaning up" rm -rf $TMP_DIR -- GitLab From fe2799dfb0e92eab722d52dd8b54e8256a290753 Mon Sep 17 00:00:00 2001 From: Anna R Date: Thu, 6 Dec 2018 17:10:55 -0800 Subject: [PATCH 176/873] Internal change. PiperOrigin-RevId: 224435855 --- tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh b/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh index a6871dd8f1..2820e466f3 100644 --- a/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh +++ b/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh @@ -109,7 +109,7 @@ if [[ "$TF_NIGHTLY" == 1 ]]; then if [ -z ${PROJECT_NAME} ]; then EXTRA_PIP_FLAGS="--nightly_flag" else - EXTRA_PIP_FLAGS="--project_name=${PROJECT_NAME} --nightly_flag" + EXTRA_PIP_FLAGS="--project_name ${PROJECT_NAME} --nightly_flag" fi fi -- GitLab From b397935bae2530e1cf5022ca6f85a0b898a795d6 Mon Sep 17 00:00:00 2001 From: Rohan Jain Date: Thu, 6 Dec 2018 17:13:19 -0800 Subject: [PATCH 177/873] Moving state created within a DatasetKernel to a resource_mgr owned by the IteratorResource instead. This helps us do better state management for state that is owned by dataset objects and would allow us to separate dataset and iterator kernel creation PiperOrigin-RevId: 224436180 --- tensorflow/core/framework/dataset.h | 7 + .../core/kernels/data/cache_dataset_ops.cc | 49 +++-- tensorflow/core/kernels/data/iterator_ops.cc | 12 +- .../kernels/data/multi_device_iterator_ops.cc | 5 + .../core/kernels/data/shuffle_dataset_op.cc | 176 ++++++++++++------ 5 files changed, 167 insertions(+), 82 deletions(-) diff --git a/tensorflow/core/framework/dataset.h b/tensorflow/core/framework/dataset.h index 8dcb5f3076..7d3776a6ec 100644 --- a/tensorflow/core/framework/dataset.h +++ b/tensorflow/core/framework/dataset.h @@ -285,6 +285,7 @@ class IteratorContext { function_library(ctx->function_library()), lib(ctx->lib()), function_handle_cache(ctx->function_handle_cache()), + resource_mgr(ctx->resource_mgr()), model(ctx->model()), runner(*(ctx->runner())), runner_threadpool_size(ctx->runner_threadpool_size()), @@ -324,6 +325,10 @@ class IteratorContext { // A FunctionHandleCache that owns all the function handles. Not owned. FunctionHandleCache* function_handle_cache = nullptr; + // A resource manager for storing dataset-related state, e.g. random + // seeds or cached tensors. Not owned. + ResourceMgr* resource_mgr = nullptr; + // If non-null, identifies the object used for performance modeling. std::shared_ptr model = nullptr; @@ -363,6 +368,8 @@ class IteratorContext { return params_.function_handle_cache; } + ResourceMgr* resource_mgr() { return params_.resource_mgr; } + const std::shared_ptr& model() { return params_.model; } std::function)>* runner() { diff --git a/tensorflow/core/kernels/data/cache_dataset_ops.cc b/tensorflow/core/kernels/data/cache_dataset_ops.cc index ab85736890..f00b38e732 100644 --- a/tensorflow/core/kernels/data/cache_dataset_ops.cc +++ b/tensorflow/core/kernels/data/cache_dataset_ops.cc @@ -14,6 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/core/framework/dataset.h" #include "tensorflow/core/framework/partial_tensor_shape.h" +#include "tensorflow/core/framework/resource_mgr.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/env.h" @@ -564,9 +565,7 @@ class CacheDatasetOp : public UnaryDatasetOpKernel { class MemoryDataset : public DatasetBase { public: explicit MemoryDataset(OpKernelContext* ctx, const DatasetBase* input) - : DatasetBase(DatasetContext(ctx)), - input_(input), - cache_(new MemoryCache()) { + : DatasetBase(DatasetContext(ctx)), input_(input) { input->Ref(); } @@ -574,8 +573,8 @@ class CacheDatasetOp : public UnaryDatasetOpKernel { std::unique_ptr MakeIteratorInternal( const string& prefix) const override { - return std::unique_ptr(new MemoryIterator( - {this, strings::StrCat(prefix, "::MemoryCache")}, cache_)); + return std::unique_ptr( + new MemoryIterator({this, strings::StrCat(prefix, "::MemoryCache")})); } const DataTypeVector& output_dtypes() const override { @@ -611,10 +610,12 @@ class CacheDatasetOp : public UnaryDatasetOpKernel { // The expected use is that a single `MemoryWriterIterator` populates the // cache with dataset elements. Once all elements are cached, the cache can // be used by one or more `MemoryReaderIterator`s. - class MemoryCache { + class MemoryCache : public ResourceBase { public: MemoryCache() = default; + string DebugString() override { return "CacheDataset::MemoryCache"; } + // Marks the cache as completed. void Complete() { mutex_lock l(mu_); @@ -681,15 +682,25 @@ class CacheDatasetOp : public UnaryDatasetOpKernel { class MemoryIterator : public DatasetIterator { public: - explicit MemoryIterator(const Params& params, - const std::shared_ptr& cache) - : DatasetIterator(params), cache_(cache) { - mode_ = cache->MaybeClaim() ? Mode::write : Mode::read; - InitializeIterator(); - } + explicit MemoryIterator(const Params& params) + : DatasetIterator(params) {} + + ~MemoryIterator() override { cache_->Unref(); } Status Initialize(IteratorContext* ctx) override { mutex_lock l(mu_); + // Use the resource manager in the iterator context to get / create + // a cache. + ResourceMgr* mgr = ctx->resource_mgr(); + const string name = + strings::StrCat(prefix(), "::", dataset()->name(), "::MemoryCache"); + TF_RETURN_IF_ERROR(mgr->LookupOrCreate( + "tf_data", name, &cache_, [](MemoryCache** cache) { + *cache = new MemoryCache(); + return Status::OK(); + })); + mode_ = cache_->MaybeClaim() ? Mode::write : Mode::read; + InitializeIterator(); if (mode_ == Mode::read && !cache_->IsCompleted()) { return errors::Internal( "Cache should only be read after it has been completed."); @@ -788,8 +799,7 @@ class CacheDatasetOp : public UnaryDatasetOpKernel { private: class MemoryWriterIterator : public DatasetIterator { public: - explicit MemoryWriterIterator(const Params& params, - const std::shared_ptr& cache) + explicit MemoryWriterIterator(const Params& params, MemoryCache* cache) : DatasetIterator(params), cache_(cache) { CHECK(cache_); } @@ -848,13 +858,12 @@ class CacheDatasetOp : public UnaryDatasetOpKernel { private: mutex mu_; std::unique_ptr input_impl_ GUARDED_BY(mu_); - std::shared_ptr cache_; + MemoryCache* const cache_ GUARDED_BY(mu_); // not owned. }; // MemoryWriterIterator class MemoryReaderIterator : public DatasetIterator { public: - explicit MemoryReaderIterator(const Params& params, - const std::shared_ptr& cache) + explicit MemoryReaderIterator(const Params& params, MemoryCache* cache) : DatasetIterator(params), cache_(cache), index_(0) { CHECK(cache); } @@ -865,6 +874,7 @@ class CacheDatasetOp : public UnaryDatasetOpKernel { // thus we record the memory allocated for the cache here. The caveat // is that this is incorrect if there are concurrent instances of this // iterator. + tf_shared_lock l(mu_); for (size_t i = 0; i < cache_->size(); ++i) { RecordBufferEnqueue(ctx, cache_->at(i)); } @@ -914,7 +924,7 @@ class CacheDatasetOp : public UnaryDatasetOpKernel { private: mutex mu_; - const std::shared_ptr cache_; + MemoryCache* const cache_ GUARDED_BY(mu_); // not owned. size_t index_ GUARDED_BY(mu_); }; // MemoryReaderIterator @@ -931,14 +941,13 @@ class CacheDatasetOp : public UnaryDatasetOpKernel { } mutex mu_; - std::shared_ptr cache_; + MemoryCache* cache_ GUARDED_BY(mu_); // not owned. enum Mode { read, write }; Mode mode_ GUARDED_BY(mu_); std::unique_ptr iterator_ GUARDED_BY(mu_); }; // MemoryIterator const DatasetBase* const input_; - const std::shared_ptr cache_; }; // MemoryDataset }; // CacheDatasetOp diff --git a/tensorflow/core/kernels/data/iterator_ops.cc b/tensorflow/core/kernels/data/iterator_ops.cc index cb7477f9e2..d5b4bfa5c5 100644 --- a/tensorflow/core/kernels/data/iterator_ops.cc +++ b/tensorflow/core/kernels/data/iterator_ops.cc @@ -77,6 +77,7 @@ class IteratorResource : public ResourceBase { params.lib = captured_state->lib; params.function_handle_cache = captured_state->function_handle_cache.get(); + params.resource_mgr = &captured_state->resource_mgr; return captured_state->iterator->GetNext( IteratorContext(std::move(params)), out_tensors, end_of_sequence); } else { @@ -135,8 +136,8 @@ class IteratorResource : public ResourceBase { std::unique_ptr pflr(nullptr); TF_RETURN_IF_ERROR(ctx->function_library()->Clone(&flib_def, &pflr, &lib)); TF_RETURN_IF_ERROR(flib_def->AddLibrary(graph_def.library())); - std::unique_ptr new_state( - new State(std::move(flib_def), std::move(pflr), lib, nullptr)); + std::unique_ptr new_state(new State( + std::move(flib_def), std::move(pflr), lib, nullptr /* iterator */)); TF_RETURN_IF_ERROR( graph_runner.Run(&graph, new_state->lib, {}, {output_node}, &outputs)); @@ -145,6 +146,7 @@ class IteratorResource : public ResourceBase { IteratorContext::Params params(ctx); params.lib = new_state->lib; params.function_handle_cache = new_state->function_handle_cache.get(); + params.resource_mgr = &new_state->resource_mgr; TF_RETURN_IF_ERROR(dataset->MakeIterator(IteratorContext(std::move(params)), "Iterator", &new_state->iterator)); TF_RETURN_IF_ERROR( @@ -156,6 +158,7 @@ class IteratorResource : public ResourceBase { IteratorContext::Params params(ctx); params.lib = new_state->lib; params.function_handle_cache = new_state->function_handle_cache.get(); + params.resource_mgr = &new_state->resource_mgr; DeviceBase* device = new_state->lib->device(); params.allocator_getter = [device](AllocatorAttributes attrs) { return device->GetAllocator(attrs); @@ -180,7 +183,8 @@ class IteratorResource : public ResourceBase { tf_shared_lock l(mu_); new_state.reset(new State(iterator_state_->flib_def, iterator_state_->pflr, iterator_state_->lib, - nullptr, nullptr)); + nullptr /* function_handle_cache */, + nullptr /* iterator */)); } // Ensure that the iterator has access to all functions in the current @@ -212,6 +216,7 @@ class IteratorResource : public ResourceBase { IteratorContext::Params params(ctx); params.lib = new_state->lib; params.function_handle_cache = new_state->function_handle_cache.get(); + params.resource_mgr = &new_state->resource_mgr; TF_RETURN_IF_ERROR(dataset->MakeIterator(IteratorContext(std::move(params)), "Iterator", &iterator)); TF_RETURN_IF_ERROR( @@ -259,6 +264,7 @@ class IteratorResource : public ResourceBase { std::shared_ptr pflr; FunctionLibraryRuntime* lib = nullptr; // not owned. std::unique_ptr function_handle_cache; + ResourceMgr resource_mgr; std::unique_ptr iterator; }; diff --git a/tensorflow/core/kernels/data/multi_device_iterator_ops.cc b/tensorflow/core/kernels/data/multi_device_iterator_ops.cc index a070456414..ba2125a66e 100644 --- a/tensorflow/core/kernels/data/multi_device_iterator_ops.cc +++ b/tensorflow/core/kernels/data/multi_device_iterator_ops.cc @@ -98,6 +98,7 @@ class MultiDeviceIterator : public ResourceBase { IteratorContext::Params params(ctx); params.lib = lib_; params.function_handle_cache = function_handle_cache_.get(); + params.resource_mgr = &resource_mgr_; IteratorContext iter_ctx(std::move(params)); tf_shared_lock l(mu_); multi_device_buffer_->GetNextFromShard( @@ -125,6 +126,8 @@ class MultiDeviceIterator : public ResourceBase { return function_handle_cache_.get(); } + ResourceMgr* resource_mgr() { return &resource_mgr_; } + private: // A private class that uses a background thread to keep a per device buffer // full. @@ -350,6 +353,7 @@ class MultiDeviceIterator : public ResourceBase { const std::unique_ptr pflr_; FunctionLibraryRuntime* const lib_ = nullptr; // not owned. const std::unique_ptr function_handle_cache_; + ResourceMgr resource_mgr_; std::shared_ptr lib_def_ GUARDED_BY(mu_); int64 incarnation_id_ GUARDED_BY(mu_) = 0; @@ -477,6 +481,7 @@ class MultiDeviceIteratorInitOp : public OpKernel { IteratorContext::Params params(ctx); params.lib = resource->lib(); params.function_handle_cache = resource->function_handle_cache(); + params.resource_mgr = resource->resource_mgr(); IteratorContext iter_ctx(std::move(params)); OP_REQUIRES_OK( ctx, dataset->MakeIterator(std::move(iter_ctx), "Iterator", &iterator)); diff --git a/tensorflow/core/kernels/data/shuffle_dataset_op.cc b/tensorflow/core/kernels/data/shuffle_dataset_op.cc index 3cf005dbda..7134793e26 100644 --- a/tensorflow/core/kernels/data/shuffle_dataset_op.cc +++ b/tensorflow/core/kernels/data/shuffle_dataset_op.cc @@ -18,6 +18,7 @@ limitations under the License. #include "tensorflow/core/framework/dataset.h" #include "tensorflow/core/framework/partial_tensor_shape.h" +#include "tensorflow/core/framework/resource_mgr.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/lib/random/philox_random.h" #include "tensorflow/core/lib/random/random.h" @@ -70,9 +71,9 @@ class ShuffleDatasetOpBase : public UnaryDatasetOpKernel { explicit Iterator(const typename DatasetIterator::Params& params, int64 seed, int64 seed2) : DatasetIterator(params), - input_impl_(nullptr), seed_(seed), seed2_(seed2), + input_impl_(nullptr), epoch_(0), num_elements_(0), parent_generator_(seed, seed2), @@ -174,6 +175,14 @@ class ShuffleDatasetOpBase : public UnaryDatasetOpKernel { /*ratio=*/1); } + void ResetRngs() EXCLUSIVE_LOCKS_REQUIRED(mu_) { + // Reset the generators based on the current iterator seeds. + parent_generator_ = random::PhiloxRandom(seed_, seed2_); + generator_ = random::SingleSampleAdapter( + &parent_generator_); + generator_.Skip(num_random_samples_); + } + Status SaveInternal(IteratorStateWriter* writer) override { mutex_lock l(mu_); // Save state needed to restore the random number generators. @@ -281,6 +290,10 @@ class ShuffleDatasetOpBase : public UnaryDatasetOpKernel { return Status::OK(); } + mutex mu_; + int64 seed_ GUARDED_BY(mu_); + int64 seed2_ GUARDED_BY(mu_); + private: // Used to represent slices of `buffer_` that belong to different epochs. // The invariant maintained by the implementation is: `start` <= `end`. @@ -301,19 +314,8 @@ class ShuffleDatasetOpBase : public UnaryDatasetOpKernel { return out; } - void ResetRngs() EXCLUSIVE_LOCKS_REQUIRED(mu_) { - // Reset the generators based on the current iterator seeds. - parent_generator_ = random::PhiloxRandom(seed_, seed2_); - generator_ = random::SingleSampleAdapter( - &parent_generator_); - generator_.Skip(num_random_samples_); - } - - mutex mu_; std::unique_ptr[]> buffer_ GUARDED_BY(mu_); std::unique_ptr input_impl_ GUARDED_BY(mu_); - int64 seed_ GUARDED_BY(mu_); - int64 seed2_ GUARDED_BY(mu_); int64 epoch_ GUARDED_BY(mu_); int64 num_elements_ GUARDED_BY(mu_); std::deque> slices_ GUARDED_BY(mu_); @@ -370,7 +372,7 @@ class ShuffleDatasetOp : public ShuffleDatasetOpBase { } private: - // A dataset that uses a pseduorandom sequence of seeds for the iterators + // A dataset that uses a pseudorandom sequence of seeds for the iterators // created from it. Used when `reshuffle_each_iteration` is true. class ReshufflingDataset : public ShuffleDatasetBase { public: @@ -378,37 +380,114 @@ class ShuffleDatasetOp : public ShuffleDatasetOpBase { int64 buffer_size, int64 seed, int64 seed2, int64 count) : ShuffleDatasetBase(ctx, input, buffer_size, count), seed_(seed), - seed2_(seed2), - parent_generator_(seed, seed2), - generator_(&parent_generator_) {} + seed2_(seed2) {} string DebugString() const override { - mutex_lock l(mu_); return strings::StrCat("ShuffleDatasetOp(", buffer_size_, ", ", seed_, ", ", seed2_, ")::ReshufflingDataset"); } std::unique_ptr MakeIteratorInternal( const string& prefix) const override { - int64 iterator_seed; - int64 iterator_seed2; - { - mutex_lock l(mu_); - iterator_seed = Random(); - iterator_seed2 = Random(); - } - return std::unique_ptr( - new Iterator({this, strings::StrCat(prefix, "::Shuffle")}, - iterator_seed, iterator_seed2)); + return std::unique_ptr(new Iterator( + {this, strings::StrCat(prefix, "::Shuffle")}, seed_, seed2_)); } protected: + class RandomSeedGenerator : public ResourceBase { + public: + RandomSeedGenerator(int64 seed, int64 seed2) + : seed_(seed), + seed2_(seed2), + parent_generator_(seed, seed2), + generator_(&parent_generator_) {} + + string DebugString() override { + return "ReshufflingDataset::RandomSeedGenerator"; + } + + void GenerateRandomSeeds(int64* seed1, int64* seed2) { + mutex_lock l(mu_); + num_random_samples_++; + *seed1 = generator_(); + num_random_samples_++; + *seed2 = generator_(); + } + + int64 num_random_samples() { + tf_shared_lock l(mu_); + return num_random_samples_; + } + + void set_num_random_samples(int64 num_random_samples) { + mutex_lock l(mu_); + num_random_samples_ = num_random_samples; + } + + void Reset() { + mutex_lock l(mu_); + // Reset the generators based on the current seeds. + parent_generator_ = random::PhiloxRandom(seed_, seed2_); + generator_ = random::SingleSampleAdapter( + &parent_generator_); + generator_.Skip(num_random_samples_); + } + + private: + const int64 seed_; + const int64 seed2_; + mutex mu_; + random::PhiloxRandom parent_generator_ GUARDED_BY(mu_); + random::SingleSampleAdapter generator_ + GUARDED_BY(mu_); + int64 num_random_samples_ GUARDED_BY(mu_) = 0; + }; + class Iterator : public ShuffleDatasetBase::Iterator { public: explicit Iterator(const Params& params, int64 seed, int64 seed2) : ShuffleDatasetBase::Iterator(params, seed, seed2) {} + ~Iterator() override { seed_generator_->Unref(); } + + Status Initialize(IteratorContext* ctx) override { + // Firstly, lookup or create a seed generator from the IteratorResource + // resource_mgr. + ResourceMgr* mgr = ctx->resource_mgr(); + RandomSeedGenerator* seed_generator; + const string name = strings::StrCat(prefix(), "::", dataset()->name(), + "::RandomSeedGenerator"); + + int64 dataset_seed, dataset_seed2; + { + tf_shared_lock l(mu_); + // Ideally we'd like to hold this lock in the LookupOrCreate method, + // but that trips up our Deadlock detection code. + dataset_seed = seed_; + dataset_seed2 = seed2_; + } + TF_RETURN_IF_ERROR(mgr->LookupOrCreate( + "tf_data", name, &seed_generator, + [dataset_seed, + dataset_seed2](RandomSeedGenerator** seed_generator) { + // On the first iterator creation, use the original seeds from the + // dataset to seed a `RandomSeedGenerator` that will provide seeds + // for subsequent repetitions of the same dataset. + *seed_generator = + new RandomSeedGenerator(dataset_seed, dataset_seed2); + return Status::OK(); + })); + // Now use the seed generator to update the base class Iterator seeds + // and random number generator with generated seeds for the current + // repetition. + mutex_lock l(mu_); + seed_generator->GenerateRandomSeeds(&seed_, &seed2_); + ResetRngs(); + seed_generator_ = seed_generator; + return Status::OK(); + } + protected: std::shared_ptr CreateNode( IteratorContext* ctx, model::Node::Args args) const override { @@ -417,12 +496,10 @@ class ShuffleDatasetOp : public ShuffleDatasetOpBase { } Status SaveInternal(IteratorStateWriter* writer) override { - mutex_lock l(dataset()->mu_); - // Save RNG state of Dataset. TF_RETURN_IF_ERROR( writer->WriteScalar(full_name("ds_num_random_samples"), - dataset()->num_random_samples_)); + seed_generator_->num_random_samples())); // Save the Iterator. return ShuffleDatasetBase::Iterator::SaveInternal( @@ -431,24 +508,25 @@ class ShuffleDatasetOp : public ShuffleDatasetOpBase { Status RestoreInternal(IteratorContext* ctx, IteratorStateReader* reader) override { - mutex_lock l(dataset()->mu_); - // Restore RNG state of Dataset. - TF_RETURN_IF_ERROR( - reader->ReadScalar(full_name("ds_num_random_samples"), - &dataset()->num_random_samples_)); - dataset()->ResetRngs(); + int64 num_random_samples; + TF_RETURN_IF_ERROR(reader->ReadScalar( + full_name("ds_num_random_samples"), &num_random_samples)); + seed_generator_->set_num_random_samples(num_random_samples); + seed_generator_->Reset(); // Restore the Iterator. return ShuffleDatasetBase::Iterator< ReshufflingDataset>::RestoreInternal(ctx, reader); } + + private: + RandomSeedGenerator* seed_generator_; }; Status AsGraphDefInternal(SerializationContext* ctx, DatasetGraphDefBuilder* b, Node** output) const override { - mutex_lock l(mu_); Node* input_graph_node = nullptr; TF_RETURN_IF_ERROR(b->AddInputDataset(ctx, input_, &input_graph_node)); Node* buffer_size = nullptr; @@ -469,28 +547,8 @@ class ShuffleDatasetOp : public ShuffleDatasetOpBase { } private: - random::SingleSampleAdapter::ResultType Random() const - EXCLUSIVE_LOCKS_REQUIRED(mu_) { - num_random_samples_++; - auto out = generator_(); - return out; - } - - void ResetRngs() const EXCLUSIVE_LOCKS_REQUIRED(mu_) { - // Reset the generators based on the current seeds. - parent_generator_ = random::PhiloxRandom(seed_, seed2_); - generator_ = - random::SingleSampleAdapter(&parent_generator_); - generator_.Skip(num_random_samples_); - } - - mutable int64 seed_ GUARDED_BY(mu_); - mutable int64 seed2_ GUARDED_BY(mu_); - mutable mutex mu_; - mutable random::PhiloxRandom parent_generator_ GUARDED_BY(mu_); - mutable random::SingleSampleAdapter generator_ - GUARDED_BY(mu_); - mutable int64 num_random_samples_ GUARDED_BY(mu_) = 0; + const int64 seed_; + const int64 seed2_; }; // A dataset that uses the same fixed seed for all iterators created from it. -- GitLab From 983b7403151f07740fa81ef9a58b7f3768e9431f Mon Sep 17 00:00:00 2001 From: Rohan Jain Date: Thu, 6 Dec 2018 17:14:35 -0800 Subject: [PATCH 178/873] Adding a whitelited_stateful_ops argument to the old Defun in which stateful ops belonging to this whitelist are recreated instead of being re-used when captured by value. This is useful for dataset make_one_shot_iterator which captures all inputs by value. PiperOrigin-RevId: 224436333 --- tensorflow/python/framework/function.py | 40 +++++++++++++++----- tensorflow/python/framework/function_test.py | 22 +++++++++++ 2 files changed, 53 insertions(+), 9 deletions(-) diff --git a/tensorflow/python/framework/function.py b/tensorflow/python/framework/function.py index 622686ce00..cfdc915a1b 100644 --- a/tensorflow/python/framework/function.py +++ b/tensorflow/python/framework/function.py @@ -209,6 +209,7 @@ class _DefinedFunction(object): out_names=None, shape_func=None, capture_by_value=False, + whitelisted_stateful_ops=None, **kwargs): """Creates _DefinedFunction. @@ -229,6 +230,8 @@ class _DefinedFunction(object): output shapes. capture_by_value: Boolean (defaults to False). If True, captured values will be copied into the function body. + whitelisted_stateful_ops: A set of ops that if stateful we ignore and + copy into the function body, when `capture_by_value` is True. **kwargs: The keyword arguments. **kwargs is passed to every call site of this function. @@ -244,6 +247,9 @@ class _DefinedFunction(object): self._out_names = out_names self._shape_func = shape_func self._capture_by_value = capture_by_value + self._whitelisted_stateful_ops = whitelisted_stateful_ops + if self._whitelisted_stateful_ops is None: + self._whitelisted_stateful_ops = set() self._extra_kwargs = kwargs # Constructed only when C API is disabled, lazily self._definition = None @@ -340,8 +346,13 @@ class _DefinedFunction(object): return temp_graph = func_graph_from_py_func( - self._func, self._arg_names, self._arg_types, self._func_name, - self._capture_by_value, self._caller_device) + self._func, + self._arg_names, + self._arg_types, + self._func_name, + self._capture_by_value, + self._caller_device, + whitelisted_stateful_ops=self._whitelisted_stateful_ops) self._extra_inputs = temp_graph.extra_inputs # pylint: disable=protected-access @@ -625,9 +636,11 @@ class _FuncGraph(ops.Graph): function argument and the caller passes in the captured tensor. """ - def __init__(self, name, capture_by_value, *args, **kwargs): + def __init__(self, name, capture_by_value, whitelisted_stateful_ops, *args, + **kwargs): super(_FuncGraph, self).__init__(*args, **kwargs) self._capture_by_value = capture_by_value + self._whitelisted_stateful_ops = whitelisted_stateful_ops self._building_function = True self._outer_graph = ops.get_default_graph() self._vscope = vs.get_variable_scope() @@ -785,7 +798,7 @@ class _FuncGraph(ops.Graph): # pylint: disable=protected-access op_def = graph_to_function_def._get_op_def(op) # pylint: enable=protected-access - if op_def.is_stateful: + if op_def.is_stateful and op not in self._whitelisted_stateful_ops: raise ValueError("Cannot capture a stateful node (name:%s, type:%s) " "by value." % (op.name, op.type)) elif op.type in ("Placeholder", "PlaceholderV2"): @@ -807,10 +820,17 @@ class _FuncGraph(ops.Graph): return captured_op -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, arg_shapes=None): +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, + arg_shapes=None, + whitelisted_stateful_ops=None): """Returns a _FuncGraph generated from `func`. Args: @@ -828,6 +848,8 @@ def func_graph_from_py_func(func, arg_names, arg_types, name=None, collections_ref: A reference to a collections dict the _FuncGraph should use internally. arg_shapes: A sequence of the function's argument shapes. + whitelisted_stateful_ops: A set of ops that if stateful we ignore and + re-create. Returns: A _FuncGraph. @@ -837,7 +859,7 @@ def func_graph_from_py_func(func, arg_names, arg_types, name=None, """ if not name: name = function_utils.get_func_name(func) - func_graph = _FuncGraph(name, capture_by_value) + func_graph = _FuncGraph(name, capture_by_value, whitelisted_stateful_ops) with func_graph.as_default(), ops.device(device): # pylint: disable=protected-access diff --git a/tensorflow/python/framework/function_test.py b/tensorflow/python/framework/function_test.py index d71f06ea52..6ec71ba8e9 100644 --- a/tensorflow/python/framework/function_test.py +++ b/tensorflow/python/framework/function_test.py @@ -1054,6 +1054,28 @@ class FunctionTest(test.TestCase): self.assertFalse(all(val3 == val1)) self.assertFalse(all(val4 == val2)) + def testStatefulFunctionWithWhitelisting(self): + t = random_ops.random_uniform([100], maxval=10, dtype=dtypes.int32) + + @function.Defun(capture_by_value=True) + def StatefulFn(): + return t + constant_op.constant(3, dtype=dtypes.int32) + + # First time we try to capture a stateful RandomUniform op. + with self.assertRaisesRegexp(ValueError, "Cannot capture a stateful node"): + res = StatefulFn() + + # This time we whitelist this op, so that its recreated. + @function.Defun(capture_by_value=True, whitelisted_stateful_ops=set([t.op])) + def StatefulFn2(): + return t + constant_op.constant(3, dtype=dtypes.int32) + + res = StatefulFn2() + with session.Session() as sess: + r = sess.run(res) + for i in r: + self.assertGreaterEqual(i, 3) + @test_util.run_deprecated_v1 def testSameFunctionOnTwoDevices(self): -- GitLab From 9e4dca5db91d9ac587514cd4cf19f334130f9837 Mon Sep 17 00:00:00 2001 From: Gaurav Jain Date: Thu, 6 Dec 2018 17:27:18 -0800 Subject: [PATCH 179/873] Make SelfAdjointEigGradTest use gradient_checker_v2 PiperOrigin-RevId: 224437857 --- .../kernel_tests/self_adjoint_eig_op_test.py | 60 +++++++++---------- 1 file changed, 29 insertions(+), 31 deletions(-) diff --git a/tensorflow/python/kernel_tests/self_adjoint_eig_op_test.py b/tensorflow/python/kernel_tests/self_adjoint_eig_op_test.py index 42577f7e42..323d14b7d8 100644 --- a/tensorflow/python/kernel_tests/self_adjoint_eig_op_test.py +++ b/tensorflow/python/kernel_tests/self_adjoint_eig_op_test.py @@ -23,7 +23,7 @@ import numpy as np from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes as dtypes_lib from tensorflow.python.ops import array_ops -from tensorflow.python.ops import gradient_checker +from tensorflow.python.ops import gradient_checker_v2 from tensorflow.python.ops import linalg_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops @@ -185,53 +185,51 @@ def _GetSelfAdjointEigGradTest(dtype_, shape_, compute_v_): n = shape_[-1] batch_shape = shape_[:-2] np_dtype = dtype_.as_numpy_dtype - a = np.random.uniform( - low=-1.0, high=1.0, size=n * n).reshape([n, n]).astype(np_dtype) - if dtype_.is_complex: - a += 1j * np.random.uniform( + + def RandomInput(): + a = np.random.uniform( low=-1.0, high=1.0, size=n * n).reshape([n, n]).astype(np_dtype) - a += np.conj(a.T) - a = np.tile(a, batch_shape + (1, 1)) + if dtype_.is_complex: + a += 1j * np.random.uniform( + low=-1.0, high=1.0, size=n * n).reshape([n, n]).astype(np_dtype) + a += np.conj(a.T) + a = np.tile(a, batch_shape + (1, 1)) + return a + # Optimal stepsize for central difference is O(epsilon^{1/3}). epsilon = np.finfo(np_dtype).eps delta = 0.1 * epsilon**(1.0 / 3.0) # tolerance obtained by looking at actual differences using # np.linalg.norm(theoretical-numerical, np.inf) on -mavx build + # after discarding one random input sample + _ = RandomInput() if dtype_ in (dtypes_lib.float32, dtypes_lib.complex64): tol = 1e-2 else: tol = 1e-7 with self.session(use_gpu=True): - tf_a = constant_op.constant(a) - if compute_v_: - tf_e, tf_v = linalg_ops.self_adjoint_eig(tf_a) + def Compute(x): + e, v = linalg_ops.self_adjoint_eig(x) # (complex) Eigenvectors are only unique up to an arbitrary phase # We normalize the vectors such that the first component has phase 0. - top_rows = tf_v[..., 0:1, :] - if tf_a.dtype.is_complex: + top_rows = v[..., 0:1, :] + if dtype_.is_complex: angle = -math_ops.angle(top_rows) phase = math_ops.complex(math_ops.cos(angle), math_ops.sin(angle)) else: phase = math_ops.sign(top_rows) - tf_v *= phase - outputs = [tf_e, tf_v] + v *= phase + return e, v + + if compute_v_: + funcs = [lambda x: Compute(x)[0], lambda x: Compute(x)[1]] else: - tf_e = linalg_ops.self_adjoint_eigvals(tf_a) - outputs = [tf_e] - for b in outputs: - x_init = np.random.uniform( - low=-1.0, high=1.0, size=n * n).reshape([n, n]).astype(np_dtype) - if dtype_.is_complex: - x_init += 1j * np.random.uniform( - low=-1.0, high=1.0, size=n * n).reshape([n, n]).astype(np_dtype) - x_init += np.conj(x_init.T) - x_init = np.tile(x_init, batch_shape + (1, 1)) - theoretical, numerical = gradient_checker.compute_gradient( - tf_a, - tf_a.get_shape().as_list(), - b, - b.get_shape().as_list(), - x_init_value=x_init, + funcs = [linalg_ops.self_adjoint_eigvals] + + for f in funcs: + theoretical, numerical = gradient_checker_v2.compute_gradient( + f, + [RandomInput()], delta=delta) self.assertAllClose(theoretical, numerical, atol=tol, rtol=tol) @@ -245,7 +243,7 @@ if __name__ == "__main__": for size in 1, 2, 5, 10: for batch_dims in [(), (3,)] + [(3, 2)] * (max(size, size) < 10): shape = batch_dims + (size, size) - name = "%s_%s_%s" % (dtype, "_".join(map(str, shape)), compute_v) + name = "%s_%s_%s" % (dtype.name, "_".join(map(str, shape)), compute_v) _AddTest(SelfAdjointEigTest, "SelfAdjointEig", name, _GetSelfAdjointEigTest(dtype, shape, compute_v)) _AddTest(SelfAdjointEigGradTest, "SelfAdjointEigGrad", name, -- GitLab From 24cd8686127694c5fadc1abc3e5183c7f8e2bb87 Mon Sep 17 00:00:00 2001 From: Jianwei Xie Date: Thu, 6 Dec 2018 18:02:42 -0800 Subject: [PATCH 180/873] Fixed the metrics for TPUStrategy with Keras. And added a test for that. PiperOrigin-RevId: 224441940 --- .../contrib/distribute/python/keras_test.py | 16 ++++++++++++---- .../python/keras/engine/training_distributed.py | 14 ++++++++++---- 2 files changed, 22 insertions(+), 8 deletions(-) diff --git a/tensorflow/contrib/distribute/python/keras_test.py b/tensorflow/contrib/distribute/python/keras_test.py index 796c4ed9f6..771c48bdd8 100644 --- a/tensorflow/contrib/distribute/python/keras_test.py +++ b/tensorflow/contrib/distribute/python/keras_test.py @@ -165,7 +165,9 @@ def get_multi_inputs_multi_outputs_data(): return (train_data, test_data) -def batch_wrapper(dataset, batch_size, distribution): +def batch_wrapper(dataset, batch_size, distribution, repeat=None): + if repeat: + dataset = dataset.repeat(repeat) # TPUs currently require fully defined input shapes, drop_remainder ensures # the input will have fully defined shapes. if isinstance(distribution, tpu_strategy.TPUStrategy): @@ -216,6 +218,7 @@ def get_correctness_test_inputs(use_numpy, use_validation_data, with_distribution, x_train, y_train, x_predict): """Generates the inputs for correctness check when enable Keras with DS.""" + training_epochs = 2 global_batch_size = 64 batch_size = global_batch_size # TODO(b/118776054): Use global batch size for Keras/DS support. @@ -231,7 +234,7 @@ def get_correctness_test_inputs(use_numpy, use_validation_data, 'batch_size': batch_size, 'x': x_train, 'y': y_train, - 'epochs': 1, + 'epochs': training_epochs, 'shuffle': False, } @@ -252,13 +255,14 @@ def get_correctness_test_inputs(use_numpy, use_validation_data, # keras.fit/evaluate/predict. The batch size is part of the dataset. train_dataset = dataset_ops.Dataset.from_tensor_slices( (x_train, y_train)) - x = batch_wrapper(train_dataset, batch_size, with_distribution) + x = batch_wrapper( + train_dataset, batch_size, with_distribution, repeat=training_epochs) training_inputs = { 'batch_size': None, 'x': x, 'y': None, - 'epochs': 1, + 'epochs': training_epochs, 'shuffle': False, 'steps_per_epoch': len(x_train) // global_batch_size, } @@ -1301,11 +1305,14 @@ class TestDistributionStrategyCorrectness(test.TestCase, with self.cached_session(): tolerance = 1e-5 + metrics = ['mse'] if isinstance(distribution, (mirrored_strategy.MirroredStrategy, mirrored_strategy.CoreMirroredStrategy)): # TODO(b/119257215): use the default one once the flakyness is fixed. tolerance = 1e-4 + # TODO(b/120570676): Enable metrics check once the bug is fixed. + metrics = None keras.backend.set_image_data_format('channels_last') np.random.seed(_RANDOM_SEED) @@ -1346,6 +1353,7 @@ class TestDistributionStrategyCorrectness(test.TestCase, model.compile( loss=keras.losses.mean_squared_error, optimizer=gradient_descent_keras.SGD(0.5), + metrics=metrics, distribute=with_distribution) training_inputs, eval_inputs, predict_inputs = ( diff --git a/tensorflow/python/keras/engine/training_distributed.py b/tensorflow/python/keras/engine/training_distributed.py index fc72f6bd15..d20d092d8e 100644 --- a/tensorflow/python/keras/engine/training_distributed.py +++ b/tensorflow/python/keras/engine/training_distributed.py @@ -350,11 +350,18 @@ def experimental_test_loop(model, for step in range(steps): _, batch_outs = K.get_session().run([test_op, output_tensors]) for i, label in enumerate(model.metrics_names): - outs[i] += batch_outs[label] + if i == 0: + # Loss is stateless metrics. + outs[i] += batch_outs[label] + else: + # For all stateful metrics, the aggregation is handled by mirrored vars. + outs[i] = batch_outs[label] + if verbose >= 1: progbar.update(step + 1) - for i in range(len(outs)): - outs[i] /= (steps) + + if len(outs) >= 0: + outs[0] /= (steps) if initialize_finalize_strategy: K.get_session().run(current_strategy.finalize()) @@ -724,4 +731,3 @@ def _reset_metrics(model, distributed_model=None): distributed_model or model._distribution_strategy.unwrap(model._grouped_model)[0]) distributed_model.reset_metrics() - -- GitLab From 3640da49c3731807a3dbc27d813e8ab68a86328a Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 6 Dec 2018 18:38:24 -0800 Subject: [PATCH 181/873] Use tpu.rewrite() instead of tpu.rewrite_for_inference() in tpu_estimator.py for TPUEstimator.export_savedmodel(). PiperOrigin-RevId: 224445962 --- tensorflow/contrib/tpu/python/tpu/tpu_estimator.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index 84816d70d0..96b9556e13 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -2234,7 +2234,7 @@ class TPUEstimator(estimator_lib.Estimator): def computation(): """Compute tpu tensors used in export_outputs. - Passed to rewrite_for_inference so that model_fn will be called under + Passed to rewrite so that model_fn will be called under the rewriting contexts. Only tpu tensors are returned, but export_outputs and scaffold are captured. @@ -2243,7 +2243,7 @@ class TPUEstimator(estimator_lib.Estimator): outside_compilation. """ # We should only call model fn once and it should be inside `computation` - # so that building the graph will happen under `rewrite_for_inference`. + # so that building the graph will happen under `rewrite`. mode = model_fn_lib.ModeKeys.PREDICT estimator_spec = self._call_model_fn(features, labels, mode, config) @@ -2260,7 +2260,7 @@ class TPUEstimator(estimator_lib.Estimator): capture.capture((estimator_spec, tensors_dict, tensors)) return tpu_tensors - tpu_tensors_on_cpu = tpu.rewrite_for_inference(computation) + tpu_tensors_on_cpu = tpu.rewrite(computation) estimator_spec, tensors_dict, tensors = capture.get() # Reconstruct `tensors`, but with `tpu_tensors` replaced with -- GitLab From 607d43181c55cb17eab67497c66384ddf66fdd2f Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Thu, 6 Dec 2018 21:59:20 -0800 Subject: [PATCH 182/873] Set bazel version to 0.20.0 --- tensorflow/tools/ci_build/install/install_bazel.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/tools/ci_build/install/install_bazel.sh b/tensorflow/tools/ci_build/install/install_bazel.sh index 7472053209..f45ac3eab3 100755 --- a/tensorflow/tools/ci_build/install/install_bazel.sh +++ b/tensorflow/tools/ci_build/install/install_bazel.sh @@ -15,7 +15,7 @@ # ============================================================================== # Select bazel version. -BAZEL_VERSION="0.18.0" +BAZEL_VERSION="0.20.0" set +e local_bazel_ver=$(bazel version 2>&1 | grep -i label | awk '{print $3}') -- GitLab From b34707000d4cd408f4e286dc083ae0328b98009a Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Thu, 6 Dec 2018 21:59:35 -0800 Subject: [PATCH 183/873] Set bazel version to 0.20.0 --- tensorflow/tools/ci_build/install/install_bazel_from_source.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/tools/ci_build/install/install_bazel_from_source.sh b/tensorflow/tools/ci_build/install/install_bazel_from_source.sh index 4f83815d77..9501a6d94b 100755 --- a/tensorflow/tools/ci_build/install/install_bazel_from_source.sh +++ b/tensorflow/tools/ci_build/install/install_bazel_from_source.sh @@ -18,7 +18,7 @@ # It will compile bazel from source and install it in /usr/local/bin # Select bazel version. -BAZEL_VERSION="0.18.0" +BAZEL_VERSION="0.20.0" set +e local_bazel_ver=$(bazel version 2>&1 | grep -i label | awk '{print $3}') -- GitLab From 55bbb4c92567732ee6712c0201b94bef50df6083 Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Thu, 6 Dec 2018 21:59:54 -0800 Subject: [PATCH 184/873] Set bazel version to 0.20.0 --- tensorflow/tools/docker/Dockerfile.devel | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/tools/docker/Dockerfile.devel b/tensorflow/tools/docker/Dockerfile.devel index 5ddcd3a2fd..9ea29c0e20 100644 --- a/tensorflow/tools/docker/Dockerfile.devel +++ b/tensorflow/tools/docker/Dockerfile.devel @@ -65,7 +65,7 @@ RUN echo "startup --batch" >>/etc/bazel.bazelrc RUN echo "build --spawn_strategy=standalone --genrule_strategy=standalone" \ >>/etc/bazel.bazelrc # Install the most recent bazel release. -ENV BAZEL_VERSION 0.18.0 +ENV BAZEL_VERSION 0.20.0 WORKDIR / RUN mkdir /bazel && \ cd /bazel && \ -- GitLab From f674bcc9d8e057406c727fe6449053356c69d598 Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Thu, 6 Dec 2018 22:00:14 -0800 Subject: [PATCH 185/873] Set bazel version to 0.20.0 --- tensorflow/tools/docker/Dockerfile.devel-gpu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/tools/docker/Dockerfile.devel-gpu b/tensorflow/tools/docker/Dockerfile.devel-gpu index 767e5f4a4f..1ad359ddcc 100644 --- a/tensorflow/tools/docker/Dockerfile.devel-gpu +++ b/tensorflow/tools/docker/Dockerfile.devel-gpu @@ -87,7 +87,7 @@ RUN echo "startup --batch" >>/etc/bazel.bazelrc RUN echo "build --spawn_strategy=standalone --genrule_strategy=standalone" \ >>/etc/bazel.bazelrc # Install the most recent bazel release. -ENV BAZEL_VERSION 0.18.0 +ENV BAZEL_VERSION 0.20.0 WORKDIR / RUN mkdir /bazel && \ cd /bazel && \ -- GitLab From 15a1ba9bdc56ef3e32bd7e0f86480f1a8d9af3ec Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Thu, 6 Dec 2018 22:00:32 -0800 Subject: [PATCH 186/873] Set bazel version to 0.20.0 --- tensorflow/tools/docker/Dockerfile.devel-mkl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/tools/docker/Dockerfile.devel-mkl b/tensorflow/tools/docker/Dockerfile.devel-mkl index 0980502bcc..4eefd31d00 100755 --- a/tensorflow/tools/docker/Dockerfile.devel-mkl +++ b/tensorflow/tools/docker/Dockerfile.devel-mkl @@ -88,7 +88,7 @@ RUN echo "startup --batch" >>/etc/bazel.bazelrc RUN echo "build --spawn_strategy=standalone --genrule_strategy=standalone" \ >>/etc/bazel.bazelrc # Install the most recent bazel release. -ENV BAZEL_VERSION 0.18.0 +ENV BAZEL_VERSION 0.20.0 WORKDIR / RUN mkdir /bazel && \ cd /bazel && \ -- GitLab From 234959092788197d674f9c49495a979f47f75a7b Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Thu, 6 Dec 2018 22:00:47 -0800 Subject: [PATCH 187/873] Set bazel version to 0.20.0 --- tensorflow/tools/docker/Dockerfile.devel-mkl-horovod | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/tools/docker/Dockerfile.devel-mkl-horovod b/tensorflow/tools/docker/Dockerfile.devel-mkl-horovod index 90db249e3d..3810daefa5 100755 --- a/tensorflow/tools/docker/Dockerfile.devel-mkl-horovod +++ b/tensorflow/tools/docker/Dockerfile.devel-mkl-horovod @@ -79,7 +79,7 @@ RUN echo "startup --batch" >>/etc/bazel.bazelrc RUN echo "build --spawn_strategy=standalone --genrule_strategy=standalone" \ >>/etc/bazel.bazelrc # Install the most recent bazel release. -ENV BAZEL_VERSION 0.18.0 +ENV BAZEL_VERSION 0.20.0 WORKDIR / RUN mkdir /bazel && \ cd /bazel && \ -- GitLab From 9408b2835c84f7bd1ad25b95b2cb85682ee3f348 Mon Sep 17 00:00:00 2001 From: James Ring Date: Thu, 6 Dec 2018 22:01:21 -0800 Subject: [PATCH 188/873] Add the beginnings of ::tensorflow::Env support for C. PiperOrigin-RevId: 224461412 --- tensorflow/c/BUILD | 49 +++++++++++++ tensorflow/c/env.cc | 147 ++++++++++++++++++++++++++++++++++++++ tensorflow/c/env.h | 148 +++++++++++++++++++++++++++++++++++++++ tensorflow/c/env_test.cc | 94 +++++++++++++++++++++++++ tensorflow/core/BUILD | 5 +- 5 files changed, 442 insertions(+), 1 deletion(-) create mode 100644 tensorflow/c/env.cc create mode 100644 tensorflow/c/env.h create mode 100644 tensorflow/c/env_test.cc diff --git a/tensorflow/c/BUILD b/tensorflow/c/BUILD index ad2ae08a37..25df970eca 100644 --- a/tensorflow/c/BUILD +++ b/tensorflow/c/BUILD @@ -175,6 +175,34 @@ tf_cuda_library( ], ) +tf_cuda_library( + name = "env", + srcs = [ + "env.cc", + ], + hdrs = [ + "env.h", + ], + copts = tf_copts(), + visibility = ["//visibility:public"], + deps = select({ + "//tensorflow:android": [ + ":c_api", + ":tf_status_helper", + "//tensorflow/core:android_tensorflow_lib_lite", + "//tensorflow/core:platform_env", + "//tensorflow/core:lib", + ], + "//conditions:default": [ + ":c_api", + ":tf_status_helper", + "//tensorflow/core:framework", + "//tensorflow/core:platform_env", + "//tensorflow/core:lib", + ], + }) + [":c_api_internal"], +) + tf_cuda_library( name = "kernels", srcs = [ @@ -334,6 +362,27 @@ tf_kernel_library( alwayslink = 1, ) +tf_cuda_cc_test( + name = "env_test", + size = "small", + srcs = ["env_test.cc"], + linkopts = select({ + "//tensorflow:darwin": ["-headerpad_max_install_names"], + "//conditions:default": [], + }), + tags = ["noasan"], + # We must ensure that the dependencies can be dynamically linked since + # the shared library must be able to use core:framework. + # linkstatic = tf_kernel_tests_linkstatic(), + deps = [ + ":c_api", + ":env", + "//tensorflow/core:lib", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) + tf_cuda_cc_test( name = "kernels_test", size = "small", diff --git a/tensorflow/c/env.cc b/tensorflow/c/env.cc new file mode 100644 index 0000000000..fcff1e413d --- /dev/null +++ b/tensorflow/c/env.cc @@ -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. +==============================================================================*/ + +#include "tensorflow/c/env.h" + +#include "tensorflow/c/c_api_internal.h" +#include "tensorflow/c/tf_status_helper.h" +#include "tensorflow/core/platform/env.h" +#include "tensorflow/core/platform/types.h" + +struct TF_StringStream { + std::vector<::tensorflow::string>* list; + size_t position; +}; + +void TF_CreateDir(const char* dirname, TF_Status* status) { + TF_SetStatus(status, TF_OK, ""); + ::tensorflow::Set_TF_Status_from_Status( + status, ::tensorflow::Env::Default()->CreateDir(dirname)); +} + +void TF_DeleteDir(const char* dirname, TF_Status* status) { + TF_SetStatus(status, TF_OK, ""); + ::tensorflow::Set_TF_Status_from_Status( + status, ::tensorflow::Env::Default()->DeleteDir(dirname)); +} + +void TF_DeleteRecursively(const char* dirname, int64_t* undeleted_file_count, + int64_t* undeleted_dir_count, TF_Status* status) { + ::tensorflow::int64 f, d; + + TF_SetStatus(status, TF_OK, ""); + ::tensorflow::Set_TF_Status_from_Status( + status, ::tensorflow::Env::Default()->DeleteRecursively(dirname, &f, &d)); + *undeleted_file_count = f; + *undeleted_dir_count = d; +} + +void TF_FileStat(const char* filename, TF_FileStatistics* stats, + TF_Status* status) { + ::tensorflow::FileStatistics cc_stats; + TF_SetStatus(status, TF_OK, ""); + ::tensorflow::Status s = + ::tensorflow::Env::Default()->Stat(filename, &cc_stats); + ::tensorflow::Set_TF_Status_from_Status(status, s); + if (s.ok()) { + stats->length = cc_stats.length; + stats->mtime_nsec = cc_stats.mtime_nsec; + stats->is_directory = cc_stats.is_directory; + } +} + +void TF_NewWritableFile(const char* filename, TF_WritableFileHandle** handle, + TF_Status* status) { + std::unique_ptr<::tensorflow::WritableFile> f; + TF_SetStatus(status, TF_OK, ""); + ::tensorflow::Status s = + ::tensorflow::Env::Default()->NewWritableFile(filename, &f); + ::tensorflow::Set_TF_Status_from_Status(status, s); + + if (s.ok()) { + *handle = reinterpret_cast(f.release()); + } +} + +void TF_CloseWritableFile(TF_WritableFileHandle* handle, TF_Status* status) { + auto* cc_file = reinterpret_cast<::tensorflow::WritableFile*>(handle); + TF_SetStatus(status, TF_OK, ""); + ::tensorflow::Set_TF_Status_from_Status(status, cc_file->Close()); + delete cc_file; +} + +void TF_SyncWritableFile(TF_WritableFileHandle* handle, TF_Status* status) { + auto* cc_file = reinterpret_cast<::tensorflow::WritableFile*>(handle); + TF_SetStatus(status, TF_OK, ""); + ::tensorflow::Set_TF_Status_from_Status(status, cc_file->Sync()); +} + +void TF_FlushWritableFile(TF_WritableFileHandle* handle, TF_Status* status) { + auto* cc_file = reinterpret_cast<::tensorflow::WritableFile*>(handle); + TF_SetStatus(status, TF_OK, ""); + ::tensorflow::Set_TF_Status_from_Status(status, cc_file->Flush()); +} + +void TF_AppendWritableFile(TF_WritableFileHandle* handle, const char* data, + size_t length, TF_Status* status) { + auto* cc_file = reinterpret_cast<::tensorflow::WritableFile*>(handle); + TF_SetStatus(status, TF_OK, ""); + ::tensorflow::Set_TF_Status_from_Status( + status, cc_file->Append(::tensorflow::StringPiece{data, length})); +} + +void TF_DeleteFile(const char* filename, TF_Status* status) { + TF_SetStatus(status, TF_OK, ""); + ::tensorflow::Set_TF_Status_from_Status( + status, ::tensorflow::Env::Default()->DeleteFile(filename)); +} + +bool TF_StringStreamNext(TF_StringStream* list, const char** result) { + if (list->position >= list->list->size()) { + *result = nullptr; + return false; + } + + *result = list->list->at(list->position++).c_str(); + return true; +} + +void TF_StringStreamDone(TF_StringStream* list) { + delete list->list; + delete list; +} +TF_StringStream* TF_GetChildren(const char* dirname, TF_Status* status) { + auto* children = new std::vector<::tensorflow::string>; + + TF_SetStatus(status, TF_OK, ""); + ::tensorflow::Set_TF_Status_from_Status( + status, ::tensorflow::Env::Default()->GetChildren(dirname, children)); + + auto* list = new TF_StringStream; + list->list = children; + list->position = 0; + return list; +} + +TF_StringStream* TF_GetLocalTempDirectories() { + auto* tmpdirs = new std::vector<::tensorflow::string>; + + ::tensorflow::Env::Default()->GetLocalTempDirectories(tmpdirs); + + auto* list = new TF_StringStream; + list->list = tmpdirs; + list->position = 0; + return list; +} diff --git a/tensorflow/c/env.h b/tensorflow/c/env.h new file mode 100644 index 0000000000..32d3f1f745 --- /dev/null +++ b/tensorflow/c/env.h @@ -0,0 +1,148 @@ +/* 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_C_ENV_H_ +#define TENSORFLOW_C_ENV_H_ + +#include "tensorflow/c/c_api.h" + +// -------------------------------------------------------------------------- +// C API for tensorflow::Env. + +struct TF_WritableFileHandle; +struct TF_StringStream; + +#ifdef __cplusplus +extern "C" { +#endif + +typedef struct TF_FileStatistics { + // The length of the file in bytes. + int64_t length; + // The last modified time in nanoseconds. + int64_t mtime_nsec; + // Whether the name refers to a directory. + bool is_directory; +} TF_FileStatistics; + +// Creates the specified directory. Typical status code are: +// * TF_OK - successfully created the directory +// * TF_ALREADY_EXISTS - directory already exists +// * TF_PERMISSION_DENIED - dirname is not writable +TF_CAPI_EXPORT extern void TF_CreateDir(const char* dirname, TF_Status* status); + +// Deletes the specified directory. Typical status codes are: +// * TF_OK - successfully deleted the directory +// * TF_FAILED_PRECONDITION - the directory is not empty +TF_CAPI_EXPORT extern void TF_DeleteDir(const char* dirname, TF_Status* status); + +// Deletes the specified directory and all subdirectories and files underneath +// it. This is accomplished by traversing the directory tree rooted at dirname +// and deleting entries as they are encountered. +// +// If dirname itself is not readable or does not exist, *undeleted_dir_count is +// set to 1, *undeleted_file_count is set to 0 and an appropriate status (e.g. +// TF_NOT_FOUND) is returned. +// +// If dirname and all its descendants were successfully deleted, TF_OK is +// returned and both error counters are set to zero. +// +// Otherwise, while traversing the tree, undeleted_file_count and +// undeleted_dir_count are updated if an entry of the corresponding type could +// not be deleted. The returned error status represents the reason that any one +// of these entries could not be deleted. +// +// Typical status codes: +// * TF_OK - dirname exists and we were able to delete everything underneath +// * TF_NOT_FOUND - dirname doesn't exist +// * TF_PERMISSION_DENIED - dirname or some descendant is not writable +// * TF_UNIMPLEMENTED - some underlying functions (like Delete) are not +// implemented +TF_CAPI_EXPORT extern void TF_DeleteRecursively(const char* dirname, + int64_t* undeleted_file_count, + int64_t* undeleted_dir_count, + TF_Status* status); + +// Obtains statistics for the given path. If status is TF_OK, *stats is +// updated, otherwise it is not touched. +TF_CAPI_EXPORT extern void TF_FileStat(const char* filename, + TF_FileStatistics* stats, + TF_Status* status); + +// Creates or truncates the given filename and returns a handle to be used for +// appending data to the file. If status is TF_OK, *handle is updated and the +// caller is responsible for freeing it (see TF_CloseWritableFile). +TF_CAPI_EXPORT extern void TF_NewWritableFile(const char* filename, + TF_WritableFileHandle** handle, + TF_Status* status); + +// Closes the given handle and frees its memory. If there was a problem closing +// the file, it is indicated by status. Memory is freed in any case. +TF_CAPI_EXPORT extern void TF_CloseWritableFile(TF_WritableFileHandle* handle, + TF_Status* status); + +// Syncs content of the handle to the filesystem. Blocks waiting for the +// filesystem to indicate that the content has been persisted. +TF_CAPI_EXPORT extern void TF_SyncWritableFile(TF_WritableFileHandle* handle, + TF_Status* status); + +// Flush local buffers to the filesystem. If the process terminates after a +// successful flush, the contents may still be persisted, since the underlying +// filesystem may eventually flush the contents. If the OS or machine crashes +// after a successful flush, the contents may or may not be persisted, depending +// on the implementation. +TF_CAPI_EXPORT extern void TF_FlushWritableFile(TF_WritableFileHandle* handle, + TF_Status* status); + +// Appends the given bytes to the file. Any failure to do so is indicated in +// status. +TF_CAPI_EXPORT extern void TF_AppendWritableFile(TF_WritableFileHandle* handle, + const char* data, + size_t length, + TF_Status* status); + +// Deletes the named file and indicates whether successful in *status. +TF_CAPI_EXPORT extern void TF_DeleteFile(const char* filename, + TF_Status* status); + +// Retrieves the next item from the given TF_StringStream and places a pointer +// to it in *result. If no more items are in the list, *result is set to NULL +// and false is returned. +// +// Ownership of the items retrieved with this function remains with the library. +// Item points are invalidated after a call to TF_StringStreamDone. +TF_CAPI_EXPORT extern bool TF_StringStreamNext(TF_StringStream* list, + const char** result); + +// Frees the resources associated with given string list. All pointers returned +// by TF_StringStreamNext are invalid after this call. +TF_CAPI_EXPORT extern void TF_StringStreamDone(TF_StringStream* list); + +// Retrieves the list of children of the given directory. You can iterate +// through the list with TF_StringStreamNext. The caller is responsible for +// freeing the list (see TF_StringStreamDone). +TF_CAPI_EXPORT extern TF_StringStream* TF_GetChildren(const char* filename, + TF_Status* status); + +// Retrieves a list of directory names on the local machine that may be used for +// temporary storage. You can iterate through the list with TF_StringStreamNext. +// The caller is responsible for freeing the list (see TF_StringStreamDone). +TF_CAPI_EXPORT extern TF_StringStream* TF_GetLocalTempDirectories(void); + +#ifdef __cplusplus +} +#endif + +#endif // TENSORFLOW_C_ENV_H_ diff --git a/tensorflow/c/env_test.cc b/tensorflow/c/env_test.cc new file mode 100644 index 0000000000..08e803f12f --- /dev/null +++ b/tensorflow/c/env_test.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 "tensorflow/c/env.h" + +#include "tensorflow/c/c_api.h" +#include "tensorflow/core/lib/io/path.h" +#include "tensorflow/core/platform/test.h" +#include "tensorflow/core/platform/types.h" + +#define ASSERT_TF_OK(x) ASSERT_EQ(TF_OK, TF_GetCode(x)) + +TEST(TestEnv, TestDirHandling) { + TF_StringStream* tempdirs = TF_GetLocalTempDirectories(); + const char* tempdir; + bool found = false; + while (TF_StringStreamNext(tempdirs, &tempdir)) { + found = true; + + TF_Status* s = TF_NewStatus(); + + ::tensorflow::string dirpath = + ::tensorflow::io::JoinPath(tempdir, "somedir"); + TF_CreateDir(dirpath.c_str(), s); + ASSERT_TF_OK(s) << "TF_CreateDir failed for " << dirpath << ": " + << TF_Message(s); + + ::tensorflow::string filepath = + ::tensorflow::io::JoinPath(dirpath, "somefile.txt"); + TF_WritableFileHandle* handle; + TF_NewWritableFile(filepath.c_str(), &handle, s); + ASSERT_TF_OK(s) << "NewWritableFile failed for " << filepath << ": " + << TF_Message(s); + + const char* data = "Hello, world!\n"; + TF_AppendWritableFile(handle, data, strlen(data), s); + ASSERT_TF_OK(s) << "TF_AppendWritableFile failed to append data to file at " + << filepath << ": " << TF_Message(s); + + TF_CloseWritableFile(handle, s); + ASSERT_TF_OK(s) << "TF_CloseWritableFile failed to close handle to " + << filepath << ": " << TF_Message(s); + + TF_StringStream* children = TF_GetChildren(dirpath.c_str(), s); + ASSERT_TF_OK(s) << "TF_GetChildren failed for " << dirpath; + const char* childpath; + ASSERT_TRUE(TF_StringStreamNext(children, &childpath)); + ASSERT_EQ(::tensorflow::string(childpath), "somefile.txt"); + // There should only be one file in this directory. + ASSERT_FALSE(TF_StringStreamNext(children, &childpath)); + ASSERT_EQ(childpath, nullptr); + TF_StringStreamDone(children); + + TF_FileStatistics stats; + TF_FileStat(filepath.c_str(), &stats, s); + ASSERT_EQ(stats.length, strlen(data)); + ASSERT_FALSE(stats.is_directory); + ASSERT_GT(stats.mtime_nsec, 0); + + // Trying to delete a non-empty directory should fail. + TF_DeleteDir(dirpath.c_str(), s); + ASSERT_NE(TF_OK, TF_GetCode(s)) + << "TF_DeleteDir unexpectedly succeeded with a non-empty directory " + << dirpath; + + TF_DeleteFile(filepath.c_str(), s); + ASSERT_TF_OK(s) << "TF_DeleteFile failed for " << filepath << ": " + << TF_Message(s); + + // Now deleting the directory should work. + TF_DeleteDir(dirpath.c_str(), s); + ASSERT_TF_OK(s) << "TF_DeleteDir failed for " << dirpath << ": " + << TF_Message(s); + + TF_DeleteStatus(s); + break; + } + + ASSERT_TRUE(found) << "expected at least one temp dir"; + + TF_StringStreamDone(tempdirs); +} diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 575edfe7a9..328bfe474d 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -492,7 +492,10 @@ cc_library( ":platform_env_internal_hdrs", ], copts = tf_copts(), - visibility = ["//tensorflow/core:__subpackages__"], + visibility = [ + "//tensorflow/c:__subpackages__", + "//tensorflow/core:__subpackages__", + ], deps = [ ":error_codes_proto_cc", ":lib", -- GitLab From 3570d7957ae81380a5584d8c00ab08ffb583fef4 Mon Sep 17 00:00:00 2001 From: Pan Daoxin Date: Fri, 7 Dec 2018 14:13:22 +0800 Subject: [PATCH 189/873] Some minor changes. --- tensorflow/core/kernels/mkl_slice_op.cc | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/tensorflow/core/kernels/mkl_slice_op.cc b/tensorflow/core/kernels/mkl_slice_op.cc index a85d80f9b3..577aa5c8db 100644 --- a/tensorflow/core/kernels/mkl_slice_op.cc +++ b/tensorflow/core/kernels/mkl_slice_op.cc @@ -59,11 +59,10 @@ gtl::InlinedVector IntTensorToInt64Vec(const Tensor& tensor) { typedef Eigen::ThreadPoolDevice CPUDevice; // A version of SharedValidation (slice_op.h) written for input that is in -// either Mkl layout or Tensorflow layout. -// A shared code to validate input shapes and check for identity, which is not -// dependent on the type of T. -// We do this to reduce code size by not duplicating -// all this for all T (float, double, int32, etc.) +// either Mkl layout or Tensorflow layout. A shared code to validate input +// shapes and check for identity, which is not dependent on the type of T. +// We do this to reduce code size by not duplicating all this for all T +// (float, double, int32, etc.) static void ValidateMklInputs(OpKernelContext* context, bool* is_identity, gtl::InlinedVector* begin, gtl::InlinedVector* size) { -- GitLab From faa7db2df4b467cc97fab6184faa6de5ea88f7f0 Mon Sep 17 00:00:00 2001 From: Priya Gupta Date: Thu, 6 Dec 2018 22:36:48 -0800 Subject: [PATCH 190/873] TF MirroredStrategy: Respect control_dependencies added in replica context during a merge_call. Also enable keras metrics correctness which was broken due to this issue. PiperOrigin-RevId: 224463968 --- .../contrib/distribute/python/keras_test.py | 39 ++++++++++++------- .../python/distribute/mirrored_strategy.py | 9 ++++- tensorflow/python/keras/engine/training.py | 16 +------- 3 files changed, 34 insertions(+), 30 deletions(-) diff --git a/tensorflow/contrib/distribute/python/keras_test.py b/tensorflow/contrib/distribute/python/keras_test.py index 771c48bdd8..d66fc52cf2 100644 --- a/tensorflow/contrib/distribute/python/keras_test.py +++ b/tensorflow/contrib/distribute/python/keras_test.py @@ -1298,22 +1298,20 @@ class TestDistributionStrategyCorrectness(test.TestCase, self.assertEqual(outs[1], 0.) self.assertEqual(outs[2], 0.) - # TODO(priyag): Add metrics correctness to this test to compare with and - # without distribution strategies. @combinations.generate(strategy_and_input_combinations()) def test_correctness(self, distribution, use_numpy, use_validation_data): with self.cached_session(): - tolerance = 1e-5 - metrics = ['mse'] + weights_tolerance = 1e-5 + metrics_tolerance = 1e-4 if isinstance(distribution, (mirrored_strategy.MirroredStrategy, mirrored_strategy.CoreMirroredStrategy)): - # TODO(b/119257215): use the default one once the flakyness is fixed. - tolerance = 1e-4 - # TODO(b/120570676): Enable metrics check once the bug is fixed. - metrics = None + # TODO(b/119257215): Weights are not exactly the same, so use lower + # tolerance for now. + weights_tolerance = 1e-4 + metrics = ['mse'] keras.backend.set_image_data_format('channels_last') np.random.seed(_RANDOM_SEED) random_seed.set_random_seed(_RANDOM_SEED) @@ -1383,22 +1381,33 @@ class TestDistributionStrategyCorrectness(test.TestCase, # Verify that the weights, training history, eval results, predict outputs # are the same within some limits of tolerance. self.assertAllClose( - wts_with_ds, wts_without_ds, atol=tolerance, rtol=tolerance, + wts_with_ds, + wts_without_ds, + atol=weights_tolerance, + rtol=weights_tolerance, msg='Fail to assert weights after training.') - self.assertAllClose( - eval_with_ds, eval_without_ds, atol=tolerance, rtol=tolerance, + eval_with_ds, + eval_without_ds, + atol=metrics_tolerance, + rtol=metrics_tolerance, msg='Fail to assert eval results.') self.assertAllClose( - predict_with_ds, predict_without_ds, atol=tolerance, rtol=tolerance, + predict_with_ds, + predict_without_ds, + atol=weights_tolerance, + rtol=weights_tolerance, msg='Fail to assert predict results.') - if not (isinstance(distribution, tpu_strategy.TPUStrategy) - and distribution.extended.steps_per_run > 1): + if not (isinstance(distribution, tpu_strategy.TPUStrategy) and + distribution.extended.steps_per_run > 1): # TODO(b/119894254): Enable this test for all cases once the underlying # bug is fixed. self.assertAllClose( - history_with_ds, history_without_ds, atol=tolerance, rtol=tolerance, + history_with_ds, + history_without_ds, + atol=metrics_tolerance, + rtol=metrics_tolerance, msg='Fail to assert training history.') diff --git a/tensorflow/python/distribute/mirrored_strategy.py b/tensorflow/python/distribute/mirrored_strategy.py index 56948b2bcb..cb94dfcfbd 100644 --- a/tensorflow/python/distribute/mirrored_strategy.py +++ b/tensorflow/python/distribute/mirrored_strategy.py @@ -169,7 +169,12 @@ def _call_for_each_replica(distribution, fn, args, kwargs): # capture the name_scope from the first MRT and assume it is # the same for all other MRTs. mtt_captured_name_scope = threads[0].captured_name_scope - with ops.name_scope(mtt_captured_name_scope): + # Capture and merge the control dependencies from all the threads. + mtt_captured_control_deps = set() + for t in threads: + mtt_captured_control_deps.update(t.captured_control_deps) + with ops.name_scope(mtt_captured_name_scope),\ + ops.control_dependencies(mtt_captured_control_deps): merge_result = threads[0].merge_fn(distribution, *merge_args, **merge_kwargs) for t in threads: @@ -898,6 +903,8 @@ class MirroredReplicaContext(distribute_lib.ReplicaContext): # Adding a "/" at end lets us re-enter this scope later. if t.captured_name_scope: t.captured_name_scope += "/" + + t.captured_control_deps = t.graph._current_control_dependencies() # pylint: disable=protected-access t.has_paused.set() t.should_run.wait() t.should_run.clear() diff --git a/tensorflow/python/keras/engine/training.py b/tensorflow/python/keras/engine/training.py index 90f8a7b252..462694fda6 100644 --- a/tensorflow/python/keras/engine/training.py +++ b/tensorflow/python/keras/engine/training.py @@ -875,17 +875,11 @@ class Model(Network): [self.total_loss] + metrics_tensors) def _make_fit_function(self): - # TODO(psv/anjalisridhar): Remove updates after we fix b/118841692 - # Stateful metrics updates - metric_updates = [] - for m in self.metrics: - metric_updates += m.updates - metrics_tensors = [ self._all_stateful_metrics_tensors[m] for m in self.metrics_names[1:] ] self._make_train_function_helper( - '_fit_function', [self.total_loss] + metrics_tensors, metric_updates) + '_fit_function', [self.total_loss] + metrics_tensors) def _make_test_function_helper(self, fn_name, outputs, metric_updates=None): if not hasattr(self, fn_name): @@ -918,17 +912,11 @@ class Model(Network): [self.total_loss] + metrics_tensors) def _make_eval_function(self): - # TODO(psv,anjalisridhar): Remove updates after we fix b/118841692 - # Stateful metrics updates - metric_updates = [] - for m in self.metrics: - metric_updates += m.updates - metrics_tensors = [ self._all_stateful_metrics_tensors[m] for m in self.metrics_names[1:] ] self._make_test_function_helper( - '_eval_function', [self.total_loss] + metrics_tensors, metric_updates) + '_eval_function', [self.total_loss] + metrics_tensors) def _make_predict_function(self): if not hasattr(self, 'predict_function'): -- GitLab From cd4cc57282ad9621c369f103e9b48961863b9c9d Mon Sep 17 00:00:00 2001 From: Yuefeng Zhou Date: Fri, 7 Dec 2018 00:04:50 -0800 Subject: [PATCH 191/873] Fix a typo in estimator_training. PiperOrigin-RevId: 224470630 --- tensorflow/python/distribute/estimator_training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/distribute/estimator_training.py b/tensorflow/python/distribute/estimator_training.py index 549fa8fb8a..7d5f231c37 100644 --- a/tensorflow/python/distribute/estimator_training.py +++ b/tensorflow/python/distribute/estimator_training.py @@ -354,7 +354,7 @@ def estimator_evaluate(estimator, evaluate_distributed_fn, hooks): if (estimator._config._distribute_coordinator_mode != dc.CoordinatorMode.STANDALONE_CLIENT): raise ValueError('Only `STANDALONE_CLIENT` mode is supported when you call ' - '`Estimator.train`') + '`Estimator.evaluate`') if estimator._config._eval_distribute.extended.experimental_between_graph: # TODO(yuefengz): remove this limitation once we figure out how to merge -- GitLab From 6bcc66c2d2b413a5d0487638c3d6b6bb63716e8f Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 7 Dec 2018 01:02:31 -0800 Subject: [PATCH 192/873] compat: Update forward compatibility horizon to 2018-12-07 PiperOrigin-RevId: 224475966 --- tensorflow/python/compat/compat.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/compat/compat.py b/tensorflow/python/compat/compat.py index 269d2e286a..e66c29ae39 100644 --- a/tensorflow/python/compat/compat.py +++ b/tensorflow/python/compat/compat.py @@ -32,7 +32,7 @@ from tensorflow.python.ops import variable_scope from tensorflow.python.util import tf_contextlib from tensorflow.python.util.tf_export import tf_export -_FORWARD_COMPATIBILITY_HORIZON = datetime.date(2018, 12, 6) +_FORWARD_COMPATIBILITY_HORIZON = datetime.date(2018, 12, 7) @tf_export("compat.forward_compatible") -- GitLab From c2e2ecedfef715b8f756c5e2df4dab3b88531253 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 7 Dec 2018 01:02:57 -0800 Subject: [PATCH 193/873] Internal change. PiperOrigin-RevId: 224476105 --- tensorflow/core/BUILD | 70 +++++++++++++++++++++++-------------------- 1 file changed, 37 insertions(+), 33 deletions(-) diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 328bfe474d..66714235b5 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -1682,6 +1682,9 @@ filegroup( # operators, use :android_tensorflow_lib if you want full operator # support. # +# If you just need TensorFlow types, e.g. Tensors, use +# :android_tensorflow_lib_lite_no_runtime. +# # Compiles to a trivial library on non-Android to prevent irrelevant # build errors. If not building this as part of an android_binary, # a command such as the following must be used: @@ -1692,7 +1695,33 @@ filegroup( cc_library( name = "android_tensorflow_lib_lite", srcs = if_android(["//tensorflow/core:android_srcs"]), - copts = tf_copts(android_optimization_level_override = None), + copts = tf_copts(android_optimization_level_override = None) + [ + "-DSUPPORT_SELECTIVE_REGISTRATION", + ], + linkopts = ["-lz"], + tags = [ + "manual", + "notap", + ], + visibility = ["//visibility:public"], + deps = [ + ":mobile_additional_lib_deps", + ":protos_all_cc_impl", + ":stats_calculator_portable", + "//third_party/eigen3", + "@double_conversion//:double-conversion", + "@nsync//:nsync_cpp", + "@protobuf_archive//:protobuf", + ], + alwayslink = 1, +) + +cc_library( + name = "android_tensorflow_lib_lite_nortti", + srcs = if_android(["//tensorflow/core:android_srcs"]), + copts = tf_copts(android_optimization_level_override = None) + [ + "-DSUPPORT_SELECTIVE_REGISTRATION", + ] + tf_opts_nortti_if_android(), linkopts = ["-lz"], tags = [ "manual", @@ -1804,46 +1833,21 @@ cc_library( # Does not contain operators. In contrast to android_tensorflow_lib_lite, # this links in framework support for all types, relying on selective # registration of ops to prune code size. -cc_library( +# +# TODO(gonnet): Move all users of these aliases to the corresponding +# :android_tensorflow_lib_lite* targets and remove. +alias( name = "android_tensorflow_lib_selective_registration", - srcs = if_android(["//tensorflow/core:android_srcs_only_runtime"]), - copts = tf_copts(android_optimization_level_override = None) + [ - "-DSUPPORT_SELECTIVE_REGISTRATION", - ], - linkopts = if_android(["-lz"]), - tags = [ - "manual", - "notap", - ], + actual = ":android_tensorflow_lib_lite", visibility = ["//visibility:public"], - deps = [ - ":protos_all_cc_impl", - "@com_google_absl//absl/container:flat_hash_set", - "@protobuf_archive//:protobuf", - ], - alwayslink = 1, ) # Android library for use with the SELECTIVE_REGISTRATION feature with # no proto_rtti. -cc_library( +alias( name = "android_tensorflow_lib_selective_registration_nortti", - srcs = if_android(["//tensorflow/core:android_srcs_only_runtime"]), - copts = tf_copts(android_optimization_level_override = None) + tf_opts_nortti_if_android() + [ - "-DSUPPORT_SELECTIVE_REGISTRATION", - ], - linkopts = if_android(["-lz"]), - tags = [ - "manual", - "notap", - ], + actual = ":android_tensorflow_lib_lite_nortti", visibility = ["//visibility:public"], - deps = [ - ":protos_all_cc_impl", - "@com_google_absl//absl/container:flat_hash_set", - "@protobuf_archive//:protobuf", - ], - alwayslink = 1, ) filegroup( -- GitLab From 3bd4a240020d1c62d84c1c940e8b1f5b3d838d3d Mon Sep 17 00:00:00 2001 From: Gaurav Jain Date: Fri, 7 Dec 2018 03:08:17 -0800 Subject: [PATCH 194/873] Enable @run_v1_only to be applied on a class Some entire suite of tests do not work on v1. Rather than annotating the individual test cases, it makes more sense to annotate the entire class. Note that we do not intend this for the @run_deprecated_v1 annotation. PiperOrigin-RevId: 224489413 --- tensorflow/python/framework/test_util.py | 20 ++++++++++++++++---- 1 file changed, 16 insertions(+), 4 deletions(-) diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py index fc1a5fbe85..4bc9bf01d2 100644 --- a/tensorflow/python/framework/test_util.py +++ b/tensorflow/python/framework/test_util.py @@ -494,7 +494,8 @@ def with_control_flow_v2(cls): return cls for name, value in cls.__dict__.copy().items(): - if (callable(value) and name.startswith("test") and + if (callable(value) and + name.startswith(unittest.TestLoader.testMethodPrefix) and not getattr(value, "_disable_control_flow_v2", False)): setattr(cls, name + "WithControlFlowV2", enable_control_flow_v2(value)) return cls @@ -893,8 +894,10 @@ def run_all_in_graph_and_eager_modes(cls): """Execute all test methods in the given class with and without eager.""" base_decorator = run_in_graph_and_eager_modes for name, value in cls.__dict__.copy().items(): - if callable(value) and name.startswith("test") and not ( - name.startswith("testSkipEager") or name.startswith("test_skip_eager")): + if (callable(value) and + name.startswith(unittest.TestLoader.testMethodPrefix) and + not (name.startswith("testSkipEager") + or name.startswith("test_skip_eager"))): setattr(cls, name, base_decorator(value)) return cls @@ -1059,7 +1062,16 @@ def run_v1_only(reason, func=None): def decorator(f): if tf_inspect.isclass(f): - raise ValueError("`run_v1_only` only supports test methods.") + setup = f.__dict__.get("setUp") + if setup is not None: + setattr(f, "setUp", decorator(setup)) + + for name, value in f.__dict__.copy().items(): + if (callable(value) and + name.startswith(unittest.TestLoader.testMethodPrefix)): + setattr(f, name, decorator(value)) + + return f def decorated(self, *args, **kwargs): if tf2.enabled(): -- GitLab From 0d3b58cfe91c6b865a14701345d7a84ce949c0e3 Mon Sep 17 00:00:00 2001 From: Gaurav Jain Date: Fri, 7 Dec 2018 04:17:58 -0800 Subject: [PATCH 195/873] Disable some tests using placeholders when running in v2 In order to do so we introduce the concept of a FakeEagerSession which allows tests to run sess.run() with an empty feed_dict. PiperOrigin-RevId: 224495103 --- .../python/kernel_tests/lanczos_test.py | 4 +- .../python/kernel_tests/least_squares_test.py | 4 +- .../kernel_tests/linear_equations_test.py | 4 +- tensorflow/python/framework/test_util.py | 59 ++++++++++++++++++- .../kernel_tests/batch_matmul_op_test.py | 1 + .../kernel_tests/matrix_solve_ls_op_test.py | 16 +++-- tensorflow/python/kernel_tests/qr_op_test.py | 8 ++- tensorflow/python/kernel_tests/svd_op_test.py | 8 ++- 8 files changed, 89 insertions(+), 15 deletions(-) diff --git a/tensorflow/contrib/solvers/python/kernel_tests/lanczos_test.py b/tensorflow/contrib/solvers/python/kernel_tests/lanczos_test.py index 8fcd7aeef6..f31bdbd399 100644 --- a/tensorflow/contrib/solvers/python/kernel_tests/lanczos_test.py +++ b/tensorflow/contrib/solvers/python/kernel_tests/lanczos_test.py @@ -19,6 +19,7 @@ from __future__ import print_function import numpy as np +from tensorflow.python import tf2 from tensorflow.contrib.solvers.python.ops import lanczos from tensorflow.contrib.solvers.python.ops import util from tensorflow.python.framework import constant_op @@ -80,7 +81,8 @@ if __name__ == "__main__": for shape in [[4, 4], [7, 4], [5, 8]]: for orthogonalize in True, False: for steps in range(1, min(shape) + 1): - for use_static_shape in True, False: + # TF2 does not support placeholders so we skip it + for use_static_shape in set([True, tf2.enabled()]): arg_string = "%s_%s_%s_%s_staticshape_%s" % ( dtype.__name__, "_".join(map(str, shape)), orthogonalize, steps, use_static_shape) diff --git a/tensorflow/contrib/solvers/python/kernel_tests/least_squares_test.py b/tensorflow/contrib/solvers/python/kernel_tests/least_squares_test.py index 2a9100903a..841a41a233 100644 --- a/tensorflow/contrib/solvers/python/kernel_tests/least_squares_test.py +++ b/tensorflow/contrib/solvers/python/kernel_tests/least_squares_test.py @@ -19,6 +19,7 @@ from __future__ import print_function import numpy as np +from tensorflow.python import tf2 from tensorflow.contrib.solvers.python.ops import least_squares from tensorflow.contrib.solvers.python.ops import util from tensorflow.python.framework import constant_op @@ -76,7 +77,8 @@ def _get_least_squares_tests(dtype_, use_static_shape_, shape_): if __name__ == "__main__": for dtype in np.float32, np.float64: for shape in [[4, 4], [8, 5], [3, 7]]: - for use_static_shape in True, False: + # TF2 does not support placeholders under eager so we skip it + for use_static_shape in set([True, tf2.enabled()]): arg_string = "%s_%s_staticshape_%s" % (dtype.__name__, "_".join(map(str, shape)), use_static_shape) diff --git a/tensorflow/contrib/solvers/python/kernel_tests/linear_equations_test.py b/tensorflow/contrib/solvers/python/kernel_tests/linear_equations_test.py index a0e6eb87bc..10807f7a80 100644 --- a/tensorflow/contrib/solvers/python/kernel_tests/linear_equations_test.py +++ b/tensorflow/contrib/solvers/python/kernel_tests/linear_equations_test.py @@ -19,6 +19,7 @@ from __future__ import print_function import numpy as np +from tensorflow.python import tf2 from tensorflow.contrib.solvers.python.ops import linear_equations from tensorflow.contrib.solvers.python.ops import util from tensorflow.python.framework import constant_op @@ -113,7 +114,8 @@ def _get_linear_equations_tests(dtype_, use_static_shape_, shape_): if __name__ == "__main__": for dtype in np.float32, np.float64: for size in 1, 4, 10: - for use_static_shape in True, False: + # TF2 does not support placeholders under eager so we skip it + for use_static_shape in set([True, tf2.enabled()]): shape = [size, size] arg_string = "%s_%s_staticshape_%s" % (dtype.__name__, size, use_static_shape) diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py index 4bc9bf01d2..b0c3c9b506 100644 --- a/tensorflow/python/framework/test_util.py +++ b/tensorflow/python/framework/test_util.py @@ -1277,6 +1277,63 @@ class CapturedWrites(object): return output_data +class FakeEagerSession(object): + """Fake session so tests that conditionally use placeholders can use eager. + + There are a number of tests that conditionally use placeholders for shape + inference. The pattern is demonstrated here: + + ```python + with self.cached_session() as sess: + if static_shape: + y = math_ops.matmul(x, ...) + feed_dict = {} + else: + x_ph = array_ops.placeholder(...) + y = math_ops.matmul(x_ph, ...) + feed_dict = {x_ph: x} + val = sess.run(y, feed_dict=feed_dict) + ``` + + Since the feed_dict is empty when not using placeholders we should be able to + call self.evaluate(), however this requires rewriting the test case. + This class shold be considered a stop-gap solution to get tests running with + eager with minimal changes to the actual test. + """ + + def __init__(self, test_case): + self._test_case = test_case + + def run(self, fetches, *args, **kwargs): + """Evalaute `fetches`. + + Fail if additional args are specified. + + Args: + fetches: A Tensor or a nested list/tuple of Tensors. + *args: Positional arguments + **kwargs: Keyword arguments + + Raises: + RuntimeError: If args or kwargs are specified. + + Returns: + Tensors as numpy values. + """ + feed_dict = kwargs.pop("feed_dict", {}) + if feed_dict: + raise RuntimeError( + "feed_dict is not supported when eager execution is enabled " + "(in this case, sess.run(t) is shorthand for t.numpy()") + + if args or kwargs: + raise RuntimeError( + "Optional args are not supported when eager execution is enabled " + "(in this case, sess.run(t) is shorthand for t.numpy()") + + return self._test_case.evaluate(fetches) + + class ErrorLoggingSession(session.Session): """Wrapper around a Session that logs errors in run(). """ @@ -1584,7 +1641,7 @@ class TensorFlowTestCase(googletest.TestCase): the graph building and execution code in a test case. """ if context.executing_eagerly(): - yield None + yield FakeEagerSession(self) else: sess = self._get_cached_session( graph, config, force_gpu, crash_if_inconsistent_args=True) diff --git a/tensorflow/python/kernel_tests/batch_matmul_op_test.py b/tensorflow/python/kernel_tests/batch_matmul_op_test.py index 931bb41424..f2f0291b89 100644 --- a/tensorflow/python/kernel_tests/batch_matmul_op_test.py +++ b/tensorflow/python/kernel_tests/batch_matmul_op_test.py @@ -198,6 +198,7 @@ if __name__ == "__main__": for adjoint_a_ in False, True: for adjoint_b_ in False, True: name = "%s_%s_%s" % (dtype_.__name__, adjoint_a_, adjoint_b_) + # TF2 does not support placeholders under eager so we skip it for use_static_shape_ in set([True, tf2.enabled()]): setattr(BatchMatmulOpTest, "testBatchMatmulOp_" + name + ("_%s" % use_static_shape_), diff --git a/tensorflow/python/kernel_tests/matrix_solve_ls_op_test.py b/tensorflow/python/kernel_tests/matrix_solve_ls_op_test.py index a6f5da9d3d..42fd95d311 100644 --- a/tensorflow/python/kernel_tests/matrix_solve_ls_op_test.py +++ b/tensorflow/python/kernel_tests/matrix_solve_ls_op_test.py @@ -20,6 +20,7 @@ from __future__ import print_function import numpy as np +from tensorflow.python import tf2 from tensorflow.python.client import session from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes @@ -150,13 +151,17 @@ class MatrixSolveLsOpTest(test_lib.TestCase): empty1 = np.empty([0, 2]) for fast in [True, False]: with self.cached_session(use_gpu=True): - tf_ans = linalg_ops.matrix_solve_ls(empty0, empty0, fast=fast).eval() + tf_ans = self.evaluate( + linalg_ops.matrix_solve_ls(empty0, empty0, fast=fast)) self.assertEqual(tf_ans.shape, (0, 0)) - tf_ans = linalg_ops.matrix_solve_ls(empty0, full, fast=fast).eval() + tf_ans = self.evaluate( + linalg_ops.matrix_solve_ls(empty0, full, fast=fast)) self.assertEqual(tf_ans.shape, (0, 2)) - tf_ans = linalg_ops.matrix_solve_ls(full, empty0, fast=fast).eval() + tf_ans = self.evaluate( + linalg_ops.matrix_solve_ls(full, empty0, fast=fast)) self.assertEqual(tf_ans.shape, (2, 0)) - tf_ans = linalg_ops.matrix_solve_ls(empty1, empty1, fast=fast).eval() + tf_ans = self.evaluate( + linalg_ops.matrix_solve_ls(empty1, empty1, fast=fast)) self.assertEqual(tf_ans.shape, (2, 2)) @test_util.run_deprecated_v1 @@ -350,7 +355,8 @@ class MatrixSolveLsBenchmark(test_lib.Benchmark): if __name__ == "__main__": for dtype_ in [np.float32, np.float64, np.complex64, np.complex128]: - for use_placeholder_ in [True, False]: + # TF2 does not support placeholders under eager so we skip it + for use_placeholder_ in set([False, not tf2.enabled()]): for fast_ in [True, False]: l2_regularizers = [0] if dtype_ == np.complex128 else [0, 0.1] for l2_regularizer_ in l2_regularizers: diff --git a/tensorflow/python/kernel_tests/qr_op_test.py b/tensorflow/python/kernel_tests/qr_op_test.py index 0f2537b371..5e9767f20c 100644 --- a/tensorflow/python/kernel_tests/qr_op_test.py +++ b/tensorflow/python/kernel_tests/qr_op_test.py @@ -20,6 +20,7 @@ from __future__ import print_function import numpy as np +from tensorflow.python import tf2 from tensorflow.python.framework import constant_op from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops @@ -102,7 +103,7 @@ def _GetQrOpTest(dtype_, shape_, full_matrices_, use_static_shape_): tol = 1e-14 # Tests that a ~= q*r. a_recon = math_ops.matmul(q, r) - self.assertAllClose(a_recon.eval(), a, rtol=tol, atol=tol) + self.assertAllClose(a_recon, a, rtol=tol, atol=tol) def CheckUnitary(self, x): # Tests that x[...,:,:]^H * x[...,:,:] is close to the identity. @@ -112,7 +113,7 @@ def _GetQrOpTest(dtype_, shape_, full_matrices_, use_static_shape_): tol = 1e-5 else: tol = 1e-14 - self.assertAllClose(identity.eval(), self.evaluate(xx), atol=tol) + self.assertAllClose(identity, xx, atol=tol) def Test(self): np.random.seed(1) @@ -202,7 +203,8 @@ if __name__ == "__main__": for cols in 1, 2, 5, 10, 32, 100: for full_matrices in False, True: for batch_dims in [(), (3,)] + [(3, 2)] * (max(rows, cols) < 10): - for use_static_shape in True, False: + # TF2 does not support placeholders under eager so we skip it + for use_static_shape in set([True, tf2.enabled()]): shape = batch_dims + (rows, cols) name = "%s_%s_full_%s_static_%s" % (dtype.__name__, "_".join(map(str, shape)), diff --git a/tensorflow/python/kernel_tests/svd_op_test.py b/tensorflow/python/kernel_tests/svd_op_test.py index 97a280ef51..94155fd117 100644 --- a/tensorflow/python/kernel_tests/svd_op_test.py +++ b/tensorflow/python/kernel_tests/svd_op_test.py @@ -20,6 +20,7 @@ from __future__ import print_function import numpy as np +from tensorflow.python import tf2 from tensorflow.python.framework import constant_op from tensorflow.python.ops import array_ops from tensorflow.python.ops import gradient_checker @@ -117,13 +118,13 @@ def _GetSvdOpTest(dtype_, shape_, use_static_shape_, compute_uv_, diag_s = array_ops.concat([diag_s, zeros], a.ndim - 1) a_recon = math_ops.matmul(u, diag_s) a_recon = math_ops.matmul(a_recon, v, adjoint_b=True) - self.assertAllClose(a_recon.eval(), a, rtol=tol, atol=tol) + self.assertAllClose(a_recon, a, rtol=tol, atol=tol) def CheckUnitary(self, x, tol): # Tests that x[...,:,:]^H * x[...,:,:] is close to the identity. xx = math_ops.matmul(x, x, adjoint_a=True) identity = array_ops.matrix_band_part(array_ops.ones_like(xx), 0, 0) - self.assertAllClose(identity.eval(), self.evaluate(xx), atol=tol) + self.assertAllClose(identity, xx, atol=tol) def Test(self): is_complex = dtype_ in (np.complex64, np.complex128) @@ -263,7 +264,8 @@ if __name__ == "__main__": for cols in 1, 2, 5, 10, 32, 100: for batch_dims in [(), (3,)] + [(3, 2)] * (max(rows, cols) < 10): shape = batch_dims + (rows, cols) - for use_static_shape in True, False: + # TF2 does not support placeholders under eager so we skip it + for use_static_shape in set([True, tf2.enabled()]): name = "%s_%s_static_shape_%s__compute_uv_%s_full_%s" % ( dtype.__name__, "_".join(map(str, shape)), use_static_shape, compute_uv, full_matrices) -- GitLab From 9109842678c385b4ab31825c53bf4373fcadf15e Mon Sep 17 00:00:00 2001 From: James Ring Date: Fri, 7 Dec 2018 07:42:26 -0800 Subject: [PATCH 196/873] Add time functions to TF_Env PiperOrigin-RevId: 224513745 --- tensorflow/c/env.cc | 14 ++++++++++++++ tensorflow/c/env.h | 9 +++++++++ tensorflow/c/env_test.cc | 6 ++++++ 3 files changed, 29 insertions(+) diff --git a/tensorflow/c/env.cc b/tensorflow/c/env.cc index fcff1e413d..07b9e8b940 100644 --- a/tensorflow/c/env.cc +++ b/tensorflow/c/env.cc @@ -145,3 +145,17 @@ TF_StringStream* TF_GetLocalTempDirectories() { list->position = 0; return list; } + +TF_CAPI_EXPORT extern uint64_t TF_NowNanos(void) { + return ::tensorflow::Env::Default()->NowNanos(); +} + +// Returns the number of microseconds since the Unix epoch. +TF_CAPI_EXPORT extern uint64_t TF_NowMicros(void) { + return ::tensorflow::Env::Default()->NowMicros(); +} + +// Returns the number of seconds since the Unix epoch. +TF_CAPI_EXPORT extern uint64_t TF_NowSeconds(void) { + return ::tensorflow::Env::Default()->NowSeconds(); +} diff --git a/tensorflow/c/env.h b/tensorflow/c/env.h index 32d3f1f745..9d27c5da37 100644 --- a/tensorflow/c/env.h +++ b/tensorflow/c/env.h @@ -141,6 +141,15 @@ TF_CAPI_EXPORT extern TF_StringStream* TF_GetChildren(const char* filename, // The caller is responsible for freeing the list (see TF_StringStreamDone). TF_CAPI_EXPORT extern TF_StringStream* TF_GetLocalTempDirectories(void); +// Returns the number of nanoseconds since the Unix epoch. +TF_CAPI_EXPORT extern uint64_t TF_NowNanos(void); + +// Returns the number of microseconds since the Unix epoch. +TF_CAPI_EXPORT extern uint64_t TF_NowMicros(void); + +// Returns the number of seconds since the Unix epoch. +TF_CAPI_EXPORT extern uint64_t TF_NowSeconds(void); + #ifdef __cplusplus } #endif diff --git a/tensorflow/c/env_test.cc b/tensorflow/c/env_test.cc index 08e803f12f..e2206c6bef 100644 --- a/tensorflow/c/env_test.cc +++ b/tensorflow/c/env_test.cc @@ -92,3 +92,9 @@ TEST(TestEnv, TestDirHandling) { TF_StringStreamDone(tempdirs); } + +TEST(TestEnv, TestTimeFunctions) { + ASSERT_GE(TF_NowSeconds(), 946684800); // Midnight Jan 1, 2000 + ASSERT_GE(TF_NowMicros(), 946684800 * 1e6); + ASSERT_GE(TF_NowNanos(), 946684800 * 1e9); +} -- GitLab From 840be3029ce5ee97b345a8c317a76a68eb4e5374 Mon Sep 17 00:00:00 2001 From: Brennan Saeta Date: Fri, 7 Dec 2018 08:21:42 -0800 Subject: [PATCH 197/873] [Distributed Runtime] Mark SessionOptions as const. #cleanup PiperOrigin-RevId: 224518435 --- tensorflow/core/distributed_runtime/rpc/grpc_session.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_session.h b/tensorflow/core/distributed_runtime/rpc/grpc_session.h index e00cf97e38..a3ed3ec736 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_session.h +++ b/tensorflow/core/distributed_runtime/rpc/grpc_session.h @@ -111,7 +111,7 @@ class GrpcSession : public Session { LOCKS_EXCLUDED(mu_); private: - SessionOptions options_; + const SessionOptions options_; std::unique_ptr master_; mutex mu_; -- GitLab From 989e78c412a7e0f5361d4d7dfdfb230c8136e749 Mon Sep 17 00:00:00 2001 From: Eugene Brevdo Date: Fri, 7 Dec 2018 09:32:55 -0800 Subject: [PATCH 198/873] [TF Keras] keras.Flatten()(tensor : shaped (batch,)) -> tensor : shaped (batch, 1) This provides a consistent API for users who have scalar "channels". PiperOrigin-RevId: 224528301 --- .../features/python/conditioning_utils_impl.py | 3 +++ .../features/python/conditioning_utils_test.py | 2 +- .../contrib/layers/python/layers/layers_test.py | 13 ++++++------- tensorflow/python/keras/engine/training_utils.py | 14 +++++++++++--- tensorflow/python/keras/layers/core.py | 13 ++++++++++--- tensorflow/python/keras/layers/core_test.py | 14 ++++++++++++++ tensorflow/python/keras/testing_utils.py | 6 +++++- tensorflow/python/layers/core_test.py | 9 ++++++--- 8 files changed, 56 insertions(+), 18 deletions(-) diff --git a/tensorflow/contrib/gan/python/features/python/conditioning_utils_impl.py b/tensorflow/contrib/gan/python/features/python/conditioning_utils_impl.py index e2594faf85..364fa4eb46 100644 --- a/tensorflow/contrib/gan/python/features/python/conditioning_utils_impl.py +++ b/tensorflow/contrib/gan/python/features/python/conditioning_utils_impl.py @@ -64,6 +64,9 @@ def condition_tensor(tensor, conditioning): """ tensor.shape[1:].assert_is_fully_defined() num_features = tensor.shape[1:].num_elements() + if conditioning.shape.ndims < 2: + raise ValueError('conditioning must be at least 2D, but saw shape: %s' + % conditioning.shape) mapped_conditioning = layers.linear( layers.flatten(conditioning), num_features) diff --git a/tensorflow/contrib/gan/python/features/python/conditioning_utils_test.py b/tensorflow/contrib/gan/python/features/python/conditioning_utils_test.py index 0aad769793..f5c7d53cf2 100644 --- a/tensorflow/contrib/gan/python/features/python/conditioning_utils_test.py +++ b/tensorflow/contrib/gan/python/features/python/conditioning_utils_test.py @@ -45,7 +45,7 @@ class ConditioningUtilsTest(test.TestCase): array_ops.placeholder(dtypes.float32, (5, None)), array_ops.placeholder(dtypes.float32, (5, 1))) - with self.assertRaisesRegexp(ValueError, 'expected min_ndim=2'): + with self.assertRaisesRegexp(ValueError, 'at least 2D'): conditioning_utils.condition_tensor( array_ops.placeholder(dtypes.float32, (5, 2)), array_ops.placeholder(dtypes.float32, (5))) diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py index 0a4d2c6d4c..d791418c9d 100644 --- a/tensorflow/contrib/layers/python/layers/layers_test.py +++ b/tensorflow/contrib/layers/python/layers/layers_test.py @@ -1459,13 +1459,6 @@ class DropoutTest(test.TestCase): class FlattenTest(test.TestCase): - def testInvalidRank(self): - with ops.Graph().as_default() as g, self.session(g): - inputs = array_ops.placeholder(dtype=dtypes.float32) - inputs.set_shape(tensor_shape.TensorShape((5,))) - with self.assertRaisesRegexp(ValueError, 'incompatible with the layer'): - _layers.flatten(inputs) - def testUnknownLastDim(self): with ops.Graph().as_default() as g, self.session(g): inputs = array_ops.placeholder(dtype=dtypes.float32) @@ -1502,6 +1495,12 @@ class FlattenTest(test.TestCase): images.get_shape().num_elements()) self.assertEqual(output.get_shape()[0], images.get_shape()[0]) + def testFlatten0D(self): + with self.cached_session(): + scalars = random_ops.random_uniform((5,), seed=1, name='scalars') + output = _layers.flatten(scalars) + self.assertEqual(output.shape, (5, 1)) + def testFlattenBatchSize(self): height, width = 3, 3 with self.cached_session() as sess: diff --git a/tensorflow/python/keras/engine/training_utils.py b/tensorflow/python/keras/engine/training_utils.py index 9301302f4a..01a09eb031 100644 --- a/tensorflow/python/keras/engine/training_utils.py +++ b/tensorflow/python/keras/engine/training_utils.py @@ -232,10 +232,14 @@ def check_num_samples(ins, return None # Edge case where ins == [static_learning_phase] -def standardize_single_array(x): +def standardize_single_array(x, expected_shape=None): + """Expand data of shape (x,) to (x, 1), unless len(expected_shape)==1.""" if x is None: return None - if x.shape is not None and len(x.shape) == 1: + + if (x.shape is not None + and len(x.shape) == 1 + and (expected_shape is None or len(expected_shape) != 1)): if tensor_util.is_tensor(x): x = array_ops.expand_dims(x, axis=1) else: @@ -301,7 +305,11 @@ def standardize_input_data(data, else: data = data.values if data.__class__.__name__ == 'DataFrame' else data data = [data] - data = [standardize_single_array(x) for x in data] + if shapes is not None: + data = [standardize_single_array(x, shape) + for (x, shape) in zip(data, shapes)] + else: + data = [standardize_single_array(x) for x in data] if len(data) != len(names): if data and hasattr(data[0], 'shape'): diff --git a/tensorflow/python/keras/layers/core.py b/tensorflow/python/keras/layers/core.py index 56dd70558c..854774c569 100644 --- a/tensorflow/python/keras/layers/core.py +++ b/tensorflow/python/keras/layers/core.py @@ -506,6 +506,9 @@ class Permute(Layer): class Flatten(Layer): """Flattens the input. Does not affect the batch size. + If inputs are shaped `(batch,)` without a channel dimension, then flattening + adds an extra channel dimension and output shapes are `(batch, 1)`. + Arguments: data_format: A string, one of `channels_last` (default) or `channels_first`. @@ -534,23 +537,27 @@ class Flatten(Layer): def __init__(self, data_format=None, **kwargs): super(Flatten, self).__init__(**kwargs) self.data_format = conv_utils.normalize_data_format(data_format) - self.input_spec = InputSpec(min_ndim=2) + self.input_spec = InputSpec(min_ndim=1) def call(self, inputs): - if self.data_format == 'channels_first': + if (self.data_format == 'channels_first' + and K.ndim(inputs) is not None and K.ndim(inputs) > 1): permutation = [0] permutation.extend([i for i in range(2, K.ndim(inputs))]) permutation.append(1) inputs = array_ops.transpose(inputs, perm=permutation) - outputs = array_ops.reshape(inputs, (array_ops.shape(inputs)[0], -1)) + outputs = array_ops.reshape( + inputs, (inputs.shape[0].value or array_ops.shape(inputs)[0], -1)) if not context.executing_eagerly(): outputs.set_shape(self.compute_output_shape(inputs.get_shape())) return outputs def compute_output_shape(self, input_shape): input_shape = tensor_shape.TensorShape(input_shape).as_list() + if not input_shape: + output_shape = tensor_shape.TensorShape([1]) output_shape = [input_shape[0]] if all(input_shape[1:]): output_shape += [np.prod(input_shape[1:])] diff --git a/tensorflow/python/keras/layers/core_test.py b/tensorflow/python/keras/layers/core_test.py index aad6ab8171..f138adf760 100644 --- a/tensorflow/python/keras/layers/core_test.py +++ b/tensorflow/python/keras/layers/core_test.py @@ -149,6 +149,20 @@ class CoreLayersTest(test.TestCase): np.transpose(inputs, (0, 2, 3, 1)), (-1, 5 * 5 * 3)) self.assertAllClose(outputs, target_outputs) + @tf_test_util.run_in_graph_and_eager_modes + def test_flatten_scalar_channels(self): + testing_utils.layer_test( + keras.layers.Flatten, kwargs={}, input_shape=(3,)) + + # Test channels_first + inputs = np.random.random((10,)).astype('float32') + outputs = testing_utils.layer_test( + keras.layers.Flatten, + kwargs={'data_format': 'channels_first'}, + input_data=inputs) + target_outputs = np.expand_dims(inputs, -1) + self.assertAllClose(outputs, target_outputs) + @tf_test_util.run_in_graph_and_eager_modes def test_repeat_vector(self): testing_utils.layer_test( diff --git a/tensorflow/python/keras/testing_utils.py b/tensorflow/python/keras/testing_utils.py index 42f8f10fca..fd062b0ab3 100644 --- a/tensorflow/python/keras/testing_utils.py +++ b/tensorflow/python/keras/testing_utils.py @@ -77,9 +77,13 @@ def layer_test(layer_cls, kwargs=None, input_shape=None, input_dtype=None, Returns: The output data (Numpy array) returned by the layer, for additional checks to be done by the calling code. + + Raises: + ValueError: if `input_shape is None`. """ if input_data is None: - assert input_shape + if input_shape is None: + raise ValueError('input_shape is None') if not input_dtype: input_dtype = 'float32' input_data_shape = list(input_shape) diff --git a/tensorflow/python/layers/core_test.py b/tensorflow/python/layers/core_test.py index cf6f0fbb70..b40a268238 100644 --- a/tensorflow/python/layers/core_test.py +++ b/tensorflow/python/layers/core_test.py @@ -533,10 +533,13 @@ class FlattenTest(test.TestCase): self.assertEqual(y.get_shape().as_list(), [None, 6]) @test_util.run_deprecated_v1 - def testFlattenValueError(self): + def testFlatten0D(self): x = array_ops.placeholder(shape=(None,), dtype='float32') - with self.assertRaises(ValueError): - core_layers.Flatten()(x) + y = core_layers.Flatten()(x) + with self.cached_session() as sess: + np_output = sess.run(y, feed_dict={x: np.zeros((5,))}) + self.assertEqual(list(np_output.shape), [5, 1]) + self.assertEqual(y.shape.as_list(), [None, 1]) @test_util.run_deprecated_v1 def testFlattenUnknownAxes(self): -- GitLab From 8d3be0f555b14013d4d93336cd5206153a6fa63b Mon Sep 17 00:00:00 2001 From: Martin Wicke Date: Fri, 7 Dec 2018 09:45:24 -0800 Subject: [PATCH 199/873] Add default value for topdown aerg of io.gfile.walk. The default exists in Python's os.walk and in the v1 gfile too. PiperOrigin-RevId: 224530098 --- tensorflow/python/lib/io/file_io.py | 2 +- tensorflow/tools/api/golden/v1/tensorflow.io.gfile.pbtxt | 2 +- tensorflow/tools/api/golden/v2/tensorflow.io.gfile.pbtxt | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/tensorflow/python/lib/io/file_io.py b/tensorflow/python/lib/io/file_io.py index 4caa5750bf..ee55d89bff 100644 --- a/tensorflow/python/lib/io/file_io.py +++ b/tensorflow/python/lib/io/file_io.py @@ -671,7 +671,7 @@ def walk(top, in_order=True): @tf_export("io.gfile.walk") -def walk_v2(top, topdown, onerror=None): +def walk_v2(top, topdown=True, onerror=None): """Recursive directory tree generator for directories. Args: diff --git a/tensorflow/tools/api/golden/v1/tensorflow.io.gfile.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.io.gfile.pbtxt index 93d9b0fd75..cfa3372b12 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.io.gfile.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.io.gfile.pbtxt @@ -46,6 +46,6 @@ tf_module { } member_method { name: "walk" - argspec: "args=[\'top\', \'topdown\', \'onerror\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'top\', \'topdown\', \'onerror\'], varargs=None, keywords=None, defaults=[\'True\', \'None\'], " } } diff --git a/tensorflow/tools/api/golden/v2/tensorflow.io.gfile.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.io.gfile.pbtxt index 93d9b0fd75..cfa3372b12 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.io.gfile.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.io.gfile.pbtxt @@ -46,6 +46,6 @@ tf_module { } member_method { name: "walk" - argspec: "args=[\'top\', \'topdown\', \'onerror\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'top\', \'topdown\', \'onerror\'], varargs=None, keywords=None, defaults=[\'True\', \'None\'], " } } -- GitLab From 0d9781711b16a897a220e33f5f6c60a038fb5bac Mon Sep 17 00:00:00 2001 From: Rohan Jain Date: Fri, 7 Dec 2018 09:53:37 -0800 Subject: [PATCH 200/873] Creating a wrapper around DatasetVariantWrapper's that can be passed from CPU to GPU while keeping everything in host memory. This is necessary for when we move to creating datasets on the go and for transformations such as copy_to_device we need to ferry the DatasetVariantWrapper. PiperOrigin-RevId: 224531292 --- tensorflow/compiler/jit/xla_device_ops.h | 2 + .../api_def_UnwrapDatasetVariant.pbtxt | 4 + .../base_api/api_def_WrapDatasetVariant.pbtxt | 4 + tensorflow/core/framework/dataset.cc | 109 ++++++++++++++++++ tensorflow/core/kernels/function_ops.cc | 3 + tensorflow/core/ops/dataset_ops.cc | 10 ++ .../data/experimental/kernel_tests/BUILD | 11 ++ .../kernel_tests/wrap_unwrap_test.py | 69 +++++++++++ 8 files changed, 212 insertions(+) create mode 100644 tensorflow/core/api_def/base_api/api_def_UnwrapDatasetVariant.pbtxt create mode 100644 tensorflow/core/api_def/base_api/api_def_WrapDatasetVariant.pbtxt create mode 100644 tensorflow/python/data/experimental/kernel_tests/wrap_unwrap_test.py diff --git a/tensorflow/compiler/jit/xla_device_ops.h b/tensorflow/compiler/jit/xla_device_ops.h index adf0f994b8..927f983ba9 100644 --- a/tensorflow/compiler/jit/xla_device_ops.h +++ b/tensorflow/compiler/jit/xla_device_ops.h @@ -203,6 +203,8 @@ class XlaAssignVariableOp : public OpKernel { .HostMemory("output") \ .TypeConstraint("T"), \ ArgOp); \ + REGISTER_KERNEL_BUILDER( \ + Name(kArgOp).Device(DEVICE).TypeConstraint("T"), ArgOp); \ \ REGISTER_KERNEL_BUILDER(Name(kRetOp) \ .Device(DEVICE) \ diff --git a/tensorflow/core/api_def/base_api/api_def_UnwrapDatasetVariant.pbtxt b/tensorflow/core/api_def/base_api/api_def_UnwrapDatasetVariant.pbtxt new file mode 100644 index 0000000000..7b3f88a1da --- /dev/null +++ b/tensorflow/core/api_def/base_api/api_def_UnwrapDatasetVariant.pbtxt @@ -0,0 +1,4 @@ +op { + graph_op_name: "UnwrapDatasetVariant" + visibility: HIDDEN +} diff --git a/tensorflow/core/api_def/base_api/api_def_WrapDatasetVariant.pbtxt b/tensorflow/core/api_def/base_api/api_def_WrapDatasetVariant.pbtxt new file mode 100644 index 0000000000..40f5c7a0d2 --- /dev/null +++ b/tensorflow/core/api_def/base_api/api_def_WrapDatasetVariant.pbtxt @@ -0,0 +1,4 @@ +op { + graph_op_name: "WrapDatasetVariant" + visibility: HIDDEN +} diff --git a/tensorflow/core/framework/dataset.cc b/tensorflow/core/framework/dataset.cc index 0e48932da7..6e21433271 100644 --- a/tensorflow/core/framework/dataset.cc +++ b/tensorflow/core/framework/dataset.cc @@ -17,6 +17,8 @@ limitations under the License. #include "tensorflow/core/framework/device_base.h" #include "tensorflow/core/framework/function.h" +#include "tensorflow/core/framework/variant_encode_decode.h" +#include "tensorflow/core/framework/variant_op_registry.h" #include "tensorflow/core/graph/graph_def_builder.h" #include "tensorflow/core/graph/node_builder.h" #include "tensorflow/core/platform/mutex.h" @@ -74,6 +76,113 @@ class DatasetVariantWrapper { DatasetBase* const dataset_; // Owns one reference. }; +const char kWrappedDatasetVariantTypeName[] = + "tensorflow::data::WrappedDatasetVariant"; + +class WrappedDatasetVariantWrapper { + public: + WrappedDatasetVariantWrapper() {} + + explicit WrappedDatasetVariantWrapper(const Tensor& ds_tensor) + : ds_tensor_(ds_tensor) {} + + Tensor get() const { return ds_tensor_; } + + string TypeName() const { return "tensorflow::WrappedDatasetVariantWrapper"; } + + string DebugString() const { + return "tensorflow::WrappedDatasetVariantWrapper::DebugString"; + } + + void Encode(VariantTensorData* data) const { + *(data->add_tensors()) = ds_tensor_; + } + + bool Decode(const VariantTensorData& data) { + ds_tensor_ = data.tensors(0); + return true; + } + + private: + Tensor ds_tensor_; +}; + +class WrapDatasetVariantOp : public OpKernel { + public: + explicit WrapDatasetVariantOp(OpKernelConstruction* ctx) : OpKernel(ctx) {} + + void Compute(OpKernelContext* ctx) override { + const Tensor& tensor = ctx->input(0); + OP_REQUIRES(ctx, + tensor.dtype() == DT_VARIANT && + TensorShapeUtils::IsScalar(tensor.shape()), + errors::InvalidArgument( + "Dataset tensor must be a scalar of dtype DT_VARIANT.")); + DatasetBase* unused; + OP_REQUIRES_OK(ctx, GetDatasetFromVariantTensor(tensor, &unused)); + Tensor* output = nullptr; + OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({}), &output)); + output->scalar()() = WrappedDatasetVariantWrapper(tensor); + } +}; + +REGISTER_KERNEL_BUILDER(Name("WrapDatasetVariant").Device(DEVICE_CPU), + WrapDatasetVariantOp); +REGISTER_KERNEL_BUILDER(Name("WrapDatasetVariant") + .HostMemory("input_handle") + .HostMemory("output_handle") + .Device(DEVICE_GPU), + WrapDatasetVariantOp); + +class UnwrapDatasetVariantOp : public OpKernel { + public: + explicit UnwrapDatasetVariantOp(OpKernelConstruction* ctx) : OpKernel(ctx) {} + + void Compute(OpKernelContext* ctx) override { + const Tensor& tensor = ctx->input(0); + OP_REQUIRES(ctx, + tensor.dtype() == DT_VARIANT && + TensorShapeUtils::IsScalar(tensor.shape()), + errors::InvalidArgument( + "Dataset tensor must be a scalar of dtype DT_VARIANT.")); + Variant variant = tensor.scalar()(); + const WrappedDatasetVariantWrapper* wrapper = + variant.get(); + OP_REQUIRES(ctx, wrapper != nullptr, + errors::InvalidArgument( + "Tensor must be a WrappedDataset variant object.")); + Tensor ds_tensor = wrapper->get(); + OP_REQUIRES_OK(ctx, ctx->set_output("output_handle", ds_tensor)); + } +}; + +REGISTER_KERNEL_BUILDER(Name("UnwrapDatasetVariant").Device(DEVICE_CPU), + UnwrapDatasetVariantOp); +REGISTER_KERNEL_BUILDER(Name("UnwrapDatasetVariant") + .HostMemory("input_handle") + .HostMemory("output_handle") + .Device(DEVICE_GPU), + UnwrapDatasetVariantOp); + +static Status WrappedDatasetVariantDeviceCopy( + const WrappedDatasetVariantWrapper& from, WrappedDatasetVariantWrapper* to, + const UnaryVariantOpRegistry::AsyncTensorDeviceCopyFn& copy) { + *to = WrappedDatasetVariantWrapper(from); + return Status::OK(); +} + +#define REGISTER_OPTIONAL_COPY(DIRECTION) \ + INTERNAL_REGISTER_UNARY_VARIANT_DEVICE_COPY_FUNCTION( \ + WrappedDatasetVariantWrapper, DIRECTION, \ + WrappedDatasetVariantDeviceCopy) + +REGISTER_OPTIONAL_COPY(VariantDeviceCopyDirection::HOST_TO_DEVICE); +REGISTER_OPTIONAL_COPY(VariantDeviceCopyDirection::DEVICE_TO_HOST); +REGISTER_OPTIONAL_COPY(VariantDeviceCopyDirection::DEVICE_TO_DEVICE); + +REGISTER_UNARY_VARIANT_DECODE_FUNCTION(WrappedDatasetVariantWrapper, + kWrappedDatasetVariantTypeName); + } // namespace Status GraphDefBuilderWrapper::AddDataset( diff --git a/tensorflow/core/kernels/function_ops.cc b/tensorflow/core/kernels/function_ops.cc index cca3cfbd7c..90f94ee4a0 100644 --- a/tensorflow/core/kernels/function_ops.cc +++ b/tensorflow/core/kernels/function_ops.cc @@ -122,6 +122,9 @@ REGISTER_KERNEL_BUILDER(Name(kArgOp) .TypeConstraint("T"), ArgOp); +REGISTER_KERNEL_BUILDER( + Name(kArgOp).Device(DEVICE_GPU).TypeConstraint("T"), ArgOp); + #define REGISTER(type) \ REGISTER_KERNEL_BUILDER( \ Name(kRetOp).Device(DEVICE_GPU).TypeConstraint("T"), RetvalOp); diff --git a/tensorflow/core/ops/dataset_ops.cc b/tensorflow/core/ops/dataset_ops.cc index 9163ed715d..1c117166de 100644 --- a/tensorflow/core/ops/dataset_ops.cc +++ b/tensorflow/core/ops/dataset_ops.cc @@ -687,6 +687,16 @@ REGISTER_OP("MapDefun") return Status::OK(); }); +REGISTER_OP("WrapDatasetVariant") + .Input("input_handle: variant") + .Output("output_handle: variant") + .SetShapeFn(shape_inference::ScalarShape); + +REGISTER_OP("UnwrapDatasetVariant") + .Input("input_handle: variant") + .Output("output_handle: variant") + .SetShapeFn(shape_inference::ScalarShape); + REGISTER_OP("MultiDeviceIterator") .Output("handle: resource") .Attr("devices: list(string) >= 1") diff --git a/tensorflow/python/data/experimental/kernel_tests/BUILD b/tensorflow/python/data/experimental/kernel_tests/BUILD index 07ed4d2bce..548eb422ed 100644 --- a/tensorflow/python/data/experimental/kernel_tests/BUILD +++ b/tensorflow/python/data/experimental/kernel_tests/BUILD @@ -716,3 +716,14 @@ py_test( "//tensorflow/python/data/ops:dataset_ops", ], ) + +cuda_py_test( + name = "wrap_unwrap_test", + size = "small", + srcs = ["wrap_unwrap_test.py"], + additional_deps = [ + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/kernel_tests:test_base", + "//tensorflow/python/data/ops:dataset_ops", + ], +) diff --git a/tensorflow/python/data/experimental/kernel_tests/wrap_unwrap_test.py b/tensorflow/python/data/experimental/kernel_tests/wrap_unwrap_test.py new file mode 100644 index 0000000000..9c734b65e0 --- /dev/null +++ b/tensorflow/python/data/experimental/kernel_tests/wrap_unwrap_test.py @@ -0,0 +1,69 @@ +# 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 Wrapping / Unwrapping dataset variants.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.data.kernel_tests import test_base +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import gen_dataset_ops +from tensorflow.python.platform import test + + +class WrapDatasetVariantTest(test_base.DatasetTestBase): + + def testBasic(self): + ds = dataset_ops.Dataset.range(100) + ds_variant = ds._as_variant_tensor() # pylint: disable=protected-access + + wrapped_variant = gen_dataset_ops.wrap_dataset_variant(ds_variant) + unwrapped_variant = gen_dataset_ops.unwrap_dataset_variant(wrapped_variant) + + variant_ds = dataset_ops._VariantDataset(unwrapped_variant, + ds._element_structure) + iterator = dataset_ops.make_initializable_iterator(variant_ds) + get_next = iterator.get_next() + + with self.cached_session(): + self.evaluate(iterator.initializer) + for i in range(100): + self.assertEqual(i, self.evaluate(get_next)) + + def testGPU(self): + ds = dataset_ops.Dataset.range(100) + ds_variant = ds._as_variant_tensor() # pylint: disable=protected-access + wrapped_variant = gen_dataset_ops.wrap_dataset_variant(ds_variant) + + with ops.device("/gpu:0"): + gpu_wrapped_variant = array_ops.identity(wrapped_variant) + + unwrapped_variant = gen_dataset_ops.unwrap_dataset_variant( + gpu_wrapped_variant) + variant_ds = dataset_ops._VariantDataset(unwrapped_variant, + ds._element_structure) + iterator = dataset_ops.make_initializable_iterator(variant_ds) + get_next = iterator.get_next() + + with self.cached_session(): + self.evaluate(iterator.initializer) + for i in range(100): + self.assertEqual(i, self.evaluate(get_next)) + + +if __name__ == "__main__": + test.main() -- GitLab From b56d14fbcb33d974f593e0c874bc404d1f1192b3 Mon Sep 17 00:00:00 2001 From: Scott Zhu Date: Fri, 7 Dec 2018 09:56:19 -0800 Subject: [PATCH 201/873] Rework the Cudnn LSTM implementation to get rid the extra input bias. This heavily clear up the logic for save/load model since the weights for input bias is gone. The new implementation is mathematically same as the canonical LSTM. PiperOrigin-RevId: 224531752 --- tensorflow/python/keras/engine/saving.py | 58 ------------- tensorflow/python/keras/engine/saving_test.py | 64 -------------- tensorflow/python/keras/layers/recurrent.py | 46 +++------- .../python/keras/layers/unified_lstm_test.py | 84 +++++++++++++++++++ 4 files changed, 94 insertions(+), 158 deletions(-) diff --git a/tensorflow/python/keras/engine/saving.py b/tensorflow/python/keras/engine/saving.py index 15ba5f78d9..54d9e32fb2 100644 --- a/tensorflow/python/keras/engine/saving.py +++ b/tensorflow/python/keras/engine/saving.py @@ -551,68 +551,10 @@ def preprocess_weights_for_loading(layer, if layer.__class__.__name__ == 'ConvLSTM2D': weights[1] = np.transpose(weights[1], (3, 2, 0, 1)) - weights = _convert_unified_lstm_weights(layer, weights) - # convert CuDNN layers return _convert_rnn_weights(layer, weights) -def _convert_unified_lstm_weights(layer, weights): - """Converts weights for Unified LSTM layer. - - The input weights suppose to have 2, 3 or 4 items. - 1. kernel. (i, f, c, o gates concat among axis 1) - 2. recurrent_kernel. (i, f, c, o concat among axis 1) - 3. recurrent_bias. (optional, only available when use bias) - 4. input_bias (optional, only available when use bias and cudnn). - Kernel and recurrent_kernel does not need any conversion. During load(), - since the layer could be built with the parameter that does not support the - defun approach, it is possible that cudnn_bias variable is not created, or - even created but not used during actual run. Because of that, we sum up the - value of two biases, and give it to recurrent_bias only. Mathematically, the - LSTM is calculated as following formula: - - i_t = sigmoid(w_i * x_t + r_i * h_(t-1) + b_wi + b_ri) - f_t = sigmoid(w_f * x_t + r_f * h_(t-1) + b_wf + b_rf) - o_t = sigmoid(w_o * x_t + r_o * h_(t-1) + b_wo + b_ro) - c'_t = tanh(w_c * x_t + r_c * h_(t-1) + b_wc + b_rc) - c_t = f_t . c_(t-1) + i_t . c'_t - h_t = o_t . tanh(c_t) - - Note that b_w{x} is the input_bias, and b_r{x} is the recurrent_bias. - Since it is a linear add, it is fine to give b_r{x} 100% and b_w{x} 0%, as - long as the sum are the same. - - Args: - layer: The keras layer that will be loaded with weights. - weights: the list of numpy arrays which hold the weights to be loaded. - - Returns: - weights: the processed list of numpy arrays. - """ - if layer.__class__.__name__ == 'UnifiedLSTM': - if len(weights) not in [3, 4]: - # Only handles the bias conversion in this function, in the case when - # bias is not used or weights in unexpected length, do nothing and return. - return weights - - if len(weights) == 3: - recurrent_bias = weights[2] - else: - # Add all the bias value to recurrent_bias - recurrent_bias = weights[2] + weights[3] - - if len(layer.weights) == 3: - weights = weights[:2] + [recurrent_bias] - elif len(layer.weights) == 4: - # Create a zero filled input_bias, since all the weights have given - # to recurrent bias. - input_bias = np.zeros_like(recurrent_bias) - weights = weights[:2] + [recurrent_bias, input_bias] - - return weights - - def _convert_rnn_weights(layer, weights): """Converts weights for RNN layers between native and CuDNN format. diff --git a/tensorflow/python/keras/engine/saving_test.py b/tensorflow/python/keras/engine/saving_test.py index 8fcefce748..6d9d9a2fca 100644 --- a/tensorflow/python/keras/engine/saving_test.py +++ b/tensorflow/python/keras/engine/saving_test.py @@ -221,70 +221,6 @@ class TestWeightSavingAndLoading(test.TestCase, parameterized.TestCase): for (x, y) in zip(weights1, weights2) ] - @parameterized.named_parameters( - # test_name, use_bias, bias_initializer, activation - ('normal', True, 'zeros', 'tanh'), - ('no_bias', False, 'zeros', 'tanh'), - # TODO(scottzhu): Reenable this test case when the approach is decided. - # ('random_bias', True, 'random_uniform', 'tanh'), - ('no_cudnn_bias', True, 'zeros', 'relu') - ) - def test_process_weights_for_loading_unified_lstm( - self, use_bias, bias_initializer, activation): - if h5py is None: - return - - temp_dir = self.get_temp_dir() - self.addCleanup(shutil.rmtree, temp_dir) - h5_path = os.path.join(temp_dir, 'test.h5') - - batch = 10 - timestep = 3 - input_dim = 5 - units = 2 - - x = np.random.random((batch, timestep, input_dim)) - - def build_model(): - inputs = keras.layers.Input( - shape=[timestep, input_dim], dtype=dtypes.float32) - layer = keras.layers.UnifiedLSTM( - units, - activation=activation, - use_bias=use_bias, - bias_initializer=bias_initializer) - output = layer(inputs) - return keras.models.Model(inputs, output), layer - - with self.cached_session(): - model, layer = build_model() - y_ref = model.predict(x) - model.save_weights(h5_path) - - cloned_model, new_layer = build_model() - cloned_model.load_weights(h5_path) - y = cloned_model.predict(x) - - self.assertAllClose(y, y_ref) - - # Test the individual layer weights. - weights1 = layer.get_weights() - weights2 = new_layer.get_weights() - self.assertLen(weights1, len(weights2)) - # kernel and current kernel should be the same. - self.assertAllClose(weights1[:2], weights2[:2]) - - if len(weights2) >= 3: - # Test recurrent bias - expected_recurrent_bias = weights1[2] - if len(weights1) == 4: - expected_recurrent_bias += weights1[3] - self.assertAllClose(weights2[2], expected_recurrent_bias) - - if len(weights2) == 4: - # Test recovered input_gate_bias to be always zero - self.assertAllClose(weights2[3], np.zeros_like(weights1[3])) - def test_sequential_weight_loading(self): if h5py is None: return diff --git a/tensorflow/python/keras/layers/recurrent.py b/tensorflow/python/keras/layers/recurrent.py index 189ad98794..fdc2acd538 100644 --- a/tensorflow/python/keras/layers/recurrent.py +++ b/tensorflow/python/keras/layers/recurrent.py @@ -2666,21 +2666,6 @@ class UnifiedLSTM(LSTM): activation == 'tanh' and recurrent_dropout == 0 and not unroll and use_bias and bias_regularizer is None) - def build(self, input_shape): - super(UnifiedLSTM, self).build(input_shape) - if self.could_use_cudnn: - # Add a new set of bias for CuDNN implementation only. Standard LSTM only - # has bias for recurrent kernel, while CuDNN LSTM has an extra set for - # input gate as well. - self.cudnn_bias = self.add_weight( - shape=(self.units * 4,), - name='cudnn_bias', - use_resource=True, - initializer=self.bias_initializer, - regularizer=self.bias_regularizer, - constraint=self.bias_constraint) - self.built = True - def call(self, inputs, mask=None, training=None, initial_state=None): # LSTM does not support constants. Ignore it during process. inputs, initial_state, _ = self._process_inputs(inputs, initial_state, None) @@ -2721,8 +2706,6 @@ class UnifiedLSTM(LSTM): # Reverse time axis. inputs = K.reverse(inputs, 1) - combined_bias = array_ops.concat([self.cudnn_bias, self.cell.bias], 0) - if 0 < self.dropout < 1: if self._dropout_mask is None: self._dropout_mask = _generate_dropout_mask( @@ -2757,23 +2740,23 @@ class UnifiedLSTM(LSTM): if context.num_gpus() > 0: last_output, outputs, new_h, new_c, runtime = defun_cudnn_lstm( inputs, initial_state[0], initial_state[1], self.cell.kernel, - self.cell.recurrent_kernel, combined_bias, self.time_major) + self.cell.recurrent_kernel, self.cell.bias, self.time_major) else: last_output, outputs, new_h, new_c, runtime = defun_standard_lstm( inputs, initial_state[0], initial_state[1], self.cell.kernel, - self.cell.recurrent_kernel, combined_bias, self.activation, + self.cell.recurrent_kernel, self.cell.bias, self.activation, self.recurrent_activation, self.time_major) else: # Call the normal LSTM impl and register the CuDNN impl function. The # grappler will kick in during session execution to optimize the graph. last_output, outputs, new_h, new_c, runtime = defun_standard_lstm( inputs, initial_state[0], initial_state[1], self.cell.kernel, - self.cell.recurrent_kernel, combined_bias, self.activation, + self.cell.recurrent_kernel, self.cell.bias, self.activation, self.recurrent_activation, self.time_major) function.register(defun_cudnn_lstm, inputs, initial_state[0], initial_state[1], self.cell.kernel, - self.cell.recurrent_kernel, combined_bias, + self.cell.recurrent_kernel, self.cell.bias, self.time_major) states = [new_h, new_c] @@ -2800,8 +2783,6 @@ class UnifiedLSTM(LSTM): if self.trainable: weights = [] weights += self.cell.trainable_weights - if getattr(self, 'cudnn_bias', None) is not None: - weights += [self.cudnn_bias] return weights return [] @@ -2810,8 +2791,6 @@ class UnifiedLSTM(LSTM): if not self.trainable: weights = [] weights += self.cell.non_trainable_weights - if getattr(self, 'cudnn_bias', None) is not None: - weights += [self.cudnn_bias] return weights return [] @@ -2830,8 +2809,6 @@ class UnifiedLSTM(LSTM): def get_weights(self): weights = [] weights += self.cell.weights - if getattr(self, 'cudnn_bias', None) is not None: - weights += [self.cudnn_bias] return K.batch_get_value(weights) def set_weights(self, weights): @@ -2839,10 +2816,6 @@ class UnifiedLSTM(LSTM): cell_weights = weights[:len(self.cell.weights)] if cell_weights: tuples.append((self.cell.weights, cell_weights)) - if getattr(self, 'cudnn_bias', None) is not None: - cudnn_bias_weights = weights[len(self.cell.weights):] - if cudnn_bias_weights: - tuples.append((self.cudnn_bias, cudnn_bias_weights)) K.batch_set_value(tuples) @@ -2919,9 +2892,6 @@ def standard_lstm(inputs, init_h, init_c, kernel, recurrent_kernel, bias, input_shape = K.int_shape(inputs) timesteps = input_shape[0] if time_major else input_shape[1] - # Only use the second half of the bias weights. - _, real_bias = array_ops.split(bias, 2) - def step(cell_inputs, cell_states): """Step function that will be used by Keras RNN backend.""" h_tm1 = cell_states[0] # previous memory state @@ -2929,7 +2899,7 @@ def standard_lstm(inputs, init_h, init_c, kernel, recurrent_kernel, bias, z = K.dot(cell_inputs, kernel) z += K.dot(h_tm1, recurrent_kernel) - z = K.bias_add(z, real_bias) + z = K.bias_add(z, bias) z0, z1, z2, z3 = array_ops.split(z, 4, axis=1) @@ -2962,9 +2932,13 @@ def cudnn_lstm(inputs, input_h, input_c, kernel, recurrent_kernel, bias, weights = array_ops.split(kernel, 4, axis=1) weights += array_ops.split(recurrent_kernel, 4, axis=1) + # CuDNN has an extra set of bias for inputs, we disable them (setting to 0), + # so that mathematically it is same as the canonical LSTM implementation. + full_bias = array_ops.concat((array_ops.zeros_like(bias), bias), 0) + params = _canonical_to_params( weights=weights, - biases=array_ops.split(bias, 8), + biases=array_ops.split(full_bias, 8), shape=constant_op.constant([-1]), transpose_weights=True) diff --git a/tensorflow/python/keras/layers/unified_lstm_test.py b/tensorflow/python/keras/layers/unified_lstm_test.py index b004284140..2cb3eff8fd 100644 --- a/tensorflow/python/keras/layers/unified_lstm_test.py +++ b/tensorflow/python/keras/layers/unified_lstm_test.py @@ -18,6 +18,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import os +import shutil import time from absl.testing import parameterized @@ -26,6 +28,7 @@ import numpy as np from tensorflow.core.protobuf import config_pb2 from tensorflow.core.protobuf import rewriter_config_pb2 from tensorflow.python import keras +from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import test_util @@ -175,6 +178,86 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): bias_regularizer=bias_regularizer) self.assertFalse(layer.could_use_cudnn) + def test_unified_lstm_feature_parity_with_canonical_lstm(self): + with context.eager_mode(): + # Run this test under eager only due to b/120160788 for model.set_weights. + input_shape = 10 + rnn_state_size = 8 + timestep = 4 + batch = 20 + + (x_train, y_train), _ = testing_utils.get_test_data( + train_samples=batch, + test_samples=0, + input_shape=(timestep, input_shape), + num_classes=rnn_state_size) + y_train = keras.utils.to_categorical(y_train, rnn_state_size) + + inputs = keras.layers.Input( + shape=[timestep, input_shape], dtype=dtypes.float32) + lstm_layer = keras.layers.LSTM(rnn_state_size, + recurrent_activation='sigmoid') + output = lstm_layer(inputs) + lstm_model = keras.models.Model(inputs, output) + weights = lstm_model.get_weights() + y_1 = lstm_model.predict(x_train) + lstm_model.compile('rmsprop', 'mse') + lstm_model.fit(x_train, y_train) + y_2 = lstm_model.predict(x_train) + + with test_util.device(use_gpu=True): + cudnn_layer = keras.layers.UnifiedLSTM(rnn_state_size, + recurrent_activation='sigmoid') + cudnn_model = keras.models.Model(inputs, cudnn_layer(inputs)) + cudnn_model.set_weights(weights) + y_3 = cudnn_model.predict(x_train) + cudnn_model.compile('rmsprop', 'mse') + cudnn_model.fit(x_train, y_train) + y_4 = cudnn_model.predict(x_train) + + self.assertAllClose(y_1, y_3) + self.assertAllClose(y_2, y_4) + + @parameterized.named_parameters( + # test_name, use_bias, bias_initializer, activation + ('normal', True, 'zeros'), + ('no_bias', False, 'zeros'), + ('random_bias', True, 'random_uniform'), + ) + @test_util.run_in_graph_and_eager_modes(config=_config) + def test_unified_lstm_model_save_load(self, use_bias, bias_initializer): + temp_dir = self.get_temp_dir() + self.addCleanup(shutil.rmtree, temp_dir) + h5_path = os.path.join(temp_dir, 'test.h5') + + batch = 10 + timestep = 3 + input_dim = 5 + units = 2 + + x = np.random.random((batch, timestep, input_dim)) + + def build_model(): + inputs = keras.layers.Input( + shape=[timestep, input_dim], dtype=dtypes.float32) + layer = keras.layers.UnifiedLSTM( + units, + use_bias=use_bias, + bias_initializer=bias_initializer) + output = layer(inputs) + return keras.models.Model(inputs, output), layer + + model, layer = build_model() + y_ref = model.predict(x) + model.save_weights(h5_path) + + cloned_model, new_layer = build_model() + cloned_model.load_weights(h5_path) + y = cloned_model.predict(x) + + self.assertAllClose(y, y_ref) + self.assertAllClose(layer.get_weights(), new_layer.get_weights()) + @test_util.run_in_graph_and_eager_modes(config=_config) def test_unified_lstm_output_on_multiple_kernel(self): input_shape = 10 @@ -240,6 +323,7 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): model.compile('rmsprop', loss='mse') model.fit(x_train, y_train, epochs=epoch) model.evaluate(x_train, y_train) + model.predict(x_train) @test_util.run_in_graph_and_eager_modes(config=_config) def test_return_sequences_LSTM(self): -- GitLab From 8f2bc575d908bf1d1e743dd0e67c7115c5b757a9 Mon Sep 17 00:00:00 2001 From: Ayush Dubey Date: Fri, 7 Dec 2018 09:57:11 -0800 Subject: [PATCH 202/873] Reuse GPUs for multiple ranks in NcclManager test. Since NCCL 2.1.2, NCCL uses cooperative group launch by default which results in deadlocks in the NcclManager test if number of ranks > number of GPUs. Setting NCCL_LAUNCH_MODE to PARALLEL, which was the default before NCCL 2.1.2, fixes this. PiperOrigin-RevId: 224531851 --- tensorflow/core/nccl/nccl_manager_test.cc | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/tensorflow/core/nccl/nccl_manager_test.cc b/tensorflow/core/nccl/nccl_manager_test.cc index f43103e120..f9ed4d0b9a 100644 --- a/tensorflow/core/nccl/nccl_manager_test.cc +++ b/tensorflow/core/nccl/nccl_manager_test.cc @@ -65,6 +65,7 @@ class NcclManagerTest : public ::testing::Test { static void SetUpTestCase() { setenv("NCCL_DEBUG", "WARN", 1 /* replace */); + setenv("NCCL_LAUNCH_MODE", "PARALLEL", 1 /* replace */); devices_ = new std::vector>(GetGPUDevices()); LOG(ERROR) << "Running test with " << devices_->size() << " gpus"; } @@ -200,7 +201,7 @@ TYPED_TEST_CASE(NcclManagerTest, TypeList); // Test basic sum reduction. TYPED_TEST(NcclManagerTest, BasicSumReduction) { - const int num_ranks = this->NumGPUs(); + const int num_ranks = 4; for (int op = 0; op < 4; ++op) { ncclRedOp_t reduction_op = static_cast(op); @@ -208,6 +209,7 @@ TYPED_TEST(NcclManagerTest, BasicSumReduction) { this->MakeTestCase(num_ranks, reduction_op, TensorShape({2, 3}), 0.0f)); for (int rank = 0; rank < num_ranks; ++rank) { auto* device = this->GetDevice(rank); + VLOG(2) << "rank " << rank << " device " << device->name(); auto* event_mgr = device->tensorflow_gpu_device_info()->event_mgr; auto* stream = device->tensorflow_gpu_device_info()->stream; NcclManager::instance()->AddToAllReduce( @@ -224,14 +226,12 @@ TYPED_TEST(NcclManagerTest, BasicSumReduction) { // Same as the Basic test, but with multiple threads launching parts of many // reductions. // -// Testing the multi-rank execution is currently reduced as it can hang when run -// with num_ranks > devices->size(), for some GPUs (e.g. K20m). -// To test the higher settings, increase num_ranks, -// num_collectives_per_iteration and time_limit_micros. +// To run test longer, increase num_ranks, num_collectives_per_iteration and +// time_limit_micros. TYPED_TEST(NcclManagerTest, MultipleCallers) { - const int num_ranks = this->NumGPUs(); + const int num_ranks = 4; const int num_collectives_per_iteration = 10; // 1000; - const int num_threads = 3; + const int num_threads = num_ranks * 2; const int time_limit_micros = 100; // 60 * 30 * 1000 * 1000; int64 start = Env::Default()->NowMicros(); -- GitLab From 19b07482d6a80943c832d167df6f49c0a4008462 Mon Sep 17 00:00:00 2001 From: Smit Hinsu Date: Fri, 7 Dec 2018 09:57:49 -0800 Subject: [PATCH 203/873] Fix tensorflow nccl CODEOWNERS PiperOrigin-RevId: 224531965 --- CODEOWNERS | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CODEOWNERS b/CODEOWNERS index bfcdc2a23f..cb3fa23124 100644 --- a/CODEOWNERS +++ b/CODEOWNERS @@ -1,7 +1,7 @@ # Where component owners are known, add them here. /tenosrflow/core/debug @caisq -/tensorflow/core/nccl/ @azaks @csigg +/tensorflow/core/nccl/ @azaks2 @chsigg /tensorflow/core/platform/windows/ @mrry /tensorflow/core/platform/s3 @yongtang /tensorflow/go @asimshankar -- GitLab From cc079ee316c75993f86a19c6b8820d2bc36a33b6 Mon Sep 17 00:00:00 2001 From: Jianwei Xie Date: Fri, 7 Dec 2018 10:09:13 -0800 Subject: [PATCH 204/873] Lower the tol for metrics to 1e-5. And add a predict_tol. PiperOrigin-RevId: 224534209 --- tensorflow/contrib/distribute/python/keras_test.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/tensorflow/contrib/distribute/python/keras_test.py b/tensorflow/contrib/distribute/python/keras_test.py index d66fc52cf2..dea208232a 100644 --- a/tensorflow/contrib/distribute/python/keras_test.py +++ b/tensorflow/contrib/distribute/python/keras_test.py @@ -1302,16 +1302,18 @@ class TestDistributionStrategyCorrectness(test.TestCase, def test_correctness(self, distribution, use_numpy, use_validation_data): with self.cached_session(): - weights_tolerance = 1e-5 - metrics_tolerance = 1e-4 + default_tolerance = 1e-5 + weights_tolerance = default_tolerance + metrics_tolerance = default_tolerance + predict_tolerance = default_tolerance if isinstance(distribution, (mirrored_strategy.MirroredStrategy, mirrored_strategy.CoreMirroredStrategy)): # TODO(b/119257215): Weights are not exactly the same, so use lower # tolerance for now. weights_tolerance = 1e-4 + predict_tolerance = 1e-4 - metrics = ['mse'] keras.backend.set_image_data_format('channels_last') np.random.seed(_RANDOM_SEED) random_seed.set_random_seed(_RANDOM_SEED) @@ -1351,7 +1353,7 @@ class TestDistributionStrategyCorrectness(test.TestCase, model.compile( loss=keras.losses.mean_squared_error, optimizer=gradient_descent_keras.SGD(0.5), - metrics=metrics, + metrics=['mse'], distribute=with_distribution) training_inputs, eval_inputs, predict_inputs = ( @@ -1395,8 +1397,8 @@ class TestDistributionStrategyCorrectness(test.TestCase, self.assertAllClose( predict_with_ds, predict_without_ds, - atol=weights_tolerance, - rtol=weights_tolerance, + atol=predict_tolerance, + rtol=predict_tolerance, msg='Fail to assert predict results.') if not (isinstance(distribution, tpu_strategy.TPUStrategy) and -- GitLab From 89912db5f751778b0a24c7e77e4083bbe7c042a3 Mon Sep 17 00:00:00 2001 From: Mihai Maruseac Date: Fri, 7 Dec 2018 10:14:25 -0800 Subject: [PATCH 205/873] Change API for TF 2.0 for tf.space_to_batch and tf.space_to_batch_nd. PiperOrigin-RevId: 224535013 --- tensorflow/python/ops/array_ops.py | 10 +++++++++- tensorflow/tools/api/golden/v2/tensorflow.nn.pbtxt | 2 +- tensorflow/tools/api/golden/v2/tensorflow.pbtxt | 4 ++++ tensorflow/tools/compatibility/renames_v2.py | 1 - tensorflow/tools/compatibility/reorders_v2.py | 2 ++ tensorflow/tools/compatibility/tf_upgrade_v2.py | 8 ++++++++ .../tools/compatibility/tf_upgrade_v2_test.py | 13 +++++++++++++ 7 files changed, 37 insertions(+), 3 deletions(-) diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py index 185452e1ab..216b08a8b0 100644 --- a/tensorflow/python/ops/array_ops.py +++ b/tensorflow/python/ops/array_ops.py @@ -2652,7 +2652,7 @@ def required_space_to_batch_paddings(input_shape, return result_paddings, result_crops -@tf_export("nn.space_to_batch", v1=["nn.space_to_batch", "space_to_batch"]) +@tf_export(v1=["nn.space_to_batch", "space_to_batch"]) @deprecation.deprecated_endpoints("space_to_batch") def space_to_batch(input, paddings, block_size, name=None): # pylint: disable=redefined-builtin result = space_to_batch_nd( @@ -2667,6 +2667,14 @@ def space_to_batch(input, paddings, block_size, name=None): # pylint: disable=r space_to_batch.__doc__ = gen_array_ops.space_to_batch.__doc__ +@tf_export("space_to_batch", "nn.space_to_batch", v1=[]) +def space_to_batch_v2(input, block_shape, paddings, name=None): # pylint: disable=redefined-builtin + return space_to_batch_nd(input, block_shape, paddings, name) + + +space_to_batch_v2.__doc__ = gen_array_ops.space_to_batch_nd.__doc__ + + @tf_export(v1=["nn.space_to_depth", "space_to_depth"]) @deprecation.deprecated_endpoints("space_to_depth") def space_to_depth(input, block_size, name=None, data_format="NHWC"): # pylint: disable=redefined-builtin diff --git a/tensorflow/tools/api/golden/v2/tensorflow.nn.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.nn.pbtxt index 9e5e82483a..c75c75f2ef 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.nn.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.nn.pbtxt @@ -270,7 +270,7 @@ tf_module { } member_method { name: "space_to_batch" - argspec: "args=[\'input\', \'paddings\', \'block_size\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + argspec: "args=[\'input\', \'block_shape\', \'paddings\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "space_to_depth" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.pbtxt index 542e5cbe9a..4432cae53b 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.pbtxt @@ -928,6 +928,10 @@ tf_module { name: "sort" argspec: "args=[\'values\', \'axis\', \'direction\', \'name\'], varargs=None, keywords=None, defaults=[\'-1\', \'ASCENDING\', \'None\'], " } + member_method { + name: "space_to_batch" + argspec: "args=[\'input\', \'block_shape\', \'paddings\', \'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\'], " diff --git a/tensorflow/tools/compatibility/renames_v2.py b/tensorflow/tools/compatibility/renames_v2.py index b27fd5acc7..e4ef8eb528 100644 --- a/tensorflow/tools/compatibility/renames_v2.py +++ b/tensorflow/tools/compatibility/renames_v2.py @@ -511,7 +511,6 @@ renames = { 'tf.sets.set_intersection': 'tf.sets.intersection', 'tf.sets.set_size': 'tf.sets.size', 'tf.sets.set_union': 'tf.sets.union', - 'tf.space_to_batch': 'tf.nn.space_to_batch', 'tf.space_to_depth': 'tf.compat.v1.space_to_depth', 'tf.sparse.matmul': 'tf.sparse.sparse_dense_matmul', 'tf.sparse.merge': 'tf.compat.v1.sparse.merge', diff --git a/tensorflow/tools/compatibility/reorders_v2.py b/tensorflow/tools/compatibility/reorders_v2.py index 76f71d5804..e6596755c3 100644 --- a/tensorflow/tools/compatibility/reorders_v2.py +++ b/tensorflow/tools/compatibility/reorders_v2.py @@ -63,6 +63,7 @@ reorders = { 'tf.nn.moments': ['x', 'axes', 'shift', 'name', 'keep_dims'], 'tf.nn.pool': ['input', 'window_shape', 'pooling_type', 'padding', 'dilation_rate', 'strides', 'name', 'data_format'], 'tf.nn.separable_conv2d': ['input', 'depthwise_filter', 'pointwise_filter', 'strides', 'padding', 'rate', 'name', 'data_format'], + 'tf.nn.space_to_batch': ['input', 'paddings', 'block_size', 'name'], 'tf.nn.space_to_depth': ['input', 'block_size', 'name', 'data_format'], 'tf.nn.weighted_moments': ['x', 'axes', 'frequency_weights', 'name', 'keep_dims'], 'tf.pad': ['tensor', 'paddings', 'mode', 'name', 'constant_values'], @@ -85,6 +86,7 @@ reorders = { 'tf.serialize_sparse': ['sp_input', 'name', 'out_type'], 'tf.shape': ['input', 'name', 'out_type'], 'tf.size': ['input', 'name', 'out_type'], + 'tf.space_to_batch': ['input', 'paddings', 'block_size', 'name'], 'tf.space_to_depth': ['input', 'block_size', 'name', 'data_format'], 'tf.sparse.add': ['a', 'b', 'threshold', 'thresh'], 'tf.sparse.concat': ['axis', 'sp_inputs', 'name', 'expand_nonconcat_dim', 'concat_dim'], diff --git a/tensorflow/tools/compatibility/tf_upgrade_v2.py b/tensorflow/tools/compatibility/tf_upgrade_v2.py index 0630ce28cf..b2477a541e 100644 --- a/tensorflow/tools/compatibility/tf_upgrade_v2.py +++ b/tensorflow/tools/compatibility/tf_upgrade_v2.py @@ -51,6 +51,9 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec): "tf.batch_to_space": { "block_size": "block_shape", }, + "tf.space_to_batch": { + "block_size": "block_shape", + }, "tf.constant": { "verify_shape": "verify_shape_is_now_always_true", }, @@ -294,6 +297,10 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec): self.manual_symbol_renames = { "tf.batch_to_space_nd": "tf.batch_to_space", + "tf.space_to_batch_nd": + "tf.space_to_batch", + "tf.nn.space_to_batch": + "tf.space_to_batch", "tf.extract_image_patches": "tf.image.extract_image_patches", "tf.gfile.Copy": @@ -497,6 +504,7 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec): "tf.argmax", "tf.argmin", "tf.batch_to_space", + "tf.nn.space_to_batch", "tf.boolean_mask", "tf.convert_to_tensor", "tf.nn.moments", diff --git a/tensorflow/tools/compatibility/tf_upgrade_v2_test.py b/tensorflow/tools/compatibility/tf_upgrade_v2_test.py index b362a8081f..4460ad5182 100644 --- a/tensorflow/tools/compatibility/tf_upgrade_v2_test.py +++ b/tensorflow/tools/compatibility/tf_upgrade_v2_test.py @@ -567,6 +567,19 @@ bazel-bin/tensorflow/tools/compatibility/update/generate_v2_reorders_map _, unused_report, unused_errors, new_text = self._upgrade(text) self.assertEqual(new_text, expected_text) + def testSpacetoBatch(self): + text = "tf.space_to_batch_nd(input, shape, paddings, name)" + expected_text = "tf.space_to_batch(input, shape, paddings, name)" + _, unused_report, unused_errors, new_text = self._upgrade(text) + self.assertEqual(new_text, expected_text) + + text = "tf.nn.space_to_batch(input, paddings, block_size, name)" + expected_text = ( + "tf.space_to_batch(input=input, paddings=paddings, " + "block_size=block_size, name=name)") + _, unused_report, unused_errors, new_text = self._upgrade(text) + self.assertEqual(new_text, expected_text) + def testInTopK(self): text = "tf.math.in_top_k(a, b, c, n)" expected_text = ( -- GitLab From a40ad0ce882e5cc482b16892162f2ce061aea055 Mon Sep 17 00:00:00 2001 From: Rachel Lim Date: Fri, 7 Dec 2018 10:14:36 -0800 Subject: [PATCH 206/873] [tf.data] tiny test change PiperOrigin-RevId: 224535049 --- .../python/data/experimental/kernel_tests/optimization/BUILD | 1 - .../kernel_tests/optimization/noop_elimination_test.py | 5 ----- 2 files changed, 6 deletions(-) diff --git a/tensorflow/python/data/experimental/kernel_tests/optimization/BUILD b/tensorflow/python/data/experimental/kernel_tests/optimization/BUILD index f214944254..2fc243aa13 100644 --- a/tensorflow/python/data/experimental/kernel_tests/optimization/BUILD +++ b/tensorflow/python/data/experimental/kernel_tests/optimization/BUILD @@ -279,7 +279,6 @@ py_test( "//tensorflow/python:errors", "//tensorflow/python:math_ops", "//tensorflow/python/data/experimental/ops:optimization", - "//tensorflow/python/data/experimental/ops:optimization_options", "//tensorflow/python/data/kernel_tests:test_base", "//tensorflow/python/data/ops:dataset_ops", ], diff --git a/tensorflow/python/data/experimental/kernel_tests/optimization/noop_elimination_test.py b/tensorflow/python/data/experimental/kernel_tests/optimization/noop_elimination_test.py index ce86bfa4e0..8058f53eea 100644 --- a/tensorflow/python/data/experimental/kernel_tests/optimization/noop_elimination_test.py +++ b/tensorflow/python/data/experimental/kernel_tests/optimization/noop_elimination_test.py @@ -18,7 +18,6 @@ from __future__ import division from __future__ import print_function from tensorflow.python.data.experimental.ops import optimization -from tensorflow.python.data.experimental.ops.optimization_options import OptimizationOptions from tensorflow.python.data.kernel_tests import test_base from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import constant_op @@ -42,10 +41,6 @@ class NoopEliminationTest(test_base.DatasetTestBase): ["FiniteRepeat", "FiniteSkip", "Prefetch", "MemoryCacheImpl"])) dataset = dataset.repeat(some_tensor).skip(5).take(-1).skip(0).repeat( 1).prefetch(0).prefetch(1).cache() - options = dataset_ops.Options() - options.experimental_optimization = OptimizationOptions() - options.experimental_optimization.noop_elimination = True - dataset = dataset.with_options(options) self.assertDatasetProduces(dataset, expected_output=range(5)) -- GitLab From 6d456192027c309a902fb63e15c57254e4d7ab42 Mon Sep 17 00:00:00 2001 From: Yu-Cheng Ling Date: Fri, 7 Dec 2018 10:43:54 -0800 Subject: [PATCH 207/873] Fix TFLite iOS example Podfile PiperOrigin-RevId: 224540375 --- tensorflow/lite/examples/ios/camera/Podfile | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tensorflow/lite/examples/ios/camera/Podfile b/tensorflow/lite/examples/ios/camera/Podfile index 96a0d23426..2ed5c8da1e 100644 --- a/tensorflow/lite/examples/ios/camera/Podfile +++ b/tensorflow/lite/examples/ios/camera/Podfile @@ -1,5 +1,7 @@ platform :ios, '8.0' inhibit_all_warnings! +project 'tflite_camera_example.xcodeproj' + target 'tflite_camera_example' pod 'TensorFlowLite', '1.12.0' -- GitLab From c8e92c679ec4c098bb9404bf9f38a5f4f02e3c42 Mon Sep 17 00:00:00 2001 From: Alexandre Passos Date: Fri, 7 Dec 2018 10:52:12 -0800 Subject: [PATCH 208/873] Better propagation of variable shape inference. PiperOrigin-RevId: 224541903 --- .../resource_variable_ops_test.py | 29 +++++++++++++++++++ tensorflow/python/ops/array_ops.py | 6 +++- tensorflow/python/ops/custom_gradient.py | 4 +++ 3 files changed, 38 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/kernel_tests/resource_variable_ops_test.py b/tensorflow/python/kernel_tests/resource_variable_ops_test.py index b57d9d47aa..3992d6bdfb 100644 --- a/tensorflow/python/kernel_tests/resource_variable_ops_test.py +++ b/tensorflow/python/kernel_tests/resource_variable_ops_test.py @@ -33,6 +33,8 @@ from tensorflow.python.framework import tensor_util from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import custom_gradient +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 @@ -585,6 +587,33 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): v.load(2.0) self.assertEqual(2.0, self.evaluate(v.value())) + def testShapePassedToGradient(self): + with ops.Graph().as_default(): + @custom_gradient.custom_gradient + def differentiable_scatter_update(handle, indices, values): + with ops.control_dependencies([ + resource_variable_ops.resource_scatter_update( + handle, indices, values)]): + new_handle = array_ops.identity(handle) + + def grad(dresult): + self.assertIsNotNone( + tensor_util.constant_value(dresult.dense_shape)) + return [dresult, None, None] + + return new_handle, grad + + var = variable_scope.get_variable( + "foo", shape=[20], initializer=init_ops.zeros_initializer, + dtype=dtypes.float64, use_resource=True) + + indices = math_ops.range(10) + updates = math_ops.range(9, -1, -1, dtype=dtypes.float64) + new_handle = differentiable_scatter_update(var.handle, indices, updates) + gathered = resource_variable_ops.resource_gather( + new_handle, indices, dtype=var.dtype) + gradients_impl.gradients([gathered], [updates]) + def testToFromProtoCachedValue(self): with ops.Graph().as_default(): v_def = resource_variable_ops.ResourceVariable( diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py index 216b08a8b0..9dabbffb13 100644 --- a/tensorflow/python/ops/array_ops.py +++ b/tensorflow/python/ops/array_ops.py @@ -78,7 +78,11 @@ def identity(input, name=None): # pylint: disable=redefined-builtin return input._copy() # pylint: disable=protected-access return input else: - return gen_array_ops.identity(input, name=name) + ret = gen_array_ops.identity(input, name=name) + # Propagate handle data for happier shape inference for resource variables. + if hasattr(input, "_handle_data"): + ret._handle_data = input._handle_data # pylint: disable=protected-access + return ret # pylint: disable=redefined-builtin,protected-access diff --git a/tensorflow/python/ops/custom_gradient.py b/tensorflow/python/ops/custom_gradient.py index 1426e8851c..d96601ac21 100644 --- a/tensorflow/python/ops/custom_gradient.py +++ b/tensorflow/python/ops/custom_gradient.py @@ -236,6 +236,10 @@ def _graph_mode_decorator(f, *args, **kwargs): original_tensors = all_tensors with ops.get_default_graph().gradient_override_map({"IdentityN": name}): all_tensors = array_ops.identity_n(all_tensors) + # Propagate handle data for happier shape inference for resource variables. + for i, t in enumerate(original_tensors): + if t.dtype == dtypes.resource and hasattr(t, "_handle_data"): + all_tensors[i]._handle_data = t._handle_data # pylint: disable=protected-access tape_lib.record_operation( f.__name__, all_tensors, original_tensors, tape_grad_fn) for ot, t in zip(original_tensors, all_tensors): -- GitLab From 504afac32db928bfb196299030ac2af28ce06d9d Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 7 Dec 2018 10:58:45 -0800 Subject: [PATCH 209/873] Increase max buffer size. PiperOrigin-RevId: 224543122 --- .../lite/kernels/internal/optimized/optimized_ops.h | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/lite/kernels/internal/optimized/optimized_ops.h index c7691e2763..c79b69a22e 100644 --- a/tensorflow/lite/kernels/internal/optimized/optimized_ops.h +++ b/tensorflow/lite/kernels/internal/optimized/optimized_ops.h @@ -3582,8 +3582,8 @@ inline void AveragePool(const PoolParams& params, std::min(params.filter_height, input_height - in_y_origin); const int filter_count = (filter_x_end - filter_x_start) * (filter_y_end - filter_y_start); - // 1280 required by Inception v3 - static constexpr int kAccBufferMaxSize = 2048; + // 2560 is required by MobileNetV2 with depth multiplier 2. + static constexpr int kAccBufferMaxSize = 4096; TFLITE_DCHECK_LE(depth, kAccBufferMaxSize); uint16 acc[kAccBufferMaxSize]; memset(acc, 0, depth * sizeof(acc[0])); @@ -3748,8 +3748,8 @@ inline void MaxPool(const PoolParams& params, const RuntimeShape& input_shape, const int filter_y_start = std::max(0, -in_y_origin); const int filter_y_end = std::min(params.filter_height, input_height - in_y_origin); - // 2048 required by Inception v3 - static constexpr int kAccBufferMaxSize = 2048; + // 2560 is required by MobileNetV2 with depth multiplier 2. + static constexpr int kAccBufferMaxSize = 4096; TFLITE_DCHECK_LE(depth, kAccBufferMaxSize); uint8 acc[kAccBufferMaxSize]; memset(acc, 0, depth * sizeof(acc[0])); -- GitLab From 629834552e79edf13dbdf2306420698b3d64def2 Mon Sep 17 00:00:00 2001 From: Gaurav Jain Date: Fri, 7 Dec 2018 11:00:57 -0800 Subject: [PATCH 210/873] Fix up control_flow_ops_tests to work with v2 - Add cond_v2/while_v2 dependencies - Add run_deprecated_v1 and run_v1_only where needed - Remove non-strict cond tests PiperOrigin-RevId: 224543529 --- tensorflow/python/BUILD | 2 + .../python/ops/control_flow_ops_test.py | 367 ++++++++++-------- 2 files changed, 199 insertions(+), 170 deletions(-) diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index bc6dc413e7..cc36f1fc0e 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -3236,6 +3236,7 @@ cuda_py_test( srcs = ["ops/control_flow_ops_test.py"], additional_deps = [ ":array_ops", + ":cond_v2", ":control_flow_ops", ":embedding_ops", ":framework_for_generated_wrappers", @@ -3251,6 +3252,7 @@ cuda_py_test( ":util", ":variable_scope", ":variables", + ":while_v2", "//tensorflow/python/eager:def_function", ], ) diff --git a/tensorflow/python/ops/control_flow_ops_test.py b/tensorflow/python/ops/control_flow_ops_test.py index b19ec4bd61..0c18b7208f 100644 --- a/tensorflow/python/ops/control_flow_ops_test.py +++ b/tensorflow/python/ops/control_flow_ops_test.py @@ -21,9 +21,9 @@ from __future__ import print_function import collections import numpy as np +from tensorflow.python import tf2 from tensorflow.core.framework import graph_pb2 from tensorflow.core.framework import node_def_pb2 -from tensorflow.python.client import session from tensorflow.python.eager import def_function from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes @@ -127,56 +127,56 @@ class GroupTestCase(test_util.TensorFlowTestCase): node { name: "root" op: "NoOp" input: "^a" input: "^b" } """, self._StripGraph(gd)) + @test_util.run_deprecated_v1 def testPassingNonTensors(self): - with ops.Graph().as_default(): - with self.assertRaises(TypeError): - control_flow_ops.group(1, 2) + with self.assertRaises(TypeError): + control_flow_ops.group(1, 2) class ShapeTestCase(test_util.TensorFlowTestCase): def testShape(self): - with ops.Graph().as_default(): - tensor = constant_op.constant([1.0, 2.0]) - self.assertEquals([2], tensor.get_shape()) - self.assertEquals([2], - control_flow_ops.with_dependencies( - [constant_op.constant(1.0)], tensor).get_shape()) + tensor = constant_op.constant([1.0, 2.0]) + self.assertEquals([2], tensor.get_shape()) + self.assertEquals([2], + control_flow_ops.with_dependencies( + [constant_op.constant(1.0)], tensor).get_shape()) class WithDependenciesTestCase(test_util.TensorFlowTestCase): + @test_util.run_deprecated_v1 def testTupleDependencies(self): - with ops.Graph().as_default(): - counter = variable_scope.get_variable( - "my_counter", shape=[], initializer=init_ops.zeros_initializer()) - increment_counter = state_ops.assign_add(counter, 1) - const_with_dep = control_flow_ops.with_dependencies( - (increment_counter, constant_op.constant(42)), - constant_op.constant(7)) - with self.cached_session(): - variables.global_variables_initializer().run() - self.assertEquals(0, self.evaluate(counter)) - self.assertEquals(7, self.evaluate(const_with_dep)) - self.assertEquals(1, self.evaluate(counter)) - + counter = variable_scope.get_variable( + "my_counter", shape=[], initializer=init_ops.zeros_initializer()) + increment_counter = state_ops.assign_add(counter, 1) + const_with_dep = control_flow_ops.with_dependencies( + (increment_counter, constant_op.constant(42)), + constant_op.constant(7)) + + self.evaluate(variables.global_variables_initializer()) + self.assertEquals(0, self.evaluate(counter)) + self.assertEquals(7, self.evaluate(const_with_dep)) + self.assertEquals(1, self.evaluate(counter)) + + @test_util.run_deprecated_v1 def testListDependencies(self): - with ops.Graph().as_default(): - counter = variable_scope.get_variable( - "my_counter", shape=[], initializer=init_ops.zeros_initializer()) - increment_counter = state_ops.assign_add(counter, 1) - const_with_dep = control_flow_ops.with_dependencies( - [increment_counter, constant_op.constant(42)], - constant_op.constant(7)) - with self.cached_session(): - variables.global_variables_initializer().run() - self.assertEquals(0, self.evaluate(counter)) - self.assertEquals(7, self.evaluate(const_with_dep)) - self.assertEquals(1, self.evaluate(counter)) + counter = variable_scope.get_variable( + "my_counter", shape=[], initializer=init_ops.zeros_initializer()) + increment_counter = state_ops.assign_add(counter, 1) + const_with_dep = control_flow_ops.with_dependencies( + [increment_counter, constant_op.constant(42)], + constant_op.constant(7)) + + self.evaluate(variables.global_variables_initializer()) + self.assertEquals(0, self.evaluate(counter)) + self.assertEquals(7, self.evaluate(const_with_dep)) + self.assertEquals(1, self.evaluate(counter)) class SwitchTestCase(test_util.TensorFlowTestCase): + @test_util.run_deprecated_v1 def testIndexedSlicesWithDenseShape(self): with self.cached_session(): data = ops.IndexedSlices( @@ -190,68 +190,64 @@ class SwitchTestCase(test_util.TensorFlowTestCase): self.assertAllEqual([1, 2, 3], switch_true.values.eval()) self.assertAllEqual([0, 1], switch_true.indices.eval()) + @test_util.run_deprecated_v1 def testIndexedSlicesGradient(self): - with ops.Graph().as_default(): - embedding_matrix = variable_scope.get_variable( - "embedding_matrix", [5, 5], - initializer=init_ops.random_normal_initializer()) - - def cond(it, _): - return it < 5 - - def body(it, cost): - embedding = embedding_ops.embedding_lookup(embedding_matrix + 0.0, [0]) - cost += math_ops.reduce_sum(embedding) - return it + 1, cost - - _, cost = control_flow_ops.while_loop( - cond, body, [constant_op.constant(0), - constant_op.constant(0.0)]) - optimizer = momentum.MomentumOptimizer(0.1, 0.9) - train_op = optimizer.minimize(cost) - with self.cached_session() as sess: - self.evaluate(variables.global_variables_initializer()) - for _ in range(10): - self.evaluate([train_op]) + embedding_matrix = variable_scope.get_variable( + "embedding_matrix", [5, 5], + initializer=init_ops.random_normal_initializer()) + + def cond(it, _): + return it < 5 + + def body(it, cost): + embedding = embedding_ops.embedding_lookup(embedding_matrix + 0.0, [0]) + cost += math_ops.reduce_sum(embedding) + return it + 1, cost + + _, cost = control_flow_ops.while_loop( + cond, body, [constant_op.constant(0), + constant_op.constant(0.0)]) + optimizer = momentum.MomentumOptimizer(0.1, 0.9) + train_op = optimizer.minimize(cost) + with self.cached_session(): + self.evaluate(variables.global_variables_initializer()) + for _ in range(10): + self.evaluate([train_op]) def testResourceReadInLoop(self): - with ops.Graph().as_default(): - embedding_matrix = variable_scope.get_variable( - "embedding_matrix", - initializer=[[2.0], [3.0]], - use_resource=True) + embedding_matrix = variable_scope.get_variable( + "embedding_matrix", initializer=[[2.0], [3.0]], use_resource=True) - def cond(it, _): - return it < 5 + def cond(it, _): + return it < 5 - def body(it, cost): - embedding = embedding_ops.embedding_lookup(embedding_matrix, [0]) - cost += math_ops.reduce_sum(embedding) - return it + 1, cost + def body(it, cost): + embedding = embedding_ops.embedding_lookup(embedding_matrix, [0]) + cost += math_ops.reduce_sum(embedding) + return it + 1, cost - _, cost = control_flow_ops.while_loop( - cond, body, [constant_op.constant(0), - constant_op.constant(0.0)]) - with self.cached_session() as sess: - self.evaluate(variables.global_variables_initializer()) - self.assertAllEqual(10.0, self.evaluate(cost)) + _, cost = control_flow_ops.while_loop( + cond, body, [constant_op.constant(0), + constant_op.constant(0.0)]) + with self.cached_session(): + self.evaluate(variables.global_variables_initializer()) + self.assertAllEqual(10.0, self.evaluate(cost)) def doTestIndexedSlicesGradientInCondInWhileLoop(self, use_resource=False): - with ops.Graph().as_default(): - embedding_matrix = variable_scope.get_variable( - "embedding_matrix", [5, 5], - initializer=init_ops.random_normal_initializer(), - use_resource=use_resource) - - def cond(it, _): - return it < 5 - - def body(it, cost): - embedding = embedding_ops.embedding_lookup(embedding_matrix, [0]) - cost = control_flow_ops.cond( - math_ops.equal(it, 3), lambda: math_ops.square(cost), - lambda: cost + math_ops.reduce_sum(embedding)) - return it + 1, cost + embedding_matrix = variable_scope.get_variable( + "embedding_matrix", [5, 5], + initializer=init_ops.random_normal_initializer(), + use_resource=use_resource) + + def cond(it, _): + return it < 5 + + def body(it, cost): + embedding = embedding_ops.embedding_lookup(embedding_matrix, [0]) + cost = control_flow_ops.cond( + math_ops.equal(it, 3), lambda: math_ops.square(cost), + (lambda: cost + math_ops.reduce_sum(embedding))) + return it + 1, cost _, cost = control_flow_ops.while_loop( cond, body, [constant_op.constant(0), @@ -269,7 +265,7 @@ class SwitchTestCase(test_util.TensorFlowTestCase): static_grads = math_ops.segment_sum(static_grads.values, static_grads.indices) - with self.cached_session() as sess: + with self.cached_session(): self.evaluate(variables.global_variables_initializer()) self.assertAllEqual(*self.evaluate([static_grads, dynamic_grads])) @@ -279,6 +275,7 @@ class SwitchTestCase(test_util.TensorFlowTestCase): def testIndexedSlicesGradientInCondInWhileLoopResource(self): self.doTestIndexedSlicesGradientInCondInWhileLoop(use_resource=True) + @test_util.run_v1_only("b/120545219") def testIndexedSlicesWithShapeGradientInWhileLoop(self): for dtype in [dtypes.float32, dtypes.float64]: with self.cached_session() as sess: @@ -308,6 +305,7 @@ class SwitchTestCase(test_util.TensorFlowTestCase): self.assertEquals(o, 20) self.assertAllEqual(grad, [1] * num_steps) + @test_util.run_v1_only("b/120545219") def testIndexedSlicesWithDynamicShapeGradientInWhileLoop(self): for dtype in [dtypes.float32, dtypes.float64]: with self.cached_session() as sess: @@ -335,105 +333,94 @@ class SwitchTestCase(test_util.TensorFlowTestCase): self.assertEquals(o, 6) self.assertAllEqual(grad, [1] * 3) + @test_util.run_deprecated_v1 def testGradientThroughSingleBranchOutsideOfContext(self): - with self.cached_session(): - x = constant_op.constant(2.) - s = constant_op.constant(True) - x_false, x_true = control_flow_ops.switch(x, s) - grad_x_true = gradients_impl.gradients(x_true, x)[0] - grad_x_false = gradients_impl.gradients(x_false, x)[0] - self.assertEquals(grad_x_true.eval(), 1.) - self.assertEquals(grad_x_false.eval(), 0.) + x = constant_op.constant(2.) + s = constant_op.constant(True) + x_false, x_true = control_flow_ops.switch(x, s) + grad_x_true = gradients_impl.gradients(x_true, x)[0] + grad_x_false = gradients_impl.gradients(x_false, x)[0] + self.assertEquals(self.evaluate(grad_x_true), 1.) + self.assertEquals(self.evaluate(grad_x_false), 0.) class CondTest(test_util.TensorFlowTestCase): def testCondTrue(self): - with ops.Graph().as_default(): - with session.Session(): - x = constant_op.constant(2) - y = constant_op.constant(5) - z = control_flow_ops.cond( - math_ops.less(x, y), lambda: math_ops.multiply(x, 17), - lambda: math_ops.add(y, 23)) - self.assertEquals(z.eval(), 34) + x = constant_op.constant(2) + y = constant_op.constant(5) + z = control_flow_ops.cond( + math_ops.less( + x, + y), lambda: math_ops.multiply(x, 17), lambda: math_ops.add(y, 23)) + self.assertEquals(self.evaluate(z), 34) def testCondFalse(self): - with ops.Graph().as_default(): - with session.Session(): - x = constant_op.constant(2) - y = constant_op.constant(1) - z = control_flow_ops.cond( - math_ops.less(x, y), lambda: math_ops.multiply(x, 17), - lambda: math_ops.add(y, 23)) - self.assertEquals(z.eval(), 24) + x = constant_op.constant(2) + y = constant_op.constant(1) + z = control_flow_ops.cond( + math_ops.less( + x, + y), lambda: math_ops.multiply(x, 17), lambda: math_ops.add(y, 23)) + self.assertEquals(self.evaluate(z), 24) def testCondTrueLegacy(self): - with ops.Graph().as_default(): - with session.Session(): - x = constant_op.constant(2) - y = constant_op.constant(5) - z = control_flow_ops.cond( - math_ops.less(x, y), fn1=lambda: math_ops.multiply(x, 17), - fn2=lambda: math_ops.add(y, 23)) - self.assertEquals(z.eval(), 34) + x = constant_op.constant(2) + y = constant_op.constant(5) + z = control_flow_ops.cond( + math_ops.less(x, y), + fn1=lambda: math_ops.multiply(x, 17), + fn2=lambda: math_ops.add(y, 23)) + self.assertEquals(self.evaluate(z), 34) def testCondFalseLegacy(self): - with ops.Graph().as_default(): - with session.Session(): - x = constant_op.constant(2) - y = constant_op.constant(1) - z = control_flow_ops.cond( - math_ops.less(x, y), fn1=lambda: math_ops.multiply(x, 17), - fn2=lambda: math_ops.add(y, 23)) - self.assertEquals(z.eval(), 24) - + x = constant_op.constant(2) + y = constant_op.constant(1) + z = control_flow_ops.cond( + math_ops.less(x, y), + fn1=lambda: math_ops.multiply(x, 17), + fn2=lambda: math_ops.add(y, 23)) + self.assertEquals(self.evaluate(z), 24) + + @test_util.run_deprecated_v1 def testCondModifyBoolPred(self): # This test in particular used to fail only when running in GPU, hence # use_gpu=True. - with ops.Graph().as_default(): - with session.Session() as sess: - bool_var = variable_scope.get_variable("bool_var", dtype=dtypes.bool, - initializer=True) - cond_on_bool_var = control_flow_ops.cond( - pred=bool_var, - true_fn=lambda: state_ops.assign(bool_var, False), - false_fn=lambda: True) - self.evaluate(bool_var.initializer) - self.assertEquals(self.evaluate(cond_on_bool_var), False) - self.assertEquals(self.evaluate(cond_on_bool_var), True) + with test_util.use_gpu(): + bool_var = variable_scope.get_variable( + "bool_var", dtype=dtypes.bool, initializer=True) + cond_on_bool_var = control_flow_ops.cond( + pred=bool_var, + true_fn=lambda: state_ops.assign(bool_var, False), + false_fn=lambda: True) + self.evaluate(bool_var.initializer) + self.assertEquals(self.evaluate(cond_on_bool_var), False) + self.assertEquals(self.evaluate(cond_on_bool_var), True) def testCondMissingArg1(self): - with ops.Graph().as_default(): - with session.Session(): - x = constant_op.constant(1) - with self.assertRaises(TypeError): - control_flow_ops.cond(True, false_fn=lambda: x) + x = constant_op.constant(1) + with self.assertRaises(TypeError): + control_flow_ops.cond(True, false_fn=lambda: x) def testCondMissingArg2(self): - with ops.Graph().as_default(): - with session.Session(): - x = constant_op.constant(1) - with self.assertRaises(TypeError): - control_flow_ops.cond(True, lambda: x) + x = constant_op.constant(1) + with self.assertRaises(TypeError): + control_flow_ops.cond(True, lambda: x) def testCondDuplicateArg1(self): - with ops.Graph().as_default(): - with session.Session(): - x = constant_op.constant(1) - with self.assertRaises(TypeError): - control_flow_ops.cond(True, lambda: x, lambda: x, fn1=lambda: x) + x = constant_op.constant(1) + with self.assertRaises(TypeError): + control_flow_ops.cond(True, lambda: x, lambda: x, fn1=lambda: x) def testCondDuplicateArg2(self): - with ops.Graph().as_default(): - with session.Session(): - x = constant_op.constant(1) - with self.assertRaises(TypeError): - control_flow_ops.cond(True, lambda: x, lambda: x, fn2=lambda: x) + x = constant_op.constant(1) + with self.assertRaises(TypeError): + control_flow_ops.cond(True, lambda: x, lambda: x, fn2=lambda: x) class ContextTest(test_util.TensorFlowTestCase): + @test_util.run_deprecated_v1 def testCondContext(self): with self.cached_session() as sess: x = constant_op.constant(2) @@ -463,12 +450,15 @@ class ContextTest(test_util.TensorFlowTestCase): control_flow_ops.WhileContext.from_proto( control_flow_context.to_proto()).to_proto()) + @test_util.run_deprecated_v1 def testWhileContext(self): self._testWhileContextHelper() + @test_util.run_deprecated_v1 def testWhileContextWithMaximumIterations(self): self._testWhileContextHelper(maximum_iterations=10) + @test_util.run_deprecated_v1 def testControlContextImportScope(self): class NoABCControlFlowContext(control_flow_ops.ControlFlowContext): """A noop wrapper around `ControlFlowContext`. @@ -591,6 +581,7 @@ class DataTypesTest(test_util.TensorFlowTestCase): if check_cond: self.assertAllEqualNested(result_case, expected_value_false) + @test_util.run_deprecated_v1 def test_int(self): shape = tensor_shape.TensorShape([]) fn_true = lambda: 1 @@ -600,6 +591,7 @@ class DataTypesTest(test_util.TensorFlowTestCase): self._testShape(fn_true, fn_false, shape, strict=True) self._testReturnValues(fn_true, fn_false, 1, 2, strict=True) + @test_util.run_deprecated_v1 def test_float(self): shape = tensor_shape.TensorShape([]) fn_true = lambda: 1.0 @@ -607,12 +599,14 @@ class DataTypesTest(test_util.TensorFlowTestCase): self._testShape(fn_true, fn_false, shape) self._testReturnValues(fn_true, fn_false, 1.0, 2.0) + @test_util.run_deprecated_v1 def test_noop(self): shape = tensor_shape.TensorShape(None) self._testShape(control_flow_ops.no_op, control_flow_ops.no_op, shape) self._testReturnValues(control_flow_ops.no_op, control_flow_ops.no_op, True, False, check_cond=False) + @test_util.run_deprecated_v1 def test_string(self): shape = tensor_shape.TensorShape([]) fn_true = lambda: "abc" @@ -620,6 +614,7 @@ class DataTypesTest(test_util.TensorFlowTestCase): self._testShape(fn_true, fn_false, shape) self._testReturnValues(fn_true, fn_false, b"abc", b"xyz") + @test_util.run_deprecated_v1 def test_variable(self): shape = tensor_shape.TensorShape([]) fn_true = lambda: variables.Variable(3.0) @@ -627,6 +622,7 @@ class DataTypesTest(test_util.TensorFlowTestCase): self._testShape(fn_true, fn_false, shape) self._testReturnValues(fn_true, fn_false, 3.0, 4.0) + @test_util.run_v1_only("b/120553181") def test_none(self): fn_none = lambda: None fn_tensor = lambda: constant_op.constant(1) @@ -637,6 +633,7 @@ class DataTypesTest(test_util.TensorFlowTestCase): with self.assertRaises(ValueError): control_flow_ops.cond(constant_op.constant(True), fn_tensor, fn_none) + @test_util.run_deprecated_v1 def test_tensors(self): def _build_true_branch(dtype): @@ -665,6 +662,7 @@ class DataTypesTest(test_util.TensorFlowTestCase): (np.zeros([2, 2]), np.ones([3, 3])), (np.ones([2, 2]), np.zeros([3, 3]))) + @test_util.run_deprecated_v1 def test_tensors_unknown_shape(self): def _build_true_branch(dtype): @@ -693,6 +691,7 @@ class DataTypesTest(test_util.TensorFlowTestCase): feed_dict={true_tensor: np.zeros([2, 2]), false_tensor: np.ones([2, 2])}) + @test_util.run_deprecated_v1 def test_sparse_tensors(self): shape = tensor_shape.TensorShape([None, None]) @@ -708,11 +707,14 @@ class DataTypesTest(test_util.TensorFlowTestCase): values=[1, 2], dense_shape=[3, 4]) value2 = sparse_tensor.SparseTensorValue(indices=[[0, 0], [2, 1]], values=[3, 4], dense_shape=[3, 4]) - self._testShape(true_fn, false_fn, shape) - self._testReturnValues(true_fn, false_fn, value1, value2) + # Non-strict cond is only available in v1 + if not tf2.enabled(): + self._testShape(true_fn, false_fn, shape) + self._testReturnValues(true_fn, false_fn, value1, value2) self._testShape(true_fn, false_fn, [shape], strict=True) self._testReturnValues(true_fn, false_fn, [value1], [value2], strict=True) + @test_util.run_deprecated_v1 def test_tensors_with_partially_specified_shapes(self): def _build_branch(dtype, shape): @@ -742,6 +744,7 @@ class DataTypesTest(test_util.TensorFlowTestCase): true_tensors[2]: np.ones([3, 3]), false_tensors[2]: np.ones([3, 3])}) + @test_util.run_deprecated_v1 def test_tensor_arrays(self): element_shape = tensor_shape.TensorShape([2]) ta1 = _create_tensor_array(4, element_shape) @@ -751,6 +754,7 @@ class DataTypesTest(test_util.TensorFlowTestCase): fn_false = lambda: ta2 self._testShape(fn_true, fn_false, shape) + @test_util.run_deprecated_v1 def test_tensor_array_reads(self): shape = tensor_shape.TensorShape([2]) ta = _create_tensor_array(4, shape) @@ -758,6 +762,7 @@ class DataTypesTest(test_util.TensorFlowTestCase): fn_false = lambda: ta.read(1) self._testShape(fn_true, fn_false, shape) + @test_util.run_deprecated_v1 def test_list(self): shape = [tensor_shape.TensorShape([]), tensor_shape.TensorShape([]), tensor_shape.TensorShape([])] @@ -766,6 +771,7 @@ class DataTypesTest(test_util.TensorFlowTestCase): self._testShape(fn_true, fn_false, shape) self._testReturnValues(fn_true, fn_false, [1, 2, 3.0], [3, 4, 5.0]) + @test_util.run_v1_only("Non-strict cond is only available in v1") def test_non_strict(self): shape = tensor_shape.TensorShape([]) fn_tensor = lambda: constant_op.constant(1) @@ -778,6 +784,7 @@ class DataTypesTest(test_util.TensorFlowTestCase): self._testReturnValues(fn_tensor, fn_tuple, 1, 3) self._testReturnValues(fn_list, fn_tuple, 2, 3) + @test_util.run_v1_only("b/120553181") def test_singleton_strict(self): fn_tensor = lambda: constant_op.constant(1) fn_list = lambda: [constant_op.constant(2)] @@ -799,36 +806,46 @@ class DataTypesTest(test_util.TensorFlowTestCase): control_flow_ops.case([(constant_op.constant(True), fn_list)], fn_tuple, strict=True) + @test_util.run_deprecated_v1 def test_singleton_list(self): shape = tensor_shape.TensorShape([]) fn_true = lambda: [constant_op.constant(1)] fn_false = lambda: [constant_op.constant(3)] - self._testShape(fn_true, fn_false, shape) - self._testReturnValues(fn_true, fn_false, 1, 3) + # Non-strict cond is only available in v1 + if not tf2.enabled(): + self._testShape(fn_true, fn_false, shape) + self._testReturnValues(fn_true, fn_false, 1, 3) self._testShape(fn_true, fn_false, [shape], strict=True) self._testReturnValues(fn_true, fn_false, [1], [3], strict=True) + @test_util.run_deprecated_v1 def test_singleton_tuple(self): shape = tensor_shape.TensorShape([]) fn_true = lambda: (constant_op.constant(1),) fn_false = lambda: (constant_op.constant(3),) - self._testShape(fn_true, fn_false, shape) - self._testReturnValues(fn_true, fn_false, 1, 3) + # Non-strict cond is only available in v1 + if not tf2.enabled(): + self._testShape(fn_true, fn_false, shape) + self._testReturnValues(fn_true, fn_false, 1, 3) self._testShape(fn_true, fn_false, (shape,), strict=True) self._testReturnValues(fn_true, fn_false, (1,), (3,), strict=True) + @test_util.run_deprecated_v1 def test_singleton_namedtuple(self): shape = tensor_shape.TensorShape([]) fn_true = lambda: SingletonTestTuple(constant_op.constant(1)) fn_false = lambda: SingletonTestTuple(constant_op.constant(3)) - self._testShape(fn_true, fn_false, shape) - self._testReturnValues(fn_true, fn_false, 1, 3) + # Non-strict cond is only available in v1 + if not tf2.enabled(): + self._testShape(fn_true, fn_false, shape) + self._testReturnValues(fn_true, fn_false, 1, 3) self._testShape(fn_true, fn_false, SingletonTestTuple(shape), strict=True) self._testReturnValues(fn_true, fn_false, SingletonTestTuple(1), SingletonTestTuple(3), strict=True) + @test_util.run_deprecated_v1 def test_tuple(self): shape = (tensor_shape.TensorShape([]), tensor_shape.TensorShape([])) fn_true = lambda: (constant_op.constant(1), 2) @@ -836,6 +853,7 @@ class DataTypesTest(test_util.TensorFlowTestCase): self._testShape(fn_true, fn_false, shape) self._testReturnValues(fn_true, fn_false, (1, 2), (3, 4)) + @test_util.run_deprecated_v1 def test_namedtuple(self): shape = TestTuple(tensor_shape.TensorShape([]), tensor_shape.TensorShape([])) @@ -844,6 +862,7 @@ class DataTypesTest(test_util.TensorFlowTestCase): self._testShape(fn_true, fn_false, shape) self._testReturnValues(fn_true, fn_false, TestTuple(1, 2), TestTuple(3, 4)) + @test_util.run_deprecated_v1 def test_nested(self): shape = [tensor_shape.TensorShape([]), TestTuple(tensor_shape.TensorShape([]), @@ -869,6 +888,7 @@ class DataTypesTest(test_util.TensorFlowTestCase): [11, TestTuple(12, [13, 14]), np.ones([5, 5]), 16]) + @test_util.run_deprecated_v1 def test_cond_inside_while_loop(self): def body(i, matrix): @@ -890,6 +910,7 @@ class DataTypesTest(test_util.TensorFlowTestCase): class CaseTest(test_util.TensorFlowTestCase): + @test_util.run_deprecated_v1 def testCase_withDefault(self): x = array_ops.placeholder(dtype=dtypes.int32, shape=[]) conditions = [(math_ops.equal(x, 1), lambda: constant_op.constant(2)), @@ -901,6 +922,7 @@ class CaseTest(test_util.TensorFlowTestCase): self.assertEqual(sess.run(output, feed_dict={x: 2}), 4) self.assertEqual(sess.run(output, feed_dict={x: 3}), 6) + @test_util.run_deprecated_v1 def testCase_multiple_matches_exclusive(self): x = array_ops.placeholder(dtype=dtypes.int32, shape=[]) conditions = [(math_ops.equal(x, 1), lambda: constant_op.constant(2)), @@ -914,6 +936,7 @@ class CaseTest(test_util.TensorFlowTestCase): with self.assertRaisesRegexp(errors.InvalidArgumentError, "Input error:"): sess.run(output, feed_dict={x: 2}) + @test_util.run_deprecated_v1 def testCase_multiple_matches_non_exclusive(self): x = array_ops.placeholder(dtype=dtypes.int32, shape=[]) conditions = [(math_ops.equal(x, 1), lambda: constant_op.constant(2)), @@ -926,6 +949,7 @@ class CaseTest(test_util.TensorFlowTestCase): self.assertEqual(sess.run(output, feed_dict={x: 2}), 4) self.assertEqual(sess.run(output, feed_dict={x: 3}), 8) + @test_util.run_deprecated_v1 def testCase_withoutDefault(self): x = array_ops.placeholder(dtype=dtypes.int32, shape=[]) conditions = [(math_ops.equal(x, 1), lambda: constant_op.constant(2)), @@ -939,6 +963,7 @@ class CaseTest(test_util.TensorFlowTestCase): with self.assertRaisesRegexp(errors.InvalidArgumentError, "Input error:"): sess.run(output, feed_dict={x: 4}) + @test_util.run_deprecated_v1 def testCase_withoutDefault_oneCondition(self): x = array_ops.placeholder(dtype=dtypes.int32, shape=[]) conditions = [(math_ops.equal(x, 1), lambda: constant_op.constant(2))] @@ -980,6 +1005,7 @@ class WhileLoopTestCase(test_util.TensorFlowTestCase): # Expect a tuple since that is what the body returns. self.assertEqual(self.evaluate(r), (10,)) + @test_util.run_deprecated_v1 def testWhileLoopSameReturnShape_False(self): i = constant_op.constant(0) c = lambda i, _: math_ops.less(i, 10) @@ -1005,6 +1031,7 @@ class WhileLoopTestCase(test_util.TensorFlowTestCase): class AssertTest(test_util.TensorFlowTestCase): + @test_util.run_deprecated_v1 def testAssert(self): i = constant_op.constant(0) c = control_flow_ops.Assert(i < 10, [i, [10], [i + 1]]) -- GitLab From 49aa21b5b6468309e0acef82a6a15314ba8f5a8f Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 7 Dec 2018 11:24:44 -0800 Subject: [PATCH 211/873] Automated rollback of commit 51561e0354a1556e848c920cd47a718360dc720d PiperOrigin-RevId: 224548216 --- .../python/estimator_training_test.py | 7 ++-- .../python/multi_worker_test_base.py | 10 ++---- .../distribute/distribute_coordinator.py | 35 ++++++------------- 3 files changed, 16 insertions(+), 36 deletions(-) diff --git a/tensorflow/contrib/distribute/python/estimator_training_test.py b/tensorflow/contrib/distribute/python/estimator_training_test.py index 3f55a8a1c8..b369a7fefe 100644 --- a/tensorflow/contrib/distribute/python/estimator_training_test.py +++ b/tensorflow/contrib/distribute/python/estimator_training_test.py @@ -375,13 +375,11 @@ class DistributeCoordinatorIntegrationTest( threads = self.run_multiple_tasks_in_threads(self._independent_worker_fn, cluster_spec, train_distribute, eval_distribute) - threads_to_join = [] for task_type, ts in threads.items(): if task_type == PS: continue for t in ts: - threads_to_join.append(t) - self.join_independent_workers(threads_to_join) + t.join() estimator = self._get_estimator(train_distribute, eval_distribute) self._inspect_train_and_eval_events(estimator) @@ -415,7 +413,8 @@ class DistributeCoordinatorIntegrationTest( threads = self.run_multiple_tasks_in_threads(self._independent_worker_fn, cluster_spec, train_distribute, eval_distribute) - self.join_independent_workers([threads[WORKER][0], threads[EVALUATOR][0]]) + threads[WORKER][0].join() + threads[EVALUATOR][0].join() estimator = self._get_estimator(train_distribute, eval_distribute) self._inspect_train_and_eval_events(estimator) diff --git a/tensorflow/contrib/distribute/python/multi_worker_test_base.py b/tensorflow/contrib/distribute/python/multi_worker_test_base.py index b05aac431f..147c9b83f8 100644 --- a/tensorflow/contrib/distribute/python/multi_worker_test_base.py +++ b/tensorflow/contrib/distribute/python/multi_worker_test_base.py @@ -40,7 +40,6 @@ from tensorflow.python.client import session from tensorflow.python.estimator import run_config from tensorflow.python.platform import test from tensorflow.python.platform import tf_logging as logging -from tensorflow.python.training import coordinator from tensorflow.python.training import server_lib ASSIGNED_PORTS = set() @@ -361,7 +360,6 @@ class IndependentWorkerTestBase(test.TestCase): self._mock_os_env = MockOsEnv() self._mock_context = test.mock.patch.object(os, 'environ', self._mock_os_env) - self._coord = coordinator.Coordinator() super(IndependentWorkerTestBase, self).setUp() self._mock_context.__enter__() @@ -370,9 +368,8 @@ class IndependentWorkerTestBase(test.TestCase): super(IndependentWorkerTestBase, self).tearDown() def _task_thread(self, task_fn, tf_config, *args, **kwargs): - with self._coord.stop_on_exception(): - os.environ['TF_CONFIG'] = json.dumps(tf_config) - task_fn(*args, **kwargs) + os.environ['TF_CONFIG'] = json.dumps(tf_config) + task_fn(*args, **kwargs) def _run_task_in_thread(self, task_fn, cluster_spec, task_type, task_id, *args, **kwargs): @@ -406,6 +403,3 @@ class IndependentWorkerTestBase(test.TestCase): *args, **kwargs) threads[task_type].append(t) return threads - - def join_independent_workers(self, worker_threads): - self._coord.join(worker_threads) diff --git a/tensorflow/python/distribute/distribute_coordinator.py b/tensorflow/python/distribute/distribute_coordinator.py index 78c995a578..c0f9b8a1fd 100644 --- a/tensorflow/python/distribute/distribute_coordinator.py +++ b/tensorflow/python/distribute/distribute_coordinator.py @@ -29,7 +29,6 @@ from tensorflow.python.client import session from tensorflow.python.distribute import distribute_coordinator_context from tensorflow.python.distribute import multi_worker_util from tensorflow.python.platform import tf_logging as logging -from tensorflow.python.training import coordinator from tensorflow.python.training import monitored_session from tensorflow.python.training import server_lib @@ -329,8 +328,7 @@ def _run_single_worker(worker_fn, task_id, session_config, rpc_layer="", - worker_barrier=None, - coord=None): + worker_barrier=None): """Runs a single worker by calling `worker_fn` under context.""" session_config = copy.deepcopy(session_config) strategy = copy.deepcopy(strategy) @@ -352,11 +350,7 @@ def _run_single_worker(worker_fn, rpc_layer=rpc_layer, worker_barrier=worker_barrier) with context: - if coord: - with coord.stop_on_exception(): - return worker_fn(strategy) - else: - return worker_fn(strategy) + return worker_fn(strategy) def _split_cluster_for_evaluator(cluster_spec, task_type): @@ -429,7 +423,6 @@ def _run_std_server(cluster_spec=None, def _run_between_graph_client(worker_fn, strategy, eval_fn, eval_strategy, cluster_spec, session_config, rpc_layer): """Runs a standalone client for between-graph replication.""" - coord = coordinator.Coordinator() eval_thread = None if _TaskType.EVALUATOR in cluster_spec.jobs: eval_thread = threading.Thread( @@ -438,7 +431,6 @@ def _run_between_graph_client(worker_fn, strategy, eval_fn, eval_strategy, session_config), kwargs={ "rpc_layer": rpc_layer, - "coord": coord, }) eval_thread.start() @@ -452,18 +444,18 @@ def _run_between_graph_client(worker_fn, strategy, eval_fn, eval_strategy, session_config), kwargs={ "rpc_layer": rpc_layer, - "worker_barrier": worker_barrier, - "coord": coord, + "worker_barrier": worker_barrier }) t.start() threads.append(t) + # TODO(yuefengz): wrap threads into thread coordinator? + for t in threads: + t.join() + + # TODO(yuefengz): is it necessary to join eval thread? if eval_thread: - # TODO(yuefengz): is it necessary to join eval thread? - threads_to_join = threads + [eval_thread] - else: - threads_to_join = threads - coord.join(threads_to_join) + eval_thread.join() # TODO(yuefengz): we probably want to return results from all workers? return None @@ -472,7 +464,6 @@ def _run_between_graph_client(worker_fn, strategy, eval_fn, eval_strategy, def _run_in_graph_client(worker_fn, strategy, eval_fn, eval_strategy, cluster_spec, session_config, rpc_layer): """Runs a standalone client for in-graph replication.""" - coord = coordinator.Coordinator() eval_thread = None if _TaskType.EVALUATOR in cluster_spec.jobs: eval_thread = threading.Thread( @@ -481,7 +472,6 @@ def _run_in_graph_client(worker_fn, strategy, eval_fn, eval_strategy, session_config), kwargs={ "rpc_layer": rpc_layer, - "coord": coord, }) eval_thread.start() @@ -492,12 +482,9 @@ def _run_in_graph_client(worker_fn, strategy, eval_fn, eval_strategy, None, None, session_config, - rpc_layer=rpc_layer, - coord=coord) - + rpc_layer=rpc_layer) if eval_thread: - coord.join([eval_thread]) - + eval_thread.join() return worker_result -- GitLab From 0b2afecc4b5d76c4d4976b3fb8155c2ac8ee6c5a Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Fri, 7 Dec 2018 19:41:11 +0000 Subject: [PATCH 212/873] Add float16 suport for scatter_max/scatter_min on gpu This fix tries to address the issue raised in 24219 where there were no float16 supports for scatter_max/scatter_min on gpu. This fix adds the float16 support for scatter_max/scatter_min on gpu. This fix fixes 24219. Signed-off-by: Yong Tang --- tensorflow/core/kernels/scatter_op.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/core/kernels/scatter_op.cc b/tensorflow/core/kernels/scatter_op.cc index 0fbde764d5..ee3c583347 100644 --- a/tensorflow/core/kernels/scatter_op.cc +++ b/tensorflow/core/kernels/scatter_op.cc @@ -288,7 +288,7 @@ TF_CALL_ALL_TYPES(REGISTER_SCATTER_UPDATE_CPU); #define REGISTER_SCATTER_UPDATE_GPU(type) REGISTER_SCATTER_UPDATE(type, GPU); TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ARITHMETIC_GPU); -TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_MINMAX_GPU); +TF_CALL_GPU_NUMBER_TYPES(REGISTER_SCATTER_MINMAX_GPU); TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_UPDATE_GPU); #endif // GOOGLE_CUDA -- GitLab From fda42f7afdd11155d2267669e2b41f94beb725de Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Fri, 7 Dec 2018 19:45:37 +0000 Subject: [PATCH 213/873] Enable template specification for ScatterMax/Min on gpu Signed-off-by: Yong Tang --- tensorflow/core/kernels/scatter_op_gpu.cu.cc | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/core/kernels/scatter_op_gpu.cu.cc b/tensorflow/core/kernels/scatter_op_gpu.cu.cc index 0df329310f..d4defb8503 100644 --- a/tensorflow/core/kernels/scatter_op_gpu.cu.cc +++ b/tensorflow/core/kernels/scatter_op_gpu.cu.cc @@ -41,6 +41,7 @@ typedef Eigen::GpuDevice GPUDevice; DEFINE_GPU_SPECS_INDEX(T, int32); \ DEFINE_GPU_SPECS_INDEX(T, int64); +DEFINE_GPU_SPECS(Eigen::half); DEFINE_GPU_SPECS(float); DEFINE_GPU_SPECS(double); // TODO: The following fails to compile. -- GitLab From 22497517ac15d961697bbda4d20d78c3a6e33141 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Fri, 7 Dec 2018 19:48:07 +0000 Subject: [PATCH 214/873] Add test case for float16 of scatter_max/min Signed-off-by: Yong Tang --- tensorflow/python/kernel_tests/scatter_ops_test.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/tensorflow/python/kernel_tests/scatter_ops_test.py b/tensorflow/python/kernel_tests/scatter_ops_test.py index 1c7006ac0b..44431791ef 100644 --- a/tensorflow/python/kernel_tests/scatter_ops_test.py +++ b/tensorflow/python/kernel_tests/scatter_ops_test.py @@ -191,6 +191,10 @@ class ScatterTest(test.TestCase): if tf_scatter != state_ops.scatter_div: vtypes.append(np.int32) + if (tf_scatter == state_ops.scatter_min or + tf_scatter == state_ops.scatter_max): + vtypes.append(np.float16) + for vtype in vtypes: for itype in (np.int32, np.int64): self._VariableRankTest(tf_scatter, vtype, itype, repeat_indices, -- GitLab From 48a224c33e6cc258226e029a6eacb71aca0aa7b9 Mon Sep 17 00:00:00 2001 From: Jared Duke Date: Fri, 7 Dec 2018 12:27:20 -0800 Subject: [PATCH 215/873] Update documentation on string support for TensorFlow Lite Strings are not compatible with ByteBuffer inputs/outputs. Make that clear in the documentation, also highlighting how they differ from primitive types. PiperOrigin-RevId: 224559052 --- tensorflow/lite/g3doc/apis.md | 7 ++++ .../java/org/tensorflow/lite/Interpreter.java | 37 +++++++++++++++---- 2 files changed, 36 insertions(+), 8 deletions(-) diff --git a/tensorflow/lite/g3doc/apis.md b/tensorflow/lite/g3doc/apis.md index b85570f41c..b15159ce41 100644 --- a/tensorflow/lite/g3doc/apis.md +++ b/tensorflow/lite/g3doc/apis.md @@ -304,6 +304,13 @@ one of the following primitive types: * `long` * `byte` +`String` types are also supported, but they are encoded differently than the +primitive types. In particular, the shape of a string Tensor dictates the number +and arrangement of strings in the Tensor, with each element itself being a +variable length string. In this sense, the (byte) size of the Tensor cannot be +computed from the shape and type alone, and consequently strings cannot be +provided as a single, flat `ByteBuffer` argument. + If other data types, including boxed types like `Integer` and `Float`, are used, an `IllegalArgumentException` will be thrown. diff --git a/tensorflow/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java b/tensorflow/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java index a03d7b5676..2203d5fbdb 100644 --- a/tensorflow/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java +++ b/tensorflow/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java @@ -43,15 +43,34 @@ import org.checkerframework.checker.nullness.qual.NonNull; * 
{@code
  * Object[] inputs = {input0, input1, ...};
  * Map map_of_indices_to_outputs = new HashMap<>();
- * float[][][] ith_output = new float[3][2][4];
+ * ByteBuffer ith_output = ByteBuffer.allocateDirect(3 * 2 * 4 * 4);  // Float tensor, shape 3x2x4.
+ * ith_output.order(ByteOrder.nativeOrder());
  * map_of_indices_to_outputs.put(i, ith_output);
  * try (Interpreter interpreter = new Interpreter(file_of_a_tensorflowlite_model)) {
  *   interpreter.runForMultipleInputsOutputs(inputs, map_of_indices_to_outputs);
  * }
  * }
* + *

If a model takes or produces string tensors: + * + * 

{@code
+ * String[] input = {"foo", "bar"};  // Input tensor shape is [2].
+ * String[] output = new String[3][2];  // Output tensor shape is [3, 2].
+ * try (Interpreter interpreter = new Interpreter(file_of_a_tensorflowlite_model)) {
+ *   interpreter.runForMultipleInputsOutputs(input, output);
+ * }
+ * }
+ * *

Orders of inputs and outputs are determined when converting TensorFlow model to TensorFlowLite - * model with Toco. + * model with Toco, as are the default shapes of the inputs. + * + *

When inputs are provided as (multi-dimensional) arrays, the corresponding input tensor(s) will + * be implicitly resized according to that array's shape. When inputs are provided as {@link + * ByteBuffer} types, no implicit resizing is done; the caller must ensure that the {@link + * ByteBuffer} byte size either matches that of the corresponding tensor, or that they first resize + * the tensor via {@link #resizeInput()}. Tensor shape and type information can be obtained via the + * {@link Tensor} class, available via {@link #getInputTensor(int)} and {@link + * #getOutputTensor(int)}. * *

WARNING:Instances of a {@code Interpreter} is not thread-safe. A {@code * Interpreter} owns resources that must be explicitly freed by invoking {@link #close()} @@ -192,12 +211,13 @@ public final class Interpreter implements AutoCloseable { * Runs model inference if the model takes only one input, and provides only one output. * *

Warning: The API runs much faster if {@link ByteBuffer} is used as input data type. Please - * consider using {@link ByteBuffer} to feed input data for better performance. + * consider using {@link ByteBuffer} to feed primitive input data for better performance. * * @param input an array or multidimensional array, or a {@link ByteBuffer} of primitive types * including int, float, long, and byte. {@link ByteBuffer} is the preferred way to pass large - * input data. When {@link ByteBuffer} is used, its content should remain unchanged until - * model inference is done. + * input data for primitive types, whereas string types require using the (multi-dimensional) + * array input path. When {@link ByteBuffer} is used, its content should remain unchanged + * until model inference is done. * @param output a multidimensional array of output data, or a {@link ByteBuffer} of primitive * types including int, float, long, and byte. */ @@ -212,13 +232,14 @@ public final class Interpreter implements AutoCloseable { * Runs model inference if the model takes multiple inputs, or returns multiple outputs. * *

Warning: The API runs much faster if {@link ByteBuffer} is used as input data type. Please - * consider using {@link ByteBuffer} to feed input data for better performance. + * consider using {@link ByteBuffer} to feed primitive input data for better performance. * * @param inputs an array of input data. The inputs should be in the same order as inputs of the * model. Each input can be an array or multidimensional array, or a {@link ByteBuffer} of * primitive types including int, float, long, and byte. {@link ByteBuffer} is the preferred - * way to pass large input data. When {@link ByteBuffer} is used, its content should remain - * unchanged until model inference is done. + * way to pass large input data, whereas string types require using the (multi-dimensional) + * array input path. When {@link ByteBuffer} is used, its content should remain unchanged + * until model inference is done. * @param outputs a map mapping output indices to multidimensional arrays of output data or {@link * ByteBuffer}s of primitive types including int, float, long, and byte. It only needs to keep * entries for the outputs to be used. -- GitLab From 9fffd26041e40bb3c89eabebfd35f92fe37b2c2a Mon Sep 17 00:00:00 2001 From: Igor Ganichev Date: Fri, 7 Dec 2018 12:39:30 -0800 Subject: [PATCH 216/873] Support XLA_CPU/GPU devices in eager runtime Compiling a defun can be done with: @function.defun def f(x): return x + 1 with tf.device("XLA_GPU"): f(5) PiperOrigin-RevId: 224560868 --- tensorflow/compiler/jit/BUILD | 2 ++ .../compiler/jit/xla_compile_on_demand_op.cc | 14 +++++++++++++- tensorflow/compiler/jit/xla_cpu_device.cc | 14 +++++++++++++- tensorflow/compiler/tests/BUILD | 7 ------- tensorflow/core/common_runtime/eager/execute.cc | 3 ++- .../core/common_runtime/eager/tensor_handle.cc | 5 +---- tensorflow/python/framework/func_graph.py | 4 +++- 7 files changed, 34 insertions(+), 15 deletions(-) diff --git a/tensorflow/compiler/jit/BUILD b/tensorflow/compiler/jit/BUILD index 7ebcd120f6..15dcbb2641 100644 --- a/tensorflow/compiler/jit/BUILD +++ b/tensorflow/compiler/jit/BUILD @@ -76,6 +76,7 @@ cc_library( srcs = ["xla_cpu_device.cc"], visibility = [":friends"], deps = [ + ":create_xla_launch_op", # buildcleaner: keep ":flags", ":jit_compilation_passes", ":xla_device", @@ -95,6 +96,7 @@ cc_library( srcs = ["xla_gpu_device.cc"], visibility = [":friends"], deps = [ + ":create_xla_launch_op", # buildcleaner: keep ":jit_compilation_passes", ":xla_device", "//tensorflow/compiler/jit/kernels:xla_ops", diff --git a/tensorflow/compiler/jit/xla_compile_on_demand_op.cc b/tensorflow/compiler/jit/xla_compile_on_demand_op.cc index 1fe612d43d..c7e8d61d28 100644 --- a/tensorflow/compiler/jit/xla_compile_on_demand_op.cc +++ b/tensorflow/compiler/jit/xla_compile_on_demand_op.cc @@ -142,11 +142,22 @@ Status XlaCompileOnDemandOp::Compile( TF_RETURN_IF_ERROR(ctx->allocate_temp( device_tensor.dtype(), device_tensor.shape(), &host_tensor, attrs)); Notification n; + Status status; ctx->op_device_context()->CopyDeviceTensorToCPU( &device_tensor, "ConstantArgument", reinterpret_cast(ctx->device()), &host_tensor, - [&](Status status) { n.Notify(); }); + [&](Status s) { + status = s; + n.Notify(); + }); n.WaitForNotification(); + if (!status.ok()) { + LOG(ERROR) << "Copying tensor of shape " + << device_tensor.shape().DebugString() << " from " + << ctx->device()->name() << "to CPU failed with " + << status.ToString(); + return status; + } constant_arguments[i] = host_tensor; } } @@ -189,6 +200,7 @@ Status XlaCompileOnDemandOp::Compile( std::map variable_args = GetVariables(ctx); std::vector args; + TF_RETURN_IF_ERROR(XlaComputationLaunchContext::BuildXlaCompilerArguments( constant_arguments, variable_args, ctx, &args)); diff --git a/tensorflow/compiler/jit/xla_cpu_device.cc b/tensorflow/compiler/jit/xla_cpu_device.cc index 7df898ad12..e9770647e7 100644 --- a/tensorflow/compiler/jit/xla_cpu_device.cc +++ b/tensorflow/compiler/jit/xla_cpu_device.cc @@ -63,7 +63,19 @@ Status XlaCpuDeviceFactory::CreateDevices( options.device_ordinal = 0; options.compilation_device_name = DEVICE_CPU_XLA_JIT; options.use_multiple_streams = false; - devices->push_back(absl::make_unique(session_options, options)); + auto device = absl::make_unique(session_options, options); + + // Setting GpuDeviceInfo because eager runtime relies on the device + // context in tensorflow_gpu_device_info(). Also, + // tensorflow_gpu_device_info() == nullptr is used as an IsCPU test. + // We need XlaCpuDevice to be treated not as CPU because it allocates + // XlaTensors, not regular Tensors. + Status status = device->UseGpuDeviceInfo(); + if (!status.ok()) { + errors::AppendToMessage(&status, "while setting up ", DEVICE_GPU_XLA_JIT); + return status; + } + devices->push_back(std::move(device)); return Status::OK(); } diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD index bc3d60b90e..093b61629c 100644 --- a/tensorflow/compiler/tests/BUILD +++ b/tensorflow/compiler/tests/BUILD @@ -408,13 +408,6 @@ tf_xla_py_test( name = "eager_test", size = "large", srcs = ["eager_test.py"], - disabled_backends = [ - # TODO(b/78199195) Support XLA CPU devices in eager runtime - "cpu", - "cpu_ondemand", - # TODO(b/78468222) Enable GPU backend - "gpu", - ], deps = [ ":xla_test", "//tensorflow/python:array_ops", diff --git a/tensorflow/core/common_runtime/eager/execute.cc b/tensorflow/core/common_runtime/eager/execute.cc index a6199f2aeb..783baa96c9 100644 --- a/tensorflow/core/common_runtime/eager/execute.cc +++ b/tensorflow/core/common_runtime/eager/execute.cc @@ -263,7 +263,8 @@ Status EagerLocalExecute(EagerOperation* op, // 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") { + (device->device_type() == "TPU" || device->device_type() == "XLA_GPU" || + device->device_type() == "XLA_CPU")) { op->MutableAttrs()->Set(kXlaCompileAttr, true); } diff --git a/tensorflow/core/common_runtime/eager/tensor_handle.cc b/tensorflow/core/common_runtime/eager/tensor_handle.cc index d8d6b7a63b..0acd160936 100644 --- a/tensorflow/core/common_runtime/eager/tensor_handle.cc +++ b/tensorflow/core/common_runtime/eager/tensor_handle.cc @@ -184,10 +184,7 @@ Status TensorHandle::CopyToDevice(EagerContext* ctx, tensorflow::Device* dstd, bool is_same_device = (srcd == dstd) || (srcd->name() == dstd->name()); const bool dst_cpu = dstd->tensorflow_gpu_device_info() == nullptr; const bool src_cpu = srcd->tensorflow_gpu_device_info() == nullptr; - // both_on_cpu can be true and yet is_same_device is false, if one of src/dst - // has device type XLA_CPU, and the other CPU. - const bool both_on_cpu = src_cpu && dst_cpu; - if (is_same_device || both_on_cpu) { + if (is_same_device) { *output = new tensorflow::TensorHandle(*src, dstd, dstd, ctx); return tensorflow::Status::OK(); } diff --git a/tensorflow/python/framework/func_graph.py b/tensorflow/python/framework/func_graph.py index 0f6db500cc..bd4ed5553e 100644 --- a/tensorflow/python/framework/func_graph.py +++ b/tensorflow/python/framework/func_graph.py @@ -122,7 +122,9 @@ class FuncGraph(ops.Graph): # restored. if context.executing_eagerly(): self.seed = context.global_seed() - self._xla_compile = (context.context().device_spec.device_type == "TPU") + device_type = context.context().device_spec.device_type + self._xla_compile = (device_type == "TPU" or device_type == "XLA_GPU" + or device_type == "XLA_CPU") if self._distribution_strategy_stack or self._xla_compile: self._add_device_to_stack(context.context().device_name) else: -- GitLab From 4f543e588a99caeb8265372127b889a4f67edba4 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 7 Dec 2018 12:39:53 -0800 Subject: [PATCH 217/873] Fix NVPTX TargetMachine construction to use reloc/code model flags only if explicitly set. Fixes failures caused by r348585. PiperOrigin-RevId: 224560930 --- .../xla/service/gpu/llvm_gpu_backend/nvptx_backend_lib.cc | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/nvptx_backend_lib.cc b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/nvptx_backend_lib.cc index abf9e7b6d6..bd53b90b42 100644 --- a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/nvptx_backend_lib.cc +++ b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/nvptx_backend_lib.cc @@ -199,8 +199,7 @@ std::unique_ptr GetTargetMachine( } return absl::WrapUnique(target->createTargetMachine( triple.str(), llvm_ir::AsStringRef(cpu_name), "+ptx60", target_options, - Optional(RelocModel), Optional(CMModel), - codegen_opt_level)); + getRelocModel(), getCodeModel(), codegen_opt_level)); } // Adds the standard LLVM optimization passes, based on the speed optimization -- GitLab From 66ca3cd10df0bf9bb6586bf0a09ac5c5ed0a25fb Mon Sep 17 00:00:00 2001 From: Allen Lavoie Date: Fri, 7 Dec 2018 12:40:49 -0800 Subject: [PATCH 218/873] Add a functional saver, use it for object-based checkpointing Pulls some utilities out of saver.py which are necessary to actually use it. The functional saver takes only SaveableObjects, so these are utilities for taking a list of whatever users pass in and converting them to those. One other code move for object-based checkpointing to avoid circular imports. Applications which need a SaverDef still use the old Saver. Serialization to SaverDef will be added to this saver in a followup. Does not actually wrap the new Saver's methods in @tf.function yet, since there are memory issues which need to be fixed first. PiperOrigin-RevId: 224561069 --- .../training/elastic_average_optimizer.py | 5 +- .../training/moving_average_optimizer.py | 3 +- tensorflow/python/BUILD | 11 +- .../python/training/checkpoint_utils.py | 6 +- .../python/training/checkpointable/BUILD | 6 +- .../python/training/checkpointable/base.py | 40 +-- .../python/training/checkpointable/util.py | 151 +++++--- tensorflow/python/training/saver.py | 331 ++--------------- tensorflow/python/training/saving/BUILD | 55 +++ .../training/saving/functional_saver.py | 101 ++++++ .../training/saving/functional_saver_test.py | 50 +++ .../training/{ => saving}/saveable_object.py | 0 .../training/saving/saveable_object_util.py | 340 ++++++++++++++++++ .../python/training/warm_starting_util.py | 4 +- 14 files changed, 687 insertions(+), 416 deletions(-) create mode 100644 tensorflow/python/training/saving/BUILD create mode 100644 tensorflow/python/training/saving/functional_saver.py create mode 100644 tensorflow/python/training/saving/functional_saver_test.py rename tensorflow/python/training/{ => saving}/saveable_object.py (100%) create mode 100644 tensorflow/python/training/saving/saveable_object_util.py diff --git a/tensorflow/contrib/opt/python/training/elastic_average_optimizer.py b/tensorflow/contrib/opt/python/training/elastic_average_optimizer.py index 6c203e5519..fa1a7aaff0 100644 --- a/tensorflow/contrib/opt/python/training/elastic_average_optimizer.py +++ b/tensorflow/contrib/opt/python/training/elastic_average_optimizer.py @@ -30,6 +30,7 @@ from tensorflow.python.ops import variables from tensorflow.python.training import optimizer from tensorflow.python.training import saver from tensorflow.python.training import session_run_hook +from tensorflow.python.training.saving import saveable_object_util LOCAL_VARIABLE_NAME = 'local_center_variable' GLOBAL_VARIABLE_NAME = 'global_center_variable' @@ -424,7 +425,7 @@ class ElasticAverageOptimizer(optimizer.Optimizer): if var_list is None: var_list = variables.trainable_variables() if not isinstance(var_list, dict): - var_list = saver.BaseSaverBuilder.OpListToDict(var_list) + var_list = saveable_object_util.op_list_to_dict(var_list) swapped_var_list = {} for key, var in var_list.items(): @@ -464,4 +465,4 @@ class _ElasticAverageOptimizerHook(session_run_hook.SessionRunHook): def after_create_session(self, session, coord): """Run initialization ops""" - session.run(self._variable_init_op) \ No newline at end of file + session.run(self._variable_init_op) diff --git a/tensorflow/contrib/opt/python/training/moving_average_optimizer.py b/tensorflow/contrib/opt/python/training/moving_average_optimizer.py index b7fd2d2fb9..bf3e5c51f7 100644 --- a/tensorflow/contrib/opt/python/training/moving_average_optimizer.py +++ b/tensorflow/contrib/opt/python/training/moving_average_optimizer.py @@ -26,6 +26,7 @@ from tensorflow.python.ops import variables from tensorflow.python.training import moving_averages from tensorflow.python.training import optimizer from tensorflow.python.training import saver +from tensorflow.python.training.saving import saveable_object_util class MovingAverageOptimizer(optimizer.Optimizer): @@ -165,7 +166,7 @@ class MovingAverageOptimizer(optimizer.Optimizer): if var_list is None: var_list = variables.global_variables() if not isinstance(var_list, dict): - var_list = saver.BaseSaverBuilder.OpListToDict(var_list) + var_list = saveable_object_util.op_list_to_dict(var_list) v_name_to_tensor = {} for k, tensor_or_list in six.iteritems(var_list): diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index cc36f1fc0e..0a3ee65bc4 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -3515,13 +3515,13 @@ py_library( exclude = [ "**/*test*", "training/checkpointable/**/*.py", + "training/saving/**/*.py", # The following targets have their own build rules (same name as the # file): "training/basic_session_run_hooks.py", "training/checkpoint_management.py", "training/distribute.py", "training/distribution_strategy_context.py", - "training/saveable_object.py", "training/saver.py", "training/session_run_hook.py", "training/training_util.py", @@ -3596,12 +3596,6 @@ py_library( ], ) -py_library( - name = "saveable_object", - srcs = ["training/saveable_object.py"], - srcs_version = "PY2AND3", -) - py_library( name = "checkpoint_management", srcs = ["training/checkpoint_management.py"], @@ -3655,7 +3649,6 @@ py_library( ":platform", ":pywrap_tensorflow", ":resource_variable_ops", - ":saveable_object", ":session", ":state_ops", ":string_ops", @@ -3665,6 +3658,8 @@ py_library( "//tensorflow/core:protos_all_py", "//tensorflow/python/eager:context", "//tensorflow/python/training/checkpointable:base", + "//tensorflow/python/training/saving:saveable_object", + "//tensorflow/python/training/saving:saveable_object_util", "//third_party/py/numpy", "@six_archive//:six", ], diff --git a/tensorflow/python/training/checkpoint_utils.py b/tensorflow/python/training/checkpoint_utils.py index 99b1f4c0d7..74b46179e7 100644 --- a/tensorflow/python/training/checkpoint_utils.py +++ b/tensorflow/python/training/checkpoint_utils.py @@ -30,7 +30,7 @@ from tensorflow.python.ops import variables from tensorflow.python.platform import gfile from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import checkpoint_management -from tensorflow.python.training import saver +from tensorflow.python.training.saving import saveable_object_util from tensorflow.python.util.tf_export import tf_export @@ -311,10 +311,10 @@ def _set_checkpoint_initializer(variable, restore_op = io_ops.restore_v2( ckpt_file, [tensor_name], [slice_spec], [base_type], name=name)[0] - names_to_saveables = saver.BaseSaverBuilder.OpListToDict([variable]) + names_to_saveables = saveable_object_util.op_list_to_dict([variable]) saveable_objects = [] for name, op in names_to_saveables.items(): - for s in saver.BaseSaverBuilder.SaveableObjectsForOp(op, name): + for s in saveable_object_util.saveable_objects_for_op(op, name): saveable_objects.append(s) assert len(saveable_objects) == 1 # Should be only one variable. diff --git a/tensorflow/python/training/checkpointable/BUILD b/tensorflow/python/training/checkpointable/BUILD index 4ab5593d4f..26a0ac35b7 100644 --- a/tensorflow/python/training/checkpointable/BUILD +++ b/tensorflow/python/training/checkpointable/BUILD @@ -25,9 +25,9 @@ py_library( "//tensorflow/python:framework_ops", "//tensorflow/python:io_ops_gen", "//tensorflow/python:platform", - "//tensorflow/python:saveable_object", "//tensorflow/python:util", "//tensorflow/python/eager:context", + "//tensorflow/python/training/saving:saveable_object", ], ) @@ -114,7 +114,6 @@ py_library( "//tensorflow/python:init_ops", "//tensorflow/python:io_ops_gen", "//tensorflow/python:pywrap_tensorflow", - "//tensorflow/python:saveable_object", "//tensorflow/python:saver", "//tensorflow/python:session", "//tensorflow/python:tensor_shape", @@ -123,6 +122,9 @@ py_library( "//tensorflow/python:variables", "//tensorflow/python/eager:context", "//tensorflow/python/eager:def_function", + "//tensorflow/python/training/saving:functional_saver", + "//tensorflow/python/training/saving:saveable_object", + "//tensorflow/python/training/saving:saveable_object_util", ], ) diff --git a/tensorflow/python/training/checkpointable/base.py b/tensorflow/python/training/checkpointable/base.py index 095a90ddd4..3cd1c6f9c8 100644 --- a/tensorflow/python/training/checkpointable/base.py +++ b/tensorflow/python/training/checkpointable/base.py @@ -25,7 +25,6 @@ import weakref import six -from tensorflow.python import pywrap_tensorflow from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes @@ -34,7 +33,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import gen_io_ops as io_ops from tensorflow.python.platform import tf_logging as logging -from tensorflow.python.training import saveable_object +from tensorflow.python.training.saving import saveable_object from tensorflow.python.util import nest from tensorflow.python.util import serialization from tensorflow.python.util import tf_decorator @@ -374,41 +373,10 @@ class _CheckpointPosition(object): eagerly. """ (restore_ops, - named_saveables, + tensor_saveables, python_saveables) = self._gather_ops_or_named_saveables() - - # Eagerly run restorations for Python state. - reader = pywrap_tensorflow.NewCheckpointReader( - self._checkpoint.save_path_string) - for saveable in python_saveables: - spec_names = [spec.name for spec in saveable.specs] - saveable.python_restore( - [reader.get_tensor(name) for name in spec_names]) - - # If we have new SaveableObjects, extract and cache restore ops. - if named_saveables: - validated_saveables = ( - self._checkpoint.builder._ValidateAndSliceInputs(named_saveables)) # pylint: disable=protected-access - validated_names = set(saveable.name for saveable in validated_saveables) - if set(named_saveables.keys()) != validated_names: - raise AssertionError( - ("Saveable keys changed when validating. Got back %s, was " - "expecting %s") % (named_saveables.keys(), validated_names)) - all_tensors = self._checkpoint.builder.bulk_restore( - filename_tensor=self._checkpoint.save_path_tensor, - saveables=validated_saveables, preferred_shard=-1, - restore_sequentially=False) - saveable_index = 0 - for saveable in validated_saveables: - num_specs = len(saveable.specs) - saveable_tensors = all_tensors[ - saveable_index:saveable_index + num_specs] - saveable_index += num_specs - restore_op = saveable.restore(saveable_tensors, restored_shapes=None) - if not context.executing_eagerly(): - assert saveable.name not in self._checkpoint.restore_ops_by_name - self._checkpoint.restore_ops_by_name[saveable.name] = restore_op - restore_ops.append(restore_op) + restore_ops.extend(self._checkpoint.restore_saveables( + tensor_saveables, python_saveables)) return restore_ops @property diff --git a/tensorflow/python/training/checkpointable/util.py b/tensorflow/python/training/checkpointable/util.py index d183fbdcf9..a54f41a54f 100644 --- a/tensorflow/python/training/checkpointable/util.py +++ b/tensorflow/python/training/checkpointable/util.py @@ -40,11 +40,14 @@ from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.training import checkpoint_management from tensorflow.python.training import optimizer as optimizer_lib -from tensorflow.python.training import saveable_object as saveable_object_lib -from tensorflow.python.training import saver as saver_lib +from tensorflow.python.training import saver as v1_saver_lib from tensorflow.python.training.checkpointable import base from tensorflow.python.training.checkpointable import data_structures from tensorflow.python.training.checkpointable import tracking +from tensorflow.python.training.saving import functional_saver +from tensorflow.python.training.saving import saveable_object as saveable_object_lib +from tensorflow.python.training.saving import saveable_object_util +from tensorflow.python.util import compat from tensorflow.python.util import deprecation from tensorflow.python.util import tf_contextlib from tensorflow.python.util.tf_export import tf_export @@ -89,7 +92,6 @@ class _CheckpointRestoreCoordinator(object): referenced every restore (e.g. for Python state); otherwise they would create their own ops every restore. """ - self.builder = saver_lib.BulkSaverBuilder() self.object_graph_proto = object_graph_proto self.restore_uid = ops.uid() # Maps from objects to lists of attributes which were in the checkpoint but @@ -144,6 +146,57 @@ class _CheckpointRestoreCoordinator(object): if self.new_restore_ops_callback: self.new_restore_ops_callback(new_ops) # pylint: disable=not-callable + def restore_saveables(self, tensor_saveables, python_saveables): + """Run or build restore operations for SaveableObjects. + + Args: + tensor_saveables: `SaveableObject`s which correspond to Tensors. + python_saveables: `PythonStateSaveable`s which correspond to Python + values. + + Returns: + When graph building, a list of restore operations, either cached or newly + created, to restore `tensor_saveables`. + """ + restore_ops = [] + # Eagerly run restorations for Python state. + reader = pywrap_tensorflow.NewCheckpointReader( + self.save_path_string) + for saveable in python_saveables: + spec_names = [spec.name for spec in saveable.specs] + saveable.python_restore( + [reader.get_tensor(name) for name in spec_names]) + + # If we have new SaveableObjects, extract and cache restore ops. + if tensor_saveables: + validated_saveables = saveable_object_util.validate_and_slice_inputs( + tensor_saveables) + validated_names = set(saveable.name for saveable in validated_saveables) + if set(tensor_saveables.keys()) != validated_names: + raise AssertionError( + ("Saveable keys changed when validating. Got back %s, was " + "expecting %s") % (tensor_saveables.keys(), validated_names)) + for saveable in validated_saveables: + if saveable.device: + device = saveable_object_util.set_cpu0(saveable.device) + else: + device = None + with ops.device(device): + tensors = [] + for spec in saveable.specs: + tensors.append( + io_ops.restore_v2( + self.save_path_tensor, + [spec.name], + [spec.slice_spec], + [spec.dtype])[0]) + restore_op = saveable.restore(tensors, restored_shapes=None) + if not context.executing_eagerly(): + assert saveable.name not in self.restore_ops_by_name + self.restore_ops_by_name[saveable.name] = restore_op + restore_ops.append(restore_op) + return restore_ops + class _NameBasedRestoreCoordinator(object): """Keeps the status of a name-based checkpoint restore.""" @@ -183,11 +236,11 @@ class _NameBasedRestoreCoordinator(object): continue else: saveable = saveable_factory - names_to_saveables = saver_lib.BaseSaverBuilder.OpListToDict( + names_to_saveables = saveable_object_util.op_list_to_dict( [saveable], convert_variable_to_tensor=False) for name, op in names_to_saveables.items(): - for saveable_object in saver_lib.BaseSaverBuilder.SaveableObjectsForOp( + for saveable_object in saveable_object_util.saveable_objects_for_op( op=op, name=name): yield saveable_object @@ -606,10 +659,10 @@ def _add_attributes_to_object_graph( # Figure out the name-based Saver's name for this variable. If it's # already a SaveableObject we'd just get the checkpoint key back, so # we leave full_name blank. - saver_dict = saver_lib.BaseSaverBuilder.OpListToDict( + saver_dict = saveable_object_util.op_list_to_dict( [maybe_saveable], convert_variable_to_tensor=False) full_name, = saver_dict.keys() - saveables = tuple(saver_lib.BaseSaverBuilder.SaveableObjectsForOp( + saveables = tuple(saveable_object_util.saveable_objects_for_op( op=maybe_saveable, name=attribute.checkpoint_key)) for saveable in saveables: saveable.full_name = full_name @@ -1226,7 +1279,7 @@ class NameBasedSaverStatus(_LoadStatus): session = ops.get_default_session() with ops.device("/cpu:0"): saveables = self._gather_saveable_objects() - saver_lib.Saver(saveables).restore( + v1_saver_lib.Saver(saveables).restore( sess=session, save_path=self._checkpoint.save_path) def initialize_or_restore(self, session=None): @@ -1251,18 +1304,6 @@ class _SessionWithFeedDictAdditions(session_lib.SessionInterface): fetches=fetches, feed_dict=feed_dict, **kwargs) -def _copy_saver_with_new_var_list(old_saver, new_var_list): - """Copy a `tf.train.Saver`'s state to a new Saver with different variables.""" - new_saver = saver_lib.Saver(var_list=new_var_list, max_to_keep=None) - # TODO(allenl): Move to copying functionality to Saver? - # pylint: disable=protected-access - new_saver._last_checkpoints = old_saver._last_checkpoints - new_saver._checkpoints_to_be_deleted = old_saver._checkpoints_to_be_deleted - new_saver._next_checkpoint_time = old_saver._next_checkpoint_time - # pylint: enable=protected-access - return new_saver - - class CheckpointableSaver(object): """Saves and restores a `Checkpointable` object and its dependencies. @@ -1301,7 +1342,8 @@ class CheckpointableSaver(object): # Op caching for save self._object_graph_feed_tensor = None self._last_save_object_graph = None - self._last_save_saver = None + self._file_prefix_feed_tensor = None + self._cached_save_operation = None # Op caching for restore, shared between _CheckpointRestoreCoordinators self._restore_op_cache = {} @@ -1368,13 +1410,16 @@ class CheckpointableSaver(object): base.NoRestoreSaveable( tensor=object_graph_tensor, name=base.OBJECT_GRAPH_PROTO_KEY)) - # TODO(allenl, haoliang): Swap in a function-based saver here. - return saver_lib.Saver( + # TODO(allenl): Swap in a function-based saver here once it can serialize + # to a SaverDef. + return v1_saver_lib.Saver( var_list=named_saveable_objects, max_to_keep=None) - def _prepare_save(self, - object_graph_tensor=None, - saveable_object_cache=None): + def _save_cached_when_graph_building( + self, + file_prefix, + object_graph_tensor=None, + saveable_object_cache=None): """Create or retrieve save ops. When graph building, `saveable_object_cache` will typically be non-`None`, @@ -1383,15 +1428,17 @@ class CheckpointableSaver(object): unnecessarily re-creating save ops. Args: + file_prefix: The prefix for saved checkpoint files. object_graph_tensor: A `Tensor` to which the current object graph will be fed. saveable_object_cache: A dictionary; if specified, used to cache `SaveableObject`s. Returns: - A two-element tuple with a `tf.train.Saver` and a feed_dict of `Tensor`s - to feed when running save ops. The feed dict contains the current object - graph and any Python state to be saved in the checkpoint. + A two-element tuple with a filename tensor and a feed_dict of tensors to + feed when running it (if graph building). The feed dict contains the + current object graph and any Python state to be saved in the + checkpoint. When executing eagerly only the first argument is meaningful. """ (named_saveable_objects, graph_proto, feed_additions) = self._gather_saveables( @@ -1403,15 +1450,11 @@ class CheckpointableSaver(object): # constructors. That means the Saver needs to be copied with a new # var_list. or context.executing_eagerly()): - if self._last_save_object_graph is not None: - self._last_save_saver = _copy_saver_with_new_var_list( - old_saver=self._last_save_saver, - new_var_list=named_saveable_objects) - else: - self._last_save_saver = saver_lib.Saver( - var_list=named_saveable_objects, max_to_keep=None) + saver = functional_saver.Saver(named_saveable_objects) + with ops.device("/cpu:0"): + self._cached_save_operation = saver.save(file_prefix) self._last_save_object_graph = graph_proto - return self._last_save_saver, feed_additions + return self._cached_save_operation, feed_additions def save(self, file_prefix, checkpoint_number=None, session=None): """Save a training checkpoint. @@ -1435,36 +1478,42 @@ class CheckpointableSaver(object): Returns: The full path to the checkpoint. """ - feed_additions = {} + feed_dict = {} graph_building = not context.executing_eagerly() + if checkpoint_number: + file_prefix = "%s-%d" % (file_prefix, checkpoint_number) if graph_building: if self._object_graph_feed_tensor is None: with ops.device("/cpu:0"): self._object_graph_feed_tensor = constant_op.constant( "", dtype=dtypes.string) + self._file_prefix_feed_tensor = constant_op.constant( + "", dtype=dtypes.string) object_graph_tensor = self._object_graph_feed_tensor + file_prefix_tensor = self._file_prefix_feed_tensor + feed_dict[file_prefix_tensor] = file_prefix else: + with ops.device("/cpu:0"): + file_prefix_tensor = constant_op.constant( + file_prefix, dtype=dtypes.string) object_graph_tensor = None - saver, new_feed_additions = self._prepare_save( + file_io.recursive_create_dir(os.path.dirname(file_prefix)) + save_path, new_feed_additions = self._save_cached_when_graph_building( + file_prefix=file_prefix_tensor, object_graph_tensor=object_graph_tensor, saveable_object_cache=self._saveable_object_cache) if new_feed_additions: - feed_additions.update(new_feed_additions) + feed_dict.update(new_feed_additions) if not graph_building: session = None elif session is None: session = ops.get_default_session() - file_io.recursive_create_dir(os.path.dirname(file_prefix)) - with ops.device("/cpu:0"): - save_path = saver.save( - sess=_SessionWithFeedDictAdditions( - session=session, feed_additions=feed_additions), - save_path=file_prefix, - write_meta_graph=False, - write_state=False, - global_step=checkpoint_number) + if session: + save_path = session.run(save_path, feed_dict=feed_dict) + else: + save_path = save_path.numpy() return save_path def restore(self, save_path): @@ -1753,9 +1802,9 @@ class Checkpoint(tracking.Checkpointable): Returns: The full path to the checkpoint (i.e. `file_prefix`). """ - return self._saver.save( + return compat.as_str(self._saver.save( file_prefix=file_prefix, - session=session) + session=session)) @property def save_counter(self): diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py index 4cd09f8a1d..04a7216484 100644 --- a/tensorflow/python/training/saver.py +++ b/tensorflow/python/training/saver.py @@ -14,7 +14,11 @@ # ============================================================================== # pylint: disable=invalid-name -"""Save and restore variables.""" +"""Save and restore variables. + +Symbols in this file are deprecated. See replacements in +tensorflow/python/training/checkpointable and tensorflow/python/training/saving. +""" from __future__ import absolute_import from __future__ import division from __future__ import print_function @@ -25,7 +29,6 @@ import time import uuid import numpy as np -import six from tensorflow.core.protobuf import checkpointable_object_graph_pb2 from tensorflow.core.protobuf import meta_graph_pb2 @@ -42,16 +45,15 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import gen_io_ops from tensorflow.python.ops import io_ops -from tensorflow.python.ops import resource_variable_ops -from tensorflow.python.ops import state_ops from tensorflow.python.ops import string_ops from tensorflow.python.ops import variables from tensorflow.python.platform import gfile from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import checkpoint_management -from tensorflow.python.training import saveable_object from tensorflow.python.training import training_util from tensorflow.python.training.checkpointable import base as checkpointable +from tensorflow.python.training.saving import saveable_object +from tensorflow.python.training.saving import saveable_object_util from tensorflow.python.util import compat from tensorflow.python.util.tf_export import tf_export @@ -67,31 +69,6 @@ get_checkpoint_mtimes = checkpoint_management.get_checkpoint_mtimes remove_checkpoint = checkpoint_management.remove_checkpoint -# Op names which identify variable reads which should be saved. -_VARIABLE_OPS = set(["Variable", - "VariableV2", - "AutoReloadVariable", - "VarHandleOp", - "ReadVariableOp"]) - - -def _set_cpu0(device_string): - """Creates a new device string based on `device_string` but using /CPU:0. - - If the device is already on /CPU:0, this is a no-op. - - Args: - device_string: A device string. - - Returns: - A device string. - """ - parsed_device = pydev.DeviceSpec.from_string(device_string) - parsed_device.device_type = "CPU" - parsed_device.device_index = 0 - return parsed_device.to_string() - - class BaseSaverBuilder(object): """Base class for Savers. @@ -101,64 +78,9 @@ class BaseSaverBuilder(object): SaveSpec = saveable_object.SaveSpec SaveableObject = saveable_object.SaveableObject - class VariableSaveable(SaveableObject): - """SaveableObject implementation that handles Variables.""" - - def __init__(self, var, slice_spec, name): - spec = BaseSaverBuilder.SaveSpec(var, slice_spec, name, dtype=var.dtype) - super(BaseSaverBuilder.VariableSaveable, self).__init__(var, [spec], name) - - def restore(self, restored_tensors, restored_shapes): - restored_tensor = restored_tensors[0] - if restored_shapes is not None: - restored_tensor = array_ops.reshape(restored_tensor, restored_shapes[0]) - return state_ops.assign( - self.op, - restored_tensor, - validate_shape=restored_shapes is None and - self.op.get_shape().is_fully_defined()) - - class ResourceVariableSaveable(SaveableObject): - """SaveableObject implementation that handles ResourceVariables.""" - - def __init__(self, var, slice_spec, name): - self._var_device = var.device - self._var_shape = var.shape - if isinstance(var, ops.Tensor): - self.handle_op = var.op.inputs[0] - tensor = var - elif isinstance(var, resource_variable_ops.ResourceVariable): - - def _read_variable_closure(v): - def f(): - with ops.device(v.device): - x = v.read_value() - # To allow variables placed on non-CPU devices to be checkpointed, - # we copy them to CPU on the same machine first. - with ops.device("/device:CPU:0"): - return array_ops.identity(x) - return f - - self.handle_op = var.handle - tensor = _read_variable_closure(var) - else: - raise ValueError( - "Saveable is neither a resource variable nor a read operation." - " Got: %s" % repr(var)) - spec = BaseSaverBuilder.SaveSpec(tensor, slice_spec, name, - dtype=var.dtype) - super(BaseSaverBuilder.ResourceVariableSaveable, self).__init__( - var, [spec], name) - - def restore(self, restored_tensors, restored_shapes): - restored_tensor = restored_tensors[0] - if restored_shapes is not None: - restored_tensor = array_ops.reshape(restored_tensor, restored_shapes[0]) - # Copy the restored tensor to the variable's device. - with ops.device(self._var_device): - restored_tensor = array_ops.identity(restored_tensor) - return resource_variable_ops.shape_safe_assign_variable_handle( - self.handle_op, self._var_shape, restored_tensor) + # Aliases for code which was moved but still has lots of users. + VariableSaveable = saveable_object_util.ReferenceVariableSaveable + ResourceVariableSaveable = saveable_object_util.ResourceVariableSaveable def __init__(self, write_version=saver_pb2.SaverDef.V2): self._write_version = write_version @@ -224,7 +146,11 @@ class BaseSaverBuilder(object): del restore_sequentially all_tensors = [] for saveable in saveables: - with ops.device(_set_cpu0(saveable.device) if saveable.device else None): + if saveable.device: + device = saveable_object_util.set_cpu0(saveable.device) + else: + device = None + with ops.device(device): all_tensors.extend( self.restore_op(filename_tensor, saveable, preferred_shard)) return all_tensors @@ -336,7 +262,7 @@ class BaseSaverBuilder(object): last_device = None for shard, (device, saveables) in enumerate(per_device): last_device = device - with ops.device(_set_cpu0(device)): + with ops.device(saveable_object_util.set_cpu0(device)): sharded_filename = self.sharded_filename(tmp_checkpoint_prefix, shard, num_shards_tensor) sharded_prefixes.append(sharded_filename) @@ -344,7 +270,7 @@ class BaseSaverBuilder(object): with ops.control_dependencies([x.op for x in sharded_saves]): # Co-locates the merge step with the last device. - with ops.device(_set_cpu0(last_device)): + with ops.device(saveable_object_util.set_cpu0(last_device)): # V2 format write path consists of a metadata merge step. Once merged, # attempts to delete the temporary directory, "_temp". merge_step = gen_io_ops.merge_v2_checkpoints( @@ -459,10 +385,6 @@ class BaseSaverBuilder(object): name="restore_shard")) return control_flow_ops.group(*sharded_restores, name="restore_all") - @staticmethod - def _IsVariable(v): - return isinstance(v, ops.Tensor) and v.op.type in _VARIABLE_OPS - def _GroupByDevices(self, saveables): """Group Variable tensor slices per device. @@ -490,220 +412,6 @@ class BaseSaverBuilder(object): per_device[canonical_device.pop()].append(saveable) return sorted(per_device.items(), key=lambda t: t[0]) - @staticmethod - def OpListToDict(op_list, convert_variable_to_tensor=True): - """Create a dictionary of names to operation lists. - - Args: - op_list: A list, tuple, or set of Variables or SaveableObjects. - convert_variable_to_tensor: Whether or not to convert single Variables - with no slice info into Tensors. - - Returns: - A dictionary of names to the operations that must be saved under - that name. Variables with save_slice_info are grouped together under the - same key in no particular order. - - Raises: - TypeError: If the type of op_list or its elements is not supported. - ValueError: If at least two saveables share the same name. - """ - if not isinstance(op_list, (list, tuple, set)): - raise TypeError("Variables to save should be passed in a dict or a " - "list: %s" % op_list) - # When ResourceVariables are converted to Tensors, read ops are added to the - # graph. Sorting the op_list ensures that the resulting graph is always - # constructed in a deterministic way: - op_list = sorted(op_list, key=lambda x: x.name) - names_to_saveables = {} - # pylint: disable=protected-access - for var in op_list: - if isinstance(var, BaseSaverBuilder.SaveableObject): - names_to_saveables[var.name] = var - elif isinstance(var, variables.PartitionedVariable): - if var.name in names_to_saveables: - raise ValueError("At least two variables have the same name: %s" % - var.name) - names_to_saveables[var.name] = var - elif isinstance(var, variables.Variable) and var._save_slice_info: - name = var._save_slice_info.full_name - if name in names_to_saveables: - if not isinstance(names_to_saveables[name], list): - raise ValueError("Mixing slices and non-slices with the same name: " - "%s" % name) - names_to_saveables[name].append(var) - else: - names_to_saveables[name] = [var] - elif (isinstance(var, checkpointable.CheckpointableBase) - and not isinstance(var, variables.Variable)): - checkpointable_saveables = [ - (factory() if callable(factory) else factory) - for factory in var._gather_saveables_for_checkpoint().values()] - names_to_saveables.update( - BaseSaverBuilder.OpListToDict(checkpointable_saveables)) - else: - if context.executing_eagerly(): - if not isinstance(var, resource_variable_ops.ResourceVariable): - raise ValueError( - "Can only save/restore ResourceVariables when eager execution " - "is enabled, type: %s." % type(var)) - set_var = names_to_saveables.setdefault(var._shared_name, var) - if set_var is not var: - raise ValueError( - ("Two different ResourceVariable objects with the same " - "shared_name '%s' were passed to the Saver. This likely means " - "that they were created in different Graphs or isolation " - "contexts, and may not be checkpointed together.") % - (var._shared_name,)) - else: - if convert_variable_to_tensor: - if isinstance(var, resource_variable_ops.ResourceVariable): - var = var._graph_element # pylint: disable=protected-access - else: - var = ops.internal_convert_to_tensor(var, as_ref=True) - if not BaseSaverBuilder._IsVariable(var): - raise TypeError("Variable to save is not a Variable: %s" % var) - if var.op.type == "ReadVariableOp": - name = var.op.inputs[0].op.name - else: - name = var.op.name - if name in names_to_saveables: - raise ValueError("At least two variables have the same name: %s" % - name) - names_to_saveables[name] = var - - # pylint: enable=protected-access - return names_to_saveables - - @staticmethod - def SaveableObjectsForOp(op, name): - """Create `SaveableObject`s from an operation. - - Args: - op: A variable, operation, or SaveableObject to coerce into a - SaveableObject. - name: A string name for the SaveableObject. - - Yields: - `SaveableObject`s which together save/restore `op`. - - Raises: - TypeError: If `name` is not a string. - ValueError: For operations with no known conversion to SaveableObject. - """ - if not isinstance(name, six.string_types): - raise TypeError( - "names_to_saveables must be a dict mapping string names to " - "checkpointable operations. Name is not a string: %s" % name) - if isinstance(op, BaseSaverBuilder.SaveableObject): - yield op - elif isinstance(op, (list, tuple, variables.PartitionedVariable)): - if isinstance(op, variables.PartitionedVariable): - op = list(op) - # A set of slices. - slice_name = None - # pylint: disable=protected-access - for variable in op: - if not isinstance(variable, variables.Variable): - raise ValueError("Slices must all be Variables: %s" % variable) - if not variable._save_slice_info: - raise ValueError("Slices must all be slices: %s" % variable) - if slice_name is None: - slice_name = variable._save_slice_info.full_name - elif slice_name != variable._save_slice_info.full_name: - raise ValueError( - "Slices must all be from the same tensor: %s != %s" % - (slice_name, variable._save_slice_info.full_name)) - if variable.op.type in ["Variable", "VariableV2", - "AutoReloadVariable"]: - yield BaseSaverBuilder.VariableSaveable( - variable, variable._save_slice_info.spec, name) - else: - yield BaseSaverBuilder.ResourceVariableSaveable( - variable, variable._save_slice_info.spec, name) - # pylint: enable=protected-access - elif isinstance(op, checkpointable.CheckpointableBase) and not isinstance( - op, variables.Variable): - # pylint: disable=protected-access - for attr, factory in op._gather_saveables_for_checkpoint().items(): - if attr == checkpointable.VARIABLE_VALUE_KEY: - # Keep original name for classes masquerading as variables. - full_name = name - else: - full_name = name + "_" + attr - op = (factory(full_name) if callable(factory) else factory) - for op in BaseSaverBuilder.SaveableObjectsForOp(op, op.name): - yield op - # pylint: enable=protected-access - else: - # A variable or tensor. - if context.executing_eagerly(): - if not isinstance(op, resource_variable_ops.ResourceVariable): - raise ValueError("Can only save/restore ResourceVariable eager " - "mode is enabled, type: %s." % type(op)) - yield BaseSaverBuilder.ResourceVariableSaveable(op, "", name) - else: - if isinstance(op, resource_variable_ops.ResourceVariable): - variable = op._graph_element # pylint: disable=protected-access - else: - variable = ops.internal_convert_to_tensor(op, as_ref=True) - if not BaseSaverBuilder._IsVariable(variable): - raise TypeError("names_to_saveables must be a dict mapping string " - "names to Tensors/Variables. Not a variable: %s" % - variable) - if variable.op.type in ["Variable", "VariableV2", - "AutoReloadVariable"]: - yield BaseSaverBuilder.VariableSaveable(variable, "", name) - else: - yield BaseSaverBuilder.ResourceVariableSaveable( - variable, "", name) - - def _ValidateAndSliceInputs(self, names_to_saveables): - """Returns the variables and names that will be used for a Saver. - - Args: - names_to_saveables: A dict (k, v) where k is the name of an operation and - v is an operation to save or a BaseSaverBuilder.Saver. - - Returns: - A list of BaseSaverBuilder.SaveableObject objects. - - Raises: - TypeError: If any of the keys are not strings or any of the - values are not one of Tensor or Variable or a checkpointable operation. - ValueError: If the same operation is given in more than one value - (this also applies to slices of SlicedVariables). - """ - if not isinstance(names_to_saveables, dict): - names_to_saveables = BaseSaverBuilder.OpListToDict(names_to_saveables) - - saveables = [] - seen_ops = set() - for name, op in sorted(names_to_saveables.items(), - # Avoid comparing ops, sort only by name. - key=lambda x: x[0]): - for converted_saveable_object in self.SaveableObjectsForOp(op, name): - self._AddSaveable(saveables, seen_ops, converted_saveable_object) - return saveables - - def _AddSaveable(self, saveables, seen_ops, saveable): - """Adds the saveable to the saveables list. - - Args: - saveables: List to append the SaveableObject to. - seen_ops: Set of the ops of the saveables already processed. Used to - check that each saveable is only saved once. - saveable: The saveable. - - Raises: - ValueError: If the saveable has already been processed. - """ - if saveable.op in seen_ops: - raise ValueError("The same saveable will be restored with two names: %s" % - saveable.name) - saveables.append(saveable) - seen_ops.add(saveable.op) - def build(self, names_to_saveables, reshape=False, @@ -775,7 +483,8 @@ class BaseSaverBuilder(object): raise ValueError("save and restore operations need to be built together " " when eager execution is not enabled.") - saveables = self._ValidateAndSliceInputs(names_to_saveables) + saveables = saveable_object_util.validate_and_slice_inputs( + names_to_saveables) if max_to_keep is None: max_to_keep = 0 @@ -1910,7 +1619,7 @@ def saver_from_object_based_checkpoint( if builder is None: builder = BulkSaverBuilder() - saveables = builder._ValidateAndSliceInputs(var_list) # pylint: disable=protected-access + saveables = saveable_object_util.validate_and_slice_inputs(var_list) current_names = set() for saveable in saveables: for spec in saveable.specs: diff --git a/tensorflow/python/training/saving/BUILD b/tensorflow/python/training/saving/BUILD new file mode 100644 index 0000000000..67ccd59b88 --- /dev/null +++ b/tensorflow/python/training/saving/BUILD @@ -0,0 +1,55 @@ +# Description: +# Low-level utilities for reading and writing checkpoints. + +package( + default_visibility = [ + "//tensorflow:internal", + ], +) + +licenses(["notice"]) # Apache 2.0 + +exports_files(["LICENSE"]) + +load("//tensorflow:tensorflow.bzl", "cuda_py_test") + +py_library( + name = "functional_saver", + srcs = ["functional_saver.py"], + srcs_version = "PY2AND3", + deps = [ + ":saveable_object", + ":saveable_object_util", + "//tensorflow/python/eager:def_function", + ], +) + +cuda_py_test( + name = "functional_saver_test", + size = "medium", + srcs = [ + "functional_saver_test.py", + ], + additional_deps = [ + ":functional_saver", + "//tensorflow/python/eager:test", + ], +) + +py_library( + name = "saveable_object", + srcs = ["saveable_object.py"], + srcs_version = "PY2AND3", +) + +py_library( + name = "saveable_object_util", + srcs = ["saveable_object_util.py"], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow/python:resource_variable_ops", + "//tensorflow/python:variables", + "//tensorflow/python/training/checkpointable:base", + "@six_archive//:six", + ], +) diff --git a/tensorflow/python/training/saving/functional_saver.py b/tensorflow/python/training/saving/functional_saver.py new file mode 100644 index 0000000000..7eed333662 --- /dev/null +++ b/tensorflow/python/training/saving/functional_saver.py @@ -0,0 +1,101 @@ +# 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. +# ============================================================================== +"""Saves and restore variables inside traced @tf.functions.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +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 io_ops +from tensorflow.python.training.saving import saveable_object +from tensorflow.python.training.saving import saveable_object_util + + +class Saver(object): + """A minimal utility class for saving and restoring checkpoints. + + Note that this is a low-level utility which stores Tensors in the keys + specified by `SaveableObject`s. Higher-level utilities for object-based + checkpointing are built on top of it. + """ + + def __init__(self, saveable_objects): + """Specify a list of `SaveableObject`s to save and restore. + + Args: + saveable_objects: A list of `SaveableObject`s. + """ + saveable_objects = list(saveable_objects) + for saveable in saveable_objects: + if not isinstance(saveable, saveable_object.SaveableObject): + raise ValueError( + "Saver expected a list of SaveableObjects, got %s." % (saveable,)) + self._saveable_objects = saveable_objects + + # TODO(b/120569892): Use tf.function here + def save(self, file_prefix): + """Save the saveable objects to a checkpoint with `file_prefix`. + + Args: + file_prefix: A string or scalar string Tensor containing the prefix to + save under. + Returns: + A scalar string Tensor containing `file_prefix` with control dependencies + on the save ops. + """ + tensor_names = [] + tensors = [] + tensor_slices = [] + for saveable in self._saveable_objects: + for spec in saveable.specs: + tensor_names.append(spec.name) + tensors.append(spec.tensor) + tensor_slices.append(spec.slice_spec) + with ops.control_dependencies( + [io_ops.save_v2(file_prefix, tensor_names, tensor_slices, tensors)]): + return array_ops.identity(file_prefix) + + # TODO(b/120569892): Use tf.function here + def restore(self, file_prefix): + """Restore the saveable objects from a checkpoint with `file_prefix`. + + Args: + file_prefix: A string or scalar string Tensor containing the prefix for + files to read from. + + Returns: + An operation which restores the `Saver`'s `SaveableObject`s when run, or + None if executing eagerly. + """ + restore_ops = [] + for saveable in self._saveable_objects: + if saveable.device: + device = saveable_object_util.set_cpu0(saveable.device) + else: + device = None + with ops.device(device): + tensors = [] + for spec in saveable.specs: + tensors.append( + io_ops.restore_v2( + file_prefix, + [spec.name], + [spec.slice_spec], + [spec.dtype])[0]) + restore_ops.append(saveable.restore(tensors, restored_shapes=None)) + return control_flow_ops.group(restore_ops) diff --git a/tensorflow/python/training/saving/functional_saver_test.py b/tensorflow/python/training/saving/functional_saver_test.py new file mode 100644 index 0000000000..40002255aa --- /dev/null +++ b/tensorflow/python/training/saving/functional_saver_test.py @@ -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. +# ============================================================================= +"""Tests for the functional saver.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from tensorflow.python.eager import test +from tensorflow.python.framework import constant_op +from tensorflow.python.ops import resource_variable_ops +from tensorflow.python.training.saving import functional_saver +from tensorflow.python.training.saving import saveable_object_util + + +class SaverTest(test.TestCase): + + def test_resource_variable(self): + v1 = resource_variable_ops.ResourceVariable(2.) + saver = functional_saver.Saver( + saveable_object_util.saveable_objects_for_op(v1, "x")) + prefix = os.path.join(self.get_temp_dir(), "ckpt") + save_path = saver.save(constant_op.constant(prefix)) + v1.assign(1.) + saver.restore(save_path) + self.assertEqual(2., self.evaluate(v1)) + + v2 = resource_variable_ops.ResourceVariable(3.) + second_saver = functional_saver.Saver( + saveable_object_util.saveable_objects_for_op(v2, "x")) + second_saver.restore(save_path) + self.assertEqual(2., self.evaluate(v2)) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/python/training/saveable_object.py b/tensorflow/python/training/saving/saveable_object.py similarity index 100% rename from tensorflow/python/training/saveable_object.py rename to tensorflow/python/training/saving/saveable_object.py diff --git a/tensorflow/python/training/saving/saveable_object_util.py b/tensorflow/python/training/saving/saveable_object_util.py new file mode 100644 index 0000000000..fa88d2c6eb --- /dev/null +++ b/tensorflow/python/training/saving/saveable_object_util.py @@ -0,0 +1,340 @@ +# 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. +# ============================================================================== +"""Utilities for working with and creating SaveableObjects.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import six + +from tensorflow.python.eager import context +from tensorflow.python.framework import device as pydev +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import resource_variable_ops +from tensorflow.python.ops import state_ops +from tensorflow.python.ops import variables +from tensorflow.python.training.checkpointable import base as checkpointable +from tensorflow.python.training.saving import saveable_object + + +# Op names which identify variable reads which should be saved. +_VARIABLE_OPS = set(["Variable", + "VariableV2", + "AutoReloadVariable", + "VarHandleOp", + "ReadVariableOp"]) + + +def set_cpu0(device_string): + """Creates a new device string based on `device_string` but using /CPU:0. + + If the device is already on /CPU:0, this is a no-op. + + Args: + device_string: A device string. + + Returns: + A device string. + """ + parsed_device = pydev.DeviceSpec.from_string(device_string) + parsed_device.device_type = "CPU" + parsed_device.device_index = 0 + return parsed_device.to_string() + + +class ReferenceVariableSaveable(saveable_object.SaveableObject): + """SaveableObject implementation that handles reference variables.""" + + def __init__(self, var, slice_spec, name): + spec = saveable_object.SaveSpec(var, slice_spec, name, dtype=var.dtype) + super(ReferenceVariableSaveable, self).__init__(var, [spec], name) + + def restore(self, restored_tensors, restored_shapes): + restored_tensor = restored_tensors[0] + if restored_shapes is not None: + restored_tensor = array_ops.reshape(restored_tensor, restored_shapes[0]) + return state_ops.assign( + self.op, + restored_tensor, + validate_shape=restored_shapes is None and + self.op.get_shape().is_fully_defined()) + + +class ResourceVariableSaveable(saveable_object.SaveableObject): + """SaveableObject implementation that handles ResourceVariables.""" + + def __init__(self, var, slice_spec, name): + self._var_device = var.device + self._var_shape = var.shape + if isinstance(var, ops.Tensor): + self.handle_op = var.op.inputs[0] + tensor = var + elif isinstance(var, resource_variable_ops.ResourceVariable): + + def _read_variable_closure(v): + def f(): + with ops.device(v.device): + x = v.read_value() + # To allow variables placed on non-CPU devices to be checkpointed, + # we copy them to CPU on the same machine first. + with ops.device("/device:CPU:0"): + return array_ops.identity(x) + return f + + self.handle_op = var.handle + tensor = _read_variable_closure(var) + else: + raise ValueError( + "Saveable is neither a resource variable nor a read operation." + " Got: %s" % repr(var)) + spec = saveable_object.SaveSpec(tensor, slice_spec, name, + dtype=var.dtype) + super(ResourceVariableSaveable, self).__init__(var, [spec], name) + + def restore(self, restored_tensors, restored_shapes): + restored_tensor = restored_tensors[0] + if restored_shapes is not None: + restored_tensor = array_ops.reshape(restored_tensor, restored_shapes[0]) + # Copy the restored tensor to the variable's device. + with ops.device(self._var_device): + restored_tensor = array_ops.identity(restored_tensor) + return resource_variable_ops.shape_safe_assign_variable_handle( + self.handle_op, self._var_shape, restored_tensor) + + +def _tensor_comes_from_variable(v): + return isinstance(v, ops.Tensor) and v.op.type in _VARIABLE_OPS + + +def saveable_objects_for_op(op, name): + """Create `SaveableObject`s from an operation. + + Args: + op: A variable, operation, or SaveableObject to coerce into a + SaveableObject. + name: A string name for the SaveableObject. + + Yields: + `SaveableObject`s which together save/restore `op`. + + Raises: + TypeError: If `name` is not a string. + ValueError: For operations with no known conversion to SaveableObject. + """ + if not isinstance(name, six.string_types): + raise TypeError( + "names_to_saveables must be a dict mapping string names to " + "checkpointable operations. Name is not a string: %s" % name) + if isinstance(op, saveable_object.SaveableObject): + yield op + elif isinstance(op, (list, tuple, variables.PartitionedVariable)): + if isinstance(op, variables.PartitionedVariable): + op = list(op) + # A set of slices. + slice_name = None + # pylint: disable=protected-access + for variable in op: + if not isinstance(variable, variables.Variable): + raise ValueError("Slices must all be Variables: %s" % variable) + if not variable._save_slice_info: + raise ValueError("Slices must all be slices: %s" % variable) + if slice_name is None: + slice_name = variable._save_slice_info.full_name + elif slice_name != variable._save_slice_info.full_name: + raise ValueError( + "Slices must all be from the same tensor: %s != %s" % + (slice_name, variable._save_slice_info.full_name)) + if variable.op.type in ["Variable", "VariableV2", + "AutoReloadVariable"]: + yield ReferenceVariableSaveable( + variable, variable._save_slice_info.spec, name) + else: + yield ResourceVariableSaveable( + variable, variable._save_slice_info.spec, name) + # pylint: enable=protected-access + elif isinstance(op, checkpointable.CheckpointableBase) and not isinstance( + op, variables.Variable): + # pylint: disable=protected-access + for attr, factory in op._gather_saveables_for_checkpoint().items(): + if attr == checkpointable.VARIABLE_VALUE_KEY: + # Keep original name for classes masquerading as variables. + full_name = name + else: + full_name = name + "_" + attr + op = (factory(full_name) if callable(factory) else factory) + for op in saveable_objects_for_op(op, op.name): + yield op + # pylint: enable=protected-access + else: + # A variable or tensor. + if isinstance(op, resource_variable_ops.ResourceVariable): + # pylint: disable=protected-access + if op._in_graph_mode: + variable = op._graph_element + else: + variable = op + # pylint: enable=protected-access + yield ResourceVariableSaveable(variable, "", name) + else: + with ops.init_scope(): + if context.executing_eagerly(): + raise ValueError("Can only save/restore ResourceVariables when " + "executing eagerly, got type: %s." % type(op)) + + variable = ops.internal_convert_to_tensor(op, as_ref=True) + if not _tensor_comes_from_variable(variable): + raise TypeError("names_to_saveables must be a dict mapping string " + "names to Tensors/Variables. Not a variable: %s" % + variable) + if variable.op.type in ["Variable", "VariableV2", + "AutoReloadVariable"]: + yield ReferenceVariableSaveable(variable, "", name) + else: + yield ResourceVariableSaveable( + variable, "", name) + + +def op_list_to_dict(op_list, convert_variable_to_tensor=True): + """Create a dictionary of names to operation lists. + + Args: + op_list: A list, tuple, or set of Variables or SaveableObjects. + convert_variable_to_tensor: Whether or not to convert single Variables + with no slice info into Tensors. + + Returns: + A dictionary of names to the operations that must be saved under + that name. Variables with save_slice_info are grouped together under the + same key in no particular order. + + Raises: + TypeError: If the type of op_list or its elements is not supported. + ValueError: If at least two saveables share the same name. + """ + if not isinstance(op_list, (list, tuple, set)): + raise TypeError("Variables to save should be passed in a dict or a " + "list: %s" % op_list) + # When ResourceVariables are converted to Tensors, read ops are added to the + # graph. Sorting the op_list ensures that the resulting graph is always + # constructed in a deterministic way: + op_list = sorted(op_list, key=lambda x: x.name) + names_to_saveables = {} + # pylint: disable=protected-access + for var in op_list: + if isinstance(var, saveable_object.SaveableObject): + names_to_saveables[var.name] = var + elif isinstance(var, variables.PartitionedVariable): + if var.name in names_to_saveables: + raise ValueError("At least two variables have the same name: %s" % + var.name) + names_to_saveables[var.name] = var + elif isinstance(var, variables.Variable) and var._save_slice_info: + name = var._save_slice_info.full_name + if name in names_to_saveables: + if not isinstance(names_to_saveables[name], list): + raise ValueError("Mixing slices and non-slices with the same name: " + "%s" % name) + names_to_saveables[name].append(var) + else: + names_to_saveables[name] = [var] + elif (isinstance(var, checkpointable.CheckpointableBase) + and not isinstance(var, variables.Variable)): + checkpointable_saveables = [ + (factory() if callable(factory) else factory) + for factory in var._gather_saveables_for_checkpoint().values()] + names_to_saveables.update( + op_list_to_dict(checkpointable_saveables)) + else: + if context.executing_eagerly(): + if not isinstance(var, resource_variable_ops.ResourceVariable): + raise ValueError( + "Can only save/restore ResourceVariables when eager execution " + "is enabled, type: %s." % type(var)) + set_var = names_to_saveables.setdefault(var._shared_name, var) + if set_var is not var: + raise ValueError( + ("Two different ResourceVariable objects with the same " + "shared_name '%s' were passed to the Saver. This likely means " + "that they were created in different Graphs or isolation " + "contexts, and may not be checkpointed together.") % + (var._shared_name,)) + else: + if convert_variable_to_tensor: + if isinstance(var, resource_variable_ops.ResourceVariable): + var = var._graph_element # pylint: disable=protected-access + else: + var = ops.internal_convert_to_tensor(var, as_ref=True) + if not _tensor_comes_from_variable(var): + raise TypeError("Variable to save is not a Variable: %s" % var) + if var.op.type == "ReadVariableOp": + name = var.op.inputs[0].op.name + else: + name = var.op.name + if name in names_to_saveables: + raise ValueError("At least two variables have the same name: %s" % + name) + names_to_saveables[name] = var + + # pylint: enable=protected-access + return names_to_saveables + + +def _add_saveable(saveables, seen_ops, saveable): + """Adds the saveable to the saveables list. + + Args: + saveables: List to append the SaveableObject to. + seen_ops: Set of the ops of the saveables already processed. Used to + check that each saveable is only saved once. + saveable: The saveable. + + Raises: + ValueError: If the saveable has already been processed. + """ + if saveable.op in seen_ops: + raise ValueError("The same saveable will be restored with two names: %s" % + saveable.name) + saveables.append(saveable) + seen_ops.add(saveable.op) + + +def validate_and_slice_inputs(names_to_saveables): + """Returns the variables and names that will be used for a Saver. + + Args: + names_to_saveables: A dict (k, v) where k is the name of an operation and + v is an operation to save or a BaseSaverBuilder.Saver. + + Returns: + A list of SaveableObjects. + + Raises: + TypeError: If any of the keys are not strings or any of the + values are not one of Tensor or Variable or a checkpointable operation. + ValueError: If the same operation is given in more than one value + (this also applies to slices of SlicedVariables). + """ + if not isinstance(names_to_saveables, dict): + names_to_saveables = op_list_to_dict(names_to_saveables) + + saveables = [] + seen_ops = set() + for name, op in sorted(names_to_saveables.items(), + # Avoid comparing ops, sort only by name. + key=lambda x: x[0]): + for converted_saveable_object in saveable_objects_for_op(op, name): + _add_saveable(saveables, seen_ops, converted_saveable_object) + return saveables diff --git a/tensorflow/python/training/warm_starting_util.py b/tensorflow/python/training/warm_starting_util.py index 8c97f101da..1382b8ce72 100644 --- a/tensorflow/python/training/warm_starting_util.py +++ b/tensorflow/python/training/warm_starting_util.py @@ -28,7 +28,7 @@ from tensorflow.python.ops import variables as variables_lib from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import checkpoint_ops from tensorflow.python.training import checkpoint_utils -from tensorflow.python.training import saver +from tensorflow.python.training.saving import saveable_object_util from tensorflow.python.util.tf_export import tf_export @@ -139,7 +139,7 @@ def _infer_var_name(var): Returns: Name of the `var` """ - name_to_var_dict = saver.BaseSaverBuilder.OpListToDict(var) + name_to_var_dict = saveable_object_util.op_list_to_dict(var) if len(name_to_var_dict) > 1: raise TypeError("`var` = %s passed as arg violates the constraints. " "name_to_var_dict = %s" % (var, name_to_var_dict)) -- GitLab From abc2610c5ca689dce98683c63ccf8d886c42c37a Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 7 Dec 2018 13:09:16 -0800 Subject: [PATCH 219/873] Update ops-related pbtxt files. PiperOrigin-RevId: 224565203 --- .../core/ops/compat/ops_history.v1.pbtxt | 22 +++++++++++++++++++ tensorflow/core/ops/ops.pbtxt | 22 +++++++++++++++++++ 2 files changed, 44 insertions(+) diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt index ce71b21507..1492741e8b 100644 --- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt +++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt @@ -79062,6 +79062,17 @@ op { } is_stateful: true } +op { + name: "UnwrapDatasetVariant" + input_arg { + name: "input_handle" + type: DT_VARIANT + } + output_arg { + name: "output_handle" + type: DT_VARIANT + } +} op { name: "UpperBound" input_arg { @@ -79495,6 +79506,17 @@ op { minimum: 1 } } +op { + name: "WrapDatasetVariant" + input_arg { + name: "input_handle" + type: DT_VARIANT + } + output_arg { + name: "output_handle" + type: DT_VARIANT + } +} op { name: "WriteAudioSummary" input_arg { diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt index 1fa59b7087..89bdcc571e 100644 --- a/tensorflow/core/ops/ops.pbtxt +++ b/tensorflow/core/ops/ops.pbtxt @@ -38153,6 +38153,17 @@ op { } is_stateful: true } +op { + name: "UnwrapDatasetVariant" + input_arg { + name: "input_handle" + type: DT_VARIANT + } + output_arg { + name: "output_handle" + type: DT_VARIANT + } +} op { name: "UpperBound" input_arg { @@ -38471,6 +38482,17 @@ op { minimum: 1 } } +op { + name: "WrapDatasetVariant" + input_arg { + name: "input_handle" + type: DT_VARIANT + } + output_arg { + name: "output_handle" + type: DT_VARIANT + } +} op { name: "WriteAudioSummary" input_arg { -- GitLab From 32b16083085c0bf954228e8c9160561a576ec405 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 7 Dec 2018 13:11:24 -0800 Subject: [PATCH 220/873] Go: Update generated wrapper functions for TensorFlow ops. PiperOrigin-RevId: 224565479 --- tensorflow/go/op/wrappers.go | 266 +++++++++++++++++------------------ 1 file changed, 133 insertions(+), 133 deletions(-) diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index 440756bb38..6e49fbb9ea 100644 --- a/tensorflow/go/op/wrappers.go +++ b/tensorflow/go/op/wrappers.go @@ -5862,6 +5862,121 @@ func MapStage(scope *Scope, key tf.Output, indices tf.Output, values []tf.Output return scope.AddOperation(opspec) } +// 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 +// +// 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 +// +// 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. +// +// 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{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StageClear", + + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// StageSizeAttr is an optional argument to StageSize. +type StageSizeAttr func(optionalAttr) + +// StageSizeCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func StageSizeCapacity(value int64) StageSizeAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// StageSizeMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func StageSizeMemoryLimit(value int64) StageSizeAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// StageSizeContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func StageSizeContainer(value string) StageSizeAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// StageSizeSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func StageSizeSharedName(value string) StageSizeAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op returns the number of elements in the underlying container. +func StageSize(scope *Scope, dtypes []tf.DataType, optional ...StageSizeAttr) (size tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StageSize", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Compute the regularized incomplete beta integral \\(I_x(a, b)\\). // // The regularized incomplete beta integral is defined as: @@ -11434,24 +11549,6 @@ func RandomShuffle(scope *Scope, value tf.Output, optional ...RandomShuffleAttr) return op.Output(0) } -// 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) -} - // ResourceSparseApplyRMSPropAttr is an optional argument to ResourceSparseApplyRMSProp. type ResourceSparseApplyRMSPropAttr func(optionalAttr) @@ -17210,64 +17307,6 @@ func NonMaxSuppressionWithOverlaps(scope *Scope, overlaps tf.Output, scores tf.O return op.Output(0) } -// 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 -// -// 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 -// -// 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. -// -// 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{}{"dtypes": dtypes} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "StageClear", - - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - // Computes softmax cross entropy cost and gradients to backpropagate. // // Unlike `SoftmaxCrossEntropyWithLogits`, this operation does not accept @@ -20236,63 +20275,6 @@ func NotEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { return op.Output(0) } -// StageSizeAttr is an optional argument to StageSize. -type StageSizeAttr func(optionalAttr) - -// StageSizeCapacity sets the optional capacity attribute to value. -// If not specified, defaults to 0 -// -// REQUIRES: value >= 0 -func StageSizeCapacity(value int64) StageSizeAttr { - return func(m optionalAttr) { - m["capacity"] = value - } -} - -// StageSizeMemoryLimit sets the optional memory_limit attribute to value. -// If not specified, defaults to 0 -// -// REQUIRES: value >= 0 -func StageSizeMemoryLimit(value int64) StageSizeAttr { - return func(m optionalAttr) { - m["memory_limit"] = value - } -} - -// StageSizeContainer sets the optional container attribute to value. -// If not specified, defaults to "" -func StageSizeContainer(value string) StageSizeAttr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// StageSizeSharedName sets the optional shared_name attribute to value. -// If not specified, defaults to "" -func StageSizeSharedName(value string) StageSizeAttr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// Op returns the number of elements in the underlying container. -func StageSize(scope *Scope, dtypes []tf.DataType, optional ...StageSizeAttr) (size tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtypes": dtypes} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "StageSize", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Produces the max pool of the input tensor for quantized types. // // Arguments: @@ -31550,6 +31532,24 @@ func ModelDataset(scope *Scope, input_dataset tf.Output, output_types []tf.DataT return op.Output(0) } +// 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) +} + // Performs a padding as a preprocess during a convolution. // // Similar to FusedResizeAndPadConv2d, this op allows for an optimized -- GitLab From d09435e0cc8b21e5b10eb0f9750e7a24c2031e85 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 7 Dec 2018 13:13:45 -0800 Subject: [PATCH 221/873] Change `Status::error_message()` usage to `Status::message()`. PiperOrigin-RevId: 224565798 --- tensorflow/contrib/tpu/profiler/dump_tpu_profile.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/tpu/profiler/dump_tpu_profile.cc b/tensorflow/contrib/tpu/profiler/dump_tpu_profile.cc index b4b06a40a2..ef35e84ba5 100644 --- a/tensorflow/contrib/tpu/profiler/dump_tpu_profile.cc +++ b/tensorflow/contrib/tpu/profiler/dump_tpu_profile.cc @@ -98,7 +98,7 @@ Status DumpOpProfileToLogDirectory(StringPiece run_dir, if (!status.ok()) { return errors::Internal( "Failed to convert op profile to json. Skipping... ", - string(status.error_message())); + string(status.message())); } TF_RETURN_IF_ERROR(WriteStringToFile(Env::Default(), path, json)); if (os) { -- GitLab From 939206b56b6dd243f1aae52184c680e6b6f8b30e Mon Sep 17 00:00:00 2001 From: Scott Zhu Date: Fri, 7 Dec 2018 13:25:27 -0800 Subject: [PATCH 222/873] Update the performance test for unified LSTM to be benchmark test. PiperOrigin-RevId: 224567398 --- .../python/keras/layers/unified_lstm_test.py | 51 +++++++++++-------- 1 file changed, 30 insertions(+), 21 deletions(-) diff --git a/tensorflow/python/keras/layers/unified_lstm_test.py b/tensorflow/python/keras/layers/unified_lstm_test.py index 2cb3eff8fd..8cbc370633 100644 --- a/tensorflow/python/keras/layers/unified_lstm_test.py +++ b/tensorflow/python/keras/layers/unified_lstm_test.py @@ -28,6 +28,7 @@ import numpy as np from tensorflow.core.protobuf import config_pb2 from tensorflow.core.protobuf import rewriter_config_pb2 from tensorflow.python import keras +from tensorflow.python.client import session as session_lib from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes @@ -732,7 +733,7 @@ class LSTMLayerGraphOnlyTest(test.TestCase): self.assertEqual(len(layer.get_losses_for(x)), 1) -class UnifiedLSTMPerformanceTest(test.TestCase): +class UnifiedLSTMPerformanceTest(test.Benchmark): def _measure_performance(self, test_config, model, x_train, y_train): batch = test_config['batch'] @@ -808,11 +809,11 @@ class UnifiedLSTMPerformanceTest(test.TestCase): 'Normal LSTM', sec_per_epoch) return sec_per_epoch - @test_util.run_in_graph_and_eager_modes(config=_config, use_gpu=True) - def test_performance_with_standard_cudnn_impl(self): + def _benchmark_performance_with_standard_cudnn_impl(self): if not test.is_gpu_available(): self.skipTest('performance test will only run on GPU') + mode = 'eager' if context.executing_eagerly() else 'graph' batch = 64 num_batch = 10 test_config = { @@ -832,34 +833,42 @@ class UnifiedLSTMPerformanceTest(test.TestCase): num_classes=test_config['output_shape']) y_train = keras.utils.to_categorical(y_train, test_config['output_shape']) - cudnn_duration = self._time_performance_run_cudnn_lstm( + cudnn_sec_per_epoch = self._time_performance_run_cudnn_lstm( test_config, x_train, y_train) - unified_lstm_gpu_duration = self._time_performance_run_unifed_lstm_gpu( + unified_lstm_sec_per_epoch = self._time_performance_run_unifed_lstm_gpu( test_config, x_train, y_train) - normal_lstm_duration = self._time_performance_run_normal_lstm( + normal_lstm_sec_per_epoch = self._time_performance_run_normal_lstm( test_config, x_train, y_train) - cudnn_vs_unified = cudnn_duration / unified_lstm_gpu_duration - unified_vs_normal = normal_lstm_duration / unified_lstm_gpu_duration + cudnn_vs_unified = cudnn_sec_per_epoch / unified_lstm_sec_per_epoch + unified_vs_normal = normal_lstm_sec_per_epoch / unified_lstm_sec_per_epoch + + self.report_benchmark(name='keras_cudnn_lstm_' + mode, + wall_time=cudnn_sec_per_epoch, + iters=test_config['epoch'], + extras=test_config) + self.report_benchmark(name='keras_unified_lstm_' + mode, + wall_time=unified_lstm_sec_per_epoch, + iters=test_config['epoch'], + extras=test_config) + self.report_benchmark(name='keras_canonical_lstm_' + mode, + wall_time=normal_lstm_sec_per_epoch, + iters=test_config['epoch'], + extras=test_config) - # TODO(scottzhu): reeanble the test after moving it to benchmark test suite. - # The current test has performance flakiness issue. logging.info('Expect the performance of Unified LSTM is within 80% of ' 'CuDNN LSTM, got {0:.2f}%'.format(cudnn_vs_unified * 100)) logging.info('Expect the performance of Unified LSTM is more than 5 times' ' of normal LSTM, got {0:.2f}'.format(unified_vs_normal)) - # Assert the performance diff should be within 80% of the native cudnn. - # self.assertGreaterEqual( - # cudnn_vs_unified, 0.80, - # 'Expect the performance of Unified LSTM is within 80% of CuDNN LSTM, ' - # 'but got {0:.2f}%'.format(cudnn_vs_unified * 100)) - # # Assert the performance diff between CPU impl and GPU impl should be more - # # than 5 times. - # self.assertGreaterEqual( - # unified_vs_normal, 5, - # 'Expect the performance of Unified LSTM is more than 5 times of ' - # 'normal LSTM, but got {0:.2f}'.format(unified_vs_normal)) + def benchmark_performance_graph(self): + with context.graph_mode(), session_lib.Session(config=_config): + self._benchmark_performance_with_standard_cudnn_impl() + + def benchmark_performance_eager(self): + with context.eager_mode(): + self._benchmark_performance_with_standard_cudnn_impl() + if __name__ == '__main__': test.main() -- GitLab From 56429fce51cc81bb49d57c0217bd2c59deff774f Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 7 Dec 2018 13:42:24 -0800 Subject: [PATCH 223/873] Enables some previously disabled Keras test parameterizations. PiperOrigin-RevId: 224570187 --- .../keras/engine/training_dataset_test.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/tensorflow/python/keras/engine/training_dataset_test.py b/tensorflow/python/keras/engine/training_dataset_test.py index a8a81fa842..d6cc93d1ef 100644 --- a/tensorflow/python/keras/engine/training_dataset_test.py +++ b/tensorflow/python/keras/engine/training_dataset_test.py @@ -24,6 +24,7 @@ import numpy as np from tensorflow.python import keras from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.eager import context from tensorflow.python.framework import ops from tensorflow.python.framework import test_util as tf_test_util from tensorflow.python.keras import keras_parameterized @@ -147,11 +148,14 @@ class TestTrainingWithDatasetIterators(keras_parameterized.TestCase): class TestTrainingWithDataset(keras_parameterized.TestCase): - # TODO(kaftan) Run w/ all model types. - # Seems like subclass models has a bug, file ticket - @keras_parameterized.run_with_all_model_types(exclude_models='subclass') + @keras_parameterized.run_with_all_model_types @keras_parameterized.run_all_keras_modes def test_calling_model_on_same_dataset(self): + if ((not testing_utils.should_run_eagerly()) + and testing_utils.get_model_type() == 'subclass' + and context.executing_eagerly()): + self.skipTest('b/120673224') + model = testing_utils.get_small_mlp(1, 4, input_dim=3) optimizer = RMSPropOptimizer(learning_rate=0.001) loss = 'mse' @@ -234,9 +238,7 @@ class TestTrainingWithDataset(keras_parameterized.TestCase): 'you should specify the `steps` argument'): model.predict(dataset, verbose=0) - # TODO(kaftan) Run w/ all model types. - # Seems like subclass models has a bug, file ticket - @keras_parameterized.run_with_all_model_types(exclude_models='subclass') + @keras_parameterized.run_with_all_model_types @keras_parameterized.run_all_keras_modes def test_dataset_with_sample_weights(self): model = testing_utils.get_small_mlp(1, 4, input_dim=3) @@ -308,9 +310,7 @@ class TestTrainingWithDataset(keras_parameterized.TestCase): class TestMetricsWithDatasetIterators(keras_parameterized.TestCase): - # TODO(kaftan) Run w/ all model types. - # Seems like subclass models has a bug, file ticket - @keras_parameterized.run_with_all_model_types(exclude_models='subclass') + @keras_parameterized.run_with_all_model_types @keras_parameterized.run_all_keras_modes def test_metrics_correctness_with_iterator(self): layers = [ -- GitLab From 68e8a87bcd2b97a7f9a09f4cdba86ec254fad978 Mon Sep 17 00:00:00 2001 From: Jianwei Xie Date: Fri, 7 Dec 2018 13:45:51 -0800 Subject: [PATCH 224/873] Improve the correctness test by invoking train/eval multiple times (w/w.o. validation data). PiperOrigin-RevId: 224570819 --- .../contrib/distribute/python/keras_test.py | 86 +++++++++---------- 1 file changed, 39 insertions(+), 47 deletions(-) diff --git a/tensorflow/contrib/distribute/python/keras_test.py b/tensorflow/contrib/distribute/python/keras_test.py index dea208232a..c0bcb5ecd5 100644 --- a/tensorflow/contrib/distribute/python/keras_test.py +++ b/tensorflow/contrib/distribute/python/keras_test.py @@ -1303,16 +1303,17 @@ class TestDistributionStrategyCorrectness(test.TestCase, with self.cached_session(): default_tolerance = 1e-5 - weights_tolerance = default_tolerance - metrics_tolerance = default_tolerance - predict_tolerance = default_tolerance + tol_table = {} if isinstance(distribution, (mirrored_strategy.MirroredStrategy, mirrored_strategy.CoreMirroredStrategy)): - # TODO(b/119257215): Weights are not exactly the same, so use lower - # tolerance for now. - weights_tolerance = 1e-4 - predict_tolerance = 1e-4 + # TODO(b/119257215): Weights are not exactly the same, so use larger + # tolerance for now. Predict should be related to weights. + tol_table = { + 'weights_1': 1e-4, + 'weights_2': 1e-4, + 'predict_result_1': 1e-4, + } keras.backend.set_image_data_format('channels_last') np.random.seed(_RANDOM_SEED) @@ -1361,56 +1362,47 @@ class TestDistributionStrategyCorrectness(test.TestCase, with_distribution, x_train, y_train, x_predict)) - training_history = model.fit(**training_inputs).history + result = {} + result['training_history_1'] = model.fit(**training_inputs).history if eval_inputs is not None: - eval_result = model.evaluate(**eval_inputs) - else: - # Creates a dummy identical eval_result to be compared later. - eval_result = 1.0 + result['eval_result_1'] = model.evaluate(**eval_inputs) - weights = model.get_weights() - predict_result = model.predict(**predict_inputs) + result['weights_1'] = model.get_weights() + result['predict_result_1'] = model.predict(**predict_inputs) - return weights, training_history, eval_result, predict_result + # Train and eval again to mimic user's flow. - wts_with_ds, history_with_ds, eval_with_ds, predict_with_ds = ( - fit_eval_and_predict(with_distribution=distribution)) + result['training_history_2'] = model.fit(**training_inputs).history - (wts_without_ds, history_without_ds, eval_without_ds, - predict_without_ds) = fit_eval_and_predict(with_distribution=None) + if eval_inputs is not None: + result['eval_result_2'] = model.evaluate(**eval_inputs) + + result['weights_2'] = model.get_weights() + + return result + + results_with_ds = fit_eval_and_predict(with_distribution=distribution) + results_without_ds = fit_eval_and_predict(with_distribution=None) # Verify that the weights, training history, eval results, predict outputs # are the same within some limits of tolerance. - self.assertAllClose( - wts_with_ds, - wts_without_ds, - atol=weights_tolerance, - rtol=weights_tolerance, - msg='Fail to assert weights after training.') - self.assertAllClose( - eval_with_ds, - eval_without_ds, - atol=metrics_tolerance, - rtol=metrics_tolerance, - msg='Fail to assert eval results.') - self.assertAllClose( - predict_with_ds, - predict_without_ds, - atol=predict_tolerance, - rtol=predict_tolerance, - msg='Fail to assert predict results.') - - if not (isinstance(distribution, tpu_strategy.TPUStrategy) and - distribution.extended.steps_per_run > 1): - # TODO(b/119894254): Enable this test for all cases once the underlying - # bug is fixed. + for key in results_with_ds: + if (key.startswith('training_history') and + isinstance(distribution, tpu_strategy.TPUStrategy) and + distribution.extended.steps_per_run > 1): + # TODO(b/119894254): Enable this test for all cases once the + # underlying bug is fixed. + continue + + tolerance = tol_table.get(key, default_tolerance) + self.assertAllClose( - history_with_ds, - history_without_ds, - atol=metrics_tolerance, - rtol=metrics_tolerance, - msg='Fail to assert training history.') + results_with_ds[key], + results_without_ds[key], + atol=tolerance, + rtol=tolerance, + msg='Fail to assert {}.'.format(key)) if __name__ == '__main__': -- GitLab From 1b03648fb58913992ae44d706f9e5fce06a919e5 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 7 Dec 2018 13:52:06 -0800 Subject: [PATCH 225/873] Update cmd line quantization documentation link. PiperOrigin-RevId: 224571863 --- tensorflow/lite/g3doc/convert/cmdline_examples.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/tensorflow/lite/g3doc/convert/cmdline_examples.md b/tensorflow/lite/g3doc/convert/cmdline_examples.md index 59f26b3505..de81e2cfdd 100644 --- a/tensorflow/lite/g3doc/convert/cmdline_examples.md +++ b/tensorflow/lite/g3doc/convert/cmdline_examples.md @@ -94,11 +94,12 @@ tflite_convert \ ### Convert a TensorFlow GraphDef for quantized inference The TensorFlow Lite Converter is compatible with fixed point quantization models -described [here](https://www.tensorflow.org/performance/quantization). These are -float models with `FakeQuant*` ops inserted at the boundaries of fused layers -to record min-max range information. This generates a quantized inference -workload that reproduces the quantization behavior that was used during -training. +described +[here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/q +uantize/README.md). These are float models with `FakeQuant*` ops inserted at the +boundaries of fused layers to record min-max range information. This generates a +quantized inference workload that reproduces the quantization behavior that was +used during training. The following command generates a quantized TensorFlow Lite FlatBuffer from a "quantized" TensorFlow GraphDef. -- GitLab From a64a8d8d02f9d15b1f5d6fbdbc0fa0db3f37f97d Mon Sep 17 00:00:00 2001 From: Gaurav Jain Date: Fri, 7 Dec 2018 13:58:08 -0800 Subject: [PATCH 226/873] Fix up a few tests to interact better with v2 mode PiperOrigin-RevId: 224572844 --- .../tutorials/mnist/mnist_with_summaries.py | 3 ++- .../python/debug/examples/debug_errors.py | 3 ++- .../python/debug/examples/debug_fibonacci.py | 3 ++- .../python/debug/examples/debug_keras.py | 3 ++- .../python/debug/examples/debug_mnist.py | 3 ++- tensorflow/python/keras/models_test.py | 4 ++- tensorflow/python/kernel_tests/lu_op_test.py | 27 ++++++++++--------- .../python/kernel_tests/matmul_op_test.py | 4 ++- .../kernel_tests/self_adjoint_eig_op_test.py | 4 +-- .../python/kernel_tests/tensordot_op_test.py | 10 ++++--- 10 files changed, 39 insertions(+), 25 deletions(-) diff --git a/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py b/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py index 7967e22d6a..1854e84d49 100644 --- a/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py +++ b/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py @@ -183,7 +183,8 @@ def main(_): if tf.gfile.Exists(FLAGS.log_dir): tf.gfile.DeleteRecursively(FLAGS.log_dir) tf.gfile.MakeDirs(FLAGS.log_dir) - train() + with tf.Graph().as_default(): + train() if __name__ == '__main__': diff --git a/tensorflow/python/debug/examples/debug_errors.py b/tensorflow/python/debug/examples/debug_errors.py index 28abc97343..e3692072cc 100644 --- a/tensorflow/python/debug/examples/debug_errors.py +++ b/tensorflow/python/debug/examples/debug_errors.py @@ -77,4 +77,5 @@ if __name__ == "__main__": default=False, help="Use debugger to track down bad values during training") FLAGS, unparsed = parser.parse_known_args() - tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) + with tf.Graph().as_default(): + tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/tensorflow/python/debug/examples/debug_fibonacci.py b/tensorflow/python/debug/examples/debug_fibonacci.py index 3821b393ec..777fb08988 100644 --- a/tensorflow/python/debug/examples/debug_fibonacci.py +++ b/tensorflow/python/debug/examples/debug_fibonacci.py @@ -100,4 +100,5 @@ if __name__ == "__main__": "--debug flag.") FLAGS, unparsed = parser.parse_known_args() - tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) + with tf.Graph().as_default(): + tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/tensorflow/python/debug/examples/debug_keras.py b/tensorflow/python/debug/examples/debug_keras.py index 3272d85ade..019121fa0a 100644 --- a/tensorflow/python/debug/examples/debug_keras.py +++ b/tensorflow/python/debug/examples/debug_keras.py @@ -86,4 +86,5 @@ if __name__ == "__main__": default=2, help="Number of epochs to train the model for.") FLAGS, unparsed = parser.parse_known_args() - tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) + with tf.Graph().as_default(): + tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/tensorflow/python/debug/examples/debug_mnist.py b/tensorflow/python/debug/examples/debug_mnist.py index ab1c90371c..09fb06c9c0 100644 --- a/tensorflow/python/debug/examples/debug_mnist.py +++ b/tensorflow/python/debug/examples/debug_mnist.py @@ -190,4 +190,5 @@ if __name__ == "__main__": "the gRPC address (e.g., localhost:1234). Mutually exclusive with the " "--debug flag.") FLAGS, unparsed = parser.parse_known_args() - tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) + with tf.Graph().as_default(): + tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/tensorflow/python/keras/models_test.py b/tensorflow/python/keras/models_test.py index b0872ae3ab..907ac41d0e 100644 --- a/tensorflow/python/keras/models_test.py +++ b/tensorflow/python/keras/models_test.py @@ -365,7 +365,9 @@ class TestCloneAndBuildModel(test.TestCase): self.assertEqual('mse', model.loss) self.assertTrue( - isinstance(model.optimizer, keras.optimizers.RMSprop)) + isinstance(model.optimizer, + (keras.optimizers.RMSprop, + keras.optimizer_v2.rmsprop.RMSprop))) self.assertEqual(['acc', metrics.categorical_accuracy], model._compile_metrics) diff --git a/tensorflow/python/kernel_tests/lu_op_test.py b/tensorflow/python/kernel_tests/lu_op_test.py index f2defcae85..06deb0e1c8 100644 --- a/tensorflow/python/kernel_tests/lu_op_test.py +++ b/tensorflow/python/kernel_tests/lu_op_test.py @@ -24,6 +24,7 @@ from tensorflow.python.client import session from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import ops +from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import functional_ops @@ -41,9 +42,9 @@ class LuOpTest(test.TestCase): def float_types(self): return set((np.float64, np.float32, np.complex64, np.complex128)) - def _verifyLuBase(self, sess, x, lower, upper, perm, verification, + def _verifyLuBase(self, x, lower, upper, perm, verification, output_idx_type): - lower_np, upper_np, perm_np, verification_np = sess.run( + lower_np, upper_np, perm_np, verification_np = self.evaluate( [lower, upper, perm, verification]) self.assertAllClose(x, verification_np) @@ -65,7 +66,7 @@ class LuOpTest(test.TestCase): def _verifyLu(self, x, output_idx_type=dtypes.int64): # Verify that Px = LU. - with self.cached_session(use_gpu=True) as sess: + with test_util.use_gpu(): lu, perm = linalg_ops.lu(x, output_idx_type=output_idx_type) @@ -121,7 +122,7 @@ class LuOpTest(test.TestCase): verification = array_ops.reshape(permuted_verification_reshaped, lu_shape) - self._verifyLuBase(sess, x, lower, upper, perm, verification, + self._verifyLuBase(x, lower, upper, perm, verification, output_idx_type) def testBasic(self): @@ -139,7 +140,7 @@ class LuOpTest(test.TestCase): self._verifyLu(complex_data, output_idx_type=output_idx_type) def testPivoting(self): - with self.session(use_gpu=True) as sess: + with test_util.use_gpu(): # This matrix triggers partial pivoting because the first diagonal entry # is small. data = np.array([[1e-9, 1., 0.], [1., 0., 0], [0., 1., 5]]) @@ -148,7 +149,7 @@ class LuOpTest(test.TestCase): for dtype in (np.float32, np.float64): self._verifyLu(data.astype(dtype)) _, p = linalg_ops.lu(data) - p_val = sess.run([p]) + p_val = self.evaluate([p]) # Make sure p_val is not the identity permutation. self.assertNotAllClose(np.arange(3), p_val) @@ -158,7 +159,7 @@ class LuOpTest(test.TestCase): complex_data += data self._verifyLu(complex_data) _, p = linalg_ops.lu(data) - p_val = sess.run([p]) + p_val = self.evaluate([p]) # Make sure p_val is not the identity permutation. self.assertNotAllClose(np.arange(3), p_val) @@ -166,15 +167,15 @@ class LuOpTest(test.TestCase): # LU factorization gives an error when the input is singular. # Note: A singular matrix may return without error but it won't be a valid # factorization. - with self.session(use_gpu=True) as sess: + with test_util.use_gpu(): for dtype in self.float_types: with self.assertRaises(errors.InvalidArgumentError): - sess.run( + self.evaluate( linalg_ops.lu( np.array([[1., 2., 3.], [2., 4., 6.], [2., 3., 4.]], dtype=dtype))) with self.assertRaises(errors.InvalidArgumentError): - sess.run( + self.evaluate( linalg_ops.lu( np.array([[[1., 2., 3.], [2., 4., 6.], [1., 2., 3.]], [[1., 2., 3.], [3., 4., 5.], [5., 6., 7.]]], @@ -212,17 +213,19 @@ class LuOpTest(test.TestCase): data = np.random.rand(n, n) + 1j * np.random.rand(n, n) self._verifyLu(data) + @test_util.run_v1_only("b/120545219") def testEmpty(self): self._verifyLu(np.empty([0, 2, 2])) self._verifyLu(np.empty([2, 0, 0])) + @test_util.run_deprecated_v1 def testConcurrentExecutesWithoutError(self): - with self.session(use_gpu=True) as sess: + with test_util.use_gpu(): matrix1 = random_ops.random_normal([5, 5], seed=42) matrix2 = random_ops.random_normal([5, 5], seed=42) lu1, p1 = linalg_ops.lu(matrix1) lu2, p2 = linalg_ops.lu(matrix2) - lu1_val, p1_val, lu2_val, p2_val = sess.run([lu1, p1, lu2, p2]) + lu1_val, p1_val, lu2_val, p2_val = self.evaluate([lu1, p1, lu2, p2]) self.assertAllEqual(lu1_val, lu2_val) self.assertAllEqual(p1_val, p2_val) diff --git a/tensorflow/python/kernel_tests/matmul_op_test.py b/tensorflow/python/kernel_tests/matmul_op_test.py index cd99f1774a..d31ecbcd3f 100644 --- a/tensorflow/python/kernel_tests/matmul_op_test.py +++ b/tensorflow/python/kernel_tests/matmul_op_test.py @@ -21,6 +21,7 @@ from __future__ import print_function import operator import numpy as np +from tensorflow.python import tf2 from tensorflow.python.framework import constant_op from tensorflow.python.framework import ops from tensorflow.python.framework import test_util @@ -224,7 +225,8 @@ class MatMulInfixOperatorTest(test_lib.TestCase): if __name__ == "__main__": sizes = [1, 3, 5] trans_options = [[False, False], [True, False], [False, True]] - for use_static_shape in [False, True]: + # TF2 does not support placeholders under eager so we skip it + for use_static_shape in set([True, tf2.enabled()]): for dtype in (np.int32, np.int64, np.float16, np.float32, np.float64, np.complex64, np.complex128): if not use_static_shape and (dtype == np.int32 or dtype == np.int64): diff --git a/tensorflow/python/kernel_tests/self_adjoint_eig_op_test.py b/tensorflow/python/kernel_tests/self_adjoint_eig_op_test.py index 323d14b7d8..ce782dbc28 100644 --- a/tensorflow/python/kernel_tests/self_adjoint_eig_op_test.py +++ b/tensorflow/python/kernel_tests/self_adjoint_eig_op_test.py @@ -161,7 +161,7 @@ def _GetSelfAdjointEigTest(dtype_, shape_, compute_v_): math_ops.matmul(tf_v, array_ops.matrix_diag(tf_e)), tf_v, adjoint_b=True) - self.assertAllClose(a_ev.eval(), a, atol=atol) + self.assertAllClose(self.evaluate(a_ev), a, atol=atol) # Compare to numpy.linalg.eigh. CompareEigenDecompositions(self, np_e, np_v, self.evaluate(tf_e), @@ -169,7 +169,7 @@ def _GetSelfAdjointEigTest(dtype_, shape_, compute_v_): else: tf_e = linalg_ops.self_adjoint_eigvals(constant_op.constant(a)) self.assertAllClose( - np.sort(np_e, -1), np.sort(tf_e.eval(), -1), atol=atol) + np.sort(np_e, -1), np.sort(self.evaluate(tf_e), -1), atol=atol) return Test diff --git a/tensorflow/python/kernel_tests/tensordot_op_test.py b/tensorflow/python/kernel_tests/tensordot_op_test.py index 123c9b376c..2894b7dd50 100644 --- a/tensorflow/python/kernel_tests/tensordot_op_test.py +++ b/tensorflow/python/kernel_tests/tensordot_op_test.py @@ -20,6 +20,7 @@ from __future__ import print_function import numpy as np +from tensorflow.python import tf2 from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors_impl @@ -99,7 +100,7 @@ class TensordotTest(test_lib.TestCase): tf_a = array_ops.ones((3, 3), dtype=dtypes.float32) tf_b = constant_op.constant([2, 3, 1], dtype=dtypes.float32)[None, None] - tf_ans = math_ops.tensordot(tf_a, tf_b, axes_value).eval() + tf_ans = math_ops.tensordot(tf_a, tf_b, axes_value) self.assertAllEqual(tf_ans.shape, np_ans.shape) self.assertAllEqual(tf_ans, np_ans) @@ -178,7 +179,7 @@ def _get_tensordot_tests(dtype_, rank_a_, rank_b_, num_dims_, dynamic_shape_): axes: (a_dims_np, b_dims_np) }) else: - tf_ans = math_ops.tensordot(a_np, b_np, (a_dims_np, b_dims_np)).eval() + tf_ans = math_ops.tensordot(a_np, b_np, (a_dims_np, b_dims_np)) self.assertAllClose(tf_ans, np_ans, rtol=tol, atol=tol) self.assertAllEqual(tf_ans.shape, np_ans.shape) @@ -208,7 +209,7 @@ def _get_tensordot_tests(dtype_, rank_a_, rank_b_, num_dims_, dynamic_shape_): c = math_ops.tensordot(a, b, axes=axes) tf_ans = sess.run(c, feed_dict={a: a_np, b: b_np}) else: - tf_ans = math_ops.tensordot(a_np, b_np, axes=axes).eval() + tf_ans = math_ops.tensordot(a_np, b_np, axes=axes) self.assertAllClose(tf_ans, np_ans, rtol=tol, atol=tol) self.assertAllEqual(tf_ans.shape, np_ans.shape) @@ -220,7 +221,8 @@ if __name__ == "__main__": for rank_a in 1, 2, 4, 5: for rank_b in 1, 2, 4, 5: for num_dims in range(0, min(rank_a, rank_b) + 1): - for dynamic_shape in False, True: + # TF2 does not support placeholders under eager so we skip it + for dynamic_shape in set([False, not tf2.enabled()]): for testcase in _get_tensordot_tests(dtype, rank_a, rank_b, num_dims, dynamic_shape): name = "%s_%s_%s_%s_%s_%s" % (testcase.__name__, dtype.__name__, -- GitLab From 71ea120a1bdfea823c4e3d27c55c2d2b885b147d Mon Sep 17 00:00:00 2001 From: Dan Moldovan Date: Fri, 7 Dec 2018 14:09:06 -0800 Subject: [PATCH 227/873] Use eager_py_func instead of the deprecated py_func. Add a bit of extra logic to account for the potential use of tf.print in Python 2. PiperOrigin-RevId: 224574979 --- .../python/autograph/operators/py_builtins.py | 2 ++ tensorflow/python/autograph/pyct/parser.py | 13 ++++++++++++- tensorflow/python/autograph/utils/py_func.py | 5 +++-- tensorflow/python/autograph/utils/py_func_test.py | 14 +++++++------- 4 files changed, 24 insertions(+), 10 deletions(-) diff --git a/tensorflow/python/autograph/operators/py_builtins.py b/tensorflow/python/autograph/operators/py_builtins.py index 2f55d53892..ddf05f73f3 100644 --- a/tensorflow/python/autograph/operators/py_builtins.py +++ b/tensorflow/python/autograph/operators/py_builtins.py @@ -174,6 +174,7 @@ def _tf_py_func_print(objects, kwargs): override_kwargs['flush'] = True def print_wrapper(*vals): + vals = tuple(v.numpy() if tensor_util.is_tensor(v) else v for v in vals) if six.PY3: # TensorFlow doesn't seem to generate Unicode when passing strings to # py_func. This causes the print to add a "b'" wrapper to the output, @@ -193,6 +194,7 @@ def range_(start_or_stop, stop=UNDEFINED, step=UNDEFINED): def _tf_range(start_or_stop, stop, step): + """Overload of range_ that generates a TF range tensor.""" # Note: for static inputs (e.g. constants), tf.range errors out at graph # construction time, instead of returning an empty tensor. Preventing the # graph construction error aligns the semantics with Python. diff --git a/tensorflow/python/autograph/pyct/parser.py b/tensorflow/python/autograph/pyct/parser.py index 8f4037c5e2..39fc1a7ed0 100644 --- a/tensorflow/python/autograph/pyct/parser.py +++ b/tensorflow/python/autograph/pyct/parser.py @@ -24,6 +24,7 @@ from __future__ import print_function import textwrap import gast +import six from tensorflow.python.util import tf_inspect @@ -91,7 +92,17 @@ def parse_entity(entity): def parse_str(src): """Returns the AST of given piece of code.""" # TODO(mdan): This should exclude the module things are autowrapped in. - return gast.parse(src) + + if six.PY2 and '.print(' in src: + # This special treatment is required because gast.parse is not aware of + # whether print_function was present in the original context. + src = 'from __future__ import print_function\n' + src + parsed_module = gast.parse(src) + parsed_module.body = parsed_module.body[1:] + else: + parsed_module = gast.parse(src) + + return parsed_module def parse_expression(src): diff --git a/tensorflow/python/autograph/utils/py_func.py b/tensorflow/python/autograph/utils/py_func.py index 11ebfb2e49..ee8b46b520 100644 --- a/tensorflow/python/autograph/utils/py_func.py +++ b/tensorflow/python/autograph/utils/py_func.py @@ -127,5 +127,6 @@ def wrap_py_func(f, return_dtypes, args, kwargs=None, use_dummy_return=False): retval = f(*f_args, **f_kwargs) return 1 if use_dummy_return else retval - return script_ops.py_func(f_wrapper, tensor_args, dtypes.int64 - if use_dummy_return else return_dtypes) + if use_dummy_return: + return_dtypes = dtypes.int32 + return script_ops.eager_py_func(f_wrapper, tensor_args, return_dtypes) diff --git a/tensorflow/python/autograph/utils/py_func_test.py b/tensorflow/python/autograph/utils/py_func_test.py index 28cefd8c3e..d17ede7714 100644 --- a/tensorflow/python/autograph/utils/py_func_test.py +++ b/tensorflow/python/autograph/utils/py_func_test.py @@ -32,13 +32,13 @@ class PyFuncTest(test.TestCase): return a + b + c with self.cached_session() as sess: - result = py_func.wrap_py_func(test_fn, dtypes.int64, + result = py_func.wrap_py_func(test_fn, dtypes.int32, (1, constant_op.constant(1), 1)) self.assertEqual(3, self.evaluate(result)) - result = py_func.wrap_py_func(test_fn, dtypes.int64, (1, 1, 1)) + result = py_func.wrap_py_func(test_fn, dtypes.int32, (1, 1, 1)) self.assertEqual(3, self.evaluate(result)) result = py_func.wrap_py_func( - test_fn, dtypes.int64, + test_fn, dtypes.int32, (constant_op.constant(1), 1, constant_op.constant(1))) self.assertEqual(3, self.evaluate(result)) @@ -53,9 +53,9 @@ class PyFuncTest(test.TestCase): return a * b.foo with self.cached_session() as sess: - result = py_func.wrap_py_func(test_fn, dtypes.int64, (7, TestClass())) + result = py_func.wrap_py_func(test_fn, dtypes.int32, (7, TestClass())) self.assertEqual(35, self.evaluate(result)) - result = py_func.wrap_py_func(test_fn, dtypes.int64, + result = py_func.wrap_py_func(test_fn, dtypes.int32, (constant_op.constant(7), TestClass())) self.assertEqual(35, self.evaluate(result)) @@ -70,12 +70,12 @@ class PyFuncTest(test.TestCase): return a * b.foo + c * d.foo with self.cached_session() as sess: - result = py_func.wrap_py_func(test_fn, dtypes.int64, (7, TestClass(5)), { + result = py_func.wrap_py_func(test_fn, dtypes.int32, (7, TestClass(5)), { 'c': 11, 'd': TestClass(13) }) self.assertEqual(178, self.evaluate(result)) - result = py_func.wrap_py_func(test_fn, dtypes.int64, + result = py_func.wrap_py_func(test_fn, dtypes.int32, (constant_op.constant(7), TestClass(5)), { 'c': constant_op.constant(11), 'd': TestClass(13) -- GitLab From 093f0363238c773255c580ea1390e9fdf666d69c Mon Sep 17 00:00:00 2001 From: Alexandre Passos Date: Fri, 7 Dec 2018 14:14:36 -0800 Subject: [PATCH 228/873] Utility to run tests inside tf.function and eager. Relies on being able to run the assert* test methods inside a py_func to run them inside the graph, so there's no need for self.evaluate or similar methods which create a graph/eager hybrid programming model. PiperOrigin-RevId: 224575790 --- tensorflow/python/eager/function_test.py | 9 +-- tensorflow/python/framework/test_util.py | 72 ++++++++++++++++++- .../kernel_tests/variable_scope_test.py | 5 +- 3 files changed, 78 insertions(+), 8 deletions(-) diff --git a/tensorflow/python/eager/function_test.py b/tensorflow/python/eager/function_test.py index 71afbd24d8..e0854b0632 100644 --- a/tensorflow/python/eager/function_test.py +++ b/tensorflow/python/eager/function_test.py @@ -428,20 +428,21 @@ class FunctionTest(test.TestCase, parameterized.TestCase): self.evaluate(variables.global_variables_initializer()) self.assertEqual(self.evaluate(value), 2.0) - @test_util.run_in_graph_and_eager_modes + @test_util.also_run_as_tf_function def testInitScopeTensorInitializationInFunction(self): @def_function.function def tensor_init(): with ops.init_scope(): const = constant_op.constant(2.0) + # Note: this variable bypasses tf.function's variable creation + # requirements by bypassing variable_creator_scope by using + # ResourceVariable instead of Variable. self.v = resource_variable_ops.ResourceVariable(const) return self.v.read_value() value = tensor_init() - if not context.executing_eagerly(): - self.evaluate(variables.global_variables_initializer()) - self.assertEqual(self.evaluate(value), 2.0) + self.assertAllEqual(value, 2.0) def testDefunShapeInferenceWithCapturedResourceVariable(self): v = resource_variable_ops.ResourceVariable([[1, 2], [3, 4]]) diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py index b0c3c9b506..06316ce2e9 100644 --- a/tensorflow/python/framework/test_util.py +++ b/tensorflow/python/framework/test_util.py @@ -54,6 +54,7 @@ from tensorflow.python import tf2 from tensorflow.python.client import device_lib from tensorflow.python.client import session from tensorflow.python.eager import context +from tensorflow.python.eager import def_function from tensorflow.python.eager import tape from tensorflow.python.framework import device as pydev from tensorflow.python.framework import dtypes @@ -67,6 +68,7 @@ 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 control_flow_ops +from tensorflow.python.ops import script_ops from tensorflow.python.ops import tensor_array_ops from tensorflow.python.ops import variables from tensorflow.python.platform import googletest @@ -76,6 +78,7 @@ from tensorflow.python.util import compat from tensorflow.python.util import deprecation from tensorflow.python.util import memory from tensorflow.python.util import nest +from tensorflow.python.util import tf_decorator from tensorflow.python.util import tf_inspect from tensorflow.python.util.protobuf import compare from tensorflow.python.util.tf_export import tf_export @@ -1009,6 +1012,58 @@ def run_in_graph_and_eager_modes(func=None, return decorator +def py_func_if_in_function(f): + + def decorated(*args, **kwds): + if not ops.get_default_graph()._building_function: + return f(*args, **kwds) + + tensor_args, tensor_indices = zip( + *[(x, i) for i, x in enumerate(args) + if isinstance(x, (ops.Tensor, variables.Variable))]) + + def inner_f(*inner_tensor_args): + my_args = list(args) + for i, n in zip(tensor_indices, inner_tensor_args): + my_args[i] = n + return f(*my_args, **kwds) + + return script_ops.py_func(inner_f, tensor_args, []) + + return tf_decorator.make_decorator(f, decorated) + + +def also_run_as_tf_function(f): + """Runs the decorated test twice--once as is, once inside a tf.function. + + This allows you to run a test both in eager execution and inside a + tf.function, exercising the two execution modes supported in tf 2.0. The test + assertions are automatically done inside tf.py_funcs, and tf.function ensures + that they run in the proper order and with the proper side effects. + + Currently variable creation is not supported in tests annotated with this + decorator since it's tricky to ensure the variable doesn't get repeatedly + created when retracing the tf.function. + + Args: + f: the test method to be decorated + + Returns: + The decorated test method, which will run both in eager and inside a + tf.function. + """ + + def decorated(*args, **kwds): + with context.eager_mode(): + # Running in eager mode + f(*args, **kwds) + + defun_f = def_function.function(f) + defun_f(*args, **kwds) + + return decorated + + def run_deprecated_v1(func=None): """Execute the decorated test in graph mode. @@ -1783,8 +1838,8 @@ class TensorFlowTestCase(googletest.TestCase): return ret -# pylint: enable=invalid-name - + # pylint: enable=invalid-name + @py_func_if_in_function def assertNear(self, f1, f2, err, msg=None): """Asserts that two floats are near each other. @@ -1803,6 +1858,7 @@ class TensorFlowTestCase(googletest.TestCase): "%f != %f +/- %f%s" % (f1, f2, err, " (%s)" % msg if msg is not None else "")) + @py_func_if_in_function def assertArrayNear(self, farray1, farray2, err, msg=None): """Asserts that two float arrays are near each other. @@ -1822,6 +1878,7 @@ class TensorFlowTestCase(googletest.TestCase): def _NDArrayNear(self, ndarray1, ndarray2, err): return np.linalg.norm(ndarray1 - ndarray2) < err + @py_func_if_in_function def assertNDArrayNear(self, ndarray1, ndarray2, err, msg=None): """Asserts that two numpy arrays have near values. @@ -1959,6 +2016,7 @@ class TensorFlowTestCase(googletest.TestCase): e.args = ((e.args[0] + " : " + msg,) + e.args[1:]) raise + @py_func_if_in_function def assertAllClose(self, a, b, rtol=1e-6, atol=1e-6, msg=None): """Asserts that two structures of numpy arrays or Tensors, have near values. @@ -1984,6 +2042,7 @@ class TensorFlowTestCase(googletest.TestCase): """ self._assertAllCloseRecursive(a, b, rtol=rtol, atol=atol, msg=msg) + @py_func_if_in_function def assertAllCloseAccordingToType(self, a, b, @@ -2031,6 +2090,7 @@ class TensorFlowTestCase(googletest.TestCase): self.assertAllClose(a, b, rtol=rtol, atol=atol, msg=msg) + @py_func_if_in_function def assertNotAllClose(self, a, b, **kwargs): """Assert that two numpy arrays, or or Tensors, do not have near values. @@ -2049,6 +2109,7 @@ class TensorFlowTestCase(googletest.TestCase): return raise AssertionError("The two values are close at all elements") + @py_func_if_in_function def assertAllEqual(self, a, b, msg=None): """Asserts that two numpy arrays or Tensors have the same values. @@ -2091,6 +2152,7 @@ class TensorFlowTestCase(googletest.TestCase): msgs.append("not equal rhs = {}".format(y)) np.testing.assert_array_equal(a, b, err_msg="\n".join(msgs)) + @py_func_if_in_function def assertAllGreater(self, a, comparison_target): """Assert element values are all greater than a target value. @@ -2102,6 +2164,7 @@ class TensorFlowTestCase(googletest.TestCase): a = self._GetNdArray(a) self.assertGreater(np.min(a), comparison_target) + @py_func_if_in_function def assertAllLess(self, a, comparison_target): """Assert element values are all less than a target value. @@ -2113,6 +2176,7 @@ class TensorFlowTestCase(googletest.TestCase): a = self._GetNdArray(a) self.assertLess(np.max(a), comparison_target) + @py_func_if_in_function def assertAllGreaterEqual(self, a, comparison_target): """Assert element values are all greater than or equal to a target value. @@ -2124,6 +2188,7 @@ class TensorFlowTestCase(googletest.TestCase): a = self._GetNdArray(a) self.assertGreaterEqual(np.min(a), comparison_target) + @py_func_if_in_function def assertAllLessEqual(self, a, comparison_target): """Assert element values are all less than or equal to a target value. @@ -2166,6 +2231,7 @@ class TensorFlowTestCase(googletest.TestCase): lines.append(prefix + "...") return lines + @py_func_if_in_function def assertAllInRange(self, target, lower_bound, @@ -2224,6 +2290,7 @@ class TensorFlowTestCase(googletest.TestCase): "Subscript(s) and value(s) of the offending elements:\n" + "\n".join(self._format_subscripts(violation_subscripts, target))) + @py_func_if_in_function def assertAllInSet(self, target, expected_set): """Assert that elements of a Tensor are all in a given closed set. @@ -2245,6 +2312,7 @@ class TensorFlowTestCase(googletest.TestCase): raise AssertionError("%d unique element(s) are not in the set %s: %s" % (np.size(diff), expected_set, diff)) + @py_func_if_in_function def assertDTypeEqual(self, target, expected_dtype): """Assert ndarray data type is equal to expected. diff --git a/tensorflow/python/kernel_tests/variable_scope_test.py b/tensorflow/python/kernel_tests/variable_scope_test.py index 44d4bd5e30..451eb38530 100644 --- a/tensorflow/python/kernel_tests/variable_scope_test.py +++ b/tensorflow/python/kernel_tests/variable_scope_test.py @@ -237,7 +237,8 @@ class VariableScopeTest(test.TestCase): _ = d2(x) self.assertEqual(len(d2.variables), 2) v3, v4 = d2.variables - self.assertAllEqual([v1, v2], [v3, v4]) + self.assertEqual(v1, v3) + self.assertEqual(v2, v4) f() # TODO(mihaimaruseac): Not converted to use wrap_function because of @@ -1684,7 +1685,7 @@ class VariableScopeWithCustomGetterTest(test.TestCase): with variable_scope.variable_creator_scope(creator_b): variable_scope.variable(1.0, name="one_name") - self.assertAllEqual(variable_names, ["forced_name"]) + self.assertEqual(variable_names[0], "forced_name") called = [False] -- GitLab From 2a050766bf0556d7d92eea62d40fd2bebbcb399f Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 7 Dec 2018 14:18:35 -0800 Subject: [PATCH 229/873] human_readable_json.h uses the correct switched namespace protobuf but the implementation directly references google::protobuf. Fix it so that it agrees with its header file. PiperOrigin-RevId: 224576410 --- tensorflow/core/platform/default/human_readable_json.cc | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/tensorflow/core/platform/default/human_readable_json.cc b/tensorflow/core/platform/default/human_readable_json.cc index 9f97c8272c..bf9c7b7620 100644 --- a/tensorflow/core/platform/default/human_readable_json.cc +++ b/tensorflow/core/platform/default/human_readable_json.cc @@ -20,7 +20,7 @@ limitations under the License. namespace tensorflow { -Status ProtoToHumanReadableJson(const ::google::protobuf::Message& proto, +Status ProtoToHumanReadableJson(const protobuf::Message& proto, string* result) { #ifdef TENSORFLOW_LITE_PROTOS *result = "[human readable output not available on Android]"; @@ -28,7 +28,7 @@ Status ProtoToHumanReadableJson(const ::google::protobuf::Message& proto, #else result->clear(); - auto status = google::protobuf::util::MessageToJsonString(proto, result); + auto status = protobuf::util::MessageToJsonString(proto, result); if (!status.ok()) { // Convert error_msg google::protobuf::StringPiece to // tensorflow::StringPiece. @@ -41,8 +41,7 @@ Status ProtoToHumanReadableJson(const ::google::protobuf::Message& proto, #endif } -Status HumanReadableJsonToProto(const string& str, - ::google::protobuf::Message* proto) { +Status HumanReadableJsonToProto(const string& str, protobuf::Message* proto) { #ifdef TENSORFLOW_LITE_PROTOS return errors::Internal("Cannot parse JSON protos on Android"); #else -- GitLab From 9ed800cea0d5872511c1b45708d0e8970e4fc958 Mon Sep 17 00:00:00 2001 From: Scott Zhu Date: Fri, 7 Dec 2018 14:23:42 -0800 Subject: [PATCH 230/873] Bug fix for unified LSTM with time major and go_backward combined. Also added test case to cover that. PiperOrigin-RevId: 224577299 --- tensorflow/python/keras/layers/recurrent.py | 2 +- .../python/keras/layers/unified_lstm_test.py | 44 +++++++++++++++++++ 2 files changed, 45 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/keras/layers/recurrent.py b/tensorflow/python/keras/layers/recurrent.py index fdc2acd538..86a69e45d9 100644 --- a/tensorflow/python/keras/layers/recurrent.py +++ b/tensorflow/python/keras/layers/recurrent.py @@ -2704,7 +2704,7 @@ class UnifiedLSTM(LSTM): # both normal and CuDNN implementations. if self.go_backwards: # Reverse time axis. - inputs = K.reverse(inputs, 1) + inputs = K.reverse(inputs, 0 if self.time_major else 1) if 0 < self.dropout < 1: if self._dropout_mask is None: diff --git a/tensorflow/python/keras/layers/unified_lstm_test.py b/tensorflow/python/keras/layers/unified_lstm_test.py index 8cbc370633..33351948ee 100644 --- a/tensorflow/python/keras/layers/unified_lstm_test.py +++ b/tensorflow/python/keras/layers/unified_lstm_test.py @@ -298,6 +298,50 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): self.assertAllClose(y_1, y_2) self.assertAllClose(y_2, y_3) + @parameterized.named_parameters( + # test_name, time_major, go_backwards + ('normal', False, False), + ('time_major', True, False), + ('go_backwards', False, True), + ('both', True, True), + ) + @test_util.run_in_graph_and_eager_modes(config=_config) + def test_time_major_and_go_backward(self, time_major, go_backwards): + input_shape = 10 + rnn_state_size = 8 + timestep = 4 + batch = 100 + + x_train = np.random.random((batch, timestep, input_shape)) + + def build_model(layer_cls): + inputs = keras.layers.Input( + shape=[timestep, input_shape], dtype=dtypes.float32) + layer = layer_cls(rnn_state_size, + recurrent_activation='sigmoid', + time_major=time_major, + return_sequences=True, + go_backwards=go_backwards) + if time_major: + converted_input = keras.layers.Lambda( + lambda t: array_ops.transpose(t, [1, 0, 2]))(inputs) + outputs = layer(converted_input) + outputs = keras.layers.Lambda( + lambda t: array_ops.transpose(t, [1, 0, 2]))(outputs) + else: + outputs = layer(inputs) + return keras.models.Model(inputs, outputs) + + lstm_model = build_model(keras.layers.LSTM) + y_ref = lstm_model.predict(x_train) + weights = lstm_model.get_weights() + + unified_lstm_model = build_model(keras.layers.UnifiedLSTM) + unified_lstm_model.set_weights(weights) + y = unified_lstm_model.predict(x_train) + + self.assertAllClose(y, y_ref) + @test_util.run_in_graph_and_eager_modes(config=_config) def test_keras_model_with_lstm(self): input_shape = 10 -- GitLab From 7eeb77ab979acf284d68fbd9b48d54152fc5d90b Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 7 Dec 2018 14:29:40 -0800 Subject: [PATCH 231/873] [TF:XLA] Rename misnamed variables in a test. PiperOrigin-RevId: 224578284 --- .../compiler/xla/tests/array_elementwise_ops_test.cc | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc index f6be27bee2..915b456b52 100644 --- a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc +++ b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc @@ -329,13 +329,13 @@ TEST_P(ArrayElementwiseOpTestParamCount, AddManyValues) { Literal b_literal = LiteralUtil::CreateR1({b_values}); std::unique_ptr b_data = client_->TransferToServer(b_literal).ConsumeValueOrDie(); - auto b_constant = Parameter(&builder, 1, a_literal.shape(), "b_param"); - auto b_param = ConstantR1(&builder, b_values); + auto b_param = Parameter(&builder, 1, a_literal.shape(), "b_param"); + auto b_constant = ConstantR1(&builder, b_values); - auto sum1 = Add(a_constant, b_constant); - auto sum2 = Add(a_constant, b_param); - auto sum3 = Add(a_param, b_constant); - auto sum4 = Add(a_param, b_param); + auto sum1 = Add(a_constant, b_param); + auto sum2 = Add(a_constant, b_constant); + auto sum3 = Add(a_param, b_param); + auto sum4 = Add(a_param, b_constant); auto sum = Add(sum1, sum2); sum = Add(sum, sum3); -- GitLab From 9c5d513df64aa61e8e7a8ba3939e32fdd13f49f1 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 7 Dec 2018 14:41:30 -0800 Subject: [PATCH 232/873] Update the remote execution toolchain to make it work with Bazel 0.20.0 PiperOrigin-RevId: 224580438 --- .../preconfig/win_1803/bazel_018/BUILD | 7 ++++ .../preconfig/win_1803/bazel_018/CROSSTOOL | 36 ------------------- 2 files changed, 7 insertions(+), 36 deletions(-) diff --git a/third_party/toolchains/preconfig/win_1803/bazel_018/BUILD b/third_party/toolchains/preconfig/win_1803/bazel_018/BUILD index c00f005e46..edd9583648 100644 --- a/third_party/toolchains/preconfig/win_1803/bazel_018/BUILD +++ b/third_party/toolchains/preconfig/win_1803/bazel_018/BUILD @@ -39,6 +39,9 @@ cc_toolchain_suite( "x64_windows|msvc-cl": ":cc-compiler-x64_windows", "x64_windows|msys-gcc": ":cc-compiler-x64_windows_msys", "x64_windows|mingw-gcc": ":cc-compiler-x64_windows_mingw", + "x64_windows_msys": ":cc-compiler-x64_windows_msys", + "x64_windows": ":cc-compiler-x64_windows", + "armeabi-v7a": ":cc-compiler-armeabi-v7a", }, ) @@ -54,6 +57,7 @@ cc_toolchain( static_runtime_libs = [":empty"], strip_files = ":empty", supports_param_files = 1, + toolchain_identifier = "msys_x64", ) toolchain( @@ -83,6 +87,7 @@ cc_toolchain( static_runtime_libs = [":empty"], strip_files = ":empty", supports_param_files = 0, + toolchain_identifier = "msys_x64_mingw", ) toolchain( @@ -112,6 +117,7 @@ cc_toolchain( static_runtime_libs = [":empty"], strip_files = ":empty", supports_param_files = 1, + toolchain_identifier = "msvc_x64", ) toolchain( @@ -140,6 +146,7 @@ cc_toolchain( static_runtime_libs = [":empty"], strip_files = ":empty", supports_param_files = 1, + toolchain_identifier = "stub_armeabi-v7a", ) toolchain( diff --git a/third_party/toolchains/preconfig/win_1803/bazel_018/CROSSTOOL b/third_party/toolchains/preconfig/win_1803/bazel_018/CROSSTOOL index 04c8bcae45..38a80c22da 100644 --- a/third_party/toolchains/preconfig/win_1803/bazel_018/CROSSTOOL +++ b/third_party/toolchains/preconfig/win_1803/bazel_018/CROSSTOOL @@ -14,42 +14,6 @@ major_version: "local" minor_version: "" -default_target_cpu: "same_as_host" - -default_toolchain { - cpu: "x64_windows" - toolchain_identifier: "msvc_x64" -} - -default_toolchain { - cpu: "local" - toolchain_identifier: "stub_armeabi-v7a" -} - -default_toolchain { - cpu: "armeabi-v7a" - toolchain_identifier: "stub_armeabi-v7a" -} - -default_toolchain { - cpu: "x64_windows" - toolchain_identifier: "msvc_x64" -} - -default_toolchain { - cpu: "x64_windows_msvc" - toolchain_identifier: "msvc_x64" -} - -default_toolchain { - cpu: "x64_windows_msys" - toolchain_identifier: "msys_x64" -} - -default_toolchain { - cpu: "s390x" - toolchain_identifier: "msys_x64" -} # Android tooling requires a default toolchain for the armeabi-v7a cpu. toolchain { -- GitLab From 5c4a09cc0b0597aba582ccaf41cd23cb8c541d23 Mon Sep 17 00:00:00 2001 From: Suharsh Sivakumar Date: Fri, 7 Dec 2018 14:50:34 -0800 Subject: [PATCH 233/873] Int8 dequantize implementation. PiperOrigin-RevId: 224581893 --- tensorflow/lite/kernels/dequantize.cc | 27 +++++++++--- tensorflow/lite/kernels/dequantize_test.cc | 33 ++++++++++++--- tensorflow/lite/kernels/internal/BUILD | 1 + .../internal/reference/integer_ops/README.md | 8 ++++ .../reference/integer_ops/dequantize.h | 42 +++++++++++++++++++ tensorflow/lite/kernels/register.cc | 4 +- tensorflow/lite/toco/tflite/operator.cc | 24 +++++++++++ 7 files changed, 127 insertions(+), 12 deletions(-) create mode 100644 tensorflow/lite/kernels/internal/reference/integer_ops/README.md create mode 100644 tensorflow/lite/kernels/internal/reference/integer_ops/dequantize.h diff --git a/tensorflow/lite/kernels/dequantize.cc b/tensorflow/lite/kernels/dequantize.cc index b2825bb9ea..7f03c73c9c 100644 --- a/tensorflow/lite/kernels/dequantize.cc +++ b/tensorflow/lite/kernels/dequantize.cc @@ -18,6 +18,7 @@ limitations under the License. #include "tensorflow/lite/c/builtin_op_data.h" #include "tensorflow/lite/c/c_api_internal.h" #include "tensorflow/lite/kernels/internal/optimized/optimized_ops.h" +#include "tensorflow/lite/kernels/internal/reference/integer_ops/dequantize.h" #include "tensorflow/lite/kernels/internal/tensor.h" #include "tensorflow/lite/kernels/kernel_util.h" #include "tensorflow/lite/kernels/op_macros.h" @@ -57,7 +58,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { OpContext op_context(context, node); - TF_LITE_ENSURE(context, op_context.input->type == kTfLiteUInt8); + TF_LITE_ENSURE(context, op_context.input->type == kTfLiteUInt8 || + op_context.input->type == kTfLiteInt8); op_context.output->type = kTfLiteFloat32; // If the input tensor is constant, we can persist the dequantized value in @@ -80,10 +82,25 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { tflite::DequantizationParams op_params; op_params.zero_point = op_context.input->params.zero_point; op_params.scale = op_context.input->params.scale; - optimized_ops::Dequantize(op_params, GetTensorShape(op_context.input), - GetTensorData(op_context.input), - GetTensorShape(op_context.output), - GetTensorData(op_context.output)); + switch (op_context.input->type) { + case kTfLiteUInt8: + optimized_ops::Dequantize(op_params, GetTensorShape(op_context.input), + GetTensorData(op_context.input), + GetTensorShape(op_context.output), + GetTensorData(op_context.output)); + break; + case kTfLiteInt8: + reference_integer_ops::Dequantize( + op_params, GetTensorShape(op_context.input), + GetTensorData(op_context.input), + GetTensorShape(op_context.output), + GetTensorData(op_context.output)); + break; + default: + context->ReportError(context, "Type %d not supported.", + op_context.input->type); + return kTfLiteError; + } if (IsConstantTensor(op_context.input)) { op_data->float_dequantized_weights_initialized = true; diff --git a/tensorflow/lite/kernels/dequantize_test.cc b/tensorflow/lite/kernels/dequantize_test.cc index 55265d93e5..bb5f1e74a8 100644 --- a/tensorflow/lite/kernels/dequantize_test.cc +++ b/tensorflow/lite/kernels/dequantize_test.cc @@ -25,8 +25,16 @@ using ::testing::ElementsAreArray; class DequantizeOpModel : public SingleOpModel { public: - DequantizeOpModel(std::initializer_list shape, float min, float max) { - input_ = AddInput({TensorType_UINT8, shape, min, max}); + DequantizeOpModel(TensorType type, std::initializer_list shape, + float scale, int32_t zero_point) { + TensorData input_tensor_data; + input_tensor_data.type = type; + input_tensor_data.shape = shape; + input_tensor_data.min = 0; + input_tensor_data.max = 0; + input_tensor_data.scale = scale; + input_tensor_data.zero_point = zero_point; + input_ = AddInput(input_tensor_data); output_ = AddOutput({TensorType_FLOAT32, shape}); SetBuiltinOp(BuiltinOperator_DEQUANTIZE, BuiltinOptions_DequantizeOptions, CreateDequantizeOptions(builder_).Union()); @@ -34,7 +42,8 @@ class DequantizeOpModel : public SingleOpModel { BuildInterpreter({GetShape(input_)}); } - void SetInput(std::initializer_list data) { + template + void SetInput(std::initializer_list data) { PopulateTensor(input_, data); } @@ -45,10 +54,22 @@ class DequantizeOpModel : public SingleOpModel { int output_; }; -TEST(SplitOpTest, FourDimensional) { - DequantizeOpModel m({2, 5}, -63.5, 64); +TEST(DequantizeOpTest, UINT8) { + // [-63.5, 64] -> scale=0.5 zero_point=127 for UINT8 + DequantizeOpModel m(TensorType_UINT8, {2, 5}, 0.5, 127); - m.SetInput({0, 1, 2, 3, 4, 251, 252, 253, 254, 255}); + m.SetInput({0, 1, 2, 3, 4, 251, 252, 253, 254, 255}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear( + {-63.5, -63, -62.5, -62, -61.5, 62, 62.5, 63, 63.5, 64}))); +} + +TEST(DequantizeOpTest, INT8) { + // [-63.5, 64] -> scale=0.5, zero_point=1 for INT8 + DequantizeOpModel m(TensorType_INT8, {2, 5}, 0.5, -1); + + m.SetInput({-128, -127, -126, -125, -124, 123, 124, 125, 126, 127}); m.Invoke(); EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear( diff --git a/tensorflow/lite/kernels/internal/BUILD b/tensorflow/lite/kernels/internal/BUILD index d2f7f455e4..69816583f5 100644 --- a/tensorflow/lite/kernels/internal/BUILD +++ b/tensorflow/lite/kernels/internal/BUILD @@ -313,6 +313,7 @@ cc_library( "reference/depthwiseconv_float.h", "reference/depthwiseconv_uint8.h", "reference/fully_connected.h", + "reference/integer_ops/dequantize.h", "reference/reference_ops.h", "reference/softmax.h", ], diff --git a/tensorflow/lite/kernels/internal/reference/integer_ops/README.md b/tensorflow/lite/kernels/internal/reference/integer_ops/README.md new file mode 100644 index 0000000000..4b1d3c91d5 --- /dev/null +++ b/tensorflow/lite/kernels/internal/reference/integer_ops/README.md @@ -0,0 +1,8 @@ +This directory contains reference implementations for int8 fully integer kernels. + +Weight filters of convs are expected to be symmetric per-channel quantized in +the range [-127, 127]. +Inputs/activations are expected to be asymmetric per-layer quantized in the +range [-128, 127]. + +THESE ARE EXPERIMENTAL AND PRONE TO CHANGE. diff --git a/tensorflow/lite/kernels/internal/reference/integer_ops/dequantize.h b/tensorflow/lite/kernels/internal/reference/integer_ops/dequantize.h new file mode 100644 index 0000000000..03dcb6c220 --- /dev/null +++ b/tensorflow/lite/kernels/internal/reference/integer_ops/dequantize.h @@ -0,0 +1,42 @@ +/* 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_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_DEQUANTIZE_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_DEQUANTIZE_H_ + +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite { +namespace reference_integer_ops { + +inline void Dequantize(const tflite::DequantizationParams& op_params, + const RuntimeShape& input_shape, const int8* input_data, + const RuntimeShape& output_shape, float* output_data) { + const int32 zero_point = op_params.zero_point; + const double scale = op_params.scale; + const int flat_size = MatchingFlatSize(input_shape, output_shape); + + for (int i = 0; i < flat_size; i++) { + const int32 val = input_data[i]; + const float result = static_cast(scale * (val - zero_point)); + output_data[i] = result; + } +} + +} // namespace reference_integer_ops +} // namespace tflite + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_DEQUANTIZE_H_ diff --git a/tensorflow/lite/kernels/register.cc b/tensorflow/lite/kernels/register.cc index 3c60d281b3..c0e6f6994f 100644 --- a/tensorflow/lite/kernels/register.cc +++ b/tensorflow/lite/kernels/register.cc @@ -222,7 +222,9 @@ BuiltinOpResolver::BuiltinOpResolver() { AddBuiltin(BuiltinOperator_LOG, Register_LOG()); AddBuiltin(BuiltinOperator_LOG_SOFTMAX, Register_LOG_SOFTMAX()); AddBuiltin(BuiltinOperator_CAST, Register_CAST()); - AddBuiltin(BuiltinOperator_DEQUANTIZE, Register_DEQUANTIZE()); + AddBuiltin(BuiltinOperator_DEQUANTIZE, Register_DEQUANTIZE(), + /* min_version */ 1, + /* max_version */ 2); AddBuiltin(BuiltinOperator_PRELU, Register_PRELU()); AddBuiltin(BuiltinOperator_MAXIMUM, Register_MAXIMUM()); AddBuiltin(BuiltinOperator_MINIMUM, Register_MINIMUM()); diff --git a/tensorflow/lite/toco/tflite/operator.cc b/tensorflow/lite/toco/tflite/operator.cc index e0faed4927..205af23da5 100644 --- a/tensorflow/lite/toco/tflite/operator.cc +++ b/tensorflow/lite/toco/tflite/operator.cc @@ -1482,6 +1482,30 @@ class TensorFlowUnsupported : public BaseOperator { const bool enable_select_tf_ops_; }; +class Dequantize + : public BuiltinOperator { + public: + using BuiltinOperator::BuiltinOperator; + + flatbuffers::Offset WriteOptions( + const TocoOperator& op, + flatbuffers::FlatBufferBuilder* builder) const override { + return ::tflite::CreateDequantizeOptions(*builder); + } + + void ReadOptions(const TfLiteOptions& options, + TocoOperator* op) const override {} + + int GetVersion(const Operator& op) const override { + // TODO(suharshs): Dequantize now supports INT8 in addition to + // QUANTIZED_UINT8. When TOCO can create models with INT8, we need + // to find a way to see the type here and return version 2. Right now + // version 2 will only be added by post training quantization tools. + return 1; + } +}; + namespace { // Build a vector containing all the known operators. std::vector> BuildOperatorList( -- GitLab From 6e010b0d414f4aca6c7e87fdada46b683b5c9846 Mon Sep 17 00:00:00 2001 From: Niranjan Hasabnis Date: Fri, 7 Dec 2018 14:58:00 -0800 Subject: [PATCH 234/873] Addressing review comments --- tensorflow/core/graph/mkl_graph_util.h | 8 ----- tensorflow/core/graph/mkl_layout_pass.cc | 6 ++-- tensorflow/core/kernels/mkl_conv_ops.cc | 36 +++++++++---------- tensorflow/core/kernels/mkl_fused_ops_test.cc | 12 +++---- 4 files changed, 25 insertions(+), 37 deletions(-) diff --git a/tensorflow/core/graph/mkl_graph_util.h b/tensorflow/core/graph/mkl_graph_util.h index a599ce3620..990b2fe9b0 100644 --- a/tensorflow/core/graph/mkl_graph_util.h +++ b/tensorflow/core/graph/mkl_graph_util.h @@ -72,14 +72,6 @@ int inline GetTensorMetaDataIndex(int n, int total_tensors) { return DataIndexToMetaDataIndex(tidx, total_tensors); } -// Helper function to compare fused_ops attribute strings -// TODO(Intel) this code is also defined in mkl_conv_ops.h, we need to move to -// mkl_util.h so we have only one version. -inline bool CompareFusedOps(const std::vector& fused_ops, - const std::vector& expected) { - return fused_ops == expected; -} - namespace mkl_op_registry { static const char* kMklOpLabel = "MklOp"; static const char* kMklOpLabelPattern = "label='MklOp'"; diff --git a/tensorflow/core/graph/mkl_layout_pass.cc b/tensorflow/core/graph/mkl_layout_pass.cc index 6933b033b1..4c060f54ca 100644 --- a/tensorflow/core/graph/mkl_layout_pass.cc +++ b/tensorflow/core/graph/mkl_layout_pass.cc @@ -1131,9 +1131,9 @@ class MklLayoutRewritePass : public GraphOptimizationPass { std::vector fused_ops; TF_CHECK_OK(GetNodeAttr(n->def(), "fused_ops", &fused_ops)); - return (CompareFusedOps(fused_ops, {"BiasAdd"}) || - CompareFusedOps(fused_ops, {"Relu"}) || - CompareFusedOps(fused_ops, {"BiasAdd", "Relu"})); + return (fused_ops == std::vector{"BiasAdd"} || + fused_ops == std::vector{"Relu"} || + fused_ops == std::vector{"BiasAdd", "Relu"}); } // Rewrites input node to a new node specified by its matching rewrite info. diff --git a/tensorflow/core/kernels/mkl_conv_ops.cc b/tensorflow/core/kernels/mkl_conv_ops.cc index 4a4aaffead..d3bbb3d9e3 100644 --- a/tensorflow/core/kernels/mkl_conv_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_ops.cc @@ -1099,12 +1099,12 @@ class MklConvOp : public OpKernel { } protected: - void FuseBiasAdd(bool fuse_bias_add) { fuse_biasadd_ = fuse_bias_add; } - void FuseRelu(bool fuse_relu) { fuse_relu_ = fuse_relu; } + void set_fuse_biasadd(bool fuse_biasadd) { fuse_biasadd_ = fuse_biasadd; } + void set_fuse_relu(bool fuse_relu) { fuse_relu_ = fuse_relu; } - // This method is called for the base class MklConvOp, which handles the + // This method is for the base class MklConvOp, which handles the // floating point implementation of Conv. The quantized conv implementations - // will use overiddern versions of this method. + // will use overidden versions of this method. virtual void ExtendConvFwdParams(OpKernelContext* context, MklConvFwdParams& params) { // Create a string from data types of input, filter, bias, and output. @@ -1114,6 +1114,8 @@ class MklConvOp : public OpKernel { params.dtypes.append(typeid(Toutput).name()); // Add fusions as post ops + // Note: Fusion of BiasAdd is handled directly inside MklConvOp by + // checking fuse_biasadd_ flag. if (fuse_relu_) params.post_op_params.push_back({"relu", {1.0, 0.0, 0.0}}); } @@ -1169,7 +1171,7 @@ class MklConvOp : public OpKernel { Padding padding_; TensorFormat data_format_; - // Initialize to value the template is instantiated with + // Initialize to values the template is instantiated with bool fuse_biasadd_ = biasEnabled; bool fuse_relu_ = false; @@ -1177,11 +1179,6 @@ class MklConvOp : public OpKernel { const int kOutputIndex_Dst = 0, kOutputIndex_Filter = 1; const int kDilationH = 0, kDilationW = 1; - // Helper function to compare fused_ops attribute strings - bool CompareFusedOps(const std::vector& fused_ops, - const std::vector& expected) { - return fused_ops == expected; - } // Allocate filter output tensor. void AllocateFilterOutputTensor( OpKernelContext* context, @@ -1254,27 +1251,27 @@ class MklFusedConvOp : public MklConvOp( context) { - // Since we came here through the registration of _MklFusedConv2D then get + // Since we came here through the registration of _MklFusedConv2D, get // all information from 'fused_ops' and 'num_args' std::vector fused_ops; OP_REQUIRES_OK(context, context->GetAttr("fused_ops", &fused_ops)); int num_args; OP_REQUIRES_OK(context, context->GetAttr("num_args", &num_args)); - OP_REQUIRES(context, (num_args == 0 || !fused_ops.empty()), + OP_REQUIRES(context, !fused_ops.empty(), errors::InvalidArgument( "Fused Conv2D must have at least one fused op.")); - if (CompareFusedOps(fused_ops, {"BiasAdd"})) { - this->FuseBiasAdd(true); + if (fused_ops == std::vector{"BiasAdd"}) { + this->set_fuse_biasadd(true); OP_REQUIRES(context, num_args == 1, errors::InvalidArgument( "Fused Conv2D must have one extra argument: bias.")); - } else if (CompareFusedOps(fused_ops, {"Relu"})) { - this->FuseRelu(true); - } else if (CompareFusedOps(fused_ops, {"BiasAdd", "Relu"})) { - this->FuseBiasAdd(true); - this->FuseRelu(true); + } else if (fused_ops == std::vector{"Relu"}) { + this->set_fuse_relu(true); + } else if (fused_ops == std::vector{"BiasAdd", "Relu"}) { + this->set_fuse_biasadd(true); + this->set_fuse_relu(true); OP_REQUIRES(context, num_args == 1, errors::InvalidArgument( "Fused Conv2D must have one extra argument: bias.")); @@ -1873,7 +1870,6 @@ TF_CALL_float(REGISTER_MKL_CPU_2D); .TypeConstraint("T") \ .Label(mkl_op_registry::kMklOpLabel), \ MklFusedConvOp); -// Note we are registering _MklFusedConv2D. // We check the fused_ops attributes to decide if bias is enabled or not. TF_CALL_float(REGISTER_MKL_CPU_2D_FUSED); diff --git a/tensorflow/core/kernels/mkl_fused_ops_test.cc b/tensorflow/core/kernels/mkl_fused_ops_test.cc index 7f1965de85..657b3e63ff 100644 --- a/tensorflow/core/kernels/mkl_fused_ops_test.cc +++ b/tensorflow/core/kernels/mkl_fused_ops_test.cc @@ -66,7 +66,7 @@ class ConvMklToTF : public OpsTestBase { PerformConversion(dtype, tensor, mkl_meta_tensor, &output); test::ExpectTensorNear(expected, output, 1e-5); } - void TestBody(){}; + void TestBody() {} }; // Testing MKL's fused convolution ops @@ -175,6 +175,8 @@ class MklFusedConv2DOpTest : public OpsTestBase { // Compare output to expected results const Tensor& output_tensor = *GetOutput(0); + // Index 2 will need to be changed if the number of outputs produced + // by MklConv2D change. const Tensor& output_meta_tensor = *GetOutput(2); ConvMklToTF conv_comp; conv_comp.PerformConversion(dtype, output_tensor, output_meta_tensor, @@ -207,7 +209,7 @@ class MklFusedConv2DOpTest : public OpsTestBase { ASSERT_EQ(conv_2d.dtype(), fused_conv_2d.dtype()); ASSERT_EQ(conv_2d.shape(), fused_conv_2d.shape()); - test::ExpectTensorNear(conv_2d, fused_conv_2d, 1e-5); + test::ExpectClose(conv_2d, fused_conv_2d); } // Verifies that computing Conv2D+BiasAdd in a graph is identical to @@ -293,10 +295,8 @@ TYPED_TEST_P(MklFusedConv2DWithBiasOpTest, SpatialConvolutionAndRelu) { this->VerifyConv2DWithBiasAndRelu(filter_size, filter_count); } -REGISTER_TYPED_TEST_CASE_P(MklFusedConv2DWithBiasOpTest, - OneByOneConvolution, // - SpatialConvolution, // - OneByOneConvolutionAndRelu, // +REGISTER_TYPED_TEST_CASE_P(MklFusedConv2DWithBiasOpTest, OneByOneConvolution, + SpatialConvolution, OneByOneConvolutionAndRelu, SpatialConvolutionAndRelu); using MklFusedBiasAddDataTypes = ::testing::Types; -- GitLab From cd231a62a664e577aa5171d7b3a44a04867f2035 Mon Sep 17 00:00:00 2001 From: Derek Murray Date: Fri, 7 Dec 2018 15:05:59 -0800 Subject: [PATCH 235/873] [tf.data] Expose `tf.data.experimental.NestedStructure`. PiperOrigin-RevId: 224584699 --- .../python/data/experimental/__init__.py | 2 ++ tensorflow/python/data/util/structure.py | 1 + ...w.data.experimental.-nested-structure.pbtxt | 18 ++++++++++++++++++ .../v1/tensorflow.data.experimental.pbtxt | 4 ++++ ...w.data.experimental.-nested-structure.pbtxt | 18 ++++++++++++++++++ .../v2/tensorflow.data.experimental.pbtxt | 4 ++++ 6 files changed, 47 insertions(+) create mode 100644 tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-nested-structure.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-nested-structure.pbtxt diff --git a/tensorflow/python/data/experimental/__init__.py b/tensorflow/python/data/experimental/__init__.py index 365c53405c..ffc2e5ef5f 100644 --- a/tensorflow/python/data/experimental/__init__.py +++ b/tensorflow/python/data/experimental/__init__.py @@ -26,6 +26,7 @@ See [Importing Data](https://tensorflow.org/guide/datasets) for an overview. @@CheckpointInputPipelineHook @@CsvDataset @@DatasetStructure +@@NestedStructure @@OptimizationOptions @@Optional @@OptionalStructure @@ -121,6 +122,7 @@ from tensorflow.python.data.ops.dataset_ops import DatasetStructure from tensorflow.python.data.ops.iterator_ops import get_next_as_optional from tensorflow.python.data.ops.optional_ops import Optional from tensorflow.python.data.ops.optional_ops import OptionalStructure +from tensorflow.python.data.util.structure import NestedStructure from tensorflow.python.data.util.structure import SparseTensorStructure from tensorflow.python.data.util.structure import Structure from tensorflow.python.data.util.structure import TensorStructure diff --git a/tensorflow/python/data/util/structure.py b/tensorflow/python/data/util/structure.py index 4f2c6cd853..5e3addacaa 100644 --- a/tensorflow/python/data/util/structure.py +++ b/tensorflow/python/data/util/structure.py @@ -273,6 +273,7 @@ def convert_legacy_structure(output_types, output_shapes, output_classes): # NOTE(mrry): The following classes make extensive use of non-public methods of # their base class, so we disable the protected-access lint warning once here. # pylint: disable=protected-access +@tf_export("data.experimental.NestedStructure") class NestedStructure(Structure): """Represents a nested structure in which each leaf is a `Structure`.""" diff --git a/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-nested-structure.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-nested-structure.pbtxt new file mode 100644 index 0000000000..b4b066e563 --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-nested-structure.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.data.experimental.NestedStructure" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'nested_structure\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_value" + argspec: "args=[\'value\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_compatible_with" + argspec: "args=[\'self\', \'other\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.pbtxt index 234507e5de..2d11590492 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.data.experimental.pbtxt @@ -20,6 +20,10 @@ tf_module { name: "INFINITE_CARDINALITY" mtype: "" } + member { + name: "NestedStructure" + mtype: "" + } member { name: "OptimizationOptions" mtype: "" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-nested-structure.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-nested-structure.pbtxt new file mode 100644 index 0000000000..b4b066e563 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-nested-structure.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.data.experimental.NestedStructure" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'nested_structure\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_value" + argspec: "args=[\'value\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_compatible_with" + argspec: "args=[\'self\', \'other\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.pbtxt index 234507e5de..2d11590492 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.data.experimental.pbtxt @@ -20,6 +20,10 @@ tf_module { name: "INFINITE_CARDINALITY" mtype: "" } + member { + name: "NestedStructure" + mtype: "" + } member { name: "OptimizationOptions" mtype: "" -- GitLab From 47b751c9324522c0178536f792cc47a970f4f62b Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 7 Dec 2018 15:17:02 -0800 Subject: [PATCH 236/873] Changed CPU <-> NNAPI toggle UI element from ToggleButton to RadioButton. PiperOrigin-RevId: 224586292 --- .../Camera2BasicFragment.java | 41 ++++++++++++++----- .../tflitecamerademo/ImageClassifier.java | 9 +++- .../layout-land/fragment_camera2_basic.xml | 25 ++++++++--- .../res/layout-v26/fragment_camera2_basic.xml | 29 +++++++++---- .../res/layout/fragment_camera2_basic.xml | 30 +++++++++----- .../demo/app/src/main/res/values/strings.xml | 1 + 6 files changed, 98 insertions(+), 37 deletions(-) diff --git a/tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/Camera2BasicFragment.java b/tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/Camera2BasicFragment.java index 3596e42011..20e96f586a 100644 --- a/tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/Camera2BasicFragment.java +++ b/tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/Camera2BasicFragment.java @@ -56,11 +56,10 @@ import android.view.Surface; import android.view.TextureView; import android.view.View; import android.view.ViewGroup; -import android.widget.CompoundButton; import android.widget.NumberPicker; +import android.widget.RadioButton; import android.widget.TextView; import android.widget.Toast; -import android.widget.ToggleButton; import java.io.IOException; import java.util.ArrayList; import java.util.Arrays; @@ -87,10 +86,12 @@ public class Camera2BasicFragment extends Fragment private boolean runClassifier = false; private boolean checkedPermissions = false; private TextView textView; - private ToggleButton toggle; private NumberPicker np; private ImageClassifier classifier; + enum InferenceEngine { CPU, NNAPI }; + private InferenceEngine inferenceEngine = InferenceEngine.CPU; + /** Max preview width that is guaranteed by Camera2 API */ private static final int MAX_PREVIEW_WIDTH = 1920; @@ -303,14 +304,6 @@ public class Camera2BasicFragment extends Fragment public void onViewCreated(final View view, Bundle savedInstanceState) { textureView = (AutoFitTextureView) view.findViewById(R.id.texture); textView = (TextView) view.findViewById(R.id.text); - toggle = (ToggleButton) view.findViewById(R.id.button); - - toggle.setOnCheckedChangeListener( - new CompoundButton.OnCheckedChangeListener() { - public void onCheckedChanged(CompoundButton buttonView, boolean isChecked) { - backgroundHandler.post(() -> classifier.setUseNNAPI(isChecked)); - } - }); np = (NumberPicker) view.findViewById(R.id.np); np.setMinValue(1); @@ -323,6 +316,32 @@ public class Camera2BasicFragment extends Fragment backgroundHandler.post(() -> classifier.setNumThreads(newVal)); } }); + + RadioButton cpuButton = (RadioButton) view.findViewById(R.id.radio_cpu); + cpuButton.setChecked(true); // TFLite runs on CPU by default. + cpuButton.setOnClickListener( + new View.OnClickListener() { + @Override + public void onClick(View view) { + if (inferenceEngine == InferenceEngine.CPU) { + return; + } + inferenceEngine = InferenceEngine.CPU; + backgroundHandler.post(() -> classifier.useCPU()); + } + }); + + ((RadioButton) view.findViewById(R.id.radio_nnapi)).setOnClickListener( + new View.OnClickListener() { + @Override + public void onClick(View view) { + if (inferenceEngine == InferenceEngine.NNAPI) { + return; + } + inferenceEngine = InferenceEngine.NNAPI; + backgroundHandler.post(() -> classifier.useNNAPI()); + } + }); } /** Load the model and labels. */ diff --git a/tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifier.java b/tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifier.java index 39057aa776..700efc1c1a 100644 --- a/tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifier.java +++ b/tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifier.java @@ -163,8 +163,13 @@ public abstract class ImageClassifier { } } - public void setUseNNAPI(Boolean nnapi) { - tfliteOptions.setUseNNAPI(nnapi); + public void useCPU() { + tfliteOptions.setUseNNAPI(false); + recreateInterpreter(); + } + + public void useNNAPI() { + tfliteOptions.setUseNNAPI(true); recreateInterpreter(); } diff --git a/tensorflow/lite/java/demo/app/src/main/res/layout-land/fragment_camera2_basic.xml b/tensorflow/lite/java/demo/app/src/main/res/layout-land/fragment_camera2_basic.xml index ef8a9e0845..ee71ab808f 100644 --- a/tensorflow/lite/java/demo/app/src/main/res/layout-land/fragment_camera2_basic.xml +++ b/tensorflow/lite/java/demo/app/src/main/res/layout-land/fragment_camera2_basic.xml @@ -40,12 +40,27 @@ android:scaleType="centerInside" android:src="@drawable/logo"/> - + android:layout_height="match_parent" + android:orientation="horizontal"> + + + + - + + + + diff --git a/tensorflow/lite/java/demo/app/src/main/res/layout/fragment_camera2_basic.xml b/tensorflow/lite/java/demo/app/src/main/res/layout/fragment_camera2_basic.xml index e567009a42..be66eeac75 100644 --- a/tensorflow/lite/java/demo/app/src/main/res/layout/fragment_camera2_basic.xml +++ b/tensorflow/lite/java/demo/app/src/main/res/layout/fragment_camera2_basic.xml @@ -80,15 +80,25 @@ android:layout_marginLeft="10dp" android:theme="@style/AppTheme.Picker" android:visibility="visible" /> - - + + + + diff --git a/tensorflow/lite/java/demo/app/src/main/res/values/strings.xml b/tensorflow/lite/java/demo/app/src/main/res/values/strings.xml index 29a033bcd4..45b12850e5 100644 --- a/tensorflow/lite/java/demo/app/src/main/res/values/strings.xml +++ b/tensorflow/lite/java/demo/app/src/main/res/values/strings.xml @@ -23,4 +23,5 @@ Use NNAPI tflite NNAPI + CPU -- GitLab From c707bb2b4da16b9fdf228c3680cf279034c3e9fb Mon Sep 17 00:00:00 2001 From: Derek Murray Date: Fri, 7 Dec 2018 15:38:23 -0800 Subject: [PATCH 237/873] [tf.data] Update datasets that use legacy structures to use `Structure`. PiperOrigin-RevId: 224589556 --- .../experimental/ops/get_single_element.py | 12 ++--- .../data/experimental/ops/prefetching_ops.py | 26 +++------ .../python/data/experimental/ops/scan_ops.py | 53 ++++++++++--------- 3 files changed, 37 insertions(+), 54 deletions(-) diff --git a/tensorflow/python/data/experimental/ops/get_single_element.py b/tensorflow/python/data/experimental/ops/get_single_element.py index 73116edf12..d649a07012 100644 --- a/tensorflow/python/data/experimental/ops/get_single_element.py +++ b/tensorflow/python/data/experimental/ops/get_single_element.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.ops import gen_dataset_ops from tensorflow.python.util.tf_export import tf_export @@ -63,10 +61,8 @@ def get_single_element(dataset): if not isinstance(dataset, dataset_ops.DatasetV2): raise TypeError("`dataset` must be a `tf.data.Dataset` object.") - nested_ret = nest.pack_sequence_as( - dataset.output_types, gen_dataset_ops.dataset_to_single_element( - dataset._as_variant_tensor(), # pylint: disable=protected-access + # pylint: disable=protected-access + return dataset._element_structure._from_compatible_tensor_list( + gen_dataset_ops.dataset_to_single_element( + dataset._as_variant_tensor(), **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/python/data/experimental/ops/prefetching_ops.py b/tensorflow/python/data/experimental/ops/prefetching_ops.py index aba1786646..e46dfb6568 100644 --- a/tensorflow/python/data/experimental/ops/prefetching_ops.py +++ b/tensorflow/python/data/experimental/ops/prefetching_ops.py @@ -20,8 +20,6 @@ from __future__ import print_function from tensorflow.python.data.experimental.ops import optimization_options from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import iterator_ops -from tensorflow.python.data.util import nest -from tensorflow.python.data.util import sparse from tensorflow.python.eager import function from tensorflow.python.framework import device as framework_device from tensorflow.python.framework import dtypes @@ -106,13 +104,6 @@ class _CopyToDeviceDataset(dataset_ops.UnaryUnchangedStructureDataset): self._source_device_string = source_device self._source_device = ops.convert_to_tensor(source_device) - self._flat_output_shapes = nest.flatten( - sparse.as_dense_shapes(self._input_dataset.output_shapes, - self._input_dataset.output_classes)) - self._flat_output_types = nest.flatten( - sparse.as_dense_types(self._input_dataset.output_types, - self._input_dataset.output_classes)) - @function.defun() def _init_func(): """Creates an iterator for the input dataset. @@ -123,8 +114,7 @@ class _CopyToDeviceDataset(dataset_ops.UnaryUnchangedStructureDataset): # pylint: disable=protected-access ds_variant = self._input_dataset._as_variant_tensor() resource = gen_dataset_ops.anonymous_iterator( - output_types=self._flat_output_types, - output_shapes=self._flat_output_shapes) + **dataset_ops.flat_structure(self._input_dataset)) with ops.control_dependencies( [gen_dataset_ops.make_iterator(ds_variant, resource)]): return gen_dataset_ops.iterator_to_string_handle(resource) @@ -155,8 +145,7 @@ class _CopyToDeviceDataset(dataset_ops.UnaryUnchangedStructureDataset): iterator = iterator_ops.Iterator.from_string_handle( string_handle, self.output_types, self.output_shapes, self.output_classes) - ret = iterator.get_next() - return nest.flatten(sparse.serialize_sparse_tensors(ret)) + return self._element_structure._to_tensor_list(iterator.get_next()) # pylint: disable=protected-access next_func_concrete = _next_func._get_concrete_function_internal() # pylint: disable=protected-access @@ -166,7 +155,7 @@ class _CopyToDeviceDataset(dataset_ops.UnaryUnchangedStructureDataset): target=self._source_device, args=[string_handle] + next_func_concrete.captured_inputs, - Tout=self._flat_output_types, + Tout=self._input_dataset._element_structure._flat_types, # pylint: disable=protected-access f=next_func_concrete) self._next_func = _remote_next_func._get_concrete_function_internal() # pylint: disable=protected-access @@ -183,8 +172,7 @@ class _CopyToDeviceDataset(dataset_ops.UnaryUnchangedStructureDataset): """ iterator_resource = gen_dataset_ops.iterator_from_string_handle_v2( string_handle, - output_types=self._flat_output_types, - output_shapes=self._flat_output_shapes) + **dataset_ops.flat_structure(self._input_dataset)) with ops.control_dependencies([ resource_variable_ops.destroy_resource_op( iterator_resource, ignore_lookup_error=True)]): @@ -196,8 +184,7 @@ class _CopyToDeviceDataset(dataset_ops.UnaryUnchangedStructureDataset): def _remote_finalize_func(string_handle): return functional_ops.remote_call( target=self._source_device, - args=[string_handle] + - finalize_func_concrete.captured_inputs, + args=[string_handle] + finalize_func_concrete.captured_inputs, Tout=[dtypes.int64], f=finalize_func_concrete) @@ -233,8 +220,7 @@ class _CopyToDeviceDataset(dataset_ops.UnaryUnchangedStructureDataset): init_func=self._init_func, next_func=self._next_func, finalize_func=self._finalize_func, - output_types=self._flat_output_types, - output_shapes=self._flat_output_shapes) + **dataset_ops.flat_structure(self._input_dataset)) class _MapOnGpuDataset(dataset_ops.UnaryDataset): diff --git a/tensorflow/python/data/experimental/ops/scan_ops.py b/tensorflow/python/data/experimental/ops/scan_ops.py index 661f2c5928..5c77ad7343 100644 --- a/tensorflow/python/data/experimental/ops/scan_ops.py +++ b/tensorflow/python/data/experimental/ops/scan_ops.py @@ -21,7 +21,6 @@ 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.data.util import structure from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor @@ -50,13 +49,7 @@ class _ScanDataset(dataset_ops.UnaryDataset): # Compute initial values for the state classes, shapes and types based on # the initial state. The shapes may be refined by running `tf_scan_func` one # or more times below. - self._state_classes = sparse.get_classes(self._initial_state) - self._state_shapes = nest.pack_sequence_as( - self._initial_state, - [t.get_shape() for t in nest.flatten(self._initial_state)]) - self._state_types = nest.pack_sequence_as( - self._initial_state, - [t.dtype for t in nest.flatten(self._initial_state)]) + self._state_structure = structure.Structure.from_value(self._initial_state) # Iteratively rerun the scan function until reaching a fixed point on # `self._state_shapes`. @@ -66,9 +59,8 @@ class _ScanDataset(dataset_ops.UnaryDataset): wrapped_func = dataset_ops.StructuredFunctionWrapper( scan_func, self._transformation_name(), - 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_structure=structure.NestedStructure( + (self._state_structure, input_dataset._element_structure)), # pylint: disable=protected-access add_to_graph=False) if not ( isinstance(wrapped_func.output_types, collections.Sequence) and @@ -76,35 +68,38 @@ class _ScanDataset(dataset_ops.UnaryDataset): raise TypeError("The scan function must return a pair comprising the " "new state and the output value.") - new_state_classes, output_classes = wrapped_func.output_classes + 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( + new_state_classes, output_classes = wrapped_func.output_classes + old_state_classes = self._state_structure._to_legacy_output_classes() # pylint: disable=protected-access + for new_state_class, old_state_class in zip( nest.flatten(new_state_classes), - nest.flatten(self._state_classes)): - if not issubclass(new_state_class, state_class): + nest.flatten(old_state_classes)): + if not issubclass(new_state_class, old_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)) + (old_state_classes, new_state_classes)) # Extract and validate type information from the returned values. new_state_types, 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: + old_state_types = self._state_structure._to_legacy_output_types() # pylint: disable=protected-access + for new_state_type, old_state_type in zip( + nest.flatten(new_state_types), nest.flatten(old_state_types)): + if new_state_type != old_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)) + (old_state_types, new_state_types)) # Extract shape information from the returned values. new_state_shapes, output_shapes = wrapped_func.output_shapes - + old_state_shapes = self._state_structure._to_legacy_output_shapes() # pylint: disable=protected-access self._structure = structure.convert_legacy_structure( output_types, output_shapes, output_classes) - flat_state_shapes = nest.flatten(self._state_shapes) + flat_state_shapes = nest.flatten(old_state_shapes) flat_new_state_shapes = nest.flatten(new_state_shapes) weakened_state_shapes = [ original.most_specific_compatible_shape(new) @@ -121,8 +116,13 @@ class _ScanDataset(dataset_ops.UnaryDataset): break if need_to_rerun: - self._state_shapes = nest.pack_sequence_as(self._state_shapes, - weakened_state_shapes) + # TODO(b/110122868): Support a "most specific compatible structure" + # method for combining structures, to avoid using legacy structures + # in this method. + self._state_structure = structure.convert_legacy_structure( + old_state_types, + nest.pack_sequence_as(old_state_shapes, weakened_state_shapes), + old_state_classes) self._scan_func = wrapped_func self._scan_func.function.add_to_graph(ops.get_default_graph()) @@ -131,10 +131,11 @@ class _ScanDataset(dataset_ops.UnaryDataset): return [self._scan_func] def _as_variant_tensor(self): - input_t = self._input_dataset._as_variant_tensor() # pylint: disable=protected-access + # pylint: disable=protected-access + input_t = self._input_dataset._as_variant_tensor() return gen_experimental_dataset_ops.experimental_scan_dataset( input_t, - nest.flatten(sparse.serialize_sparse_tensors(self._initial_state)), + self._state_structure._to_tensor_list(self._initial_state), self._scan_func.function.captured_inputs, f=self._scan_func.function, preserve_cardinality=True, -- GitLab From 078e8fa05791cc55e71a13ea33475a436e62dba5 Mon Sep 17 00:00:00 2001 From: Dimitris Vardoulakis Date: Fri, 7 Dec 2018 15:58:35 -0800 Subject: [PATCH 238/873] [TF:XLA] Change ArCrsCombiner to use division instead of subtraction. This way, when we move the AR down the graph, the adjustment stays before the AR, not after, and the AR moves closer to the CRS. Then, it's easier to move the AR past multiple ops one-at-a-time in future CLs. Also, some bug fixes in InstructionsComputeSameValue. PiperOrigin-RevId: 224592476 --- .../compiler/xla/service/ar_crs_combiner.cc | 69 +++++++------- .../compiler/xla/service/ar_crs_combiner.h | 6 +- .../xla/service/ar_crs_combiner_test.cc | 94 +++++++++++++++++-- 3 files changed, 124 insertions(+), 45 deletions(-) diff --git a/tensorflow/compiler/xla/service/ar_crs_combiner.cc b/tensorflow/compiler/xla/service/ar_crs_combiner.cc index 24de693822..362bc44a1c 100644 --- a/tensorflow/compiler/xla/service/ar_crs_combiner.cc +++ b/tensorflow/compiler/xla/service/ar_crs_combiner.cc @@ -60,7 +60,7 @@ absl::optional MatchesArCrsPattern( absl::optional ArCrsCombiner::WhileFromBodyParameter( HloInstruction* instruction) { - CHECK(HloOpcode::kParameter == instruction->opcode()); + CHECK_EQ(HloOpcode::kParameter, instruction->opcode()); HloComputation* computation = instruction->parent(); auto caller_instructions = call_graph_->GetComputationCallers(computation); if (caller_instructions.size() == 1) { @@ -120,7 +120,7 @@ bool ArCrsCombiner::TupleElementsComputeSameValue( return false; } for (auto tuple : tuples) { - CHECK(tuple->opcode() == HloOpcode::kTuple); + CHECK_EQ(tuple->opcode(), HloOpcode::kTuple); if (!InstructionsComputeSameValue(tuple->mutable_operand(i1), tuple->mutable_operand(i2), visited_pairs)) { @@ -133,7 +133,7 @@ bool ArCrsCombiner::TupleElementsComputeSameValue( /* static */ bool ArCrsCombiner::TestInstructionsComputeSameValue(HloInstruction* i1, HloInstruction* i2) { - ArCrsCombiner combiner(/*num_spatial_partitions=*/2, /*num_replicas=*/1); + ArCrsCombiner combiner(/*num_spatial_partitions=*/2); auto module = i1->parent()->parent(); CHECK_EQ(module, i2->parent()->parent()); combiner.call_graph_ = CallGraph::Build(module); @@ -160,13 +160,6 @@ bool ArCrsCombiner::InstructionsComputeSameValue( if (opcode1 != i2->opcode() || operands1.size() != i2->operands().size()) { return false; } - if (opcode1 == HloOpcode::kConstant || i1->IsCrossModuleAllReduce()) { - return i1->Identical( - *i2, - /*eq_operands=*/std::equal_to(), - /*eq_computations=*/std::equal_to(), - /*layout_sensitive=*/false); - } visited_pairs->emplace(min_uid, max_uid); for (int i = 0; i < operands1.size(); ++i) { auto operand1 = operands1[i]; @@ -175,14 +168,28 @@ bool ArCrsCombiner::InstructionsComputeSameValue( return false; } } + if (opcode1 == HloOpcode::kParameter) { + // In the general case, we don't try to prove equality of parameters. + // We only try in the context of get-tuple-element + // (see TupleElementsComputeSameValue). + return false; + } if (opcode1 == HloOpcode::kGetTupleElement) { - if (i1->tuple_index() == i2->tuple_index()) { - return true; - } - return TupleElementsComputeSameValue(operands1[0], i1->tuple_index(), + return i1->tuple_index() == i2->tuple_index() || + TupleElementsComputeSameValue(operands1[0], i1->tuple_index(), i2->tuple_index(), visited_pairs); } - return true; + // Don't check that the operands are identical, because Identical can + // return false for instructions that compute the same value but are not + // identical, which we don't want. We have checked the arguments with + // InstructionsComputeSameValue earlier. + auto eq_instructions = [](const HloInstruction* i1, + const HloInstruction* i2) -> bool { return true; }; + auto eq_computations = [](const HloComputation* a, const HloComputation* b) { + return *a == *b; + }; + return i1->Identical(*i2, eq_instructions, eq_computations, + /*layout_sensitive=*/false); } void ArCrsCombiner::GroupAllReducesById(HloModule* module) { @@ -203,12 +210,12 @@ void ArCrsCombiner::KeepProvablyEqualInstructionGroups() { auto instr_0 = instruction_vec[0]; auto add_0 = instr_0->users()[0]->users()[0]; - CHECK(HloOpcode::kAdd == add_0->opcode()); + CHECK_EQ(HloOpcode::kAdd, add_0->opcode()); for (int i = 1; i < instruction_vec.size(); ++i) { auto instr_i = instruction_vec[i]; auto add_i = instr_i->users()[0]->users()[0]; - CHECK(HloOpcode::kAdd == add_i->opcode()); + CHECK_EQ(HloOpcode::kAdd, add_i->opcode()); absl::flat_hash_map visited_pairs; if (!InstructionsComputeSameValue(add_0, add_i, &visited_pairs)) { all_reduce_map_.erase(it.first); @@ -242,26 +249,22 @@ StatusOr ArCrsCombiner::RewriteGraph() { HloInstruction* other_summand = (add->operands()[0] == convert) ? add->operands()[1] : add->operands()[0]; - // Remove the AllReduce and replace the CRS with an all-core AllReduce, - // then subtract: - // other_summand * num_replicas_ * (num_spatial_partitions_ - 1) + // To move the AR past the addition, we need to divide other_summand by + // the number of spatial partitions. + CHECK_EQ(all_reduce->user_count(), 1); TF_CHECK_OK( all_reduce->ReplaceAllUsesWith(all_reduce->mutable_operand(0))); - crs->set_all_reduce_id(all_reduce->all_reduce_id()); - auto new_shape = crs->shape(); - Literal lit(new_shape); - lit.PopulateWithValue(num_replicas_ * - (num_spatial_partitions_ - 1)); - auto partitions_minus_1_const = parent_computation->AddInstruction( + auto shape = other_summand->shape(); + Literal lit(shape); + lit.PopulateWithValue(num_spatial_partitions_); + auto divisor = parent_computation->AddInstruction( HloInstruction::CreateConstant(lit.Clone())); - auto to_subtract = + auto division = parent_computation->AddInstruction(HloInstruction::CreateBinary( - new_shape, HloOpcode::kMultiply, other_summand, - partitions_minus_1_const)); - auto sub = - parent_computation->AddInstruction(HloInstruction::CreateBinary( - new_shape, HloOpcode::kSubtract, crs, to_subtract)); - TF_CHECK_OK(crs->ReplaceAllUsesWith(sub)); + shape, HloOpcode::kDivide, other_summand, divisor)); + TF_CHECK_OK(other_summand->ReplaceUseWith(add, division)); + // The AllReduce and the CRS are combined to an all-core AllReduce. + crs->set_all_reduce_id(all_reduce->all_reduce_id()); TF_CHECK_OK(parent_computation->RemoveInstruction(all_reduce)); } } diff --git a/tensorflow/compiler/xla/service/ar_crs_combiner.h b/tensorflow/compiler/xla/service/ar_crs_combiner.h index 4abdb1f57d..f6a7ef76ec 100644 --- a/tensorflow/compiler/xla/service/ar_crs_combiner.h +++ b/tensorflow/compiler/xla/service/ar_crs_combiner.h @@ -30,9 +30,8 @@ namespace xla { // fully utilizes the interconnect bandwidth. class ArCrsCombiner : public HloModulePass { public: - ArCrsCombiner(int num_spatial_partitions, int num_replicas) - : num_spatial_partitions_(num_spatial_partitions), - num_replicas_(num_replicas) {} + ArCrsCombiner(int num_spatial_partitions) + : num_spatial_partitions_(num_spatial_partitions) {} absl::string_view name() const override { return "ar-crs-combiner"; } StatusOr Run(HloModule* module) override; @@ -77,7 +76,6 @@ class ArCrsCombiner : public HloModulePass { StatusOr RewriteGraph(); int num_spatial_partitions_; - int num_replicas_; // Map from all-reduce ids to the all reduce instructions. absl::flat_hash_map> all_reduce_map_; diff --git a/tensorflow/compiler/xla/service/ar_crs_combiner_test.cc b/tensorflow/compiler/xla/service/ar_crs_combiner_test.cc index 23d9aa9eb3..10171835d8 100644 --- a/tensorflow/compiler/xla/service/ar_crs_combiner_test.cc +++ b/tensorflow/compiler/xla/service/ar_crs_combiner_test.cc @@ -48,6 +48,43 @@ ENTRY %entrycomp (p: f32[2,2]) -> (f32[2,2], f32[2,2]) { EXPECT_TRUE(ArCrsCombiner::TestInstructionsComputeSameValue(i1, i2)); } +TEST_F(ArCrsCombinerTest, SameValueTestBasecase2) { + const char* module_str = R"( +HloModule foobar + +ENTRY %entrycomp (x: f32[]) -> (f32[], f32[]) { + %x = f32[] parameter(0) + ROOT %tuple = (f32[], f32[]) tuple(%x, %x) +} +)"; + + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, + ParseAndReturnVerifiedModule(module_str)); + auto root_tuple = module->entry_computation()->root_instruction(); + auto i1 = root_tuple->operands()[0]; + auto i2 = root_tuple->operands()[1]; + EXPECT_TRUE(ArCrsCombiner::TestInstructionsComputeSameValue(i1, i2)); +} + +TEST_F(ArCrsCombinerTest, SameValueTestBasecase3) { + const char* module_str = R"( +HloModule foobar + +ENTRY %entrycomp (x: f32[], y: f32[]) -> (f32[], f32[]) { + %x = f32[] parameter(0) + %y = f32[] parameter(1) + ROOT %tuple = (f32[], f32[]) tuple(%x, %y) +} +)"; + + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, + ParseAndReturnVerifiedModule(module_str)); + auto root_tuple = module->entry_computation()->root_instruction(); + auto i1 = root_tuple->operands()[0]; + auto i2 = root_tuple->operands()[1]; + EXPECT_FALSE(ArCrsCombiner::TestInstructionsComputeSameValue(i1, i2)); +} + TEST_F(ArCrsCombinerTest, SameValueTestNumOperands) { const char* module_str = R"( HloModule foobar @@ -69,6 +106,46 @@ ENTRY %entrycomp (p: f32[2,2]) -> ((f32[2,2]), (f32[2,2], f32[2,2])) { EXPECT_FALSE(ArCrsCombiner::TestInstructionsComputeSameValue(i1, i2)); } +TEST_F(ArCrsCombinerTest, SameValueTestSliceIndicesMatch) { + const char* module_str = R"( +HloModule foobar + +ENTRY %entrycomp (p: f32[2]) -> (f32[1], f32[1]) { + %p = f32[2] parameter(0) + %slice.1 = f32[1] slice(f32[2] %p), slice={[0:1]} + %slice.2 = f32[1] slice(f32[2] %p), slice={[0:1]} + ROOT %tuple = (f32[1], f32[1]) tuple(%slice.1, %slice.2) +} +)"; + + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, + ParseAndReturnVerifiedModule(module_str)); + auto root_tuple = module->entry_computation()->root_instruction(); + auto i1 = root_tuple->operands()[0]; + auto i2 = root_tuple->operands()[1]; + EXPECT_TRUE(ArCrsCombiner::TestInstructionsComputeSameValue(i1, i2)); +} + +TEST_F(ArCrsCombinerTest, SameValueTestSliceIndicesDontMatch) { + const char* module_str = R"( +HloModule foobar + +ENTRY %entrycomp (p: f32[2]) -> (f32[1], f32[1]) { + %p = f32[2] parameter(0) + %slice.1 = f32[1] slice(f32[2] %p), slice={[0:1]} + %slice.2 = f32[1] slice(f32[2] %p), slice={[1:2]} + ROOT %tuple = (f32[1], f32[1]) tuple(%slice.1, %slice.2) +} +)"; + + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, + ParseAndReturnVerifiedModule(module_str)); + auto root_tuple = module->entry_computation()->root_instruction(); + auto i1 = root_tuple->operands()[0]; + auto i2 = root_tuple->operands()[1]; + EXPECT_FALSE(ArCrsCombiner::TestInstructionsComputeSameValue(i1, i2)); +} + TEST_F(ArCrsCombinerTest, SameValueTestTupleElementSameIndex) { const char* module_str = R"( HloModule foobar @@ -317,17 +394,18 @@ ENTRY %entrycomp (p: f32[2,2]) -> (f32[2,2], f32[2,2]) { auto crs_before = module->entry_computation()->root_instruction()->operands()[0]; auto replica_groups_before = crs_before->replica_groups(); - ArCrsCombiner combiner(2, 1); + ArCrsCombiner combiner(2); auto changed = combiner.Run(module.get()).ValueOrDie(); EXPECT_TRUE(changed); EXPECT_THAT( module->entry_computation()->root_instruction(), - op::Tuple(op::Subtract(op::CrossReplicaSum(), - op::Multiply(op::Constant(), op::Constant())), - op::Subtract(op::CrossReplicaSum(), - op::Multiply(op::Constant(), op::Constant())))); - auto sub = module->entry_computation()->root_instruction()->operands()[0]; - auto crs_after = sub->operands()[0]; + op::Tuple( + op::CrossReplicaSum(op::Add( + op::Divide(op::Constant(), op::Constant()), op::Convert())), + op::CrossReplicaSum(op::Add( + op::Divide(op::Constant(), op::Constant()), op::Convert())))); + auto crs_after = + module->entry_computation()->root_instruction()->operands()[0]; auto replica_groups_after = crs_after->replica_groups(); ASSERT_EQ(replica_groups_before.size(), replica_groups_after.size()); for (int i = 0; i < replica_groups_before.size(); ++i) { @@ -409,7 +487,7 @@ ENTRY %entrycomp (p: f32[2,2]) -> (f32[2,2], f32[2,2]) { TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, ParseAndReturnVerifiedModule(module_str)); - ArCrsCombiner combiner(2, 1); + ArCrsCombiner combiner(2); auto changed = combiner.Run(module.get()).ValueOrDie(); EXPECT_FALSE(changed); } -- GitLab From 44157872848ae4f07b533a1fea35a6b00d97cc1e Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 7 Dec 2018 16:13:24 -0800 Subject: [PATCH 239/873] Track XRT memory allocations. PiperOrigin-RevId: 224594734 --- tensorflow/compiler/xrt/xrt_state.cc | 47 +++++++++++++++++++++++++++- 1 file changed, 46 insertions(+), 1 deletion(-) diff --git a/tensorflow/compiler/xrt/xrt_state.cc b/tensorflow/compiler/xrt/xrt_state.cc index 5c7c537c34..31603e044d 100644 --- a/tensorflow/compiler/xrt/xrt_state.cc +++ b/tensorflow/compiler/xrt/xrt_state.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xrt/xrt_state.h" #include +#include #include #include #include @@ -34,6 +35,7 @@ limitations under the License. #include "tensorflow/core/framework/resource_mgr.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/lib/random/random.h" +#include "tensorflow/core/platform/mutex.h" #include "tensorflow/core/platform/types.h" #include "tensorflow/stream_executor/stream_executor.h" @@ -41,6 +43,34 @@ namespace tensorflow { namespace { +class BufferAllocStats { + public: + struct Stats { + int64 count = 0; + int64 size = 0; + }; + + Stats ReportAlloc(int64 device, int64 msize) { + mutex_lock lock(lock_); + Stats* device_stats = &stats_[device]; + device_stats->count += 1; + device_stats->size += msize; + return *device_stats; + } + + Stats ReportFree(int64 device, int64 msize) { + mutex_lock lock(lock_); + Stats* device_stats = &stats_[device]; + device_stats->count -= 1; + device_stats->size -= msize; + return *device_stats; + } + + private: + mutable mutex lock_; + std::map stats_; +}; + const char* kTupleContainer = "tuples"; int64 get_uid() { @@ -48,6 +78,11 @@ int64 get_uid() { return static_cast(unsigned_rand); } +BufferAllocStats* GetAllocStats() { + static BufferAllocStats* stats = new BufferAllocStats(); + return stats; +} + Status AllocateScopedShapedBuffer( xla::Backend* backend, int device_ordinal, const xla::Shape& shape, std::unique_ptr* buffer) { @@ -100,9 +135,19 @@ XRTBufferAllocation::XRTBufferAllocation(const se::DeviceMemoryBase& allocation, xla::DeviceMemoryAllocator* allocator) : allocation_(allocation), device_ordinal_(device_ordinal), - allocator_(allocator) {} + allocator_(allocator) { + if (VLOG_IS_ON(2)) { + auto stats = + GetAllocStats()->ReportAlloc(device_ordinal_, allocation_.size()); + LOG(INFO) << "XRT Allocation Stats: device=" << device_ordinal_ + << " count=" << stats.count << " size=" << stats.size; + } +} XRTBufferAllocation::~XRTBufferAllocation() { + if (VLOG_IS_ON(2)) { + GetAllocStats()->ReportFree(device_ordinal_, allocation_.size()); + } // Deallocate explicitly allows allocation_ to be null. Status s = allocator_->Deallocate(device_ordinal_, allocation_); // Nothing to do but check fail here if memory datastructures are corrupted. -- GitLab From 294ad7140234fb383e28bd059d6d761ba879c09e Mon Sep 17 00:00:00 2001 From: Anna R Date: Fri, 7 Dec 2018 16:22:49 -0800 Subject: [PATCH 240/873] Internal change. PiperOrigin-RevId: 224596031 --- tensorflow/tools/api/tests/api_compatibility_test.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tensorflow/tools/api/tests/api_compatibility_test.py b/tensorflow/tools/api/tests/api_compatibility_test.py index e7f23a1174..40162daf14 100644 --- a/tensorflow/tools/api/tests/api_compatibility_test.py +++ b/tensorflow/tools/api/tests/api_compatibility_test.py @@ -277,6 +277,9 @@ class ApiCompatibilityTest(test.TestCase): public_api_visitor = public_api.PublicAPIVisitor(visitor) public_api_visitor.private_map['tf'] = ['contrib'] + if api_version == 2: + public_api_visitor.private_map['tf'].append('enable_v2_behavior') + public_api_visitor.do_not_descend_map['tf.GPUOptions'] = ['Experimental'] if FLAGS.only_test_core_api: public_api_visitor.do_not_descend_map['tf'].extend(_NON_CORE_PACKAGES) -- GitLab From 5dfbcfbba04197117cff01a07397f499682d3f56 Mon Sep 17 00:00:00 2001 From: Jared Duke Date: Fri, 7 Dec 2018 16:23:28 -0800 Subject: [PATCH 241/873] Consolidate JNI inclusion code Introduce a single proxy target that exports JNI headers appropriately for the given platform. PiperOrigin-RevId: 224596100 --- tensorflow/lite/java/jni/BUILD | 47 ++++++++++++++ tensorflow/lite/java/src/main/native/BUILD | 62 ++----------------- tensorflow/lite/java/src/test/native/BUILD | 19 ++---- .../models/smartreply/demo/app/src/main/BUILD | 1 + 4 files changed, 58 insertions(+), 71 deletions(-) create mode 100644 tensorflow/lite/java/jni/BUILD diff --git a/tensorflow/lite/java/jni/BUILD b/tensorflow/lite/java/jni/BUILD new file mode 100644 index 0000000000..ce17ac4fa0 --- /dev/null +++ b/tensorflow/lite/java/jni/BUILD @@ -0,0 +1,47 @@ +package(default_visibility = ["//tensorflow/lite:__subpackages__"]) + +licenses(["notice"]) # Apache 2.0 + +# Helper target for exposing JNI headers across multiple platforms. +cc_library( + name = "jni", + hdrs = select({ + # The Android toolchain makes "jni.h" available in the include path. + # For non-Android toolchains, generate jni.h and jni_md.h. + "//tensorflow:android": [], + "//conditions:default": [ + ":jni.h", + ":jni_md.h", + ], + }), + includes = select({ + "//tensorflow:android": [], + "//conditions:default": ["."], + }), +) + +# Silly rules to make +# #include +# in the source headers work +# (in combination with the "includes" attribute of the tf_cuda_library rule +# above. Not needed when using the Android toolchain). +# +# Inspired from: +# https://github.com/bazelbuild/bazel/blob/f99a0543f8d97339d32075c7176b79f35be84606/src/main/native/BUILD +# but hopefully there is a simpler alternative to this. +genrule( + name = "copy_jni_h", + srcs = ["@bazel_tools//tools/jdk:jni_header"], + outs = ["jni.h"], + cmd = "cp -f $< $@", +) + +genrule( + name = "copy_jni_md_h", + srcs = select({ + "//tensorflow:darwin": ["@bazel_tools//tools/jdk:jni_md_header-darwin"], + "//conditions:default": ["@bazel_tools//tools/jdk:jni_md_header-linux"], + }), + outs = ["jni_md.h"], + cmd = "cp -f $< $@", +) diff --git a/tensorflow/lite/java/src/main/native/BUILD b/tensorflow/lite/java/src/main/native/BUILD index 8f95f14518..52194e86db 100644 --- a/tensorflow/lite/java/src/main/native/BUILD +++ b/tensorflow/lite/java/src/main/native/BUILD @@ -15,15 +15,7 @@ cc_library( "nativeinterpreterwrapper_jni.cc", "tensor_jni.cc", "tensorflow_lite_jni.cc", - ] + select({ - # The Android toolchain makes "jni.h" available in the include path. - # For non-Android toolchains, generate jni.h and jni_md.h. - "//tensorflow:android": [], - "//conditions:default": [ - ":jni.h", - ":jni_md.h", - ], - }), + ], hdrs = [ "exception_jni.h", "nativeinterpreterwrapper_jni.h", @@ -31,75 +23,31 @@ cc_library( "tensorflow_lite_jni.h", ], copts = tflite_copts(), - includes = select({ - "//tensorflow:android": [], - "//conditions:default": ["."], - }), linkopts = [ "-lm", "-ldl", ], deps = [ - "//tensorflow/lite:context", "//tensorflow/lite:framework", "//tensorflow/lite:schema_fbs_version", "//tensorflow/lite:string_util", + "//tensorflow/lite/c:c_api_internal", + "//tensorflow/lite/java/jni", ], alwayslink = 1, ) -# Silly rules to make -# #include -# in the source headers work -# (in combination with the "includes" attribute of the tf_cuda_library rule -# above. Not needed when using the Android toolchain). -# -# Inspired from: -# https://github.com/bazelbuild/bazel/blob/f99a0543f8d97339d32075c7176b79f35be84606/src/main/native/BUILD -# but hopefully there is a simpler alternative to this. -genrule( - name = "copy_jni_h", - srcs = ["@bazel_tools//tools/jdk:jni_header"], - outs = ["jni.h"], - cmd = "cp -f $< $@", -) - -genrule( - name = "copy_jni_md_h", - srcs = select({ - "//tensorflow:darwin": ["@bazel_tools//tools/jdk:jni_md_header-darwin"], - "//conditions:default": ["@bazel_tools//tools/jdk:jni_md_header-linux"], - }), - outs = ["jni_md.h"], - cmd = "cp -f $< $@", -) - cc_library( name = "init_tensorflow", srcs = [ "init_tensorflow_jni.cc", - ] + select({ - # The Android toolchain makes "jni.h" available in the include path. - # For non-Android toolchains, generate jni.h and jni_md.h. - "//tensorflow:android": [], - "//conditions:default": [ - ":jni.h", - ":jni_md.h", - ], - }), + ], hdrs = [ "init_tensorflow_jni.h", ], copts = tflite_copts(), - includes = select({ - "//tensorflow:android": [], - "//conditions:default": ["."], - }), - linkopts = [ - "-lm", - "-ldl", - ], deps = [ + "//tensorflow/lite/java/jni", "//tensorflow/lite/testing:init_tensorflow", ], alwayslink = 1, diff --git a/tensorflow/lite/java/src/test/native/BUILD b/tensorflow/lite/java/src/test/native/BUILD index 4d3e82b1ac..481aea7ecd 100644 --- a/tensorflow/lite/java/src/test/native/BUILD +++ b/tensorflow/lite/java/src/test/native/BUILD @@ -12,20 +12,11 @@ cc_library( testonly = 1, srcs = [ "interpreter_test_jni.cc", - ] + select({ - # The Android toolchain makes "jni.h" available in the include path. - # For non-Android toolchains, generate jni.h and jni_md.h. - "//tensorflow:android": [], - "//conditions:default": [ - "//tensorflow/lite/java/src/main/native:jni.h", - "//tensorflow/lite/java/src/main/native:jni_md.h", - ], - }), - includes = select({ - "//tensorflow:android": [], - "//conditions:default": ["../../main/native/."], - }), - deps = ["//tensorflow/lite/c:c_api_internal"], + ], + deps = [ + "//tensorflow/lite/c:c_api_internal", + "//tensorflow/lite/java/jni", + ], ) tflite_jni_binary( diff --git a/tensorflow/lite/models/smartreply/demo/app/src/main/BUILD b/tensorflow/lite/models/smartreply/demo/app/src/main/BUILD index b14af4cb20..73326e994b 100644 --- a/tensorflow/lite/models/smartreply/demo/app/src/main/BUILD +++ b/tensorflow/lite/models/smartreply/demo/app/src/main/BUILD @@ -62,6 +62,7 @@ cc_library( ], deps = [ "//tensorflow/lite:framework", + "//tensorflow/lite/java/jni", "//tensorflow/lite/models/smartreply:predictor_lib", ], alwayslink = 1, -- GitLab From f0e09e4ab12414aba6c3c47750287515ea12feea Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Fri, 7 Dec 2018 16:37:37 -0800 Subject: [PATCH 242/873] Fix bazel test failure with mac llvm This fix tries to address the issue raised in 24212 where the bazel test for trt_allocator_test fails. The reason was that 1ul is unsigned long while uint64_t is unsigned long long. This fix fixes the issue. Signed-off-by: Yong Tang --- tensorflow/contrib/tensorrt/resources/trt_allocator_test.cc | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/tensorrt/resources/trt_allocator_test.cc b/tensorflow/contrib/tensorrt/resources/trt_allocator_test.cc index ad6b1d7d4c..ab3541ef6e 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_allocator_test.cc +++ b/tensorflow/contrib/tensorrt/resources/trt_allocator_test.cc @@ -48,11 +48,11 @@ TEST(TRTAllocatorTest, Align) { 513ul, 700ul, 12345ul, 1ul << 32}) { for (uint64_t alignment = 1; alignment <= space * 4; alignment *= 2) { for (const uintptr_t ptr_val : - {1ul, alignment == 1 ? 1ul : alignment - 1, alignment, alignment + 1, + {1ull, alignment == 1 ? 1ull : alignment - 1, alignment, alignment + 1, alignment + (alignment / 2)}) { if (ptr_val % alignment == 0) { for (const uint64_t size : - {1ul, space == 1 ? 1ul : space - 1, space, space + 1}) { + {1ull, space == 1 ? 1ull : space - 1, space, space + 1}) { EXPECT_EQ(space >= size, RunTest(alignment, size, ptr_val, space)); } } else { -- GitLab From e87bf9a3da0c396fe88d664d5a0c84a892eed9af Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Fri, 7 Dec 2018 16:40:06 -0800 Subject: [PATCH 243/873] Additional fix of trt_allocator_test.cc Signed-off-by: Yong Tang --- tensorflow/contrib/tensorrt/resources/trt_allocator_test.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/tensorrt/resources/trt_allocator_test.cc b/tensorflow/contrib/tensorrt/resources/trt_allocator_test.cc index ab3541ef6e..18b983b9ed 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_allocator_test.cc +++ b/tensorflow/contrib/tensorrt/resources/trt_allocator_test.cc @@ -62,7 +62,7 @@ TEST(TRTAllocatorTest, Align) { EXPECT_TRUE( RunTest(alignment, space - diff, ptr_val + diff, space - diff)); for (const uint64_t size : - {1ul, space - diff > 1 ? space - diff - 1 : 1ul, space - diff, + {1ull, space - diff > 1 ? space - diff - 1 : 1ull, space - diff, space - diff + 1, space - 1}) { EXPECT_EQ(space - diff >= size, RunTest(alignment, size, ptr_val, space)); -- GitLab From f9f50b6cf831cdfef15d952152f43ba6542a14ad Mon Sep 17 00:00:00 2001 From: Eddie Zhou Date: Fri, 7 Dec 2018 16:44:06 -0800 Subject: [PATCH 244/873] Move skipping of test_session to setUp(), so child tests will also skip setUp(), which can be expensive. PiperOrigin-RevId: 224598690 --- tensorflow/python/framework/test_util.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py index 06316ce2e9..28f25daa6d 100644 --- a/tensorflow/python/framework/test_util.py +++ b/tensorflow/python/framework/test_util.py @@ -1430,6 +1430,10 @@ class TensorFlowTestCase(googletest.TestCase): ops.reset_default_graph() random_seed.set_random_seed(random_seed.DEFAULT_GRAPH_SEED) + # Avoiding calling setUp() for the poorly named test_session method. + if self.id().endswith(".test_session"): + self.skipTest("Not a test.") + def tearDown(self): for thread in self._threads: thread.check_termination() @@ -1713,9 +1717,6 @@ class TensorFlowTestCase(googletest.TestCase): use_gpu=False, force_gpu=False): """Use cached_session instead.""" - if self.id().endswith(".test_session"): - self.skipTest("Not a test.") - if context.executing_eagerly(): yield None else: -- GitLab From ff563b9436509a35bbb5087952c7fbfda44df46f Mon Sep 17 00:00:00 2001 From: Yuefeng Zhou Date: Fri, 7 Dec 2018 16:44:40 -0800 Subject: [PATCH 245/873] Fixed the bug in keras where the callback attributes are not correctly checked. PiperOrigin-RevId: 224598769 --- tensorflow/contrib/distribute/python/BUILD | 1 - .../contrib/distribute/python/keras_test.py | 14 ++++---------- .../keras/engine/distributed_training_utils.py | 16 ++++++++++++---- 3 files changed, 16 insertions(+), 15 deletions(-) diff --git a/tensorflow/contrib/distribute/python/BUILD b/tensorflow/contrib/distribute/python/BUILD index 4ea1fa050a..4c9c35da5a 100644 --- a/tensorflow/contrib/distribute/python/BUILD +++ b/tensorflow/contrib/distribute/python/BUILD @@ -611,7 +611,6 @@ cuda_py_test( "no_oss", # TODO(b/117919883): Fix python error. "no_pip", "no_windows_gpu", - "noguitar", # TODO(b/120025010): Re-enable this test on Guitar. "notsan", ], ) diff --git a/tensorflow/contrib/distribute/python/keras_test.py b/tensorflow/contrib/distribute/python/keras_test.py index c0bcb5ecd5..683cc89bfb 100644 --- a/tensorflow/contrib/distribute/python/keras_test.py +++ b/tensorflow/contrib/distribute/python/keras_test.py @@ -1085,8 +1085,8 @@ class TestDistributionStrategyErrorCases(test.TestCase, parameterized.TestCase): @combinations.generate(combinations.combine( distribution=[ - combinations.mirrored_strategy_with_two_gpus, - combinations.core_mirrored_strategy_with_two_gpus], + combinations.mirrored_strategy_with_gpu_and_cpu, + combinations.core_mirrored_strategy_with_gpu_and_cpu], mode=['graph', 'eager'])) def test_unsupported_features(self, distribution): with self.cached_session(): @@ -1134,8 +1134,8 @@ class TestDistributionStrategyErrorCases(test.TestCase, parameterized.TestCase): @combinations.generate(combinations.combine( distribution=[ - combinations.mirrored_strategy_with_two_gpus, - combinations.core_mirrored_strategy_with_two_gpus], + combinations.mirrored_strategy_with_gpu_and_cpu, + combinations.core_mirrored_strategy_with_gpu_and_cpu], mode=['graph', 'eager'])) def test_calling_with_unsupported_predefined_callbacks(self, distribution): with self.cached_session(): @@ -1161,12 +1161,6 @@ class TestDistributionStrategyErrorCases(test.TestCase, parameterized.TestCase): 'using'): model.fit(dataset, epochs=1, steps_per_epoch=2, verbose=0, callbacks=[keras.callbacks.ReduceLROnPlateau()]) - with self.assertRaisesRegexp(ValueError, - 'histogram_freq in the TensorBoard callback ' - 'is not supported when using ' - 'DistributionStrategy.'): - model.fit(dataset, epochs=1, steps_per_epoch=2, verbose=0, - callbacks=[keras.callbacks.TensorBoard(histogram_freq=10)]) class TestDistributionStrategyWithLossMasking(test.TestCase, diff --git a/tensorflow/python/keras/engine/distributed_training_utils.py b/tensorflow/python/keras/engine/distributed_training_utils.py index d100182381..32129afe64 100644 --- a/tensorflow/python/keras/engine/distributed_training_utils.py +++ b/tensorflow/python/keras/engine/distributed_training_utils.py @@ -199,11 +199,19 @@ def validate_callbacks(input_callbacks, optimizer, current_strategy): # running ops. if isinstance(callback, callbacks.TensorBoard): if callback.__getattribute__('histogram_freq'): - raise ValueError('histogram_freq in the TensorBoard callback is not ' - 'supported when using DistributionStrategy.') + logging.warning( + UserWarning( + '`histogram_freq` in the TensorBoard callback is not ' + 'supported when using DistributionStrategy. Setting ' + '`histogram_freq` to `0`.')) + callback.histogram_freq = 0 if callback.__getattribute__('write_grads'): - raise ValueError('write_grads in the TensorBoard callback is not ' - 'supported when using DistributionStrategy.') + logging.warning( + UserWarning( + '`write_grads` in the TensorBoard callback is not supported ' + 'when using DistributionStrategy. Setting `write_grads` ' + 'to `False`.')) + callback.histogram_freq = False def validate_distributed_dataset_inputs(distribution_strategy, x, y, -- GitLab From b659523f8acec043478ee7378dd17c8df4c132e2 Mon Sep 17 00:00:00 2001 From: Russell Power Date: Fri, 7 Dec 2018 16:48:22 -0800 Subject: [PATCH 246/873] Use parent sessions configuration to ensure we connect to the same set of workers. PiperOrigin-RevId: 224599211 --- .../contrib/tpu/python/tpu/session_support.py | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/session_support.py b/tensorflow/contrib/tpu/python/tpu/session_support.py index a952754878..3e463823c8 100644 --- a/tensorflow/contrib/tpu/python/tpu/session_support.py +++ b/tensorflow/contrib/tpu/python/tpu/session_support.py @@ -43,12 +43,19 @@ class CoordinatorShutdownException(Exception): pass +def _clone_session(session, graph=None): + return session_lib.Session( + target=session.sess_str, + config=session._config, # pylint: disable=protected-access + graph=graph if graph else session.graph) + + def _make_heartbeat_op(session, device, request_ph): """Return a heartbeat op or None if heartbeats are not supported by device.""" try: # Test if we can connect in a isolated graph + session with ops.Graph().as_default(): - with session_lib.Session(target=session.sess_str) as temp_session: + with _clone_session(session) as temp_session: with ops.device(device): heartbeat_op = tpu_ops.worker_heartbeat('') options = config_pb2.RunOptions(timeout_in_ms=5000) @@ -220,6 +227,7 @@ class WatchdogManager(threading.Thread): self.ping_interval = ping_interval self.shutdown_timeout = shutdown_timeout self.daemon = True + self._config = session._config # pylint: disable=protected-access self._target = session.sess_str self._running = False self._devices = devices @@ -234,6 +242,7 @@ class WatchdogManager(threading.Thread): self._session = session_lib.Session( target=self._target, graph=self._graph, + config=self._config, ) if self._devices is None: @@ -334,8 +343,7 @@ class GracefulShutdownHook(session_run_hook.SessionRunHook): with self._graph.as_default(): logging.info('Installing graceful shutdown hook.') - self._session = session_lib.Session( - target=training_session.sess_str, graph=self._graph) + self._session = _clone_session(training_session, self._graph) self._workers = WorkerHeartbeatManager.from_devices( self._session, all_worker_devices(self._session)) self._heartbeat_supported = self._workers.num_workers() > 0 -- GitLab From 8e1eb82d53c1d60936910847bd38844227c0bd3b Mon Sep 17 00:00:00 2001 From: Anna R Date: Fri, 7 Dec 2018 16:49:43 -0800 Subject: [PATCH 247/873] Split keyword argument test in tf_upgrade_v2_test.py into 2 tests. Extend one of the tests to run API conversion for all TF functions instead of just the ones updated by tf_upgrade_v2 script. Fix issues found by this extended test. PiperOrigin-RevId: 224599358 --- tensorflow/python/ops/image_ops_impl.py | 5 +- tensorflow/python/ops/sparse_ops.py | 10 +- tensorflow/tools/compatibility/reorders_v2.py | 7 + .../tools/compatibility/tf_upgrade_v2.py | 115 ++++++++++++- .../tools/compatibility/tf_upgrade_v2_test.py | 151 +++++++++++++++--- 5 files changed, 258 insertions(+), 30 deletions(-) diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py index 229393c970..24d049b726 100644 --- a/tensorflow/python/ops/image_ops_impl.py +++ b/tensorflow/python/ops/image_ops_impl.py @@ -2046,9 +2046,8 @@ def sample_distorted_bounding_box_v2(image_size, 3-D with shape `[batch, N, 4]` describing the N bounding boxes associated with the image. seed: An optional `int`. Defaults to `0`. - 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 `seed` is set to non-zero, the random number generator is seeded by + the given `seed`. Otherwise, it is seeded by a random seed. min_object_covered: A Tensor of type `float32`. Defaults to `0.1`. The cropped area of the image must contain at least this fraction of any bounding box supplied. The value of this parameter should diff --git a/tensorflow/python/ops/sparse_ops.py b/tensorflow/python/ops/sparse_ops.py index d7346b7371..097b485a11 100644 --- a/tensorflow/python/ops/sparse_ops.py +++ b/tensorflow/python/ops/sparse_ops.py @@ -1093,6 +1093,9 @@ def sparse_reduce_max_v2( @deprecation.deprecated_endpoints("sparse_reduce_max") @deprecation.deprecated_args( None, "keep_dims is deprecated, use keepdims instead", "keep_dims") +@deprecation.deprecated_args( + None, "reduction_axes is deprecated, use axis instead", + "reduction_axes") def sparse_reduce_max(sp_input, axis=None, keepdims=None, reduction_axes=None, keep_dims=None): """Computes the max of elements across dimensions of a SparseTensor. @@ -1141,7 +1144,7 @@ def sparse_reduce_max(sp_input, axis=None, keepdims=None, axis: The dimensions to reduce; list or scalar. If `None` (the default), reduces all dimensions. keepdims: If true, retain reduced dimensions with length 1. - reduction_axes: Deprecated name of axis. + reduction_axes: Deprecated name of `axis`. keep_dims: Deprecated alias for `keepdims`. Returns: @@ -1279,6 +1282,9 @@ def sparse_reduce_sum_v2( @deprecation.deprecated_endpoints("sparse_reduce_sum") @deprecation.deprecated_args( None, "keep_dims is deprecated, use keepdims instead", "keep_dims") +@deprecation.deprecated_args( + None, "reduction_axes is deprecated, use axis instead", + "reduction_axes") def sparse_reduce_sum(sp_input, axis=None, keepdims=None, reduction_axes=None, keep_dims=None): """Computes the sum of elements across dimensions of a SparseTensor. @@ -1314,7 +1320,7 @@ def sparse_reduce_sum(sp_input, axis=None, keepdims=None, axis: The dimensions to reduce; list or scalar. If `None` (the default), reduces all dimensions. keepdims: If true, retain reduced dimensions with length 1. - reduction_axes: Deprecated name of axis. + reduction_axes: Deprecated name of `axis`. keep_dims: Deprecated alias for `keepdims`. Returns: diff --git a/tensorflow/tools/compatibility/reorders_v2.py b/tensorflow/tools/compatibility/reorders_v2.py index e6596755c3..44494ac148 100644 --- a/tensorflow/tools/compatibility/reorders_v2.py +++ b/tensorflow/tools/compatibility/reorders_v2.py @@ -40,6 +40,7 @@ reorders = { 'tf.io.parse_single_example': ['serialized', 'features', 'name', 'example_names'], 'tf.io.serialize_many_sparse': ['sp_input', 'name', 'out_type'], 'tf.io.serialize_sparse': ['sp_input', 'name', 'out_type'], + 'tf.linalg.norm': ['tensor', 'ord', 'axis', 'keepdims', 'name', 'keep_dims'], 'tf.math.argmax': ['input', 'axis', 'name', 'dimension', 'output_type'], 'tf.math.argmin': ['input', 'axis', 'name', 'dimension', 'output_type'], 'tf.math.confusion_matrix': ['labels', 'predictions', 'num_classes', 'dtype', 'name', 'weights'], @@ -66,6 +67,7 @@ reorders = { 'tf.nn.space_to_batch': ['input', 'paddings', 'block_size', 'name'], 'tf.nn.space_to_depth': ['input', 'block_size', 'name', 'data_format'], 'tf.nn.weighted_moments': ['x', 'axes', 'frequency_weights', 'name', 'keep_dims'], + 'tf.norm': ['tensor', 'ord', 'axis', 'keepdims', 'name', 'keep_dims'], 'tf.pad': ['tensor', 'paddings', 'mode', 'name', 'constant_values'], 'tf.parse_example': ['serialized', 'features', 'name', 'example_names'], 'tf.parse_single_example': ['serialized', 'features', 'name', 'example_names'], @@ -82,6 +84,7 @@ reorders = { 'tf.reduce_min': ['input_tensor', 'axis', 'keepdims', 'name', 'reduction_indices', 'keep_dims'], 'tf.reduce_prod': ['input_tensor', 'axis', 'keepdims', 'name', 'reduction_indices', 'keep_dims'], 'tf.reduce_sum': ['input_tensor', 'axis', 'keepdims', 'name', 'reduction_indices', 'keep_dims'], + 'tf.reverse_sequence': ['input', 'seq_lengths', 'seq_axis', 'batch_axis', 'name', 'seq_dim', 'batch_dim'], 'tf.serialize_many_sparse': ['sp_input', 'name', 'out_type'], 'tf.serialize_sparse': ['sp_input', 'name', 'out_type'], 'tf.shape': ['input', 'name', 'out_type'], @@ -90,15 +93,19 @@ reorders = { 'tf.space_to_depth': ['input', 'block_size', 'name', 'data_format'], 'tf.sparse.add': ['a', 'b', 'threshold', 'thresh'], 'tf.sparse.concat': ['axis', 'sp_inputs', 'name', 'expand_nonconcat_dim', 'concat_dim'], + 'tf.sparse.reduce_max': ['sp_input', 'axis', 'keepdims', 'reduction_axes', 'keep_dims'], 'tf.sparse.segment_mean': ['data', 'indices', 'segment_ids', 'name', 'num_segments'], 'tf.sparse.segment_sqrt_n': ['data', 'indices', 'segment_ids', 'name', 'num_segments'], 'tf.sparse.segment_sum': ['data', 'indices', 'segment_ids', 'name', 'num_segments'], + 'tf.sparse.split': ['keyword_required', 'sp_input', 'num_split', 'axis', 'name', 'split_dim'], 'tf.sparse_add': ['a', 'b', 'threshold', 'thresh'], 'tf.sparse_concat': ['axis', 'sp_inputs', 'name', 'expand_nonconcat_dim', 'concat_dim'], 'tf.sparse_matmul': ['a', 'b', 'transpose_a', 'transpose_b', 'a_is_sparse', 'b_is_sparse', 'name'], + 'tf.sparse_reduce_max': ['sp_input', 'axis', 'keepdims', 'reduction_axes', 'keep_dims'], 'tf.sparse_segment_mean': ['data', 'indices', 'segment_ids', 'name', 'num_segments'], 'tf.sparse_segment_sqrt_n': ['data', 'indices', 'segment_ids', 'name', 'num_segments'], 'tf.sparse_segment_sum': ['data', 'indices', 'segment_ids', 'name', 'num_segments'], + 'tf.sparse_split': ['keyword_required', 'sp_input', 'num_split', 'axis', 'name', 'split_dim'], 'tf.strings.length': ['input', 'name', 'unit'], 'tf.strings.reduce_join': ['inputs', 'axis', 'keep_dims', 'separator', 'name', 'reduction_indices'], 'tf.strings.substr': ['input', 'pos', 'len', 'name', 'unit'], diff --git a/tensorflow/tools/compatibility/tf_upgrade_v2.py b/tensorflow/tools/compatibility/tf_upgrade_v2.py index b2477a541e..ea86da42f6 100644 --- a/tensorflow/tools/compatibility/tf_upgrade_v2.py +++ b/tensorflow/tools/compatibility/tf_upgrade_v2.py @@ -36,6 +36,18 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec): "tf.argmax": { "dimension": "axis", }, + "tf.arg_min": { + "dimension": "axis", + }, + "tf.arg_max": { + "dimension": "axis", + }, + "tf.math.argmin": { + "dimension": "axis", + }, + "tf.math.argmax": { + "dimension": "axis", + }, "tf.image.crop_and_resize": { "box_ind": "box_indices", }, @@ -54,6 +66,9 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec): "tf.space_to_batch": { "block_size": "block_shape", }, + "tf.nn.space_to_batch": { + "block_size": "block_shape", + }, "tf.constant": { "verify_shape": "verify_shape_is_now_always_true", }, @@ -66,6 +81,15 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec): "tf.linalg.l2_normalize": { "dim": "axis", }, + "tf.linalg.norm": { + "keep_dims": "keepdims", + }, + "tf.norm": { + "keep_dims": "keepdims", + }, + "tf.load_file_system_library": { + "library_filename": "library_location", + }, "tf.math.count_nonzero": { "input_tensor": "input", "keep_dims": "keepdims", @@ -99,6 +123,9 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec): "tf.nn.separable_conv2d": { "rate": "dilations" }, + "tf.nn.depthwise_conv2d": { + "rate": "dilations" + }, "tf.nn.softmax": { "dim": "axis" }, @@ -117,14 +144,35 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec): }, "tf.sparse.concat": { "concat_dim": "axis", + "expand_nonconcat_dim": "expand_nonconcat_dims", }, "tf.sparse_concat": { "concat_dim": "axis", + "expand_nonconcat_dim": "expand_nonconcat_dims", }, "tf.sparse.split": { "split_dim": "axis", }, - "tf.max_pool_with_argmax": { + "tf.sparse_split": { + "split_dim": "axis", + }, + "tf.sparse.reduce_max": { + "reduction_axes": "axis", + "keep_dims": "keepdims", + }, + "tf.sparse_reduce_max": { + "reduction_axes": "axis", + "keep_dims": "keepdims", + }, + "tf.sparse.reduce_sum": { + "reduction_axes": "axis", + "keep_dims": "keepdims", + }, + "tf.sparse_reduce_sum": { + "reduction_axes": "axis", + "keep_dims": "keepdims", + }, + "tf.nn.max_pool_with_argmax": { "Targmax": "output_dtype", }, "tf.multinomial": { @@ -133,6 +181,10 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec): "tf.random.multinomial": { "output_dtype": "dtype", }, + "tf.reverse_sequence": { + "seq_dim": "seq_axis", + "batch_dim": "batch_axis", + }, "tf.nn.batch_norm_with_global_normalization": { "t": "input", "m": "mean", @@ -151,6 +203,10 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec): "tf.ones_like": { "tensor": "input", }, + "tf.nn.conv2d_transpose": { + "value": "input", + "filter": "filters", + }, "tf.nn.conv3d_transpose": { "value": "input", "filter": "filters", @@ -287,6 +343,9 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec): "tf.squeeze": { "squeeze_dims": "axis", }, + "tf.nn.weighted_moments": { + "keep_dims": "keepdims" + }, } # pylint: disable=line-too-long @@ -427,6 +486,10 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec): "tf.sparse.split", "tf.sparse_matmul": "tf.linalg.matmul", + "tf.sparse_reduce_sum": + "tf.sparse.reduce_sum", + "tf.sparse_reduce_max": + "tf.sparse.reduce_max", "tf.random.stateless_multinomial": "tf.random.stateless_categorical", "tf.string_to_hash_bucket": @@ -481,6 +544,10 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec): "tf.argmax", "tf.arg_min": "tf.argmin", + # tf.nn.ctc_loss is still available in 2.0 but behavior + # changed significantly. + "tf.nn.ctc_loss": + "tf.compat.v1.nn.ctc_loss", } # pylint: enable=line-too-long @@ -530,6 +597,8 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec): "tf.sparse.segment_sqrt_n", "tf.sparse.segment_sum", "tf.sparse_matmul", + "tf.sparse.reduce_max", + "tf.sparse_reduce_max", "tf.io.decode_csv", "tf.strings.substr", "tf.strings.reduce_join", @@ -566,6 +635,10 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec): "tf.nn.embedding_lookup_sparse", "tf.nn.in_top_k", "tf.nn.space_to_depth", + "tf.linalg.norm", + "tf.norm", + "tf.reverse_sequence", + "tf.sparse_split", } # Functions that were reordered should be changed to the new keyword args @@ -634,6 +707,9 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec): assert_return_type_comment, "tf.assert_rank": assert_rank_comment, + "tf.cond": "tf.cond no longer takes 'strict'. " + "Now 'strict' defaults to True." + "fn1/fn2 arguments are replaced by true_fn/false_fn.", "tf.debugging.assert_equal": assert_return_type_comment, "tf.debugging.assert_greater": @@ -664,6 +740,9 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec): assert_rank_comment, "tf.debugging.assert_rank_in": assert_rank_comment, + "tf.device": "tf.device no longer takes function as an argument. " + "'devide_name_or_function' argument has been renamed to " + "'device_name'.", "tf.flags": "tf.flags has been removed, please use the argparse or absl" " module if you need command line parsing.", @@ -701,6 +780,10 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec): default_loss_reduction_changed, "tf.estimator.BaselineRegressor": default_loss_reduction_changed, + "tf.hessians": "tf.hessians no longer takes " + "'colocate_gradients_with_ops' argument. Also, " + "arguments have been reordered so that 'name' is the " + "last argument.", "tf.nn.conv1d": "WARNING: use_cudnn_on_gpu argument has been removed and \"value\"" " was renamed to \"input\"", @@ -727,9 +810,33 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec): tf_01s_like_no_optimize_comment, "tf.nn.embedding_lookup": "WARNING: validate_indices argument has been removed.", - "tf.sparse.concat": - ("WARNING: expand_nonconcat_dim was renamed to " - "expand_nonconcat_dims."), + "tf.while_loop": + "tf.while_loop no longer takes 'return_same_structure' argument. " + "'return_same_structure' now defaults to True. Also, 'name'" + "argument is now the last argument.", + "tf.image.sample_distorted_bounding_box": + "tf.image.sample_distorted_bounding_box no longer takes 'seed2' " + "argument.", + "tf.nn.ctc_beam_search_decoder": + "tf.nn.ctc_beam_search_decoder no longer takes 'merge_repeated' " + "argument. 'merge_repeated' now defaults to False.", + "tf.nn.fractional_avg_pool": + "tf.nn.fractional_avg_pool no longer takes 'seed2' and " + "'deterministic' arguments. Now it takes a single 'seed' arg. If " + "'seed' is zero, the execution is random and deterministic " + "otherwise", + "tf.nn.fractional_max_pool": + "tf.nn.fractional_max_pool no longer takes 'seed2' and " + "'deterministic' arguments. Now it takes a single 'seed' arg. If " + "'seed' is zero, the execution is random and deterministic " + "otherwise", + "tf.nn.softmax_cross_entropy_with_logits": + "tf.nn.softmax_cross_entropy_with_logits behavior has changed. " + "'labels' needs to be wrapped with tf.stop_gradient to keep the " + "old behavior. Also, 'dim' argument has been renamed to 'axis'.", + "tf.test.assert_equal_graph_def": + "tf.assert_equal_graph_def no longer takes 'checkpoint_v2' " + "argument. 'checkpoint_v2' now defaults to True.", } self.symbol_renames = { diff --git a/tensorflow/tools/compatibility/tf_upgrade_v2_test.py b/tensorflow/tools/compatibility/tf_upgrade_v2_test.py index 4460ad5182..63aa5f0c6b 100644 --- a/tensorflow/tools/compatibility/tf_upgrade_v2_test.py +++ b/tensorflow/tools/compatibility/tf_upgrade_v2_test.py @@ -18,6 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import inspect import os import tempfile @@ -25,7 +26,6 @@ import six import tensorflow as tf # OSS TF V2 import placeholder. - from tensorflow.python.framework import test_util from tensorflow.python.platform import test as test_lib from tensorflow.python.util import tf_decorator @@ -72,6 +72,15 @@ def get_symbol_for_name(root, name): return symbol +def get_args(symbol): + if hasattr(inspect, "signature"): + signature = inspect.signature(symbol) + # Ignore *args and **kwargs for now. + return [param.name for param in signature.parameters.values() + if param.kind == param.POSITIONAL_OR_KEYWORD] + return tf_inspect.getargspec(symbol)[0] + + def get_func_and_args_from_str(call_str): """Parse call string to get function and argument names. @@ -88,6 +97,7 @@ def get_func_and_args_from_str(call_str): function_name = call_str[:call_str.find("(")] args = call_str[open_paren_index+1:close_paren_index].split(",") args = [arg.split("=")[0].strip() for arg in args] + args = [arg for arg in args if arg] # filter out empty strings return function_name, args @@ -200,17 +210,12 @@ class TestUpgrade(test_util.TensorFlowTestCase): collect = False traverse.traverse(tf.compat.v1, visitor) - def testKeywordArgNames(self): - if not hasattr(tf.compat, "v2"): - return - + def testV1KeywordArgNames(self): all_keyword_renames = ( tf_upgrade_v2.TFAPIChangeSpec().function_keyword_renames) - v2_name_exceptions = {"verify_shape_is_now_always_true"} - # Visitor that verifies V1 argument names, converts to V2 and checks - # V2 argument names. - def conversion_visitor(unused_path, unused_parent, children): + # Visitor that verifies V1 argument names. + def arg_test_visitor(unused_path, unused_parent, children): for child in children: _, attr = tf_decorator.unwrap(child[1]) names_v1 = get_v1_names(attr) @@ -230,25 +235,77 @@ class TestUpgrade(test_util.TensorFlowTestCase): "%s not found in %s arguments: %s" % (from_name, name, str(arg_names_v1))) + visitor = public_api.PublicAPIVisitor(arg_test_visitor) + visitor.do_not_descend_map["tf"].append("contrib") + visitor.private_map["tf.compat"] = ["v1", "v2"] + traverse.traverse(tf.compat.v1, visitor) + + def testV2KeywordArgNames(self): + # This test converts a call of the form: + # tf.foo(arg1=0, arg2=1, ...) + # to 2.0. Then, checks that converted function has valid argument names. + if not hasattr(tf.compat, "v2"): + return + v2_arg_exceptions = { + "verify_shape_is_now_always_true", + # These arguments should not be used, they just specify + # that a function takes named arguments. + "keyword_required", + "_sentinel", + } + v1_name_exceptions = { + "tf.print", # requires print_function import + } + function_warnings = ( + tf_upgrade_v2.TFAPIChangeSpec().function_warnings) + function_handles = ( + tf_upgrade_v2.TFAPIChangeSpec().function_handle) + keyword_renames = ( + tf_upgrade_v2.TFAPIChangeSpec().function_keyword_renames) + + # Visitor that converts to V2 and checks V2 argument names. + def conversion_visitor(unused_path, unused_parent, children): + for child in children: + _, attr = tf_decorator.unwrap(child[1]) + if not tf_inspect.isfunction(attr): + continue + names_v1 = get_v1_names(attr) + arg_names_v1 = get_args(attr) + + for name in names_v1: + tf_name = "tf.%s" % name + if tf_name in function_warnings or tf_name in function_handles: + continue # These require manual change + if tf_name in v1_name_exceptions: + continue # Assert that arg names after converting to v2 are present in # v2 function. # 1. First, create an input of the form: # tf.foo(arg1=val1, arg2=val2, ...) args = ",".join( ["%s=%d" % (from_name, from_index) - for from_index, from_name in enumerate(keyword_renames.keys())]) - text_input = "%s(%s)" % (name, args) + for from_index, from_name in enumerate(arg_names_v1)]) + text_input = "%s(%s)" % (tf_name, args) # 2. Convert the input to V2. _, _, _, text = self._upgrade(text_input) new_function_name, new_args = get_func_and_args_from_str(text) + if new_function_name == "tf.compat.v1.%s" % name: + if tf_name in keyword_renames: + # If we rename arguments, new function must be available in 2.0. + # We should not be using compat.v1 in this case. + self.assertFalse( + "Function '%s' is not in 2.0 when converting\n%s\nto\n%s" % + (new_function_name, text_input, text)) + continue # 3. Verify V2 function and arguments. - # Note: If we rename arguments, new function must be available in 2.0. - # We should not be using compat.v1 in this case. - self.assertIn(new_function_name, self.v2_symbols) - args_v2 = tf_inspect.getargspec(self.v2_symbols[new_function_name])[0] - args_v2.extend(v2_name_exceptions) + args_v2 = get_args(self.v2_symbols[new_function_name]) + args_v2.extend(v2_arg_exceptions) for new_arg in new_args: - self.assertIn(new_arg, args_v2) + self.assertIn( + new_arg, args_v2, + "Invalid argument '%s' in 2.0 when converting\n%s\nto\n%s.\n" + "Supported arguments: %s" % ( + new_arg, text_input, text, str(args_v2))) visitor = public_api.PublicAPIVisitor(conversion_visitor) visitor.do_not_descend_map["tf"].append("contrib") @@ -526,9 +583,24 @@ bazel-bin/tensorflow/tools/compatibility/update/generate_v2_reorders_map text = "tf.nn.softmax_cross_entropy_with_logits_v2(labels, logits, dim=2)" expected_text = ( "tf.nn.softmax_cross_entropy_with_logits(labels, logits, axis=2)") - _, unused_report, unused_errors, new_text = self._upgrade(text) + _, unused_report, errors, new_text = self._upgrade(text) self.assertEqual(new_text, expected_text) + self.assertFalse(errors) + + def testSoftMaxCrossEntropyWithLogits(self): + text = "tf.nn.softmax_cross_entropy_with_logits(labels, logits, dim=2)" + expected_text = ( + "tf.nn.softmax_cross_entropy_with_logits(labels, logits, dim=2)") + _, report, errors, new_text = self._upgrade(text) + self.assertEqual(new_text, expected_text) + self.assertIn( + "tf.nn.softmax_cross_entropy_with_logits requires manual check.", + errors[0]) + self.assertIn( + "tf.nn.softmax_cross_entropy_with_logits behavior has changed. ", + report) + def testSparseMatmul(self): text = ("tf.sparse_matmul(a, b, c, d, e, f, g)\n") expected_text = ("tf.linalg.matmul(a=a, b=b, transpose_a=c, transpose_b=d, " @@ -540,7 +612,7 @@ bazel-bin/tensorflow/tools/compatibility/update/generate_v2_reorders_map text = "tf.nn.weighted_moments(x, axes, freq, name, kd)" expected_text = ( "tf.nn.weighted_moments(x=x, axes=axes, frequency_weights=freq, " - "name=name, keep_dims=kd)") + "name=name, keepdims=kd)") _, unused_report, unused_errors, new_text = self._upgrade(text) self.assertEqual(new_text, expected_text) @@ -554,7 +626,7 @@ bazel-bin/tensorflow/tools/compatibility/update/generate_v2_reorders_map text = "tf.sparse.concat(ax, inp, name, exp, concat)" expected_text = ( "tf.sparse.concat(axis=ax, sp_inputs=inp, name=name, " - "expand_nonconcat_dim=exp, axis=concat)") + "expand_nonconcat_dims=exp, axis=concat)") _, unused_report, unused_errors, new_text = self._upgrade(text) self.assertEqual(new_text, expected_text) @@ -576,7 +648,7 @@ bazel-bin/tensorflow/tools/compatibility/update/generate_v2_reorders_map text = "tf.nn.space_to_batch(input, paddings, block_size, name)" expected_text = ( "tf.space_to_batch(input=input, paddings=paddings, " - "block_size=block_size, name=name)") + "block_shape=block_size, name=name)") _, unused_report, unused_errors, new_text = self._upgrade(text) self.assertEqual(new_text, expected_text) @@ -628,6 +700,43 @@ bazel-bin/tensorflow/tools/compatibility/update/generate_v2_reorders_map _, unused_report, unused_errors, new_text = self._upgrade(text) self.assertEqual(new_text, expected_text) + def testPrint(self): + # tf.print() cannot be parsed unless we import print_function + text = """from __future__ import print_function +tf.print() +tf.print('abc') +""" + _, unused_report, unused_errors, new_text = self._upgrade(text) + self.assertEqual(new_text, text) # Text should stay the same + + def testSparseSplit(self): + text = ( + "tf.sparse_split(sp_input=sp_input, num_split=num_split, axis=axis, " + "name=name)") + expected_text = ( + "tf.sparse.split(sp_input=sp_input, num_split=num_split, axis=axis, " + "name=name)") + _, unused_report, unused_errors, new_text = self._upgrade(text) + self.assertEqual(new_text, expected_text) + + text = ( + "tf.sparse_split(sp_input=sp_input, num_split=num_split, " + "name=name, split_dim=axis)") + expected_text = ( + "tf.sparse.split(sp_input=sp_input, num_split=num_split, " + "name=name, axis=axis)") + _, unused_report, unused_errors, new_text = self._upgrade(text) + self.assertEqual(new_text, expected_text) + + text = ( + "tf.sparse.split(sp_input=sp_input, num_split=num_split, " + "name=name, split_dim=axis)") + expected_text = ( + "tf.sparse.split(sp_input=sp_input, num_split=num_split, " + "name=name, axis=axis)") + _, unused_report, unused_errors, new_text = self._upgrade(text) + self.assertEqual(new_text, expected_text) + class TestUpgradeFiles(test_util.TensorFlowTestCase): -- GitLab From 623cc7e829568118683070d761c21823c7038338 Mon Sep 17 00:00:00 2001 From: Rachel Lim Date: Fri, 7 Dec 2018 16:54:42 -0800 Subject: [PATCH 248/873] [tf.data] Enable `map_and_batch_fusion` optimization PiperOrigin-RevId: 224599990 --- .../benchmarks/map_and_batch_benchmark.py | 43 ++++++++++--------- .../benchmarks/map_vectorization_benchmark.py | 21 ++++++--- .../optimization/map_and_batch_fusion_test.py | 5 --- .../optimization/map_vectorization_test.py | 19 +++++--- .../optimization/optimize_dataset_test.py | 5 ++- .../dataset_serialization_test_base.py | 33 ++++++++++---- .../experimental/ops/optimization_options.py | 2 +- 7 files changed, 81 insertions(+), 47 deletions(-) diff --git a/tensorflow/python/data/experimental/benchmarks/map_and_batch_benchmark.py b/tensorflow/python/data/experimental/benchmarks/map_and_batch_benchmark.py index fbd06a5a78..b17f2bcd12 100644 --- a/tensorflow/python/data/experimental/benchmarks/map_and_batch_benchmark.py +++ b/tensorflow/python/data/experimental/benchmarks/map_and_batch_benchmark.py @@ -26,6 +26,7 @@ import numpy as np from tensorflow.core.protobuf import config_pb2 from tensorflow.python.client import session from tensorflow.python.data.experimental.ops import batching +from tensorflow.python.data.experimental.ops.optimization_options import OptimizationOptions from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes @@ -139,47 +140,49 @@ class MapAndBatchBenchmark(test.Benchmark): num_iters = 1024 // ( (element_size * batch_size) // min(num_calls, inter_op)) - dataset = make_base_dataset(element_size) - chained_dataset = dataset.map( + fused_dataset = make_base_dataset(element_size) + fused_dataset = fused_dataset.map( math_ops.matmul, num_parallel_calls=num_calls).batch(batch_size=batch_size) - chained_iterator = dataset_ops.make_one_shot_iterator(chained_dataset) - chained_get_next = chained_iterator.get_next() - chained_deltas = [] + fused_iterator = dataset_ops.make_one_shot_iterator(fused_dataset) + fused_get_next = fused_iterator.get_next() + + fused_deltas = [] with session.Session( config=config_pb2.ConfigProto( inter_op_parallelism_threads=inter_op, use_per_session_threads=True)) as sess: + for _ in range(5): - sess.run(chained_get_next.op) + sess.run(fused_get_next.op) for _ in range(num_iters): start = time.time() - sess.run(chained_get_next.op) + sess.run(fused_get_next.op) end = time.time() - chained_deltas.append(end - start) + fused_deltas.append(end - start) - fused_dataset = dataset.apply( - batching.map_and_batch( - math_ops.matmul, - num_parallel_calls=num_calls, - batch_size=batch_size)) - fused_iterator = dataset_ops.make_one_shot_iterator(fused_dataset) - fused_get_next = fused_iterator.get_next() + # `map_and_batch_fusion` is optimized by default. To get the chained + # dataset, with have to disable it. + options = dataset_ops.Options() + options.experimental_optimization = OptimizationOptions() + options.experimental_optimization.map_and_batch_fusion = False + chained_dataset = fused_dataset.with_options(options) + chained_iterator = dataset_ops.make_one_shot_iterator(chained_dataset) + chained_get_next = chained_iterator.get_next() - fused_deltas = [] + chained_deltas = [] with session.Session( config=config_pb2.ConfigProto( inter_op_parallelism_threads=inter_op, use_per_session_threads=True)) as sess: - for _ in range(5): - sess.run(fused_get_next.op) + sess.run(chained_get_next.op) for _ in range(num_iters): start = time.time() - sess.run(fused_get_next.op) + sess.run(chained_get_next.op) end = time.time() - fused_deltas.append(end - start) + chained_deltas.append(end - start) print( "batch size: %d, num parallel calls: %d, inter-op parallelism: %d, " diff --git a/tensorflow/python/data/experimental/benchmarks/map_vectorization_benchmark.py b/tensorflow/python/data/experimental/benchmarks/map_vectorization_benchmark.py index 47ec6391f7..a60ba0a857 100644 --- a/tensorflow/python/data/experimental/benchmarks/map_vectorization_benchmark.py +++ b/tensorflow/python/data/experimental/benchmarks/map_vectorization_benchmark.py @@ -24,6 +24,7 @@ import numpy as np from tensorflow.core.example import example_pb2 from tensorflow.core.example import feature_pb2 from tensorflow.python.client import session +from tensorflow.python.data.experimental.ops import optimization_options from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.util import nest from tensorflow.python.framework import constant_op @@ -115,13 +116,23 @@ class MapVectorizationBenchmark(test.Benchmark): def _compare(self, input_dataset, map_fn, batch_size, input_size, str_id): num_elems = int(np.sum([np.prod(x) for x in input_size])) name_template = "{}__batch_size_{}_input_element_size_{}_{}" - unoptimized = input_dataset.map(map_fn).batch(batch_size) - unoptimized_op = dataset_ops.make_one_shot_iterator(unoptimized).get_next() - optimized = input_dataset.map(map_fn).batch(batch_size) + base_dataset = input_dataset.map(map_fn).batch(batch_size) + options = dataset_ops.Options() - options.experimental_map_vectorization = True - optimized = optimized.with_options(options) + opt_options = optimization_options.OptimizationOptions() + # Disable default map_and_batch_fusion optimization + opt_options.map_and_batch_fusion = False + options.experimental_optimization = opt_options + base_dataset = base_dataset.with_options(options) + + unoptimized_op = dataset_ops.make_one_shot_iterator(base_dataset).get_next() + + optimized_options = dataset_ops.Options() + opt_options = optimization_options.OptimizationOptions() + opt_options.map_vectorization = True + optimized_options.experimental_optimization = opt_options + optimized = base_dataset.with_options(optimized_options) optimized_op = dataset_ops.make_one_shot_iterator(optimized).get_next() unoptimized_time = self._run( diff --git a/tensorflow/python/data/experimental/kernel_tests/optimization/map_and_batch_fusion_test.py b/tensorflow/python/data/experimental/kernel_tests/optimization/map_and_batch_fusion_test.py index 801f664f09..e2ff3116ec 100644 --- a/tensorflow/python/data/experimental/kernel_tests/optimization/map_and_batch_fusion_test.py +++ b/tensorflow/python/data/experimental/kernel_tests/optimization/map_and_batch_fusion_test.py @@ -18,7 +18,6 @@ from __future__ import division from __future__ import print_function from tensorflow.python.data.experimental.ops import optimization -from tensorflow.python.data.experimental.ops.optimization_options import OptimizationOptions from tensorflow.python.data.kernel_tests import test_base from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import test_util @@ -32,10 +31,6 @@ class MapAndBatchFusionTest(test_base.DatasetTestBase): dataset = dataset_ops.Dataset.range(10).apply( optimization.assert_next( ["MapAndBatch"])).map(lambda x: x * x).batch(10) - options = dataset_ops.Options() - options.experimental_optimization = OptimizationOptions() - options.experimental_optimization.map_and_batch_fusion = True - dataset = dataset.with_options(options) self.assertDatasetProduces( dataset, expected_output=[[x * x for x in range(10)]]) diff --git a/tensorflow/python/data/experimental/kernel_tests/optimization/map_vectorization_test.py b/tensorflow/python/data/experimental/kernel_tests/optimization/map_vectorization_test.py index c2e08e2cd8..adc411bfb5 100644 --- a/tensorflow/python/data/experimental/kernel_tests/optimization/map_vectorization_test.py +++ b/tensorflow/python/data/experimental/kernel_tests/optimization/map_vectorization_test.py @@ -23,7 +23,7 @@ import numpy as np from tensorflow.core.example import example_pb2 from tensorflow.core.example import feature_pb2 from tensorflow.python.data.experimental.ops import optimization -from tensorflow.python.data.experimental.ops.optimization_options import OptimizationOptions +from tensorflow.python.data.experimental.ops import optimization_options from tensorflow.python.data.kernel_tests import test_base from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import constant_op @@ -344,18 +344,25 @@ class MapVectorizationTest(test_base.DatasetTestBase, parameterized.TestCase): Tuple of (unoptimized dataset, optimized dataset). """ map_node_name = "Map" if num_parallel_calls is None else "ParallelMap" - batch_size = 100 def _make_dataset(node_names): - return base_dataset.apply(optimization.assert_next(node_names)).map( - map_fn, num_parallel_calls=num_parallel_calls).batch(batch_size) + dataset = base_dataset.apply(optimization.assert_next(node_names)) + dataset = dataset.map(map_fn, num_parallel_calls) + dataset = dataset.batch(100) + options = dataset_ops.Options() + opt_options = optimization_options.OptimizationOptions() + opt_options.map_and_batch_fusion = False + options.experimental_optimization = opt_options + dataset = dataset.with_options(options) + return dataset unoptimized = _make_dataset([map_node_name, "Batch"]) optimized = _make_dataset(["Batch", map_node_name] if expect_optimized else [map_node_name, "Batch"]) options = dataset_ops.Options() - options.experimental_optimization = OptimizationOptions() - options.experimental_optimization.map_vectorization = True + opt_options = optimization_options.OptimizationOptions() + opt_options.map_vectorization = True + options.experimental_optimization = opt_options optimized = optimized.with_options(options) return unoptimized, optimized diff --git a/tensorflow/python/data/experimental/kernel_tests/optimization/optimize_dataset_test.py b/tensorflow/python/data/experimental/kernel_tests/optimization/optimize_dataset_test.py index 150cc7b5e4..05d88eb071 100644 --- a/tensorflow/python/data/experimental/kernel_tests/optimization/optimize_dataset_test.py +++ b/tensorflow/python/data/experimental/kernel_tests/optimization/optimize_dataset_test.py @@ -256,8 +256,9 @@ class OptimizeDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): def testOptimizationEnabledByDefault(self): """Tests that some optimizations are applied to datasets by default.""" options = dataset_ops.Options() - expected_optimizations = ["noop_elimination"] - self.assertEqual(options._static_optimizations(), expected_optimizations) + expected_optimizations = ["noop_elimination", "map_and_batch_fusion"] + self.assertEqual( + set(options._static_optimizations()), set(expected_optimizations)) def testOptimizationDisableDefault(self): """Tests that we can disable all static optimizations enabled by default. diff --git a/tensorflow/python/data/experimental/kernel_tests/serialization/dataset_serialization_test_base.py b/tensorflow/python/data/experimental/kernel_tests/serialization/dataset_serialization_test_base.py index e65aa44d06..bdbd8702b7 100644 --- a/tensorflow/python/data/experimental/kernel_tests/serialization/dataset_serialization_test_base.py +++ b/tensorflow/python/data/experimental/kernel_tests/serialization/dataset_serialization_test_base.py @@ -23,6 +23,7 @@ import os import numpy as np from tensorflow.python.data.experimental.ops import iterator_ops as contrib_iterator_ops +from tensorflow.python.data.experimental.ops.optimization_options import OptimizationOptions from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import iterator_ops from tensorflow.python.framework import dtypes @@ -74,23 +75,39 @@ class DatasetSerializationTestBase(test.TestCase): Raises: AssertionError if any test fails. """ + # NOTE: We disable all default optimizations in serialization tests in order + # to test the actual dataset in question. + options = dataset_ops.Options() + options.experimental_optimization = OptimizationOptions() + options.experimental_optimization.apply_default_optimizations = False + + def ds_fn1_no_opt(): + return ds_fn1().with_options(options) + self.verify_unused_iterator( - ds_fn1, num_outputs, sparse_tensors=sparse_tensors) + ds_fn1_no_opt, num_outputs, sparse_tensors=sparse_tensors) self.verify_fully_used_iterator( - ds_fn1, num_outputs, sparse_tensors=sparse_tensors) + ds_fn1_no_opt, num_outputs, sparse_tensors=sparse_tensors) self.verify_exhausted_iterator( - ds_fn1, num_outputs, sparse_tensors=sparse_tensors) + ds_fn1_no_opt, num_outputs, sparse_tensors=sparse_tensors) self.verify_init_before_restore( - ds_fn1, num_outputs, sparse_tensors=sparse_tensors) + ds_fn1_no_opt, num_outputs, sparse_tensors=sparse_tensors) self.verify_multiple_breaks( - ds_fn1, num_outputs, sparse_tensors=sparse_tensors) + ds_fn1_no_opt, num_outputs, sparse_tensors=sparse_tensors) self.verify_reset_restored_iterator( - ds_fn1, num_outputs, sparse_tensors=sparse_tensors) + ds_fn1_no_opt, num_outputs, sparse_tensors=sparse_tensors) self.verify_restore_in_empty_graph( - ds_fn1, num_outputs, sparse_tensors=sparse_tensors) + ds_fn1_no_opt, num_outputs, sparse_tensors=sparse_tensors) if ds_fn2: + + def ds_fn2_no_opt(): + return ds_fn2().with_options(options) + self.verify_restore_in_modified_graph( - ds_fn1, ds_fn2, num_outputs, sparse_tensors=sparse_tensors) + ds_fn1_no_opt, + ds_fn2_no_opt, + num_outputs, + sparse_tensors=sparse_tensors) def verify_unused_iterator(self, ds_fn, diff --git a/tensorflow/python/data/experimental/ops/optimization_options.py b/tensorflow/python/data/experimental/ops/optimization_options.py index daf65cd5cd..73849435a9 100644 --- a/tensorflow/python/data/experimental/ops/optimization_options.py +++ b/tensorflow/python/data/experimental/ops/optimization_options.py @@ -94,7 +94,6 @@ class OptimizationOptions(options.OptionsBase): optimizations_to_enable = [ "filter_fusion", "hoist_random_uniform", - "map_and_batch_fusion", "map_and_filter_fusion", "map_fusion", "map_parallelization", @@ -109,6 +108,7 @@ class OptimizationOptions(options.OptionsBase): # The following optimizations are turned on by default, unless the # user explicitly disables them. optimizations_to_disable = [ + "map_and_batch_fusion", "noop_elimination", ] for optimization in optimizations_to_disable: -- GitLab From 034565a9a1683201040de985a0d251615921219b Mon Sep 17 00:00:00 2001 From: Yu-Cheng Ling Date: Fri, 7 Dec 2018 17:01:20 -0800 Subject: [PATCH 249/873] Update TFLite iOS Camera example to use GPU Delegate. Note: GPU Delegate library isn't released yet. We're working on it. PiperOrigin-RevId: 224600723 --- .../ios/camera/CameraExampleViewController.h | 26 ++++++++--- .../ios/camera/CameraExampleViewController.mm | 36 ++++++++++++++- tensorflow/lite/examples/ios/camera/Podfile | 8 +++- .../project.pbxproj | 44 +++---------------- .../lite/examples/ios/download_models.sh | 4 +- 5 files changed, 70 insertions(+), 48 deletions(-) diff --git a/tensorflow/lite/examples/ios/camera/CameraExampleViewController.h b/tensorflow/lite/examples/ios/camera/CameraExampleViewController.h index fb5800e86d..438e6adc79 100644 --- a/tensorflow/lite/examples/ios/camera/CameraExampleViewController.h +++ b/tensorflow/lite/examples/ios/camera/CameraExampleViewController.h @@ -17,8 +17,26 @@ #include -#include "tensorflow/contrib/lite/kernels/register.h" -#include "tensorflow/contrib/lite/model.h" +// TensorFlow Lite was migrated out of `contrib/` directory. The change +// wasn't reflected in newest CocoaPod release yet (1.12.0). +// Change this to 0 when using a TFLite version which is newer than 1.12.0. +// TODO(ycling): Remove the macro when we release the next version. +#ifndef TFLITE_USE_CONTRIB_LITE +#define TFLITE_USE_CONTRIB_LITE 1 +#endif + +// Set TFLITE_USE_GPU_DELEGATE to 1 to use TFLite GPU Delegate. +// Note: TFLite GPU Delegate binary isn't releast yet, and we're working +// on it. +#ifndef TFLITE_USE_GPU_DELEGATE +#define TFLITE_USE_GPU_DELEGATE 0 +#endif + +#if TFLITE_USE_GPU_DELEGATE && TFLITE_USE_CONTRIB_LITE +// Sanity check. +#error "GPU Delegate only works with newer TFLite " \ + "after migrating out of contrib" +#endif @interface CameraExampleViewController : UIViewController { @@ -33,10 +51,6 @@ AVCaptureSession* session; std::vector labels; - std::unique_ptr model; - tflite::ops::builtin::BuiltinOpResolver resolver; - std::unique_ptr interpreter; - double total_latency; int total_count; } diff --git a/tensorflow/lite/examples/ios/camera/CameraExampleViewController.mm b/tensorflow/lite/examples/ios/camera/CameraExampleViewController.mm index a3e6e11095..48cd313c9d 100644 --- a/tensorflow/lite/examples/ios/camera/CameraExampleViewController.mm +++ b/tensorflow/lite/examples/ios/camera/CameraExampleViewController.mm @@ -23,10 +23,20 @@ #include #include +#if TFLITE_USE_CONTRIB_LITE #include "tensorflow/contrib/lite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/op_resolver.h" #include "tensorflow/contrib/lite/string_util.h" +#else +#include "tensorflow/lite/kernels/register.h" +#include "tensorflow/lite/model.h" +#include "tensorflow/lite/op_resolver.h" +#include "tensorflow/lite/string_util.h" +#if TFLITE_USE_GPU_DELEGATE +#include "tensorflow/lite/delegates/gpu/metal_delegate.h" +#endif +#endif #define LOG(x) std::cerr @@ -34,7 +44,12 @@ namespace { // If you have your own model, modify this to the file name, and make sure // you've added the file to your app resources too. +#if TFLITE_USE_GPU_DELEGATE +// GPU Delegate only supports float model now. NSString* model_file_name = @"mobilenet_v1_1.0_224"; +#else +NSString* model_file_name = @"mobilenet_quant_v1_224.tflite"; +#endif NSString* model_file_type = @"tflite"; // If you have your own model, point this to the labels file. NSString* labels_file_name = @"labels"; @@ -151,7 +166,12 @@ void ProcessInputWithQuantizedModel( - (void)teardownAVCapture; @end -@implementation CameraExampleViewController +@implementation CameraExampleViewController { + std::unique_ptr model; + tflite::ops::builtin::BuiltinOpResolver resolver; + std::unique_ptr interpreter; + TfLiteDelegate* delegate; +} - (void)setupAVCapture { NSError* error = nil; @@ -363,6 +383,11 @@ void ProcessInputWithQuantizedModel( } - (void)dealloc { +#if TFLITE_USE_GPU_DELEGATE + if (delegate) { + DeleteGpuDelegate(delegate); + } +#endif [self teardownAVCapture]; } @@ -388,6 +413,15 @@ void ProcessInputWithQuantizedModel( LoadLabels(labels_file_name, labels_file_type, &labels); tflite::InterpreterBuilder(*model, resolver)(&interpreter); + +#if TFLITE_USE_GPU_DELEGATE + GpuDelegateOptions options; + options.allow_precision_loss = true; + options.wait_type = GpuDelegateOptions::WaitType::kActive; + delegate = NewGpuDelegate(&options); + interpreter->ModifyGraphWithDelegate(delegate); +#endif + // Explicitly resize the input tensor. { int input = interpreter->inputs()[0]; diff --git a/tensorflow/lite/examples/ios/camera/Podfile b/tensorflow/lite/examples/ios/camera/Podfile index 2ed5c8da1e..2e15cc63de 100644 --- a/tensorflow/lite/examples/ios/camera/Podfile +++ b/tensorflow/lite/examples/ios/camera/Podfile @@ -4,4 +4,10 @@ inhibit_all_warnings! project 'tflite_camera_example.xcodeproj' target 'tflite_camera_example' - pod 'TensorFlowLite', '1.12.0' + # Comment 'TensorFlowLite' pod and un-comment 'TensorFlowLiteGpuExperimental' + # to use TFLite GPU Delegate. + # Note: TFLite GPU Delegate binary isn't releast yet, and we're working + # on it. + + pod 'TensorFlowLite', '1.12.0' + # pod 'TensorFlowLiteGpuExperimental', '0.0.1' diff --git a/tensorflow/lite/examples/ios/camera/tflite_camera_example.xcodeproj/project.pbxproj b/tensorflow/lite/examples/ios/camera/tflite_camera_example.xcodeproj/project.pbxproj index 9522c41dea..9b5c2b32a8 100644 --- a/tensorflow/lite/examples/ios/camera/tflite_camera_example.xcodeproj/project.pbxproj +++ b/tensorflow/lite/examples/ios/camera/tflite_camera_example.xcodeproj/project.pbxproj @@ -15,6 +15,7 @@ 1CDB2D4A1ED3A9CD007929E9 /* CameraExampleViewController.mm in Sources */ = {isa = PBXBuildFile; fileRef = 1CDB2D451ED3A9CD007929E9 /* CameraExampleViewController.mm */; }; 54DC6C3C5F734F3A58069F0C /* libPods-tflite_camera_example.a in Frameworks */ = {isa = PBXBuildFile; fileRef = 3BA8BF92C84895BFE59D8236 /* libPods-tflite_camera_example.a */; }; AC1F82661FBA3CBD0052BA77 /* labels.txt in Resources */ = {isa = PBXBuildFile; fileRef = AC1F82641FBA3CBD0052BA77 /* labels.txt */; }; + AC31178921BB3FF900AFF1D2 /* mobilenet_quant_v1_224.tflite in Resources */ = {isa = PBXBuildFile; fileRef = AC31178821BB3FF900AFF1D2 /* mobilenet_quant_v1_224.tflite */; }; AC3BB41720114C400084552C /* mobilenet_v1_1.0_224.tflite in Resources */ = {isa = PBXBuildFile; fileRef = AC3BB41620114C400084552C /* mobilenet_v1_1.0_224.tflite */; }; /* End PBXBuildFile section */ @@ -36,6 +37,7 @@ 3BC5BE4BBD09374D3E98F082 /* Pods-tflite_camera_example.debug.xcconfig */ = {isa = PBXFileReference; includeInIndex = 1; lastKnownFileType = text.xcconfig; name = "Pods-tflite_camera_example.debug.xcconfig"; path = "Pods/Target Support Files/Pods-tflite_camera_example/Pods-tflite_camera_example.debug.xcconfig"; sourceTree = ""; }; 55ED318E8D29C8AFEF03DF1E /* Pods-tflite_camera_example.release.xcconfig */ = {isa = PBXFileReference; includeInIndex = 1; lastKnownFileType = text.xcconfig; name = "Pods-tflite_camera_example.release.xcconfig"; path = "Pods/Target Support Files/Pods-tflite_camera_example/Pods-tflite_camera_example.release.xcconfig"; sourceTree = ""; }; AC1F82641FBA3CBD0052BA77 /* labels.txt */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = text; path = labels.txt; sourceTree = ""; }; + AC31178821BB3FF900AFF1D2 /* mobilenet_quant_v1_224.tflite */ = {isa = PBXFileReference; lastKnownFileType = file; path = mobilenet_quant_v1_224.tflite; sourceTree = ""; }; AC3BB41620114C400084552C /* mobilenet_v1_1.0_224.tflite */ = {isa = PBXFileReference; lastKnownFileType = file; path = mobilenet_v1_1.0_224.tflite; sourceTree = ""; }; /* End PBXFileReference section */ @@ -103,6 +105,7 @@ 59A3CFF31CF4E68100C4259F /* data */ = { isa = PBXGroup; children = ( + AC31178821BB3FF900AFF1D2 /* mobilenet_quant_v1_224.tflite */, AC3BB41620114C400084552C /* mobilenet_v1_1.0_224.tflite */, AC1F82641FBA3CBD0052BA77 /* labels.txt */, ); @@ -120,8 +123,6 @@ 1C564C091ED3A92E00087306 /* Sources */, 1C564C0A1ED3A92E00087306 /* Frameworks */, 1C564C0B1ED3A92E00087306 /* Resources */, - 00E875C3B066535AE6B77101 /* [CP] Embed Pods Frameworks */, - 5C2D02120E3E5E09567AA946 /* [CP] Copy Pods Resources */, ); buildRules = ( ); @@ -175,42 +176,13 @@ AC3BB41720114C400084552C /* mobilenet_v1_1.0_224.tflite in Resources */, 1C99111C1ED3B0E600A6BFB9 /* MainStoryboard_iPhone.storyboard in Resources */, AC1F82661FBA3CBD0052BA77 /* labels.txt in Resources */, + AC31178921BB3FF900AFF1D2 /* mobilenet_quant_v1_224.tflite in Resources */, ); runOnlyForDeploymentPostprocessing = 0; }; /* End PBXResourcesBuildPhase section */ /* Begin PBXShellScriptBuildPhase section */ - 00E875C3B066535AE6B77101 /* [CP] Embed Pods Frameworks */ = { - isa = PBXShellScriptBuildPhase; - buildActionMask = 2147483647; - files = ( - ); - inputPaths = ( - ); - name = "[CP] Embed Pods Frameworks"; - outputPaths = ( - ); - runOnlyForDeploymentPostprocessing = 0; - shellPath = /bin/sh; - shellScript = "\"${SRCROOT}/Pods/Target Support Files/Pods-tflite_camera_example/Pods-tflite_camera_example-frameworks.sh\"\n"; - showEnvVarsInLog = 0; - }; - 5C2D02120E3E5E09567AA946 /* [CP] Copy Pods Resources */ = { - isa = PBXShellScriptBuildPhase; - buildActionMask = 2147483647; - files = ( - ); - inputPaths = ( - ); - name = "[CP] Copy Pods Resources"; - outputPaths = ( - ); - runOnlyForDeploymentPostprocessing = 0; - shellPath = /bin/sh; - shellScript = "\"${SRCROOT}/Pods/Target Support Files/Pods-tflite_camera_example/Pods-tflite_camera_example-resources.sh\"\n"; - showEnvVarsInLog = 0; - }; 66DAEAAEE9EF6550C3A061E0 /* [CP] Check Pods Manifest.lock */ = { isa = PBXShellScriptBuildPhase; buildActionMask = 2147483647; @@ -322,9 +294,7 @@ GCC_WARN_UNINITIALIZED_AUTOS = YES_AGGRESSIVE; GCC_WARN_UNUSED_FUNCTION = YES; GCC_WARN_UNUSED_VARIABLE = YES; - HEADER_SEARCH_PATHS = ( - "$(inherited)", - ); + HEADER_SEARCH_PATHS = "$(inherited)"; IPHONEOS_DEPLOYMENT_TARGET = 8.0; MTL_ENABLE_DEBUG_INFO = YES; ONLY_ACTIVE_ARCH = YES; @@ -365,9 +335,7 @@ GCC_WARN_UNINITIALIZED_AUTOS = YES_AGGRESSIVE; GCC_WARN_UNUSED_FUNCTION = YES; GCC_WARN_UNUSED_VARIABLE = YES; - HEADER_SEARCH_PATHS = ( - "$(inherited)", - ); + HEADER_SEARCH_PATHS = "$(inherited)"; IPHONEOS_DEPLOYMENT_TARGET = 8.0; MTL_ENABLE_DEBUG_INFO = NO; SDKROOT = iphoneos; diff --git a/tensorflow/lite/examples/ios/download_models.sh b/tensorflow/lite/examples/ios/download_models.sh index ad6ccd1b0a..4828617d95 100755 --- a/tensorflow/lite/examples/ios/download_models.sh +++ b/tensorflow/lite/examples/ios/download_models.sh @@ -53,6 +53,6 @@ download_and_extract "${QUANTIZED_MODELS_URL}" "${DOWNLOADS_DIR}/quantized_model file ${DOWNLOADS_DIR}/models cp ${DOWNLOADS_DIR}/models/models/* simple/data/ -cp "${DOWNLOADS_DIR}/quantized_models/labels.txt" camera/data/ +cp ${DOWNLOADS_DIR}/models/models/* camera/data/ cp "${DOWNLOADS_DIR}/quantized_models/mobilenet_quant_v1_224.tflite" \ - 'camera/data/mobilenet_v1_1.0_224.tflite' + 'camera/data/mobilenet_quant_v1_224.tflite' -- GitLab From 7b6a1018b9185542be1630b3267075012fe6e862 Mon Sep 17 00:00:00 2001 From: Derek Murray Date: Fri, 7 Dec 2018 17:01:29 -0800 Subject: [PATCH 250/873] [tf.data] Update `tf.data.Iterator` to use `Structure` more internally. PiperOrigin-RevId: 224600744 --- tensorflow/python/data/ops/iterator_ops.py | 45 ++++++++++------------ 1 file changed, 20 insertions(+), 25 deletions(-) diff --git a/tensorflow/python/data/ops/iterator_ops.py b/tensorflow/python/data/ops/iterator_ops.py index 6f9c494f39..d0e91b01f9 100644 --- a/tensorflow/python/data/ops/iterator_ops.py +++ b/tensorflow/python/data/ops/iterator_ops.py @@ -23,7 +23,6 @@ import warnings from tensorflow.python.compat import compat from tensorflow.python.data.ops import optional_ops from tensorflow.python.data.util import nest -from tensorflow.python.data.util import sparse from tensorflow.python.data.util import structure as structure_lib from tensorflow.python.eager import context from tensorflow.python.framework import dtypes @@ -188,34 +187,32 @@ class Iterator(checkpointable.CheckpointableBase): if output_classes is None: output_classes = nest.map_structure(lambda _: ops.Tensor, output_types) nest.assert_same_structure(output_types, output_shapes) + output_structure = structure_lib.convert_legacy_structure( + output_types, output_shapes, output_classes) if shared_name is None: shared_name = "" + # pylint: disable=protected-access if compat.forward_compatible(2018, 8, 3): if _device_stack_is_empty(): with ops.device("/cpu:0"): iterator_resource = gen_dataset_ops.iterator_v2( container="", shared_name=shared_name, - output_types=nest.flatten( - sparse.as_dense_types(output_types, output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(output_shapes, output_classes))) + output_types=output_structure._flat_types, + output_shapes=output_structure._flat_shapes) else: iterator_resource = gen_dataset_ops.iterator_v2( container="", shared_name=shared_name, - output_types=nest.flatten( - sparse.as_dense_types(output_types, output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(output_shapes, output_classes))) + output_types=output_structure._flat_types, + output_shapes=output_structure._flat_shapes) else: iterator_resource = gen_dataset_ops.iterator( container="", shared_name=shared_name, - output_types=nest.flatten( - sparse.as_dense_types(output_types, output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(output_shapes, output_classes))) + output_types=output_structure._flat_types, + output_shapes=output_structure._flat_shapes) + # pylint: enable=protected-access return Iterator(iterator_resource, None, output_types, output_shapes, output_classes) @@ -278,30 +275,28 @@ class Iterator(checkpointable.CheckpointableBase): if output_classes is None: output_classes = nest.map_structure(lambda _: ops.Tensor, output_types) nest.assert_same_structure(output_types, output_shapes) + output_structure = structure_lib.convert_legacy_structure( + output_types, output_shapes, output_classes) string_handle = ops.convert_to_tensor(string_handle, dtype=dtypes.string) + # pylint: disable=protected-access if compat.forward_compatible(2018, 8, 3): if _device_stack_is_empty(): with ops.device("/cpu:0"): iterator_resource = gen_dataset_ops.iterator_from_string_handle_v2( string_handle, - output_types=nest.flatten( - sparse.as_dense_types(output_types, output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(output_shapes, output_classes))) + output_types=output_structure._flat_types, + output_shapes=output_structure._flat_shapes) else: iterator_resource = gen_dataset_ops.iterator_from_string_handle_v2( string_handle, - output_types=nest.flatten( - sparse.as_dense_types(output_types, output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(output_shapes, output_classes))) + output_types=output_structure._flat_types, + output_shapes=output_structure._flat_shapes) else: iterator_resource = gen_dataset_ops.iterator_from_string_handle( string_handle, - output_types=nest.flatten( - sparse.as_dense_types(output_types, output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(output_shapes, output_classes))) + output_types=output_structure._flat_types, + output_shapes=output_structure._flat_shapes) + # pylint: enable=protected-access return Iterator(iterator_resource, None, output_types, output_shapes, output_classes) -- GitLab From 602b6b86848c27439686f869590f432a54425bb3 Mon Sep 17 00:00:00 2001 From: Derek Murray Date: Fri, 7 Dec 2018 17:02:42 -0800 Subject: [PATCH 251/873] [tf.data] Update `Dataset.reduce()` to use (more of) `Structure`. We still explode the `Structure` into the types/shapes/classes to perform the "most specific compatible structure" calculation. It would probably make sense to support that calculation as a method on `Structure` and its subclasses. PiperOrigin-RevId: 224600910 --- tensorflow/python/data/ops/dataset_ops.py | 49 +++++++++++------------ 1 file changed, 23 insertions(+), 26 deletions(-) diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py index a7cd2f54c7..e08a3af852 100644 --- a/tensorflow/python/data/ops/dataset_ops.py +++ b/tensorflow/python/data/ops/dataset_ops.py @@ -1195,27 +1195,23 @@ class DatasetV2(object): # Compute initial values for the state classes, shapes and types based on # the initial state. - state_classes = sparse.get_classes(initial_state) - state_shapes = nest.pack_sequence_as( - initial_state, [t.get_shape() for t in nest.flatten(initial_state)]) - state_types = nest.pack_sequence_as( - initial_state, [t.dtype for t in nest.flatten(initial_state)]) + state_structure = structure_lib.Structure.from_value(initial_state) # Iteratively rerun the reduce function until reaching a fixed point on - # `self._state_shapes`. + # `state_structure`. need_to_rerun = True while need_to_rerun: wrapped_func = StructuredFunctionWrapper( reduce_func, "reduce()", - input_classes=(state_classes, self.output_classes), - input_shapes=(state_shapes, self.output_shapes), - input_types=(state_types, self.output_types), + input_structure=structure_lib.NestedStructure( + (state_structure, self._element_structure)), add_to_graph=False) # Extract and validate class information from the returned values. output_classes = wrapped_func.output_classes + state_classes = state_structure._to_legacy_output_classes() # pylint: disable=protected-access for new_state_class, state_class in zip( nest.flatten(output_classes), nest.flatten(state_classes)): if not issubclass(new_state_class, state_class): @@ -1226,6 +1222,7 @@ class DatasetV2(object): # Extract and validate type information from the returned values. output_types = wrapped_func.output_types + state_types = state_structure._to_legacy_output_types() # pylint: disable=protected-access for new_state_type, state_type in zip( nest.flatten(output_types), nest.flatten(state_types)): if new_state_type != state_type: @@ -1236,6 +1233,7 @@ class DatasetV2(object): # Extract shape information from the returned values. output_shapes = wrapped_func.output_shapes + state_shapes = state_structure._to_legacy_output_shapes() # pylint: disable=protected-access flat_state_shapes = nest.flatten(state_shapes) flat_new_state_shapes = nest.flatten(output_shapes) weakened_state_shapes = [ @@ -1253,27 +1251,26 @@ class DatasetV2(object): break if need_to_rerun: - state_shapes = nest.pack_sequence_as(state_shapes, - weakened_state_shapes) + # TODO(b/110122868): Support a "most specific compatible structure" + # method for combining structures, to avoid using legacy structures + # here. + state_structure = structure_lib.convert_legacy_structure( + state_types, + nest.pack_sequence_as(state_shapes, weakened_state_shapes), + state_classes) reduce_func = wrapped_func.function reduce_func.add_to_graph(ops.get_default_graph()) - return sparse.deserialize_sparse_tensors( - nest.pack_sequence_as( - output_types, - gen_dataset_ops.reduce_dataset( - self._as_variant_tensor(), # pylint: disable=protected-access - nest.flatten(sparse.serialize_sparse_tensors(initial_state)), - reduce_func.captured_inputs, - f=reduce_func, - output_shapes=nest.flatten( - sparse.as_dense_shapes(output_shapes, output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(output_types, output_classes)))), - output_types, - output_shapes, - output_classes) + # pylint: disable=protected-access + return state_structure._from_compatible_tensor_list( + gen_dataset_ops.reduce_dataset( + self._as_variant_tensor(), + state_structure._to_tensor_list(initial_state), + reduce_func.captured_inputs, + f=reduce_func, + output_shapes=state_structure._flat_shapes, + output_types=state_structure._flat_types)) def with_options(self, options): """Returns a new `tf.data.Dataset` with the given options set. -- GitLab From a9bfbc26263ca6af4ea9410daea093633a9b49fa Mon Sep 17 00:00:00 2001 From: Jared Duke Date: Fri, 7 Dec 2018 17:16:07 -0800 Subject: [PATCH 252/873] Add an Android APK wrapper for the benchmark_model utility This APK offers a more faithful representation of on-device performance by executing the benchmark in the context of a foreground Activity. See the README for more details. PiperOrigin-RevId: 224602509 --- tensorflow/lite/build_def.bzl | 4 +- tensorflow/lite/tools/benchmark/README.md | 5 + .../benchmark/android/AndroidManifest.xml | 42 +++++++++ tensorflow/lite/tools/benchmark/android/BUILD | 44 +++++++++ .../lite/tools/benchmark/android/README.md | 65 +++++++++++++ .../android/jni/benchmark_model_jni.cc | 92 +++++++++++++++++++ .../lite/benchmark/BenchmarkModel.java | 32 +++++++ .../benchmark/BenchmarkModelActivity.java | 44 +++++++++ 8 files changed, 327 insertions(+), 1 deletion(-) create mode 100644 tensorflow/lite/tools/benchmark/android/AndroidManifest.xml create mode 100644 tensorflow/lite/tools/benchmark/android/BUILD create mode 100644 tensorflow/lite/tools/benchmark/android/README.md create mode 100644 tensorflow/lite/tools/benchmark/android/jni/benchmark_model_jni.cc create mode 100644 tensorflow/lite/tools/benchmark/android/src/org/tensorflow/lite/benchmark/BenchmarkModel.java create mode 100644 tensorflow/lite/tools/benchmark/android/src/org/tensorflow/lite/benchmark/BenchmarkModelActivity.java diff --git a/tensorflow/lite/build_def.bzl b/tensorflow/lite/build_def.bzl index c61ee63c7e..c17eddf47b 100644 --- a/tensorflow/lite/build_def.bzl +++ b/tensorflow/lite/build_def.bzl @@ -112,7 +112,8 @@ def tflite_jni_binary( linkshared = 1, linkstatic = 1, testonly = 0, - deps = []): + deps = [], + srcs = []): """Builds a jni binary for TFLite.""" linkopts = linkopts + [ "-Wl,--version-script", # Export only jni functions & classes. @@ -124,6 +125,7 @@ def tflite_jni_binary( linkshared = linkshared, linkstatic = linkstatic, deps = deps + [linkscript], + srcs = srcs, linkopts = linkopts, testonly = testonly, ) diff --git a/tensorflow/lite/tools/benchmark/README.md b/tensorflow/lite/tools/benchmark/README.md index a71a2fa1c0..a4d9c879eb 100644 --- a/tensorflow/lite/tools/benchmark/README.md +++ b/tensorflow/lite/tools/benchmark/README.md @@ -11,6 +11,11 @@ The instructions below are for running the binary on Desktop and Android, for iOS please use the [iOS benchmark app](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/benchmark/ios). +An experimental Android APK wrapper for the benchmark model utility offers more +faithful execution behavior on Android (via a foreground Activity). It is +located +[here](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/benchmark/android). + ## Parameters The binary takes the following required parameters: diff --git a/tensorflow/lite/tools/benchmark/android/AndroidManifest.xml b/tensorflow/lite/tools/benchmark/android/AndroidManifest.xml new file mode 100644 index 0000000000..7cdca2885d --- /dev/null +++ b/tensorflow/lite/tools/benchmark/android/AndroidManifest.xml @@ -0,0 +1,42 @@ + + + + + + + + + + + + + + + + + diff --git a/tensorflow/lite/tools/benchmark/android/BUILD b/tensorflow/lite/tools/benchmark/android/BUILD new file mode 100644 index 0000000000..a291effddc --- /dev/null +++ b/tensorflow/lite/tools/benchmark/android/BUILD @@ -0,0 +1,44 @@ +# Description: +# BenchmarkModel Android harness for TensorFlow Lite benchmarks. +package(default_visibility = ["//visibility:private"]) + +licenses(["notice"]) # Apache 2.0 + +exports_files(["LICENSE"]) + +load("//tensorflow/lite:build_def.bzl", "tflite_jni_binary") +load("@build_bazel_rules_android//android:rules.bzl", "android_binary") + +# See README.md for details about building and executing this benchmark. +android_binary( + name = "benchmark_model", + srcs = glob([ + "src/**/*.java", + ]), + custom_package = "org.tensorflow.lite.benchmark", + manifest = "AndroidManifest.xml", + # In some platforms we don't have an Android SDK/NDK and this target + # can't be built. We need to prevent the build system from trying to + # use the target in that case. + tags = ["manual"], + deps = [":tensorflowlite_benchmark_native"], +) + +tflite_jni_binary( + name = "libtensorflowlite_benchmark.so", + srcs = glob([ + "jni/**/*.cc", + "jni/**/*.h", + ]), + deps = [ + "//tensorflow/lite/java/jni", + "//tensorflow/lite/tools/benchmark:benchmark_tflite_model_lib", + "//tensorflow/lite/tools/benchmark:logging", + ], +) + +cc_library( + name = "tensorflowlite_benchmark_native", + srcs = ["libtensorflowlite_benchmark.so"], + visibility = ["//visibility:private"], +) diff --git a/tensorflow/lite/tools/benchmark/android/README.md b/tensorflow/lite/tools/benchmark/android/README.md new file mode 100644 index 0000000000..f5b67e3f79 --- /dev/null +++ b/tensorflow/lite/tools/benchmark/android/README.md @@ -0,0 +1,65 @@ +# TFLite Android Model Benchmark Tool + +## Description + +This Android benchmark app is a simple wrapper around the TensorFlow Lite +[command-line benchmark utility](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/benchmark). + +Pushing and executing binaries directly on Android is a valid approach to +benchmarking, but it can result in subtle (but observable) differences in +performance relative to execution within an actual Android app. In particular, +Android's scheduler tailors behavior based on thread and process priorities, +which differ between a foreground Activity/Application and a regular background +binary executed via `adb shell ...`. This tailored behavior is most evident when +enabling multi-threaded CPU execution with TensorFlow Lite. + +To that end, this app offers perhaps a more faithful view of runtime performance +that developers can expected when deploying TensorFlow Lite with their +application. + +## To build/install/run + +(0) Refer to +https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android +to edit the `WORKSPACE` to configure the android NDK/SDK. + +(1) Build for your specific platform, e.g.: + +``` +bazel build -c opt \ + --config=android_arm64 \ + --cxxopt='--std=c++11' \ + tensorflow/lite/tools/benchmark/android:benchmark_model +``` + +(2) Connect your phone. Install the benchmark APK to your phone with adb: + +``` +adb install -r -d bazel-bin/tensorflow/lite/tools/benchmark/android/benchmark_model.apk +``` + +(3) Push the compute graph that you need to test. + +``` +adb push mobilenet_quant_v1_224.tflite /data/local/tmp +``` + +(4) Run the benchmark. Additional command-line flags are documented +[here](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/benchmark/README.md) +and can be appended to the `args` string alongside the required `--graph` flag +(note that all args must be nested in the single quoted string that follows the +args key). + +``` +adb shell am start -S -n + org.tensorflow.lite.benchmark/org.tensorflow.lite.benchmark.BenchmarkModelActivity \ + --es args '"--graph=/data/local/tmp/mobilenet_quant_v1_224.tflite --num_threads=4"' +``` + +(5) The results will be available in Android's logcat, e.g.: + +``` +adb logcat | grep "Average inference" + +... tflite : Average inference timings in us: Warmup: 91471, Init: 4108, Inference: 80660.1 +``` diff --git a/tensorflow/lite/tools/benchmark/android/jni/benchmark_model_jni.cc b/tensorflow/lite/tools/benchmark/android/jni/benchmark_model_jni.cc new file mode 100644 index 0000000000..ee67bdafb0 --- /dev/null +++ b/tensorflow/lite/tools/benchmark/android/jni/benchmark_model_jni.cc @@ -0,0 +1,92 @@ +/* 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 "tensorflow/lite/tools/benchmark/benchmark_tflite_model.h" +#include "tensorflow/lite/tools/benchmark/logging.h" + +#ifdef __ANDROID__ +#include +#endif + +namespace tflite { +namespace benchmark { +namespace { + +class AndroidBenchmarkLoggingListener : public BenchmarkListener { + void OnBenchmarkEnd(const BenchmarkResults& results) override { + auto inference_us = results.inference_time_us(); + auto init_us = results.startup_latency_us(); + auto warmup_us = results.warmup_time_us(); + std::stringstream results_output; + results_output << "Average inference timings in us: " + << "Warmup: " << warmup_us.avg() << ", " + << "Init: " << init_us << ", " + << "Inference: " << inference_us.avg(); +#ifdef __ANDROID__ + __android_log_print(ANDROID_LOG_ERROR, "tflite", "%s", + results_output.str().c_str()); +#else + fprintf(stderr, "%s", results_output.str().c_str()); +#endif + } +}; + +void Run(int argc, char** argv) { + BenchmarkTfLiteModel benchmark; + AndroidBenchmarkLoggingListener listener; + benchmark.AddListener(&listener); + benchmark.Run(argc, argv); +} + +} // namespace +} // namespace benchmark +} // namespace tflite + +#ifdef __cplusplus +extern "C" { +#endif + +JNIEXPORT void JNICALL +Java_org_tensorflow_lite_benchmark_BenchmarkModel_nativeRun(JNIEnv* env, + jclass clazz, + jstring args_obj) { + const char* args_chars = env->GetStringUTFChars(args_obj, nullptr); + + // Split the args string into individual arg tokens. + std::istringstream iss(args_chars); + std::vector args_split{std::istream_iterator(iss), + {}}; + + // Construct a fake argv command-line object for the benchmark. + std::vector argv; + std::string arg0 = "(BenchmarkModelAndroid)"; + argv.push_back(const_cast(arg0.data())); + for (auto& arg : args_split) { + argv.push_back(const_cast(arg.data())); + } + + tflite::benchmark::Run(static_cast(argv.size()), argv.data()); + + env->ReleaseStringUTFChars(args_obj, args_chars); +} + +#ifdef __cplusplus +} // extern "C" +#endif // __cplusplus diff --git a/tensorflow/lite/tools/benchmark/android/src/org/tensorflow/lite/benchmark/BenchmarkModel.java b/tensorflow/lite/tools/benchmark/android/src/org/tensorflow/lite/benchmark/BenchmarkModel.java new file mode 100644 index 0000000000..a6cf8d78d5 --- /dev/null +++ b/tensorflow/lite/tools/benchmark/android/src/org/tensorflow/lite/benchmark/BenchmarkModel.java @@ -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. +==============================================================================*/ + +package org.tensorflow.lite.benchmark; + +/** Helper class for running a native TensorFlow Lite benchmark. */ +class BenchmarkModel { + static { + System.loadLibrary("tensorflowlite_benchmark"); + } + + // Executes a standard TensorFlow Lite benchmark according to the provided args. + // + // Note that {@code args} will be split by the native execution code. + public static void run(String args) { + nativeRun(args); + } + + private static native void nativeRun(String args); +} diff --git a/tensorflow/lite/tools/benchmark/android/src/org/tensorflow/lite/benchmark/BenchmarkModelActivity.java b/tensorflow/lite/tools/benchmark/android/src/org/tensorflow/lite/benchmark/BenchmarkModelActivity.java new file mode 100644 index 0000000000..12410adf3d --- /dev/null +++ b/tensorflow/lite/tools/benchmark/android/src/org/tensorflow/lite/benchmark/BenchmarkModelActivity.java @@ -0,0 +1,44 @@ +/* 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 org.tensorflow.lite.benchmark; + +import android.app.Activity; +import android.content.Intent; +import android.os.Bundle; +import android.util.Log; + +/** Main {@code Activity} class for the benchmark app. */ +public class BenchmarkModelActivity extends Activity { + + private static final String TAG = "tflite_BenchmarkModelActivity"; + + private static final String ARGS_INTENT_KEY_0 = "args"; + private static final String ARGS_INTENT_KEY_1 = "--args"; + + @Override + public void onCreate(Bundle savedInstanceState) { + super.onCreate(savedInstanceState); + + Intent intent = getIntent(); + Bundle bundle = intent.getExtras(); + String args = bundle.getString(ARGS_INTENT_KEY_0, bundle.getString(ARGS_INTENT_KEY_1)); + Log.i(TAG, "Running TensorFlow Lite benchmark with args: " + args); + + BenchmarkModel.run(args); + + finish(); + } +} -- GitLab From bcd4d48cee1ec070f00a401107a85ae416c57abc Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 7 Dec 2018 17:17:17 -0800 Subject: [PATCH 253/873] Automated rollback of commit c05e0bd84fb8a20f746addde640ac0bc35f5026d. Revert #24081. PiperOrigin-RevId: 224602628 --- tensorflow/stream_executor/cuda/cuda_dnn.cc | 459 +++++--------------- tensorflow/stream_executor/cuda/cuda_dnn.h | 194 +-------- tensorflow/stream_executor/dnn.h | 207 ++------- tensorflow/stream_executor/stream.cc | 172 ++------ 4 files changed, 179 insertions(+), 853 deletions(-) diff --git a/tensorflow/stream_executor/cuda/cuda_dnn.cc b/tensorflow/stream_executor/cuda/cuda_dnn.cc index 387afefc21..1f2e2f48bb 100644 --- a/tensorflow/stream_executor/cuda/cuda_dnn.cc +++ b/tensorflow/stream_executor/cuda/cuda_dnn.cc @@ -2380,7 +2380,7 @@ bool ShouldIncludeWinogradNonfusedAlgo( } // namespace template -port::Status CudnnSupport::PrepareForConvolutionImpl( +port::Status CudnnSupport::DoConvolveImpl( Stream* stream, const dnn::BatchDescriptor& input_descriptor, const DeviceMemory& input_data, const dnn::FilterDescriptor& filter_descriptor, @@ -2389,34 +2389,6 @@ port::Status CudnnSupport::PrepareForConvolutionImpl( const dnn::BatchDescriptor& output_descriptor, DeviceMemory* output_data, dnn::DataType accumulator_type, ScratchAllocator* scratch_allocator, const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, DeviceMemory* scratch_memory) { - cudnnDataType_t cudnn_type = GetCudnnDataType(); - CudnnTensorDescriptor input_nd(input_descriptor, cudnn_type); - CudnnTensorDescriptor output_nd(output_descriptor, cudnn_type); - CudnnFilterDescriptor filter(filter_descriptor, cudnn_type); - CudnnConvolutionDescriptor conv(convolution_descriptor, - ToCudnnDataType(accumulator_type)); - - auto cudnn = cudnn_->GetHandle(parent_, stream); - - SE_ASSIGN_OR_RETURN(*algorithm_desc, - GetCudnnConvolutionForwardAlgorithm( - stream, cudnn, algorithm_config, input_nd, filter, - conv, output_nd, scratch_allocator, scratch_memory)); - - return port::Status::OK(); -} - -template -port::Status CudnnSupport::DoConvolveImpl( - Stream* stream, const dnn::BatchDescriptor& input_descriptor, - const DeviceMemory& input_data, - const dnn::FilterDescriptor& filter_descriptor, - const DeviceMemory& filter_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::BatchDescriptor& output_descriptor, DeviceMemory* output_data, - dnn::DataType accumulator_type, const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, dnn::ProfileResult* output_profile_result) { cudnnDataType_t cudnn_type = GetCudnnDataType(); CudnnTensorDescriptor input_nd(input_descriptor, cudnn_type); @@ -2439,6 +2411,12 @@ port::Status CudnnSupport::DoConvolveImpl( const bool is_profiling = output_profile_result != nullptr; + DeviceMemory scratch; + SE_ASSIGN_OR_RETURN(dnn::AlgorithmDesc algo_desc, + GetCudnnConvolutionForwardAlgorithm( + stream, cudnn, algorithm_config, input_nd, filter, + conv, output_nd, scratch_allocator, &scratch)); + std::unique_ptr timer; if (is_profiling) { timer.reset(new CUDATimer(parent_)); // NOLINT @@ -2454,7 +2432,7 @@ port::Status CudnnSupport::DoConvolveImpl( // memory. See nvbugs/2138754, b/80018418. if (CUDNN_VERSION < 7300) { SE_RETURN_IF_ERROR([&] { - if (algorithm_desc.algo_id() != CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING) { + if (algo_desc.algo_id() != CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING) { return port::Status::OK(); } if (input_descriptor.ndims() < 3) { @@ -2479,8 +2457,7 @@ port::Status CudnnSupport::DoConvolveImpl( }()); } - if (algorithm_desc.algo_id() == - CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED && + if (algo_desc.algo_id() == CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED && !ShouldIncludeWinogradNonfusedAlgo(input_descriptor, output_descriptor)) { return port::Status(port::error::FAILED_PRECONDITION, "This configuration has potential integer overflow in " @@ -2492,19 +2469,18 @@ port::Status CudnnSupport::DoConvolveImpl( /*alpha=*/alpha, /*srcDesc=*/input_nd.handle(), /*srcData=*/input_data.opaque(), /*filterDesc=*/filter.handle(), /*filterData=*/filter_data.opaque(), /*convDesc=*/conv.handle(), - /*algo=*/ToConvForwardAlgo(algorithm_desc), - /*workSpace=*/scratch_memory->opaque(), - /*workSpaceSizeInBytes=*/scratch_memory->size(), /*beta=*/beta, + /*algo=*/ToConvForwardAlgo(algo_desc), /*workSpace=*/scratch.opaque(), + /*workSpaceSizeInBytes=*/scratch.size(), /*beta=*/beta, /*yDesc=*/output_nd.handle(), /*y=*/output_data->opaque())); if (is_profiling) { if (!timer->Stop(AsCUDAStream(stream))) { return port::Status(port::error::INTERNAL, "Failed to stop timer"); } - output_profile_result->set_algorithm(algorithm_desc); + output_profile_result->set_algorithm(algo_desc); output_profile_result->set_elapsed_time_in_ms( timer->GetElapsedMilliseconds()); - output_profile_result->set_scratch_size(scratch_memory->size()); + output_profile_result->set_scratch_size(scratch.size()); } return port::Status::OK(); @@ -2901,7 +2877,7 @@ port::Status CudnnSupport::DoBatchNormalizationBackwardImpl( return port::Status::OK(); } -bool CudnnSupport::PrepareForConvolution( +bool CudnnSupport::DoConvolve( Stream* stream, const dnn::BatchDescriptor& batch_descriptor, const DeviceMemory& input_data, const dnn::FilterDescriptor& filter_descriptor, @@ -2910,70 +2886,12 @@ bool CudnnSupport::PrepareForConvolution( const dnn::BatchDescriptor& output_descriptor, DeviceMemory* output_data, ScratchAllocator* scratch_allocator, const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, DeviceMemory* scratch_memory) { - return IsStatusOk(PrepareForConvolutionImpl( - stream, batch_descriptor, input_data, filter_descriptor, - filter_data, convolution_descriptor, output_descriptor, - output_data, dnn::DataType::kFloat, scratch_allocator, - algorithm_config, algorithm_desc, scratch_memory), - /*report_error=*/true); -} - -bool CudnnSupport::PrepareForConvolution( - Stream* stream, const dnn::BatchDescriptor& batch_descriptor, - const DeviceMemory& input_data, - const dnn::FilterDescriptor& filter_descriptor, - const DeviceMemory& filter_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::BatchDescriptor& output_descriptor, - DeviceMemory* output_data, ScratchAllocator* scratch_allocator, - const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, DeviceMemory* scratch_memory) { - return IsStatusOk(PrepareForConvolutionImpl( - stream, batch_descriptor, input_data, filter_descriptor, - filter_data, convolution_descriptor, output_descriptor, - output_data, dnn::DataType::kDouble, scratch_allocator, - algorithm_config, algorithm_desc, scratch_memory), - /*report_error=*/true); -} - -bool CudnnSupport::PrepareForConvolution( - Stream* stream, const dnn::BatchDescriptor& batch_descriptor, - const DeviceMemory& input_data, - const dnn::FilterDescriptor& filter_descriptor, - const DeviceMemory& filter_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::BatchDescriptor& output_descriptor, - DeviceMemory* output_data, ScratchAllocator* scratch_allocator, - const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, DeviceMemory* scratch_memory) { - dnn::DataType acc_type = - CudnnEnvVar::IsEnabled() - ? dnn::DataType::kFloat - : dnn::DataType::kHalf; - return IsStatusOk( - PrepareForConvolutionImpl( - stream, batch_descriptor, input_data, filter_descriptor, filter_data, - convolution_descriptor, output_descriptor, output_data, acc_type, - scratch_allocator, algorithm_config, algorithm_desc, scratch_memory), - /*report_error=*/true); -} - -bool CudnnSupport::DoConvolve( - Stream* stream, const dnn::BatchDescriptor& batch_descriptor, - const DeviceMemory& input_data, - const dnn::FilterDescriptor& filter_descriptor, - const DeviceMemory& filter_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::BatchDescriptor& output_descriptor, - DeviceMemory* output_data, const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, dnn::ProfileResult* output_profile_result) { return IsStatusOk( DoConvolveImpl(stream, batch_descriptor, input_data, filter_descriptor, filter_data, convolution_descriptor, output_descriptor, - output_data, dnn::DataType::kFloat, algorithm_desc, - scratch_memory, output_profile_result), + output_data, dnn::DataType::kFloat, scratch_allocator, + algorithm_config, output_profile_result), /*report_error=*/!output_profile_result); } @@ -2984,14 +2902,14 @@ bool CudnnSupport::DoConvolve( const DeviceMemory& filter_data, const dnn::ConvolutionDescriptor& convolution_descriptor, const dnn::BatchDescriptor& output_descriptor, - DeviceMemory* output_data, const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, + DeviceMemory* output_data, ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, dnn::ProfileResult* output_profile_result) { return IsStatusOk( DoConvolveImpl(stream, batch_descriptor, input_data, filter_descriptor, filter_data, convolution_descriptor, output_descriptor, - output_data, dnn::DataType::kDouble, algorithm_desc, - scratch_memory, output_profile_result), + output_data, dnn::DataType::kDouble, scratch_allocator, + algorithm_config, output_profile_result), /*report_error=*/!output_profile_result); } @@ -3002,9 +2920,8 @@ bool CudnnSupport::DoConvolve( const DeviceMemory& filter_data, const dnn::ConvolutionDescriptor& convolution_descriptor, const dnn::BatchDescriptor& output_descriptor, - DeviceMemory* output_data, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, + DeviceMemory* output_data, ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, dnn::ProfileResult* output_profile_result) { dnn::DataType acc_type = CudnnEnvVar::IsEnabled() @@ -3013,7 +2930,7 @@ bool CudnnSupport::DoConvolve( return IsStatusOk( DoConvolveImpl(stream, batch_descriptor, input_data, filter_descriptor, filter_data, convolution_descriptor, output_descriptor, - output_data, acc_type, algorithm_desc, scratch_memory, + output_data, acc_type, scratch_allocator, algorithm_config, output_profile_result), /*report_error=*/!output_profile_result); } @@ -3149,7 +3066,7 @@ bool CudnnSupport::DoTransformTensor(Stream* stream, } template -port::Status CudnnSupport::PrepareForConvolutionBackwardDataImpl( +port::Status CudnnSupport::DoConvolveBackwardDataImpl( Stream* stream, const dnn::FilterDescriptor& filter_descriptor, const DeviceMemory& filter_data, const dnn::BatchDescriptor& output_descriptor, @@ -3159,36 +3076,6 @@ port::Status CudnnSupport::PrepareForConvolutionBackwardDataImpl( DeviceMemory* backward_input_data, dnn::DataType accumulator_type, ScratchAllocator* scratch_allocator, const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, DeviceMemory* scratch_memory) { - cudnnDataType_t cudnn_type = GetCudnnDataType(); - auto cudnn = cudnn_->GetHandle(parent_, stream); - - CudnnTensorDescriptor out_back_nd(output_descriptor, cudnn_type); - CudnnTensorDescriptor in_back_nd(input_descriptor, cudnn_type); - CudnnFilterDescriptor filter(filter_descriptor, cudnn_type); - CudnnConvolutionDescriptor conv(convolution_descriptor, - ToCudnnDataType(accumulator_type)); - - SE_ASSIGN_OR_RETURN( - *algorithm_desc, - GetCudnnConvolutionBackwardDataAlgorithm( - stream, cudnn, algorithm_config, in_back_nd, filter, conv, - out_back_nd, scratch_allocator, scratch_memory)); - - return port::Status::OK(); -} - -template -port::Status CudnnSupport::DoConvolveBackwardDataImpl( - Stream* stream, const dnn::FilterDescriptor& filter_descriptor, - const DeviceMemory& filter_data, - const dnn::BatchDescriptor& output_descriptor, - DeviceMemory backward_output_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::BatchDescriptor& input_descriptor, - DeviceMemory* backward_input_data, dnn::DataType accumulator_type, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, dnn::ProfileResult* output_profile_result) { cudnnDataType_t cudnn_type = GetCudnnDataType(); // Alpha is the scaling factor for input. @@ -3212,6 +3099,12 @@ port::Status CudnnSupport::DoConvolveBackwardDataImpl( const bool is_profiling = output_profile_result != nullptr; + DeviceMemory scratch; + SE_ASSIGN_OR_RETURN(dnn::AlgorithmDesc algo_desc, + GetCudnnConvolutionBackwardDataAlgorithm( + stream, cudnn, algorithm_config, in_back_nd, filter, + conv, out_back_nd, scratch_allocator, &scratch)); + std::unique_ptr timer; if (is_profiling) { timer.reset(new CUDATimer(parent_)); // NOLINT @@ -3223,8 +3116,7 @@ port::Status CudnnSupport::DoConvolveBackwardDataImpl( } } - if (algorithm_desc.algo_id() == - CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED && + if (algo_desc.algo_id() == CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED && !ShouldIncludeWinogradNonfusedAlgo(input_descriptor, output_descriptor)) { return port::Status(port::error::FAILED_PRECONDITION, "This configuration has potential integer overflow in " @@ -3234,44 +3126,44 @@ port::Status CudnnSupport::DoConvolveBackwardDataImpl( // Cudnn 7.1.4 has a bug if the workspace of the following convolution is not // zero-initialized, nvbugs/2254619. if (CUDNN_VERSION >= 7000 && CUDNN_VERSION < 7300 && - algorithm_desc.algo_id() == CUDNN_CONVOLUTION_BWD_DATA_ALGO_1 && - cudnn_type == CUDNN_DATA_HALF && algorithm_desc.tensor_ops_enabled() && + algo_desc.algo_id() == CUDNN_CONVOLUTION_BWD_DATA_ALGO_1 && + cudnn_type == CUDNN_DATA_HALF && algo_desc.tensor_ops_enabled() && input_descriptor.layout() == dnn::DataLayout::kBatchYXDepth && filter_descriptor.layout() == dnn::FilterLayout::kOutputInputYX && output_descriptor.layout() == dnn::DataLayout::kBatchDepthYX && (convolution_descriptor.vertical_filter_stride() > 1 || convolution_descriptor.horizontal_filter_stride() > 1)) { - stream->ThenMemZero(scratch_memory, scratch_memory->size()); + stream->ThenMemZero(&scratch, scratch.size()); } - RETURN_IF_CUDNN_ERROR(cudnnConvolutionBackwardData( - cudnn.handle(), - /*alpha=*/alpha, - /*wDesc=*/filter.handle(), - /*w=*/filter_data.opaque(), - /*dyDesc=*/out_back_nd.handle(), - /*dy=*/backward_output_data.opaque(), - /*convDesc=*/conv.handle(), - /*algo=*/ToConvBackwardDataAlgo(algorithm_desc), - /*workSpace=*/scratch_memory->opaque(), - /*workSpaceSizeInBytes=*/scratch_memory->size(), - /*beta=*/beta, - /*dxDesc=*/in_back_nd.handle(), - /*dx=*/backward_input_data->opaque())); + RETURN_IF_CUDNN_ERROR( + cudnnConvolutionBackwardData(cudnn.handle(), + /*alpha=*/alpha, + /*wDesc=*/filter.handle(), + /*w=*/filter_data.opaque(), + /*dyDesc=*/out_back_nd.handle(), + /*dy=*/backward_output_data.opaque(), + /*convDesc=*/conv.handle(), + /*algo=*/ToConvBackwardDataAlgo(algo_desc), + /*workSpace=*/scratch.opaque(), + /*workSpaceSizeInBytes=*/scratch.size(), + /*beta=*/beta, + /*dxDesc=*/in_back_nd.handle(), + /*dx=*/backward_input_data->opaque())); if (is_profiling) { if (!timer->Stop(AsCUDAStream(stream))) { return port::Status(port::error::INTERNAL, "Failed to stop timer"); } - output_profile_result->set_algorithm(algorithm_desc); + output_profile_result->set_algorithm(algo_desc); output_profile_result->set_elapsed_time_in_ms( timer->GetElapsedMilliseconds()); - output_profile_result->set_scratch_size(scratch_memory->size()); + output_profile_result->set_scratch_size(scratch.size()); } return port::Status::OK(); } -bool CudnnSupport::PrepareForConvolutionBackwardData( +bool CudnnSupport::DoConvolveBackwardData( Stream* stream, const dnn::FilterDescriptor& filter_descriptor, const DeviceMemory& filter_data, const dnn::BatchDescriptor& output_descriptor, @@ -3281,77 +3173,13 @@ bool CudnnSupport::PrepareForConvolutionBackwardData( DeviceMemory* backward_input_data, ScratchAllocator* scratch_allocator, const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, DeviceMemory* scratch_memory) { - return IsStatusOk( - PrepareForConvolutionBackwardDataImpl( - stream, filter_descriptor, filter_data, output_descriptor, - backward_output_data, convolution_descriptor, input_descriptor, - backward_input_data, dnn::DataType::kDouble, scratch_allocator, - algorithm_config, algorithm_desc, scratch_memory), - /*report_error=*/true); -} - -bool CudnnSupport::PrepareForConvolutionBackwardData( - Stream* stream, const dnn::FilterDescriptor& filter_descriptor, - const DeviceMemory& filter_data, - const dnn::BatchDescriptor& output_descriptor, - DeviceMemory backward_output_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::BatchDescriptor& input_descriptor, - DeviceMemory* backward_input_data, - ScratchAllocator* scratch_allocator, - const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, DeviceMemory* scratch_memory) { - return IsStatusOk( - PrepareForConvolutionBackwardDataImpl( - stream, filter_descriptor, filter_data, output_descriptor, - backward_output_data, convolution_descriptor, input_descriptor, - backward_input_data, dnn::DataType::kFloat, scratch_allocator, - algorithm_config, algorithm_desc, scratch_memory), - /*report_error=*/true); -} - -bool CudnnSupport::PrepareForConvolutionBackwardData( - Stream* stream, const dnn::FilterDescriptor& filter_descriptor, - const DeviceMemory& filter_data, - const dnn::BatchDescriptor& output_descriptor, - DeviceMemory backward_output_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::BatchDescriptor& input_descriptor, - DeviceMemory* backward_input_data, - ScratchAllocator* scratch_allocator, - const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, DeviceMemory* scratch_memory) { - dnn::DataType acc_type = - CudnnEnvVar::IsEnabled() - ? dnn::DataType::kFloat - : dnn::DataType::kHalf; - return IsStatusOk( - PrepareForConvolutionBackwardDataImpl( - stream, filter_descriptor, filter_data, output_descriptor, - backward_output_data, convolution_descriptor, input_descriptor, - backward_input_data, acc_type, scratch_allocator, algorithm_config, - algorithm_desc, scratch_memory), - /*report_error=*/true); -} - -bool CudnnSupport::DoConvolveBackwardData( - Stream* stream, const dnn::FilterDescriptor& filter_descriptor, - const DeviceMemory& filter_data, - const dnn::BatchDescriptor& output_descriptor, - DeviceMemory backward_output_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::BatchDescriptor& input_descriptor, - DeviceMemory* backward_input_data, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, dnn::ProfileResult* output_profile_result) { return IsStatusOk( DoConvolveBackwardDataImpl( stream, filter_descriptor, filter_data, output_descriptor, backward_output_data, convolution_descriptor, input_descriptor, - backward_input_data, dnn::DataType::kDouble, algorithm_desc, - scratch_memory, output_profile_result), + backward_input_data, dnn::DataType::kDouble, scratch_allocator, + algorithm_config, output_profile_result), /*report_error=*/!output_profile_result); } @@ -3363,15 +3191,15 @@ bool CudnnSupport::DoConvolveBackwardData( const dnn::ConvolutionDescriptor& convolution_descriptor, const dnn::BatchDescriptor& input_descriptor, DeviceMemory* backward_input_data, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, dnn::ProfileResult* output_profile_result) { return IsStatusOk( DoConvolveBackwardDataImpl( stream, filter_descriptor, filter_data, output_descriptor, backward_output_data, convolution_descriptor, input_descriptor, - backward_input_data, dnn::DataType::kFloat, algorithm_desc, - scratch_memory, output_profile_result), + backward_input_data, dnn::DataType::kFloat, scratch_allocator, + algorithm_config, output_profile_result), /*report_error=*/!output_profile_result); } @@ -3383,24 +3211,24 @@ bool CudnnSupport::DoConvolveBackwardData( const dnn::ConvolutionDescriptor& convolution_descriptor, const dnn::BatchDescriptor& input_descriptor, DeviceMemory* backward_input_data, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, dnn::ProfileResult* output_profile_result) { dnn::DataType acc_type = CudnnEnvVar::IsEnabled() ? dnn::DataType::kFloat : dnn::DataType::kHalf; return IsStatusOk( - DoConvolveBackwardDataImpl(stream, filter_descriptor, filter_data, - output_descriptor, backward_output_data, - convolution_descriptor, input_descriptor, - backward_input_data, acc_type, algorithm_desc, - scratch_memory, output_profile_result), + DoConvolveBackwardDataImpl( + stream, filter_descriptor, filter_data, output_descriptor, + backward_output_data, convolution_descriptor, input_descriptor, + backward_input_data, acc_type, scratch_allocator, algorithm_config, + output_profile_result), /*report_error=*/!output_profile_result); } template -port::Status CudnnSupport::PrepareForConvolutionBackwardFilterImpl( +port::Status CudnnSupport::DoConvolveBackwardFilterImpl( Stream* stream, const dnn::BatchDescriptor& input_descriptor, const DeviceMemory& input_data, const dnn::BatchDescriptor& output_descriptor, @@ -3410,36 +3238,6 @@ port::Status CudnnSupport::PrepareForConvolutionBackwardFilterImpl( DeviceMemory* backward_filter_data, dnn::DataType accumulator_type, ScratchAllocator* scratch_allocator, const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, DeviceMemory* scratch_memory) { - cudnnDataType_t cudnn_type = GetCudnnDataType(); - auto cudnn = cudnn_->GetHandle(parent_, stream); - - CudnnTensorDescriptor out_back_nd(output_descriptor, cudnn_type); - CudnnTensorDescriptor input_nd(input_descriptor, cudnn_type); - CudnnFilterDescriptor filter(filter_descriptor, cudnn_type); - CudnnConvolutionDescriptor conv(convolution_descriptor, - ToCudnnDataType(accumulator_type)); - - SE_ASSIGN_OR_RETURN( - *algorithm_desc, - GetCudnnConvolutionBackwardFilterAlgorithm( - stream, cudnn, algorithm_config, input_nd, filter, conv, out_back_nd, - scratch_allocator, scratch_memory)); - - return port::Status::OK(); -} - -template -port::Status CudnnSupport::DoConvolveBackwardFilterImpl( - Stream* stream, const dnn::BatchDescriptor& input_descriptor, - const DeviceMemory& input_data, - const dnn::BatchDescriptor& output_descriptor, - DeviceMemory backward_output_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::FilterDescriptor& filter_descriptor, - DeviceMemory* backward_filter_data, dnn::DataType accumulator_type, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, dnn::ProfileResult* output_profile_result) { cudnnDataType_t cudnn_type = GetCudnnDataType(); // Alpha is the scaling factor for input. @@ -3463,6 +3261,12 @@ port::Status CudnnSupport::DoConvolveBackwardFilterImpl( const bool is_profiling = output_profile_result != nullptr; + DeviceMemory scratch; + SE_ASSIGN_OR_RETURN(dnn::AlgorithmDesc algo_desc, + GetCudnnConvolutionBackwardFilterAlgorithm( + stream, cudnn, algorithm_config, input_nd, filter, + conv, out_back_nd, scratch_allocator, &scratch)); + std::unique_ptr timer; if (is_profiling) { timer.reset(new CUDATimer(parent_)); // NOLINT @@ -3478,8 +3282,7 @@ port::Status CudnnSupport::DoConvolveBackwardFilterImpl( // results. See nvbugs/2072856 if (CUDNN_VERSION < 7300) { SE_RETURN_IF_ERROR([&] { - if (algorithm_desc.algo_id() != - CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT_TILING) { + if (algo_desc.algo_id() != CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT_TILING) { return port::Status::OK(); } if (output_descriptor.height() > 1 && output_descriptor.width() > 1) { @@ -3505,8 +3308,7 @@ port::Status CudnnSupport::DoConvolveBackwardFilterImpl( }()); } - if (algorithm_desc.algo_id() == - CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED && + if (algo_desc.algo_id() == CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED && !ShouldIncludeWinogradNonfusedAlgo(input_descriptor, output_descriptor)) { return port::Status(port::error::FAILED_PRECONDITION, "This configuration has potential integer overflow in " @@ -3522,7 +3324,7 @@ port::Status CudnnSupport::DoConvolveBackwardFilterImpl( // // See nvbugs/2379553. if (CUDNN_VERSION >= 7100 && CUDNN_VERSION < 7300 && - algorithm_desc.algo_id() == CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1 && + algo_desc.algo_id() == CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1 && cudnn_type == CUDNN_DATA_HALF && input_descriptor.layout() == dnn::DataLayout::kBatchYXDepth && filter_descriptor.layout() == dnn::FilterLayout::kOutputYXInput && @@ -3540,9 +3342,9 @@ port::Status CudnnSupport::DoConvolveBackwardFilterImpl( /*diffDesc=*/out_back_nd.handle(), /*diffData=*/backward_output_data.opaque(), /*convDesc=*/conv.handle(), - /*algo=*/ToConvBackwardFilterAlgo(algorithm_desc), - /*workSpace=*/scratch_memory->opaque(), - /*workSpaceSizeInBytes=*/scratch_memory->size(), + /*algo=*/ToConvBackwardFilterAlgo(algo_desc), + /*workSpace=*/scratch.opaque(), + /*workSpaceSizeInBytes=*/scratch.size(), /*beta=*/beta, /*gradDesc=*/filter.handle(), /*dw=*/backward_filter_data->opaque())); @@ -3550,16 +3352,16 @@ port::Status CudnnSupport::DoConvolveBackwardFilterImpl( if (!timer->Stop(AsCUDAStream(stream))) { return port::Status(port::error::INTERNAL, "Failed to stop timer"); } - output_profile_result->set_algorithm(algorithm_desc); + output_profile_result->set_algorithm(algo_desc); output_profile_result->set_elapsed_time_in_ms( timer->GetElapsedMilliseconds()); - output_profile_result->set_scratch_size(scratch_memory->size()); + output_profile_result->set_scratch_size(scratch.size()); } return port::Status::OK(); } -bool CudnnSupport::PrepareForConvolutionBackwardFilter( +bool CudnnSupport::DoConvolveBackwardFilter( Stream* stream, const dnn::BatchDescriptor& input_descriptor, const DeviceMemory& input_data, const dnn::BatchDescriptor& output_descriptor, @@ -3569,17 +3371,18 @@ bool CudnnSupport::PrepareForConvolutionBackwardFilter( DeviceMemory* backward_filter_data, ScratchAllocator* scratch_allocator, const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, DeviceMemory* scratch_memory) { + dnn::ProfileResult* output_profile_result) { return IsStatusOk( - PrepareForConvolutionBackwardFilterImpl( + DoConvolveBackwardFilterImpl( stream, input_descriptor, input_data, output_descriptor, backward_output_data, convolution_descriptor, filter_descriptor, - backward_filter_data, dnn::DataType::kDouble, scratch_allocator, - algorithm_config, algorithm_desc, scratch_memory), - /*report_error=*/true); + backward_filter_data, dnn::DataType::kDouble, + + scratch_allocator, algorithm_config, output_profile_result), + /*report_error=*/!output_profile_result); } -bool CudnnSupport::PrepareForConvolutionBackwardFilter( +bool CudnnSupport::DoConvolveBackwardFilter( Stream* stream, const dnn::BatchDescriptor& input_descriptor, const DeviceMemory& input_data, const dnn::BatchDescriptor& output_descriptor, @@ -3589,17 +3392,18 @@ bool CudnnSupport::PrepareForConvolutionBackwardFilter( DeviceMemory* backward_filter_data, ScratchAllocator* scratch_allocator, const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, DeviceMemory* scratch_memory) { - return IsStatusOk( - PrepareForConvolutionBackwardFilterImpl( - stream, input_descriptor, input_data, output_descriptor, - backward_output_data, convolution_descriptor, filter_descriptor, - backward_filter_data, dnn::DataType::kFloat, scratch_allocator, - algorithm_config, algorithm_desc, scratch_memory), - /*report_error=*/true); + dnn::ProfileResult* output_profile_result) { + return IsStatusOk(DoConvolveBackwardFilterImpl( + stream, input_descriptor, input_data, output_descriptor, + backward_output_data, convolution_descriptor, + filter_descriptor, backward_filter_data, + + dnn::DataType::kFloat, scratch_allocator, + algorithm_config, output_profile_result), + /*report_error=*/!output_profile_result); } -bool CudnnSupport::PrepareForConvolutionBackwardFilter( +bool CudnnSupport::DoConvolveBackwardFilter( Stream* stream, const dnn::BatchDescriptor& input_descriptor, const DeviceMemory& input_data, const dnn::BatchDescriptor& output_descriptor, @@ -3609,83 +3413,20 @@ bool CudnnSupport::PrepareForConvolutionBackwardFilter( DeviceMemory* backward_filter_data, ScratchAllocator* scratch_allocator, const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, DeviceMemory* scratch_memory) { + dnn::ProfileResult* output_profile_result) { dnn::DataType acc_type = CudnnEnvVar::IsEnabled() ? dnn::DataType::kFloat : dnn::DataType::kHalf; - return IsStatusOk( - PrepareForConvolutionBackwardFilterImpl( - stream, input_descriptor, input_data, output_descriptor, - backward_output_data, convolution_descriptor, filter_descriptor, - backward_filter_data, acc_type, scratch_allocator, algorithm_config, - algorithm_desc, scratch_memory), - /*report_error=*/true); -} - -bool CudnnSupport::DoConvolveBackwardFilter( - Stream* stream, const dnn::BatchDescriptor& input_descriptor, - const DeviceMemory& input_data, - const dnn::BatchDescriptor& output_descriptor, - DeviceMemory backward_output_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::FilterDescriptor& filter_descriptor, - DeviceMemory* backward_filter_data, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, - dnn::ProfileResult* output_profile_result) { return IsStatusOk( DoConvolveBackwardFilterImpl( stream, input_descriptor, input_data, output_descriptor, backward_output_data, convolution_descriptor, filter_descriptor, - backward_filter_data, dnn::DataType::kDouble, algorithm_desc, - scratch_memory, output_profile_result), - /*report_error=*/!output_profile_result); -} - -bool CudnnSupport::DoConvolveBackwardFilter( - Stream* stream, const dnn::BatchDescriptor& input_descriptor, - const DeviceMemory& input_data, - const dnn::BatchDescriptor& output_descriptor, - DeviceMemory backward_output_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::FilterDescriptor& filter_descriptor, - DeviceMemory* backward_filter_data, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, - dnn::ProfileResult* output_profile_result) { - return IsStatusOk( - DoConvolveBackwardFilterImpl( - stream, input_descriptor, input_data, output_descriptor, - backward_output_data, convolution_descriptor, filter_descriptor, - backward_filter_data, dnn::DataType::kFloat, algorithm_desc, - scratch_memory, output_profile_result), + backward_filter_data, acc_type, scratch_allocator, algorithm_config, + output_profile_result), /*report_error=*/!output_profile_result); } -bool CudnnSupport::DoConvolveBackwardFilter( - Stream* stream, const dnn::BatchDescriptor& input_descriptor, - const DeviceMemory& input_data, - const dnn::BatchDescriptor& output_descriptor, - DeviceMemory backward_output_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::FilterDescriptor& filter_descriptor, - DeviceMemory* backward_filter_data, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, - dnn::ProfileResult* output_profile_result) { - dnn::DataType acc_type = - CudnnEnvVar::IsEnabled() - ? dnn::DataType::kFloat - : dnn::DataType::kHalf; - return IsStatusOk(DoConvolveBackwardFilterImpl( - stream, input_descriptor, input_data, output_descriptor, - backward_output_data, convolution_descriptor, - filter_descriptor, backward_filter_data, acc_type, - algorithm_desc, scratch_memory, output_profile_result), - /*report_error=*/!output_profile_result); -} - template port::Status CudnnSupport::DoConvolveBackwardBiasImpl( Stream* stream, const dnn::BatchDescriptor& input_descriptor, diff --git a/tensorflow/stream_executor/cuda/cuda_dnn.h b/tensorflow/stream_executor/cuda/cuda_dnn.h index 6b5b6c690f..0641be140d 100644 --- a/tensorflow/stream_executor/cuda/cuda_dnn.h +++ b/tensorflow/stream_executor/cuda/cuda_dnn.h @@ -252,43 +252,6 @@ class CudnnSupport : public dnn::DnnSupport { DeviceMemory* scale_backprop, DeviceMemory* offset_backprop) override; - bool PrepareForConvolution( - Stream* stream, const dnn::BatchDescriptor& batch_descriptor, - const DeviceMemory& input_data, - const dnn::FilterDescriptor& filter_descriptor, - const DeviceMemory& filter_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::BatchDescriptor& output_descriptor, - DeviceMemory* output_data, ScratchAllocator* scratch_allocator, - const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, - DeviceMemory* scratch_memory) override; - - bool PrepareForConvolution( - Stream* stream, const dnn::BatchDescriptor& batch_descriptor, - const DeviceMemory& input_data, - const dnn::FilterDescriptor& filter_descriptor, - const DeviceMemory& filter_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::BatchDescriptor& output_descriptor, - DeviceMemory* output_data, ScratchAllocator* scratch_allocator, - const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, - DeviceMemory* scratch_memory) override; - - bool PrepareForConvolution( - Stream* stream, const dnn::BatchDescriptor& batch_descriptor, - const DeviceMemory& input_data, - const dnn::FilterDescriptor& filter_descriptor, - const DeviceMemory& filter_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::BatchDescriptor& output_descriptor, - DeviceMemory* output_data, - ScratchAllocator* scratch_allocator, - const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, - DeviceMemory* scratch_memory) override; - bool DoConvolve(Stream* stream, const dnn::BatchDescriptor& batch_descriptor, const DeviceMemory& input_data, const dnn::FilterDescriptor& filter_descriptor, @@ -296,8 +259,8 @@ class CudnnSupport : public dnn::DnnSupport { const dnn::ConvolutionDescriptor& convolution_descriptor, const dnn::BatchDescriptor& output_descriptor, DeviceMemory* output_data, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, dnn::ProfileResult* output_profile_result) override; bool DoConvolve(Stream* stream, const dnn::BatchDescriptor& batch_descriptor, @@ -307,8 +270,8 @@ class CudnnSupport : public dnn::DnnSupport { const dnn::ConvolutionDescriptor& convolution_descriptor, const dnn::BatchDescriptor& output_descriptor, DeviceMemory* output_data, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, dnn::ProfileResult* output_profile_result) override; bool DoConvolve(Stream* stream, const dnn::BatchDescriptor& batch_descriptor, @@ -318,8 +281,8 @@ class CudnnSupport : public dnn::DnnSupport { const dnn::ConvolutionDescriptor& convolution_descriptor, const dnn::BatchDescriptor& output_descriptor, DeviceMemory* output_data, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, dnn::ProfileResult* output_profile_result) override; bool DoFusedConvolve( @@ -421,20 +384,7 @@ class CudnnSupport : public dnn::DnnSupport { return false; } - bool PrepareForConvolutionBackwardData( - Stream* stream, const dnn::FilterDescriptor& filter_descriptor, - const DeviceMemory& filter_data, - const dnn::BatchDescriptor& output_descriptor, - DeviceMemory backward_output_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::BatchDescriptor& input_descriptor, - DeviceMemory* backward_input_data, - ScratchAllocator* scratch_allocator, - const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, - DeviceMemory* scratch_memory) override; - - bool PrepareForConvolutionBackwardData( + bool DoConvolveBackwardData( Stream* stream, const dnn::FilterDescriptor& filter_descriptor, const DeviceMemory& filter_data, const dnn::BatchDescriptor& output_descriptor, @@ -444,32 +394,6 @@ class CudnnSupport : public dnn::DnnSupport { DeviceMemory* backward_input_data, ScratchAllocator* scratch_allocator, const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, - DeviceMemory* scratch_memory) override; - - bool PrepareForConvolutionBackwardData( - Stream* stream, const dnn::FilterDescriptor& filter_descriptor, - const DeviceMemory& filter_data, - const dnn::BatchDescriptor& output_descriptor, - DeviceMemory backward_output_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::BatchDescriptor& input_descriptor, - DeviceMemory* backward_input_data, - ScratchAllocator* scratch_allocator, - const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, - DeviceMemory* scratch_memory) override; - - bool DoConvolveBackwardData( - Stream* stream, const dnn::FilterDescriptor& filter_descriptor, - const DeviceMemory& filter_data, - const dnn::BatchDescriptor& output_descriptor, - DeviceMemory backward_output_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::BatchDescriptor& input_descriptor, - DeviceMemory* backward_input_data, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, dnn::ProfileResult* output_profile_result) override; bool DoConvolveBackwardData( @@ -480,8 +404,8 @@ class CudnnSupport : public dnn::DnnSupport { const dnn::ConvolutionDescriptor& convolution_descriptor, const dnn::BatchDescriptor& input_descriptor, DeviceMemory* backward_input_data, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, dnn::ProfileResult* output_profile_result) override; bool DoConvolveBackwardData( @@ -492,48 +416,9 @@ class CudnnSupport : public dnn::DnnSupport { const dnn::ConvolutionDescriptor& convolution_descriptor, const dnn::BatchDescriptor& input_descriptor, DeviceMemory* backward_input_data, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, - dnn::ProfileResult* output_profile_result) override; - - bool PrepareForConvolutionBackwardFilter( - Stream* stream, const dnn::BatchDescriptor& input_descriptor, - const DeviceMemory& input_data, - const dnn::BatchDescriptor& output_descriptor, - DeviceMemory backward_output_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::FilterDescriptor& filter_descriptor, - DeviceMemory* backward_filter_data, - ScratchAllocator* scratch_allocator, - const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, - DeviceMemory* scratch_memory) override; - - bool PrepareForConvolutionBackwardFilter( - Stream* stream, const dnn::BatchDescriptor& input_descriptor, - const DeviceMemory& input_data, - const dnn::BatchDescriptor& output_descriptor, - DeviceMemory backward_output_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::FilterDescriptor& filter_descriptor, - DeviceMemory* backward_filter_data, - ScratchAllocator* scratch_allocator, - const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, - DeviceMemory* scratch_memory) override; - - bool PrepareForConvolutionBackwardFilter( - Stream* stream, const dnn::BatchDescriptor& input_descriptor, - const DeviceMemory& input_data, - const dnn::BatchDescriptor& output_descriptor, - DeviceMemory backward_output_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::FilterDescriptor& filter_descriptor, - DeviceMemory* backward_filter_data, ScratchAllocator* scratch_allocator, const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, - DeviceMemory* scratch_memory) override; + dnn::ProfileResult* output_profile_result) override; bool DoConvolveBackwardFilter( Stream* stream, const dnn::BatchDescriptor& input_descriptor, @@ -543,8 +428,8 @@ class CudnnSupport : public dnn::DnnSupport { const dnn::ConvolutionDescriptor& convolution_descriptor, const dnn::FilterDescriptor& filter_descriptor, DeviceMemory* backward_filter_data, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, dnn::ProfileResult* output_profile_result) override; bool DoConvolveBackwardFilter( @@ -555,8 +440,8 @@ class CudnnSupport : public dnn::DnnSupport { const dnn::ConvolutionDescriptor& convolution_descriptor, const dnn::FilterDescriptor& filter_descriptor, DeviceMemory* backward_filter_data, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, dnn::ProfileResult* output_profile_result) override; bool DoConvolveBackwardFilter( @@ -567,8 +452,8 @@ class CudnnSupport : public dnn::DnnSupport { const dnn::ConvolutionDescriptor& convolution_descriptor, const dnn::FilterDescriptor& filter_descriptor, DeviceMemory* backward_filter_data, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, dnn::ProfileResult* output_profile_result) override; bool DoConvolveBackwardBias( @@ -778,7 +663,7 @@ class CudnnSupport : public dnn::DnnSupport { DeviceMemory* offset_backprop); template - port::Status PrepareForConvolutionImpl( + port::Status DoConvolveImpl( Stream* stream, const dnn::BatchDescriptor& input_descriptor, const DeviceMemory& input_data, const dnn::FilterDescriptor& filter_descriptor, @@ -788,19 +673,6 @@ class CudnnSupport : public dnn::DnnSupport { DeviceMemory* output_data, dnn::DataType accumulator_type, ScratchAllocator* scratch_allocator, const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, DeviceMemory* scratch_memory); - - template - port::Status DoConvolveImpl( - Stream* stream, const dnn::BatchDescriptor& input_descriptor, - const DeviceMemory& input_data, - const dnn::FilterDescriptor& filter_descriptor, - const DeviceMemory& filter_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::BatchDescriptor& output_descriptor, - DeviceMemory* output_data, dnn::DataType accumulator_type, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, dnn::ProfileResult* output_profile_result); template @@ -820,19 +692,6 @@ class CudnnSupport : public dnn::DnnSupport { const dnn::AlgorithmConfig& algorithm_config, dnn::ProfileResult* output_profile_result); - template - port::Status PrepareForConvolutionBackwardDataImpl( - Stream* stream, const dnn::FilterDescriptor& filter_descriptor, - const DeviceMemory& filter_data, - const dnn::BatchDescriptor& output_descriptor, - DeviceMemory backward_output_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::BatchDescriptor& input_descriptor, - DeviceMemory* backward_input_data, dnn::DataType accumulator_type, - ScratchAllocator* scratch_allocator, - const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, DeviceMemory* scratch_memory); - template port::Status DoConvolveBackwardDataImpl( Stream* stream, const dnn::FilterDescriptor& filter_descriptor, @@ -842,22 +701,9 @@ class CudnnSupport : public dnn::DnnSupport { const dnn::ConvolutionDescriptor& convolution_descriptor, const dnn::BatchDescriptor& input_descriptor, DeviceMemory* backward_input_data, dnn::DataType accumulator_type, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, - dnn::ProfileResult* output_profile_result); - - template - port::Status PrepareForConvolutionBackwardFilterImpl( - Stream* stream, const dnn::BatchDescriptor& input_descriptor, - const DeviceMemory& input_data, - const dnn::BatchDescriptor& output_descriptor_in, - DeviceMemory backward_output_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::FilterDescriptor& filter_descriptor, - DeviceMemory* backward_filter_data, dnn::DataType accumulator_type, ScratchAllocator* scratch_allocator, const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, DeviceMemory* scratch_memory); + dnn::ProfileResult* output_profile_result); template port::Status DoConvolveBackwardFilterImpl( @@ -868,8 +714,8 @@ class CudnnSupport : public dnn::DnnSupport { const dnn::ConvolutionDescriptor& convolution_descriptor, const dnn::FilterDescriptor& filter_descriptor, DeviceMemory* backward_filter_data, dnn::DataType accumulator_type, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, dnn::ProfileResult* output_profile_result); template diff --git a/tensorflow/stream_executor/dnn.h b/tensorflow/stream_executor/dnn.h index 04949c4813..c044a356ef 100644 --- a/tensorflow/stream_executor/dnn.h +++ b/tensorflow/stream_executor/dnn.h @@ -730,7 +730,6 @@ class PoolingDescriptor { class AlgorithmDesc { public: typedef int64 Index; - AlgorithmDesc() : AlgorithmDesc(0, false) {} AlgorithmDesc(Index a, bool use_tensor_ops) { proto_.set_algo_id(a); proto_.set_math_type(use_tensor_ops ? AlgorithmProto::TENSOR_OP_MATH @@ -1176,52 +1175,6 @@ class DnnSupport { return false; } - virtual bool PrepareForConvolution( - Stream* stream, const BatchDescriptor& batch_descriptor, - const DeviceMemory& input_data, - const FilterDescriptor& filter_descriptor, - const DeviceMemory& filter_data, - const ConvolutionDescriptor& convolution_descriptor, - const BatchDescriptor& output_descriptor, - DeviceMemory* output_data, ScratchAllocator* scratch_allocator, - const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, DeviceMemory* scratch_memory) { - *algorithm_desc = {}; - *scratch_memory = {}; - return true; - } - - virtual bool PrepareForConvolution( - Stream* stream, const BatchDescriptor& batch_descriptor, - const DeviceMemory& input_data, - const FilterDescriptor& filter_descriptor, - const DeviceMemory& filter_data, - const ConvolutionDescriptor& convolution_descriptor, - const BatchDescriptor& output_descriptor, - DeviceMemory* output_data, ScratchAllocator* scratch_allocator, - const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, DeviceMemory* scratch_memory) { - *algorithm_desc = {}; - *scratch_memory = {}; - return true; - } - - virtual bool PrepareForConvolution( - Stream* stream, const BatchDescriptor& batch_descriptor, - const DeviceMemory& input_data, - const FilterDescriptor& filter_descriptor, - const DeviceMemory& filter_data, - const ConvolutionDescriptor& convolution_descriptor, - const BatchDescriptor& output_descriptor, - DeviceMemory* output_data, - ScratchAllocator* scratch_allocator, - const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, DeviceMemory* scratch_memory) { - *algorithm_desc = {}; - *scratch_memory = {}; - return true; - } - // Enqueues a single-precision convolution operation onto the stream. // // Arguments (all borrowed): @@ -1235,10 +1188,10 @@ class DnnSupport { // output_descriptor: dimensions of the output layer. // output_data: un-owned device memory region in which to place the // convolution result. - // algorithm_desc: specifies which algorithm should be used for the + // scratch_allocator: un-owned, may-be-null object that may allocate scratch + // space in order to speed up the convolution operation. + // algorithm_config: specifies which algorithm should be used for the // operation. - // scratch: un-owned device memory for scratch space in order to speed up - // the convolution operation. // output_profile_result: the output profile result for this call. The // profiling is only enabled when this is not nullptr. // @@ -1263,9 +1216,8 @@ class DnnSupport { const DeviceMemory& filter_data, const dnn::ConvolutionDescriptor& convolution_descriptor, const dnn::BatchDescriptor& output_descriptor, - DeviceMemory* output_data, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, + DeviceMemory* output_data, ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, ProfileResult* output_profile_result) = 0; // Enqueues a double-precision convolution operation onto the stream. @@ -1277,9 +1229,8 @@ class DnnSupport { const DeviceMemory& filter_data, const dnn::ConvolutionDescriptor& convolution_descriptor, const dnn::BatchDescriptor& output_descriptor, - DeviceMemory* output_data, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, + DeviceMemory* output_data, ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, dnn::ProfileResult* output_profile_result) = 0; // Enqueues a half-precision convolution operation onto the stream. @@ -1292,8 +1243,8 @@ class DnnSupport { const dnn::ConvolutionDescriptor& convolution_descriptor, const dnn::BatchDescriptor& output_descriptor, DeviceMemory* output_data, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, ProfileResult* output_profile_result) = 0; // Return a list of algorithms supported by the forward convolution pass. @@ -1349,54 +1300,6 @@ class DnnSupport { const BatchDescriptor& output_descriptor, DeviceMemory* output_data) = 0; - virtual bool PrepareForConvolutionBackwardData( - Stream* stream, const dnn::FilterDescriptor& filter_descriptor, - const DeviceMemory& filter_data, - const dnn::BatchDescriptor& output_descriptor, - DeviceMemory backward_output_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::BatchDescriptor& input_descriptor, - DeviceMemory* backward_input_data, - ScratchAllocator* scratch_allocator, - const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, DeviceMemory* scratch_memory) { - *algorithm_desc = {}; - *scratch_memory = {}; - return true; - } - - virtual bool PrepareForConvolutionBackwardData( - Stream* stream, const dnn::FilterDescriptor& filter_descriptor, - const DeviceMemory& filter_data, - const dnn::BatchDescriptor& output_descriptor, - DeviceMemory backward_output_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::BatchDescriptor& input_descriptor, - DeviceMemory* backward_input_data, - ScratchAllocator* scratch_allocator, - const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, DeviceMemory* scratch_memory) { - *algorithm_desc = {}; - *scratch_memory = {}; - return true; - } - - virtual bool PrepareForConvolutionBackwardData( - Stream* stream, const dnn::FilterDescriptor& filter_descriptor, - const DeviceMemory& filter_data, - const dnn::BatchDescriptor& output_descriptor, - DeviceMemory backward_output_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::BatchDescriptor& input_descriptor, - DeviceMemory* backward_input_data, - ScratchAllocator* scratch_allocator, - const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, DeviceMemory* scratch_memory) { - *algorithm_desc = {}; - *scratch_memory = {}; - return true; - } - // Enqueues a single-precision backward convolution (for data) operation onto // the stream. // @@ -1416,15 +1319,15 @@ class DnnSupport { // scratch_allocator: un-owned, may-be-null object that may allocate scratch // space in order to speed up the convolution operation. virtual bool DoConvolveBackwardData( - Stream* stream, const dnn::FilterDescriptor& filter_descriptor, + Stream* stream, const FilterDescriptor& filter_descriptor, const DeviceMemory& filter_data, - const dnn::BatchDescriptor& output_descriptor, + const BatchDescriptor& output_descriptor, DeviceMemory backward_output_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::BatchDescriptor& input_descriptor, + const ConvolutionDescriptor& convolution_descriptor, + const BatchDescriptor& input_descriptor, DeviceMemory* backward_input_data, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, ProfileResult* output_profile_result) = 0; // Return a list of algorithms supported by the backward convolution pass for @@ -1434,76 +1337,28 @@ class DnnSupport { std::vector* out_algorithms); virtual bool DoConvolveBackwardData( - Stream* stream, const dnn::FilterDescriptor& filter_descriptor, + Stream* stream, const FilterDescriptor& filter_descriptor, const DeviceMemory& filter_data, - const dnn::BatchDescriptor& output_descriptor, - DeviceMemory backward_output_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::BatchDescriptor& input_descriptor, - DeviceMemory* backward_input_data, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, - ProfileResult* output_profile_result) = 0; - - virtual bool DoConvolveBackwardData( - Stream* stream, const dnn::FilterDescriptor& filter_descriptor, - const DeviceMemory& filter_data, - const dnn::BatchDescriptor& output_descriptor, - DeviceMemory backward_output_data, - const dnn::ConvolutionDescriptor& convolution_descriptor, - const dnn::BatchDescriptor& input_descriptor, - DeviceMemory* backward_input_data, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, - ProfileResult* output_profile_result) = 0; - - virtual bool PrepareForConvolutionBackwardFilter( - Stream* stream, const BatchDescriptor& input_descriptor, - const DeviceMemory& input_data, - const BatchDescriptor& output_descriptor, - DeviceMemory backward_output_data, - const ConvolutionDescriptor& convolution_descriptor, - const FilterDescriptor& filter_descriptor, - DeviceMemory* backward_filter_data, - ScratchAllocator* scratch_allocator, - const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, DeviceMemory* scratch_memory) { - *algorithm_desc = {}; - *scratch_memory = {}; - return true; - } - - virtual bool PrepareForConvolutionBackwardFilter( - Stream* stream, const BatchDescriptor& input_descriptor, - const DeviceMemory& input_data, const BatchDescriptor& output_descriptor, DeviceMemory backward_output_data, const ConvolutionDescriptor& convolution_descriptor, - const FilterDescriptor& filter_descriptor, - DeviceMemory* backward_filter_data, + const BatchDescriptor& input_descriptor, + DeviceMemory* backward_input_data, ScratchAllocator* scratch_allocator, const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, DeviceMemory* scratch_memory) { - *algorithm_desc = {}; - *scratch_memory = {}; - return true; - } + ProfileResult* output_profile_result) = 0; - virtual bool PrepareForConvolutionBackwardFilter( - Stream* stream, const BatchDescriptor& input_descriptor, - const DeviceMemory& input_data, + virtual bool DoConvolveBackwardData( + Stream* stream, const FilterDescriptor& filter_descriptor, + const DeviceMemory& filter_data, const BatchDescriptor& output_descriptor, DeviceMemory backward_output_data, const ConvolutionDescriptor& convolution_descriptor, - const FilterDescriptor& filter_descriptor, - DeviceMemory* backward_filter_data, + const BatchDescriptor& input_descriptor, + DeviceMemory* backward_input_data, ScratchAllocator* scratch_allocator, const dnn::AlgorithmConfig& algorithm_config, - dnn::AlgorithmDesc* algorithm_desc, DeviceMemory* scratch_memory) { - *algorithm_desc = {}; - *scratch_memory = {}; - return true; - } + ProfileResult* output_profile_result) = 0; // Enqueues a single-precision backward convolution (for filter) operation // onto the stream. @@ -1532,8 +1387,8 @@ class DnnSupport { const ConvolutionDescriptor& convolution_descriptor, const FilterDescriptor& filter_descriptor, DeviceMemory* backward_filter_data, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, ProfileResult* output_profile_result) = 0; // Return a list of algorithms supported by the backward convolution pass for @@ -1550,8 +1405,8 @@ class DnnSupport { const ConvolutionDescriptor& convolution_descriptor, const FilterDescriptor& filter_descriptor, DeviceMemory* backward_filter_data, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, ProfileResult* output_profile_result) = 0; virtual bool DoConvolveBackwardFilter( @@ -1562,8 +1417,8 @@ class DnnSupport { const ConvolutionDescriptor& convolution_descriptor, const FilterDescriptor& filter_descriptor, DeviceMemory* backward_filter_data, - const dnn::AlgorithmDesc& algorithm_desc, - DeviceMemory* scratch_memory, + ScratchAllocator* scratch_allocator, + const dnn::AlgorithmConfig& algorithm_config, ProfileResult* output_profile_result) = 0; // Enqueues a single-precision backward convolution (for bias) operation onto diff --git a/tensorflow/stream_executor/stream.cc b/tensorflow/stream_executor/stream.cc index 4503127bee..3edc66cde8 100644 --- a/tensorflow/stream_executor/stream.cc +++ b/tensorflow/stream_executor/stream.cc @@ -549,16 +549,11 @@ Stream &Stream::ThenConvolveWithScratch( if (ok()) { if (dnn::DnnSupport *dnn = parent_->AsDnn()) { - DeviceMemory scratch_memory; - dnn::AlgorithmDesc algorithm_desc; - CheckError(dnn->PrepareForConvolution( - this, input_descriptor, input_data, filter_descriptor, filter_data, - convolution_descriptor, output_descriptor, output, scratch_allocator, - dnn::AlgorithmConfig(), &algorithm_desc, &scratch_memory)); CheckError(dnn->DoConvolve( this, input_descriptor, input_data, filter_descriptor, filter_data, - convolution_descriptor, output_descriptor, output, algorithm_desc, - &scratch_memory, nullptr)); + convolution_descriptor, output_descriptor, output, scratch_allocator, + dnn::AlgorithmConfig(), + /*output_profile_result=*/nullptr)); } else { SetErrorAndLogNoDnnSupport(); } @@ -581,16 +576,11 @@ Stream &Stream::ThenConvolveWithScratch( if (ok()) { if (dnn::DnnSupport *dnn = parent_->AsDnn()) { - DeviceMemory scratch_memory; - dnn::AlgorithmDesc algorithm_desc; - CheckError(dnn->PrepareForConvolution( - this, input_descriptor, input_data, filter_descriptor, filter_data, - convolution_descriptor, output_descriptor, output, scratch_allocator, - dnn::AlgorithmConfig(), &algorithm_desc, &scratch_memory)); CheckError(dnn->DoConvolve( this, input_descriptor, input_data, filter_descriptor, filter_data, - convolution_descriptor, output_descriptor, output, algorithm_desc, - &scratch_memory, nullptr)); + convolution_descriptor, output_descriptor, output, scratch_allocator, + dnn::AlgorithmConfig(), + /*output_profile_result=*/nullptr)); } else { SetErrorAndLogNoDnnSupport(); } @@ -768,18 +758,10 @@ Stream &Stream::ThenConvolveWithAlgorithm( if (ok()) { if (dnn::DnnSupport *dnn = parent_->AsDnn()) { - DeviceMemory scratch_memory; - dnn::AlgorithmDesc algorithm_desc; - auto status = dnn->PrepareForConvolution( + auto status = dnn->DoConvolve( this, input_descriptor, input_data, filter_descriptor, filter_data, convolution_descriptor, output_descriptor, output, scratch_allocator, - algorithm_config, &algorithm_desc, &scratch_memory); - if (status) { - status = dnn->DoConvolve( - this, input_descriptor, input_data, filter_descriptor, filter_data, - convolution_descriptor, output_descriptor, output, algorithm_desc, - &scratch_memory, output_profile_result); - } + algorithm_config, output_profile_result); if (!status && !output_profile_result) { SetError(); } @@ -807,18 +789,10 @@ Stream &Stream::ThenConvolveWithAlgorithm( if (ok()) { if (dnn::DnnSupport *dnn = parent_->AsDnn()) { - DeviceMemory scratch_memory; - dnn::AlgorithmDesc algorithm_desc; - auto status = dnn->PrepareForConvolution( + auto status = dnn->DoConvolve( this, input_descriptor, input_data, filter_descriptor, filter_data, convolution_descriptor, output_descriptor, output, scratch_allocator, - algorithm_config, &algorithm_desc, &scratch_memory); - if (status) { - status = dnn->DoConvolve( - this, input_descriptor, input_data, filter_descriptor, filter_data, - convolution_descriptor, output_descriptor, output, algorithm_desc, - &scratch_memory, output_profile_result); - } + algorithm_config, output_profile_result); if (!status && !output_profile_result) { SetError(); } @@ -846,18 +820,10 @@ Stream &Stream::ThenConvolveWithAlgorithm( if (ok()) { if (dnn::DnnSupport *dnn = parent_->AsDnn()) { - DeviceMemory scratch_memory; - dnn::AlgorithmDesc algorithm_desc; - auto status = dnn->PrepareForConvolution( + auto status = dnn->DoConvolve( this, input_descriptor, input_data, filter_descriptor, filter_data, convolution_descriptor, output_descriptor, output, scratch_allocator, - algorithm_config, &algorithm_desc, &scratch_memory); - if (status) { - status = dnn->DoConvolve( - this, input_descriptor, input_data, filter_descriptor, filter_data, - convolution_descriptor, output_descriptor, output, algorithm_desc, - &scratch_memory, output_profile_result); - } + algorithm_config, output_profile_result); if (!status && !output_profile_result) { SetError(); } @@ -1003,17 +969,10 @@ Stream &Stream::ThenConvolveBackwardDataWithScratch( if (ok()) { if (dnn::DnnSupport *dnn = parent_->AsDnn()) { - DeviceMemory scratch_memory; - dnn::AlgorithmDesc algorithm_desc; - CheckError(dnn->PrepareForConvolutionBackwardData( - this, filter_descriptor, filter_data, output_descriptor, - backward_output_data, convolution_descriptor, input_descriptor, - backward_input_data, scratch_allocator, dnn::AlgorithmConfig(), - &algorithm_desc, &scratch_memory)); CheckError(dnn->DoConvolveBackwardData( this, filter_descriptor, filter_data, output_descriptor, backward_output_data, convolution_descriptor, input_descriptor, - backward_input_data, algorithm_desc, &scratch_memory, + backward_input_data, scratch_allocator, dnn::AlgorithmConfig(), /*output_profile_result=*/nullptr)); } else { SetErrorAndLogNoDnnSupport(); @@ -1040,20 +999,11 @@ Stream &Stream::ThenConvolveBackwardDataWithAlgorithm( if (ok()) { if (dnn::DnnSupport *dnn = parent_->AsDnn()) { - DeviceMemory scratch_memory; - dnn::AlgorithmDesc algorithm_desc; - auto status = dnn->PrepareForConvolutionBackwardData( + auto status = dnn->DoConvolveBackwardData( this, filter_descriptor, filter_data, output_descriptor, backward_output_data, convolution_descriptor, input_descriptor, backward_input_data, scratch_allocator, algorithm_config, - &algorithm_desc, &scratch_memory); - if (status) { - status = dnn->DoConvolveBackwardData( - this, filter_descriptor, filter_data, output_descriptor, - backward_output_data, convolution_descriptor, input_descriptor, - backward_input_data, algorithm_desc, &scratch_memory, - output_profile_result); - } + output_profile_result); if (!status && !output_profile_result) { SetError(); } @@ -1082,20 +1032,11 @@ Stream &Stream::ThenConvolveBackwardDataWithAlgorithm( if (ok()) { if (dnn::DnnSupport *dnn = parent_->AsDnn()) { - DeviceMemory scratch_memory; - dnn::AlgorithmDesc algorithm_desc; - auto status = dnn->PrepareForConvolutionBackwardData( + auto status = dnn->DoConvolveBackwardData( this, filter_descriptor, filter_data, output_descriptor, backward_output_data, convolution_descriptor, input_descriptor, backward_input_data, scratch_allocator, algorithm_config, - &algorithm_desc, &scratch_memory); - if (status) { - status = dnn->DoConvolveBackwardData( - this, filter_descriptor, filter_data, output_descriptor, - backward_output_data, convolution_descriptor, input_descriptor, - backward_input_data, algorithm_desc, &scratch_memory, - output_profile_result); - } + output_profile_result); if (!status && !output_profile_result) { SetError(); } @@ -1124,20 +1065,11 @@ Stream &Stream::ThenConvolveBackwardDataWithAlgorithm( if (ok()) { if (dnn::DnnSupport *dnn = parent_->AsDnn()) { - DeviceMemory scratch_memory; - dnn::AlgorithmDesc algorithm_desc; - auto status = dnn->PrepareForConvolutionBackwardData( + auto status = dnn->DoConvolveBackwardData( this, filter_descriptor, filter_data, output_descriptor, backward_output_data, convolution_descriptor, input_descriptor, backward_input_data, scratch_allocator, algorithm_config, - &algorithm_desc, &scratch_memory); - if (status) { - status = dnn->DoConvolveBackwardData( - this, filter_descriptor, filter_data, output_descriptor, - backward_output_data, convolution_descriptor, input_descriptor, - backward_input_data, algorithm_desc, &scratch_memory, - output_profile_result); - } + output_profile_result); if (!status && !output_profile_result) { SetError(); } @@ -1164,17 +1096,10 @@ Stream &Stream::ThenConvolveBackwardDataWithScratch( if (ok()) { if (dnn::DnnSupport *dnn = parent_->AsDnn()) { - DeviceMemory scratch_memory; - dnn::AlgorithmDesc algorithm_desc; - CheckError(dnn->PrepareForConvolutionBackwardData( - this, filter_descriptor, filter_data, output_descriptor, - backward_output_data, convolution_descriptor, input_descriptor, - backward_input_data, scratch_allocator, dnn::AlgorithmConfig(), - &algorithm_desc, &scratch_memory)); CheckError(dnn->DoConvolveBackwardData( this, filter_descriptor, filter_data, output_descriptor, backward_output_data, convolution_descriptor, input_descriptor, - backward_input_data, algorithm_desc, &scratch_memory, + backward_input_data, scratch_allocator, dnn::AlgorithmConfig(), /*output_profile_result=*/nullptr)); } else { SetErrorAndLogNoDnnSupport(); @@ -1213,17 +1138,10 @@ Stream &Stream::ThenConvolveBackwardFilterWithScratch( if (ok()) { if (dnn::DnnSupport *dnn = parent_->AsDnn()) { - DeviceMemory scratch_memory; - dnn::AlgorithmDesc algorithm_desc; - CheckError(dnn->PrepareForConvolutionBackwardFilter( - this, input_descriptor, input_data, output_descriptor, - backward_output_data, convolution_descriptor, filter_descriptor, - backward_filter_data, scratch_allocator, dnn::AlgorithmConfig(), - &algorithm_desc, &scratch_memory)); CheckError(dnn->DoConvolveBackwardFilter( this, input_descriptor, input_data, output_descriptor, backward_output_data, convolution_descriptor, filter_descriptor, - backward_filter_data, algorithm_desc, &scratch_memory, + backward_filter_data, scratch_allocator, dnn::AlgorithmConfig(), /*output_profile_result=*/nullptr)); } else { SetErrorAndLogNoDnnSupport(); @@ -1250,20 +1168,11 @@ Stream &Stream::ThenConvolveBackwardFilterWithAlgorithm( if (ok()) { if (dnn::DnnSupport *dnn = parent_->AsDnn()) { - DeviceMemory scratch_memory; - dnn::AlgorithmDesc algorithm_desc; - auto status = dnn->PrepareForConvolutionBackwardFilter( + auto status = dnn->DoConvolveBackwardFilter( this, input_descriptor, input_data, output_descriptor, backward_output_data, convolution_descriptor, filter_descriptor, backward_filter_data, scratch_allocator, algorithm_config, - &algorithm_desc, &scratch_memory); - if (status) { - status = dnn->DoConvolveBackwardFilter( - this, input_descriptor, input_data, output_descriptor, - backward_output_data, convolution_descriptor, filter_descriptor, - backward_filter_data, algorithm_desc, &scratch_memory, - output_profile_result); - } + output_profile_result); if (!status && !output_profile_result) { SetError(); } @@ -1292,20 +1201,11 @@ Stream &Stream::ThenConvolveBackwardFilterWithAlgorithm( if (ok()) { if (dnn::DnnSupport *dnn = parent_->AsDnn()) { - DeviceMemory scratch_memory; - dnn::AlgorithmDesc algorithm_desc; - auto status = dnn->PrepareForConvolutionBackwardFilter( + auto status = dnn->DoConvolveBackwardFilter( this, input_descriptor, input_data, output_descriptor, backward_output_data, convolution_descriptor, filter_descriptor, backward_filter_data, scratch_allocator, algorithm_config, - &algorithm_desc, &scratch_memory); - if (status) { - status = dnn->DoConvolveBackwardFilter( - this, input_descriptor, input_data, output_descriptor, - backward_output_data, convolution_descriptor, filter_descriptor, - backward_filter_data, algorithm_desc, &scratch_memory, - output_profile_result); - } + output_profile_result); if (!status && !output_profile_result) { SetError(); } @@ -1332,17 +1232,10 @@ Stream &Stream::ThenConvolveBackwardFilterWithScratch( if (ok()) { if (dnn::DnnSupport *dnn = parent_->AsDnn()) { - DeviceMemory scratch_memory; - dnn::AlgorithmDesc algorithm_desc; - CheckError(dnn->PrepareForConvolutionBackwardFilter( - this, input_descriptor, input_data, output_descriptor, - backward_output_data, convolution_descriptor, filter_descriptor, - backward_filter_data, scratch_allocator, dnn::AlgorithmConfig(), - &algorithm_desc, &scratch_memory)); CheckError(dnn->DoConvolveBackwardFilter( this, input_descriptor, input_data, output_descriptor, backward_output_data, convolution_descriptor, filter_descriptor, - backward_filter_data, algorithm_desc, &scratch_memory, + backward_filter_data, scratch_allocator, dnn::AlgorithmConfig(), /*output_profile_result=*/nullptr)); } else { SetErrorAndLogNoDnnSupport(); @@ -1369,20 +1262,11 @@ Stream &Stream::ThenConvolveBackwardFilterWithAlgorithm( if (ok()) { if (dnn::DnnSupport *dnn = parent_->AsDnn()) { - DeviceMemory scratch_memory; - dnn::AlgorithmDesc algorithm_desc; - auto status = dnn->PrepareForConvolutionBackwardFilter( + auto status = dnn->DoConvolveBackwardFilter( this, input_descriptor, input_data, output_descriptor, backward_output_data, convolution_descriptor, filter_descriptor, backward_filter_data, scratch_allocator, algorithm_config, - &algorithm_desc, &scratch_memory); - if (status) { - status = dnn->DoConvolveBackwardFilter( - this, input_descriptor, input_data, output_descriptor, - backward_output_data, convolution_descriptor, filter_descriptor, - backward_filter_data, algorithm_desc, &scratch_memory, - output_profile_result); - } + output_profile_result); if (!status && !output_profile_result) { SetError(); } -- GitLab From d35b41c2619cd520c0fa3fd786160d19dcc6339c Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Fri, 7 Dec 2018 17:21:52 -0800 Subject: [PATCH 254/873] [TF:XLA] Bump open source abseil revision to f197d7c72a54064cfde5a2058f1513a4a0ee36fb PiperOrigin-RevId: 224603073 --- tensorflow/workspace.bzl | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index ee91cd677b..60dcca3207 100755 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -123,11 +123,11 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""): tf_http_archive( name = "com_google_absl", build_file = clean_dep("//third_party:com_google_absl.BUILD"), - sha256 = "f7e3734c3c5854cf2bae1e193fbbd8f7d261673e0f2e042b1fca52732f688a0a", - strip_prefix = "abseil-cpp-284378a71b32dfb3af4e3661f585e671d1b603a3", + sha256 = "3ad76de484192b2d5afd49d90492b5ed0bc59eb1a4e8e0deecc7a2a077a90251", + strip_prefix = "abseil-cpp-f197d7c72a54064cfde5a2058f1513a4a0ee36fb", urls = [ - "https://mirror.bazel.build/github.com/abseil/abseil-cpp/archive/284378a71b32dfb3af4e3661f585e671d1b603a3.tar.gz", - "https://github.com/abseil/abseil-cpp/archive/284378a71b32dfb3af4e3661f585e671d1b603a3.tar.gz", + "https://mirror.bazel.build/github.com/abseil/abseil-cpp/archive/f197d7c72a54064cfde5a2058f1513a4a0ee36fb.tar.gz", + "https://github.com/abseil/abseil-cpp/archive/f197d7c72a54064cfde5a2058f1513a4a0ee36fb.tar.gz", ], ) -- GitLab From 4890b781b6b77fcc6c751d351c2f320ef9043e8d Mon Sep 17 00:00:00 2001 From: Gaurav Jain Date: Fri, 7 Dec 2018 17:36:37 -0800 Subject: [PATCH 255/873] Annotate tests as @run_v1_only Skip individual test cases or entire suites that are not running in v1. Also replace some @run_deprecated_v1 annotations since simply running the test in graph mode was not enough. PiperOrigin-RevId: 224604547 --- .../audio_microfrontend_op_test.py | 5 + .../lite/python/convert_saved_model_test.py | 7 ++ tensorflow/lite/python/convert_test.py | 2 + tensorflow/lite/python/lite_test.py | 4 + .../model_coverage/model_coverage_lib_test.py | 10 ++ tensorflow/python/autograph/impl/api_test.py | 1 + tensorflow/python/client/session_test.py | 23 ++++ .../data/kernel_tests/interleave_test.py | 2 + .../kernel_tests/iterator_cluster_test.py | 5 + .../python/data/kernel_tests/map_test.py | 2 + .../multi_device_iterator_test.py | 9 ++ .../python/debug/cli/analyzer_cli_test.py | 36 +----- .../python/debug/cli/cli_shared_test.py | 3 +- .../debug/cli/profile_analyzer_cli_test.py | 7 +- .../python/debug/cli/stepper_cli_test.py | 1 + .../python/debug/lib/debug_gradients_test.py | 15 +-- .../python/debug/lib/debug_utils_test.py | 20 +-- .../debug/lib/dist_session_debug_grpc_test.py | 1 + .../debug/lib/session_debug_file_test.py | 2 +- .../debug/lib/session_debug_grpc_test.py | 3 + .../python/debug/lib/session_debug_testlib.py | 1 + .../python/debug/lib/source_utils_test.py | 1 + tensorflow/python/debug/lib/stepper_test.py | 4 + .../python/debug/wrappers/disk_usage_test.py | 1 + .../debug/wrappers/dumping_wrapper_test.py | 1 + .../python/debug/wrappers/framework_test.py | 1 + .../debug/wrappers/local_cli_wrapper_test.py | 1 + .../distribute/distribute_coordinator_test.py | 2 + tensorflow/python/eager/backprop_test.py | 19 +++ .../python/eager/function_gradients_test.py | 2 +- tensorflow/python/eager/function_test.py | 2 + .../framework/auto_control_deps_test.py | 7 ++ .../framework/error_interpolation_test.py | 8 ++ .../python/framework/graph_util_test.py | 2 +- .../python/framework/meta_graph_test.py | 2 +- tensorflow/python/framework/ops_test.py | 13 +- tensorflow/python/framework/subscribe_test.py | 6 +- tensorflow/python/grappler/item_test.py | 2 +- .../python/grappler/memory_optimizer_test.py | 2 +- .../python/grappler/tf_optimizer_test.py | 4 +- tensorflow/python/keras/backend_test.py | 2 +- tensorflow/python/keras/callbacks_test.py | 10 +- tensorflow/python/keras/engine/saving_test.py | 6 +- .../python/keras/engine/sequential_test.py | 2 +- .../python/keras/engine/topology_test.py | 3 +- .../python/keras/engine/training_test.py | 8 +- tensorflow/python/keras/integration_test.py | 9 +- tensorflow/python/keras/layers/lstm_test.py | 6 +- .../python/keras/layers/normalization_test.py | 3 +- .../python/keras/layers/simplernn_test.py | 4 +- .../python/keras/layers/unified_lstm_test.py | 4 +- .../python/keras/layers/wrappers_test.py | 10 +- .../python/keras/model_subclassing_test.py | 2 + tensorflow/python/keras/models_test.py | 13 +- tensorflow/python/keras/optimizers_test.py | 18 +-- .../kernel_tests/atrous_convolution_test.py | 3 + .../python/kernel_tests/base64_ops_test.py | 1 + .../kernel_tests/batch_matmul_op_test.py | 3 + .../boosted_trees/quantile_ops_test.py | 8 +- .../kernel_tests/checkpoint_ops_test.py | 1 + .../python/kernel_tests/cholesky_op_test.py | 2 + .../python/kernel_tests/cond_v2_test.py | 4 + .../conditional_accumulator_test.py | 12 +- .../kernel_tests/control_flow_ops_py_test.py | 118 +++++++++++++++--- .../kernel_tests/control_flow_util_test.py | 4 + .../python/kernel_tests/ctc_loss_op_test.py | 36 +++--- .../dense_update_ops_no_tsan_test.py | 8 +- .../kernel_tests/dense_update_ops_test.py | 6 +- .../kernel_tests/depthwise_conv_op_test.py | 4 + .../kernel_tests/determinant_op_test.py | 3 + .../distributions/multinomial_test.py | 17 +++ .../python/kernel_tests/fifo_queue_test.py | 4 + .../kernel_tests/functional_ops_test.py | 10 +- .../kernel_tests/identity_op_py_test.py | 2 +- .../linalg/linear_operator_test.py | 6 +- .../python/kernel_tests/linalg_grad_test.py | 2 + tensorflow/python/kernel_tests/losses_test.py | 52 ++++---- .../kernel_tests/matrix_band_part_op_test.py | 3 + .../kernel_tests/matrix_logarithm_op_test.py | 3 + .../kernel_tests/matrix_solve_ls_op_test.py | 5 +- .../matrix_square_root_op_test.py | 3 + .../python/kernel_tests/norm_op_test.py | 4 + .../python/kernel_tests/numerics_test.py | 6 +- .../kernel_tests/padding_fifo_queue_test.py | 3 + .../partitioned_variables_test.py | 8 +- .../kernel_tests/priority_queue_test.py | 7 ++ .../python/kernel_tests/py_func_test.py | 26 +++- tensorflow/python/kernel_tests/qr_op_test.py | 3 + .../random/random_shuffle_queue_test.py | 3 + .../resource_variable_ops_test.py | 10 +- tensorflow/python/kernel_tests/rnn_test.py | 2 +- .../kernel_tests/scatter_nd_ops_test.py | 11 +- .../kernel_tests/self_adjoint_eig_op_test.py | 3 + .../python/kernel_tests/session_ops_test.py | 5 + .../signal/reconstruction_ops_test.py | 3 + .../sparse_conditional_accumulator_test.py | 24 ++-- .../python/kernel_tests/stack_ops_test.py | 4 +- tensorflow/python/kernel_tests/svd_op_test.py | 5 + .../kernel_tests/tensor_array_ops_test.py | 32 ++--- .../python/kernel_tests/tensordot_op_test.py | 4 + .../kernel_tests/unicode_encode_op_test.py | 13 ++ .../python/kernel_tests/variables_test.py | 26 ++-- .../python/kernel_tests/while_v2_test.py | 4 +- .../python/layers/normalization_test.py | 37 +----- tensorflow/python/ops/gradients_test.py | 23 ++++ tensorflow/python/ops/nn_test.py | 1 + .../ops/ragged/ragged_batch_gather_op_test.py | 4 +- .../ops/ragged/ragged_boolean_mask_op_test.py | 2 +- .../ops/ragged/ragged_concat_op_test.py | 4 +- .../python/ops/ragged/ragged_dispatch_test.py | 2 +- .../ops/ragged/ragged_from_sparse_op_test.py | 8 +- .../ops/ragged/ragged_from_tensor_op_test.py | 6 +- .../ops/ragged/ragged_gather_nd_op_test.py | 4 +- .../ops/ragged/ragged_gather_op_test.py | 10 +- .../ops/ragged/ragged_map_fn_op_test.py | 6 +- .../ragged/ragged_map_inner_values_op_test.py | 18 +-- .../ops/ragged/ragged_operators_test.py | 4 +- .../ops/ragged/ragged_reduce_op_test.py | 2 +- .../ops/ragged/ragged_row_lengths_op_test.py | 2 +- .../ops/ragged/ragged_segment_op_test.py | 4 +- .../python/ops/ragged/ragged_stack_op_test.py | 2 +- .../ops/ragged/ragged_tensor_shape_test.py | 6 + .../python/ops/ragged/ragged_tensor_test.py | 12 +- .../ops/ragged/ragged_to_sparse_op_test.py | 4 +- .../python/ops/ragged/ragged_util_test.py | 2 + .../python/ops/ragged/ragged_where_op_test.py | 2 +- .../profiler/internal/run_metadata_test.py | 2 +- .../python/profiler/model_analyzer_test.py | 4 + .../python/profiler/pprof_profiler_test.py | 2 +- tensorflow/python/saved_model/loader_test.py | 10 +- tensorflow/python/saved_model/save_test.py | 2 +- .../python/saved_model/saved_model_test.py | 15 +-- .../saved_model/signature_def_utils_test.py | 2 + tensorflow/python/saved_model/utils_test.py | 8 ++ tensorflow/python/tools/freeze_graph_test.py | 4 +- tensorflow/python/training/adagrad_test.py | 4 +- .../training/basic_session_run_hooks_test.py | 4 +- .../python/training/checkpoint_ops_test.py | 2 + .../checkpointable/data_structures_test.py | 4 + .../training/checkpointable/util_test.py | 1 + tensorflow/python/training/input_test.py | 32 ++--- .../training/learning_rate_decay_test.py | 2 +- .../localhost_cluster_performance_test.py | 2 + .../python/training/monitored_session_test.py | 1 + .../python/training/moving_averages_test.py | 32 ++--- .../python/training/quantize_training_test.py | 2 +- .../python/training/queue_runner_test.py | 8 +- tensorflow/python/training/saver_test.py | 24 ++-- ...server_lib_same_variables_no_clear_test.py | 2 +- tensorflow/python/training/server_lib_test.py | 7 ++ .../python/training/session_manager_test.py | 30 ++--- .../python/training/slot_creator_test.py | 6 +- tensorflow/python/training/supervisor_test.py | 22 ++-- .../training/sync_replicas_optimizer_test.py | 5 + .../training/tensorboard_logging_test.py | 4 + .../python/training/training_ops_test.py | 14 +-- .../python/training/training_util_test.py | 4 +- .../tools/api/tests/api_compatibility_test.py | 4 +- .../compatibility/testdata/test_file_v0_11.py | 8 ++ .../compatibility/testdata/test_file_v1_12.py | 4 + 160 files changed, 827 insertions(+), 480 deletions(-) diff --git a/tensorflow/lite/experimental/microfrontend/python/kernel_tests/audio_microfrontend_op_test.py b/tensorflow/lite/experimental/microfrontend/python/kernel_tests/audio_microfrontend_op_test.py index 561f5f7a50..3ce861707f 100644 --- a/tensorflow/lite/experimental/microfrontend/python/kernel_tests/audio_microfrontend_op_test.py +++ b/tensorflow/lite/experimental/microfrontend/python/kernel_tests/audio_microfrontend_op_test.py @@ -21,6 +21,7 @@ from __future__ import print_function import tensorflow as tf from tensorflow.lite.experimental.microfrontend.python.ops import audio_microfrontend_op as frontend_op +from tensorflow.python.framework import test_util SAMPLE_RATE = 1000 WINDOW_SIZE = 25 @@ -33,6 +34,7 @@ SMOOTHING_BITS = 10 class AudioFeatureGenerationTest(tf.test.TestCase): + @test_util.run_v1_only("b/120545219") def testSimple(self): with self.test_session(): audio = tf.constant( @@ -51,6 +53,7 @@ class AudioFeatureGenerationTest(tf.test.TestCase): self.assertAllEqual(filterbanks.eval(), [[479, 425], [436, 378], [410, 350], [391, 325]]) + @test_util.run_v1_only("b/120545219") def testSimpleFloatScaled(self): with self.test_session(): audio = tf.constant( @@ -72,6 +75,7 @@ class AudioFeatureGenerationTest(tf.test.TestCase): [[7.484375, 6.640625], [6.8125, 5.90625], [6.40625, 5.46875], [6.109375, 5.078125]]) + @test_util.run_v1_only("b/120545219") def testStacking(self): with self.test_session(): audio = tf.constant( @@ -114,6 +118,7 @@ class AudioFeatureGenerationTest(tf.test.TestCase): [[479, 425, 479, 425, 436, 378], [479, 425, 436, 378, 410, 350], [436, 378, 410, 350, 391, 325], [410, 350, 391, 325, 391, 325]]) + @test_util.run_v1_only("b/120545219") def testStackingDropFrame(self): with self.test_session(): audio = tf.constant( diff --git a/tensorflow/lite/python/convert_saved_model_test.py b/tensorflow/lite/python/convert_saved_model_test.py index 76113853ca..11bfcdc795 100644 --- a/tensorflow/lite/python/convert_saved_model_test.py +++ b/tensorflow/lite/python/convert_saved_model_test.py @@ -39,6 +39,7 @@ from tensorflow.python.saved_model import tag_constants class TensorFunctionsTest(test_util.TensorFlowTestCase): + @test_util.run_v1_only("b/120545219") def testGetTensorsValid(self): in_tensor = array_ops.placeholder( shape=[1, 16, 16, 3], dtype=dtypes.float32) @@ -49,6 +50,7 @@ class TensorFunctionsTest(test_util.TensorFlowTestCase): sess.graph, ["Placeholder"]) self.assertEqual("Placeholder:0", tensors[0].name) + @test_util.run_v1_only("b/120545219") def testGetTensorsInvalid(self): in_tensor = array_ops.placeholder( shape=[1, 16, 16, 3], dtype=dtypes.float32) @@ -61,6 +63,7 @@ class TensorFunctionsTest(test_util.TensorFlowTestCase): self.assertEqual("Invalid tensors 'invalid-input' were found.", str(error.exception)) + @test_util.run_v1_only("b/120545219") def testSetTensorShapeValid(self): tensor = array_ops.placeholder(shape=[None, 3, 5], dtype=dtypes.float32) self.assertEqual([None, 3, 5], tensor.shape.as_list()) @@ -68,6 +71,7 @@ class TensorFunctionsTest(test_util.TensorFlowTestCase): convert_saved_model.set_tensor_shapes([tensor], {"Placeholder": [5, 3, 5]}) self.assertEqual([5, 3, 5], tensor.shape.as_list()) + @test_util.run_v1_only("b/120545219") def testSetTensorShapeNoneValid(self): tensor = array_ops.placeholder(dtype=dtypes.float32) self.assertEqual(None, tensor.shape) @@ -75,6 +79,7 @@ class TensorFunctionsTest(test_util.TensorFlowTestCase): convert_saved_model.set_tensor_shapes([tensor], {"Placeholder": [1, 3, 5]}) self.assertEqual([1, 3, 5], tensor.shape.as_list()) + @test_util.run_v1_only("b/120545219") def testSetTensorShapeArrayInvalid(self): # Tests set_tensor_shape where the tensor name passed in doesn't exist. tensor = array_ops.placeholder(shape=[None, 3, 5], dtype=dtypes.float32) @@ -88,6 +93,7 @@ class TensorFunctionsTest(test_util.TensorFlowTestCase): str(error.exception)) self.assertEqual([None, 3, 5], tensor.shape.as_list()) + @test_util.run_v1_only("b/120545219") def testSetTensorShapeDimensionInvalid(self): # Tests set_tensor_shape where the shape passed in is incompatiable. tensor = array_ops.placeholder(shape=[None, 3, 5], dtype=dtypes.float32) @@ -101,6 +107,7 @@ class TensorFunctionsTest(test_util.TensorFlowTestCase): "(?, 3, 5) to [1, 5, 5].", str(error.exception)) self.assertEqual([None, 3, 5], tensor.shape.as_list()) + @test_util.run_v1_only("b/120545219") def testSetTensorShapeEmpty(self): tensor = array_ops.placeholder(shape=[None, 3, 5], dtype=dtypes.float32) self.assertEqual([None, 3, 5], tensor.shape.as_list()) diff --git a/tensorflow/lite/python/convert_test.py b/tensorflow/lite/python/convert_test.py index 2a6f1f634f..cf49ee2b47 100644 --- a/tensorflow/lite/python/convert_test.py +++ b/tensorflow/lite/python/convert_test.py @@ -34,6 +34,7 @@ from tensorflow.python.ops import math_ops from tensorflow.python.platform import test +@test_util.run_v1_only("b/120545219") class ConvertTest(test_util.TensorFlowTestCase): def testBasic(self): @@ -176,6 +177,7 @@ class ConvertTest(test_util.TensorFlowTestCase): "QUANTIZED_UINT8.", str(error.exception)) +@test_util.run_v1_only("b/120545219") class ConvertTestOpHint(test_util.TensorFlowTestCase): """Test the hint to stub functionality.""" diff --git a/tensorflow/lite/python/lite_test.py b/tensorflow/lite/python/lite_test.py index 2c630588ca..1f9c768b44 100644 --- a/tensorflow/lite/python/lite_test.py +++ b/tensorflow/lite/python/lite_test.py @@ -80,6 +80,7 @@ class FromConstructor(test_util.TensorFlowTestCase): self.assertTrue(converter._has_valid_tensors()) +@test_util.run_v1_only('b/120545219') class FromSessionTest(test_util.TensorFlowTestCase): def testFloat(self): @@ -497,6 +498,7 @@ class FromSessionTest(test_util.TensorFlowTestCase): interpreter.allocate_tensors() +@test_util.run_v1_only('b/120545219') class FromFrozenGraphFile(test_util.TensorFlowTestCase): def testFloat(self): @@ -744,6 +746,7 @@ class FromFrozenGraphFile(test_util.TensorFlowTestCase): interpreter.allocate_tensors() +@test_util.run_v1_only('b/120545219') class FromSavedModelTest(test_util.TensorFlowTestCase): def _createSavedModel(self, shape): @@ -888,6 +891,7 @@ class FromSavedModelTest(test_util.TensorFlowTestCase): interpreter.allocate_tensors() +@test_util.run_v1_only('b/120545219') class FromKerasFile(test_util.TensorFlowTestCase): def setUp(self): diff --git a/tensorflow/lite/testing/model_coverage/model_coverage_lib_test.py b/tensorflow/lite/testing/model_coverage/model_coverage_lib_test.py index 6b4e7427ed..4e329ac97d 100644 --- a/tensorflow/lite/testing/model_coverage/model_coverage_lib_test.py +++ b/tensorflow/lite/testing/model_coverage/model_coverage_lib_test.py @@ -28,6 +28,7 @@ from tensorflow.python import keras from tensorflow.python.client import session from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes +from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.platform import test @@ -42,6 +43,7 @@ class EvaluateFrozenGraph(test.TestCase): write_graph(sess.graph_def, '', graph_def_file, False) return graph_def_file + @test_util.run_v1_only('b/120545219') def testFloat(self): with session.Session().as_default() as sess: in_tensor = array_ops.placeholder( @@ -51,6 +53,7 @@ class EvaluateFrozenGraph(test.TestCase): model_coverage.test_frozen_graph(filename, ['Placeholder'], ['add']) + @test_util.run_v1_only('b/120545219') def testMultipleOutputs(self): with session.Session().as_default() as sess: in_tensor_1 = array_ops.placeholder( @@ -84,15 +87,18 @@ class EvaluateFrozenGraph(test.TestCase): filename = self._saveFrozenGraph(sess) return filename + @test_util.run_v1_only('b/120545219') def testQuantized(self): filename = self._getQuantizedModel() model_coverage.test_frozen_graph_quant(filename, ['inputA'], ['output']) + @test_util.run_v1_only('b/120545219') def testQuantizedInputShapes(self): filename = self._getQuantizedModel() model_coverage.test_frozen_graph_quant( filename, ['inputA'], ['output'], input_shapes={'inputA': [33, 33]}) + @test_util.run_v1_only('b/120545219') def testQuantizedFlexAll(self): filename = self._getQuantizedModel() model_coverage.test_frozen_graph_quant( @@ -102,6 +108,7 @@ class EvaluateFrozenGraph(test.TestCase): class EvaluateSavedModel(test.TestCase): + @test_util.run_v1_only('b/120545219') def testFloat(self): saved_model_dir = os.path.join(self.get_temp_dir(), 'simple_savedmodel') with session.Session().as_default() as sess: @@ -139,18 +146,21 @@ class EvaluateKerasModel(test.TestCase): os.close(fd) return keras_file + @test_util.run_v1_only('b/120545219') def testFloat(self): model = self._getSingleInputKerasModel() keras_file = self._saveKerasModel(model) model_coverage.test_keras_model(keras_file) + @test_util.run_v1_only('b/120545219') def testPostTrainingQuantize(self): model = self._getSingleInputKerasModel() keras_file = self._saveKerasModel(model) model_coverage.test_keras_model(keras_file, post_training_quantize=True) + @test_util.run_v1_only('b/120545219') def testTargetOps(self): model = self._getSingleInputKerasModel() keras_file = self._saveKerasModel(model) diff --git a/tensorflow/python/autograph/impl/api_test.py b/tensorflow/python/autograph/impl/api_test.py index 66edda5119..d5561ba824 100644 --- a/tensorflow/python/autograph/impl/api_test.py +++ b/tensorflow/python/autograph/impl/api_test.py @@ -218,6 +218,7 @@ class ApiTest(test.TestCase): constant_op.constant(-1)) self.assertEqual(1, self.evaluate(x)) + @test_util.run_v1_only('b/120545219') def test_converted_call_functools_partial(self): def test_fn(x, y, z): diff --git a/tensorflow/python/client/session_test.py b/tensorflow/python/client/session_test.py index 347833ce8f..c4a118a414 100644 --- a/tensorflow/python/client/session_test.py +++ b/tensorflow/python/client/session_test.py @@ -312,6 +312,7 @@ class SessionTest(test_util.TensorFlowTestCase): self.assertEqual(None, res[2]) self.assertEqual(44.0, res[1]) + @test_util.run_v1_only('b/120545219') def testFetchAttrs(self): if attr is None: self.skipTest('attr module is unavailable.') @@ -340,6 +341,7 @@ class SessionTest(test_util.TensorFlowTestCase): self.assertAllEqual(val3, result.field1) self.assertAllEqual(val2, result.field2) + @test_util.run_v1_only('b/120545219') def testFetchNestedAttrs(self): if attr is None: self.skipTest('attr module is unavailable.') @@ -1024,6 +1026,7 @@ class SessionTest(test_util.TensorFlowTestCase): fed_c_val = c.eval(feed_dict={a.name: [[4.0, 4.0]]}) self.assertAllEqual([[16.0, 16.0, 16.0]], fed_c_val) + @test_util.run_v1_only('b/120545219') def testOperationRunMethod(self): with session.Session(): a = constant_op.constant(1.0, shape=[1, 2]) @@ -1154,6 +1157,7 @@ class SessionTest(test_util.TensorFlowTestCase): else: importer.import_graph_def(gdef, name='import') + @test_util.run_v1_only('b/120545219') def testParallelRunAndSingleBuild(self): with session.Session() as sess: c = constant_op.constant(5.0) @@ -1174,6 +1178,7 @@ class SessionTest(test_util.TensorFlowTestCase): for t in threads: t.join() + @test_util.run_v1_only('b/120545219') def testParallelRunAndParallelBuild(self): with session.Session() as sess: c = constant_op.constant(5.0) @@ -1274,6 +1279,7 @@ class SessionTest(test_util.TensorFlowTestCase): with self.assertRaisesRegexp(RuntimeError, 'The Session graph is empty.'): sess.run({}) + @test_util.run_v1_only('b/120545219') def testNotEntered(self): # pylint: disable=protected-access self.assertEqual(ops._default_session_stack.get_default(), None) @@ -1289,6 +1295,7 @@ class SessionTest(test_util.TensorFlowTestCase): ValueError, lambda e: 'No default session is registered.' in str(e)): c_2.eval() + @test_util.run_v1_only('b/120545219') def testInteractive(self): with ops.device('/cpu:0'): sess = session.InteractiveSession() @@ -1301,6 +1308,7 @@ class SessionTest(test_util.TensorFlowTestCase): self.assertAllEqual([[24.0]], e.eval()) sess.close() + @test_util.run_v1_only('b/120545219') def testMultipleInteractiveSessionsWarning(self): # Reinitialize the global state to ensure that the expected warnings will # be emitted. @@ -1328,6 +1336,7 @@ class SessionTest(test_util.TensorFlowTestCase): sess2.close() sess.close() + @test_util.run_v1_only('b/120545219') def testInteractivePlacePrunedGraph(self): sess = session.InteractiveSession() @@ -1349,6 +1358,7 @@ class SessionTest(test_util.TensorFlowTestCase): a.eval() sess.close() + @test_util.run_v1_only('b/120545219') def testDefaultSessionPlacePrunedGraph(self): sess = session.Session() @@ -1769,9 +1779,11 @@ class SessionTest(test_util.TensorFlowTestCase): sess.run(a, run_metadata=run_metadata) self.assertEqual(len(run_metadata.partition_graphs), 0) + @test_util.run_v1_only('b/120545219') def testOutputPartitionGraphsDirect(self): self.runTestOutputPartitionGraphs(session.Session()) + @test_util.run_v1_only('b/120545219') def testOutputPartitionGraphsDistributed(self): server = server_lib.Server.create_local_server() self.runTestOutputPartitionGraphs(session.Session(server.target)) @@ -1796,6 +1808,7 @@ class SessionTest(test_util.TensorFlowTestCase): del sess1 del sess2 + @test_util.run_v1_only('b/120545219') def testAsDefault(self): c = constant_op.constant(37) sess = session.Session() @@ -1821,6 +1834,7 @@ class SessionTest(test_util.TensorFlowTestCase): with self.assertRaisesRegexp(TypeError, 'graph must be a tf.Graph'): session.Session(graph=37) + @test_util.run_v1_only('b/120545219') def testTimeoutWithShortOperations(self): num_epochs = 5 q = data_flow_ops.FIFOQueue(capacity=50, dtypes=[dtypes.int32], shapes=[()]) @@ -1834,6 +1848,7 @@ class SessionTest(test_util.TensorFlowTestCase): sess.run(enqueue_op) self.assertEqual(sess.run(q.size()), num_epochs * 2) + @test_util.run_v1_only('b/120545219') def testRegisterFetchAndFeedConversionFunctions(self): class SquaredTensor(object): @@ -1865,6 +1880,7 @@ class SessionTest(test_util.TensorFlowTestCase): squared_eval = sess.partial_run(partial_run, squared_tensor) self.assertAllClose(np2 * np2, squared_eval) + @test_util.run_v1_only('b/120545219') def testDefaultLogDevicePlacement(self): class CaptureStderr(str): @@ -1914,6 +1930,7 @@ class SessionTest(test_util.TensorFlowTestCase): self.assertTrue('/job:local/replica:0/task:0/device:CPU:0' in str(log), str(log)) + @test_util.run_v1_only('b/120545219') def testLocalMasterSessionTimeout(self): # Test that the timeout passed in a config to the session works correctly. config = config_pb2.ConfigProto(operation_timeout_in_ms=1000) @@ -1927,6 +1944,7 @@ class SessionTest(test_util.TensorFlowTestCase): with self.assertRaises(errors.DeadlineExceededError): sess.run(dequeued_t) + @test_util.run_v1_only('b/120545219') def testDefaultServerTimeout(self): # Test that the default server config timeout gets used when no Session # config is provided. @@ -1952,9 +1970,11 @@ class SessionTest(test_util.TensorFlowTestCase): with self.assertRaisesOpError('has inputs from different frames'): sess.run(res, feed_dict={data: 1.0}) + @test_util.run_v1_only('b/120545219') def testBuildGraphErrorDirect(self): self.runTestBuildGraphError(session.Session()) + @test_util.run_v1_only('b/120545219') def testBuildGraphErrorDist(self): server = server_lib.Server.create_local_server() self.runTestBuildGraphError(session.Session(server.target)) @@ -1993,9 +2013,11 @@ class SessionTest(test_util.TensorFlowTestCase): result = sess.run(f) self.assertEqual(result, 2.0) + @test_util.run_v1_only('b/120545219') def testAddFunctionToSession(self): self.runTestAddFunctionToSession() + @test_util.run_v1_only('b/120545219') def testAddFunctionToGrpcSession(self): server = server_lib.Server.create_local_server() self.runTestAddFunctionToSession(server.target) @@ -2009,6 +2031,7 @@ class SessionTest(test_util.TensorFlowTestCase): with session.Session(): pass + @test_util.run_v1_only('b/120545219') def testAutoConvertAndCheckData(self): with self.cached_session() as sess: a = array_ops.placeholder(dtype=dtypes.string) diff --git a/tensorflow/python/data/kernel_tests/interleave_test.py b/tensorflow/python/data/kernel_tests/interleave_test.py index c3450e6525..05a211afcc 100644 --- a/tensorflow/python/data/kernel_tests/interleave_test.py +++ b/tensorflow/python/data/kernel_tests/interleave_test.py @@ -264,6 +264,7 @@ class InterleaveTest(test_base.DatasetTestBase, parameterized.TestCase): ("8", np.int64([4, 0, 6]), 2, 3, 1), ("9", np.int64([4, 0, 6]), 2, 3, 2), ) + @test_util.run_v1_only("b/120545219") def testSkipEagerSloppyInterleaveInOrder(self, input_values, cycle_length, block_length, num_parallel_calls): get_next, coordination_events = _make_coordinated_sloppy_dataset( @@ -286,6 +287,7 @@ class InterleaveTest(test_base.DatasetTestBase, parameterized.TestCase): ("3", np.int64([4, 5, 6]), 3, 2, 3), ("4", np.int64([4, 0, 6]), 2, 3, 2), ) + @test_util.run_v1_only("b/120545219") def testSkipEagerSloppyInterleaveOutOfOrder(self, input_values, cycle_length, block_length, num_parallel_calls): get_next, coordination_events = _make_coordinated_sloppy_dataset( diff --git a/tensorflow/python/data/kernel_tests/iterator_cluster_test.py b/tensorflow/python/data/kernel_tests/iterator_cluster_test.py index 728bed20a1..2008823495 100644 --- a/tensorflow/python/data/kernel_tests/iterator_cluster_test.py +++ b/tensorflow/python/data/kernel_tests/iterator_cluster_test.py @@ -39,6 +39,7 @@ from tensorflow.python.platform import test class IteratorClusterTest(test.TestCase): + @test_util.run_v1_only("b/120545219") def testRemoteIteratorWithoutRemoteCallFail(self): worker_config = config_pb2.ConfigProto() worker_config.device_count["CPU"] = 2 @@ -92,6 +93,7 @@ class IteratorClusterTest(test.TestCase): with self.assertRaises(errors.OutOfRangeError): sess.run(remote_op, feed_dict={target_placeholder: device1}) + @test_util.run_v1_only("b/120545219") def testRemoteIteratorUsingRemoteCallOp(self): worker_config = config_pb2.ConfigProto() worker_config.device_count["CPU"] = 2 @@ -102,6 +104,7 @@ class IteratorClusterTest(test.TestCase): "/job:worker/replica:0/task:0/cpu:1", worker[0].target) + @test_util.run_v1_only("b/120545219") def testRemoteIteratorUsingRemoteCallOpCrossProcess(self): workers, _ = test_util.create_local_cluster(2, 1) @@ -109,6 +112,7 @@ class IteratorClusterTest(test.TestCase): "/job:worker/replica:0/task:1/cpu:0", workers[0].target) + @test_util.run_v1_only("b/120545219") def testCaptureHashTableInSharedIterator(self): worker, _ = test_util.create_local_cluster(1, 1) @@ -143,6 +147,7 @@ class IteratorClusterTest(test.TestCase): with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) + @test_util.run_v1_only("b/120545219") def testImplicitDisposeParallelMapDataset(self): # Tests whether a parallel map dataset will be cleaned up correctly when # the pipeline does not run it until exhaustion. diff --git a/tensorflow/python/data/kernel_tests/map_test.py b/tensorflow/python/data/kernel_tests/map_test.py index 8b22542e11..e07706413d 100644 --- a/tensorflow/python/data/kernel_tests/map_test.py +++ b/tensorflow/python/data/kernel_tests/map_test.py @@ -34,6 +34,7 @@ from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import tensor_util +from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import data_flow_ops @@ -82,6 +83,7 @@ def _make_coordinated_sloppy_dataset(num_elements, num_parallel_calls): return next_element, coordination_events +@test_util.run_v1_only("b/120545219") class MapDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): def _buildMapDataset(self, components, count): diff --git a/tensorflow/python/data/kernel_tests/multi_device_iterator_test.py b/tensorflow/python/data/kernel_tests/multi_device_iterator_test.py index 622ebb55de..0322d1f2c6 100644 --- a/tensorflow/python/data/kernel_tests/multi_device_iterator_test.py +++ b/tensorflow/python/data/kernel_tests/multi_device_iterator_test.py @@ -35,6 +35,7 @@ from tensorflow.python.platform import test # TODO(b/117581999): Add eager coverage. class MultiDeviceIteratorTest(test_base.DatasetTestBase): + @test_util.run_v1_only("b/120545219") def testNoGetNext(self): dataset = dataset_ops.Dataset.range(10) multi_device_iterator = multi_device_iterator_ops.MultiDeviceIterator( @@ -44,6 +45,7 @@ class MultiDeviceIteratorTest(test_base.DatasetTestBase): with self.test_session(config=config) as sess: self.evaluate(multi_device_iterator.initializer) + @test_util.run_v1_only("b/120545219") def testBasic(self): dataset = dataset_ops.Dataset.range(10) multi_device_iterator = multi_device_iterator_ops.MultiDeviceIterator( @@ -60,6 +62,7 @@ class MultiDeviceIteratorTest(test_base.DatasetTestBase): self.evaluate(elem_on_1) self.evaluate(elem_on_2) + @test_util.run_v1_only("b/120545219") def testOneOnSameDevice(self): with ops.device("/cpu:0"): dataset = dataset_ops.Dataset.range(10) @@ -77,6 +80,7 @@ class MultiDeviceIteratorTest(test_base.DatasetTestBase): self.evaluate(elem_on_1) self.evaluate(elem_on_2) + @test_util.run_v1_only("b/120545219") def testRepeatDevices(self): with ops.device("/cpu:0"): dataset = dataset_ops.Dataset.range(20) @@ -99,6 +103,7 @@ class MultiDeviceIteratorTest(test_base.DatasetTestBase): self.evaluate(elem_on_3) self.evaluate(elem_on_4) + @test_util.run_v1_only("b/120545219") def testNotFullyDivisible(self): dataset = dataset_ops.Dataset.range(9) multi_device_iterator = multi_device_iterator_ops.MultiDeviceIterator( @@ -116,6 +121,7 @@ class MultiDeviceIteratorTest(test_base.DatasetTestBase): self.evaluate(elem_on_1) self.evaluate(elem_on_2) + @test_util.run_v1_only("b/120545219") def testGetNextAsOptional(self): dataset = dataset_ops.Dataset.range(9) multi_device_iterator = multi_device_iterator_ops.MultiDeviceIterator( @@ -149,6 +155,7 @@ class MultiDeviceIteratorTest(test_base.DatasetTestBase): with self.assertRaises(errors.InvalidArgumentError): self.evaluate(elem_on_2_t) + @test_util.run_v1_only("b/120545219") def testUneven(self): dataset = dataset_ops.Dataset.range(10) multi_device_iterator = multi_device_iterator_ops.MultiDeviceIterator( @@ -166,6 +173,7 @@ class MultiDeviceIteratorTest(test_base.DatasetTestBase): self.evaluate(elem_on_1) self.evaluate(elem_on_2) + @test_util.run_v1_only("b/120545219") def testMultipleInitializations(self): with ops.device("/cpu:0"): epoch = array_ops.placeholder(dtypes.int64, shape=[]) @@ -259,6 +267,7 @@ class MultiDeviceIteratorTest(test_base.DatasetTestBase): with self.assertRaises(errors.InvalidArgumentError): self.evaluate(elem_on_2_t) + @test_util.run_v1_only("b/120545219") def testOptimization(self): dataset = dataset_ops.Dataset.range(10) dataset = dataset.apply(optimization.assert_next(["MemoryCacheImpl"])) diff --git a/tensorflow/python/debug/cli/analyzer_cli_test.py b/tensorflow/python/debug/cli/analyzer_cli_test.py index 322ecf9466..586982dc4b 100644 --- a/tensorflow/python/debug/cli/analyzer_cli_test.py +++ b/tensorflow/python/debug/cli/analyzer_cli_test.py @@ -573,6 +573,7 @@ def create_analyzer_cli(dump): return analyzer, registry +@test_util.run_v1_only("b/120545219") class AnalyzerCLISimpleMulAddTest(test_util.TensorFlowTestCase): @classmethod @@ -645,7 +646,6 @@ class AnalyzerCLISimpleMulAddTest(test_util.TensorFlowTestCase): self.assertEqual(len("Size (B)") + 1, dump_size_col_width) self.assertEqual(len("Op type") + 1, op_type_col_width) - @test_util.run_deprecated_v1 def testMeasureTensorListColumnWidthsGivesRightAnswerForData(self): dump = self._debug_dump.dumped_tensor_data[0] self.assertLess(dump.dump_size_bytes, 1000) @@ -661,7 +661,6 @@ class AnalyzerCLISimpleMulAddTest(test_util.TensorFlowTestCase): # column should be determined by the length of "VariableV2". self.assertEqual(len("VariableV2") + 1, op_type_col_width) - @test_util.run_deprecated_v1 def testListTensors(self): # Use shorthand alias for the command prefix. out = self._registry.dispatch_command("lt", []) @@ -675,7 +674,6 @@ class AnalyzerCLISimpleMulAddTest(test_util.TensorFlowTestCase): # Check the main menu. check_main_menu(self, out, list_tensors_enabled=False) - @test_util.run_deprecated_v1 def testListTensorsInReverseTimeOrderWorks(self): # Use shorthand alias for the command prefix. out = self._registry.dispatch_command("lt", ["-s", "timestamp", "-r"]) @@ -691,7 +689,6 @@ class AnalyzerCLISimpleMulAddTest(test_util.TensorFlowTestCase): reverse=True) check_main_menu(self, out, list_tensors_enabled=False) - @test_util.run_deprecated_v1 def testListTensorsInDumpSizeOrderWorks(self): out = self._registry.dispatch_command("lt", ["-s", "dump_size"]) assert_listed_tensors( @@ -705,7 +702,6 @@ class AnalyzerCLISimpleMulAddTest(test_util.TensorFlowTestCase): sort_by="dump_size") check_main_menu(self, out, list_tensors_enabled=False) - @test_util.run_deprecated_v1 def testListTensorsInReverseDumpSizeOrderWorks(self): out = self._registry.dispatch_command("lt", ["-s", "dump_size", "-r"]) assert_listed_tensors( @@ -725,7 +721,6 @@ class AnalyzerCLISimpleMulAddTest(test_util.TensorFlowTestCase): self.assertIn("ValueError: Unsupported key to sort tensors by: foobar", out.lines) - @test_util.run_deprecated_v1 def testListTensorsInOpTypeOrderWorks(self): # Use shorthand alias for the command prefix. out = self._registry.dispatch_command("lt", ["-s", "op_type"]) @@ -741,7 +736,6 @@ class AnalyzerCLISimpleMulAddTest(test_util.TensorFlowTestCase): reverse=False) check_main_menu(self, out, list_tensors_enabled=False) - @test_util.run_deprecated_v1 def testListTensorsInReverseOpTypeOrderWorks(self): # Use shorthand alias for the command prefix. out = self._registry.dispatch_command("lt", ["-s", "op_type", "-r"]) @@ -757,7 +751,6 @@ class AnalyzerCLISimpleMulAddTest(test_util.TensorFlowTestCase): reverse=True) check_main_menu(self, out, list_tensors_enabled=False) - @test_util.run_deprecated_v1 def testListTensorsInTensorNameOrderWorks(self): # Use shorthand alias for the command prefix. out = self._registry.dispatch_command("lt", ["-s", "tensor_name"]) @@ -773,7 +766,6 @@ class AnalyzerCLISimpleMulAddTest(test_util.TensorFlowTestCase): reverse=False) check_main_menu(self, out, list_tensors_enabled=False) - @test_util.run_deprecated_v1 def testListTensorsInReverseTensorNameOrderWorks(self): # Use shorthand alias for the command prefix. out = self._registry.dispatch_command("lt", ["-s", "tensor_name", "-r"]) @@ -789,7 +781,6 @@ class AnalyzerCLISimpleMulAddTest(test_util.TensorFlowTestCase): reverse=True) check_main_menu(self, out, list_tensors_enabled=False) - @test_util.run_deprecated_v1 def testListTensorsFilterByNodeNameRegex(self): out = self._registry.dispatch_command("list_tensors", ["--node_name_filter", ".*read.*"]) @@ -803,7 +794,6 @@ class AnalyzerCLISimpleMulAddTest(test_util.TensorFlowTestCase): assert_listed_tensors(self, out, [], [], node_name_regex="^read") check_main_menu(self, out, list_tensors_enabled=False) - @test_util.run_deprecated_v1 def testListTensorFilterByOpTypeRegex(self): out = self._registry.dispatch_command("list_tensors", ["--op_type_filter", "Identity"]) @@ -832,7 +822,6 @@ class AnalyzerCLISimpleMulAddTest(test_util.TensorFlowTestCase): op_type_regex="(Add|MatMul)") check_main_menu(self, out, list_tensors_enabled=False) - @test_util.run_deprecated_v1 def testListTensorWithFilterAndNodeNameExclusionWorks(self): # First, create and register the filter. def is_2x1_vector(datum, tensor): @@ -889,7 +878,6 @@ class AnalyzerCLISimpleMulAddTest(test_util.TensorFlowTestCase): out = self._registry.dispatch_command("list_tensors", ["--bar"]) check_syntax_error_output(self, out, "list_tensors") - @test_util.run_deprecated_v1 def testNodeInfoByNodeName(self): node_name = "simple_mul_add/matmul" out = self._registry.dispatch_command("node_info", [node_name]) @@ -914,7 +902,6 @@ class AnalyzerCLISimpleMulAddTest(test_util.TensorFlowTestCase): [(len(out.lines[0]) - len(node_name), len(out.lines[0]), "bold")], out.font_attr_segs[0]) - @test_util.run_deprecated_v1 def testNodeInfoShowAttributes(self): node_name = "simple_mul_add/matmul" out = self._registry.dispatch_command("node_info", ["-a", node_name]) @@ -938,7 +925,6 @@ class AnalyzerCLISimpleMulAddTest(test_util.TensorFlowTestCase): print_tensor_node_name=node_name, list_outputs_node_name=node_name) - @test_util.run_deprecated_v1 def testNodeInfoShowDumps(self): node_name = "simple_mul_add/matmul" out = self._registry.dispatch_command("node_info", ["-d", node_name]) @@ -963,7 +949,6 @@ class AnalyzerCLISimpleMulAddTest(test_util.TensorFlowTestCase): len(out.lines[16]) - len(out.lines[16].strip()), len(out.lines[16]), "pt %s:0 -n 0" % node_name) - @test_util.run_deprecated_v1 def testNodeInfoShowStackTraceUnavailableIsIndicated(self): self._debug_dump.set_python_graph(None) @@ -987,7 +972,6 @@ class AnalyzerCLISimpleMulAddTest(test_util.TensorFlowTestCase): print_tensor_node_name=node_name, list_outputs_node_name=node_name) - @test_util.run_deprecated_v1 def testNodeInfoShowStackTraceAvailableWorks(self): self._debug_dump.set_python_graph(self._sess.graph) @@ -1011,7 +995,6 @@ class AnalyzerCLISimpleMulAddTest(test_util.TensorFlowTestCase): print_tensor_node_name=node_name, list_outputs_node_name=node_name) - @test_util.run_deprecated_v1 def testNodeInfoByTensorName(self): node_name = "simple_mul_add/u/read" tensor_name = node_name + ":0" @@ -1381,7 +1364,6 @@ class AnalyzerCLISimpleMulAddTest(test_util.TensorFlowTestCase): break return index - @test_util.run_deprecated_v1 def testPrintSourceForOpNamesWholeFileWorks(self): self._debug_dump.set_python_graph(self._sess.graph) out = self._registry.dispatch_command( @@ -1434,7 +1416,6 @@ class AnalyzerCLISimpleMulAddTest(test_util.TensorFlowTestCase): self.assertEqual("pt simple_mul_add/add", out.font_attr_segs[index + 1][0][2].content) - @test_util.run_deprecated_v1 def testPrintSourceForTensorNamesWholeFileWorks(self): self._debug_dump.set_python_graph(self._sess.graph) out = self._registry.dispatch_command( @@ -1455,7 +1436,6 @@ class AnalyzerCLISimpleMulAddTest(test_util.TensorFlowTestCase): self.assertEqual("pt simple_mul_add/u:0", out.font_attr_segs[index + 2][0][2].content) - @test_util.run_deprecated_v1 def testPrintSourceForOpNamesStartingAtSpecifiedLineWorks(self): self._debug_dump.set_python_graph(self._sess.graph) out = self._registry.dispatch_command( @@ -1482,7 +1462,6 @@ class AnalyzerCLISimpleMulAddTest(test_util.TensorFlowTestCase): self.assertEqual("pt simple_mul_add/u/read", out.font_attr_segs[index + 3][0][2].content) - @test_util.run_deprecated_v1 def testPrintSourceForOpNameSettingMaximumElementCountWorks(self): self._debug_dump.set_python_graph(self._sess.graph) out = self._registry.dispatch_command( @@ -1527,7 +1506,6 @@ class AnalyzerCLISimpleMulAddTest(test_util.TensorFlowTestCase): self.assertTrue(cli_shared.COLOR_GRAY in attr_seg[2] or attr_seg[2] == cli_shared.COLOR_GRAY) - @test_util.run_deprecated_v1 def testListSourceWithNodeNameFilterWithMatchesWorks(self): self._debug_dump.set_python_graph(self._sess.graph) out = self._registry.dispatch_command("list_source", ["-n", ".*/read"]) @@ -1691,6 +1669,7 @@ class AnalyzerCLIPrintLargeTensorTest(test_util.TensorFlowTestCase): self.assertNotIn("...,", out.lines[4]) +@test_util.run_v1_only("b/120545219") class AnalyzerCLIControlDepTest(test_util.TensorFlowTestCase): @classmethod @@ -1742,7 +1721,6 @@ class AnalyzerCLIControlDepTest(test_util.TensorFlowTestCase): # Tear down temporary dump directory. shutil.rmtree(cls._dump_root) - @test_util.run_deprecated_v1 def testNodeInfoWithControlDependencies(self): # Call node_info on a node with control inputs. out = self._registry.dispatch_command("node_info", @@ -1783,7 +1761,6 @@ class AnalyzerCLIControlDepTest(test_util.TensorFlowTestCase): len(out.lines[z_line]), "ni -a -d -t control_deps/ctrl_dep_z") - @test_util.run_deprecated_v1 def testListInputsNonRecursiveNoControl(self): """List inputs non-recursively, without any control inputs.""" @@ -1826,7 +1803,6 @@ class AnalyzerCLIControlDepTest(test_util.TensorFlowTestCase): len(out.lines[3]) - len("control_deps/ctrl_dep_y"), len(out.lines[3]), "li -c -r control_deps/ctrl_dep_y") - @test_util.run_deprecated_v1 def testListInputsNonRecursiveNoControlUsingTensorName(self): """List inputs using the name of an output tensor of the node.""" @@ -1855,7 +1831,6 @@ class AnalyzerCLIControlDepTest(test_util.TensorFlowTestCase): len(out.lines[3]) - len("control_deps/ctrl_dep_y"), len(out.lines[3]), "li -c -r control_deps/ctrl_dep_y") - @test_util.run_deprecated_v1 def testListInputsNonRecursiveWithControls(self): """List inputs non-recursively, with control inputs.""" node_name = "control_deps/ctrl_dep_z" @@ -1886,7 +1861,6 @@ class AnalyzerCLIControlDepTest(test_util.TensorFlowTestCase): len(out.lines[5]) - len("control_deps/x"), len(out.lines[5]), "li -c -r control_deps/x") - @test_util.run_deprecated_v1 def testListInputsRecursiveWithControls(self): """List inputs recursively, with control inputs.""" node_name = "control_deps/ctrl_dep_z" @@ -1932,7 +1906,6 @@ class AnalyzerCLIControlDepTest(test_util.TensorFlowTestCase): len(out.lines[18]) - len("control_deps/x"), len(out.lines[18]), "li -c -r control_deps/x") - @test_util.run_deprecated_v1 def testListInputsRecursiveWithControlsWithDepthLimit(self): """List inputs recursively, with control inputs and a depth limit.""" node_name = "control_deps/ctrl_dep_z" @@ -1992,7 +1965,6 @@ class AnalyzerCLIControlDepTest(test_util.TensorFlowTestCase): "ERROR: There is no node named \"control_deps/z/foo\" in the " "partition graphs"], out.lines) - @test_util.run_deprecated_v1 def testListRecipientsRecursiveWithControlsWithDepthLimit(self): """List recipients recursively, with control inputs and a depth limit.""" @@ -2025,6 +1997,7 @@ class AnalyzerCLIControlDepTest(test_util.TensorFlowTestCase): out.font_attr_segs[0]) +@test_util.run_v1_only("b/120545219") class AnalyzerCLIWhileLoopTest(test_util.TensorFlowTestCase): @classmethod @@ -2064,7 +2037,6 @@ class AnalyzerCLIWhileLoopTest(test_util.TensorFlowTestCase): # Tear down temporary dump directory. shutil.rmtree(cls._dump_root) - @test_util.run_deprecated_v1 def testMultipleDumpsPrintTensorNoNumber(self): output = self._registry.dispatch_command("pt", ["while/Identity:0"]) @@ -2082,7 +2054,6 @@ class AnalyzerCLIWhileLoopTest(test_util.TensorFlowTestCase): self.assertEqual("For example:", output.lines[-2]) self.assertEqual(" print_tensor while/Identity:0 -n 0", output.lines[-1]) - @test_util.run_deprecated_v1 def testMultipleDumpsPrintTensorWithNumber(self): for i in xrange(5): output = self._registry.dispatch_command( @@ -2096,7 +2067,6 @@ class AnalyzerCLIWhileLoopTest(test_util.TensorFlowTestCase): self.assertTrue(output.lines[4].startswith("array(%d" % i)) self.assertTrue(output.lines[4].endswith(")")) - @test_util.run_deprecated_v1 def testMultipleDumpsPrintTensorInvalidNumber(self): output = self._registry.dispatch_command("pt", ["while/Identity:0", "-n", "10"]) diff --git a/tensorflow/python/debug/cli/cli_shared_test.py b/tensorflow/python/debug/cli/cli_shared_test.py index d191a234fd..66a12efda5 100644 --- a/tensorflow/python/debug/cli/cli_shared_test.py +++ b/tensorflow/python/debug/cli/cli_shared_test.py @@ -105,6 +105,7 @@ class TimeToReadableStrTest(test_util.TensorFlowTestCase): cli_shared.time_to_readable_str(100, force_time_unit="ks") +@test_util.run_v1_only("b/120545219") class GetRunStartIntroAndDescriptionTest(test_util.TensorFlowTestCase): def setUp(self): @@ -334,6 +335,7 @@ class GetRunStartIntroAndDescriptionTest(test_util.TensorFlowTestCase): self.assertEqual("run #1: 1 fetch (a:0); 1 feed (foo)", short_description) +@test_util.run_v1_only("b/120545219") class GetErrorIntroTest(test_util.TensorFlowTestCase): def setUp(self): @@ -342,7 +344,6 @@ class GetErrorIntroTest(test_util.TensorFlowTestCase): def tearDown(self): ops.reset_default_graph() - @test_util.run_deprecated_v1 def testShapeError(self): tf_error = errors.OpError(None, self.var_a.initializer, "foo description", None) diff --git a/tensorflow/python/debug/cli/profile_analyzer_cli_test.py b/tensorflow/python/debug/cli/profile_analyzer_cli_test.py index effcd500c7..d6d2b58b5f 100644 --- a/tensorflow/python/debug/cli/profile_analyzer_cli_test.py +++ b/tensorflow/python/debug/cli/profile_analyzer_cli_test.py @@ -70,6 +70,7 @@ def _assert_no_lines_match(pattern, lines): "%s matched at least one line in %s." % (pattern, str(lines))) +@test_util.run_v1_only("b/120545219") class ProfileAnalyzerListProfileTest(test_util.TensorFlowTestCase): def testNodeInfoEmpty(self): @@ -321,6 +322,7 @@ class ProfileAnalyzerListProfileTest(test_util.TensorFlowTestCase): _assert_at_least_one_line_matches(r"Device Total.*0\.009ms", prof_output) +@test_util.run_v1_only("b/120545219") class ProfileAnalyzerPrintSourceTest(test_util.TensorFlowTestCase): def setUp(self): @@ -348,7 +350,6 @@ class ProfileAnalyzerPrintSourceTest(test_util.TensorFlowTestCase): ops.reset_default_graph() super(ProfileAnalyzerPrintSourceTest, self).tearDown() - @test_util.run_deprecated_v1 def testPrintSourceForWhileLoop(self): prof_output = self.prof_analyzer.print_source([__file__]) @@ -362,7 +363,6 @@ class ProfileAnalyzerPrintSourceTest(test_util.TensorFlowTestCase): r"\[(\|)+(\s)*\] .*us .*7\(55\) .*L%d.*(\S)+" % self.loop_lineno, prof_output.lines) - @test_util.run_deprecated_v1 def testPrintSourceOutputContainsClickableLinks(self): prof_output = self.prof_analyzer.print_source([__file__]) any_match, line_index = _at_least_one_line_matches( @@ -379,7 +379,6 @@ class ProfileAnalyzerPrintSourceTest(test_util.TensorFlowTestCase): break self.assertTrue(any_menu_item_match) - @test_util.run_deprecated_v1 def testPrintSourceWithNonDefaultTimeUnit(self): prof_output = self.prof_analyzer.print_source([ __file__, "--time_unit", "ms"]) @@ -394,7 +393,6 @@ class ProfileAnalyzerPrintSourceTest(test_util.TensorFlowTestCase): r"\[(\|)+(\s)*\] .*ms .*7\(55\) .*L%d.*(\S)+" % self.loop_lineno, prof_output.lines) - @test_util.run_deprecated_v1 def testPrintSourceWithNodeNameFilter(self): prof_output = self.prof_analyzer.print_source([ __file__, "--node_name_filter", "x$"]) @@ -427,7 +425,6 @@ class ProfileAnalyzerPrintSourceTest(test_util.TensorFlowTestCase): break self.assertTrue(any_menu_item_match) - @test_util.run_deprecated_v1 def testPrintSourceWithOpTypeFilter(self): prof_output = self.prof_analyzer.print_source([ __file__, "--op_type_filter", "Less"]) diff --git a/tensorflow/python/debug/cli/stepper_cli_test.py b/tensorflow/python/debug/cli/stepper_cli_test.py index 7b8a42c253..5cf69d0168 100644 --- a/tensorflow/python/debug/cli/stepper_cli_test.py +++ b/tensorflow/python/debug/cli/stepper_cli_test.py @@ -129,6 +129,7 @@ def _parse_updated(lines): return updated +@test_util.run_v1_only("b/120545219") class NodeStepperSimpleGraphTest(test_util.TensorFlowTestCase): def setUp(self): diff --git a/tensorflow/python/debug/lib/debug_gradients_test.py b/tensorflow/python/debug/lib/debug_gradients_test.py index 1c53147863..885691c3ef 100644 --- a/tensorflow/python/debug/lib/debug_gradients_test.py +++ b/tensorflow/python/debug/lib/debug_gradients_test.py @@ -36,6 +36,7 @@ from tensorflow.python.platform import googletest from tensorflow.python.training import gradient_descent +@test_util.run_v1_only("b/120545219") class IdentifyGradientTest(test_util.TensorFlowTestCase): def setUp(self): @@ -54,7 +55,6 @@ class IdentifyGradientTest(test_util.TensorFlowTestCase): ops.reset_default_graph() debug_gradients.clear_gradient_debuggers() - @test_util.run_deprecated_v1 def testIdentifyGradientGivesCorrectTensorObjectWithoutContextManager(self): grad_debugger = debug_gradients.GradientsDebugger() id_grad_w = grad_debugger.identify_gradient(self.w) @@ -85,7 +85,6 @@ class IdentifyGradientTest(test_util.TensorFlowTestCase): self.assertIsInstance(w_grad, ops.Tensor) self.assertAllClose(1.0, self.sess.run(w_grad)) - @test_util.run_deprecated_v1 def testIdentifyGradientGivesCorrectTensorObjectWithTfGradients(self): grad_debugger = debug_gradients.GradientsDebugger() id_grad_w = grad_debugger.identify_gradient(self.w) @@ -117,7 +116,6 @@ class IdentifyGradientTest(test_util.TensorFlowTestCase): self.assertIsInstance(w_grad, ops.Tensor) self.assertAllClose(1.0, self.sess.run(w_grad)) - @test_util.run_deprecated_v1 def testCallingIdentifyGradientTwiceWithTheSameGradientsDebuggerErrors(self): grad_debugger = debug_gradients.GradientsDebugger() grad_debugger.identify_gradient(self.w) @@ -125,7 +123,6 @@ class IdentifyGradientTest(test_util.TensorFlowTestCase): "The graph already contains an op named .*"): grad_debugger.identify_gradient(self.w) - @test_util.run_deprecated_v1 def testIdentifyGradientWorksOnMultipleLosses(self): grad_debugger_1 = debug_gradients.GradientsDebugger() grad_debugger_2 = debug_gradients.GradientsDebugger() @@ -154,7 +151,6 @@ class IdentifyGradientTest(test_util.TensorFlowTestCase): self.assertAllClose(2.0 * 5.0, self.sess.run(dz1_dy)) self.assertAllClose(0.5 * (5.0**-0.5), self.sess.run(dz2_dy)) - @test_util.run_deprecated_v1 def testIdentifyGradientRaisesLookupErrorForUnknownXTensor(self): grad_debugger_1 = debug_gradients.GradientsDebugger() grad_debugger_2 = debug_gradients.GradientsDebugger() @@ -175,7 +171,6 @@ class IdentifyGradientTest(test_util.TensorFlowTestCase): r"This GradientsDebugger has not received any gradient tensor for "): grad_debugger_2.gradient_tensor(self.w) - @test_util.run_deprecated_v1 def testIdentifyGradientRaisesTypeErrorForNonTensorOrTensorNameInput(self): grad_debugger = debug_gradients.GradientsDebugger() with self.assertRaisesRegexp( @@ -184,7 +179,6 @@ class IdentifyGradientTest(test_util.TensorFlowTestCase): r"has type .*Operation.*"): grad_debugger.gradient_tensor(variables.global_variables_initializer()) - @test_util.run_deprecated_v1 def testIdentifyGradientTensorWorksWithGradientDescentOptimizer(self): grad_debugger = debug_gradients.GradientsDebugger() id_grad_w = grad_debugger.identify_gradient(self.w) @@ -200,7 +194,6 @@ class IdentifyGradientTest(test_util.TensorFlowTestCase): self.assertIsInstance(w_grad, ops.Tensor) self.assertAllClose(1.0, self.sess.run(w_grad)) - @test_util.run_deprecated_v1 def testWatchGradientsByXTensorNamesWorks(self): y = math_ops.add(self.w, -1.0, name="y") @@ -227,7 +220,6 @@ class IdentifyGradientTest(test_util.TensorFlowTestCase): self.assertIsInstance(w_grad, ops.Tensor) self.assertAllClose(1.0, self.sess.run(w_grad)) - @test_util.run_deprecated_v1 def testWatchGradientsByXTensorNamesWorksWithoutContextManager(self): y = math_ops.add(self.w, -1.0, name="y") @@ -254,7 +246,6 @@ class IdentifyGradientTest(test_util.TensorFlowTestCase): self.assertIsInstance(w_grad, ops.Tensor) self.assertAllClose(1.0, self.sess.run(w_grad)) - @test_util.run_deprecated_v1 def testWatchGradientsWorksOnRefTensor(self): y = math_ops.add(self.w, -1.0, name="y") @@ -273,7 +264,6 @@ class IdentifyGradientTest(test_util.TensorFlowTestCase): self.assertAllClose(3.0, self.sess.run( grad_debugger.gradient_tensor("u:0"))) - @test_util.run_deprecated_v1 def testWatchGradientsWorksOnMultipleTensors(self): y = math_ops.add(self.w, -1.0, name="y") @@ -294,7 +284,6 @@ class IdentifyGradientTest(test_util.TensorFlowTestCase): self.assertAllClose(3.0, self.sess.run( grad_debugger.gradient_tensor("u:0"))) - @test_util.run_deprecated_v1 def testWatchGradientsByXTensorsWorks(self): y = math_ops.add(self.w, -1.0, name="foo/y") z = math_ops.square(y, name="foo/z") @@ -317,7 +306,6 @@ class IdentifyGradientTest(test_util.TensorFlowTestCase): self.assertAllClose(10.0, self.sess.run(w_grad)) self.assertAllClose(30.0, self.sess.run(u_grad)) - @test_util.run_deprecated_v1 def testWatchGradientsByTensorCanWorkOnMultipleLosses(self): y = math_ops.add(self.w, -1.0, name="y") z1 = math_ops.square(y, name="z1") @@ -343,7 +331,6 @@ class IdentifyGradientTest(test_util.TensorFlowTestCase): self.assertAllClose(2.0 * 5.0, self.sess.run(dz1_dy)) self.assertAllClose(0.5 * (5.0**-0.5), self.sess.run(dz2_dy)) - @test_util.run_deprecated_v1 def testGradientsValuesFromDumpWorks(self): y = math_ops.add(self.w, -1.0, name="y") z = math_ops.square(y, name="z") diff --git a/tensorflow/python/debug/lib/debug_utils_test.py b/tensorflow/python/debug/lib/debug_utils_test.py index cf59b30e3d..9d59cfc179 100644 --- a/tensorflow/python/debug/lib/debug_utils_test.py +++ b/tensorflow/python/debug/lib/debug_utils_test.py @@ -185,7 +185,7 @@ class DebugUtilsTest(test_util.TensorFlowTestCase): self.assertEqual(["file:///tmp/tfdbg_1", "file:///tmp/tfdbg_2"], watch_0.debug_urls) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testWatchGraph_allNodes(self): debug_utils.watch_graph( self._run_options, @@ -217,7 +217,7 @@ class DebugUtilsTest(test_util.TensorFlowTestCase): self.assertTrue("p1" in node_names) self.assertTrue("s" in node_names) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testWatchGraph_nodeNameWhitelist(self): debug_utils.watch_graph( self._run_options, @@ -232,7 +232,7 @@ class DebugUtilsTest(test_util.TensorFlowTestCase): sorted(["a1_init", "a1", "a1/Assign", "a1/read", "p1"]), sorted(node_names)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testWatchGraph_opTypeWhitelist(self): debug_utils.watch_graph( self._run_options, @@ -258,7 +258,7 @@ class DebugUtilsTest(test_util.TensorFlowTestCase): ["DebugIdentity"], ["file:///tmp/tfdbg_1"]) self.assertEqual(["p1"], node_names) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testWatchGraph_tensorDTypeWhitelist(self): debug_utils.watch_graph( self._run_options, @@ -271,7 +271,7 @@ class DebugUtilsTest(test_util.TensorFlowTestCase): ["DebugIdentity"], ["file:///tmp/tfdbg_1"]) self.assertItemsEqual(["a1", "a1/Assign", "b", "b/Assign"], node_names) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testWatchGraph_nodeNameAndTensorDTypeWhitelists(self): debug_utils.watch_graph( self._run_options, @@ -285,7 +285,7 @@ class DebugUtilsTest(test_util.TensorFlowTestCase): ["DebugIdentity"], ["file:///tmp/tfdbg_1"]) self.assertItemsEqual(["a1", "a1/Assign"], node_names) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testWatchGraph_nodeNameBlacklist(self): debug_utils.watch_graph_with_blacklists( self._run_options, @@ -300,7 +300,7 @@ class DebugUtilsTest(test_util.TensorFlowTestCase): sorted(["b_init", "b", "b/Assign", "b/read", "c", "s"]), sorted(node_names)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testWatchGraph_opTypeBlacklist(self): debug_utils.watch_graph_with_blacklists( self._run_options, @@ -313,7 +313,7 @@ class DebugUtilsTest(test_util.TensorFlowTestCase): ["DebugIdentity"], ["file:///tmp/tfdbg_1"]) self.assertEqual(sorted(["p1", "s"]), sorted(node_names)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testWatchGraph_nodeNameAndOpTypeBlacklists(self): debug_utils.watch_graph_with_blacklists( self._run_options, @@ -327,7 +327,7 @@ class DebugUtilsTest(test_util.TensorFlowTestCase): ["DebugIdentity"], ["file:///tmp/tfdbg_1"]) self.assertEqual(["s"], node_names) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testWatchGraph_tensorDTypeBlacklists(self): debug_utils.watch_graph_with_blacklists( self._run_options, @@ -344,7 +344,7 @@ class DebugUtilsTest(test_util.TensorFlowTestCase): self.assertNotIn("b/Assign", node_names) self.assertIn("s", node_names) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testWatchGraph_nodeNameAndTensorDTypeBlacklists(self): debug_utils.watch_graph_with_blacklists( self._run_options, diff --git a/tensorflow/python/debug/lib/dist_session_debug_grpc_test.py b/tensorflow/python/debug/lib/dist_session_debug_grpc_test.py index 74498c8ea3..2405e29aaa 100644 --- a/tensorflow/python/debug/lib/dist_session_debug_grpc_test.py +++ b/tensorflow/python/debug/lib/dist_session_debug_grpc_test.py @@ -44,6 +44,7 @@ from tensorflow.python.platform import test from tensorflow.python.platform import tf_logging +@test_util.run_v1_only("b/120545219") class DistributedSessionDebugTest(test_util.TensorFlowTestCase): """Test the debugging of distributed sessions.""" diff --git a/tensorflow/python/debug/lib/session_debug_file_test.py b/tensorflow/python/debug/lib/session_debug_file_test.py index f5f9ba29ab..16ab815d92 100644 --- a/tensorflow/python/debug/lib/session_debug_file_test.py +++ b/tensorflow/python/debug/lib/session_debug_file_test.py @@ -34,6 +34,7 @@ from tensorflow.python.ops import variables from tensorflow.python.platform import googletest +@test_util.run_v1_only("b/120545219") class SessionDebugFileTest(session_debug_testlib.SessionDebugTestBase): def _debug_urls(self, run_number=None): @@ -45,7 +46,6 @@ class SessionDebugFileTest(session_debug_testlib.SessionDebugTestBase): else: return os.path.join(self._dump_root, "run_%d" % run_number) - @test_util.run_deprecated_v1 def testAllowsDifferentWatchesOnDifferentRuns(self): """Test watching different tensors on different runs of the same graph.""" diff --git a/tensorflow/python/debug/lib/session_debug_grpc_test.py b/tensorflow/python/debug/lib/session_debug_grpc_test.py index bfc9a3a382..472e244915 100644 --- a/tensorflow/python/debug/lib/session_debug_grpc_test.py +++ b/tensorflow/python/debug/lib/session_debug_grpc_test.py @@ -91,6 +91,7 @@ class GrpcDebugServerTest(test_util.TensorFlowTestCase): server.stop_server().wait() +@test_util.run_v1_only("b/120545219") class SessionDebugGrpcTest(session_debug_testlib.SessionDebugTestBase): @classmethod @@ -353,6 +354,7 @@ class SessionDebugConcurrentTest( return urls +@test_util.run_v1_only("b/120545219") class SessionDebugGrpcGatingTest(test_util.TensorFlowTestCase): """Test server gating of debug ops.""" @@ -730,6 +732,7 @@ class SessionDebugGrpcGatingTest(test_util.TensorFlowTestCase): self.assertEqual("DebugNumericSummary", debug_watch.debug_op) +@test_util.run_v1_only("b/120545219") class DelayedDebugServerTest(test_util.TensorFlowTestCase): def testDebuggedSessionRunWorksWithDelayedDebugServerStartup(self): diff --git a/tensorflow/python/debug/lib/session_debug_testlib.py b/tensorflow/python/debug/lib/session_debug_testlib.py index 25ef91b575..5165febff5 100644 --- a/tensorflow/python/debug/lib/session_debug_testlib.py +++ b/tensorflow/python/debug/lib/session_debug_testlib.py @@ -84,6 +84,7 @@ class _RNNCellForTest(rnn_cell_impl.RNNCell): return (math_ops.multiply(self._w, input_), state) +@test_util.run_v1_only("b/120545219") class SessionDebugTestBase(test_util.TensorFlowTestCase): """Base class for unit tests of tfdbg running with tf.Session.""" diff --git a/tensorflow/python/debug/lib/source_utils_test.py b/tensorflow/python/debug/lib/source_utils_test.py index 9083297fdb..4f4aea0321 100644 --- a/tensorflow/python/debug/lib/source_utils_test.py +++ b/tensorflow/python/debug/lib/source_utils_test.py @@ -216,6 +216,7 @@ class SourceHelperTest(test_util.TensorFlowTestCase): os.remove(unrelated_source_path) +@test_util.run_v1_only("b/120545219") class ListSourceAgainstDumpTest(test_util.TensorFlowTestCase): def createAndRunGraphWithWhileLoop(self): diff --git a/tensorflow/python/debug/lib/stepper_test.py b/tensorflow/python/debug/lib/stepper_test.py index 3839c67198..9e78e207b8 100644 --- a/tensorflow/python/debug/lib/stepper_test.py +++ b/tensorflow/python/debug/lib/stepper_test.py @@ -33,6 +33,7 @@ from tensorflow.python.platform import googletest from tensorflow.python.training import gradient_descent +@test_util.run_v1_only("b/120545219") class StepperTest(test_util.TensorFlowTestCase): def setUp(self): @@ -443,6 +444,7 @@ class StepperTest(test_util.TensorFlowTestCase): self.assertAllClose(-4.0, result["fz"]["z"]) +@test_util.run_v1_only("b/120545219") class StepperTestWithPlaceHolders(test_util.TensorFlowTestCase): def setUp(self): @@ -577,6 +579,7 @@ class StepperTestWithPlaceHolders(test_util.TensorFlowTestCase): self.assertAllClose([[-1.0], [6.0]], stepper.finalize()) +@test_util.run_v1_only("b/120545219") class StepperAssignAddTest(test_util.TensorFlowTestCase): def setUp(self): @@ -692,6 +695,7 @@ class StepperAssignAddTest(test_util.TensorFlowTestCase): self.assertAllClose(12.0, stepper.cont(self.v)) +@test_util.run_v1_only("b/120545219") class StepperBackwardRunTest(test_util.TensorFlowTestCase): def setUp(self): diff --git a/tensorflow/python/debug/wrappers/disk_usage_test.py b/tensorflow/python/debug/wrappers/disk_usage_test.py index 0874525966..88b1cd540d 100644 --- a/tensorflow/python/debug/wrappers/disk_usage_test.py +++ b/tensorflow/python/debug/wrappers/disk_usage_test.py @@ -32,6 +32,7 @@ from tensorflow.python.platform import googletest from tensorflow.python.training import monitored_session +@test_util.run_v1_only("b/120545219") class DumpingDebugWrapperDiskUsageLimitTest(test_util.TensorFlowTestCase): @classmethod diff --git a/tensorflow/python/debug/wrappers/dumping_wrapper_test.py b/tensorflow/python/debug/wrappers/dumping_wrapper_test.py index 11011a5c13..42e3b09382 100644 --- a/tensorflow/python/debug/wrappers/dumping_wrapper_test.py +++ b/tensorflow/python/debug/wrappers/dumping_wrapper_test.py @@ -41,6 +41,7 @@ from tensorflow.python.platform import googletest from tensorflow.python.training import monitored_session +@test_util.run_v1_only("b/120545219") class DumpingDebugWrapperSessionTest(test_util.TensorFlowTestCase): def setUp(self): diff --git a/tensorflow/python/debug/wrappers/framework_test.py b/tensorflow/python/debug/wrappers/framework_test.py index 68584b4ede..a50fa7cf4b 100644 --- a/tensorflow/python/debug/wrappers/framework_test.py +++ b/tensorflow/python/debug/wrappers/framework_test.py @@ -141,6 +141,7 @@ class TestDebugWrapperSessionBadAction(framework.BaseDebugWrapperSession): return framework.OnRunEndResponse() +@test_util.run_v1_only("b/120545219") class DebugWrapperSessionTest(test_util.TensorFlowTestCase): def _no_rewrite_session_config(self): diff --git a/tensorflow/python/debug/wrappers/local_cli_wrapper_test.py b/tensorflow/python/debug/wrappers/local_cli_wrapper_test.py index 149a7497df..e38df861f5 100644 --- a/tensorflow/python/debug/wrappers/local_cli_wrapper_test.py +++ b/tensorflow/python/debug/wrappers/local_cli_wrapper_test.py @@ -127,6 +127,7 @@ class LocalCLIDebuggerWrapperSessionForTest( return e.exit_token +@test_util.run_v1_only("b/120545219") class LocalCLIDebugWrapperSessionTest(test_util.TensorFlowTestCase): def setUp(self): diff --git a/tensorflow/python/distribute/distribute_coordinator_test.py b/tensorflow/python/distribute/distribute_coordinator_test.py index f2cb950aad..7598c105c2 100644 --- a/tensorflow/python/distribute/distribute_coordinator_test.py +++ b/tensorflow/python/distribute/distribute_coordinator_test.py @@ -427,6 +427,7 @@ class DistributeCoordinatorTestStandaloneMode(DistributeCoordinatorTestBase): # Each finished worker will increment self._result_correct. self.assertEqual(self._result_correct, NUM_WORKERS) + @test_util.run_v1_only("b/120545219") def testBetweenGraphWithMonitoredSession(self): """Test monitored session in standalone client mode.""" distribute_coordinator.run_distribute_coordinator( @@ -600,6 +601,7 @@ class DistributeCoordinatorTestInpendentWorkerMode( # Each finished worker will increment self._result_correct. self.assertEqual(self._result_correct, NUM_WORKERS) + @test_util.run_v1_only("b/120545219") def testBetweenGraphWithMonitoredSession(self): cluster_spec = self._create_cluster_spec( num_workers=NUM_WORKERS, num_ps=NUM_PS) diff --git a/tensorflow/python/eager/backprop_test.py b/tensorflow/python/eager/backprop_test.py index 3cec40a48f..61c47a29fd 100644 --- a/tensorflow/python/eager/backprop_test.py +++ b/tensorflow/python/eager/backprop_test.py @@ -648,6 +648,7 @@ class BackpropTest(test.TestCase): g.gradient(x, y) @test_util.run_in_graph_and_eager_modes + @test_util.run_v1_only('b/120545219') def testGradientTapeWithCond(self): x = constant_op.constant(3.0) @@ -669,6 +670,7 @@ class BackpropTest(test.TestCase): self.assertEqual(self.evaluate(dy), 6.0) @test_util.run_in_graph_and_eager_modes + @test_util.run_v1_only('b/120545219') def testGradientTapeWithWhileLoop(self): i = constant_op.constant(1) x = constant_op.constant(2.) @@ -704,6 +706,7 @@ class BackpropTest(test.TestCase): @test_util.assert_no_new_tensors @test_util.run_in_graph_and_eager_modes + @test_util.run_v1_only('b/120545219') def testPersistentTape(self): with backprop.GradientTape(persistent=True) as g: x = constant_op.constant(3.0) @@ -1243,16 +1246,19 @@ class JacobianTest(test.TestCase): answer = [array_ops.diag(2 * x * y), array_ops.diag(x * x)] return jacobian, answer + @test_util.run_v1_only('b/120545219') def testPfor(self): jacobian, answer = self._jacobian(experimental_use_pfor=True) for j, a in zip(jacobian, answer): self.assertAllEqual(a, j) + @test_util.run_v1_only('b/120545219') def testWhileLoop(self): jacobian, answer = self._jacobian(experimental_use_pfor=False) for j, a in zip(jacobian, answer): self.assertAllEqual(a, j) + @test_util.run_v1_only('b/120545219') def testPforDefun(self): @function.defun @@ -1263,6 +1269,7 @@ class JacobianTest(test.TestCase): for j, a in zip(jacobian, answer): self.assertAllEqual(a, j) + @test_util.run_v1_only('b/120545219') def testWhileLoopDefun(self): @function.defun @@ -1273,6 +1280,7 @@ class JacobianTest(test.TestCase): for j, a in zip(jacobian, answer): self.assertAllEqual(a, j) + @test_util.run_v1_only('b/120545219') def testPersistentTape(self): if not context.executing_eagerly(): return @@ -1283,6 +1291,7 @@ class JacobianTest(test.TestCase): with self.assertRaisesRegexp(RuntimeError, 'persistent'): g.jacobian(y, x, experimental_use_pfor=False) + @test_util.run_v1_only('b/120545219') def testPforException(self): var = variables.Variable([1.]) @@ -1303,6 +1312,7 @@ class JacobianTest(test.TestCase): with self.assertRaisesRegexp(ValueError, 'No converter'): g.jacobian(y, x, experimental_use_pfor=True) + @test_util.run_v1_only('b/120545219') def test_parallel_iterations(self): with backprop.GradientTape(persistent=True) as g: x = constant_op.constant([[1., 2], [3, 4]]) @@ -1328,14 +1338,17 @@ class BatchJacobianTest(test.TestCase): array_ops.diag(2 * x[1] * y[1])]) return batch_jacobian, answer + @test_util.run_v1_only('b/120545219') def testPfor(self): batch_jacobian, answer = self._batch_jacobian(experimental_use_pfor=True) self.assertAllEqual(answer, batch_jacobian) + @test_util.run_v1_only('b/120545219') def testWhileLoop(self): batch_jacobian, answer = self._batch_jacobian(experimental_use_pfor=False) self.assertAllEqual(answer, batch_jacobian) + @test_util.run_v1_only('b/120545219') def testPforDefun(self): @function.defun @@ -1345,6 +1358,7 @@ class BatchJacobianTest(test.TestCase): batch_jacobian, answer = _f() self.assertAllEqual(answer, batch_jacobian) + @test_util.run_v1_only('b/120545219') def testWhileLoopDefun(self): @function.defun @@ -1354,6 +1368,7 @@ class BatchJacobianTest(test.TestCase): batch_jacobian, answer = _f() self.assertAllEqual(answer, batch_jacobian) + @test_util.run_v1_only('b/120545219') def testPersistentTape(self): if not context.executing_eagerly(): return @@ -1364,6 +1379,7 @@ class BatchJacobianTest(test.TestCase): with self.assertRaisesRegexp(RuntimeError, 'persistent'): g.batch_jacobian(y, x, experimental_use_pfor=False) + @test_util.run_v1_only('b/120545219') def testBadShape(self): x = random_ops.random_uniform([2, 3]) with backprop.GradientTape() as g: @@ -1371,6 +1387,7 @@ class BatchJacobianTest(test.TestCase): with self.assertRaisesRegexp(ValueError, 'Need first dimension'): g.batch_jacobian(y, x) + @test_util.run_v1_only('b/120545219') def testBadInputRank(self): x = random_ops.random_uniform([2]) with backprop.GradientTape() as g: @@ -1385,6 +1402,7 @@ class BatchJacobianTest(test.TestCase): with self.assertRaisesRegexp(ValueError, 'must have rank at least 2'): g.batch_jacobian(y, x) + @test_util.run_v1_only('b/120545219') def testPforException(self): var = variables.Variable([1.]) @@ -1405,6 +1423,7 @@ class BatchJacobianTest(test.TestCase): with self.assertRaisesRegexp(ValueError, 'No converter'): g.batch_jacobian(y, x, experimental_use_pfor=True) + @test_util.run_v1_only('b/120545219') def test_parallel_iterations(self): with backprop.GradientTape(persistent=True) as g: x = constant_op.constant([[1., 2], [3, 4]]) diff --git a/tensorflow/python/eager/function_gradients_test.py b/tensorflow/python/eager/function_gradients_test.py index 9b83f57089..98dec0b361 100644 --- a/tensorflow/python/eager/function_gradients_test.py +++ b/tensorflow/python/eager/function_gradients_test.py @@ -187,7 +187,7 @@ class FunctionGradientsTest(test.TestCase, parameterized.TestCase): self.assertAllEqual(2, g(constant_op.constant(2.))) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testGraphModeEagerGradError(self): with context.graph_mode(): def f(): diff --git a/tensorflow/python/eager/function_test.py b/tensorflow/python/eager/function_test.py index e0854b0632..50d1b4b6f7 100644 --- a/tensorflow/python/eager/function_test.py +++ b/tensorflow/python/eager/function_test.py @@ -963,6 +963,7 @@ class FunctionTest(test.TestCase, parameterized.TestCase): # construction. Eager's configuration is controlled in `__main__`. @test_util.run_in_graph_and_eager_modes( config=config_pb2.ConfigProto(device_count={'CPU': 4})) + @test_util.run_v1_only('b/120545219') def testDeviceAnnotationsRespected(self): def multi_device_fn(): @@ -1001,6 +1002,7 @@ class FunctionTest(test.TestCase, parameterized.TestCase): @test_util.run_in_graph_and_eager_modes( config=config_pb2.ConfigProto(device_count={'CPU': 2})) + @test_util.run_v1_only('b/120545219') def testCallingGraphFunctionOnDifferentDevice(self): def func(): diff --git a/tensorflow/python/framework/auto_control_deps_test.py b/tensorflow/python/framework/auto_control_deps_test.py index a1dff9e834..5f5de45b9e 100644 --- a/tensorflow/python/framework/auto_control_deps_test.py +++ b/tensorflow/python/framework/auto_control_deps_test.py @@ -24,6 +24,7 @@ from tensorflow.python.framework import auto_control_deps as acd 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 array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import resource_variable_ops @@ -46,6 +47,7 @@ class AutomaticControlDependenciesTest(test.TestCase): val = c.mark_as_return(val) self.assertAllEqual(val.eval(), 4.0) + @test_util.run_v1_only("b/120545219") def testCondMustRun(self): with context.graph_mode(), self.cached_session(): v = resource_variable_ops.ResourceVariable(1.0) @@ -67,6 +69,7 @@ class AutomaticControlDependenciesTest(test.TestCase): self.assertAllEqual(val.eval(feed_dict={p: False}), 5.0) self.assertAllEqual(val.eval(feed_dict={p: True}), 6.0) + @test_util.run_v1_only("b/120545219") def testCondMustRunSeparateRead(self): with context.graph_mode(), self.cached_session(): v = resource_variable_ops.ResourceVariable(1.0) @@ -90,6 +93,7 @@ class AutomaticControlDependenciesTest(test.TestCase): one.eval(feed_dict={p: True}) self.assertAllEqual(v.read_value().eval(), 6.0) + @test_util.run_v1_only("b/120545219") def testCondNested(self): with context.graph_mode(), self.cached_session(): v = resource_variable_ops.ResourceVariable(1.0) @@ -124,6 +128,7 @@ class AutomaticControlDependenciesTest(test.TestCase): self.assertAllEqual(val.eval(feed_dict={p: True, q: True}), 7.0) self.assertAllEqual(val.eval(feed_dict={p: True, q: False}), 8.0) + @test_util.run_v1_only("b/120545219") def testCondOneBranch(self): with context.graph_mode(), self.cached_session(): v = resource_variable_ops.ResourceVariable(1.0) @@ -144,6 +149,7 @@ class AutomaticControlDependenciesTest(test.TestCase): self.assertAllEqual(val.eval(feed_dict={p: False}), 5.0) self.assertAllEqual(val.eval(feed_dict={p: True}), 5.0) + @test_util.run_v1_only("b/120545219") def testCondOneBranchUpdateBefore(self): with context.graph_mode(), self.cached_session(): v = resource_variable_ops.ResourceVariable(1.0) @@ -165,6 +171,7 @@ class AutomaticControlDependenciesTest(test.TestCase): self.assertAllEqual(val.eval(feed_dict={p: False}), 6.0) self.assertAllEqual(val.eval(feed_dict={p: True}), 12.0) + @test_util.run_v1_only("b/120545219") def testCondOneBranchUpdateAfter(self): with context.graph_mode(), self.cached_session(): v = resource_variable_ops.ResourceVariable(1.0) diff --git a/tensorflow/python/framework/error_interpolation_test.py b/tensorflow/python/framework/error_interpolation_test.py index 1b77548592..9eaa4a5f2d 100644 --- a/tensorflow/python/framework/error_interpolation_test.py +++ b/tensorflow/python/framework/error_interpolation_test.py @@ -23,6 +23,7 @@ import os from tensorflow.python.framework import constant_op from tensorflow.python.framework import error_interpolation from tensorflow.python.framework import ops +from tensorflow.python.framework import test_util from tensorflow.python.framework import traceable_stack from tensorflow.python.platform import test from tensorflow.python.util import tf_stack @@ -112,6 +113,7 @@ class ComputeColocationSummaryFromOpTest(test.TestCase): self.assertIn("No node-device colocations", summary) +@test_util.run_v1_only("b/120545219") class InterpolateFilenamesAndLineNumbersTest(test.TestCase): def setUp(self): @@ -193,6 +195,7 @@ class InterpolateFilenamesAndLineNumbersTest(test.TestCase): self.assertRegexpMatches(interpolated_string, "constant_op.py:[0-9]+.*") +@test_util.run_v1_only("b/120545219") class InterpolateDeviceSummaryTest(test.TestCase): def _fancy_device_function(self, unused_op): @@ -236,6 +239,7 @@ class InterpolateDeviceSummaryTest(test.TestCase): self.assertRegexpMatches(result, expected_re) +@test_util.run_v1_only("b/120545219") class InterpolateColocationSummaryTest(test.TestCase): def setUp(self): @@ -260,11 +264,13 @@ class InterpolateColocationSummaryTest(test.TestCase): self.graph = node_three.graph + @test_util.run_v1_only("b/120545219") def testNodeThreeHasColocationInterpolation(self): message = "{{colocation_node Three_with_one}}" result = error_interpolation.interpolate(message, self.graph) self.assertIn("colocate_with(One)", result) + @test_util.run_v1_only("b/120545219") def testNodeFourHasColocationInterpolationForNodeThreeOnly(self): message = "{{colocation_node Four_with_three}}" result = error_interpolation.interpolate(message, self.graph) @@ -273,12 +279,14 @@ class InterpolateColocationSummaryTest(test.TestCase): "One", result, "Node One should not appear in Four_with_three's summary:\n%s" % result) + @test_util.run_v1_only("b/120545219") def testNodeFiveHasColocationInterpolationForNodeOneAndTwo(self): message = "{{colocation_node Five_with_one_with_two}}" result = error_interpolation.interpolate(message, self.graph) self.assertIn("colocate_with(One)", result) self.assertIn("colocate_with(Two)", result) + @test_util.run_v1_only("b/120545219") def testColocationInterpolationForNodeLackingColocation(self): message = "{{colocation_node One}}" result = error_interpolation.interpolate(message, self.graph) diff --git a/tensorflow/python/framework/graph_util_test.py b/tensorflow/python/framework/graph_util_test.py index 4e7408ad49..dd26b8a78e 100644 --- a/tensorflow/python/framework/graph_util_test.py +++ b/tensorflow/python/framework/graph_util_test.py @@ -103,7 +103,7 @@ class DeviceFunctionsTest(test.TestCase): self.assertDeviceEqual(var_5.device, "/device:GPU:0") self.assertDeviceEqual(var_6.device, "/device:CPU:0") - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testNestedDeviceFunctions(self): with ops.Graph().as_default(): var_0 = variables.VariableV1(0) diff --git a/tensorflow/python/framework/meta_graph_test.py b/tensorflow/python/framework/meta_graph_test.py index 46ce4616a5..e6e8788164 100644 --- a/tensorflow/python/framework/meta_graph_test.py +++ b/tensorflow/python/framework/meta_graph_test.py @@ -528,7 +528,7 @@ class ScopedMetaGraphTest(test.TestCase): actual_grad_value = self.evaluate(grad) self.assertEqual(expected_grad_value, actual_grad_value) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testImportWhileLoopInWhileLoop(self): # Create a simple while loop. with ops.Graph().as_default(): diff --git a/tensorflow/python/framework/ops_test.py b/tensorflow/python/framework/ops_test.py index 7baa02b446..0fcbcd6ee4 100644 --- a/tensorflow/python/framework/ops_test.py +++ b/tensorflow/python/framework/ops_test.py @@ -605,6 +605,7 @@ class OperationTest(test_util.TensorFlowTestCase): x.op._update_input(1, x) # pylint: disable=protected-access @test_util.enable_control_flow_v2 + @test_util.run_v1_only("b/120545219") def testAddWhileInput(self): @eager_function.defun def test(): @@ -780,7 +781,7 @@ class CreateOpFromTFOperationTest(test_util.TensorFlowTestCase): self.assertEqual(op3.name, "myop_2") self.assertEqual(op4.name, "myop_1_1") - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testCond(self): g = ops.Graph() with g.as_default(): @@ -810,7 +811,7 @@ class CreateOpFromTFOperationTest(test_util.TensorFlowTestCase): "cond/cond_text") # pylint: enable=protected-access - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testWhileLoop(self): g = ops.Graph() with g.as_default(): @@ -840,7 +841,7 @@ class CreateOpFromTFOperationTest(test_util.TensorFlowTestCase): "myloop/while_context") # pylint: enable=protected-access - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testWhileLoopWithInternalControlDep(self): g = ops.Graph() with g.as_default(): @@ -864,7 +865,7 @@ class CreateOpFromTFOperationTest(test_util.TensorFlowTestCase): # Internal control dep is preserved self.assertEqual(op.control_inputs, [c]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testWhileLoopWithExternalControlDep(self): g = ops.Graph() with g.as_default(): @@ -2283,7 +2284,7 @@ class InitScopeTest(test_util.TensorFlowTestCase): self.assertEqual(4, int(compiled_outer(inner=compiled_inner))) self.assertEqual(7, int(compiled_outer(inner=compiled_inner))) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testFallsBackToGlobalGraphWhenAllGraphsAreBuildingFunctions(self): with context.graph_mode(): ops.reset_default_graph() @@ -2994,7 +2995,7 @@ class TracebackTest(test_util.TensorFlowTestCase): class EnableEagerExecutionTest(test_util.TensorFlowTestCase): - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testBadArgumentsToEnableEagerExecution(self): with self.assertRaisesRegexp(TypeError, "config must be a tf.ConfigProto"): ops.enable_eager_execution(context.DEVICE_PLACEMENT_SILENT) diff --git a/tensorflow/python/framework/subscribe_test.py b/tensorflow/python/framework/subscribe_test.py index 61c6ea6519..a74e96f9d9 100644 --- a/tensorflow/python/framework/subscribe_test.py +++ b/tensorflow/python/framework/subscribe_test.py @@ -215,7 +215,7 @@ class SubscribeTest(test_util.TensorFlowTestCase): self.assertIn('graph2', shared) self.assertIn('graph3', shared) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testSubscribeVariable(self): """Confirm that variables can be subscribed.""" v1 = variables.VariableV1(0.0) @@ -254,7 +254,7 @@ class SubscribeTest(test_util.TensorFlowTestCase): # Make sure the values read from the variable match the expected ones. self.assertEqual([0.0, 3.0], shared) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testResourceType(self): """Confirm that subscribe correctly handles tensors with 'resource' type.""" tensor_array = tensor_array_ops.TensorArray( @@ -344,7 +344,7 @@ class SubscribeTest(test_util.TensorFlowTestCase): self.assertEqual(add.device, add_sub.device) self.assertEqual(mul.device, mul_sub.device) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def test_subscribe_tensors_within_control_flow_context(self): """Side effect ops are added with the same control flow context.""" c1 = constant_op.constant(10) diff --git a/tensorflow/python/grappler/item_test.py b/tensorflow/python/grappler/item_test.py index 78604b259c..c02fd9f55b 100644 --- a/tensorflow/python/grappler/item_test.py +++ b/tensorflow/python/grappler/item_test.py @@ -108,7 +108,7 @@ class ItemTest(test.TestCase): newest_tf_item = grappler_item.tf_item self.assertEqual(new_tf_item, newest_tf_item) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testColocationContraints(self): with ops.Graph().as_default() as g: c = constant_op.constant([10]) diff --git a/tensorflow/python/grappler/memory_optimizer_test.py b/tensorflow/python/grappler/memory_optimizer_test.py index 6eb16fbd39..e2864ebb4d 100644 --- a/tensorflow/python/grappler/memory_optimizer_test.py +++ b/tensorflow/python/grappler/memory_optimizer_test.py @@ -62,7 +62,7 @@ class MemoryOptimizerSwapTest(test.TestCase): self.assertEqual(len(graph.node), graph_size) self.assertItemsEqual([node.name for node in graph.node], nodes) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testSimpleSwap(self): """Check that the swap annotations are followed.""" a = variables.VariableV1(10, name='a') diff --git a/tensorflow/python/grappler/tf_optimizer_test.py b/tensorflow/python/grappler/tf_optimizer_test.py index 06ccaa813f..8186c81378 100644 --- a/tensorflow/python/grappler/tf_optimizer_test.py +++ b/tensorflow/python/grappler/tf_optimizer_test.py @@ -57,7 +57,7 @@ class PyWrapOptimizeGraphTest(test.TestCase): self.assertEqual(len(graph.node), 1) self.assertItemsEqual([node.name for node in graph.node], ['d']) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testKeepNodes(self): g = ops.Graph() with g.as_default(): @@ -86,7 +86,7 @@ class PyWrapOptimizeGraphTest(test.TestCase): self.assertEqual(len(optimized_graph_nodes), len(expected_nodes)) self.assertAllInSet(optimized_graph_nodes, expected_nodes) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testLoops(self): g = ops.Graph() with g.as_default(): diff --git a/tensorflow/python/keras/backend_test.py b/tensorflow/python/keras/backend_test.py index 6401e39e53..af01b46fa9 100644 --- a/tensorflow/python/keras/backend_test.py +++ b/tensorflow/python/keras/backend_test.py @@ -1422,7 +1422,7 @@ class TestCTC(test.TestCase): decode_truth[i] == keras.backend.eval(decode_pred_tf[i]))) self.assertAllClose(log_prob_truth, log_prob_pred) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def test_ctc_batch_cost(self): with self.cached_session(): label_lens = np.expand_dims(np.asarray([5, 4]), 1) diff --git a/tensorflow/python/keras/callbacks_test.py b/tensorflow/python/keras/callbacks_test.py index 6c9a382b32..4a65ade33c 100644 --- a/tensorflow/python/keras/callbacks_test.py +++ b/tensorflow/python/keras/callbacks_test.py @@ -403,7 +403,7 @@ class KerasCallbacksTest(test.TestCase): float(keras.backend.get_value( model.optimizer.lr)) - 0.01 / 4) < keras.backend.epsilon() - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def test_ReduceLROnPlateau(self): with self.cached_session(): np.random.seed(1337) @@ -675,7 +675,7 @@ class KerasCallbacksTest(test.TestCase): self.assertEqual(len(loss), 1) self.assertEqual(loss[0], np.inf) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def test_TensorBoard(self): np.random.seed(1337) @@ -779,7 +779,7 @@ class KerasCallbacksTest(test.TestCase): data_generator(True), len(x_train), epochs=2, callbacks=cbks) assert os.path.exists(temp_dir) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def test_TensorBoard_multi_input_output(self): np.random.seed(1337) tmpdir = self.get_temp_dir() @@ -851,7 +851,7 @@ class KerasCallbacksTest(test.TestCase): callbacks=callbacks_factory(histogram_freq=1)) assert os.path.isdir(filepath) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def test_Tensorboard_histogram_summaries_in_test_function(self): class FileWriterStub(object): @@ -929,7 +929,7 @@ class KerasCallbacksTest(test.TestCase): self.assertAllEqual(tsb.writer.steps_seen, [0, 1, 2, 3, 4, 5]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def test_Tensorboard_histogram_summaries_with_generator(self): np.random.seed(1337) tmpdir = self.get_temp_dir() diff --git a/tensorflow/python/keras/engine/saving_test.py b/tensorflow/python/keras/engine/saving_test.py index 6d9d9a2fca..bc33a3ea7f 100644 --- a/tensorflow/python/keras/engine/saving_test.py +++ b/tensorflow/python/keras/engine/saving_test.py @@ -332,7 +332,7 @@ class TestWeightSavingAndLoading(test.TestCase, parameterized.TestCase): class TestWholeModelSaving(test.TestCase): - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def test_sequential_model_saving(self): if h5py is None: self.skipTest('h5py required to run this test') @@ -635,7 +635,7 @@ class TestWholeModelSaving(test.TestCase): os.close(fd) os.remove(fname) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def test_saving_model_with_long_weights_names(self): if h5py is None: self.skipTest('h5py required to run this test') @@ -756,7 +756,7 @@ class SubclassedModel(training.Model): class TestWeightSavingAndLoadingTFFormat(test.TestCase): - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def test_keras_optimizer_warning(self): graph = ops.Graph() with graph.as_default(), self.session(graph): diff --git a/tensorflow/python/keras/engine/sequential_test.py b/tensorflow/python/keras/engine/sequential_test.py index 001c3d749b..10f69da061 100644 --- a/tensorflow/python/keras/engine/sequential_test.py +++ b/tensorflow/python/keras/engine/sequential_test.py @@ -226,7 +226,7 @@ class TestSequential(keras_parameterized.TestCase): inner_model.trainable = True self.assertEqual(len(model.trainable_weights), 4) - @tf_test_util.run_deprecated_v1 + @tf_test_util.run_v1_only('b/120545219') def test_sequential_update_disabling(self): val_a = np.random.random((10, 4)) val_out = np.random.random((10, 4)) diff --git a/tensorflow/python/keras/engine/topology_test.py b/tensorflow/python/keras/engine/topology_test.py index 03bfd35589..4071e2c091 100644 --- a/tensorflow/python/keras/engine/topology_test.py +++ b/tensorflow/python/keras/engine/topology_test.py @@ -107,6 +107,7 @@ class TopologyConstructionTest(test.TestCase): self.assertEqual(len(network.updates), 5) self.assertEqual(len(network.get_updates_for(x4)), 2) + @test_util.run_v1_only('b/120545219') def test_get_updates_bn(self): x1 = input_layer_lib.Input(shape=(1,)) layer = keras.layers.BatchNormalization() @@ -833,7 +834,7 @@ class TopologyConstructionTest(test.TestCase): output_val_2 = m2.predict(x_val) self.assertAllClose(output_val, output_val_2, atol=1e-6) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def test_explicit_training_argument(self): with self.cached_session(): a = keras.layers.Input(shape=(2,)) diff --git a/tensorflow/python/keras/engine/training_test.py b/tensorflow/python/keras/engine/training_test.py index c3c3f06ffd..91a0c7cc2f 100644 --- a/tensorflow/python/keras/engine/training_test.py +++ b/tensorflow/python/keras/engine/training_test.py @@ -471,7 +471,7 @@ class TrainingTest(keras_parameterized.TestCase): metrics=['accuracy'], run_eagerly=testing_utils.should_run_eagerly()) - @tf_test_util.run_deprecated_v1 + @tf_test_util.run_v1_only('b/120545219') def test_that_trainable_disables_updates(self): val_a = np.random.random((10, 4)) val_out = np.random.random((10, 4)) @@ -864,6 +864,7 @@ class LossWeightingTest(keras_parameterized.TestCase): self.assertLess(score[0], ref_score[0]) @keras_parameterized.run_all_keras_modes + @tf_test_util.run_v1_only('b/120545219') def test_sample_weights(self): num_classes = 5 batch_size = 5 @@ -961,6 +962,7 @@ class LossWeightingTest(keras_parameterized.TestCase): self.assertTrue(msg_found) @keras_parameterized.run_all_keras_modes + @tf_test_util.run_v1_only('b/120545219') # TODO(b/120562577): Test failing with assertion error. def DISABLED_test_temporal_sample_weights(self): num_classes = 5 @@ -1283,7 +1285,7 @@ class LossMaskingTest(keras_parameterized.TestCase): class TestDynamicTrainability(keras_parameterized.TestCase): - @tf_test_util.run_deprecated_v1 + @tf_test_util.run_v1_only('b/120545219') def test_trainable_warning(self): with self.cached_session(): x = np.random.random((5, 3)) @@ -1297,7 +1299,7 @@ class TestDynamicTrainability(keras_parameterized.TestCase): model.train_on_batch(x, y) self.assertRaises(Warning) - @tf_test_util.run_deprecated_v1 + @tf_test_util.run_v1_only('b/120545219') def test_trainable_argument(self): with self.cached_session(): x = np.random.random((5, 3)) diff --git a/tensorflow/python/keras/integration_test.py b/tensorflow/python/keras/integration_test.py index f1a0932613..c516514f63 100644 --- a/tensorflow/python/keras/integration_test.py +++ b/tensorflow/python/keras/integration_test.py @@ -35,7 +35,7 @@ class KerasIntegrationTest(test.TestCase): def test_version(self): self.assertTrue(keras.__version__.endswith('-tf')) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def test_vector_classification_sequential(self): with self.cached_session(): np.random.seed(1337) @@ -134,6 +134,7 @@ class KerasIntegrationTest(test.TestCase): verbose=2) self.assertGreater(history.history['val_acc'][-1], 0.7) + @test_util.run_v1_only('b/120545219') def test_image_classification_sequential(self): with self.cached_session(): np.random.seed(1337) @@ -168,7 +169,7 @@ class KerasIntegrationTest(test.TestCase): verbose=2) self.assertGreater(history.history['val_acc'][-1], 0.7) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def test_video_classification_functional(self): with self.cached_session(): np.random.seed(1337) @@ -197,7 +198,7 @@ class KerasIntegrationTest(test.TestCase): verbose=2) self.assertGreater(history.history['val_acc'][-1], 0.7) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def test_vector_classification_shared_sequential(self): # Test that Sequential models that feature internal updates # and internal losses can be shared. @@ -232,7 +233,7 @@ class KerasIntegrationTest(test.TestCase): verbose=2) self.assertGreater(history.history['val_acc'][-1], 0.7) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def test_vector_classification_shared_model(self): # Test that functional models that feature internal updates # and internal losses can be shared. diff --git a/tensorflow/python/keras/layers/lstm_test.py b/tensorflow/python/keras/layers/lstm_test.py index 3f89cc398e..aea4261502 100644 --- a/tensorflow/python/keras/layers/lstm_test.py +++ b/tensorflow/python/keras/layers/lstm_test.py @@ -115,7 +115,7 @@ class LSTMLayerTest(test.TestCase, parameterized.TestCase): self.assertEqual(layer.cell.recurrent_kernel.constraint, r_constraint) self.assertEqual(layer.cell.bias.constraint, b_constraint) - @tf_test_util.run_deprecated_v1 + @tf_test_util.run_v1_only('b/120545219') def test_with_masking_layer_LSTM(self): layer_class = keras.layers.LSTM inputs = np.random.random((2, 3, 4)) @@ -128,7 +128,7 @@ class LSTMLayerTest(test.TestCase, parameterized.TestCase): optimizer=RMSPropOptimizer(0.01)) model.fit(inputs, targets, epochs=1, batch_size=2, verbose=1) - @tf_test_util.run_deprecated_v1 + @tf_test_util.run_v1_only('b/120545219') def test_masking_with_stacking_LSTM(self): inputs = np.random.random((2, 3, 4)) targets = np.abs(np.random.random((2, 3, 5))) @@ -314,7 +314,7 @@ class LSTMLayerTest(test.TestCase, parameterized.TestCase): class LSTMLayerGraphOnlyTest(test.TestCase): - @tf_test_util.run_deprecated_v1 + @tf_test_util.run_v1_only('b/120545219') def test_statefulness_LSTM(self): num_samples = 2 timesteps = 3 diff --git a/tensorflow/python/keras/layers/normalization_test.py b/tensorflow/python/keras/layers/normalization_test.py index 9138c0a08a..c1acc2eb3a 100644 --- a/tensorflow/python/keras/layers/normalization_test.py +++ b/tensorflow/python/keras/layers/normalization_test.py @@ -29,6 +29,7 @@ from tensorflow.python.training import gradient_descent @tf_test_util.run_all_in_graph_and_eager_modes +@tf_test_util.run_v1_only('b/120545219') class NormalizationLayersTest(test.TestCase): def test_basic_batchnorm(self): @@ -227,6 +228,7 @@ class NormalizationLayersTest(test.TestCase): norm(inp) +@tf_test_util.run_v1_only('b/120545219') class NormalizationLayersGraphModeOnlyTest(test.TestCase): def test_shared_batchnorm(self): @@ -301,7 +303,6 @@ class NormalizationLayersGraphModeOnlyTest(test.TestCase): x2 = model.predict(val_a) self.assertAllClose(x1, x2, atol=1e-7) - @tf_test_util.run_deprecated_v1 def test_batchnorm_trainable(self): """Tests that batchnorm layer is trainable when learning phase is enabled. diff --git a/tensorflow/python/keras/layers/simplernn_test.py b/tensorflow/python/keras/layers/simplernn_test.py index b49b159b71..bb3fea2692 100644 --- a/tensorflow/python/keras/layers/simplernn_test.py +++ b/tensorflow/python/keras/layers/simplernn_test.py @@ -98,7 +98,7 @@ class SimpleRNNLayerTest(test.TestCase): self.assertEqual(layer.cell.recurrent_kernel.constraint, r_constraint) self.assertEqual(layer.cell.bias.constraint, b_constraint) - @tf_test_util.run_deprecated_v1 + @tf_test_util.run_v1_only('b/120545219') def test_with_masking_layer_SimpleRNN(self): layer_class = keras.layers.SimpleRNN inputs = np.random.random((2, 3, 4)) @@ -121,7 +121,7 @@ class SimpleRNNLayerTest(test.TestCase): class SimpleRNNLayerGraphOnlyTest(test.TestCase): - @tf_test_util.run_deprecated_v1 + @tf_test_util.run_v1_only('b/120545219') def test_statefulness_SimpleRNN(self): num_samples = 2 timesteps = 3 diff --git a/tensorflow/python/keras/layers/unified_lstm_test.py b/tensorflow/python/keras/layers/unified_lstm_test.py index 33351948ee..932b2d331d 100644 --- a/tensorflow/python/keras/layers/unified_lstm_test.py +++ b/tensorflow/python/keras/layers/unified_lstm_test.py @@ -56,9 +56,9 @@ _graph_options = config_pb2.GraphOptions(rewrite_options=_rewrites) _config = config_pb2.ConfigProto(graph_options=_graph_options) +@test_util.run_v1_only('b/120545219') class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): - @test_util.run_deprecated_v1 def test_unifiedLSTM(self): input_shape = 10 rnn_state_size = 8 @@ -103,7 +103,6 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): self.assertNotEqual(existing_loss, loss_value) existing_loss = loss_value - @test_util.run_deprecated_v1 def test_unifiedLSTM_with_cond(self): # This test is to demonstrate the graph rewrite of grappler plugin under # the condition that the function returns different number of internal @@ -692,6 +691,7 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): model.train_on_batch([main_inputs] + initial_state, targets) +@test_util.run_v1_only('b/120545219') class LSTMLayerGraphOnlyTest(test.TestCase): def test_statefulness_LSTM(self): diff --git a/tensorflow/python/keras/layers/wrappers_test.py b/tensorflow/python/keras/layers/wrappers_test.py index b9196416dd..727f33dadc 100644 --- a/tensorflow/python/keras/layers/wrappers_test.py +++ b/tensorflow/python/keras/layers/wrappers_test.py @@ -165,6 +165,7 @@ class TimeDistributedTest(test.TestCase): y = model.predict(np.random.random((10, 3, 2))) self.assertAllClose(np.mean(y), 0., atol=1e-1, rtol=1e-1) + @tf_test_util.run_v1_only('b/120545219') def test_TimeDistributed_batchnorm(self): with self.cached_session(): # test that wrapped BN updates still work. @@ -187,6 +188,7 @@ class TimeDistributedTest(test.TestCase): # Verify input_map has one mapping from inputs to reshaped inputs. self.assertEqual(len(td._input_map.keys()), 1) + @tf_test_util.run_v1_only('b/120545219') def test_TimeDistributed_trainable(self): # test layers that need learning_phase to be set x = keras.layers.Input(shape=(3, 2)) @@ -201,7 +203,7 @@ class TimeDistributedTest(test.TestCase): assert len(layer.updates) == 2 assert len(layer.trainable_weights) == 2 - @tf_test_util.run_deprecated_v1 + @tf_test_util.run_v1_only('b/120545219') def test_TimeDistributed_with_masked_embedding_and_unspecified_shape(self): with self.cached_session(): # test with unspecified shape and Embeddings with mask_zero @@ -234,7 +236,7 @@ class TimeDistributedTest(test.TestCase): self.assertAllEqual(mask_outputs_val[i], ref_mask_val[i]) self.assertIs(mask_outputs[-1], None) # final layer - @tf_test_util.run_deprecated_v1 + @tf_test_util.run_v1_only('b/120545219') def test_TimeDistributed_with_masking_layer(self): with self.cached_session(): # test with Masking layer @@ -377,7 +379,7 @@ class BidirectionalTest(test.TestCase): model.compile(loss='mse', optimizer='sgd') model.fit(x, y, epochs=1, batch_size=1) - @tf_test_util.run_deprecated_v1 + @tf_test_util.run_v1_only('b/120545219') def test_Bidirectional_merged_value(self): rnn = keras.layers.LSTM samples = 2 @@ -508,7 +510,7 @@ class BidirectionalTest(test.TestCase): layer.trainable = True assert len(layer.trainable_weights) == 6 - @tf_test_util.run_deprecated_v1 + @tf_test_util.run_v1_only('b/120545219') def test_Bidirectional_updates(self): with self.cached_session(): x = keras.layers.Input(shape=(3, 2)) diff --git a/tensorflow/python/keras/model_subclassing_test.py b/tensorflow/python/keras/model_subclassing_test.py index 620275e50f..553c7fb009 100644 --- a/tensorflow/python/keras/model_subclassing_test.py +++ b/tensorflow/python/keras/model_subclassing_test.py @@ -187,6 +187,7 @@ def get_nested_model_3(input_dim, num_classes): @test_util.run_all_in_graph_and_eager_modes +@test_util.run_v1_only('b/120545219') class ModelSubclassingTest(test.TestCase): def test_custom_build(self): @@ -915,6 +916,7 @@ class ModelSubclassingTest(test.TestCase): self.assertEqual(1, len(model.get_updates_for(x))) +@test_util.run_v1_only('b/120545219') class GraphSpecificModelSubclassingTests(test.TestCase): @test_util.run_deprecated_v1 diff --git a/tensorflow/python/keras/models_test.py b/tensorflow/python/keras/models_test.py index 907ac41d0e..c466d94fed 100644 --- a/tensorflow/python/keras/models_test.py +++ b/tensorflow/python/keras/models_test.py @@ -69,7 +69,7 @@ def sequential_model(add_input_layer, include_input_shape=True): class TestModelCloning(test.TestCase): - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def test_clone_sequential_model(self): with self.cached_session(): val_a = np.random.random((10, 4)) @@ -102,10 +102,9 @@ class TestModelCloning(test.TestCase): new_model.compile('rmsprop', 'mse') new_model.train_on_batch(None, val_out) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def test_clone_sequential_model_input_layer(self): - @test_util.run_deprecated_v1 def test_input_layer(include_inputs): with self.cached_session(): val_a = np.random.random((10, 4)) @@ -142,7 +141,7 @@ class TestModelCloning(test.TestCase): test_input_layer(True) test_input_layer(False) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def test_clone_functional_model(self): with self.cached_session(): val_a = np.random.random((10, 4)) @@ -318,6 +317,7 @@ class TestModelDeepCopy(test.TestCase): model_copy.get_weights()[0])) +@test_util.run_v1_only('b/120545219') class TestCloneAndBuildModel(test.TestCase): def test_clone_and_build_non_compiled_model(self): @@ -404,7 +404,6 @@ class TestCloneAndBuildModel(test.TestCase): new_model.train_on_batch(inp, out) new_model.evaluate(inp, out) - @test_util.run_deprecated_v1 def test_clone_and_build_compiled_sequential_model(self): with self.cached_session(): model = keras.models.Sequential() @@ -417,7 +416,6 @@ class TestCloneAndBuildModel(test.TestCase): self._clone_and_build_test_helper(model) - @test_util.run_deprecated_v1 def test_clone_and_build_functional_model(self): with self.cached_session(): input_a = keras.Input(shape=(4,)) @@ -434,7 +432,6 @@ class TestCloneAndBuildModel(test.TestCase): self._clone_and_build_test_helper(model) - @test_util.run_deprecated_v1 def test_clone_and_build_subclassed_model(self): class SubclassedModel(keras.Model): @@ -483,11 +480,9 @@ class TestCloneAndBuildModel(test.TestCase): def test_replace_tf_optimizer_iterations_variable(self): self.assert_optimizer_iterations_increases(adam.AdamOptimizer(0.01)) - @test_util.run_deprecated_v1 def test_replace_keras_optimizer_iterations_variable(self): self.assert_optimizer_iterations_increases('adam') - @test_util.run_deprecated_v1 def test_clone_and_build_sequential_model_without_inputs_defined(self): with self.cached_session(): model = sequential_model(False, False) diff --git a/tensorflow/python/keras/optimizers_test.py b/tensorflow/python/keras/optimizers_test.py index d3cacb702c..77104a5d4d 100644 --- a/tensorflow/python/keras/optimizers_test.py +++ b/tensorflow/python/keras/optimizers_test.py @@ -91,26 +91,26 @@ def _test_optimizer(optimizer, target=0.75): class KerasOptimizersTest(test.TestCase): - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def test_sgd(self): with self.cached_session(): _test_optimizer(keras.optimizers.SGD(lr=0.01, momentum=0.9, nesterov=True)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def test_rmsprop(self): with self.cached_session(): _test_optimizer(keras.optimizers.RMSprop()) _test_optimizer(keras.optimizers.RMSprop(decay=1e-3)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def test_adagrad(self): with self.cached_session(): _test_optimizer(keras.optimizers.Adagrad()) _test_optimizer(keras.optimizers.Adagrad(decay=1e-3)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def test_adadelta(self): with self.cached_session(): _test_optimizer(keras.optimizers.Adadelta(), target=0.6) @@ -119,32 +119,32 @@ class KerasOptimizersTest(test.TestCase): # the accuracy. _test_optimizer(keras.optimizers.Adadelta(decay=1e-3), target=0.4) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def test_adam(self): with self.cached_session(): _test_optimizer(keras.optimizers.Adam()) _test_optimizer(keras.optimizers.Adam(decay=1e-3)) _test_optimizer(keras.optimizers.Adam(amsgrad=True)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def test_adamax(self): with self.cached_session(): _test_optimizer(keras.optimizers.Adamax()) _test_optimizer(keras.optimizers.Adamax(decay=1e-3)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def test_nadam(self): with self.cached_session(): _test_optimizer(keras.optimizers.Nadam()) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def test_clipnorm(self): with self.cached_session(): _test_optimizer(keras.optimizers.SGD(lr=0.01, momentum=0.9, clipnorm=0.5)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def test_clipvalue(self): with self.cached_session(): _test_optimizer(keras.optimizers.SGD(lr=0.01, diff --git a/tensorflow/python/kernel_tests/atrous_convolution_test.py b/tensorflow/python/kernel_tests/atrous_convolution_test.py index 6b16fca29d..2fb8a37e2b 100644 --- a/tensorflow/python/kernel_tests/atrous_convolution_test.py +++ b/tensorflow/python/kernel_tests/atrous_convolution_test.py @@ -110,6 +110,7 @@ class AtrousConvolutionTest(test.TestCase): add_check(check, y1, y2) + @test_util.run_v1_only("b/120545219") def test_unknown_spatial_dims_for_channel_last_format(self): x = array_ops.placeholder(dtypes.float32, [1, None, None, 10]) w = array_ops.zeros([3, 3, 10, 20]) @@ -117,6 +118,7 @@ class AtrousConvolutionTest(test.TestCase): x, w, "VALID", dilation_rate=[2, 2], data_format="NHWC") self.assertEqual(y.shape.as_list(), [1, None, None, 20]) + @test_util.run_v1_only("b/120545219") def test_unknown_spatial_dims_for_channel_first_format(self): x = array_ops.placeholder(dtypes.float32, [1, 10, None, None]) w = array_ops.zeros([3, 3, 10, 20]) @@ -262,6 +264,7 @@ class AtrousConvolutionTest(test.TestCase): err_tolerance = 1e-3 self.assertLess(err, err_tolerance) + @test_util.run_v1_only("b/120545219") def testGradient(self): with self.cached_session(): for padding in ["SAME", "VALID"]: diff --git a/tensorflow/python/kernel_tests/base64_ops_test.py b/tensorflow/python/kernel_tests/base64_ops_test.py index bb903d827f..381f190b8d 100644 --- a/tensorflow/python/kernel_tests/base64_ops_test.py +++ b/tensorflow/python/kernel_tests/base64_ops_test.py @@ -31,6 +31,7 @@ from tensorflow.python.ops import string_ops from tensorflow.python.platform import test +@test_util.run_v1_only("b/120545219") class Base64OpsTest(test_util.TensorFlowTestCase): def setUp(self): diff --git a/tensorflow/python/kernel_tests/batch_matmul_op_test.py b/tensorflow/python/kernel_tests/batch_matmul_op_test.py index f2f0291b89..c32a6c7e41 100644 --- a/tensorflow/python/kernel_tests/batch_matmul_op_test.py +++ b/tensorflow/python/kernel_tests/batch_matmul_op_test.py @@ -21,6 +21,7 @@ from __future__ import print_function import numpy as np from tensorflow.python import tf2 +from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import gradient_checker_v2 from tensorflow.python.ops import math_ops @@ -135,6 +136,7 @@ class BatchMatmulOpTest(test.TestCase): def _GetBatchMatmulOpTest(dtype, adjoint_a, adjoint_b, use_static_shape): + @test_util.run_v1_only("b/120545219") def Test(self): np.random.seed(42) self._testNonEmpty(dtype, adjoint_a, adjoint_b, use_static_shape) @@ -184,6 +186,7 @@ class BatchMatmulGradientTest(test.TestCase): def _GetBatchMatmulGradientTest(dtype, adjoint_a, adjoint_b): + @test_util.run_v1_only("b/120545219") def Test(self): self._compare(1, 2, 3, 5, dtype, adjoint_a, adjoint_b) self._compare(3, 4, 7, 10, dtype, adjoint_a, adjoint_b) diff --git a/tensorflow/python/kernel_tests/boosted_trees/quantile_ops_test.py b/tensorflow/python/kernel_tests/boosted_trees/quantile_ops_test.py index 390672febe..2b9863fb89 100644 --- a/tensorflow/python/kernel_tests/boosted_trees/quantile_ops_test.py +++ b/tensorflow/python/kernel_tests/boosted_trees/quantile_ops_test.py @@ -82,7 +82,7 @@ class QuantileOpsTest(test_util.TensorFlowTestCase): self.max_elements = 1 << 16 self.num_quantiles = constant_op.constant(3, dtype=dtypes.int64) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testBasicQuantileBucketsSingleResource(self): with self.cached_session() as sess: quantile_accumulator_handle = self.create_resource("floats", self.eps, @@ -107,7 +107,7 @@ class QuantileOpsTest(test_util.TensorFlowTestCase): self.assertAllClose(self._feature_0_quantiles, quantiles[0].eval()) self.assertAllClose(self._feature_1_quantiles, quantiles[1].eval()) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testBasicQuantileBucketsMultipleResources(self): with self.cached_session() as sess: quantile_accumulator_handle_0 = self.create_resource("float_0", self.eps, @@ -142,7 +142,7 @@ class QuantileOpsTest(test_util.TensorFlowTestCase): self.assertAllClose(self._feature_0_quantiles, quantiles[0].eval()) self.assertAllClose(self._feature_1_quantiles, quantiles[1].eval()) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testSaveRestoreAfterFlush(self): save_dir = os.path.join(self.get_temp_dir(), "save_restore") save_path = os.path.join(tempfile.mkdtemp(prefix=save_dir), "hash") @@ -175,7 +175,7 @@ class QuantileOpsTest(test_util.TensorFlowTestCase): self.assertAllClose(self._feature_0_boundaries, buckets[0].eval()) self.assertAllClose(self._feature_1_boundaries, buckets[1].eval()) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testSaveRestoreBeforeFlush(self): save_dir = os.path.join(self.get_temp_dir(), "save_restore") save_path = os.path.join(tempfile.mkdtemp(prefix=save_dir), "hash") diff --git a/tensorflow/python/kernel_tests/checkpoint_ops_test.py b/tensorflow/python/kernel_tests/checkpoint_ops_test.py index b8c8c9edb5..6e289bf9b7 100644 --- a/tensorflow/python/kernel_tests/checkpoint_ops_test.py +++ b/tensorflow/python/kernel_tests/checkpoint_ops_test.py @@ -105,6 +105,7 @@ class GenerateVocabRemappingTest(test.TestCase): self.assertAllEqual(expected_num_present, self.evaluate(num_present)) +@test_util.run_v1_only('b/120545219') class LoadAndRemapMatrixTest(test.TestCase): """Tests for the load_and_remap_matrix() op.""" diff --git a/tensorflow/python/kernel_tests/cholesky_op_test.py b/tensorflow/python/kernel_tests/cholesky_op_test.py index f3947236b1..a08cfe960d 100644 --- a/tensorflow/python/kernel_tests/cholesky_op_test.py +++ b/tensorflow/python/kernel_tests/cholesky_op_test.py @@ -155,6 +155,7 @@ class CholeskyOpTest(test.TestCase): np.array([[[1., 2., 3.], [3., 4., 5.]], [[1., 2., 3.], [3., 4., 5.]] ])) + @test_util.run_v1_only("b/120545219") def testWrongDimensions(self): tensor3 = constant_op.constant([1., 2.]) with self.assertRaises(ValueError): @@ -233,6 +234,7 @@ class CholeskyGradTest(test.TestCase): self.runFiniteDifferences( shapes, dtypes=(dtypes_lib.float64,), scalarTest=True) + @test_util.run_v1_only("b/120545219") def testTwoBlockMatrixComplexFloat(self): np.random.seed(0) shapes = self.getShapes([2 * self._backprop_block_size + 1]) diff --git a/tensorflow/python/kernel_tests/cond_v2_test.py b/tensorflow/python/kernel_tests/cond_v2_test.py index 843d007cc8..8fe3ba41e2 100644 --- a/tensorflow/python/kernel_tests/cond_v2_test.py +++ b/tensorflow/python/kernel_tests/cond_v2_test.py @@ -170,6 +170,7 @@ class CondV2Test(test.TestCase): self.assertRegexpMatches( cond2_op.get_attr("else_branch").name, r"foo_cond_1_false_\d*") + @test_util.run_v1_only("b/120545219") def testDefunInCond(self): x = constant_op.constant(1.0, name="x") y = constant_op.constant(2.0, name="y") @@ -189,6 +190,7 @@ class CondV2Test(test.TestCase): self._testCond(true_fn, false_fn, [x, y]) self._testCond(true_fn, false_fn, [y]) + @test_util.run_deprecated_v1 def testNestedDefunInCond(self): x = constant_op.constant(1.0, name="x") y = constant_op.constant(2.0, name="y") @@ -213,6 +215,7 @@ class CondV2Test(test.TestCase): self._testCond(true_fn, false_fn, [x, y]) self._testCond(true_fn, false_fn, [y]) + @test_util.run_deprecated_v1 def testDoubleNestedDefunInCond(self): x = constant_op.constant(1.0, name="x") y = constant_op.constant(2.0, name="y") @@ -773,6 +776,7 @@ class CondV2Test(test.TestCase): self.assertAllEqual( self.evaluate(output_t), [-5, -4, -3, -2, -1, 0, 1, 4, 9, 16]) + @test_util.run_deprecated_v1 def testForwardPassRewrite(self): x = constant_op.constant(1.0, name="x") output = cond_v2.cond_v2(constant_op.constant(True), diff --git a/tensorflow/python/kernel_tests/conditional_accumulator_test.py b/tensorflow/python/kernel_tests/conditional_accumulator_test.py index 5847e4639b..ce34201706 100644 --- a/tensorflow/python/kernel_tests/conditional_accumulator_test.py +++ b/tensorflow/python/kernel_tests/conditional_accumulator_test.py @@ -199,7 +199,7 @@ class ConditionalAccumulatorTest(test.TestCase): is_all_equal &= (val[i][j] == elems_ave[i][j]) self.assertTrue(is_all_equal) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testAccumulatorWrongDynamicShape(self): with self.cached_session() as sess: q = data_flow_ops.ConditionalAccumulator( @@ -321,7 +321,7 @@ class ConditionalAccumulatorTest(test.TestCase): shape=tensor_shape.TensorShape([1]), reduction_type="Invalid") - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testAccumulatorInvalidTakeGrad(self): with self.cached_session(): q = data_flow_ops.ConditionalAccumulator( @@ -435,7 +435,7 @@ class ConditionalAccumulatorTest(test.TestCase): if x >= ls) / sum(1 for x in local_steps if x >= ls), val) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testParallelApplyGrad(self): with self.cached_session() as sess: q = data_flow_ops.ConditionalAccumulator( @@ -461,7 +461,7 @@ class ConditionalAccumulatorTest(test.TestCase): self.assertEqual(val, sum(elems) / len(elems)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testParallelTakeGrad(self): with self.cached_session() as sess: q = data_flow_ops.ConditionalAccumulator( @@ -494,7 +494,7 @@ class ConditionalAccumulatorTest(test.TestCase): self.assertItemsEqual(elems, results) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testAccumulatorApplyAndBlockingTake(self): with self.cached_session() as sess: q = data_flow_ops.ConditionalAccumulator( @@ -528,7 +528,7 @@ class ConditionalAccumulatorTest(test.TestCase): with self.assertRaisesOpError("was cancelled"): self.evaluate(takeg_op) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testAccumulatorCancel(self): with self.cached_session() as sess: q = data_flow_ops.ConditionalAccumulator( diff --git a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py index 95420fc0f1..0fd293ebba 100644 --- a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py +++ b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py @@ -129,6 +129,7 @@ def isum(s, maximum_iterations=None): @test_util.with_control_flow_v2 class ControlFlowTest(test.TestCase): + @test_util.run_v1_only("b/120545219") def testRefIdentity(self): with self.cached_session(): v = variables.VariableV1(7) @@ -141,7 +142,7 @@ class ControlFlowTest(test.TestCase): variables.global_variables_initializer().run() self.assertEqual(9, self.evaluate(v2)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testRefEnter(self): with self.cached_session(): v = variables.VariableV1(7) @@ -155,7 +156,7 @@ class ControlFlowTest(test.TestCase): variables.global_variables_initializer().run() self.assertEqual(9, self.evaluate(v3)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testRefSwitch(self): with self.cached_session(): v = variables.VariableV1(7) @@ -193,6 +194,7 @@ class ControlFlowTest(test.TestCase): v, "frame2", is_constant=False) self.assertEqual(enter_v_non_constant.shape, None) + @test_util.run_v1_only("b/120545219") def testSwitchMergeIndexedSlices(self): with self.cached_session(): values = constant_op.constant([1, 2, 3, 4, 5, 6]) @@ -207,6 +209,7 @@ class ControlFlowTest(test.TestCase): self.assertAllEqual(np.arange(1, 7), val) self.assertAllEqual(np.arange(0, 12, 2), ind) + @test_util.run_v1_only("b/120545219") def testSwitchDeadBranch(self): with self.cached_session(): data = constant_op.constant([1, 2, 3, 4, 5, 6], name="data") @@ -219,6 +222,7 @@ class ControlFlowTest(test.TestCase): lambda e: "Retval[0] does not have value" in str(e)): self.evaluate(dead_branch) + @test_util.run_v1_only("b/120545219") def testSwitchMergeLess(self): with self.cached_session(): data = constant_op.constant([1, 2, 3, 4, 5, 6], name="data") @@ -231,6 +235,7 @@ class ControlFlowTest(test.TestCase): result = self.evaluate(merge_op) self.assertAllEqual(np.arange(1, 7), result) + @test_util.run_v1_only("b/120545219") def testSwitchMergeAddIdentity(self): with self.cached_session(): data = constant_op.constant([1, 2, 3, 4, 5, 6], name="data") @@ -244,6 +249,7 @@ class ControlFlowTest(test.TestCase): result = self.evaluate(merge_op) self.assertAllEqual(np.array([x + 1 for x in [1, 2, 3, 4, 5, 6]]), result) + @test_util.run_v1_only("b/120545219") def testSwitchMergeAddMul(self): with self.cached_session(): data = constant_op.constant([1, 2, 3, 4, 5, 6], name="data") @@ -258,6 +264,7 @@ class ControlFlowTest(test.TestCase): result = self.evaluate(merge_op) self.assertAllEqual(np.array([x * 5 for x in [1, 2, 3, 4, 5, 6]]), result) + @test_util.run_v1_only("b/120545219") def testLoop_false(self): with self.cached_session(): false = ops.convert_to_tensor(False) @@ -302,6 +309,7 @@ class ControlFlowTest(test.TestCase): result = self.evaluate(exit_i) self.assertAllEqual(10, result) + @test_util.run_v1_only("b/120545219") def testLoop_2(self): with self.cached_session(): zero = constant_op.constant(0) @@ -328,6 +336,7 @@ class ControlFlowTest(test.TestCase): result = self.evaluate(exit_i) self.assertAllEqual(10, result) + @test_util.run_v1_only("b/120545219") def testDifferentFrame(self): with self.cached_session(): data = array_ops.placeholder(dtypes.float32, shape=[]) @@ -362,6 +371,7 @@ class ControlFlowTest(test.TestCase): lambda: math_ops.subtract(x, 1.)) self.assertEqual(b.shape, tensor_shape.scalar()) + @test_util.run_v1_only("b/120545219") def testFetchable(self): with self.cached_session() as sess: x = array_ops.placeholder(dtypes.float32) @@ -378,6 +388,7 @@ class ControlFlowTest(test.TestCase): sess.run(t, feed_dict={x: 3}) @test_util.disable_control_flow_v2("Not relevant") + @test_util.run_v1_only("b/120545219") def testFeedable(self): with self.cached_session() as sess: c = constant_op.constant(2) @@ -395,6 +406,7 @@ class ControlFlowTest(test.TestCase): with self.assertRaisesRegexp(ValueError, "may not be fed"): sess.run(r, feed_dict={t: 3}) + @test_util.run_v1_only("b/120545219") def testCondIndexedSlices(self): with self.cached_session(): values = constant_op.constant(10) @@ -410,6 +422,7 @@ class ControlFlowTest(test.TestCase): self.assertAllEqual(11, val) self.assertAllEqual(0, ind) + @test_util.run_v1_only("b/120545219") def testCondSparseTensor(self): with self.cached_session(): values = constant_op.constant([2.0, 4.0], name="values") @@ -427,6 +440,7 @@ class ControlFlowTest(test.TestCase): self.assertAllEqual([[1], [4]], r.indices.eval()) self.assertAllEqual(r.values.get_shape(), (2,)) + @test_util.run_v1_only("b/120545219") def testCondResource(self): with self.cached_session(): @@ -441,6 +455,7 @@ class ControlFlowTest(test.TestCase): self.assertEqual(1.0, control_flow_ops.cond(rv, case, lambda: t).eval()) + @test_util.run_v1_only("b/120545219") def testCondWithTensorArrayGrad(self): with self.cached_session() as sess: with ops.device(test.gpu_device_name()): @@ -455,6 +470,7 @@ class ControlFlowTest(test.TestCase): self.assertAllEqual(sess.run(g, {pred: False}), [0.0, 0.0, 0.0]) @test_util.disable_control_flow_v2("b/113293074") + @test_util.run_v1_only("b/120545219") def testCondIndexedSlicesDifferentTypes(self): with self.cached_session(): values = constant_op.constant(10) @@ -472,6 +488,7 @@ class ControlFlowTest(test.TestCase): self.assertAllEqual(0, ind) self.assertTrue(ind.dtype == np.int64) + @test_util.run_v1_only("b/120545219") def testCondColocation(self): with self.session(use_gpu=True): with ops.device("/cpu:0"): @@ -576,6 +593,7 @@ class ControlFlowTest(test.TestCase): alive, count = body(i) self.assertAllEqual(4, self.evaluate(count)) + @test_util.run_v1_only("b/120545219") def testCond_6(self): with self.cached_session(): v1 = variables.Variable([7]) @@ -671,6 +689,7 @@ class ControlFlowTest(test.TestCase): test_result = self.evaluate(r) self.assertDictEqual({"a": {"c": 210}, "b": {"d": 210}}, test_result) + @test_util.run_v1_only("b/120545219") def testCheckNestedOutputStruct(self): with self.cached_session() as sess: x = constant_op.constant(10) @@ -701,7 +720,7 @@ class ControlFlowTest(test.TestCase): self.assertAllEqual([2.0], self.evaluate(r)) @test_util.disable_control_flow_v2("b/79881896 (control deps)") - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testCondWithControl(self): with self.cached_session(): control_holder = array_ops.placeholder(dtypes.float32, shape=()) @@ -717,6 +736,7 @@ class ControlFlowTest(test.TestCase): lambda: constant_op.constant(1)) self.assertEqual(5, self.evaluate(r)) + @test_util.run_v1_only("b/120545219") def testUninitializedRefIdentity(self): with self.cached_session() as sess: v = gen_state_ops.variable( @@ -771,6 +791,7 @@ class ControlFlowTest(test.TestCase): r = control_flow_ops.cond(pred, fn1, fn2) self.evaluate(r) + @test_util.run_v1_only("b/120545219") def testCondGrad_1(self): with self.cached_session(): x = constant_op.constant(10.0, name="x") @@ -845,6 +866,7 @@ class ControlFlowTest(test.TestCase): self.assertEqual(sess.run(grad_grad, {pred: True, x: 1.0, y: 2.0}), 0.0) self.assertEqual(sess.run(grad_grad, {pred: False, x: 1.0, y: 2.0}), 0.0) + @test_util.run_v1_only("b/120545219") def testNestedCond_Simple(self): with self.cached_session(): x = constant_op.constant(0., name="X") @@ -861,7 +883,7 @@ class ControlFlowTest(test.TestCase): self.assertEqual(1.0, self.evaluate(result)) @test_util.disable_control_flow_v2("b/113327884") - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testCondGrad_Gather(self): with self.cached_session() as sess: v1 = variables.Variable([1.0, 42.0]) @@ -885,6 +907,7 @@ class ControlFlowTest(test.TestCase): ] self.assertAllEqual(dense_gv, [0.0, 2.0]) + @test_util.run_v1_only("b/120545219") def testCondPredicateTensor(self): """Regression test for lowering predicate from non-first output of an op.""" @@ -1011,6 +1034,7 @@ class ControlFlowTest(test.TestCase): self.assertEqual(10000, self.evaluate(r)) @test_util.disable_control_flow_v2("b/79881896 (control deps)") + @test_util.run_v1_only("b/120545219") def testWhileExternalControlDependencies(self): with self.cached_session(): v = variables.Variable(0.0) @@ -1027,6 +1051,7 @@ class ControlFlowTest(test.TestCase): self.assertAllEqual(v.eval(), 1.0) @test_util.disable_control_flow_v2("b/79881896 (control deps)") + @test_util.run_v1_only("b/120545219") def testWhileExternalControlDependenciesNoInput(self): with self.cached_session(): v = variables.Variable(0.0) @@ -1043,7 +1068,7 @@ class ControlFlowTest(test.TestCase): self.assertAllEqual(v.eval(), 1.0) @test_util.disable_control_flow_v2("b/113324949 (RefVariable)") - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testWhileWithRefs_1(self): with self.cached_session() as sess: x = variables.VariableV1(0)._ref() # pylint: disable=protected-access @@ -1080,6 +1105,7 @@ class ControlFlowTest(test.TestCase): r = isum(s, maximum_iterations=3) self.assertAllEqual([1 + 3, 2 + 3, 3 + 3, 4 + 3, 5 + 3], self.evaluate(r)) + @test_util.run_v1_only("b/120545219") def testWhileWithMaximumIterationsAndSingleArgument(self): with self.cached_session(): r = control_flow_ops.while_loop( @@ -1087,6 +1113,7 @@ class ControlFlowTest(test.TestCase): self.assertEqual(1, self.evaluate(r)) @test_util.disable_control_flow_v2("b/115776323 (max_iters)") + @test_util.run_v1_only("b/120545219") def testSingleNestedMaximumIterationsWhileLoopGradientInXLAContext(self): v = constant_op.constant(1.0) @@ -1112,6 +1139,7 @@ class ControlFlowTest(test.TestCase): # Should execute without issue. self.assertEqual(3, self.evaluate(loop_execute)) + @test_util.run_v1_only("b/120545219") def testInvalidMaximumIterationsWhileLoopGradientInXLAContext(self): v = constant_op.constant(1.0) @@ -1172,6 +1200,7 @@ class ControlFlowTest(test.TestCase): r"context '.*' \(currently defined in '.*'\)"): _ = gradients_impl.gradients(loop_with_maxiter, v) + @test_util.run_v1_only("b/120545219") def testInvalidMaximumIterationsFromSiblingContextWhileLoopInXLAContext(self): v = constant_op.constant(1.0) @@ -1215,6 +1244,7 @@ class ControlFlowTest(test.TestCase): _ = gradients_impl.gradients(loop, v) @test_util.disable_control_flow_v2("b/118457764") + @test_util.run_v1_only("b/120545219") def testNestedWhileLoopWithMaxItersFromOuterContextInXLAContext(self): v = constant_op.constant(1.0) @@ -1326,6 +1356,7 @@ class ControlFlowTest(test.TestCase): result = r[3].eval() self.assertAllEqual(42, result) + @test_util.run_v1_only("b/120545219") def testWhile_5(self): with self.cached_session(): @@ -1351,6 +1382,7 @@ class ControlFlowTest(test.TestCase): self.assertAllEqual(np.array([0, 1, 2, 3, 4, 5, 6]), result) @test_util.disable_control_flow_v2("b/116338794 (buffer_reuse)") + @test_util.run_v1_only("b/120545219") def testBufferForwarding(self): run_options = config_pb2.RunOptions( trace_level=config_pb2.RunOptions.FULL_TRACE) @@ -1435,6 +1467,7 @@ class ControlFlowTest(test.TestCase): r = control_flow_ops.while_loop(c, b, [n], parallel_iterations=20) self.assertEqual([10000], self.evaluate(r)) + @test_util.run_v1_only("b/120545219") def testWhileShapeInference(self): with self.cached_session(): i = constant_op.constant(0) @@ -1461,6 +1494,7 @@ class ControlFlowTest(test.TestCase): r = control_flow_ops.while_loop(c, b, [i, m]) @test_util.disable_control_flow_v2("b/116328420 (SparseTensor)") + @test_util.run_v1_only("b/120545219") def testWhileShapeInferenceSparseTensor(self): with self.cached_session(): values = constant_op.constant([2.0, 4.0], name="values") @@ -1493,7 +1527,7 @@ class ControlFlowTest(test.TestCase): [i.get_shape(), tensor_shape.TensorShape([5])]) @test_util.disable_control_flow_v2("b/116282023 (IndexedSlices)") - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testWhileShapeInferenceIndexedSlices(self): with self.cached_session(): values = constant_op.constant([[2.0, 4.0], [3.0, 5.0]], name="values") @@ -1584,6 +1618,7 @@ class ControlFlowTest(test.TestCase): self._testNestedWhile_2(use_gpu=False) self._testNestedWhile_2(use_gpu=True) + @test_util.run_v1_only("b/120545219") def testWhileWithControl_1(self): with self.cached_session(): n = constant_op.constant(0) @@ -1615,6 +1650,7 @@ class ControlFlowTest(test.TestCase): condition, body, [r], parallel_iterations=1) self.assertAllEqual(12, self.evaluate(res)) + @test_util.run_v1_only("b/120545219") def testWhileWithControl_3(self): with self.cached_session() as sess: b = array_ops.placeholder(dtypes.bool) @@ -1624,6 +1660,7 @@ class ControlFlowTest(test.TestCase): r = control_flow_ops.while_loop(lambda x: x < 10, lambda x: x + c, [x0]) self.assertEqual(10, sess.run(r, {b: True})) + @test_util.run_v1_only("b/120545219") def testWhileWithControl_4(self): with self.cached_session() as sess: b = array_ops.placeholder(dtypes.bool) @@ -1635,6 +1672,7 @@ class ControlFlowTest(test.TestCase): self.assertEqual(10, sess.run(r, {b: True})) @test_util.disable_control_flow_v2("b/79881896 (control_deps)") + @test_util.run_v1_only("b/120545219") def testWhileWithControl_5(self): with self.cached_session() as sess: b = array_ops.placeholder(dtypes.bool) @@ -1663,6 +1701,7 @@ class ControlFlowTest(test.TestCase): self.assertEqual(0, self.evaluate(loop)) @test_util.disable_control_flow_v2("b/113324949 (ref vars)") + @test_util.run_v1_only("b/120545219") def testWhileCondWithControl_1(self): with self.cached_session(): v = variable_scope.get_variable( @@ -1686,6 +1725,7 @@ class ControlFlowTest(test.TestCase): self.assertAllClose(65536.0, self.evaluate(v)) @test_util.disable_control_flow_v2("b/113324949 (ref vars)") + @test_util.run_v1_only("b/120545219") def testWhileCondExitControl(self): with self.cached_session(): @@ -1855,6 +1895,7 @@ class ControlFlowTest(test.TestCase): self.assertAllClose(np.array([10.0, 10.0, 10.0]), result) @test_util.disable_control_flow_v2("b/113324949 (RefVariable)") + @test_util.run_v1_only("b/120545219") def testWhileUpdateVariable_2(self): with self.cached_session(): select1 = variables.Variable([3.0, 4.0, 5.0]) @@ -1905,7 +1946,7 @@ class ControlFlowTest(test.TestCase): self.assertAllClose(np.array([10.0, 10.0, 10.0]), result) @test_util.disable_control_flow_v2("b/113324949 (RefVariable)") - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testWhileUpdateVariable_4(self): with self.cached_session(): var_a = variables.Variable(0, name="a") @@ -1934,7 +1975,7 @@ class ControlFlowTest(test.TestCase): self.assertEqual(10, self.evaluate(var_b)) @test_util.disable_control_flow_v2("b/113324949 (RefVariable)") - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testWhileUpdateVariable_5(self): with self.cached_session(): # Create some variables. @@ -1965,6 +2006,7 @@ class ControlFlowTest(test.TestCase): self.assertEqual(10, self.evaluate(var_b)) @test_util.disable_control_flow_v2("b/113324949 (RefVariable)") + @test_util.run_v1_only("b/120545219") def testWhileUpdateVariable_6(self): with self.cached_session(): # Create some variables. @@ -1994,6 +2036,7 @@ class ControlFlowTest(test.TestCase): self.assertEqual(55, self.evaluate(var_b)) self.assertEqual(10, self.evaluate(var_a)) + @test_util.run_v1_only("b/120545219") def testWhileQueue_1(self): with self.cached_session(): q = data_flow_ops.FIFOQueue(-1, dtypes.int32) @@ -2012,6 +2055,7 @@ class ControlFlowTest(test.TestCase): for i in xrange(10): self.assertEqual([i], q.dequeue().eval()) + @test_util.run_v1_only("b/120545219") def testWhileTimeOut(self): run_options = config_pb2.RunOptions(timeout_in_ms=1) with self.cached_session() as sess: @@ -2023,6 +2067,7 @@ class ControlFlowTest(test.TestCase): sess.run(r, options=run_options) @test_util.disable_control_flow_v2("b/117119329 (stack)") + @test_util.run_v1_only("b/120545219") def testWhileStack_1(self): with self.cached_session(): s = gen_data_flow_ops.stack_v2(-1, dtypes.int32, stack_name="foo") @@ -2092,10 +2137,12 @@ class ControlFlowTest(test.TestCase): self.assertAllClose(1024.0, self.evaluate(r)) @test_util.disable_control_flow_v2("b/116351701 (colocation)") + @test_util.run_v1_only("b/120545219") def testWhileGrad_ColocateGradients(self): self._testWhileGrad_ColocateGradients(colocate=False) self._testWhileGrad_ColocateGradients(colocate=True) + @test_util.run_v1_only("b/120545219") def testWhileGrad_Square(self): with self.cached_session(): v = constant_op.constant(2.0, name="v") @@ -2107,6 +2154,7 @@ class ControlFlowTest(test.TestCase): r = gradients_impl.gradients(r, v)[0] self.assertAllClose(1024.0, self.evaluate(r)) + @test_util.run_v1_only("b/120545219") def testWhileGrad_Shape(self): with self.cached_session(): x = array_ops.placeholder(dtypes.float32, shape=[None]) @@ -2137,6 +2185,7 @@ class ControlFlowTest(test.TestCase): r = gradients_impl.gradients([r, y], x)[0] self.assertAllClose([2.0, 4.0], sess.run(r, feed_dict={x: [1.0, 2.0]})) + @test_util.run_v1_only("b/120545219") def testWhileGrad_MultipleUses(self): with self.cached_session(): v = constant_op.constant(2.0, name="v") @@ -2148,6 +2197,7 @@ class ControlFlowTest(test.TestCase): r = gradients_impl.gradients(r, v)[0] self.assertEqual(524288.0, self.evaluate(r)) + @test_util.run_v1_only("b/120545219") def testWhileGrad_LoopAdd(self): with self.cached_session(): v = constant_op.constant(2.0, name="v") @@ -2211,6 +2261,7 @@ class ControlFlowTest(test.TestCase): def testNestedWhileCondWhileGradGpu(self): self._testNestedWhileCondWhileGrad(use_gpu=True) + @test_util.run_v1_only("b/120545219") def testWhileGrad_Variable(self): with self.cached_session(): a = variables.Variable(3.0) @@ -2236,6 +2287,7 @@ class ControlFlowTest(test.TestCase): variables.global_variables_initializer().run() self.assertAllClose(216.0, g[0].eval()) + @test_util.run_v1_only("b/120545219") def testWhileGradInCond(self): with self.cached_session(): @@ -2253,7 +2305,7 @@ class ControlFlowTest(test.TestCase): self.assertAllClose(9.0, r.eval(feed_dict={x: 1.0})) @test_util.disable_control_flow_v2("b/116340060") - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testGradInWhileWrtInitialLoopVal(self): with self.cached_session(): x = array_ops.placeholder(dtypes.float32, shape=(), name="x") @@ -2271,6 +2323,7 @@ class ControlFlowTest(test.TestCase): "loop invariants or wrt the input parameters to the loop body."): control_flow_ops.while_loop(lambda i, x: i < 3, body, [0, y]) + @test_util.run_v1_only("b/120545219") def testWhileGradInWhile(self): with self.cached_session(): n = ops.convert_to_tensor(1.0, name="n") @@ -2287,6 +2340,7 @@ class ControlFlowTest(test.TestCase): [tensor_shape.unknown_shape()]) self.assertAllClose(9.0, r.eval(feed_dict={x: 1.0})) + @test_util.run_v1_only("b/120545219") def testCondGradInNestedWhiles(self): def outer_body(i, x): @@ -2375,6 +2429,7 @@ class ControlFlowTest(test.TestCase): self.assertEqual([100.0, 1.0, 102.0, 3.0, 4.0 + 100 * 2.0], self.evaluate(r_flattened)) + @test_util.run_v1_only("b/120545219") def testWhile_NestedBadArityFails(self): with self.cached_session(): named = collections.namedtuple("named", ("a", "b")) @@ -2391,6 +2446,7 @@ class ControlFlowTest(test.TestCase): with self.assertRaisesRegexp(ValueError, "the same number of elements"): control_flow_ops.while_loop(c, b, loop_vars) + @test_util.run_v1_only("b/120545219") def testWhileGrad_ys_xs(self): with self.cached_session(): x = constant_op.constant(3.0, name="x") @@ -2435,6 +2491,7 @@ class ControlFlowTest(test.TestCase): self.assertAllClose(1024.0, r[0].eval()) @test_util.disable_control_flow_v2("b/116355153 (back_prop flag)") + @test_util.run_v1_only("b/120545219") def testWhileGrad_NoGradient(self): with self.cached_session(): v = constant_op.constant(2.0, name="v") @@ -2446,6 +2503,7 @@ class ControlFlowTest(test.TestCase): self.assertAllClose(1.0, r[0].eval()) @test_util.disable_control_flow_v2("b/113324949 (RefVariable)") + @test_util.run_v1_only("b/120545219") def testWhileGrad_NoDependency(self): with self.cached_session() as sess: variable = variables.Variable(array_ops.ones([2, 3])) @@ -2486,6 +2544,7 @@ class ControlFlowTest(test.TestCase): grad = gradients_impl.gradients(cost, [c0]) self.assertAllClose(0.0, sess.run(grad[0])) + @test_util.run_v1_only("b/120545219") def testWhileGrad_SerialTwoLoops(self): with self.cached_session(): i = constant_op.constant(0, name="i") @@ -2504,6 +2563,7 @@ class ControlFlowTest(test.TestCase): r = gradients_impl.gradients([rx], x) self.assertAllClose(1024.0, r[0].eval()) + @test_util.run_v1_only("b/120545219") def testWhileGrad_ParallelTwoLoops(self): with self.cached_session(): i = constant_op.constant(0, name="i") @@ -2523,6 +2583,7 @@ class ControlFlowTest(test.TestCase): r = gradients_impl.gradients([rx], x) self.assertAllClose(64.0, r[0].eval()) + @test_util.run_v1_only("b/120545219") def testWhileGrad_OneOutputWithControlDependencyOnSecond(self): with self.cached_session(): i = constant_op.constant(0, name="i") @@ -2566,6 +2627,7 @@ class ControlFlowTest(test.TestCase): self._testNestedWhileGrad_Simple(use_gpu=False) self._testNestedWhileGrad_Simple(use_gpu=True) + @test_util.run_v1_only("b/120545219") def testNestedWhileGrad_SerialInner(self): with self.cached_session(): v = constant_op.constant(1.0) @@ -2613,6 +2675,7 @@ class ControlFlowTest(test.TestCase): r = gradients_impl.gradients(r, v)[0] self.assertAllClose(512.0, self.evaluate(r)) + @test_util.run_v1_only("b/120545219") def testNestedWhileGrad_ParallelIterations(self): # Make sure the stack pushes and pops of an inner loop are executed in # the sequential order of the iterations of its outer loop. @@ -2702,6 +2765,7 @@ class ControlFlowTest(test.TestCase): self.assertAllClose([[0.98000002, 1.98000002]], self.evaluate(x)) @test_util.disable_control_flow_v2("b/113324949 (RefVariable)") + @test_util.run_v1_only("b/120545219") def testWhileWithRefsWithGradients_1(self): with self.cached_session() as sess: x = variables.VariableV1(0.)._ref() # pylint: disable=protected-access @@ -2731,6 +2795,7 @@ class ControlFlowTest(test.TestCase): self.assertEqual(73, value_x_grad) @test_util.disable_control_flow_v2("b/116282023 (IndexedSlices)") + @test_util.run_v1_only("b/120545219") def testWhileGrad_IndexedSlices(self): with self.cached_session(): values = constant_op.constant([2.0, 4.0], name="values") @@ -2753,7 +2818,7 @@ class ControlFlowTest(test.TestCase): self.assertAllClose(np.array([1024.0, 1024.0]), self.evaluate(r)) @test_util.disable_control_flow_v2("b/116328420 (SparseTensor)") - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testWhileGrad_SparseTensor(self): with self.cached_session(): values = constant_op.constant([2.0, 4.0], name="values") @@ -2777,6 +2842,7 @@ class ControlFlowTest(test.TestCase): self.assertAllClose(np.array([1024.0, 1024.0]), self.evaluate(r)) @test_util.disable_control_flow_v2("b/115920078 (gradients)") + @test_util.run_v1_only("b/120545219") def testCallGradInLoop(self): with self.cached_session() as sess: i0 = constant_op.constant(0) @@ -2959,6 +3025,7 @@ class ControlFlowTest(test.TestCase): self.assertAllClose([0., 0.], self.evaluate(dy_dq)) @test_util.disable_control_flow_v2("b/113324949 (RefVariable)") + @test_util.run_v1_only("b/120545219") def testWhileGradientWithNontrainablePath2(self): q = variables.Variable([7., 8.]) @@ -2977,6 +3044,7 @@ class ControlFlowTest(test.TestCase): self.assertAllClose([1., 1.], self.evaluate(dy_dq)) @test_util.disable_control_flow_v2("b/115920078 (gradients)") + @test_util.run_v1_only("b/120545219") def testIssue16504(self): c = constant_op.constant(np.arange(100), dtype=dtypes.float32) w = variables.Variable( @@ -3000,6 +3068,7 @@ class ControlFlowTest(test.TestCase): grad, = gradients_impl.gradients(w, c) self.assertIsNotNone(grad) + @test_util.run_v1_only("b/120545219") def testStopGradMultiFlows(self): with self.cached_session(): @@ -3026,6 +3095,7 @@ class ControlFlowTest(test.TestCase): variables.global_variables_initializer().run() self.assertEqual(5.0, self.evaluate(result)) + @test_util.run_v1_only("b/120545219") def testOneValueCond(self): with self.cached_session(): @@ -3059,6 +3129,7 @@ class ControlFlowTest(test.TestCase): self.assertAllClose(4.0, i.eval(feed_dict={d: 1})) self.assertAllClose(2.0 * math.sqrt(2), i.eval(feed_dict={d: 2})) + @test_util.run_v1_only("b/120545219") def testCase(self): with self.cached_session(): x = constant_op.constant(1) @@ -3111,6 +3182,7 @@ class ControlFlowTest(test.TestCase): self.assertAllEqual(r6.eval(), 0) + @test_util.run_v1_only("b/120545219") def testCaseSideEffects(self): with self.cached_session() as sess: v0 = variables.Variable(-1) @@ -3147,6 +3219,7 @@ class ControlFlowTest(test.TestCase): self.assertAllEqual(self.evaluate([v0, v1, v2]), [0, -1, -1]) @test_util.disable_control_flow_v2("b/113324949 (ref vars)") + @test_util.run_v1_only("b/120545219") def testOneOpCond(self): with self.cached_session(): v = variables.Variable(0) @@ -3175,6 +3248,7 @@ class ControlFlowTest(test.TestCase): self.assertEqual(2, i.eval(feed_dict={c.name: 0})) self.assertEqual(2, self.evaluate(v)) + @test_util.run_v1_only("b/120545219") def testWithOpsDependencies(self): with self.cached_session() as sess: v = variables.VariableV1(0.0) @@ -3198,6 +3272,7 @@ class ControlFlowTest(test.TestCase): # Ensure that 'v' is initialized self.assertAllClose(0.0, real_v_val) + @test_util.run_v1_only("b/120545219") def testWithTensorDependencies(self): with self.cached_session(): v = variables.VariableV1(0.0) @@ -3224,6 +3299,7 @@ class ControlFlowTest(test.TestCase): # Ensure that 'v' is initialized self.assertAllClose(0.0, self.evaluate(v)) + @test_util.run_v1_only("b/120545219") def testWithIndexedSlicesDependencies(self): with self.cached_session(): v = variables.VariableV1( @@ -3270,6 +3346,7 @@ class ControlFlowTest(test.TestCase): self.assertDeviceEqual("", with_vdef_dep.device) self.assertEqual([b"loc:@vdef"], with_vdef_dep.op.colocation_groups()) + @test_util.run_v1_only("b/120545219") def testGroup(self): with self.cached_session() as sess: v1 = variables.VariableV1([0.0]) @@ -3289,6 +3366,7 @@ class ControlFlowTest(test.TestCase): self.assertAllClose([0.0], v1_val) self.assertAllClose([1.0], v2_val) + @test_util.run_v1_only("b/120545219") def testGroupEmpty(self): op = control_flow_ops.group() self.assertEqual(op.type, "NoOp") @@ -3349,7 +3427,7 @@ class ControlFlowTest(test.TestCase): self.assertEqual([None, None], m.get_shape().as_list()) self.assertEqual([], index.get_shape()) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testRefSelect(self): index = array_ops.placeholder(dtypes.int32) @@ -3404,6 +3482,7 @@ class ControlFlowTest(test.TestCase): with self.assertRaises(ValueError): sess.run(tensor_list[0]) + @test_util.run_v1_only("b/120545219") def testWhilePyFuncBasic(self): def func(x): @@ -3417,6 +3496,7 @@ class ControlFlowTest(test.TestCase): [tensor_shape.unknown_shape(), tensor_shape.unknown_shape()]) self.assertEqual(r[1].eval(), 65536.0) + @test_util.run_v1_only("b/120545219") def testWhileFuncBasic(self): @function.Defun(dtypes.float32) @@ -3440,6 +3520,7 @@ class ControlFlowTest(test.TestCase): ]), 1) + @test_util.run_v1_only("b/120545219") def testQIntSwitchMerge(self): with self.cached_session(force_gpu=test.is_gpu_available()) as sess: constant_qint = constant_op.constant(np.array([42]), dtypes.qint8) @@ -3448,6 +3529,7 @@ class ControlFlowTest(test.TestCase): result = control_flow_ops.merge([v_f, v_t]) self.evaluate(result) + @test_util.run_v1_only("b/120545219") def testQIntRefSwitchMerge(self): with self.cached_session(use_gpu=test.is_gpu_available()) as sess: var_qint = gen_state_ops.variable( @@ -3461,6 +3543,7 @@ class ControlFlowTest(test.TestCase): result = control_flow_ops.ref_merge([v_f, v_t]) self.evaluate(result) + @test_util.run_v1_only("b/120545219") def testUInt64SwitchMerge(self): with self.cached_session(force_gpu=test.is_gpu_available()) as sess: constant_uint64 = constant_op.constant(np.array([42]), dtypes.uint64) @@ -3508,6 +3591,7 @@ class ControlFlowContextCheckTest(test.TestCase): math_ops.less(1, 2), true_fn, lambda: constant_op.constant(0)) return cond_tensor[0] + @test_util.run_v1_only("b/120545219") def testInvalidContext(self): # Accessing a while loop tensor outside of control flow is illegal. while_tensor = self._getWhileTensor() @@ -3517,7 +3601,7 @@ class ControlFlowContextCheckTest(test.TestCase): "is in a while loop. See info log for more details."): math_ops.add(1, while_tensor) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testInvalidContextInCond(self): # Accessing a while loop tensor in cond is illegal. while_tensor = self._getWhileTensor() @@ -3530,6 +3614,7 @@ class ControlFlowContextCheckTest(test.TestCase): math_ops.less(1, 2), lambda: math_ops.add(1, while_tensor), lambda: constant_op.constant(0)) + @test_util.run_v1_only("b/120545219") def testInvalidContextInWhile(self): # Accessing a while loop tensor in a different while loop is illegal. while_tensor = self._getWhileTensor() @@ -3564,6 +3649,7 @@ class ControlFlowContextCheckTest(test.TestCase): control_flow_ops.cond(math_ops.less(1, 2), branch_fn, branch_fn) + @test_util.run_v1_only("b/120545219") def testValidWhileContext(self): # Accessing a tensor in a nested while is OK. def body(_): @@ -3572,6 +3658,7 @@ class ControlFlowContextCheckTest(test.TestCase): control_flow_ops.while_loop(lambda i: i < 5, body, [0]) + @test_util.run_v1_only("b/120545219") def testValidNestedContexts(self): # Accessing a tensor from a cond context in a while context, all inside an # outer while context, is OK. @@ -3586,7 +3673,7 @@ class ControlFlowContextCheckTest(test.TestCase): control_flow_ops.while_loop(lambda i: i < 5, body, [0]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testInvalidNestedContexts(self): # Accessing a tensor from a while context in a different while context, all # inside a cond context, is illegal. @@ -3605,6 +3692,7 @@ class ControlFlowContextCheckTest(test.TestCase): class TupleTest(test.TestCase): + @test_util.run_v1_only("b/120545219") def testTensors(self): for v1_first in [True, False]: with self.cached_session(): @@ -3635,7 +3723,7 @@ class TupleTest(test.TestCase): self.assertAllClose([30.0], self.evaluate(t2)) self.assertAllClose([1.0], self.evaluate(v1)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testIndexedSlices(self): for v1_first in [True, False]: with self.cached_session(): @@ -3887,6 +3975,7 @@ class EagerTest(test.TestCase): isum(tensor, maximum_iterations=3).numpy(), [1 + 3, 2 + 3, 3 + 3, 4 + 3, 5 + 3]) + @test_util.run_v1_only("b/120545219") def testWhileWithMaximumIterationsAndSingleArgument(self): with context.eager_mode(): tensor = constant_op.constant(0) @@ -3909,6 +3998,7 @@ class EagerTest(test.TestCase): self.assertAllEqual(t1.numpy(), tup1.numpy()) self.assertAllEqual(t2.numpy(), tup2.numpy()) + @test_util.run_v1_only("b/120545219") def testCase(self): with context.eager_mode(): x = constant_op.constant(1) diff --git a/tensorflow/python/kernel_tests/control_flow_util_test.py b/tensorflow/python/kernel_tests/control_flow_util_test.py index 762c445da0..573f4b0d25 100644 --- a/tensorflow/python/kernel_tests/control_flow_util_test.py +++ b/tensorflow/python/kernel_tests/control_flow_util_test.py @@ -22,6 +22,7 @@ from __future__ import print_function from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import test_ops +from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import control_flow_util @@ -32,6 +33,7 @@ from tensorflow.python.platform import test class ControlFlowUtilTest(test.TestCase): + @test_util.run_v1_only("b/120545219") def testIsSwitch(self): switch_false, _ = control_flow_ops.switch(1, True) switch = switch_false.op @@ -44,6 +46,7 @@ class ControlFlowUtilTest(test.TestCase): self.assertFalse(control_flow_util.IsSwitch(test_ops.int_output().op)) + @test_util.run_v1_only("b/120545219") def testIsLoopEnter(self): enter = gen_control_flow_ops.enter(1, frame_name="name").op self.assertTrue(control_flow_util.IsLoopEnter(enter)) @@ -61,6 +64,7 @@ class ControlFlowUtilTest(test.TestCase): self.assertFalse(control_flow_util.IsLoopEnter(test_ops.int_output().op)) + @test_util.run_v1_only("b/120545219") def testIsLoopExit(self): exit_op = control_flow_ops.exit(1).op self.assertTrue(control_flow_util.IsLoopExit(exit_op)) diff --git a/tensorflow/python/kernel_tests/ctc_loss_op_test.py b/tensorflow/python/kernel_tests/ctc_loss_op_test.py index e6b5835079..e24f304c1b 100644 --- a/tensorflow/python/kernel_tests/ctc_loss_op_test.py +++ b/tensorflow/python/kernel_tests/ctc_loss_op_test.py @@ -106,7 +106,7 @@ class CTCLossTest(test.TestCase): with self.assertRaisesOpError(expected_err_re): self.evaluate([loss, grad]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testBasic(self): """Test two batch entries.""" # Input and ground truth from Alex Graves' implementation. @@ -242,7 +242,7 @@ class CTCLossTest(test.TestCase): self._testCTCLoss(inputs, seq_lens, labels, loss_truth, grad_truth) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def test_time_major(self): """Testing time_major param. @@ -272,7 +272,7 @@ class CTCLossTest(test.TestCase): (tf_loss, tf_loss_transposed) = self.evaluate([loss, loss_transposed]) self.assertAllEqual(tf_loss, tf_loss_transposed) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testInvalidSecondGradient(self): inputs = np.random.randn(2, 2, 3).astype(np.float32) inputs_t = constant_op.constant(inputs) @@ -289,7 +289,7 @@ class CTCLossTest(test.TestCase): "explicitly disabled"): _ = gradients_impl._hessian_vector_product(loss, [inputs_t], v) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testEmptyBatch(self): inputs = constant_op.constant([], dtype=dtypes.float32, shape=(1, 0, 2)) sequence_lengths = constant_op.constant([], dtype=dtypes.int32) @@ -306,7 +306,7 @@ class CTCLossTest(test.TestCase): class CTCLossTestV2(test.TestCase): - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testCtcLossV2(self): random_seed.set_random_seed(5) @@ -351,7 +351,7 @@ class CTCLossTestV2(test.TestCase): logit_length=logit_length, blank_index=0)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testCtcLossDenseIsSameAsCtcLoss(self): with ops.device("/GPU:0" if test.is_gpu_available() else "/CPU:0"): random_seed.set_random_seed(5) @@ -405,7 +405,7 @@ class CTCLossTestV2(test.TestCase): rtol=2e-06, atol=2e-06) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testCtcLossDenseUniqueFastPathIsSameAsCtcLoss(self): random_seed.set_random_seed(5) @@ -459,7 +459,7 @@ class CTCLossTestV2(test.TestCase): rtol=2e-06, atol=2e-06) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testCtcLossDenseWithBlankIndexIsSameAsCtcLoss(self): random_seed.set_random_seed(5) @@ -516,7 +516,7 @@ class CTCLossTestV2(test.TestCase): rtol=2e-06, atol=2e-06) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testCtcLossDenseWithNegativeBlankIndexIsSameAsCtcLoss(self): with ops.device("/GPU:0" if test.is_gpu_available() else "/CPU:0"): random_seed.set_random_seed(5) @@ -565,7 +565,7 @@ class CTCLossTestV2(test.TestCase): rtol=2e-06, atol=2e-06) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testCollapseRepeated(self): collapsed, new_seq_lengths = ctc_ops.collapse_repeated( labels=[[1, 3, 3, 3, 0], @@ -579,7 +579,7 @@ class CTCLossTestV2(test.TestCase): [1, 4, 0, 0], [4, 2, 9, 4]]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testCollapseRepeatedPreservesDtypes(self): collapsed, new_seq_lengths = ctc_ops.collapse_repeated( labels=constant_op.constant( @@ -597,7 +597,7 @@ class CTCLossTestV2(test.TestCase): [1, 4, 0, 0], [4, 2, 9, 4]]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testCollapseRepeatedExtraPadding(self): collapsed, new_seq_lengths = ctc_ops.collapse_repeated( labels=[[1, 3, 3, 3, 0, 0, 0], @@ -611,7 +611,7 @@ class CTCLossTestV2(test.TestCase): [1, 4, 0, 0], [4, 2, 9, 4]]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testCollapseRepeatedFrontRepeats(self): collapsed, new_seq_lengths = ctc_ops.collapse_repeated( labels=[[1, 1, 1, 2, 2], @@ -625,7 +625,7 @@ class CTCLossTestV2(test.TestCase): [1, 2], [1, 0]]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testCollapseRepeatedAllLabelsTheSame(self): collapsed, new_seq_lengths = ctc_ops.collapse_repeated( labels=[[1, 1, 1, 1, 1], @@ -658,7 +658,7 @@ class CTCLossTestV2(test.TestCase): self.assertAllEqual(padded_dense, new_dense) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testUnique(self): labels = [ [3, 4, 4, 3], @@ -674,7 +674,7 @@ class CTCLossTestV2(test.TestCase): [0, 0, 0, 1], ], idx) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testSumStates(self): idx = [ [0, 1, 0, 1], @@ -694,7 +694,7 @@ class CTCLossTestV2(test.TestCase): [1.8, 0.8, 0.0, 0.0]] ], sum_of_states) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testStateToOlabel(self): labels = [ [3, 4, 3, 4], @@ -733,7 +733,7 @@ class CTCLossTestV2(test.TestCase): [22.0 + 23.0 + 24.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]], ]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testStateToOlabelUnique(self): labels = [ [3, 4, 3, 4], diff --git a/tensorflow/python/kernel_tests/dense_update_ops_no_tsan_test.py b/tensorflow/python/kernel_tests/dense_update_ops_no_tsan_test.py index 4f74e1e741..4e3da068b8 100644 --- a/tensorflow/python/kernel_tests/dense_update_ops_no_tsan_test.py +++ b/tensorflow/python/kernel_tests/dense_update_ops_no_tsan_test.py @@ -33,7 +33,7 @@ class AssignOpTest(test.TestCase): # NOTE(mrry): We exclude thess tests from the TSAN TAP target, because they # contain benign and deliberate data races when multiple threads update # the same parameters without a lock. - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testParallelUpdateWithoutLocking(self): with self.cached_session() as sess: ones_t = array_ops.fill([1024, 1024], 1.0) @@ -61,7 +61,7 @@ class AssignOpTest(test.TestCase): self.assertTrue((vals >= ones).all()) self.assertTrue((vals <= ones * 20).all()) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testParallelAssignWithoutLocking(self): with self.cached_session() as sess: ones_t = array_ops.fill([1024, 1024], float(1)) @@ -94,7 +94,7 @@ class AssignOpTest(test.TestCase): # contain non-benign but known data races between the variable assignment and # returning the output tensors. This issue will be resolved with the new # resource variables. - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testParallelUpdateWithLocking(self): with self.cached_session() as sess: zeros_t = array_ops.fill([1024, 1024], 0.0) @@ -122,7 +122,7 @@ class AssignOpTest(test.TestCase): ones = np.ones((1024, 1024)).astype(np.float32) self.assertAllEqual(vals, ones * 20) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testParallelAssignWithLocking(self): with self.cached_session() as sess: zeros_t = array_ops.fill([1024, 1024], 0.0) diff --git a/tensorflow/python/kernel_tests/dense_update_ops_test.py b/tensorflow/python/kernel_tests/dense_update_ops_test.py index 309da88bef..545de87ca1 100644 --- a/tensorflow/python/kernel_tests/dense_update_ops_test.py +++ b/tensorflow/python/kernel_tests/dense_update_ops_test.py @@ -86,7 +86,7 @@ class AssignOpTest(test.TestCase): def testBasic(self): self._testTypes(np.arange(0, 20).reshape([4, 5])) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testAssignNonStrictShapeChecking(self): with self.cached_session(): data = array_ops.fill([1024, 1024], 0) @@ -101,7 +101,7 @@ class AssignOpTest(test.TestCase): a2.op.run() self.assertAllEqual(p.eval(), self.evaluate(data2)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testInitRequiredAssignAdd(self): with self.cached_session(): p = variables.VariableV1(array_ops.fill([1024, 1024], 1), dtypes.int32) @@ -109,7 +109,7 @@ class AssignOpTest(test.TestCase): with self.assertRaisesOpError("use uninitialized"): a.op.run() - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testInitRequiredAssignSub(self): with self.cached_session(): p = variables.VariableV1(array_ops.fill([1024, 1024], 1), dtypes.int32) diff --git a/tensorflow/python/kernel_tests/depthwise_conv_op_test.py b/tensorflow/python/kernel_tests/depthwise_conv_op_test.py index f6d834c2f8..5b1a47fb03 100644 --- a/tensorflow/python/kernel_tests/depthwise_conv_op_test.py +++ b/tensorflow/python/kernel_tests/depthwise_conv_op_test.py @@ -24,6 +24,7 @@ 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.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import gradient_checker from tensorflow.python.ops import nn_impl @@ -185,6 +186,7 @@ class DepthwiseConv2DTest(test.TestCase): self.assertShapeEqual(native_result, conv_native) self.assertShapeEqual(native_result, conv_interface) + @test_util.run_v1_only("b/120545219") def testDepthwiseConv2D(self): for index, (input_size, filter_size, _, stride, padding) in enumerate(ConfigsToTest()): @@ -428,6 +430,7 @@ class DepthwiseConv2DTest(test.TestCase): use_gpu, grouped_conv, err) self.assertLess(err, tolerance) + @test_util.run_v1_only("b/120545219") def testDepthwiseConv2DInputGrad(self): for index, (input_size, filter_size, output_size, stride, padding) in enumerate(CheckGradConfigsToTest()): @@ -477,6 +480,7 @@ class DepthwiseConv2DTest(test.TestCase): use_gpu=True, data_format="NCHW") + @test_util.run_v1_only("b/120545219") def testDepthwiseConv2DFilterGrad(self): for index, (input_size, filter_size, output_size, stride, padding) in enumerate(CheckGradConfigsToTest()): diff --git a/tensorflow/python/kernel_tests/determinant_op_test.py b/tensorflow/python/kernel_tests/determinant_op_test.py index d6ef9e70b8..dbfda385ed 100644 --- a/tensorflow/python/kernel_tests/determinant_op_test.py +++ b/tensorflow/python/kernel_tests/determinant_op_test.py @@ -133,6 +133,7 @@ class DeterminantOpTest(test.TestCase): huge_matrix = np.array([[max_double, 0.0], [0.0, max_double]]) self._compareDeterminant(huge_matrix) + @test_util.run_v1_only("b/120545219") def testNonSquareMatrix(self): # When the determinant of a non-square matrix is attempted we should return # an error @@ -140,6 +141,7 @@ class DeterminantOpTest(test.TestCase): linalg_ops.matrix_determinant( np.array([[1., 2., 3.], [3., 5., 4.]]).astype(np.float32)) + @test_util.run_v1_only("b/120545219") def testWrongDimensions(self): # The input to the determinant should be a 2-dimensional tensor. tensor1 = constant_op.constant([1., 2.]) @@ -150,6 +152,7 @@ class DeterminantOpTest(test.TestCase): self._compareDeterminant(np.empty([0, 2, 2])) self._compareDeterminant(np.empty([2, 0, 0])) + @test_util.run_v1_only("b/120545219") def testConcurrentExecutesWithoutError(self): with self.session(use_gpu=True) as sess: matrix1 = random_ops.random_normal([5, 5], seed=42) diff --git a/tensorflow/python/kernel_tests/distributions/multinomial_test.py b/tensorflow/python/kernel_tests/distributions/multinomial_test.py index b3f3416a52..187ddd4cf4 100644 --- a/tensorflow/python/kernel_tests/distributions/multinomial_test.py +++ b/tensorflow/python/kernel_tests/distributions/multinomial_test.py @@ -22,6 +22,7 @@ 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.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import multinomial @@ -33,6 +34,7 @@ class MultinomialTest(test.TestCase): def setUp(self): self._rng = np.random.RandomState(42) + @test_util.run_v1_only("b/120545219") def testSimpleShapes(self): with self.cached_session(): p = [.1, .3, .6] @@ -42,6 +44,7 @@ class MultinomialTest(test.TestCase): self.assertEqual(tensor_shape.TensorShape([3]), dist.event_shape) self.assertEqual(tensor_shape.TensorShape([]), dist.batch_shape) + @test_util.run_v1_only("b/120545219") def testComplexShapes(self): with self.cached_session(): p = 0.5 * np.ones([3, 2, 2], dtype=np.float32) @@ -52,6 +55,7 @@ class MultinomialTest(test.TestCase): self.assertEqual(tensor_shape.TensorShape([2]), dist.event_shape) self.assertEqual(tensor_shape.TensorShape([3, 2]), dist.batch_shape) + @test_util.run_v1_only("b/120545219") def testN(self): p = [[0.1, 0.2, 0.7], [0.2, 0.3, 0.5]] n = [[3.], [4]] @@ -60,6 +64,7 @@ class MultinomialTest(test.TestCase): self.assertEqual((2, 1), dist.total_count.get_shape()) self.assertAllClose(n, dist.total_count.eval()) + @test_util.run_v1_only("b/120545219") def testP(self): p = [[0.1, 0.2, 0.7]] with self.cached_session(): @@ -68,6 +73,7 @@ class MultinomialTest(test.TestCase): self.assertEqual((1, 3), dist.logits.get_shape()) self.assertAllClose(p, dist.probs.eval()) + @test_util.run_v1_only("b/120545219") def testLogits(self): p = np.array([[0.1, 0.2, 0.7]], dtype=np.float32) logits = np.log(p) - 50. @@ -78,6 +84,7 @@ class MultinomialTest(test.TestCase): self.assertAllClose(p, multinom.probs.eval()) self.assertAllClose(logits, multinom.logits.eval()) + @test_util.run_v1_only("b/120545219") def testPmfUnderflow(self): logits = np.array([[-200, 0]], dtype=np.float32) with self.cached_session(): @@ -85,6 +92,7 @@ class MultinomialTest(test.TestCase): lp = dist.log_prob([1., 0.]).eval()[0] self.assertAllClose(-200, lp, atol=0, rtol=1e-6) + @test_util.run_v1_only("b/120545219") def testPmfandCountsAgree(self): p = [[0.1, 0.2, 0.7]] n = [[5.]] @@ -97,6 +105,7 @@ class MultinomialTest(test.TestCase): with self.assertRaisesOpError("counts must sum to `self.total_count`"): dist.prob([3., 3, 0]).eval() + @test_util.run_v1_only("b/120545219") def testPmfNonIntegerCounts(self): p = [[0.1, 0.2, 0.7]] n = [[5.]] @@ -157,6 +166,7 @@ class MultinomialTest(test.TestCase): self.assertAllClose([0.1, 0.9], self.evaluate(pmf)) self.assertEqual((2), pmf.get_shape()) + @test_util.run_v1_only("b/120545219") def testPmfCountsStretchedInBroadcastWhenSameRank(self): with self.cached_session(): p = [[0.1, 0.9], [0.7, 0.3]] @@ -165,6 +175,7 @@ class MultinomialTest(test.TestCase): self.assertAllClose(pmf.eval(), [0.1, 0.7]) self.assertEqual((2), pmf.get_shape()) + @test_util.run_v1_only("b/120545219") def testPmfCountsStretchedInBroadcastWhenLowerRank(self): with self.cached_session(): p = [[0.1, 0.9], [0.7, 0.3]] @@ -194,6 +205,7 @@ class MultinomialTest(test.TestCase): self.evaluate(pmf) self.assertEqual((4, 3), pmf.get_shape()) + @test_util.run_v1_only("b/120545219") def testMultinomialMean(self): with self.cached_session(): n = 5. @@ -203,6 +215,7 @@ class MultinomialTest(test.TestCase): self.assertEqual((3,), dist.mean().get_shape()) self.assertAllClose(expected_means, dist.mean().eval()) + @test_util.run_v1_only("b/120545219") def testMultinomialCovariance(self): with self.cached_session(): n = 5. @@ -214,6 +227,7 @@ class MultinomialTest(test.TestCase): self.assertEqual((3, 3), dist.covariance().get_shape()) self.assertAllClose(expected_covariances, dist.covariance().eval()) + @test_util.run_v1_only("b/120545219") def testMultinomialCovarianceBatch(self): with self.cached_session(): # Shape [2] @@ -246,6 +260,7 @@ class MultinomialTest(test.TestCase): self.assertEqual((3, 5, 4, 4), covariance.get_shape()) self.assertEqual((6, 3, 3, 3), covariance2.get_shape()) + @test_util.run_v1_only("b/120545219") def testCovarianceFromSampling(self): # We will test mean, cov, var, stddev on a DirichletMultinomial constructed # via broadcast between alpha, n. @@ -288,6 +303,7 @@ class MultinomialTest(test.TestCase): self.assertAllClose(sample_var_, analytic_var, atol=0.01, rtol=0.01) self.assertAllClose(sample_stddev_, analytic_stddev, atol=0.01, rtol=0.01) + @test_util.run_v1_only("b/120545219") def testSampleUnbiasedNonScalarBatch(self): with self.cached_session() as sess: dist = multinomial.Multinomial( @@ -317,6 +333,7 @@ class MultinomialTest(test.TestCase): self.assertAllClose( actual_covariance_, sample_covariance_, atol=0., rtol=0.20) + @test_util.run_v1_only("b/120545219") def testSampleUnbiasedScalarBatch(self): with self.cached_session() as sess: dist = multinomial.Multinomial( diff --git a/tensorflow/python/kernel_tests/fifo_queue_test.py b/tensorflow/python/kernel_tests/fifo_queue_test.py index 9655351a01..0579dddb70 100644 --- a/tensorflow/python/kernel_tests/fifo_queue_test.py +++ b/tensorflow/python/kernel_tests/fifo_queue_test.py @@ -39,6 +39,7 @@ from tensorflow.python.platform import test from tensorflow.python.util import compat +@test_util.run_v1_only("b/120545219") class FIFOQueueTest(test.TestCase): def testConstructor(self): @@ -1423,6 +1424,7 @@ class FIFOQueueTest(test.TestCase): session.run([a, c]) +@test_util.run_v1_only("b/120545219") class FIFOQueueDictTest(test.TestCase): def testConstructor(self): @@ -1583,6 +1585,7 @@ class FIFOQueueDictTest(test.TestCase): self.assertTrue([compat.as_bytes("dd"), compat.as_bytes("ee")], list(s)) +@test_util.run_v1_only("b/120545219") class FIFOQueueWithTimeoutTest(test.TestCase): def testDequeueWithTimeout(self): @@ -1617,6 +1620,7 @@ class FIFOQueueWithTimeoutTest(test.TestCase): self.assertEqual(37, self.evaluate(dequeued_t)) +@test_util.run_v1_only("b/120545219") class QueueContainerTest(test.TestCase): def testContainer(self): diff --git a/tensorflow/python/kernel_tests/functional_ops_test.py b/tensorflow/python/kernel_tests/functional_ops_test.py index c489623fe5..95ee454614 100644 --- a/tensorflow/python/kernel_tests/functional_ops_test.py +++ b/tensorflow/python/kernel_tests/functional_ops_test.py @@ -494,7 +494,7 @@ class FunctionalOpsTest(test.TestCase): @test_util.disable_control_flow_v2("b/119323354") @test_util.run_in_graph_and_eager_modes - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testMapEmptyScalar(self): map_return = functional_ops.map_fn(lambda x: 1, constant_op.constant([])) self.assertAllEqual([0], map_return.get_shape().dims) @@ -503,7 +503,7 @@ class FunctionalOpsTest(test.TestCase): # TODO(akshayka): this test fails in eager: the iterable is of length 0 so # so the body of the while loop never executes @test_util.disable_control_flow_v2("b/119323354") - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testMapEmptyTensor(self): with self.cached_session(): map_return = functional_ops.map_fn(lambda x: array_ops.zeros([3, 2]), @@ -797,7 +797,7 @@ class FunctionalOpsTest(test.TestCase): self.assertAllEqual(Run(100., False), 5050.) self.assertAllEqual(Run(100., True), 5050.) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testWhileError(self): for use_gpu in (True, False): with ops.Graph().as_default() as g: @@ -1027,7 +1027,7 @@ class FunctionalOpsTest(test.TestCase): def testForMLPWhile(self): self._testForMLP(True) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testForError(self): @function.Defun(dtypes.int32, dtypes.float32) @@ -1233,7 +1233,7 @@ class PartitionedCallTest(test.TestCase): self.assertAllEqual(expected, result) # Use an invalid executor name to test the plumbing of the executor_type attr. - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testExecutorTypeAttrExecutorNotFound(self): @function.Defun(dtypes.int32) def AddFive(x): diff --git a/tensorflow/python/kernel_tests/identity_op_py_test.py b/tensorflow/python/kernel_tests/identity_op_py_test.py index 1a6794e896..40ec9db422 100644 --- a/tensorflow/python/kernel_tests/identity_op_py_test.py +++ b/tensorflow/python/kernel_tests/identity_op_py_test.py @@ -62,7 +62,7 @@ class IdentityOpTest(test.TestCase): self.assertEquals(shape, array_ops.identity(np.array(array_2x3)).get_shape()) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testRefIdentityShape(self): with self.cached_session(): shape = [2, 3] diff --git a/tensorflow/python/kernel_tests/linalg/linear_operator_test.py b/tensorflow/python/kernel_tests/linalg/linear_operator_test.py index 8f8b15e8ed..18e13a76a0 100644 --- a/tensorflow/python/kernel_tests/linalg/linear_operator_test.py +++ b/tensorflow/python/kernel_tests/linalg/linear_operator_test.py @@ -214,7 +214,7 @@ class LinearOperatorTest(test.TestCase): operator = LinearOperatorMatmulSolve(matrix, is_square=True) self.assertTrue(operator.is_square) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def test_linear_operator_matmul_hints_closed(self): matrix = array_ops.placeholder(dtypes.float32) operator1 = LinearOperatorMatmulSolve(matrix) @@ -241,7 +241,7 @@ class LinearOperatorTest(test.TestCase): self.assertTrue(operator_matmul.is_self_adjoint) self.assertEqual(None, operator_matmul.is_positive_definite) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def test_linear_operator_matmul_hints_false(self): matrix = array_ops.placeholder(dtypes.float32) operator1 = LinearOperatorMatmulSolve( @@ -274,7 +274,7 @@ class LinearOperatorTest(test.TestCase): self.assertEqual(None, operator_matmul.is_self_adjoint) self.assertEqual(None, operator_matmul.is_positive_definite) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def test_linear_operator_matmul_hint_infer_square(self): matrix1 = array_ops.placeholder(shape=[2, 3], dtype=dtypes.float32) matrix2 = array_ops.placeholder(shape=[3, 2], dtype=dtypes.float32) diff --git a/tensorflow/python/kernel_tests/linalg_grad_test.py b/tensorflow/python/kernel_tests/linalg_grad_test.py index 28e1d7e168..ff84221611 100644 --- a/tensorflow/python/kernel_tests/linalg_grad_test.py +++ b/tensorflow/python/kernel_tests/linalg_grad_test.py @@ -61,6 +61,7 @@ class MatrixUnaryFunctorGradientTest(test_lib.TestCase): def _GetMatrixUnaryFunctorGradientTest(functor_, dtype_, shape_, **kwargs_): + @test_util.run_v1_only('b/120545219') def Test(self): with self.session(use_gpu=True): np.random.seed(1) @@ -103,6 +104,7 @@ def _GetMatrixBinaryFunctorGradientTest(functor_, float32_tol_fudge=1.0, **kwargs_): + @test_util.run_v1_only('b/120545219') def Test(self): # TODO(rmlarsen): Debug illegal address bug on CUDA and re-enable # GPU test for matrix_solve. diff --git a/tensorflow/python/kernel_tests/losses_test.py b/tensorflow/python/kernel_tests/losses_test.py index abff61f81b..4584a27e62 100644 --- a/tensorflow/python/kernel_tests/losses_test.py +++ b/tensorflow/python/kernel_tests/losses_test.py @@ -51,26 +51,26 @@ class AbsoluteDifferenceLossTest(test.TestCase): losses.absolute_difference( self._predictions, self._predictions, weights=None) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testAllCorrectNoLossWeight(self): loss = losses.absolute_difference(self._predictions, self._predictions) with self.cached_session(): self.assertAlmostEqual(0.0, self.evaluate(loss), 3) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testNonZeroLoss(self): loss = losses.absolute_difference(self._labels, self._predictions) with self.cached_session(): self.assertAlmostEqual(5.5, self.evaluate(loss), 3) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testNonZeroLossWithPythonScalarWeight(self): weights = 2.3 loss = losses.absolute_difference(self._labels, self._predictions, weights) with self.cached_session(): self.assertAlmostEqual(5.5 * weights, self.evaluate(loss), 3) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testNonZeroLossWithScalarTensorWeight(self): weights = 2.3 loss = losses.absolute_difference(self._labels, self._predictions, @@ -148,7 +148,7 @@ class SoftmaxCrossEntropyLossTest(test.TestCase): self.assertEquals(loss.op.name, 'softmax_cross_entropy_loss/value') self.assertAlmostEqual(loss.eval(), 10.0, 3) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testNonZeroLossWithPythonScalarWeight(self): logits = constant_op.constant([[10.0, 0.0, 0.0], [0.0, 10.0, 0.0], [0.0, 0.0, 10.0]]) @@ -158,7 +158,7 @@ class SoftmaxCrossEntropyLossTest(test.TestCase): loss = losses.softmax_cross_entropy(labels, logits, weights) self.assertAlmostEqual(weights * 10.0, self.evaluate(loss), 3) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testNonZeroLossWithScalarTensorWeight(self): logits = constant_op.constant([[10.0, 0.0, 0.0], [0.0, 10.0, 0.0], [0.0, 0.0, 10.0]]) @@ -311,7 +311,7 @@ class SparseSoftmaxCrossEntropyLossTest(test.TestCase): self.assertEquals(loss.op.name, 'sparse_softmax_cross_entropy_loss/value') self.assertAlmostEqual(loss.eval(), 10.0, 3) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testNonZeroLossWithPythonScalarWeight(self): logits = constant_op.constant([[10.0, 0.0, 0.0], [0.0, 10.0, 0.0], [0.0, 0.0, 10.0]]) @@ -321,7 +321,7 @@ class SparseSoftmaxCrossEntropyLossTest(test.TestCase): loss = losses.sparse_softmax_cross_entropy(labels, logits, weights) self.assertAlmostEqual(weights * 10.0, self.evaluate(loss), 3) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testNonZeroLossWithScalarTensorWeight(self): logits = constant_op.constant([[10.0, 0.0, 0.0], [0.0, 10.0, 0.0], [0.0, 0.0, 10.0]]) @@ -677,13 +677,13 @@ class LogLossTest(test.TestCase): with self.assertRaises(ValueError): losses.log_loss(self._labels, self._labels, weights=None) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testAllCorrectNoLossWeight(self): loss = losses.log_loss(self._labels, self._labels) with self.cached_session(): self.assertAlmostEqual(0.0, self.evaluate(loss), 3) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testAllCorrectNoLossWeightWithPlaceholder(self): tf_predictions = array_ops.placeholder( dtypes.float32, shape=self._np_labels.shape) @@ -692,14 +692,14 @@ class LogLossTest(test.TestCase): self.assertAlmostEqual( 0.0, loss.eval(feed_dict={tf_predictions: self._np_labels}), 3) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testNonZeroLoss(self): loss = losses.log_loss(self._labels, self._predictions) with self.cached_session(): self.assertAlmostEqual(-np.sum(self._expected_losses) / 6.0, self.evaluate(loss), 3) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testNonZeroLossWithPythonScalarWeight(self): weights = 2.3 loss = losses.log_loss(self._labels, self._predictions, weights) @@ -707,7 +707,7 @@ class LogLossTest(test.TestCase): self.assertAlmostEqual(weights * -np.sum(self._expected_losses) / 6.0, self.evaluate(loss), 3) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testNonZeroLossWithScalarTensorWeight(self): weights = 2.3 loss = losses.log_loss(self._labels, self._predictions, @@ -716,7 +716,7 @@ class LogLossTest(test.TestCase): self.assertAlmostEqual(weights * -np.sum(self._expected_losses) / 6.0, self.evaluate(loss), 3) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testNonZeroLossWithScalarTensorWeightAndPlaceholder(self): tf_predictions = array_ops.placeholder( dtypes.float32, shape=self._np_predictions.shape) @@ -728,7 +728,7 @@ class LogLossTest(test.TestCase): self.assertAlmostEqual(weights * -np.sum(self._expected_losses) / 6.0, loss, 3) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testNonZeroLossWithScalarTensorWeightAndPlaceholderWithRankOnly(self): tf_predictions = array_ops.placeholder(dtypes.float32, shape=[None, None]) weights = 2.3 @@ -788,7 +788,7 @@ class LogLossTest(test.TestCase): self.assertAlmostEqual(-np.sum(expected_losses) / 5.0, self.evaluate(loss), 3) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testNonZeroLossWithMeasurementSpecificWeightsWithPlaceholder(self): weights = np.array([3, 6, 5, 0, 4, 2]).reshape((2, 3)) expected_losses = np.multiply(self._expected_losses, weights) @@ -816,7 +816,7 @@ class LogLossTest(test.TestCase): with self.cached_session(): self.assertAlmostEqual(-np.sum(expected_losses), self.evaluate(loss), 3) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testNonZeroLossWithSampleSpecificWeightsMostZeroWithPlaceholder(self): weights = np.array([0, 0, 0, 0, 0, 2]).reshape((2, 3)) expected_losses = np.multiply(self._expected_losses, weights) @@ -955,26 +955,26 @@ class MeanSquaredErrorTest(test.TestCase): losses.mean_squared_error(predictions=constant_op.constant(0), labels=constant_op.constant(0)).eval()) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testAllCorrectNoLossWeight(self): loss = losses.mean_squared_error(self._predictions, self._predictions) with self.cached_session(): self.assertAlmostEqual(0.0, self.evaluate(loss), 3) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testNonZeroLoss(self): loss = losses.mean_squared_error(self._labels, self._predictions) with self.cached_session(): self.assertAlmostEqual(49.5, self.evaluate(loss), 3) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testNonZeroLossWithPythonScalarWeight(self): weights = 2.3 loss = losses.mean_squared_error(self._labels, self._predictions, weights) with self.cached_session(): self.assertAlmostEqual(49.5 * weights, self.evaluate(loss), 3) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testNonZeroLossWithScalarTensorWeight(self): weights = 2.3 loss = losses.mean_squared_error(self._labels, self._predictions, @@ -1068,12 +1068,12 @@ class MeanPairwiseSquaredErrorTest(test.TestCase): self.assertAlmostEqual( expected_loss, dynamic_inputs_op.eval(feed_dict=feed_dict), places=3) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testAllCorrectNoLossWeight(self): self._test_valid_weights( self._labels, self._labels, expected_loss=0.0) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testNonZeroLoss(self): self._test_valid_weights( self._labels, self._predictions, @@ -1104,7 +1104,7 @@ class MeanPairwiseSquaredErrorTest(test.TestCase): np_grad = self.evaluate(grad) self.assertFalse(np.isnan(np_grad).any()) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testNonZeroLossWithPythonScalarWeight(self): weight = 2.3 self._test_valid_weights( @@ -1112,7 +1112,7 @@ class MeanPairwiseSquaredErrorTest(test.TestCase): expected_loss=weight * np.sum(self._expected_losses), weights=weight) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testNonZeroLossWithScalarTensorWeight(self): weights = 2.3 loss = losses.mean_pairwise_squared_error( @@ -1179,7 +1179,7 @@ class MeanPairwiseSquaredErrorTest(test.TestCase): weights_placeholder: weights, }) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testInvalid3dWeighted2x0(self): labels = np.array([ [[1, 9, 2], [12, 11, 10], [9, 8, 7]], diff --git a/tensorflow/python/kernel_tests/matrix_band_part_op_test.py b/tensorflow/python/kernel_tests/matrix_band_part_op_test.py index 129ea40dfe..fdb7e4a1a4 100644 --- a/tensorflow/python/kernel_tests/matrix_band_part_op_test.py +++ b/tensorflow/python/kernel_tests/matrix_band_part_op_test.py @@ -23,6 +23,7 @@ from tensorflow.python.client import session from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes as dtypes_lib from tensorflow.python.framework import ops +from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import gradient_checker @@ -44,6 +45,7 @@ class MatrixBandPartTest(test_lib.TestCase): def _GetMatrixBandPartTest(dtype_, batch_shape_, shape_): + @test_util.run_v1_only("b/120545219") def Test(self): mat = np.ones(shape_).astype(dtype_) batch_mat = np.tile(mat, batch_shape_ + (1, 1)) @@ -73,6 +75,7 @@ class MatrixBandPartGradTest(test_lib.TestCase): def _GetMatrixBandPartGradTest(dtype_, batch_shape_, shape_): + @test_util.run_v1_only("b/120545219") def Test(self): shape = batch_shape_ + shape_ x = constant_op.constant(np.random.rand(*shape), dtype=dtype_) diff --git a/tensorflow/python/kernel_tests/matrix_logarithm_op_test.py b/tensorflow/python/kernel_tests/matrix_logarithm_op_test.py index b0bce6a1b9..682ac12adc 100644 --- a/tensorflow/python/kernel_tests/matrix_logarithm_op_test.py +++ b/tensorflow/python/kernel_tests/matrix_logarithm_op_test.py @@ -84,6 +84,7 @@ class LogarithmOpTest(test.TestCase): # Complex batch self._verifyLogarithmComplex(self._makeBatch(matrix1, matrix2)) + @test_util.run_v1_only("b/120545219") def testNonSquareMatrix(self): # When the logarithm of a non-square matrix is attempted we should return # an error @@ -91,6 +92,7 @@ class LogarithmOpTest(test.TestCase): gen_linalg_ops.matrix_logarithm( np.array([[1., 2., 3.], [3., 4., 5.]], dtype=np.complex64)) + @test_util.run_v1_only("b/120545219") def testWrongDimensions(self): # The input to the logarithm should be at least a 2-dimensional tensor. tensor3 = constant_op.constant([1., 2.], dtype=dtypes.complex64) @@ -121,6 +123,7 @@ class LogarithmOpTest(test.TestCase): size=np.prod(shape)).reshape(shape).astype(np.complex128) self._verifyLogarithmComplex(matrix) + @test_util.run_v1_only("b/120545219") def testConcurrentExecutesWithoutError(self): with self.session(use_gpu=True) as sess: matrix1 = math_ops.cast( diff --git a/tensorflow/python/kernel_tests/matrix_solve_ls_op_test.py b/tensorflow/python/kernel_tests/matrix_solve_ls_op_test.py index 42fd95d311..463477a6a2 100644 --- a/tensorflow/python/kernel_tests/matrix_solve_ls_op_test.py +++ b/tensorflow/python/kernel_tests/matrix_solve_ls_op_test.py @@ -135,7 +135,7 @@ class MatrixSolveLsOpTest(test_lib.TestCase): self.assertEqual(np_ans.shape, tf_ans_val.shape) self.assertAllClose(np_ans, tf_ans_val, atol=2 * tol, rtol=2 * tol) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testWrongDimensions(self): # The matrix and right-hand sides should have the same number of rows. with self.session(use_gpu=True): @@ -144,7 +144,6 @@ class MatrixSolveLsOpTest(test_lib.TestCase): with self.assertRaises(ValueError): linalg_ops.matrix_solve_ls(matrix, rhs) - @test_util.run_deprecated_v1 def testEmpty(self): full = np.array([[1., 2.], [3., 4.], [5., 6.]]) empty0 = np.empty([3, 0]) @@ -164,7 +163,7 @@ class MatrixSolveLsOpTest(test_lib.TestCase): linalg_ops.matrix_solve_ls(empty1, empty1, fast=fast)) self.assertEqual(tf_ans.shape, (2, 2)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testBatchResultSize(self): # 3x3x3 matrices, 3x3x1 right-hand sides. matrix = np.array([1., 2., 3., 4., 5., 6., 7., 8., 9.] * 3).reshape(3, 3, 3) diff --git a/tensorflow/python/kernel_tests/matrix_square_root_op_test.py b/tensorflow/python/kernel_tests/matrix_square_root_op_test.py index 1e2109b8c4..3edb390c72 100644 --- a/tensorflow/python/kernel_tests/matrix_square_root_op_test.py +++ b/tensorflow/python/kernel_tests/matrix_square_root_op_test.py @@ -90,17 +90,20 @@ class SquareRootOpTest(test.TestCase): self._verifySquareRootReal(np.empty([0, 2, 2])) self._verifySquareRootReal(np.empty([2, 0, 0])) + @test_util.run_v1_only("b/120545219") def testWrongDimensions(self): # The input to the square root should be at least a 2-dimensional tensor. tensor = constant_op.constant([1., 2.]) with self.assertRaises(ValueError): gen_linalg_ops.matrix_square_root(tensor) + @test_util.run_v1_only("b/120545219") def testNotSquare(self): with self.assertRaises(ValueError): tensor = constant_op.constant([[1., 0., -1.], [-1., 1., 0.]]) self.evaluate(gen_linalg_ops.matrix_square_root(tensor)) + @test_util.run_v1_only("b/120545219") def testConcurrentExecutesWithoutError(self): with test_util.use_gpu(): matrix1 = random_ops.random_normal([5, 5], seed=42) diff --git a/tensorflow/python/kernel_tests/norm_op_test.py b/tensorflow/python/kernel_tests/norm_op_test.py index 5ff0c58bf1..20b9ad95c8 100644 --- a/tensorflow/python/kernel_tests/norm_op_test.py +++ b/tensorflow/python/kernel_tests/norm_op_test.py @@ -21,6 +21,7 @@ from __future__ import print_function import numpy as np from tensorflow.python.framework import constant_op +from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import linalg_ops from tensorflow.python.platform import test as test_lib @@ -35,6 +36,7 @@ def _AddTest(test, test_name, fn): class NormOpTest(test_lib.TestCase): + @test_util.run_v1_only("b/120545219") def testBadOrder(self): matrix = [[0., 1.], [2., 3.]] for ord_ in "fro", -7, -1.1, 0: @@ -52,6 +54,7 @@ class NormOpTest(test_lib.TestCase): "'ord' must be a supported matrix norm"): linalg_ops.norm(matrix, ord=ord_, axis=[-2, -1]) + @test_util.run_v1_only("b/120545219") def testInvalidAxis(self): matrix = [[0., 1.], [2., 3.]] for axis_ in [], [1, 2, 3], [[1]], [[1], [2]], [3.1415], [1, 1]: @@ -78,6 +81,7 @@ def _GetNormOpTest(dtype_, shape_, ord_, axis_, keep_dims_, use_static_shape_): tf_norm_val = sess.run(tf_norm, feed_dict={tf_matrix: matrix}) self.assertAllClose(np_norm, tf_norm_val, rtol=1e-5, atol=1e-5) + @test_util.run_v1_only("b/120545219") def Test(self): is_matrix_norm = (isinstance(axis_, tuple) or isinstance(axis_, list)) and len(axis_) == 2 diff --git a/tensorflow/python/kernel_tests/numerics_test.py b/tensorflow/python/kernel_tests/numerics_test.py index 5751f3fe76..f13f9d6806 100644 --- a/tensorflow/python/kernel_tests/numerics_test.py +++ b/tensorflow/python/kernel_tests/numerics_test.py @@ -64,9 +64,9 @@ class VerifyTensorAllFiniteTest(test.TestCase): self.evaluate(t_verified) +@test_util.run_v1_only("b/120545219") class NumericsTest(test.TestCase): - @test_util.run_deprecated_v1 def testInf(self): with self.session(graph=ops.Graph()): t1 = constant_op.constant(1.0) @@ -77,7 +77,6 @@ class NumericsTest(test.TestCase): with self.assertRaisesOpError("Inf"): self.evaluate(a) - @test_util.run_deprecated_v1 def testNaN(self): with self.session(graph=ops.Graph()): t1 = constant_op.constant(0.0) @@ -88,7 +87,6 @@ class NumericsTest(test.TestCase): with self.assertRaisesOpError("NaN"): self.evaluate(a) - @test_util.run_deprecated_v1 def testBoth(self): with self.session(graph=ops.Graph()): t1 = constant_op.constant([1.0, 0.0]) @@ -107,7 +105,6 @@ class NumericsTest(test.TestCase): self.assertAllEqual(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]), value) self.assertEqual([2, 3], checked.get_shape()) - @test_util.run_deprecated_v1 def testControlFlowCond(self): predicate = array_ops.placeholder(dtypes.bool, shape=[]) _ = control_flow_ops.cond(predicate, @@ -120,7 +117,6 @@ class NumericsTest(test.TestCase): r"or `tf.while_loop\(\)`\."): numerics.add_check_numerics_ops() - @test_util.run_deprecated_v1 def testControlFlowWhile(self): predicate = array_ops.placeholder(dtypes.bool, shape=[]) _ = control_flow_ops.while_loop(lambda _: predicate, diff --git a/tensorflow/python/kernel_tests/padding_fifo_queue_test.py b/tensorflow/python/kernel_tests/padding_fifo_queue_test.py index b4818360d5..e3999695d0 100644 --- a/tensorflow/python/kernel_tests/padding_fifo_queue_test.py +++ b/tensorflow/python/kernel_tests/padding_fifo_queue_test.py @@ -29,11 +29,13 @@ from tensorflow.python.framework import dtypes as dtypes_lib from tensorflow.python.framework import errors_impl from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape +from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import data_flow_ops from tensorflow.python.platform import test +@test_util.run_v1_only("b/120545219") class PaddingFIFOQueueTest(test.TestCase): def testConstructor(self): @@ -1393,6 +1395,7 @@ class PaddingFIFOQueueTest(test.TestCase): with self.assertRaisesOpError("was cancelled"): self.evaluate(enqueue_many_op) + @test_util.run_deprecated_v1 def testResetOfBlockingOperation(self): with self.cached_session() as sess: q_empty = data_flow_ops.PaddingFIFOQueue(5, dtypes_lib.float32, ((),)) diff --git a/tensorflow/python/kernel_tests/partitioned_variables_test.py b/tensorflow/python/kernel_tests/partitioned_variables_test.py index 48655391fa..da79b4ecfc 100644 --- a/tensorflow/python/kernel_tests/partitioned_variables_test.py +++ b/tensorflow/python/kernel_tests/partitioned_variables_test.py @@ -412,7 +412,7 @@ class PartitionedVariablesTestCase(test.TestCase): def testResourceName(self): self._testNameHelper(use_resource=True) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testRandomInitValue(self): with self.cached_session(): rnd = variables.Variable(random_ops.random_uniform([200, 40])) @@ -430,7 +430,7 @@ class PartitionedVariablesTestCase(test.TestCase): "200 40 0,200:36,4" ]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testRandomInitUnevenPartitions(self): with self.cached_session(): rnd = variables.Variable( @@ -469,7 +469,7 @@ class PartitionedVariablesTestCase(test.TestCase): if i < len(save_specs): self._TestSaveSpec(vs, save_specs[i]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testDegenerate(self): with self.cached_session(): rnd = variables.Variable(random_ops.random_uniform([10, 43])) @@ -481,7 +481,7 @@ class PartitionedVariablesTestCase(test.TestCase): self.assertAllClose(rnd, val) self._TestSaveSpec(vs, ["10 43 0,10:0,43"]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testSliceSizeOne(self): with self.cached_session(): rnd = variables.Variable(random_ops.random_uniform([10, 43])) diff --git a/tensorflow/python/kernel_tests/priority_queue_test.py b/tensorflow/python/kernel_tests/priority_queue_test.py index 9be682ea52..49ec7ee483 100644 --- a/tensorflow/python/kernel_tests/priority_queue_test.py +++ b/tensorflow/python/kernel_tests/priority_queue_test.py @@ -27,6 +27,7 @@ import numpy as np from tensorflow.python.framework import constant_op 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 data_flow_ops import tensorflow.python.ops.nn_grad # pylint: disable=unused-import @@ -35,6 +36,7 @@ from tensorflow.python.platform import test class PriorityQueueTest(test.TestCase): + @test_util.run_v1_only("b/120545219") def testRoundTripInsertReadOnceSorts(self): with self.cached_session() as sess: q = data_flow_ops.PriorityQueue(2000, (dtypes.string, dtypes.string), ( @@ -112,6 +114,7 @@ class PriorityQueueTest(test.TestCase): missed.remove((dv0, dv1)) self.assertEqual(missed, set()) + @test_util.run_v1_only("b/120545219") def testRoundTripFillsCapacityMultiThreadedEnqueueAndDequeue(self): with self.cached_session() as sess: q = data_flow_ops.PriorityQueue(10, (dtypes.int64), (())) @@ -267,6 +270,7 @@ class PriorityQueueTest(test.TestCase): missed.remove((dv0, dv1)) self.assertEqual(missed, set()) + @test_util.run_v1_only("b/120545219") def testRoundTripInsertOnceReadOnceSorts(self): with self.cached_session() as sess: q = data_flow_ops.PriorityQueue(2000, (dtypes.string, dtypes.string), ( @@ -288,6 +292,7 @@ class PriorityQueueTest(test.TestCase): for e, dv0, dv1 in zip(deq_elem, deq_value_0, deq_value_1): self.assertTrue((dv0, dv1) in allowed[e]) + @test_util.run_v1_only("b/120545219") def testRoundTripInsertOnceReadManySorts(self): with self.cached_session(): q = data_flow_ops.PriorityQueue(2000, (dtypes.int64), (())) @@ -296,6 +301,7 @@ class PriorityQueueTest(test.TestCase): deq_values = np.hstack((q.dequeue_many(100)[0].eval() for _ in range(10))) self.assertAllEqual(deq_values, sorted(elem)) + @test_util.run_v1_only("b/120545219") def testRoundTripInsertOnceReadOnceLotsSorts(self): with self.cached_session(): q = data_flow_ops.PriorityQueue(2000, (dtypes.int64), (())) @@ -311,6 +317,7 @@ class PriorityQueueTest(test.TestCase): with self.assertRaises(TypeError): q.enqueue_many((["a", "b", "c"], ["a", "b", "c"])).run() + @test_util.run_v1_only("b/120545219") def testInsertingNonScalarFails(self): with self.cached_session() as sess: input_priority = array_ops.placeholder(dtypes.int64) diff --git a/tensorflow/python/kernel_tests/py_func_test.py b/tensorflow/python/kernel_tests/py_func_test.py index 1f3f02a9f0..482633d539 100644 --- a/tensorflow/python/kernel_tests/py_func_test.py +++ b/tensorflow/python/kernel_tests/py_func_test.py @@ -102,6 +102,7 @@ class PyFuncTest(test.TestCase): script_ops.eager_py_func(np_func, [x, y], [dtypes.float32])) self.assertEqual(z[0], np_func(1.0, 2.0).astype(np.float32)) + @test_util.run_v1_only("b/120545219") def testArray(self): with self.cached_session(): x = constant_op.constant([1.0, 2.0], dtypes.float64) @@ -168,6 +169,7 @@ class PyFuncTest(test.TestCase): (dtypes.float64, dtypes.float64))) self.assertAllClose(y, [0.0, 1.0]) + @test_util.run_v1_only("b/120545219") def testStrings(self): def read_fixed_length_numpy_strings(): @@ -185,6 +187,7 @@ class PyFuncTest(test.TestCase): script_ops.py_func(read_and_return_strings, [x, y], dtypes.string)) self.assertAllEqual(z, [b"hello there", b"hi there"]) + @test_util.run_v1_only("b/120545219") def testStringsAreConvertedToBytes(self): def read_fixed_length_numpy_strings(): @@ -202,6 +205,7 @@ class PyFuncTest(test.TestCase): script_ops.py_func(read_and_return_strings, [x, y], dtypes.string)) self.assertAllEqual(z, [b"hello there", b"hi there"]) + @test_util.run_v1_only("b/120545219") def testObjectArraysAreConvertedToBytes(self): def read_object_array(): @@ -217,12 +221,14 @@ class PyFuncTest(test.TestCase): z, = script_ops.py_func(read_and_return_strings, [x, y], [dtypes.string]) self.assertListEqual(list(z.eval()), [b"hello there", b"hi ya"]) + @test_util.run_v1_only("b/120545219") def testStringPadding(self): correct = [b"this", b"is", b"a", b"test"] with self.cached_session(): s, = script_ops.py_func(lambda: [correct], [], [dtypes.string]) self.assertAllEqual(s.eval(), correct) + @test_util.run_v1_only("b/120545219") def testStringPaddingAreConvertedToBytes(self): inp = ["this", "is", "a", "test"] correct = [b"this", b"is", b"a", b"test"] @@ -230,6 +236,7 @@ class PyFuncTest(test.TestCase): s, = script_ops.py_func(lambda: [inp], [], [dtypes.string]) self.assertAllEqual(s.eval(), correct) + @test_util.run_v1_only("b/120545219") def testLarge(self): with self.cached_session() as sess: x = array_ops.zeros([1000000], dtype=np.float32) @@ -243,6 +250,7 @@ class PyFuncTest(test.TestCase): x = self.evaluate(script_ops.py_func(lambda: 42.0, [], dtypes.float64)) self.assertAllClose(x, 42.0) + @test_util.run_v1_only("b/120545219") def testAlias(self): with self.cached_session(): np_array = np.array([1.0, 2.0], dtype=np.float32) @@ -251,6 +259,7 @@ class PyFuncTest(test.TestCase): value.op.run() self.assertAllEqual(np_array, [1.0, 2.0]) + @test_util.run_v1_only("b/120545219") def testReturnUnicodeString(self): with self.cached_session(): correct = u"你好 世界" @@ -261,6 +270,7 @@ class PyFuncTest(test.TestCase): z, = script_ops.py_func(unicode_string, [], [dtypes.string]) self.assertEqual(z.eval(), correct.encode("utf8")) + @test_util.run_v1_only("b/120545219") def testBadNumpyReturnType(self): with self.cached_session(): @@ -274,6 +284,7 @@ class PyFuncTest(test.TestCase): "Unsupported numpy type"): self.evaluate(y) + @test_util.run_v1_only("b/120545219") def testBadReturnType(self): with self.cached_session(): @@ -287,6 +298,7 @@ class PyFuncTest(test.TestCase): "Unsupported object type"): self.evaluate(z) + @test_util.run_v1_only("b/120545219") def testReturnInput(self): with self.cached_session(): @@ -321,6 +333,7 @@ class PyFuncTest(test.TestCase): self.assertEqual(self.evaluate(x), 0) self.assertEqual(self.evaluate(x), 0) + @test_util.run_v1_only("b/120545219") def testGradientFunction(self): # Input to tf.py_func is necessary, otherwise get_gradient_function() # returns None per default. @@ -330,6 +343,7 @@ class PyFuncTest(test.TestCase): self.assertEqual(None, ops.get_gradient_function(x.op)) self.assertEqual(None, ops.get_gradient_function(y.op)) + @test_util.run_v1_only("b/120545219") def testCOrder(self): with self.cached_session(): val = [[1, 2], [3, 4]] @@ -337,6 +351,7 @@ class PyFuncTest(test.TestCase): [dtypes.int64]) self.assertAllEqual(val, self.evaluate(x)) + @test_util.run_v1_only("b/120545219") def testParallel(self): # Tests that tf.py_func's can run in parallel if they release the GIL. with self.cached_session() as session: @@ -382,6 +397,7 @@ class PyFuncTest(test.TestCase): self.assertIsNone(ret) self.assertAllEqual([3], s.value) + @test_util.run_v1_only("b/120545219") def testNoReturnValueStateless(self): def do_nothing(unused_x): @@ -420,6 +436,7 @@ class PyFuncTest(test.TestCase): with self.assertRaisesWithPredicateMatch(tf_exp, expected_error_check): self.evaluate(f) + @test_util.run_v1_only("b/120545219") def testExceptionHandling(self): with self.cached_session(): self._testExceptionHandling(ValueError, errors.InvalidArgumentError) @@ -514,7 +531,7 @@ class PyFuncTest(test.TestCase): self.assertAllEqual(ret, [[3.0], [3.0], [3.0]]) @test_util.run_in_graph_and_eager_modes - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testEagerExceptionHandling(self): with test_util.device(use_gpu=True): self._testExceptionHandling( @@ -534,7 +551,7 @@ class PyFuncTest(test.TestCase): self._testExceptionHandling(WeirdError, errors.UnknownError, eager=True) @test_util.run_in_graph_and_eager_modes - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testEagerReturningVariableRaisesError(self): def return_variable(): return resource_variable_ops.ResourceVariable(0.0) @@ -558,6 +575,7 @@ class PyFuncTest(test.TestCase): dy_dx = tape.gradient(y, x) self.assertEqual(self.evaluate(dy_dx), 6.0) + @test_util.run_v1_only("b/120545219") def testEagerGradientGraph(self): def f(x): @@ -568,6 +586,7 @@ class PyFuncTest(test.TestCase): dy_dx = gradients_impl.gradients(y, x)[0] self.assertEqual(self.evaluate(dy_dx), 6.0) + @test_util.run_v1_only("b/120545219") def testEagerGradientGraphTwoOutputs(self): def f(x, y): @@ -597,6 +616,7 @@ class PyFuncTest(test.TestCase): self.assertEqual(self.evaluate(dz_dx), 6.0) self.assertEqual(self.evaluate(dz_dy), 8.0) + @test_util.run_v1_only("b/120545219") def testEagerGradientGraphMultipleArgs(self): def f(x, y): @@ -610,6 +630,7 @@ class PyFuncTest(test.TestCase): self.assertEqual(self.evaluate(dz_dx), 6.0) self.assertEqual(self.evaluate(dz_dy), 8.0) + @test_util.run_v1_only("b/120545219") def testEagerGradientGraphLogHuber(self): def log_huber(x, m): @@ -631,6 +652,7 @@ class PyFuncTest(test.TestCase): self.assertEqual(y, 1.0) self.assertEqual(dy_dx, 2.0) + @test_util.run_v1_only("b/120545219") def testEagerRespectsDevicePlacmentOfOp(self): def f(x): diff --git a/tensorflow/python/kernel_tests/qr_op_test.py b/tensorflow/python/kernel_tests/qr_op_test.py index 5e9767f20c..5adb95c7d6 100644 --- a/tensorflow/python/kernel_tests/qr_op_test.py +++ b/tensorflow/python/kernel_tests/qr_op_test.py @@ -40,6 +40,7 @@ def _AddTest(test_class, op_name, testcase_name, fn): class QrOpTest(test.TestCase): + @test_util.run_v1_only("b/120545219") def testWrongDimensions(self): # The input to qr should be a tensor of at least rank 2. scalar = constant_op.constant(1.) @@ -115,6 +116,7 @@ def _GetQrOpTest(dtype_, shape_, full_matrices_, use_static_shape_): tol = 1e-14 self.assertAllClose(identity, xx, atol=tol) + @test_util.run_v1_only("b/120545219") def Test(self): np.random.seed(1) x_np = np.random.uniform( @@ -163,6 +165,7 @@ class QrGradOpTest(test.TestCase): def _GetQrGradOpTest(dtype_, shape_, full_matrices_): + @test_util.run_v1_only("b/120545219") def Test(self): np.random.seed(42) a = np.random.uniform(low=-1.0, high=1.0, size=shape_).astype(dtype_) diff --git a/tensorflow/python/kernel_tests/random/random_shuffle_queue_test.py b/tensorflow/python/kernel_tests/random/random_shuffle_queue_test.py index ed4f5434d9..dd814a22b4 100644 --- a/tensorflow/python/kernel_tests/random/random_shuffle_queue_test.py +++ b/tensorflow/python/kernel_tests/random/random_shuffle_queue_test.py @@ -29,11 +29,13 @@ from tensorflow.python.framework import dtypes as dtypes_lib from tensorflow.python.framework import errors_impl from tensorflow.python.framework import random_seed from tensorflow.python.framework import tensor_shape +from tensorflow.python.framework import test_util from tensorflow.python.ops import data_flow_ops from tensorflow.python.platform import test from tensorflow.python.platform import tf_logging +@test_util.run_v1_only("b/120545219") class RandomShuffleQueueTest(test.TestCase): def setUp(self): @@ -1415,6 +1417,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertItemsEqual(elem, results) + @test_util.run_v1_only("b/120545219") def testBigDequeueMany(self): with self.cached_session() as sess: q = data_flow_ops.RandomShuffleQueue(2, 0, dtypes_lib.int32, ((),)) diff --git a/tensorflow/python/kernel_tests/resource_variable_ops_test.py b/tensorflow/python/kernel_tests/resource_variable_ops_test.py index 3992d6bdfb..433957fd1d 100644 --- a/tensorflow/python/kernel_tests/resource_variable_ops_test.py +++ b/tensorflow/python/kernel_tests/resource_variable_ops_test.py @@ -628,7 +628,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): variable_def=other_v_def) self.assertTrue(other_v_prime._cached_value is not None) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testVariableDefInitializedInstances(self): with ops.Graph().as_default(), self.cached_session() as sess: v_def = resource_variable_ops.ResourceVariable( @@ -733,7 +733,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): self.assertEqual(0.0, self.evaluate(v.value())) @test_util.run_in_graph_and_eager_modes - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testDestroyResource(self): v = resource_variable_ops.ResourceVariable(3.0, name="var0") self.evaluate(variables.global_variables_initializer()) @@ -792,7 +792,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): with self.assertRaises(ValueError): _ = w.value().op.get_attr("_class") - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testSharedName(self): with self.cached_session(): v = resource_variable_ops.ResourceVariable(300.0, name="var4") @@ -849,7 +849,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): v.initializer.run(feed_dict={v.initial_value: 3.0}) self.assertEqual(3.0, v.value().eval()) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testControlFlowInitialization(self): """Expects an error if an initializer is in a control-flow scope.""" @@ -986,7 +986,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): self.assertAllEqual(self.evaluate(v.assign_add(1)), [1, 2, 3, 4]) @test_util.run_in_graph_and_eager_modes - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testCopyToGraphUninitialized(self): v = resource_variable_ops.ResourceVariable([0, 1, 2, 3]) copy_to_graph = ops.Graph() diff --git a/tensorflow/python/kernel_tests/rnn_test.py b/tensorflow/python/kernel_tests/rnn_test.py index 3bc457f8fb..a49496e4ef 100644 --- a/tensorflow/python/kernel_tests/rnn_test.py +++ b/tensorflow/python/kernel_tests/rnn_test.py @@ -262,7 +262,7 @@ class RNNTest(test.TestCase): rnn.dynamic_rnn(cell, inputs, dtype=dtypes.float32, sequence_length=[4]) @test_util.run_in_graph_and_eager_modes - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testTensorArrayStateIsAccepted(self): cell = TensorArrayStateRNNCell() in_eager_mode = context.executing_eagerly() diff --git a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py index c1241ba87e..8510a08f0c 100644 --- a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py +++ b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py @@ -217,7 +217,7 @@ class StatefulScatterNdTest(test.TestCase): def testVariableRankAdd(self): self._VariableRankTests(_NumpyAdd, state_ops.scatter_nd_add) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testVariableRankSub(self): self._VariableRankTests(_NumpySub, state_ops.scatter_nd_sub) @@ -235,7 +235,7 @@ class StatefulScatterNdTest(test.TestCase): self._VariableRankTest( np_scatter, tf_scatter, vtype, itype, repeat_indices=True) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testScatterRepeatIndices(self): """This tests scatter_add using indices that repeat.""" self._ScatterRepeatIndicesTest(_NumpyAdd, state_ops.scatter_nd_add) @@ -257,7 +257,7 @@ class StatefulScatterNdTest(test.TestCase): # session.run([update0, update1]) # self.assertAllEqual([False, True], self.evaluate(var)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testScatterOutOfRangeCpu(self): # TODO(simister): Re-enable once binary size increase due to # scatter_nd ops is under control. @@ -294,7 +294,7 @@ class StatefulScatterNdTest(test.TestCase): state_ops.scatter_nd_update(ref, indices, updates).get_shape().as_list(), shape) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testResVarInvalidOutputShape(self): res = variables.Variable( initial_value=lambda: array_ops.zeros(shape=[], dtype=dtypes.float32), @@ -509,7 +509,7 @@ class ScatterNdTest(test.TestCase): ValueError, "Indices and updates specified for empty output shape"): self.scatter_nd(indices, updates, shape) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testEmptyOutputShape2(self): indices = array_ops.placeholder(dtypes.int32, shape=None) updates = array_ops.placeholder(dtypes.int32, shape=None) @@ -717,6 +717,7 @@ class ScatterNdTensorTest(test.TestCase): self.assertAllEqual(subbed, constant_op.constant([1, -10, 1, -9, -8, 1, 1, -11])) + @test_util.run_v1_only("b/120545219") def testUpdateAddSubGradients(self): with self.cached_session(): diff --git a/tensorflow/python/kernel_tests/self_adjoint_eig_op_test.py b/tensorflow/python/kernel_tests/self_adjoint_eig_op_test.py index ce782dbc28..47b22ec296 100644 --- a/tensorflow/python/kernel_tests/self_adjoint_eig_op_test.py +++ b/tensorflow/python/kernel_tests/self_adjoint_eig_op_test.py @@ -22,6 +22,7 @@ import numpy as np from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes as dtypes_lib +from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import gradient_checker_v2 from tensorflow.python.ops import linalg_ops @@ -39,6 +40,7 @@ def _AddTest(test_class, op_name, testcase_name, fn): class SelfAdjointEigTest(test.TestCase): + @test_util.run_deprecated_v1 def testWrongDimensions(self): # The input to self_adjoint_eig should be a tensor of # at least rank 2. @@ -49,6 +51,7 @@ class SelfAdjointEigTest(test.TestCase): with self.assertRaises(ValueError): linalg_ops.self_adjoint_eig(vector) + @test_util.run_deprecated_v1 def testConcurrentExecutesWithoutError(self): all_ops = [] with self.session(use_gpu=True) as sess: diff --git a/tensorflow/python/kernel_tests/session_ops_test.py b/tensorflow/python/kernel_tests/session_ops_test.py index dc663cb091..bc5d8e8151 100644 --- a/tensorflow/python/kernel_tests/session_ops_test.py +++ b/tensorflow/python/kernel_tests/session_ops_test.py @@ -20,6 +20,7 @@ from __future__ import print_function 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 array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import session_ops @@ -28,6 +29,7 @@ from tensorflow.python.ops import variables from tensorflow.python.platform import test +@test_util.run_v1_only("b/120545219") class SessionOpsTest(test.TestCase): def testHandleBasic(self): @@ -232,6 +234,7 @@ class SessionOpsTest(test.TestCase): b_p: b_handle.handle}) self.assertEqual(3.0, c_handle.eval()) + @test_util.run_v1_only("b/120545219") def testFeedOneHandleDirectly(self): with self.cached_session() as sess: a = constant_op.constant(10.0) @@ -243,6 +246,7 @@ class SessionOpsTest(test.TestCase): self.assertAllClose(2500.0, sess.run(d, feed_dict={c: h_c})) + @test_util.run_v1_only("b/120545219") def testDirectHandleFeedOverlappingWithFetches(self): with self.cached_session() as sess: a = constant_op.constant(10.0) @@ -283,6 +287,7 @@ class SessionOpsTest(test.TestCase): self.assertAllClose(48.0, sess.run(e, feed_dict={c: h_c, d: h_d})) self.assertAllClose(-48.0, sess.run(e, feed_dict={c: h_d, d: h_c})) + @test_util.run_v1_only("b/120545219") def testFeedHandleToVariableDirectly(self): with self.cached_session() as sess: a = variables.Variable(12.0) diff --git a/tensorflow/python/kernel_tests/signal/reconstruction_ops_test.py b/tensorflow/python/kernel_tests/signal/reconstruction_ops_test.py index 4cb6cedee9..e0ce06418a 100644 --- a/tensorflow/python/kernel_tests/signal/reconstruction_ops_test.py +++ b/tensorflow/python/kernel_tests/signal/reconstruction_ops_test.py @@ -66,6 +66,7 @@ class ReconstructionOpsTest(test.TestCase): self.assertAllClose(output, expected_output) + @test_util.run_deprecated_v1 def test_unknown_shapes(self): # This test uses placeholders and does not work in eager mode. if context.executing_eagerly(): @@ -85,6 +86,7 @@ class ReconstructionOpsTest(test.TestCase): self.assertAllClose(output, expected_output) + @test_util.run_deprecated_v1 def test_unknown_rank(self): # This test uses placeholders and does not work in eager mode. if context.executing_eagerly(): @@ -104,6 +106,7 @@ class ReconstructionOpsTest(test.TestCase): self.assertAllClose(output, expected_output) + @test_util.run_deprecated_v1 def test_fast_path(self): # This test uses tensor names and does not work in eager mode. if context.executing_eagerly(): diff --git a/tensorflow/python/kernel_tests/sparse_conditional_accumulator_test.py b/tensorflow/python/kernel_tests/sparse_conditional_accumulator_test.py index 275c86e534..4a967b6562 100644 --- a/tensorflow/python/kernel_tests/sparse_conditional_accumulator_test.py +++ b/tensorflow/python/kernel_tests/sparse_conditional_accumulator_test.py @@ -267,7 +267,7 @@ class IndexedSlicesConditionalAccumulatorTest(test.TestCase): self.assertAllEqual(val.values, [[5, 5], [0, 20], [30, 0]]) self.assertAllEqual(val.dense_shape, [-1, 2]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testParallelApplyGradMean(self): with self.cached_session() as sess: q = data_flow_ops.SparseConditionalAccumulator( @@ -299,7 +299,7 @@ class IndexedSlicesConditionalAccumulatorTest(test.TestCase): np.array([[expected_val, 0], [0, expected_val]]).astype(np.float32), val, sess) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testParallelApplyGradSum(self): with self.cached_session() as sess: q = data_flow_ops.SparseConditionalAccumulator( @@ -334,7 +334,7 @@ class IndexedSlicesConditionalAccumulatorTest(test.TestCase): np.array([[expected_val, 0], [0, expected_val]]).astype(np.float32), val, sess) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testParallelTakeGrad(self): with self.cached_session() as sess: q = data_flow_ops.SparseConditionalAccumulator( @@ -374,7 +374,7 @@ class IndexedSlicesConditionalAccumulatorTest(test.TestCase): self._assertEqual_nparray( np.array([[0, 0], [elems[i], 0]]), results[i], sess) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testAccumulatorApplyAndBlockingTake(self): with self.cached_session() as sess: q = data_flow_ops.SparseConditionalAccumulator( @@ -410,7 +410,7 @@ class IndexedSlicesConditionalAccumulatorTest(test.TestCase): with self.assertRaisesOpError("was cancelled"): self.evaluate(takeg_op) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testAccumulatorCancel(self): with self.cached_session() as sess: q = data_flow_ops.SparseConditionalAccumulator( @@ -430,7 +430,7 @@ class IndexedSlicesConditionalAccumulatorTest(test.TestCase): takeg_thread.join() - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testNonVectorIndices(self): with self.cached_session(): q = data_flow_ops.SparseConditionalAccumulator( @@ -443,7 +443,7 @@ class IndexedSlicesConditionalAccumulatorTest(test.TestCase): grad_indices=[[0, 1], [1, 0]], grad_values=np.array([1, 2]).astype(np.float32)).run() - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testZeroDimensionValues(self): with self.cached_session(): q = data_flow_ops.SparseConditionalAccumulator( @@ -454,7 +454,7 @@ class IndexedSlicesConditionalAccumulatorTest(test.TestCase): q.apply_grad( grad_indices=[0], grad_values=np.array(1).astype(np.float32)).run() - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testWrongNonEmptyInputValues(self): with self.cached_session(): q = data_flow_ops.SparseConditionalAccumulator( @@ -466,7 +466,7 @@ class IndexedSlicesConditionalAccumulatorTest(test.TestCase): grad_indices=[0, 1], grad_values=np.array([[0, 1, 1]]).astype(np.float32)).run() - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testDynamicNonVectorIndices(self): with self.cached_session() as sess: q = data_flow_ops.SparseConditionalAccumulator( @@ -486,7 +486,7 @@ class IndexedSlicesConditionalAccumulatorTest(test.TestCase): x_values: np.array([1, 2]).astype(np.float32) }) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testDynamicWrongNonEmptyInputValues(self): with self.cached_session() as sess: q = data_flow_ops.SparseConditionalAccumulator( @@ -505,7 +505,7 @@ class IndexedSlicesConditionalAccumulatorTest(test.TestCase): x_values: np.array([[0, 1, 1]]).astype(np.float32) }) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testEmptyShapeApply(self): with self.cached_session(): q = data_flow_ops.SparseConditionalAccumulator( @@ -531,7 +531,7 @@ class IndexedSlicesConditionalAccumulatorTest(test.TestCase): q.apply_grad(grad_indices=[0], grad_values=[1.0], grad_shape=[]).run() q.apply_grad(grad_indices=[0], grad_values=[1.0]).run() - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testValidateShape(self): with self.cached_session() as sess: q = data_flow_ops.SparseConditionalAccumulator( diff --git a/tensorflow/python/kernel_tests/stack_ops_test.py b/tensorflow/python/kernel_tests/stack_ops_test.py index d50f3f4680..1930d2484f 100644 --- a/tensorflow/python/kernel_tests/stack_ops_test.py +++ b/tensorflow/python/kernel_tests/stack_ops_test.py @@ -96,7 +96,7 @@ class StackOpTest(test.TestCase): c1, b1, [r, v], [r.get_shape(), tensor_shape.unknown_shape()]) self.assertAllClose(np.ones(2000) * 10.0, self.evaluate(ry)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testStackWhileSwap(self): self._testStackWhileSwap(use_gpu=False) self._testStackWhileSwap(use_gpu=True) @@ -248,7 +248,7 @@ class StackOpRefTest(test.TestCase): c1, b1, [r, v], [r.get_shape(), tensor_shape.unknown_shape()]) self.assertAllClose(np.ones(2000) * 10.0, self.evaluate(ry)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testStackWhileSwap(self): self._testStackWhileSwap(use_gpu=False) self._testStackWhileSwap(use_gpu=True) diff --git a/tensorflow/python/kernel_tests/svd_op_test.py b/tensorflow/python/kernel_tests/svd_op_test.py index 94155fd117..cfa9f122d1 100644 --- a/tensorflow/python/kernel_tests/svd_op_test.py +++ b/tensorflow/python/kernel_tests/svd_op_test.py @@ -22,6 +22,7 @@ import numpy as np from tensorflow.python import tf2 from tensorflow.python.framework import constant_op +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 linalg_ops @@ -39,6 +40,7 @@ def _AddTest(test_class, op_name, testcase_name, fn): class SvdOpTest(test.TestCase): + @test_util.run_v1_only("b/120545219") def testWrongDimensions(self): # The input to svd should be a tensor of at least rank 2. scalar = constant_op.constant(1.) @@ -50,6 +52,7 @@ class SvdOpTest(test.TestCase): "Shape must be at least rank 2 but is rank 1"): linalg_ops.svd(vector) + @test_util.run_v1_only("b/120545219") def testConcurrentExecutesWithoutError(self): with self.session(use_gpu=True) as sess: all_ops = [] @@ -126,6 +129,7 @@ def _GetSvdOpTest(dtype_, shape_, use_static_shape_, compute_uv_, identity = array_ops.matrix_band_part(array_ops.ones_like(xx), 0, 0) self.assertAllClose(identity, xx, atol=tol) + @test_util.run_v1_only("b/120545219") def Test(self): is_complex = dtype_ in (np.complex64, np.complex128) is_single = dtype_ in (np.float32, np.complex64) @@ -214,6 +218,7 @@ def _GetSvdGradOpTest(dtype_, shape_, compute_uv_, full_matrices_): tf_v *= phase[..., :n] return tf_s, tf_u, tf_v + @test_util.run_v1_only("b/120545219") def Test(self): np.random.seed(42) a = np.random.uniform(low=-1.0, high=1.0, size=shape_).astype(dtype_) diff --git a/tensorflow/python/kernel_tests/tensor_array_ops_test.py b/tensorflow/python/kernel_tests/tensor_array_ops_test.py index 884c04eb7a..88625841bc 100644 --- a/tensorflow/python/kernel_tests/tensor_array_ops_test.py +++ b/tensorflow/python/kernel_tests/tensor_array_ops_test.py @@ -309,7 +309,7 @@ class TensorArrayTest(test.TestCase): self._testTensorArraySplitRead(dtypes.string) @test_util.disable_control_flow_v2("v2 does not support TensorArray.grad.") - @test_util.run_deprecated_v1 + @test_util.run_v1_only("v2 does not support TensorArray.grad.") def testSkipEagerTensorGradArrayWriteRead(self): with self.session(use_gpu=True) as session: ta = tensor_array_ops.TensorArray( @@ -364,7 +364,7 @@ class TensorArrayTest(test.TestCase): self.assertAllEqual([2.0], session.run(g2)) @test_util.disable_control_flow_v2("v2 does not support TensorArray.grad.") - @test_util.run_deprecated_v1 + @test_util.run_v1_only("v2 does not support TensorArray.grad.") def testSkipEagerTensorGradArrayDynamicWriteRead(self): with self.session(use_gpu=True) as session: ta = tensor_array_ops.TensorArray( @@ -407,7 +407,7 @@ class TensorArrayTest(test.TestCase): self.assertAllEqual(3, g_vs) @test_util.disable_control_flow_v2("v2 does not support TensorArray.grad.") - @test_util.run_deprecated_v1 + @test_util.run_v1_only("v2 does not support TensorArray.grad.") def testSkipEagerTensorGradAccessTwiceReceiveSameObject(self): with self.session(use_gpu=True) as session: ta = tensor_array_ops.TensorArray( @@ -424,7 +424,7 @@ class TensorArrayTest(test.TestCase): self.assertAllEqual(t_g_ta_0, t_g_ta_1) self.assertAllEqual([[4.0, 5.0]], d_r1_0) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testTensorArrayWriteWrongIndexOrDataTypeFails(self): with self.session(use_gpu=True): ta = _make_ta(3, "foo", dtype=dtypes.float32) @@ -458,7 +458,7 @@ class TensorArrayTest(test.TestCase): with self.assertRaisesOpError(error_msg): self.evaluate(ta.write(3, 3.0).flow) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testTensorArrayReadWrongIndexOrDataTypeFails(self): with self.session(use_gpu=True): ta = _make_ta(3, "foo", dtype=dtypes.float32) @@ -493,7 +493,7 @@ class TensorArrayTest(test.TestCase): self.evaluate(ta.read(3)) @test_util.disable_control_flow_v2("v2 allows multiple writes.") - @test_util.run_deprecated_v1 + @test_util.run_v1_only("v2 allows multiple writes.") def testSkipEagerTensorArrayWriteMultipleFails(self): with self.session(use_gpu=True): ta = tensor_array_ops.TensorArray( @@ -504,7 +504,7 @@ class TensorArrayTest(test.TestCase): "it has already been written to."): self.evaluate(ta.write(2, 3.0).write(2, 3.0).flow) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testTensorArrayConcatIncompatibleShapesFails(self): with self.session(use_gpu=True): ta = tensor_array_ops.TensorArray( @@ -536,7 +536,7 @@ class TensorArrayTest(test.TestCase): with self.assertRaisesOpError("shape"): self.evaluate(w3.concat()) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testTensorArraySplitIncompatibleShapesFails(self): with self.session(use_gpu=True): in_eager_mode = context.executing_eagerly() @@ -611,14 +611,14 @@ class TensorArrayTest(test.TestCase): wb1_grad.flow.eval() @test_util.disable_control_flow_v2("v2 does not support TensorArray.grad.") - @test_util.run_deprecated_v1 + @test_util.run_v1_only("v2 does not support TensorArray.grad.") def testSkipEagerTensorArrayWriteGradientAddMultipleAdds(self): for dtype in (dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64, dtypes.complex64, dtypes.complex128): self._testTensorArrayWriteGradientAddMultipleAdds(dtype) @test_util.disable_control_flow_v2("Low level legacy TA op test.") - @test_util.run_deprecated_v1 + @test_util.run_v1_only("Low level legacy TA op test.") def testSkipEagerTensorArrayGradWithShapeKnownElementShape(self): with self.session(use_gpu=True) as sess: ta = tensor_array_ops.TensorArray( @@ -649,7 +649,7 @@ class TensorArrayTest(test.TestCase): sess.run(read_value, feed_dict={value: fed_value})) @test_util.disable_control_flow_v2("Low level legacy TA op test.") - @test_util.run_deprecated_v1 + @test_util.run_v1_only("Low level legacy TA op test.") def testSkipEagerTensorArrayGradWithShapeUnknownElementShape(self): with self.session(use_gpu=True) as sess: ta = tensor_array_ops.TensorArray( @@ -778,7 +778,7 @@ class TensorArrayTest(test.TestCase): self._testTensorArrayGradientWritePackConcatAndRead() @test_util.disable_control_flow_v2("v2 does not support clear_after_read.") - @test_util.run_deprecated_v1 + @test_util.run_v1_only("v2 does not support clear_after_read.") def testTensorArrayReadTwice(self): with self.session(use_gpu=True): value = constant_op.constant([[1.0, -1.0], [10.0, -10.0]]) @@ -1351,7 +1351,7 @@ class TensorArrayTest(test.TestCase): .ENABLE_TENSOR_ARRAY_V2 else v1_msg): ta.stack().eval() - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testSkipEagerTensorArrayEvalEmpty(self): self._testTensorArrayEvalEmpty() @@ -1443,7 +1443,7 @@ class TensorArrayTest(test.TestCase): self.assertAllEqual(expected_grad, grad_vals[0]) @test_util.disable_control_flow_v2("colocate_with not supported in v2.") - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testSkipEagerTensorArrayGetsDeviceFromFirstWrite(self): with ops.device("/job:worker/task:0/cpu:0"): # this initial device will be ignored. @@ -1493,7 +1493,7 @@ class TensorArrayTest(test.TestCase): [s for s in dev_stats[d] if "/TensorArray" in s.node_name]) @test_util.disable_control_flow_v2("colocate_with not supported in v2.") - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testSkipEagerTensorArrayGetsDeviceFromFirstWriteInWhileLoop(self): with ops.device("/job:worker/task:0/cpu:0"): ta = tensor_array_ops.TensorArray(dtype=dtypes.float32, size=2) @@ -1524,7 +1524,7 @@ class TensorArrayTest(test.TestCase): [s for s in dev_stats[d] if "TensorArray" == s.node_name]) @test_util.disable_control_flow_v2("colocate_with not supported in v2.") - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testSkipEagerTensorArrayDisabledColocateWithFirstWriteCall(self): with ops.device("/job:worker/task:0/cpu:0"): ta = tensor_array_ops.TensorArray( diff --git a/tensorflow/python/kernel_tests/tensordot_op_test.py b/tensorflow/python/kernel_tests/tensordot_op_test.py index 2894b7dd50..febfe23b16 100644 --- a/tensorflow/python/kernel_tests/tensordot_op_test.py +++ b/tensorflow/python/kernel_tests/tensordot_op_test.py @@ -24,6 +24,7 @@ from tensorflow.python import tf2 from tensorflow.python.framework import constant_op 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 math_ops from tensorflow.python.platform import test as test_lib @@ -40,6 +41,7 @@ def _add_test(test, test_name, fn): class TensordotTest(test_lib.TestCase): + @test_util.run_v1_only("b/120545219") def test_invalid_shape(self): a = [[1, 2], [3, 4]] b = [[1, 2], [3, 4], [5, 6]] @@ -63,6 +65,7 @@ class TensordotTest(test_lib.TestCase): axes_ph: (a_axes, b_axes) }) + @test_util.run_v1_only("b/120545219") def test_invalid_axes(self): a = [[1, 2], [3, 4]] b = [[1, 2], [3, 4]] @@ -105,6 +108,7 @@ class TensordotTest(test_lib.TestCase): self.assertAllEqual(tf_ans.shape, np_ans.shape) self.assertAllEqual(tf_ans, np_ans) + @test_util.run_v1_only("b/120545219") def test_partial_shape_inference(self): for axes in ([1], [0]), 1: a = array_ops.placeholder(dtypes.float32) diff --git a/tensorflow/python/kernel_tests/unicode_encode_op_test.py b/tensorflow/python/kernel_tests/unicode_encode_op_test.py index a5a5c2017c..72c8a26766 100644 --- a/tensorflow/python/kernel_tests/unicode_encode_op_test.py +++ b/tensorflow/python/kernel_tests/unicode_encode_op_test.py @@ -24,6 +24,7 @@ import numpy as np from tensorflow.python.framework import constant_op from tensorflow.python.framework import errors_impl as errors from tensorflow.python.framework import ops +from tensorflow.python.framework import test_util from tensorflow.python.ops.ragged import ragged_factory_ops from tensorflow.python.ops.ragged import ragged_string_ops from tensorflow.python.platform import test @@ -58,6 +59,7 @@ class UnicodeEncodeOpTest(test.TestCase, parameterized.TestCase): ragged_string_ops.unicode_encode(test_value, encoding, "strict").eval() @parameterized.parameters("UTF-8", "UTF-16-BE", "UTF-32-BE") + @test_util.run_v1_only("b/120545219") def testIgnoreErrors(self, encoding): test_value = np.array([72, 101, 2147483647, -1, 111], np.int32) expected_value = u"Heo".encode(encoding) @@ -69,6 +71,7 @@ class UnicodeEncodeOpTest(test.TestCase, parameterized.TestCase): self.assertAllEqual(result, expected_value) @parameterized.parameters("UTF-8", "UTF-16-BE", "UTF-32-BE") + @test_util.run_v1_only("b/120545219") def testReplaceErrors(self, encoding): test_value = np.array([72, 101, 2147483647, -1, 111], np.int32) expected_value = u"He\U0000fffd\U0000fffdo".encode(encoding) @@ -109,6 +112,7 @@ class UnicodeEncodeOpTest(test.TestCase, parameterized.TestCase): # -- regular Tensor tests -- # @parameterized.parameters("UTF-8", "UTF-16-BE", "UTF-32-BE") + @test_util.run_v1_only("b/120545219") def testVector(self, encoding): test_value = np.array([72, 101, 108, 108, 111], np.int32) expected_value = u"Hello".encode(encoding) @@ -144,6 +148,7 @@ class UnicodeEncodeOpTest(test.TestCase, parameterized.TestCase): self.assertAllEqual(result, expected_value) @parameterized.parameters("UTF-8", "UTF-16-BE", "UTF-32-BE") + @test_util.run_v1_only("b/120545219") def testMatrix(self, encoding): test_value = np.array( [[72, 128516, 108, 108, 111], [87, 128516, 114, 108, 100]], np.int32) @@ -157,6 +162,7 @@ class UnicodeEncodeOpTest(test.TestCase, parameterized.TestCase): self.assertAllEqual(result, expected_value) @parameterized.parameters("UTF-8", "UTF-16-BE", "UTF-32-BE") + @test_util.run_v1_only("b/120545219") def test3DimMatrix(self, encoding): test_value = constant_op.constant( [[[72, 101, 108, 108, 111], [87, 111, 114, 108, 100]], @@ -172,6 +178,7 @@ class UnicodeEncodeOpTest(test.TestCase, parameterized.TestCase): self.assertAllEqual(result, expected_value) @parameterized.parameters("UTF-8", "UTF-16-BE", "UTF-32-BE") + @test_util.run_v1_only("b/120545219") def test4DimMatrix(self, encoding): test_value = constant_op.constant( [[[[72, 101, 108, 108, 111]], [[87, 111, 114, 108, 100]]], @@ -192,6 +199,7 @@ class UnicodeEncodeOpTest(test.TestCase, parameterized.TestCase): # -- Ragged Tensor tests -- # @parameterized.parameters("UTF-8", "UTF-16-BE", "UTF-32-BE") + @test_util.run_v1_only("b/120545219") def testRaggedMatrix(self, encoding): test_value = ragged_factory_ops.constant( [[72, 195, 108, 108, 111], [87, 128516, 114, 108, 100, 46]], np.int32) @@ -205,6 +213,7 @@ class UnicodeEncodeOpTest(test.TestCase, parameterized.TestCase): self.assertAllEqual(result, expected_value) @parameterized.parameters("UTF-8", "UTF-16-BE", "UTF-32-BE") + @test_util.run_v1_only("b/120545219") def test3DimMatrixWithRagged2ndDim(self, encoding): test_value = ragged_factory_ops.constant( [[[72, 101, 108, 108, 111], [87, 111, 114, 108, 100]], @@ -224,6 +233,7 @@ class UnicodeEncodeOpTest(test.TestCase, parameterized.TestCase): self.assertAllEqual(result.tolist(), expected_value) @parameterized.parameters("UTF-8", "UTF-16-BE", "UTF-32-BE") + @test_util.run_v1_only("b/120545219") def test3DimMatrixWithRagged3rdDim(self, encoding): test_value = ragged_factory_ops.constant( [[[72, 101, 108, 108, 111], [87, 111, 114, 108, 100, 46]], @@ -241,6 +251,7 @@ class UnicodeEncodeOpTest(test.TestCase, parameterized.TestCase): self.assertAllEqual(result.tolist(), expected_value) @parameterized.parameters("UTF-8", "UTF-16-BE", "UTF-32-BE") + @test_util.run_v1_only("b/120545219") def test3DimMatrixWithRagged2ndAnd3rdDim(self, encoding): test_value = ragged_factory_ops.constant( [[[72, 101, 108, 108, 111], [87, 111, 114, 108, 100, 46]], [], @@ -254,6 +265,7 @@ class UnicodeEncodeOpTest(test.TestCase, parameterized.TestCase): self.assertAllEqual(result.tolist(), expected_value) @parameterized.parameters("UTF-8", "UTF-16-BE", "UTF-32-BE") + @test_util.run_v1_only("b/120545219") def test4DimRaggedMatrix(self, encoding): test_value = ragged_factory_ops.constant( [[[[72, 101, 108, 108, 111], [87, 111, 114, 108, 100]]], @@ -267,6 +279,7 @@ class UnicodeEncodeOpTest(test.TestCase, parameterized.TestCase): self.assertAllEqual(result.tolist(), expected_value) @parameterized.parameters("UTF-8", "UTF-16-BE", "UTF-32-BE") + @test_util.run_v1_only("b/120545219") def testRaggedMatrixWithMultiDimensionInnerValues(self, encoding): test_inner_values = constant_op.constant([[[72, 101, 108, 108, 111], [87, 111, 114, 108, 100]], diff --git a/tensorflow/python/kernel_tests/variables_test.py b/tensorflow/python/kernel_tests/variables_test.py index 08d885e8a8..336e9b0bca 100644 --- a/tensorflow/python/kernel_tests/variables_test.py +++ b/tensorflow/python/kernel_tests/variables_test.py @@ -43,7 +43,7 @@ from tensorflow.python.util import compat class VariablesTestCase(test.TestCase): - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testInitialization(self): with self.cached_session(): var0 = variables.VariableV1(0.0) @@ -71,7 +71,7 @@ class VariablesTestCase(test.TestCase): self.assertAllClose(0.0, self.evaluate(var0)) self.assertAllClose(1.1, self.evaluate(var1)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testInitializationOrder(self): with self.cached_session(): rnd = variables.Variable(random_ops.random_uniform([3, 6]), name="rnd") @@ -194,7 +194,7 @@ class VariablesTestCase(test.TestCase): def testCountUpToInt64(self): self._countUpToTest(dtypes.int64) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testControlDepsNone(self): with self.cached_session(): c = constant_op.constant(1.0) @@ -208,7 +208,7 @@ class VariablesTestCase(test.TestCase): self.assertEqual([], var_x.value().op.control_inputs) self.assertEqual([], var_x._ref().op.control_inputs) # pylint: disable=protected-access - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testControlFlow(self): with self.cached_session() as sess: v0 = variables.Variable(0, name="v0") @@ -245,7 +245,7 @@ class VariablesTestCase(test.TestCase): self.evaluate(v0.initializer) self.evaluate(add) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testControlFlowInitialization(self): """Expects an error if an initializer is in a control-flow scope.""" def cond(i, _): @@ -412,7 +412,7 @@ class VariablesTestCase(test.TestCase): variables.global_variables_initializer().run() self.assertAllClose([1, 12], self.evaluate(var)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testColocation(self): with ops.device("/job:ps"): var = variables.VariableV1(0, name="v") @@ -421,7 +421,7 @@ class VariablesTestCase(test.TestCase): self.assertDeviceEqual("/job:ps", assign_op.device) self.assertEqual([b"loc:@v"], assign_op.op.colocation_groups()) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testInitializerFunction(self): value = [[-42], [133.7]] shape = [2, 1] @@ -459,7 +459,7 @@ class VariablesTestCase(test.TestCase): lambda: constant_op.constant(1.), constraint=constraint) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testNoRefDataRace(self): with self.cached_session(): a = variables.Variable([1, 2, 3], dtype=dtypes.float32) @@ -489,7 +489,7 @@ class VariablesTestCase(test.TestCase): for i in v2.initializer.inputs: self.assertEqual(expected_group_v2, i.op.colocation_groups()) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testVariableDefInitializedInstances(self): with ops.Graph().as_default(), self.cached_session() as sess: v_def = variables.Variable( @@ -542,7 +542,7 @@ class VariablesTestCase(test.TestCase): self.assertAllClose(np.ones((5, 5), np.float32), self.evaluate(var)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testRepr(self): var = variables.VariableV1(np.zeros((5, 5), np.float32), name="noop") self.assertEqual( @@ -576,7 +576,7 @@ class IsInitializedTest(test.TestCase): variables.global_variables_initializer().run() self.assertEqual(0, self.evaluate(uninited).size) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testVariableList(self): with ops.Graph().as_default(), self.cached_session() as sess: v = variables.VariableV1([1, 2], name="v") @@ -614,7 +614,7 @@ class ObsoleteIsInitializedTest(test.TestCase): with ops.Graph().as_default(): self.assertEqual(None, variables.assert_variables_initialized()) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testVariables(self): with ops.Graph().as_default(), self.cached_session() as sess: v = variables.VariableV1([1, 2]) @@ -626,7 +626,7 @@ class ObsoleteIsInitializedTest(test.TestCase): variables.global_variables_initializer().run() self.evaluate(inited) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testVariableList(self): with ops.Graph().as_default(), self.cached_session() as sess: v = variables.VariableV1([1, 2]) diff --git a/tensorflow/python/kernel_tests/while_v2_test.py b/tensorflow/python/kernel_tests/while_v2_test.py index d155544f3e..cae459a34e 100644 --- a/tensorflow/python/kernel_tests/while_v2_test.py +++ b/tensorflow/python/kernel_tests/while_v2_test.py @@ -53,6 +53,7 @@ class WhileV2Test(test.TestCase, parameterized.TestCase): self.assertEqual(self.evaluate(ret), 16.) self.assertSequenceEqual(self.evaluate(grad), [32.]) + @test_util.run_v1_only("b/120545219") def testReturnSameStructureTrue(self): x = constant_op.constant(2.) ret = while_loop_v2( @@ -145,7 +146,7 @@ class WhileV2Test(test.TestCase, parameterized.TestCase): self.assertSequenceEqual(self.evaluate(grad), [32.]) self.assertSequenceEqual(self.evaluate(grad_grad), [48.]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testPruning(self): x = constant_op.constant(1) @@ -441,6 +442,7 @@ class WhileV2Test(test.TestCase, parameterized.TestCase): # grad = gradients_impl.gradients(output, [n]) # self.assertEqual(self.evaluate(grad), 3.5) + @test_util.run_deprecated_v1 def testForwardPassRewrite(self): x = constant_op.constant(1.0, name="x") output = while_v2.while_loop(lambda x: x < 10.0, diff --git a/tensorflow/python/layers/normalization_test.py b/tensorflow/python/layers/normalization_test.py index 07d8e40b75..6535f74129 100644 --- a/tensorflow/python/layers/normalization_test.py +++ b/tensorflow/python/layers/normalization_test.py @@ -38,6 +38,7 @@ from tensorflow.python.training import gradient_descent from tensorflow.python.training import saver as saver_lib +@test_util.run_v1_only('b/120545219') class BNTest(test.TestCase): def _simple_model(self, image, fused, freeze_mode): @@ -144,7 +145,6 @@ class BNTest(test.TestCase): return train_vars, loss_val - @test_util.run_deprecated_v1 def testHalfPrecision(self): ref_vars, ref_loss = self._trainEvalSequence( dtype=dtypes.float32, @@ -230,43 +230,33 @@ class BNTest(test.TestCase): ckpt_b_use_gpu, use_gpu_test_a, use_gpu_test_b, freeze_mode) - @test_util.run_deprecated_v1 def testCheckpointFusedCPUAndFusedGPU(self): self._testCheckpointCrossDevice(True, False, True, True) - @test_util.run_deprecated_v1 def testCheckpointFusedCPUAndFusedCPU(self): self._testCheckpointCrossDevice(True, False, True, False) - @test_util.run_deprecated_v1 def testCheckpointFusedGPUAndFusedGPU(self): self._testCheckpointCrossDevice(True, True, True, True) - @test_util.run_deprecated_v1 def testCheckpointNonFusedCPUAndNonFusedGPU(self): self._testCheckpointCrossDevice(False, False, False, True) - @test_util.run_deprecated_v1 def testCheckpointNonFusedCPUAndNonFusedCPU(self): self._testCheckpointCrossDevice(False, False, False, False) - @test_util.run_deprecated_v1 def testCheckpointNonFusedGPUAndNonFusedGPU(self): self._testCheckpointCrossDevice(False, True, False, True) - @test_util.run_deprecated_v1 def testCheckpointNonFusedGPUAndFusedGPU(self): self._testCheckpointCrossDevice(False, True, True, True) - @test_util.run_deprecated_v1 def testCheckpointNonFusedGPUAndFusedCPU(self): self._testCheckpointCrossDevice(False, True, True, False) - @test_util.run_deprecated_v1 def testCheckpointNonFusedCPUAndFusedCPU(self): self._testCheckpointCrossDevice(False, False, True, False) - @test_util.run_deprecated_v1 def testCreateBN(self): # Call layer. bn = normalization_layers.BatchNormalization(axis=1) @@ -293,7 +283,6 @@ class BNTest(test.TestCase): ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES), bn.trainable_variables) - @test_util.run_deprecated_v1 def testCreateFusedBNFloat16(self): # Call layer. bn = normalization_layers.BatchNormalization(axis=1, fused=True) @@ -323,7 +312,6 @@ class BNTest(test.TestCase): ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES), bn.trainable_variables) - @test_util.run_deprecated_v1 def test3DInputAxis1(self): epsilon = 1e-3 bn = normalization_layers.BatchNormalization( @@ -367,7 +355,6 @@ class BNTest(test.TestCase): self.assertAlmostEqual(np.mean(normed_np_output), 0., places=1) self.assertAlmostEqual(np.std(normed_np_output), 1., places=1) - @test_util.run_deprecated_v1 def test3DInputAxis2(self): epsilon = 1e-3 bn = normalization_layers.BatchNormalization( @@ -451,7 +438,6 @@ class BNTest(test.TestCase): self.assertAlmostEqual(np.mean(normed_np_output), 0., places=1) self.assertAlmostEqual(np.std(normed_np_output), 1., places=1) - @test_util.run_deprecated_v1 def test4DInputAxis2(self): epsilon = 1e-3 bn = normalization_layers.BatchNormalization( @@ -493,7 +479,6 @@ class BNTest(test.TestCase): self.assertAlmostEqual(np.mean(normed_np_output), 0., places=1) self.assertAlmostEqual(np.std(normed_np_output), 1., places=1) - @test_util.run_deprecated_v1 def test4DInputAxis3(self): epsilon = 1e-3 bn = normalization_layers.BatchNormalization( @@ -535,7 +520,6 @@ class BNTest(test.TestCase): self.assertAlmostEqual(np.mean(normed_np_output), 0., places=1) self.assertAlmostEqual(np.std(normed_np_output), 1., places=1) - @test_util.run_deprecated_v1 def test4DInputAxis3Fused(self): epsilon = 1e-3 bn = normalization_layers.BatchNormalization( @@ -619,7 +603,6 @@ class BNTest(test.TestCase): self.assertAlmostEqual(np.mean(normed_np_output), 0., places=1) self.assertAlmostEqual(np.std(normed_np_output), 1., places=1) - @test_util.run_deprecated_v1 def testNegativeAxis(self): epsilon = 1e-3 bn = normalization_layers.BatchNormalization( @@ -662,7 +645,6 @@ class BNTest(test.TestCase): self.assertAlmostEqual(np.mean(normed_np_output), 0., places=1) self.assertAlmostEqual(np.std(normed_np_output), 1., places=1) - @test_util.run_deprecated_v1 def testBooleanLearningPhase(self): epsilon = 1e-3 bn = normalization_layers.BatchNormalization( @@ -703,7 +685,6 @@ class BNTest(test.TestCase): self.assertAlmostEqual(np.mean(normed_np_output), 0., places=1) self.assertAlmostEqual(np.std(normed_np_output), 1., places=1) - @test_util.run_deprecated_v1 def testFunctionalNoReuse(self): inputs = variables.Variable( np.random.random((5, 4, 3, 6)), dtype=dtypes.float32) @@ -756,7 +737,6 @@ class BNTest(test.TestCase): self.assertAlmostEqual(np.mean(normed_np_output), 0., places=1) self.assertAlmostEqual(np.std(normed_np_output), 1., places=1) - @test_util.run_deprecated_v1 def testFunctionalReuse(self): inputs1 = variables.Variable( np.random.random((5, 4, 3, 6)), dtype=dtypes.float32) @@ -821,7 +801,6 @@ class BNTest(test.TestCase): self.assertAlmostEqual(np.mean(normed_np_output), 0., places=2) self.assertAlmostEqual(np.std(normed_np_output), 1., places=1) - @test_util.run_deprecated_v1 def testFunctionalReuseFromScope(self): inputs = variables.Variable( np.random.random((5, 4, 3, 6)), dtype=dtypes.float32) @@ -836,7 +815,6 @@ class BNTest(test.TestCase): inputs, axis=-1, momentum=0.9, epsilon=epsilon, training=training) self.assertEqual(len(variables.global_variables()), 5) - @test_util.run_deprecated_v1 def testNoCenter(self): bn = normalization_layers.BatchNormalization(axis=1, center=False) inputs = random_ops.random_uniform((5, 4, 3), seed=1) @@ -852,7 +830,6 @@ class BNTest(test.TestCase): self.assertEqual(len(bn.trainable_variables), 1) self.assertEqual(len(bn.non_trainable_variables), 2) - @test_util.run_deprecated_v1 def testNoScale(self): bn = normalization_layers.BatchNormalization(axis=1, scale=False) inputs = random_ops.random_uniform((5, 4, 3), seed=1) @@ -868,7 +845,6 @@ class BNTest(test.TestCase): self.assertEqual(len(bn.trainable_variables), 1) self.assertEqual(len(bn.non_trainable_variables), 2) - @test_util.run_deprecated_v1 def testRegularizers(self): reg = lambda x: 0.1 * math_ops.reduce_sum(x) bn = normalization_layers.BatchNormalization(axis=1, beta_regularizer=reg) @@ -894,7 +870,6 @@ class BNTest(test.TestCase): self.assertEqual(bn.gamma_constraint, g_constraint) self.assertEqual(bn.beta_constraint, b_constraint) - @test_util.run_deprecated_v1 def testRenorm(self): shape = (4, 3) xt = array_ops.placeholder(dtypes.float32, shape) @@ -953,7 +928,6 @@ class BNTest(test.TestCase): self.assertAllClose(y_train, yt_val_train, atol=1e-5) self.assertAllClose(y_test, yt_val_test, atol=1e-5) - @test_util.run_deprecated_v1 def testAdjustment(self): shape = (4, 3) xt = array_ops.placeholder(dtypes.float32, shape) @@ -998,7 +972,6 @@ class BNTest(test.TestCase): self.assertAllClose(y_train, yt_val_train, atol=1e-5) self.assertAllClose(y_test, yt_val_test, atol=1e-5) - @test_util.run_deprecated_v1 def testRenormWithAdjustment(self): shape = (4, 3) xt = array_ops.placeholder(dtypes.float32, shape) @@ -1069,7 +1042,6 @@ class BNTest(test.TestCase): normalization_layers.batch_normalization( inp, virtual_batch_size=-1) - @test_util.run_deprecated_v1 def testGhostBNVirtualBatchFull(self): shape = [6, 5, 4, 3] inp = random_ops.random_uniform(shape, seed=1) @@ -1095,7 +1067,6 @@ class BNTest(test.TestCase): inp, virtual_batch_size=3) self.assertListEqual(out.shape.as_list(), shape) - @test_util.run_deprecated_v1 def testGhostBNUnknownBatchSize(self): np_shape = [10, 5, 4] tf_shape = [None, 5, 4] @@ -1111,7 +1082,6 @@ class BNTest(test.TestCase): self.assertListEqual(list(y.shape), np_shape) - @test_util.run_deprecated_v1 def testGhostBN2Dims(self): shape = [6, 2] virtual_batch_size = 3 @@ -1165,7 +1135,6 @@ class BNTest(test.TestCase): self.assertAllClose(y_train, y_val_train, atol=1e-5) self.assertAllClose(y_test, y_val_test, atol=1e-5) - @test_util.run_deprecated_v1 def testGhostBN4DimsAxis3(self): shape = [6, 10, 10, 3] virtual_batch_size = 2 @@ -1219,7 +1188,6 @@ class BNTest(test.TestCase): self.assertAllClose(y_train, y_val_train, atol=1e-2) self.assertAllClose(y_test, y_val_test, atol=1e-2) - @test_util.run_deprecated_v1 def testGhostBN4DimsAxis1(self): shape = [6, 3, 10, 10] virtual_batch_size = 2 @@ -1290,7 +1258,6 @@ class BNTest(test.TestCase): normalization_layers.batch_normalization( inp, axis=[1, 2, 1]) # duplicate - @test_util.run_deprecated_v1 def test3DInputMultiAxis12(self): epsilon = 1e-3 bn = normalization_layers.BatchNormalization( @@ -1332,7 +1299,6 @@ class BNTest(test.TestCase): self.assertAlmostEqual(np.mean(normed_np_output), 0., places=1) self.assertAlmostEqual(np.std(normed_np_output), 1., places=1) - @test_util.run_deprecated_v1 def test5DInputMultiAxis123(self): epsilon = 1e-3 bn = normalization_layers.BatchNormalization( @@ -1374,7 +1340,6 @@ class BNTest(test.TestCase): self.assertAlmostEqual(np.mean(normed_np_output), 0., places=1) self.assertAlmostEqual(np.std(normed_np_output), 1., places=1) - @test_util.run_deprecated_v1 def testGhostBN5DimsMultiAxis14(self): shape = [6, 3, 10, 10, 4] virtual_batch_size = 3 diff --git a/tensorflow/python/ops/gradients_test.py b/tensorflow/python/ops/gradients_test.py index a9058c4a34..abdcbc7a3a 100644 --- a/tensorflow/python/ops/gradients_test.py +++ b/tensorflow/python/ops/gradients_test.py @@ -158,6 +158,7 @@ class GradientsTest(test_util.TensorFlowTestCase): grads = gradients.gradients(z, [x]) self.assertTrue(all(x is not None for x in grads)) + @test_util.run_v1_only("b/120545219") def testBoundaryContinue(self): # Test that we differentiate both 'x' and 'y' correctly when x is a # predecessor of y. @@ -169,6 +170,7 @@ class GradientsTest(test_util.TensorFlowTestCase): self.assertTrue(all(x is not None for x in grads)) self.assertEqual(6.0, grads[0].eval()) + @test_util.run_v1_only("b/120545219") def testAggregationMethodAccumulateN(self): with self.cached_session(): x = constant(1.0) @@ -182,6 +184,7 @@ class GradientsTest(test_util.TensorFlowTestCase): self.assertEqual(20.0, grads[0].eval()) self.assertEqual(10.0, grads[1].eval()) + @test_util.run_v1_only("b/120545219") def testAggregationMethodAddN(self): with self.cached_session(): x = constant(1.0) @@ -193,6 +196,7 @@ class GradientsTest(test_util.TensorFlowTestCase): self.assertEqual(20.0, grads[0].eval()) self.assertEqual(10.0, grads[1].eval()) + @test_util.run_v1_only("b/120545219") def testAggregationMethodTree(self): with self.cached_session(): x = constant(1.0) @@ -239,6 +243,7 @@ class GradientsTest(test_util.TensorFlowTestCase): [dx, dy], feed_dict={x: [1.0], dy.indices: [0], dy.values: [2.0]}) self.assertEqual(vdx, vdy) + @test_util.run_v1_only("b/120545219") def testNonDifferentiableSwitchInWhileLoop(self): with ops.Graph().as_default(): v = array_ops.placeholder(dtypes.float32, []) @@ -270,6 +275,7 @@ class GradientsTest(test_util.TensorFlowTestCase): gradient = gradients.gradients(graph.as_graph_element(var), var) self.assertIsNotNone(gradient) + @test_util.run_v1_only("b/120545219") def testVariableRefGradient(self): with ops.Graph().as_default(): init = constant_op.constant(100.0) @@ -277,6 +283,7 @@ class GradientsTest(test_util.TensorFlowTestCase): gradient = gradients.gradients(var._ref(), var) self.assertIsNotNone(gradient) + @test_util.run_v1_only("b/120545219") def testDependentYs(self): with self.cached_session(): x = constant_op.constant(3.0) @@ -292,6 +299,7 @@ class GradientsTest(test_util.TensorFlowTestCase): g = gradients.gradients([z, z2], x) self.assertAllClose(17502.0, g[0].eval()) + @test_util.run_v1_only("b/120545219") def testPartialDerivatives(self): with self.cached_session(): x = constant_op.constant(1.) @@ -302,6 +310,7 @@ class GradientsTest(test_util.TensorFlowTestCase): partialg = gradients.gradients(z, [x, y], stop_gradients=[x, y]) self.assertEqual([1.0, 1.0], [g.eval() for g in partialg]) + @test_util.run_v1_only("b/120545219") def testStopGradients(self): def _MakeGraph(rng, stop_gradients=()): def _FunctionOf(xs, k=3): @@ -606,6 +615,7 @@ class PreventGradientTest(test_util.TensorFlowTestCase): class HessianVectorProductTest(test_util.TensorFlowTestCase): + @test_util.run_v1_only("b/120545219") def testHessianVectorProduct(self): # Manually compute the Hessian explicitly for a low-dimensional problem # and check that HessianVectorProduct matches multiplication by the @@ -634,6 +644,7 @@ class HessianVectorProductTest(test_util.TensorFlowTestCase): class HessianTest(test_util.TensorFlowTestCase): + @test_util.run_v1_only("b/120545219") def testHessian1D(self): # Manually compute the Hessian explicitly for a low-dimensional problem # and check that `hessian` matches. Specifically, the Hessian of @@ -651,6 +662,7 @@ class HessianTest(test_util.TensorFlowTestCase): hess_actual = self.evaluate(hess) self.assertAllClose(hess_value, hess_actual) + @test_util.run_v1_only("b/120545219") def testHessian1D_multi(self): # Test the computation of the hessian with respect to multiple tensors m = 4 @@ -671,6 +683,7 @@ class HessianTest(test_util.TensorFlowTestCase): for hess_value, hess_actual in zip(hess_values, hessians_actual): self.assertAllClose(hess_value, hess_actual) + @test_util.run_v1_only("b/120545219") def testHessianInvalidDimension(self): for shape in [(10, 10), None]: with self.cached_session(use_gpu=True): @@ -679,6 +692,7 @@ class HessianTest(test_util.TensorFlowTestCase): with self.assertRaises(ValueError): gradients.hessians(x, x) + @test_util.run_v1_only("b/120545219") def testHessian2D_square_matrix(self): # Manually compute the Hessian explicitly for a low-dimensional problem # and check that `hessian` matches. Specifically, the Hessian of @@ -700,6 +714,7 @@ class HessianTest(test_util.TensorFlowTestCase): self.assertAllEqual((m, m, m, m), hess_actual.shape) self.assertAllClose(hess_value, hess_actual.reshape((m * m, m * m))) + @test_util.run_v1_only("b/120545219") def testHessian2D_non_square_matrix(self): m = 3 n = 4 @@ -722,6 +737,7 @@ class HessianTest(test_util.TensorFlowTestCase): class IndexedSlicesToTensorTest(test_util.TensorFlowTestCase): + @test_util.run_v1_only("b/120545219") def testIndexedSlicesToTensor(self): with self.cached_session(): np_val = np.random.rand(4, 4, 4, 4).astype(np.float32) @@ -731,6 +747,7 @@ class IndexedSlicesToTensorTest(test_util.TensorFlowTestCase): c_dense = math_ops.multiply(c_sparse, 1.0) self.assertAllClose(np_val, self.evaluate(c_dense)) + @test_util.run_v1_only("b/120545219") def testIndexedSlicesToTensorList(self): with self.cached_session(): numpy_list = [] @@ -747,6 +764,7 @@ class IndexedSlicesToTensorTest(test_util.TensorFlowTestCase): packed_sparse = array_ops.stack(sparse_list) self.assertAllClose(packed_dense.eval(), self.evaluate(packed_sparse)) + @test_util.run_v1_only("b/120545219") def testInt64Indices(self): with self.cached_session(): np_val = np.random.rand(4, 4, 4, 4).astype(np.float32) @@ -759,6 +777,7 @@ class IndexedSlicesToTensorTest(test_util.TensorFlowTestCase): c_dense = math_ops.multiply(c_sparse, 1.0) self.assertAllClose(np_val, self.evaluate(c_dense)) + @test_util.run_v1_only("b/120545219") def testWarnings(self): # TODO(gunan) Reenable after this issue is fixed: # https://github.com/google/protobuf/issues/2812 @@ -802,6 +821,7 @@ class IndexedSlicesToTensorTest(test_util.TensorFlowTestCase): class OnlyRealGradientsTest(test_util.TensorFlowTestCase): + @test_util.run_v1_only("b/120545219") def testRealOnly(self): x = constant_op.constant(7+3j, dtype=dtypes.complex64) y = math_ops.square(x) @@ -814,6 +834,7 @@ class OnlyRealGradientsTest(test_util.TensorFlowTestCase): class ResourceCondTest(test_util.TensorFlowTestCase): + @test_util.run_v1_only("b/120545219") def testBasic(self): gamma = resource_variable_ops.ResourceVariable( np.random.random((3,)), @@ -943,6 +964,7 @@ class CustomGradientTest(test_util.TensorFlowTestCase): self.assertEqual(6., math_ops.reduce_sum(dx).numpy()) self.assertEqual(8., math_ops.reduce_sum(dw).numpy()) + @test_util.run_v1_only("b/120545219") def testCustomGradientErrorsWithNonResourceVariables(self): def F(x, use_resource=False): @@ -993,6 +1015,7 @@ class CustomGradientTest(test_util.TensorFlowTestCase): # Smoke test to ensure numpy inputs are accepted F(x) + @test_util.run_v1_only("b/120545219") def testRVGradientsDynamicCond(self): with self.cached_session(): alpha = resource_variable_ops.ResourceVariable( diff --git a/tensorflow/python/ops/nn_test.py b/tensorflow/python/ops/nn_test.py index 414a5cdbbe..82fab74183 100644 --- a/tensorflow/python/ops/nn_test.py +++ b/tensorflow/python/ops/nn_test.py @@ -450,6 +450,7 @@ class DropoutTest(test_lib.TestCase): with self.assertRaises(ValueError): nn_ops.dropout(t, array_ops.placeholder(dtypes.float32, shape=[2])) + @test_util.run_deprecated_v1 def testInvalidRate(self): x_dim = 40 y_dim = 30 diff --git a/tensorflow/python/ops/ragged/ragged_batch_gather_op_test.py b/tensorflow/python/ops/ragged/ragged_batch_gather_op_test.py index d9d840500c..7fe185641f 100644 --- a/tensorflow/python/ops/ragged/ragged_batch_gather_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_batch_gather_op_test.py @@ -135,7 +135,7 @@ class RaggedBatchGatherOpTest(test_util.TensorFlowTestCase, expected=ragged.constant_value( [[[[b'c', b'a'], [b'd', b'd']], [[b'f', b'e']]]], ragged_rank=2)), ]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testRaggedBatchGather(self, descr, params, indices, expected): result = ragged.batch_gather(params, indices) self.assertEqual( @@ -188,7 +188,7 @@ class RaggedBatchGatherOpTest(test_util.TensorFlowTestCase, indices=[[[0]]], message='batch shape from indices does not match params shape'), ]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testRaggedBatchGatherStaticError(self, params, indices, diff --git a/tensorflow/python/ops/ragged/ragged_boolean_mask_op_test.py b/tensorflow/python/ops/ragged/ragged_boolean_mask_op_test.py index d939d9d634..763b016405 100644 --- a/tensorflow/python/ops/ragged/ragged_boolean_mask_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_boolean_mask_op_test.py @@ -298,7 +298,7 @@ class RaggedBooleanMaskOpTest(test_util.TensorFlowTestCase, keepdims=True, expected=ragged.constant_value([[[1], [4, 6]], [[7, 9], []]])), ]) # pyformat: disable - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testBooleanMask(self, descr, data, mask, keepdims, expected): actual = ragged.boolean_mask(data, mask, keepdims=keepdims) self.assertEqual( diff --git a/tensorflow/python/ops/ragged/ragged_concat_op_test.py b/tensorflow/python/ops/ragged/ragged_concat_op_test.py index 3699f90f46..ba7867418a 100644 --- a/tensorflow/python/ops/ragged/ragged_concat_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_concat_op_test.py @@ -221,7 +221,7 @@ class RaggedConcatOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): axis=0, expected=[[b'a00', b'a01'], [], [b'a20', b'a21']]), ) # pyformat: disable - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testRaggedConcat(self, descr, rt_inputs, @@ -280,7 +280,7 @@ class RaggedConcatOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): error=errors.InvalidArgumentError, message='Input tensors have incompatible shapes'), ]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testRuntimeError(self, rt_inputs, axis, error, message, ragged_ranks=None): rt_inputs = [ diff --git a/tensorflow/python/ops/ragged/ragged_dispatch_test.py b/tensorflow/python/ops/ragged/ragged_dispatch_test.py index 2bb10adce0..2533c60c4e 100644 --- a/tensorflow/python/ops/ragged/ragged_dispatch_test.py +++ b/tensorflow/python/ops/ragged/ragged_dispatch_test.py @@ -126,6 +126,7 @@ BINARY_INT_OPS = [ ] +@test_util.run_v1_only('b/120545219') class RaggedElementwiseOpsTest(test_util.TensorFlowTestCase, parameterized.TestCase): @@ -401,7 +402,6 @@ class RaggedElementwiseOpsTest(test_util.TensorFlowTestCase, result_flat_values = array_ops.reshape(result, [-1]) self.assertAllEqual(expected_flat_values, result_flat_values) - @test_util.run_deprecated_v1 def testElementwiseOpUnknownRankError(self): x = ragged.constant([[1, 2], [3]]) y = ragged.from_row_splits( diff --git a/tensorflow/python/ops/ragged/ragged_from_sparse_op_test.py b/tensorflow/python/ops/ragged/ragged_from_sparse_op_test.py index 3c0db9e8fb..674dbab112 100644 --- a/tensorflow/python/ops/ragged/ragged_from_sparse_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_from_sparse_op_test.py @@ -29,7 +29,7 @@ from tensorflow.python.platform import googletest class RaggedTensorToSparseOpTest(test_util.TensorFlowTestCase): - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testDocStringExample(self): st = sparse_tensor.SparseTensor( indices=[[0, 0], [0, 1], [0, 2], [1, 0], [3, 0]], @@ -40,7 +40,7 @@ class RaggedTensorToSparseOpTest(test_util.TensorFlowTestCase): with self.test_session(): self.assertEqual(rt.eval().tolist(), [[1, 2, 3], [4], [], [5]]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testEmpty(self): st = sparse_tensor.SparseTensor( indices=array_ops.zeros([0, 2], dtype=dtypes.int64), @@ -67,7 +67,7 @@ class RaggedTensorToSparseOpTest(test_util.TensorFlowTestCase): self.assertRaisesRegexp(ValueError, r'rank\(st_input\) must be 2', ragged.from_sparse, st3) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testGoodPartialSparseTensorRank(self): st1 = sparse_tensor.SparseTensor( indices=[[0, 0]], @@ -82,7 +82,7 @@ class RaggedTensorToSparseOpTest(test_util.TensorFlowTestCase): ragged.from_sparse(st1) ragged.from_sparse(st2) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testNonRaggedSparseTensor(self): # "index_suffix" means the value of the innermost dimension of the index # (i.e., indices[i][-1]). diff --git a/tensorflow/python/ops/ragged/ragged_from_tensor_op_test.py b/tensorflow/python/ops/ragged/ragged_from_tensor_op_test.py index 1d8a00cc18..09c17fad61 100644 --- a/tensorflow/python/ops/ragged/ragged_from_tensor_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_from_tensor_op_test.py @@ -31,7 +31,7 @@ from tensorflow.python.platform import googletest class RaggedFromTensorOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testDocStringExamples(self): # The examples from ragged.from_tensor.__doc__. dt = constant_op.constant([[5, 7, 0], [0, 3, 0], [6, 0, 0]]) @@ -263,7 +263,7 @@ class RaggedFromTensorOpTest(test_util.TensorFlowTestCase, [[[5, 6], [7]], [[0, 8], []]]] }, ) # pyformat: disable - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testRaggedFromTensor(self, tensor, expected, @@ -398,7 +398,7 @@ class RaggedFromTensorOpTest(test_util.TensorFlowTestCase, 'expected': [[], []] }, ) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testEmpty(self, dt_shape, expected, lengths=None, padding=None): dt = array_ops.zeros(dt_shape) rt = ragged.from_tensor(dt, lengths, padding) diff --git a/tensorflow/python/ops/ragged/ragged_gather_nd_op_test.py b/tensorflow/python/ops/ragged/ragged_gather_nd_op_test.py index 62c6819374..b0a4fe9d31 100644 --- a/tensorflow/python/ops/ragged/ragged_gather_nd_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_gather_nd_op_test.py @@ -28,6 +28,7 @@ from tensorflow.python.ops import ragged from tensorflow.python.platform import googletest +@test_util.run_v1_only('b/120545219') class RaggedGatherNdOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): @@ -183,7 +184,6 @@ class RaggedGatherNdOpTest(test_util.TensorFlowTestCase, indices=[[0, 0, 1], [0, 0, 0], [0, 1, 0]], expected=[[b'c', b'd'], [b'a', b'b'], [b'e', b'f']]), ]) # pyformat: disable - @test_util.run_deprecated_v1 def testRaggedGatherNd(self, descr, params, indices, expected): result = ragged.gather_nd(params, indices) self.assertEqual( @@ -193,7 +193,6 @@ class RaggedGatherNdOpTest(test_util.TensorFlowTestCase, expected = expected.tolist() self.assertEqual(self.evaluate(result).tolist(), expected) - @test_util.run_deprecated_v1 def testRaggedGatherNdUnknownRankError(self): params = ragged.constant([['a', 'b'], ['c', 'd']]) indices1 = array_ops.placeholder(dtypes.int32, shape=None) @@ -221,7 +220,6 @@ class RaggedGatherNdOpTest(test_util.TensorFlowTestCase, indices=ragged.constant([[0]]), message='The innermost dimension of indices may not be ragged'), ]) - @test_util.run_deprecated_v1 def testRaggedGatherNdStaticError(self, params, indices, diff --git a/tensorflow/python/ops/ragged/ragged_gather_op_test.py b/tensorflow/python/ops/ragged/ragged_gather_op_test.py index 76c90cdfee..d371745b73 100644 --- a/tensorflow/python/ops/ragged/ragged_gather_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_gather_op_test.py @@ -30,7 +30,7 @@ from tensorflow.python.platform import googletest class RaggedTensorOpsTest(test_util.TensorFlowTestCase): - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testDocStringExamples(self): params = constant_op.constant(['a', 'b', 'c', 'd', 'e']) indices = constant_op.constant([3, 1, 2, 1, 0]) @@ -66,7 +66,7 @@ class RaggedTensorOpsTest(test_util.TensorFlowTestCase): ragged.gather(params, indices).eval().tolist(), [[b'f'], [b'a', b'b'], [b'f'], [b'c', b'd', b'e']]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testTensorParamsAndRaggedIndices(self): params = ['a', 'b', 'c', 'd', 'e'] indices = ragged.constant([[2, 1], [1, 2, 0], [3]]) @@ -75,7 +75,7 @@ class RaggedTensorOpsTest(test_util.TensorFlowTestCase): ragged.gather(params, indices).eval().tolist(), [[b'c', b'b'], [b'b', b'c', b'a'], [b'd']]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testRaggedParamsAndRaggedIndices(self): params = ragged.constant([['a', 'b'], ['c', 'd', 'e'], ['f'], [], ['g']]) indices = ragged.constant([[2, 1], [1, 2, 0], [3]]) @@ -108,7 +108,7 @@ class RaggedTensorOpsTest(test_util.TensorFlowTestCase): [[[b'g']], [[b'g']]]] # [p2, p2]] ) # pyformat: disable - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testTensorParamsAnd4DRaggedIndices(self): indices = ragged.constant( [[[[3, 4], [0, 6]], []], [[[2, 1], [1, 0]], [[2, 5]], [[2, 3]]], @@ -123,7 +123,7 @@ class RaggedTensorOpsTest(test_util.TensorFlowTestCase): [[[b'c', b'b'], [b'b', b'a']], [[b'c', b'f']], [[b'c', b'd']]], [[[b'b', b'a']]]]) # pyformat: disable - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testOutOfBoundsError(self): tensor_params = ['a', 'b', 'c'] tensor_indices = [0, 1, 2] diff --git a/tensorflow/python/ops/ragged/ragged_map_fn_op_test.py b/tensorflow/python/ops/ragged/ragged_map_fn_op_test.py index ecd78a91b2..ab70d5a123 100644 --- a/tensorflow/python/ops/ragged/ragged_map_fn_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_map_fn_op_test.py @@ -30,6 +30,7 @@ from tensorflow.python.ops import string_ops from tensorflow.python.platform import googletest +@test_util.run_v1_only('b/120545219') class RaggedMapOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): @parameterized.parameters([ # The following test sets map over a RaggedTensor and apply a @@ -141,7 +142,6 @@ class RaggedMapOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): ), ]) - @test_util.run_deprecated_v1 def testRaggedMap( self, fn, @@ -166,7 +166,6 @@ class RaggedMapOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): output_values = self.evaluate(output) self.assertAllEqual(expected_output, output_values.tolist()) - @test_util.run_deprecated_v1 def testRaggedMapOnStructure(self): batman = ragged.constant([[1, 2, 3], [4], [5, 6, 7]]) # [[10, 20, 30], [40], [50, 60, 70]] @@ -187,7 +186,6 @@ class RaggedMapOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): self.assertAllEqual(output.eval().tolist(), [66, 44, 198]) # Test mapping over a dict of RTs can produce a dict of RTs. - @test_util.run_deprecated_v1 def testRaggedMapOnStructure_RaggedOutputs(self): batman = ragged.constant([[1, 2, 3], [4], [5, 6, 7]]) # [[10, 20, 30], [40], [50, 60, 70]] @@ -219,7 +217,6 @@ class RaggedMapOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): self.assertAllEqual(output['robin'].eval().tolist(), [[11, 21, 31], [41], [51, 61, 71]]) - @test_util.run_deprecated_v1 def testZip(self): x = ragged.constant([[10, 20], [30, 40], [50, 60], [70], [80, 90, 100]], dtypes.int64) @@ -242,7 +239,6 @@ class RaggedMapOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): result, [[[0, 10], [0, 20]], [[1, 30], [1, 40]], [[2, 50], [2, 60]], [[3, 70]], [[4, 80], [4, 90], [4, 100]]]) - @test_util.run_deprecated_v1 def testBatchGather(self): tokens = ragged.constant([['hello', '.', 'there'], ['merhaba'], ['bonjour', '.', 'ca va', '?']]) diff --git a/tensorflow/python/ops/ragged/ragged_map_inner_values_op_test.py b/tensorflow/python/ops/ragged/ragged_map_inner_values_op_test.py index b5802cb82d..dc214f4437 100644 --- a/tensorflow/python/ops/ragged/ragged_map_inner_values_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_map_inner_values_op_test.py @@ -43,7 +43,7 @@ class RaggedMapInnerValuesOpTest(test_util.TensorFlowTestCase, with self.test_session(): self.assertEqual(result.eval().tolist(), expected) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testDocStringExamples(self): """Test the examples in apply_op_to_ragged_values.__doc__.""" rt = ragged.constant([[1, 2, 3], [], [4, 5], [6]]) @@ -63,7 +63,7 @@ class RaggedMapInnerValuesOpTest(test_util.TensorFlowTestCase, args=(tensor,), expected=[[0, 0, 0], [], [0, 0]]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testOpWithTwoRaggedTensorArgs(self): x = ragged.constant([[3, 1, 4], [], [1, 5]]) y = ragged.constant([[1, 2, 3], [], [4, 5]]) @@ -76,7 +76,7 @@ class RaggedMapInnerValuesOpTest(test_util.TensorFlowTestCase, self.assertRaggedMapInnerValuesReturns( op=math_ops.multiply, args=(5, y), expected=[[5, 10, 15], [], [20, 25]]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testOpWithThreeRaggedTensorArgs(self): condition = ragged.constant( [[True, True, False], [], [True, False]]) # pyformat: disable @@ -87,7 +87,7 @@ class RaggedMapInnerValuesOpTest(test_util.TensorFlowTestCase, args=(condition, x, y), expected=[[b'a', b'b', b'C'], [], [b'd', b'E']]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testOpWithRaggedTensorListArg(self): x = ragged.constant([[1, 2, 3], [], [4, 5]]) y = ragged.constant([[10, 20, 30], [], [40, 50]]) @@ -96,7 +96,7 @@ class RaggedMapInnerValuesOpTest(test_util.TensorFlowTestCase, args=([x, y, x],), expected=[[12, 24, 36], [], [48, 60]]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testOpWithKeywordArgs(self): x = ragged.constant([[3, 1, 4], [], [1, 5]]) y = ragged.constant([[1, 2, 3], [], [4, 5]]) @@ -105,7 +105,7 @@ class RaggedMapInnerValuesOpTest(test_util.TensorFlowTestCase, kwargs=dict(x=x, y=y), expected=[[3, 2, 12], [], [4, 25]]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testOpWithMixedPositionalAndKeywordArgs(self): x = ragged.constant([[3, 1, 4], [], [1, 5]]) y = ragged.constant([[1, 2, 3], [], [4, 5]]) @@ -128,7 +128,7 @@ class RaggedMapInnerValuesOpTest(test_util.TensorFlowTestCase, }, expected=[[8, 15, 13], [], [16, 25]]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testOpWithRaggedRankGreaterThanOne(self): # ragged_rank=0 x0 = [3, 1, 4, 1, 5, 9, 2, 6, 5] @@ -173,7 +173,7 @@ class RaggedMapInnerValuesOpTest(test_util.TensorFlowTestCase, [[[54, 14], [48, 45]]] # row 3 ]) # pyformat: disable - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testOpWithRaggedRankThree(self): x = ragged.constant([[[3, 1, 4]], [], [[], [1, 5]]]) y = ragged.constant([[[1, 2, 3]], [], [[], [4, 5]]]) @@ -203,7 +203,7 @@ class RaggedMapInnerValuesOpTest(test_util.TensorFlowTestCase, r'Inputs must have identical ragged splits.*', ragged.map_inner_values, math_ops.add, x, y) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testRaggedTensorSplitsMismatchErrorAtRuntime(self): splits1 = array_ops.placeholder_with_default( constant_op.constant([0, 3, 3, 5], dtypes.int64), None) diff --git a/tensorflow/python/ops/ragged/ragged_operators_test.py b/tensorflow/python/ops/ragged/ragged_operators_test.py index 7fe8159d82..c1223db307 100644 --- a/tensorflow/python/ops/ragged/ragged_operators_test.py +++ b/tensorflow/python/ops/ragged/ragged_operators_test.py @@ -23,11 +23,11 @@ from tensorflow.python.ops import ragged from tensorflow.python.platform import googletest +@test_util.run_v1_only('b/120545219') class RaggedElementwiseOpsTest(test_util.TensorFlowTestCase): # @TODO(edloper): Test right-handed versions of operators once we add # broadcasting support for elementwise ops. - @test_util.run_deprecated_v1 def testOrderingOperators(self): x = ragged.constant([[1, 5], [3]]) y = ragged.constant([[4, 5], [1]]) @@ -41,7 +41,6 @@ class RaggedElementwiseOpsTest(test_util.TensorFlowTestCase): if a != b: print('%30s %s' % (b, a)) - @test_util.run_deprecated_v1 def testArithmeticOperators(self): x = ragged.constant([[1.0, -2.0], [8.0]]) y = ragged.constant([[4.0, 4.0], [2.0]]) @@ -77,7 +76,6 @@ class RaggedElementwiseOpsTest(test_util.TensorFlowTestCase): self.assertEqual((2.0 % y).eval().tolist(), [[2.0, 2.0], [0.0]]) self.assertEqual((x % 2.0).eval().tolist(), [[1.0, 0.0], [0.0]]) - @test_util.run_deprecated_v1 def testLogicalOperators(self): a = ragged.constant([[True, True], [False]]) b = ragged.constant([[True, False], [False]]) diff --git a/tensorflow/python/ops/ragged/ragged_reduce_op_test.py b/tensorflow/python/ops/ragged/ragged_reduce_op_test.py index 9f51d59ba3..2e4db2a423 100644 --- a/tensorflow/python/ops/ragged/ragged_reduce_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_reduce_op_test.py @@ -300,7 +300,7 @@ class RaggedReduceOpsTest(test_util.TensorFlowTestCase, parameterized.TestCase): axis=2, expected=[[mean(1, 2), mean(3, 4, 5)], [mean(6, 7), 8], [9]]), ) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testReduce(self, ragged_reduce_op, rt_input, axis, expected): rt_input = ragged.constant(rt_input) reduced = ragged_reduce_op(rt_input, axis) diff --git a/tensorflow/python/ops/ragged/ragged_row_lengths_op_test.py b/tensorflow/python/ops/ragged/ragged_row_lengths_op_test.py index 4a705be484..9f4aa1b136 100644 --- a/tensorflow/python/ops/ragged/ragged_row_lengths_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_row_lengths_op_test.py @@ -143,7 +143,7 @@ class RaggedRowLengthsOp(test_util.TensorFlowTestCase, parameterized.TestCase): expected=[[2, 3, 0], [4, 1]], expected_ragged_rank=1), ]) # pyformat: disable - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testRowLengths(self, rt_input, expected, diff --git a/tensorflow/python/ops/ragged/ragged_segment_op_test.py b/tensorflow/python/ops/ragged/ragged_segment_op_test.py index 9e4877ae3e..52fe739342 100644 --- a/tensorflow/python/ops/ragged/ragged_segment_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_segment_op_test.py @@ -182,7 +182,7 @@ class RaggedSegmentOpsTest(test_util.TensorFlowTestCase, []] # pyformat: disable self.assertEqual(self.evaluate(segmented2).tolist(), expected2) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testRaggedSegmentIds(self): rt = ragged.constant([ [[111, 112, 113, 114], [121],], # row 0 @@ -205,7 +205,7 @@ class RaggedSegmentOpsTest(test_util.TensorFlowTestCase, 'but segment_ids is ragged and data is not.', ragged.segment_sum, dt, segment_ids, 3) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testShapeMismatchError2(self): rt = ragged.constant([ [[111, 112, 113, 114], [121]], # row 0 diff --git a/tensorflow/python/ops/ragged/ragged_stack_op_test.py b/tensorflow/python/ops/ragged/ragged_stack_op_test.py index 4343471694..ed07aca6d4 100644 --- a/tensorflow/python/ops/ragged/ragged_stack_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_stack_op_test.py @@ -265,7 +265,7 @@ class RaggedStackOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): axis=0, expected=[[[b'a00', b'a01'], [], [b'a20', b'a21']]]), ) # pyformat: disable - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testRaggedStack(self, descr, rt_inputs, diff --git a/tensorflow/python/ops/ragged/ragged_tensor_shape_test.py b/tensorflow/python/ops/ragged/ragged_tensor_shape_test.py index 9c2dd26050..699dcc2bdb 100644 --- a/tensorflow/python/ops/ragged/ragged_tensor_shape_test.py +++ b/tensorflow/python/ops/ragged/ragged_tensor_shape_test.py @@ -82,6 +82,7 @@ class RaggedTensorShapeTest(test_util.TensorFlowTestCase, value=ragged.constant_value([[[1, 2], [3]], [[4, 5]]]), expected_dim_sizes=[2, [2, 1], [2, 1, 2]]), ]) + @test_util.run_v1_only('b/120545219') def testFromTensor(self, value, expected_dim_sizes): shape = ragged.RaggedTensorDynamicShape.from_tensor(value) expected = ragged.RaggedTensorDynamicShape.from_dim_sizes( @@ -105,6 +106,7 @@ class RaggedTensorShapeTest(test_util.TensorFlowTestCase, rank=5, expected_dim_sizes=[1, 3, [3, 2, 4], 2, 3]), ]) + @test_util.run_v1_only('b/120545219') def testBroadcastToRank(self, dim_sizes, rank, expected_dim_sizes): shape = ragged.RaggedTensorDynamicShape.from_dim_sizes(dim_sizes) expected = ragged.RaggedTensorDynamicShape.from_dim_sizes( @@ -281,6 +283,7 @@ class RaggedTensorShapeTest(test_util.TensorFlowTestCase, original_dim_sizes=[2, (2, 1), 2, 1], broadcast_dim_sizes=[2, (2, 1), 2, (2, 1, 2, 1, 2, 1)]), ]) # pyformat: disable + @test_util.run_v1_only('b/120545219') def testBroadcastDimension(self, axis, row_length, original_dim_sizes, broadcast_dim_sizes): """Tests for the broadcast_dimension method. @@ -370,6 +373,7 @@ class RaggedTensorShapeTest(test_util.TensorFlowTestCase, y_dims=[1, 1, 2, (2, 1)], expected_dims=[2, (2, 1), 2, (2, 1, 2, 1, 2, 1)]), ]) + @test_util.run_v1_only('b/120545219') def testBroadcastDynamicShape(self, x_dims, y_dims, expected_dims): x_shape = ragged.RaggedTensorDynamicShape.from_dim_sizes(x_dims) y_shape = ragged.RaggedTensorDynamicShape.from_dim_sizes(y_dims) @@ -416,6 +420,7 @@ class RaggedTensorShapeTest(test_util.TensorFlowTestCase, dim_sizes=[3, [3, 0, 2]], expected=ragged.constant_value([[10, 10, 10], [], [10, 10]])), ]) + @test_util.run_v1_only('b/120545219') def testRaggedBroadcastTo(self, x, dim_sizes, expected): shape = ragged.RaggedTensorDynamicShape.from_dim_sizes(dim_sizes) result = ragged.broadcast_to(x, shape) @@ -470,6 +475,7 @@ class RaggedTensorShapeTest(test_util.TensorFlowTestCase, [[[[11, 21], [32]], [[13, 23], [34]]], [[[15, 25], [36]]]])), ]) + @test_util.run_v1_only('b/120545219') def testRaggedAddWithBroadcasting(self, x, y, expected, doc): expected_rrank = getattr(expected, 'ragged_rank', 0) x = ragged.convert_to_tensor_or_ragged_tensor(x, dtype=dtypes.int32) diff --git a/tensorflow/python/ops/ragged/ragged_tensor_test.py b/tensorflow/python/ops/ragged/ragged_tensor_test.py index 608fbd6e5b..e86676f70a 100644 --- a/tensorflow/python/ops/ragged/ragged_tensor_test.py +++ b/tensorflow/python/ops/ragged/ragged_tensor_test.py @@ -861,7 +861,7 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): (SLICE_BUILDER[:, -2:], [row[-2:] for row in EXAMPLE_RAGGED_TENSOR_2D]), # TODO(edloper): Add tests for strided slices, once support is added. ) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testRaggedTensorGetItemWithRaggedRank1(self, slice_spec, expected): """Test that rt.__getitem__(slice_spec) == expected.""" # Ragged tensor @@ -903,7 +903,7 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): (SLICE_BUILDER[..., 0, 0, 0], IndexError, 'Too many indices for RaggedTensor'), ) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testRaggedTensorGetItemErrorsWithRaggedRank1(self, slice_spec, expected, message): """Test that rt.__getitem__(slice_spec) == expected.""" @@ -982,7 +982,7 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): # TODO(edloper): Add tests slicing inner ragged dimensions, one support # is added. ) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testRaggedTensorGetItemWithRaggedRank2(self, slice_spec, expected): """Test that rt.__getitem__(slice_spec) == expected.""" rt = ragged.from_nested_row_splits( @@ -1069,7 +1069,7 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): rt = ragged.from_row_splits(values, [0, 1]) self._TestGetItemException(rt, slice_spec, expected, message) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testGetItemNewAxis(self): # rt: [[[['a', 'b'], ['c', 'd']], [], [['e', 'f']]], []] splits1 = [0, 3, 3] @@ -1155,7 +1155,7 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): # RaggedTensor.with_values() and RaggedTensor.with_inner_values(). #============================================================================= - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testWithValues(self): rt1 = ragged.constant([[1, 2], [3, 4, 5], [6], [], [7]]) rt2 = ragged.constant([[[1, 2], [3, 4, 5]], [[6]], [], [[], [7]]]) @@ -1208,7 +1208,7 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): self.assertEqual(result['rt1'].tolist(), [[1, 2, 3], [4]]) self.assertEqual(result['rt2'].tolist(), [[[], [1, 2]], [[3]]]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testSessionPartialRunFeed(self): # Placeholder inputs. a = ragged.from_row_splits( diff --git a/tensorflow/python/ops/ragged/ragged_to_sparse_op_test.py b/tensorflow/python/ops/ragged/ragged_to_sparse_op_test.py index 69b31ad0e9..9863e3b583 100644 --- a/tensorflow/python/ops/ragged/ragged_to_sparse_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_to_sparse_op_test.py @@ -140,7 +140,7 @@ class RaggedTensorToSparseOpTest(test_util.TensorFlowTestCase): self.assertEqual(st.values.shape.as_list(), [7]) self.assertEqual(st.dense_shape.shape.as_list(), [3]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testKernelErrors(self): # An empty vector, defined using a placeholder to ensure that we can't # determine that it's invalid at graph-construction time. @@ -179,7 +179,7 @@ class RaggedTensorToSparseOpTest(test_util.TensorFlowTestCase): self.assertRaisesRegexp(errors.InvalidArgumentError, empty_splits_error, ragged.to_sparse(bad_rt5).eval) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testGradient(self): # rt1.shape == rt2.shape == [2, (D2), (D3), 2]. rt1 = ragged.constant([[[[1.0, 2.0], [3.0, 4.0]], [[5.0, 6.0]]]], diff --git a/tensorflow/python/ops/ragged/ragged_util_test.py b/tensorflow/python/ops/ragged/ragged_util_test.py index c24ea65353..69c605dbf9 100644 --- a/tensorflow/python/ops/ragged/ragged_util_test.py +++ b/tensorflow/python/ops/ragged/ragged_util_test.py @@ -87,6 +87,7 @@ class RaggedRepeatTest(test_util.TensorFlowTestCase, parameterized.TestCase): # Empty tensor dict(data=[], repeats=[], axis=0, expected=[]), ]) + @test_util.run_v1_only('b/120545219') def testRepeat(self, data, repeats, expected, axis=None): result = ragged_util.repeat(data, repeats, axis) with self.test_session(): @@ -135,6 +136,7 @@ class RaggedRepeatTest(test_util.TensorFlowTestCase, parameterized.TestCase): dict(data=TENSOR_4D, repeats=[1, 3, 0, 0, 2], axis=3), ] ]) + @test_util.run_v1_only('b/120545219') def testValuesMatchesNumpy(self, mode, data, repeats, axis): # Exception: we can't handle negative axis if data.ndims is unknown. if axis < 0 and mode == 'unknown_shape': diff --git a/tensorflow/python/ops/ragged/ragged_where_op_test.py b/tensorflow/python/ops/ragged/ragged_where_op_test.py index de83a54977..6d645eefac 100644 --- a/tensorflow/python/ops/ragged/ragged_where_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_where_op_test.py @@ -165,7 +165,7 @@ class RaggedWhereOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): y=ragged.constant_value([[[['a']]], [[['b']]]]), expected=ragged.constant_value([[[[], [b'A']]], [[[b'b']]]])), ]) # pyformat: disable - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testRaggedWhere(self, condition, expected, x=None, y=None): result = ragged.where(condition, x, y) self.assertEqual( diff --git a/tensorflow/python/profiler/internal/run_metadata_test.py b/tensorflow/python/profiler/internal/run_metadata_test.py index a8859f845b..f96d721f46 100644 --- a/tensorflow/python/profiler/internal/run_metadata_test.py +++ b/tensorflow/python/profiler/internal/run_metadata_test.py @@ -169,7 +169,7 @@ class RunMetadataTest(test.TestCase): ret = _extract_node(run_meta, 'MatMul:MatMul') self.assertEqual(len(ret), 0) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testLoopCPU(self): ops.reset_default_graph() with ops.device('/cpu:0'): diff --git a/tensorflow/python/profiler/model_analyzer_test.py b/tensorflow/python/profiler/model_analyzer_test.py index 8648f0b514..1c7c15be4f 100644 --- a/tensorflow/python/profiler/model_analyzer_test.py +++ b/tensorflow/python/profiler/model_analyzer_test.py @@ -76,6 +76,7 @@ class PrintModelAnalysisTest(test.TestCase): ' ScalarW (1, 1/1 params)\n', lib.CheckAndRemoveDoc(f.read())) + @test_util.run_v1_only('b/120545219') def testSelectEverythingDetail(self): ops.reset_default_graph() dev = '/device:GPU:0' if test.is_gpu_available() else '/device:CPU:0' @@ -203,6 +204,7 @@ class PrintModelAnalysisTest(test.TestCase): lib.CheckAndRemoveDoc(f.read())[0:80]) # pylint: enable=line-too-long + @test_util.run_v1_only('b/120545219') def testComplexCodeView(self): ops.reset_default_graph() outfile = os.path.join(test.get_temp_dir(), 'dump') @@ -619,6 +621,7 @@ class PrintModelAnalysisTest(test.TestCase): else: self.assertEqual(len(gfile.ListDirectory(profile_dir)), 0) + @test_util.run_v1_only('b/120545219') def testAutoProfiling(self): ops.reset_default_graph() time_dir = os.path.join(test.get_temp_dir(), 'time') @@ -706,6 +709,7 @@ class PrintModelAnalysisTest(test.TestCase): exception_str) self.assertTrue(mat is None) + @test_util.run_v1_only('b/120545219') def testTrackPersistentBytes(self): ops.reset_default_graph() a = array_ops.constant(np.ones((100, 100))) diff --git a/tensorflow/python/profiler/pprof_profiler_test.py b/tensorflow/python/profiler/pprof_profiler_test.py index 120a0d0eaa..3f5bd9e79b 100644 --- a/tensorflow/python/profiler/pprof_profiler_test.py +++ b/tensorflow/python/profiler/pprof_profiler_test.py @@ -136,7 +136,7 @@ comment: 9 profile.ParseFromString(profile_contents) self.assertEquals(expected_proto, str(profile)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testProfileWithWhileLoop(self): options = config_pb2.RunOptions() options.trace_level = config_pb2.RunOptions.FULL_TRACE diff --git a/tensorflow/python/saved_model/loader_test.py b/tensorflow/python/saved_model/loader_test.py index 3b7f0b250e..3e27c0801c 100644 --- a/tensorflow/python/saved_model/loader_test.py +++ b/tensorflow/python/saved_model/loader_test.py @@ -94,7 +94,7 @@ class SavedModelLoaderTest(test.TestCase, parameterized.TestCase): super(SavedModelLoaderTest, self).tearDown() shutil.rmtree(test.get_temp_dir(), ignore_errors=True) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def test_load_function(self, builder_cls): self.export_simple_graph(builder_cls) loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL) @@ -110,7 +110,7 @@ class SavedModelLoaderTest(test.TestCase, parameterized.TestCase): self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) self.assertEqual(7, sess.graph.get_tensor_by_name("y:0").eval()) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def test_load_graph(self, builder_cls): self.export_simple_graph(builder_cls) loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL) @@ -130,7 +130,7 @@ class SavedModelLoaderTest(test.TestCase, parameterized.TestCase): with self.assertRaises(errors.FailedPreconditionError): self.evaluate(y) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def test_load_with_import_scope(self, builder_cls): self.export_graph_with_main_op(builder_cls) loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) @@ -179,7 +179,7 @@ class SavedModelLoaderTest(test.TestCase, parameterized.TestCase): loader.restore_variables(sess, tf_saver.Saver()) self.assertEqual(55, self.evaluate(z)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def test_run_init_op(self, builder_cls): self.export_graph_with_main_op(builder_cls) loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) @@ -212,7 +212,7 @@ class SavedModelLoaderTest(test.TestCase, parameterized.TestCase): with self.assertRaises(RuntimeError): loader.get_meta_graph_def_from_tags(["not_a_graph"]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def test_load_saved_model_with_no_variables(self, builder_cls): """Test that SavedModel runs saver when there appear to be no variables. diff --git a/tensorflow/python/saved_model/save_test.py b/tensorflow/python/saved_model/save_test.py index 668a374415..97218a98ea 100644 --- a/tensorflow/python/saved_model/save_test.py +++ b/tensorflow/python/saved_model/save_test.py @@ -286,7 +286,7 @@ class SaveTest(test.TestCase): {"out": model(array_ops.ones([1, 4]))}, _import_and_infer(save_dir, {"x": [[1., 1., 1., 1.]]})) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def test_export_functional_keras_model_after_fit(self): x = input_layer.Input((1,)) y = core.Dense(1, name="y")(x) diff --git a/tensorflow/python/saved_model/saved_model_test.py b/tensorflow/python/saved_model/saved_model_test.py index d4c8d72011..8d94c7c989 100644 --- a/tensorflow/python/saved_model/saved_model_test.py +++ b/tensorflow/python/saved_model/saved_model_test.py @@ -328,7 +328,7 @@ class SavedModelTest(SavedModelTestBase): self.assertRaises(RuntimeError, loader.load, sess, ["foo", "baz"], export_dir) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testVariables(self): export_dir = self._get_export_dir("test_variables") builder = saved_model_builder._SavedModelBuilder(export_dir) @@ -474,7 +474,7 @@ class SavedModelTest(SavedModelTestBase): self.assertEqual( 42, ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)[0].eval()) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testCollections(self): export_dir = self._get_export_dir("test_collections") builder = saved_model_builder._SavedModelBuilder(export_dir) @@ -819,6 +819,7 @@ class SavedModelTest(SavedModelTestBase): self._validate_assets(export_dir, foo_graph.asset_file_def, "hello42.txt", "foo bar baz 0", "asset_file_tensor_0:0") + @test_util.run_v1_only("b/120545219") def testCustomInitOp(self): export_dir = self._get_export_dir("test_main_op") builder = saved_model_builder._SavedModelBuilder(export_dir) @@ -854,7 +855,7 @@ class SavedModelTest(SavedModelTestBase): # the main_op, following a restore. self.assertEqual(3, ops.get_collection("v")[2].eval()) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testTrainOp(self): export_dir = self._get_export_dir("test_train_op") builder = saved_model_builder._SavedModelBuilder(export_dir) @@ -882,7 +883,7 @@ class SavedModelTest(SavedModelTestBase): self.assertIsInstance( loader_impl.get_train_op(meta_graph_def), ops.Tensor) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testTrainOpGroup(self): export_dir = self._get_export_dir("test_train_op_group") builder = saved_model_builder._SavedModelBuilder(export_dir) @@ -910,7 +911,7 @@ class SavedModelTest(SavedModelTestBase): self.assertIsInstance( loader_impl.get_train_op(meta_graph_def), ops.Operation) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testTrainOpAfterVariables(self): export_dir = self._get_export_dir("test_train_op_after_variables") builder = saved_model_builder._SavedModelBuilder(export_dir) @@ -1029,7 +1030,7 @@ class SavedModelTest(SavedModelTestBase): self._validate_assets(export_dir, bar_graph.asset_file_def, "foo.txt", "content_foo", "asset_file_tensor:0") - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testOp(self): export_dir = self._get_export_dir("test_op") builder = saved_model_builder._SavedModelBuilder(export_dir) @@ -1492,7 +1493,7 @@ class SavedModelV1Test(SavedModelTestBase): self.assertIn("T", node_def.attr) self.assertIn("Tout", node_def.attr) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testLegacyInitOp(self): export_dir = self._get_export_dir("test_legacy_init_op") builder = saved_model_builder.SavedModelBuilder(export_dir) diff --git a/tensorflow/python/saved_model/signature_def_utils_test.py b/tensorflow/python/saved_model/signature_def_utils_test.py index 53c452359f..d1347eb017 100644 --- a/tensorflow/python/saved_model/signature_def_utils_test.py +++ b/tensorflow/python/saved_model/signature_def_utils_test.py @@ -423,6 +423,7 @@ class SignatureDefUtilsTest(test.TestCase): {}, signature_constants.PREDICT_METHOD_NAME) + @test_util.run_v1_only("b/120545219") def testOpSignatureDef(self): key = "adding_1_and_2_key" add_op = math_ops.add(1, 2, name="adding_1_and_2") @@ -430,6 +431,7 @@ class SignatureDefUtilsTest(test.TestCase): self.assertIn(key, signature_def.outputs) self.assertEqual(add_op.name, signature_def.outputs[key].name) + @test_util.run_v1_only("b/120545219") def testLoadOpFromSignatureDef(self): key = "adding_1_and_2_key" add_op = math_ops.add(1, 2, name="adding_1_and_2") diff --git a/tensorflow/python/saved_model/utils_test.py b/tensorflow/python/saved_model/utils_test.py index 0888dcb411..2afe8abfd6 100644 --- a/tensorflow/python/saved_model/utils_test.py +++ b/tensorflow/python/saved_model/utils_test.py @@ -24,6 +24,7 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes 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 array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.platform import test @@ -32,6 +33,7 @@ from tensorflow.python.saved_model import utils class UtilsTest(test.TestCase): + @test_util.run_v1_only("b/120545219") def testBuildTensorInfoOp(self): x = constant_op.constant(1, name="x") y = constant_op.constant(2, name="y") @@ -41,6 +43,7 @@ class UtilsTest(test.TestCase): self.assertEqual(types_pb2.DT_INVALID, z_op_info.dtype) self.assertEqual(0, len(z_op_info.tensor_shape.dim)) + @test_util.run_v1_only("b/120545219") def testBuildTensorInfoDefunOp(self): @function.defun def my_init_fn(x, y): @@ -54,6 +57,7 @@ class UtilsTest(test.TestCase): self.assertEqual(types_pb2.DT_INVALID, init_op_info.dtype) self.assertEqual(0, len(init_op_info.tensor_shape.dim)) + @test_util.run_v1_only("b/120545219") def testBuildTensorInfoDense(self): x = array_ops.placeholder(dtypes.float32, 1, name="x") x_tensor_info = utils.build_tensor_info(x) @@ -62,6 +66,7 @@ class UtilsTest(test.TestCase): self.assertEqual(1, len(x_tensor_info.tensor_shape.dim)) self.assertEqual(1, x_tensor_info.tensor_shape.dim[0].size) + @test_util.run_v1_only("b/120545219") def testBuildTensorInfoSparse(self): x = array_ops.sparse_placeholder(dtypes.float32, [42, 69], name="x") x_tensor_info = utils.build_tensor_info(x) @@ -76,6 +81,7 @@ class UtilsTest(test.TestCase): self.assertEqual(42, x_tensor_info.tensor_shape.dim[0].size) self.assertEqual(69, x_tensor_info.tensor_shape.dim[1].size) + @test_util.run_v1_only("b/120545219") def testGetTensorFromInfoDense(self): expected = array_ops.placeholder(dtypes.float32, 1, name="x") tensor_info = utils.build_tensor_info(expected) @@ -83,6 +89,7 @@ class UtilsTest(test.TestCase): self.assertIsInstance(actual, ops.Tensor) self.assertEqual(expected.name, actual.name) + @test_util.run_v1_only("b/120545219") def testGetTensorFromInfoSparse(self): expected = array_ops.sparse_placeholder(dtypes.float32, name="x") tensor_info = utils.build_tensor_info(expected) @@ -122,6 +129,7 @@ class UtilsTest(test.TestCase): import_scope="foo") self.assertEqual(expected.name, actual.name) + @test_util.run_v1_only("b/120545219") def testGetTensorFromInfoRaisesErrors(self): expected = array_ops.placeholder(dtypes.float32, 1, name="x") tensor_info = utils.build_tensor_info(expected) diff --git a/tensorflow/python/tools/freeze_graph_test.py b/tensorflow/python/tools/freeze_graph_test.py index efdf7dd2cf..de2672db3c 100644 --- a/tensorflow/python/tools/freeze_graph_test.py +++ b/tensorflow/python/tools/freeze_graph_test.py @@ -161,11 +161,11 @@ class FreezeGraphTest(test_util.TensorFlowTestCase): },) builder.save(as_text=True) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testFreezeGraphV1(self): self._testFreezeGraph(saver_pb2.SaverDef.V1) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testFreezeGraphV2(self): self._testFreezeGraph(saver_pb2.SaverDef.V2) diff --git a/tensorflow/python/training/adagrad_test.py b/tensorflow/python/training/adagrad_test.py index da26fcdb7f..1e2d29b337 100644 --- a/tensorflow/python/training/adagrad_test.py +++ b/tensorflow/python/training/adagrad_test.py @@ -306,7 +306,7 @@ class AdagradOptimizerTest(test.TestCase): np.array([2.715679168701172, 3.715679168701172]), self.evaluate(var1)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testDynamicShapeVariable_Ok(self): with self.cached_session(): v = variable_scope.get_variable("v", initializer=constant_op.constant(1.), @@ -315,7 +315,7 @@ class AdagradOptimizerTest(test.TestCase): # Creating optimizer should cause no exception. adagrad.AdagradOptimizer(3.0, initial_accumulator_value=0.1) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testDynamicShapeVariableWithCallableInit(self): var0 = variable_scope.get_variable("var0", initializer=constant_op.constant(1.), diff --git a/tensorflow/python/training/basic_session_run_hooks_test.py b/tensorflow/python/training/basic_session_run_hooks_test.py index 08942c5bb6..1af27626ba 100644 --- a/tensorflow/python/training/basic_session_run_hooks_test.py +++ b/tensorflow/python/training/basic_session_run_hooks_test.py @@ -1122,6 +1122,7 @@ class StepCounterHookTest(test.TestCase): self.assertGreater(summary_value.simple_value, 0) +@test_util.run_v1_only('b/120545219') class SummarySaverHookTest(test.TestCase): def setUp(self): @@ -1148,13 +1149,11 @@ class SummarySaverHookTest(test.TestCase): basic_session_run_hooks.SummarySaverHook( scaffold=monitored_session.Scaffold(), summary_op=self.summary_op) - @test_util.run_deprecated_v1 def test_raise_in_both_secs_and_steps(self): with self.assertRaises(ValueError): basic_session_run_hooks.SummarySaverHook( save_secs=10, save_steps=20, summary_writer=self.summary_writer) - @test_util.run_deprecated_v1 def test_raise_in_none_secs_and_steps(self): with self.assertRaises(ValueError): basic_session_run_hooks.SummarySaverHook( @@ -1405,6 +1404,7 @@ class FinalOpsHookTest(test.TestCase): hook.final_ops_values.tolist()) +@test_util.run_v1_only('b/120545219') class ResourceSummarySaverHookTest(test.TestCase): def setUp(self): diff --git a/tensorflow/python/training/checkpoint_ops_test.py b/tensorflow/python/training/checkpoint_ops_test.py index 21ad3df1c8..c481547139 100644 --- a/tensorflow/python/training/checkpoint_ops_test.py +++ b/tensorflow/python/training/checkpoint_ops_test.py @@ -23,6 +23,7 @@ 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.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import partitioned_variables @@ -33,6 +34,7 @@ from tensorflow.python.training import checkpoint_ops from tensorflow.python.training import saver as saver_lib +@test_util.run_v1_only('b/120545219') class LoadAndRemapWrappersTest(test.TestCase): """Tests for the functionality of the Python wrappers.""" diff --git a/tensorflow/python/training/checkpointable/data_structures_test.py b/tensorflow/python/training/checkpointable/data_structures_test.py index 9cefd942ac..bcec6e0100 100644 --- a/tensorflow/python/training/checkpointable/data_structures_test.py +++ b/tensorflow/python/training/checkpointable/data_structures_test.py @@ -73,6 +73,7 @@ class HasList(training.Model): class ListTests(test.TestCase): @test_util.run_in_graph_and_eager_modes + @test_util.run_v1_only("b/120545219") def testTracking(self): model = HasList() output = model(array_ops.ones([32, 2])) @@ -105,6 +106,7 @@ class ListTests(test.TestCase): self.assertIn(v, model.trainable_variables) self.assertNotIn(v, model.non_trainable_variables) + @test_util.run_v1_only("b/120545219") def testUpdatesForwarded(self): with context.graph_mode(): model = HasList() @@ -121,6 +123,7 @@ class ListTests(test.TestCase): self.assertEqual(0, len(model.updates)) @test_util.run_in_graph_and_eager_modes + @test_util.run_v1_only("b/120545219") def testLossesForwarded(self): model = HasList() model_input = array_ops.ones([32, 2]) @@ -295,6 +298,7 @@ class HasMapping(training.Model): class MappingTests(test.TestCase): @test_util.run_in_graph_and_eager_modes + @test_util.run_v1_only("b/120545219") def testTracking(self): model = HasMapping() output = model(array_ops.ones([32, 2])) diff --git a/tensorflow/python/training/checkpointable/util_test.py b/tensorflow/python/training/checkpointable/util_test.py index 78047eda90..3bdab4cb0b 100644 --- a/tensorflow/python/training/checkpointable/util_test.py +++ b/tensorflow/python/training/checkpointable/util_test.py @@ -616,6 +616,7 @@ class CheckpointingTests(test.TestCase): # pylint: disable=cell-var-from-loop @test_util.run_in_graph_and_eager_modes + @test_util.run_v1_only("b/120545219") def testWithDefun(self): num_training_steps = 2 checkpoint_directory = self.get_temp_dir() diff --git a/tensorflow/python/training/input_test.py b/tensorflow/python/training/input_test.py index a3d268a017..d89f5f3bbd 100644 --- a/tensorflow/python/training/input_test.py +++ b/tensorflow/python/training/input_test.py @@ -872,19 +872,19 @@ class BatchTest(test_lib.TestCase): for thread in threads: thread.join() - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testSingleThreadKeepInput(self): self._testKeepInputHelper(1, False) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testSingleThreadKeepInputEnqueueMany(self): self._testKeepInputHelper(1, True) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testMultipleThreadKeepInput(self): self._testKeepInputHelper(5, False) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testMultipleThreadKeepInputEnqueueMany(self): self._testKeepInputHelper(5, True) @@ -1482,19 +1482,19 @@ class BatchJoinTest(test_lib.TestCase): for thread in threads: thread.join() - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testSingleThreadKeepInput(self): self._testKeepInputHelper(1, False) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testSingleThreadKeepInputEnqueueMany(self): self._testKeepInputHelper(1, True) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testMultipleThreadKeepInput(self): self._testKeepInputHelper(5, False) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testMultipleThreadKeepInputEnqueueMany(self): self._testKeepInputHelper(5, True) @@ -1905,19 +1905,19 @@ class ShuffleBatchTest(test_lib.TestCase): for thread in threads: thread.join() - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testSingleThreadKeepInput(self): self._testKeepInputHelper(1, False) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testSingleThreadKeepInputEnqueueMany(self): self._testKeepInputHelper(1, True) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testMultipleThreadKeepInput(self): self._testKeepInputHelper(5, False) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testMultipleThreadKeepInputEnqueueMany(self): self._testKeepInputHelper(5, True) @@ -2309,19 +2309,19 @@ class ShuffleBatchJoinTest(test_lib.TestCase): for thread in threads: thread.join() - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testSingleThreadKeepInput(self): self._testKeepInputHelper(1, False) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testSingleThreadKeepInputEnqueueMany(self): self._testKeepInputHelper(1, True) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testMultipleThreadKeepInput(self): self._testKeepInputHelper(5, False) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testMultipleThreadKeepInputEnqueueMany(self): self._testKeepInputHelper(5, True) diff --git a/tensorflow/python/training/learning_rate_decay_test.py b/tensorflow/python/training/learning_rate_decay_test.py index 9de5bc8168..1029d4cea8 100644 --- a/tensorflow/python/training/learning_rate_decay_test.py +++ b/tensorflow/python/training/learning_rate_decay_test.py @@ -101,7 +101,7 @@ class LRDecayTest(test_util.TensorFlowTestCase): self.assertAllClose(self.evaluate(decayed_lr), 0.001, 1e-6) @test_util.run_in_graph_and_eager_modes - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testPiecewiseConstantEdgeCases(self): x_int = resource_variable_ops.ResourceVariable( 0, dtype=variables.dtypes.int32) diff --git a/tensorflow/python/training/localhost_cluster_performance_test.py b/tensorflow/python/training/localhost_cluster_performance_test.py index 7c097b943d..c4cbc8a55d 100644 --- a/tensorflow/python/training/localhost_cluster_performance_test.py +++ b/tensorflow/python/training/localhost_cluster_performance_test.py @@ -25,6 +25,7 @@ import numpy as np from tensorflow.python.client import session as session_lib from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops +from tensorflow.python.framework import test_util from tensorflow.python.ops import partitioned_variables from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables @@ -34,6 +35,7 @@ from tensorflow.python.training import device_setter class CreateLocalClusterTest(test.TestCase): + @test_util.run_v1_only("b/120545219") def testCreateLocalCluster(self): workers, _ = test.create_local_cluster(num_workers=2, num_ps=2) worker_sessions = [session_lib.Session(w.target) for w in workers] diff --git a/tensorflow/python/training/monitored_session_test.py b/tensorflow/python/training/monitored_session_test.py index 9dbcfa52b7..99ee9ea7e2 100644 --- a/tensorflow/python/training/monitored_session_test.py +++ b/tensorflow/python/training/monitored_session_test.py @@ -541,6 +541,7 @@ class WrappedSessionTest(test.TestCase): self.assertFalse(wrapped_sess1.should_stop()) self.assertTrue(wrapped_sess1.should_stop()) + @test_util.run_deprecated_v1 def test_close_twice(self): with self.cached_session() as sess: wrapped_sess = monitored_session._WrappedSession(sess) diff --git a/tensorflow/python/training/moving_averages_test.py b/tensorflow/python/training/moving_averages_test.py index b15f7377f0..03bcde9c84 100644 --- a/tensorflow/python/training/moving_averages_test.py +++ b/tensorflow/python/training/moving_averages_test.py @@ -219,38 +219,38 @@ class ExponentialMovingAverageTest(test.TestCase): (10.0 + 30.0) * (1 - dk)) / _Scale(dk, 2), dim) self.assertAllClose(expected, self.evaluate(avg2)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testAverageVariablesNoNumUpdates_Scalar(self): with self.cached_session(): ema = moving_averages.ExponentialMovingAverage(0.25) self._CheckDecay(ema, actual_decay=0.25, dim=1) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testAverageVariablesNoNumUpdates_Scalar_Debias(self): with self.cached_session(): ema = moving_averages.ExponentialMovingAverage(0.25, zero_debias=True) self._CheckDecay(ema, actual_decay=0.25, dim=1) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testAverageVariablesNoNumUpdates_Vector(self): with self.cached_session(): ema = moving_averages.ExponentialMovingAverage(0.25) self._CheckDecay(ema, actual_decay=0.25, dim=5) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testAverageVariablesNoNumUpdates_Vector_Debias(self): with self.cached_session(): ema = moving_averages.ExponentialMovingAverage(0.25, zero_debias=True) self._CheckDecay(ema, actual_decay=0.25, dim=5) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testAverageVariablesNumUpdates_Scalar(self): with self.cached_session(): # With num_updates 1, the decay applied is 0.1818 ema = moving_averages.ExponentialMovingAverage(0.25, num_updates=1) self._CheckDecay(ema, actual_decay=0.181818, dim=1) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testAverageVariablesNumUpdates_Scalar_Debias(self): with self.cached_session(): # With num_updates 1, the decay applied is 0.1818 @@ -258,14 +258,14 @@ class ExponentialMovingAverageTest(test.TestCase): 0.25, num_updates=1, zero_debias=True) self._CheckDecay(ema, actual_decay=0.181818, dim=1) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testAverageVariablesNumUpdates_Vector(self): with self.cached_session(): # With num_updates 1, the decay applied is 0.1818 ema = moving_averages.ExponentialMovingAverage(0.25, num_updates=1) self._CheckDecay(ema, actual_decay=0.181818, dim=5) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testAverageVariablesNumUpdates_Vector_Debias(self): with self.cached_session(): # With num_updates 1, the decay applied is 0.1818 @@ -273,7 +273,7 @@ class ExponentialMovingAverageTest(test.TestCase): 0.25, num_updates=1, zero_debias=True) self._CheckDecay(ema, actual_decay=0.181818, dim=5) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testAverageVariablesWithControlDeps(self): with self.cached_session() as sess: v0 = variables.Variable(0, name="v0") @@ -299,7 +299,7 @@ class ExponentialMovingAverageTest(test.TestCase): self.assertEqual([17.5], self.evaluate(v1_avg)) @test_util.run_in_graph_and_eager_modes - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testBasicEager(self): v0 = variables.Variable(1.0) v1 = variables.Variable(2.0) @@ -355,11 +355,11 @@ class ExponentialMovingAverageTest(test.TestCase): self.assertEqual(ema.average(v1).op.name, ema.average_name(v1)) self.assertEqual(ema.average(tensor2).op.name, ema.average_name(tensor2)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testAverageVariablesNames(self): self.averageVariablesNamesHelper(zero_debias=True) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testAverageVariablesNamesNoDebias(self): self.averageVariablesNamesHelper(zero_debias=False) @@ -405,15 +405,15 @@ class ExponentialMovingAverageTest(test.TestCase): self.assertEqual( ema.average(tensor2).op.name, ema.average_name(tensor2)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testAverageVariablesNamesRespectScope(self): self.averageVariablesNamesRespectScopeHelper(zero_debias=True) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testAverageVariablesNamesRespectScopeNoDebias(self): self.averageVariablesNamesRespectScopeHelper(zero_debias=False) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testSubsetAverageVariablesNames(self): with self.cached_session(): v0 = variables.Variable(10.0, name="v0") @@ -442,7 +442,7 @@ class ExponentialMovingAverageTest(test.TestCase): self.assertEqual(ema.average(v1).op.name, ema.average_name(v1)) self.assertEqual(ema.average(tensor2).op.name, ema.average_name(tensor2)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testAverageVariablesDeviceAssignment(self): with ops.device("/job:dev_v0"): v0 = variables.Variable(10.0, name="v0") diff --git a/tensorflow/python/training/quantize_training_test.py b/tensorflow/python/training/quantize_training_test.py index 62e783f200..2352af7e99 100644 --- a/tensorflow/python/training/quantize_training_test.py +++ b/tensorflow/python/training/quantize_training_test.py @@ -53,7 +53,7 @@ class PywrapQuantizeTrainingTest(test.TestCase): # Test that save/restoring works for EMA variables generated in the # quantized training rewrite. - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testQuantizedSaveRestore(self): save_path = os.path.join(self.get_temp_dir(), 'quantized_save_restore') diff --git a/tensorflow/python/training/queue_runner_test.py b/tensorflow/python/training/queue_runner_test.py index 4113cecf55..2f6e924f98 100644 --- a/tensorflow/python/training/queue_runner_test.py +++ b/tensorflow/python/training/queue_runner_test.py @@ -41,7 +41,7 @@ _MockOp = collections.namedtuple("MockOp", ["name"]) class QueueRunnerTest(test.TestCase): - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testBasic(self): with self.cached_session() as sess: # CountUpTo will raise OUT_OF_RANGE when it reaches the count. @@ -62,7 +62,7 @@ class QueueRunnerTest(test.TestCase): # The variable should be 3. self.assertEqual(3, self.evaluate(var)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testTwoOps(self): with self.cached_session() as sess: # CountUpTo will raise OUT_OF_RANGE when it reaches the count. @@ -132,7 +132,7 @@ class QueueRunnerTest(test.TestCase): with self.assertRaisesRegexp(errors_impl.OutOfRangeError, "is closed"): self.evaluate(dequeue1) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testRespectCoordShouldStop(self): with self.cached_session() as sess: # CountUpTo will raise OUT_OF_RANGE when it reaches the count. @@ -221,7 +221,7 @@ class QueueRunnerTest(test.TestCase): new_threads = qr.create_threads(sess, coord=coord) self.assertEqual([], new_threads) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testThreads(self): with self.cached_session() as sess: # CountUpTo will raise OUT_OF_RANGE when it reaches the count. diff --git a/tensorflow/python/training/saver_test.py b/tensorflow/python/training/saver_test.py index 5d621ba4ff..95c21cb815 100644 --- a/tensorflow/python/training/saver_test.py +++ b/tensorflow/python/training/saver_test.py @@ -390,7 +390,7 @@ class SaverTest(test.TestCase): ValueError, "The passed save_path is not a valid checkpoint:"): save.restore(sess, "invalid path") - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testInt64(self): save_path = os.path.join(self.get_temp_dir(), "int64") @@ -466,7 +466,7 @@ class SaverTest(test.TestCase): # Verify non-duplicate names work. saver_module.Saver({"v0": v0, "v2": v2.saveable}) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testBasicsWithListOfVariables(self): save_path = os.path.join(self.get_temp_dir(), "basics_with_list") @@ -667,7 +667,7 @@ class SaverTest(test.TestCase): self.assertAllClose(1.0, one.eval()) self.assertAllClose([2.0, 2.0, 2.0], twos.eval()) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testReshape(self): save_path = os.path.join(self.get_temp_dir(), "variables_reshape") with session.Session("", graph=ops_lib.Graph()) as sess: @@ -726,7 +726,7 @@ class SaverTest(test.TestCase): def testSaveWithGlobalStepWithPadding(self): self.testSaveWithGlobalStep(pad_step_number=True) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testSaveToNonexistingPath(self): file_io.write_string_to_file( os.path.join(self.get_temp_dir(), "actually_a_file"), "") @@ -1607,7 +1607,7 @@ class SaveRestoreWithVariableNameMap(test.TestCase): self.assertEqual(20.0, self.evaluate(v1)) @test_util.run_in_graph_and_eager_modes - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testNonReshapeResourceVariable(self): self._testNonReshape(resource_variable_ops.ResourceVariable) @@ -1622,7 +1622,7 @@ class MetaGraphTest(test.TestCase): gfile.MakeDirs(test_dir) return test_dir - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testAddCollectionDef(self): test_dir = self._get_test_dir("good_collection") filename = os.path.join(test_dir, "metafile") @@ -1772,13 +1772,13 @@ class MetaGraphTest(test.TestCase): v1 = sess.graph.get_tensor_by_name("v1:0") self.assertEqual(11.0, v1.eval()) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testMultiSaverCollection(self): test_dir = self._get_test_dir("saver_collection") self._testMultiSaverCollectionSave(test_dir) self._testMultiSaverCollectionRestore(test_dir) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testClearExtraneousSavers(self): test_dir = self._get_test_dir("clear_extraneous_savers") filename = os.path.join(test_dir, "metafile") @@ -1866,7 +1866,7 @@ class MetaGraphTest(test.TestCase): lambda e: "does not exist"): saver_module.import_meta_graph(filename) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testSliceVariable(self): test_dir = self._get_test_dir("slice_saver") filename = os.path.join(test_dir, "metafile") @@ -2122,7 +2122,7 @@ class MetaGraphTest(test.TestCase): lambda: math_ops.multiply(x, -1.0)))) # pylint: enable=g-long-lambda - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testStrippedOpListDef(self): with self.cached_session(): # Creates a graph. @@ -2988,7 +2988,7 @@ class CheckpointableCompatibilityTests(test.TestCase): # exception" block in Python 3. self.assertNotIn("NewCheckpointReader", cs.exception.message) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testGraphChangedForRestoreErrorRaised(self): checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") @@ -3010,7 +3010,7 @@ class CheckpointableCompatibilityTests(test.TestCase): "a mismatch between the current graph and the graph"): a_saver.restore(sess=sess, save_path=save_path) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testLoadFromObjectBasedGraph(self): checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") diff --git a/tensorflow/python/training/server_lib_same_variables_no_clear_test.py b/tensorflow/python/training/server_lib_same_variables_no_clear_test.py index 1b2d588f44..ff3fab9f37 100644 --- a/tensorflow/python/training/server_lib_same_variables_no_clear_test.py +++ b/tensorflow/python/training/server_lib_same_variables_no_clear_test.py @@ -34,7 +34,7 @@ class SameVariablesNoClearTest(test.TestCase): # TODO(b/34465411): Starting multiple servers with different configurations # in the same test is flaky. Move this test case back into # "server_lib_test.py" when this is no longer the case. - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testSameVariablesNoClear(self): server = server_lib.Server.create_local_server() diff --git a/tensorflow/python/training/server_lib_test.py b/tensorflow/python/training/server_lib_test.py index 323e94c257..92cdc1c4ad 100644 --- a/tensorflow/python/training/server_lib_test.py +++ b/tensorflow/python/training/server_lib_test.py @@ -29,6 +29,7 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors_impl from tensorflow.python.framework import ops +from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import data_flow_ops from tensorflow.python.ops import math_ops @@ -55,6 +56,7 @@ class GrpcServerTest(test.TestCase): self.assertAllEqual([[4]], sess.run(e)) # TODO(mrry): Add `server.stop()` and `server.join()` when these work. + @test_util.run_v1_only("b/120545219") def testMultipleSessions(self): server = self._cached_server @@ -73,6 +75,7 @@ class GrpcServerTest(test.TestCase): # TODO(mrry): Add `server.stop()` and `server.join()` when these work. # Verifies various reset failures. + @test_util.run_v1_only("b/120545219") def testResetFails(self): # Creates variable with container name. with ops.container("test0"): @@ -146,6 +149,7 @@ class GrpcServerTest(test.TestCase): self.assertEqual(0.5, min_val) self.assertEqual(0.5, max_val) + @test_util.run_v1_only("b/120545219") def testCloseCancelsBlockingOperation(self): server = self._cached_server sess = session.Session(server.target, config=self._useRPCConfig()) @@ -207,6 +211,7 @@ class GrpcServerTest(test.TestCase): "local": ["localhost"] }, job_name="local", task_index=0) + @test_util.run_v1_only("b/120545219") def testTimeoutRaisesException(self): server = self._cached_server q = data_flow_ops.FIFOQueue(1, [dtypes.float32]) @@ -241,6 +246,7 @@ class GrpcServerTest(test.TestCase): queue_runner_impl.start_queue_runners(sess) sess.run(var.assign(3.0)) + @test_util.run_v1_only("b/120545219") def testIsolateSessionState(self): server = self._cached_server @@ -296,6 +302,7 @@ class GrpcServerTest(test.TestCase): self.assertAllEqual(37, isolate_sess_0.run(v)) self.assertAllEqual([19, 86], isolate_sess_1.run(v)) + @test_util.run_v1_only("b/120545219") def testShapeChangingIsolateState(self): server = self._cached_server sharing_config = config_pb2.ConfigProto(isolate_session_state=False) diff --git a/tensorflow/python/training/session_manager_test.py b/tensorflow/python/training/session_manager_test.py index 4294ffa851..c9a0c56ffc 100644 --- a/tensorflow/python/training/session_manager_test.py +++ b/tensorflow/python/training/session_manager_test.py @@ -69,7 +69,7 @@ class SessionManagerTest(test.TestCase): "", init_fn=lambda sess: sess.run(v.initializer)) self.assertAllClose([125], sess.run(v)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testPrepareSessionFails(self): checkpoint_dir = os.path.join(self.get_temp_dir(), "prepare_session") checkpoint_dir2 = os.path.join(self.get_temp_dir(), "prepare_session2") @@ -154,7 +154,7 @@ class SessionManagerTest(test.TestCase): sess.graph.get_tensor_by_name("v:0")).eval(session=sess)) self.assertEquals(1, sess.run(v)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testRecoverSession(self): # Create a checkpoint. checkpoint_dir = os.path.join(self.get_temp_dir(), "recover_session") @@ -187,6 +187,7 @@ class SessionManagerTest(test.TestCase): checkpoint_filename_with_path=checkpoint_management.latest_checkpoint( checkpoint_dir)) + @test_util.run_v1_only("b/120545219") def testWaitForSessionReturnsNoneAfterTimeout(self): with ops.Graph().as_default(): variables.VariableV1(1, name="v") @@ -209,7 +210,7 @@ class SessionManagerTest(test.TestCase): variables.global_variables()), local_init_op=None) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testRecoverSessionWithReadyForLocalInitOp(self): # Create a checkpoint. checkpoint_dir = os.path.join(self.get_temp_dir(), @@ -263,7 +264,7 @@ class SessionManagerTest(test.TestCase): self.assertEquals(1, sess.run(v)) self.assertEquals(1, sess.run(w)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testRecoverSessionWithReadyForLocalInitOpFailsToReadyLocal(self): # We use ready_for_local_init_op=tf.report_uninitialized_variables(), # which causes recover_session to not run local_init_op, and to return @@ -320,7 +321,7 @@ class SessionManagerTest(test.TestCase): sess.graph.get_tensor_by_name("w:0")).eval(session=sess)) self.assertEquals(1, sess.run(v)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testRecoverSessionNoChkptStillRunsLocalInitOp(self): # This test checks for backwards compatibility. # In particular, we continue to ensure that recover_session will execute @@ -349,7 +350,7 @@ class SessionManagerTest(test.TestCase): sess.graph.get_tensor_by_name("w:0")).eval(session=sess)) self.assertEquals(1, sess.run(w)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testRecoverSessionFailsStillRunsLocalInitOp(self): # Create a checkpoint. checkpoint_dir = os.path.join( @@ -393,7 +394,7 @@ class SessionManagerTest(test.TestCase): sess.graph.get_tensor_by_name("w:0")).eval(session=sess)) self.assertEquals(1, sess.run(w)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testWaitForSessionLocalInit(self): server = server_lib.Server.create_local_server() with ops.Graph().as_default() as graph: @@ -445,7 +446,7 @@ class SessionManagerTest(test.TestCase): # because of overly restrictive ready_for_local_init_op sm.wait_for_session("", max_wait_secs=3) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testWaitForSessionInsufficientReadyForLocalInitCheck(self): with ops.Graph().as_default() as graph: v = variables.VariableV1(1, name="v") @@ -463,7 +464,7 @@ class SessionManagerTest(test.TestCase): "Session was not ready after waiting.*"): sm.wait_for_session("", max_wait_secs=3) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testPrepareSessionWithReadyForLocalInitOp(self): with ops.Graph().as_default(): v = variables.VariableV1(1, name="v") @@ -503,7 +504,7 @@ class SessionManagerTest(test.TestCase): self.assertEquals(1, sess.run(w)) self.assertEquals(3, sess.run(x)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testPrepareSessionWithPartialInitOp(self): with ops.Graph().as_default(): v = variables.VariableV1(1, name="v") @@ -570,7 +571,7 @@ class SessionManagerTest(test.TestCase): self.assertEquals(1, sess.run(w_res)) self.assertEquals(3, sess.run(x_res)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testPrepareSessionWithCyclicInitializer(self): # Regression test. Previously Variable._build_initializer_expr would enter # into an infinite recursion when the variable's initial_value involved @@ -644,7 +645,7 @@ class SessionManagerTest(test.TestCase): "Init operations did not make model ready for local_init"): sm2.prepare_session("", init_op=None) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testPrepareSessionWithInsufficientReadyForLocalInitCheck(self): with ops.Graph().as_default(): v = variables.VariableV1(1, name="v") @@ -697,7 +698,7 @@ class ObsoleteSessionManagerTest(test.TestCase): "", init_fn=lambda sess: sess.run(v.initializer)) self.assertAllClose([125], sess.run(v)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testPrepareSessionFails(self): checkpoint_dir = os.path.join(self.get_temp_dir(), "prepare_session") checkpoint_dir2 = os.path.join(self.get_temp_dir(), "prepare_session2") @@ -759,7 +760,7 @@ class ObsoleteSessionManagerTest(test.TestCase): variables.is_variable_initialized( sess.graph.get_tensor_by_name("v:0")).eval(session=sess)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testRecoverSession(self): # Create a checkpoint. checkpoint_dir = os.path.join(self.get_temp_dir(), "recover_session") @@ -798,6 +799,7 @@ class ObsoleteSessionManagerTest(test.TestCase): sess.graph.get_tensor_by_name("v:0")).eval(session=sess)) self.assertEquals(1, sess.run(v)) + @test_util.run_v1_only("b/120545219") def testWaitForSessionReturnsNoneAfterTimeout(self): with ops.Graph().as_default(): variables.VariableV1(1, name="v") diff --git a/tensorflow/python/training/slot_creator_test.py b/tensorflow/python/training/slot_creator_test.py index 1f26aaa434..f1f0d58a69 100644 --- a/tensorflow/python/training/slot_creator_test.py +++ b/tensorflow/python/training/slot_creator_test.py @@ -32,7 +32,7 @@ from tensorflow.python.training import slot_creator class SlotCreatorTest(test.TestCase): - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testCreateSlotFromVariable(self): with self.cached_session(): v = variables.Variable([1.0, 2.5], name="var") @@ -73,7 +73,7 @@ class SlotCreatorTest(test.TestCase): self.assertEqual(dtypes.float64, slot.dtype.base_dtype) self.assertAllEqual([0.0, 0.0], self.evaluate(slot)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testCreateZerosSlotFromDynamicShapedVariable(self): with self.cached_session(): dyn_shape = constant_op.constant([2], dtype=dtypes.int32) @@ -125,7 +125,7 @@ class SlotCreatorTest(test.TestCase): self.assertEqual(dtypes.float64, slot.dtype.base_dtype) self.assertAllEqual([0.0, 0.0], self.evaluate(slot)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testCreateSlotFromVariableRespectsScope(self): # See discussion on #2740. with self.cached_session(): diff --git a/tensorflow/python/training/supervisor_test.py b/tensorflow/python/training/supervisor_test.py index f6505acc9a..180ddb5287 100644 --- a/tensorflow/python/training/supervisor_test.py +++ b/tensorflow/python/training/supervisor_test.py @@ -421,7 +421,7 @@ class SupervisorTest(test.TestCase): with self.assertRaisesRegexp(RuntimeError, "requires a summary writer"): sv.summary_computed(sess, sess.run(summ)) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testLogdirButExplicitlyNoSummaryWriter(self): logdir = self._test_dir("explicit_no_summary_writer") with ops.Graph().as_default(): @@ -507,7 +507,7 @@ class SupervisorTest(test.TestCase): sv = supervisor.Supervisor(logdir="", session_manager=sm) sv.prepare_or_wait_for_session("") - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testInitOp(self): logdir = self._test_dir("default_init_op") with ops.Graph().as_default(): @@ -517,7 +517,7 @@ class SupervisorTest(test.TestCase): self.assertAllClose([1.0, 2.0, 3.0], sess.run(v)) sv.stop() - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testInitFn(self): logdir = self._test_dir("default_init_op") with ops.Graph().as_default(): @@ -531,7 +531,7 @@ class SupervisorTest(test.TestCase): self.assertAllClose([1.0, 2.0, 3.0], sess.run(v)) sv.stop() - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testInitOpWithFeedDict(self): logdir = self._test_dir("feed_dict_init_op") with ops.Graph().as_default(): @@ -545,7 +545,7 @@ class SupervisorTest(test.TestCase): self.assertAllClose([1.0, 2.0, 3.0], sess.run(v)) sv.stop() - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testReadyForLocalInitOp(self): server = server_lib.Server.create_local_server() logdir = self._test_dir("default_ready_for_local_init_op") @@ -588,7 +588,7 @@ class SupervisorTest(test.TestCase): sv0.stop() sv1.stop() - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testReadyForLocalInitOpRestoreFromCheckpoint(self): server = server_lib.Server.create_local_server() logdir = self._test_dir("ready_for_local_init_op_restore") @@ -720,7 +720,7 @@ class SupervisorTest(test.TestCase): "Variables not initialized: w"): sv.prepare_or_wait_for_session(server.target) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testSetupFail(self): logdir = self._test_dir("setup_fail") with ops.Graph().as_default(): @@ -731,7 +731,7 @@ class SupervisorTest(test.TestCase): variables.VariableV1([1.0, 2.0, 3.0], name="v") supervisor.Supervisor(logdir=logdir, is_chief=False) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testDefaultGlobalStep(self): logdir = self._test_dir("default_global_step") with ops.Graph().as_default(): @@ -741,7 +741,7 @@ class SupervisorTest(test.TestCase): self.assertEquals(287, sess.run(sv.global_step)) sv.stop() - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testRestoreFromMetaGraph(self): logdir = self._test_dir("restore_from_meta_graph") with ops.Graph().as_default(): @@ -763,7 +763,7 @@ class SupervisorTest(test.TestCase): # This test is based on the fact that the standard services start # right away and get to run once before sv.stop() returns. # We still sleep a bit to make the test robust. - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testStandardServicesWithoutGlobalStep(self): logdir = self._test_dir("standard_services_without_global_step") # Create a checkpoint. @@ -814,7 +814,7 @@ class SupervisorTest(test.TestCase): # Same as testStandardServicesNoGlobalStep but with a global step. # We should get a summary about the step time. - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testStandardServicesWithGlobalStep(self): logdir = self._test_dir("standard_services_with_global_step") # Create a checkpoint. diff --git a/tensorflow/python/training/sync_replicas_optimizer_test.py b/tensorflow/python/training/sync_replicas_optimizer_test.py index 1ef8756ef6..428583d048 100644 --- a/tensorflow/python/training/sync_replicas_optimizer_test.py +++ b/tensorflow/python/training/sync_replicas_optimizer_test.py @@ -22,6 +22,7 @@ import time from tensorflow.python.framework import constant_op from tensorflow.python.framework import ops +from tensorflow.python.framework import test_util from tensorflow.python.framework.test_util import create_local_cluster from tensorflow.python.ops import variables from tensorflow.python.platform import test @@ -88,6 +89,7 @@ class SyncReplicasOptimizerTest(test.TestCase): def _run(self, train_op, sess): sess.run(train_op) + @test_util.run_v1_only("b/120545219") def test2Workers(self): num_workers = 2 replicas_to_aggregate = 2 @@ -178,6 +180,7 @@ class SyncReplicasOptimizerTest(test.TestCase): sessions[1].run(var_1_g_1)) # 3 workers and one of them is backup. + @test_util.run_v1_only("b/120545219") def test3Workers1Backup(self): num_workers = 3 replicas_to_aggregate = 2 @@ -266,6 +269,7 @@ class SyncReplicasOptimizerHookTest(test.TestCase): "apply_gradient should be called"): hook.begin() + @test_util.run_v1_only("b/120545219") def testCanCreatedBeforeMinimizeCalled(self): """This behavior is required to be integrated with Estimators.""" opt = training.SyncReplicasOptimizer( @@ -278,6 +282,7 @@ class SyncReplicasOptimizerHookTest(test.TestCase): opt.minimize(v, global_step=global_step) hook.begin() + @test_util.run_v1_only("b/120545219") def testFetchVariableList(self): opt = training.SyncReplicasOptimizer( opt=adam.AdamOptimizer(0.01), diff --git a/tensorflow/python/training/tensorboard_logging_test.py b/tensorflow/python/training/tensorboard_logging_test.py index 5af6a0aa7b..5088ab07e5 100644 --- a/tensorflow/python/training/tensorboard_logging_test.py +++ b/tensorflow/python/training/tensorboard_logging_test.py @@ -25,6 +25,7 @@ import tempfile import time from tensorflow.core.util import event_pb2 +from tensorflow.python.framework import test_util from tensorflow.python.platform import test from tensorflow.python.platform import tf_logging as logging from tensorflow.python.summary import summary_iterator @@ -32,6 +33,7 @@ from tensorflow.python.summary.writer import writer from tensorflow.python.training import tensorboard_logging +@test_util.run_v1_only("b/120545219") class EventLoggingTest(test.TestCase): def setUp(self): @@ -85,6 +87,7 @@ class EventLoggingTest(test.TestCase): (event_pb2.LogMessage.ERROR, "format")]) self.assertEqual(2, self.logged_message_count) + @test_util.run_v1_only("b/120545219") def testVerbosity(self): tensorboard_logging.set_summary_writer(self._sw) tensorboard_logging.set_verbosity(tensorboard_logging.ERROR) @@ -112,6 +115,7 @@ class EventLoggingTest(test.TestCase): tensorboard_logging.warn("this should work") self.assertEqual(1, self.logged_message_count) + @test_util.run_v1_only("b/120545219") def testSummaryWriterFailsAfterClear(self): tensorboard_logging._clear_summary_writer() with self.assertRaises(RuntimeError): diff --git a/tensorflow/python/training/training_ops_test.py b/tensorflow/python/training/training_ops_test.py index 51f49ca081..ba0f40999b 100644 --- a/tensorflow/python/training/training_ops_test.py +++ b/tensorflow/python/training/training_ops_test.py @@ -60,7 +60,7 @@ class TrainingOpsTest(TensorFlowTestCase): self.assertShapeEqual(out, apply_sgd) self.assertAllCloseAccordingToType(x - alpha * delta, out) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testApplyGradientDescent(self): for (dtype, use_gpu) in itertools.product( [np.float16, np.float32, np.float64], [False, True]): @@ -129,7 +129,7 @@ class TrainingOpsTest(TensorFlowTestCase): self.assertAllClose(linear_update, self.evaluate(linear)) self.assertAllClose(expected_out, out) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testApplyAdagrad(self): for (dtype, use_gpu) in itertools.product( [np.float16, np.float32, np.float64], [False, True]): @@ -139,7 +139,7 @@ class TrainingOpsTest(TensorFlowTestCase): grad = np.arange(100).astype(dtype) self._testTypesForAdagrad(x, y, lr, grad, use_gpu) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testApplyFtrl(self): for dtype in [np.float16, np.float32, np.float64]: x = np.arange(100).astype(dtype) @@ -211,7 +211,7 @@ class TrainingOpsTest(TensorFlowTestCase): self.assertAllCloseAccordingToType(y[index] + grad[i] * grad[i], self.evaluate(accum)[index]) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testSparseApplyAdagrad(self): for (dtype, index_type) in itertools.product( [np.float16, np.float32, np.float64], [np.int32, np.int64]): @@ -225,7 +225,7 @@ class TrainingOpsTest(TensorFlowTestCase): indices = np.array([0, 2]).astype(index_type) self._testTypesForSparseAdagrad(x, y, lr, grad, indices) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testSparseApplyAdagradDim1(self): for (dtype, index_type) in itertools.product( [np.float16, np.float32, np.float64], [np.int32, np.int64]): @@ -239,7 +239,7 @@ class TrainingOpsTest(TensorFlowTestCase): indices = np.array([0, 2]).astype(index_type) self._testTypesForSparseAdagrad(x, y, lr, grad, indices) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testSparseApplyFtrlDim1(self): for (dtype, index_type) in itertools.product( [np.float16, np.float32, np.float64], [np.int32, np.int64]): @@ -255,7 +255,7 @@ class TrainingOpsTest(TensorFlowTestCase): indices = np.array([0, 2]).astype(index_type) self._testTypesForSparseFtrl(x, y, z, lr, grad, indices) - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testApplyAdam(self): for dtype, use_gpu in itertools.product( [np.float16, np.float32, np.float64], [False, True]): diff --git a/tensorflow/python/training/training_util_test.py b/tensorflow/python/training/training_util_test.py index 3317008fce..3f9858a33b 100644 --- a/tensorflow/python/training/training_util_test.py +++ b/tensorflow/python/training/training_util_test.py @@ -27,6 +27,7 @@ from tensorflow.python.training import monitored_session from tensorflow.python.training import training_util +@test_util.run_v1_only('b/120545219') class GlobalStepTest(test.TestCase): def _assert_global_step(self, global_step, expected_dtype=dtypes.int64): @@ -47,7 +48,6 @@ class GlobalStepTest(test.TestCase): self.assertRaisesRegexp(TypeError, 'does not have integer type', training_util.get_global_step, g) - @test_util.run_deprecated_v1 def test_invalid_shape(self): with ops.Graph().as_default() as g: self.assertIsNone(training_util.get_global_step()) @@ -72,7 +72,6 @@ class GlobalStepTest(test.TestCase): training_util.create_global_step, g) self._assert_global_step(training_util.create_global_step(ops.Graph())) - @test_util.run_deprecated_v1 def test_get_global_step(self): with ops.Graph().as_default() as g: self.assertIsNone(training_util.get_global_step()) @@ -93,6 +92,7 @@ class GlobalStepTest(test.TestCase): self._assert_global_step(training_util.get_or_create_global_step(g)) +@test_util.run_v1_only('b/120545219') class GlobalStepReadTest(test.TestCase): def test_global_step_read_is_none_if_there_is_no_global_step(self): diff --git a/tensorflow/tools/api/tests/api_compatibility_test.py b/tensorflow/tools/api/tests/api_compatibility_test.py index 40162daf14..723fceef41 100644 --- a/tensorflow/tools/api/tests/api_compatibility_test.py +++ b/tensorflow/tools/api/tests/api_compatibility_test.py @@ -314,7 +314,7 @@ class ApiCompatibilityTest(test.TestCase): update_goldens=FLAGS.update_goldens, api_version=api_version) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testAPIBackwardsCompatibility(self): api_version = 1 golden_file_pattern = os.path.join( @@ -333,7 +333,7 @@ class ApiCompatibilityTest(test.TestCase): 'tensorflow.python.util.lazy_loader.LazyLoader' in str(type(tf.contrib))) - @test_util.run_deprecated_v1 + @test_util.run_v1_only('b/120545219') def testAPIBackwardsCompatibilityV1(self): api_version = 1 golden_file_pattern = os.path.join( diff --git a/tensorflow/tools/compatibility/testdata/test_file_v0_11.py b/tensorflow/tools/compatibility/testdata/test_file_v0_11.py index 68ba7a2630..917236da4b 100644 --- a/tensorflow/tools/compatibility/testdata/test_file_v0_11.py +++ b/tensorflow/tools/compatibility/testdata/test_file_v0_11.py @@ -34,6 +34,7 @@ class TestUpgrade(test_util.TensorFlowTestCase): a unit test if the converter is successful. """ + @test_util.run_v1_only("b/120545219") def testArgRenames(self): with self.cached_session(): @@ -97,6 +98,7 @@ class TestUpgrade(test_util.TensorFlowTestCase): tf.expand_dims([[1, 2], [3, 4]], axis=1).eval(), [[[1, 2]], [[3, 4]]]) + @test_util.run_v1_only("b/120545219") def testArgMinMax(self): with self.cached_session(): self.assertAllEqual( @@ -112,6 +114,7 @@ class TestUpgrade(test_util.TensorFlowTestCase): tf.argmax([[1, 2, 3], [4, 1, 0]], dimension=0).eval(), [1, 0, 0]) + @test_util.run_v1_only("b/120545219") def testExpandAndSqueeze(self): with self.cached_session(): @@ -139,6 +142,7 @@ class TestUpgrade(test_util.TensorFlowTestCase): [[1, 2, 3]], dim=1), squeeze_dims=[1]).eval(), a) + @test_util.run_v1_only("b/120545219") def testArithmeticRenames(self): with self.cached_session() as s: stuff = tf.split(1, 2, [[1, 2, 3, 4], [4, 5, 6, 7]]) @@ -163,6 +167,7 @@ class TestUpgrade(test_util.TensorFlowTestCase): # # TODO(aselle): (tf.batch_*) # ] + @test_util.run_v1_only("b/120545219") def testBatchAndSvd(self): with self.cached_session(): mat = [[1., 2.], [2., 3.]] @@ -174,6 +179,7 @@ class TestUpgrade(test_util.TensorFlowTestCase): tf.svd(mat, False, True).eval(), tf.svd(mat, compute_uv=False, full_matrices=True).eval()) + @test_util.run_v1_only("b/120545219") def testCrossEntropy(self): # TODO(aselle): Test sparse_softmax_... with self.cached_session(): @@ -190,6 +196,7 @@ class TestUpgrade(test_util.TensorFlowTestCase): tf.nn.sigmoid_cross_entropy_with_logits( labels=labels, logits=logits).eval()) + @test_util.run_v1_only("b/120545219") def testVariables(self): with self.cached_session() as s: @@ -200,6 +207,7 @@ class TestUpgrade(test_util.TensorFlowTestCase): _ = [v.name for v in tf.all_variables()] _ = [v.name for v in tf.local_variables()] + @test_util.run_v1_only("b/120545219") def testSummaries(self): with self.cached_session() as s: var = tf.Variable([1, 2, 3], dtype=tf.float32) diff --git a/tensorflow/tools/compatibility/testdata/test_file_v1_12.py b/tensorflow/tools/compatibility/testdata/test_file_v1_12.py index fd688781b0..5ce4dd49ad 100644 --- a/tensorflow/tools/compatibility/testdata/test_file_v1_12.py +++ b/tensorflow/tools/compatibility/testdata/test_file_v1_12.py @@ -28,11 +28,13 @@ class TestUpgrade(test_util.TensorFlowTestCase): def setUp(self): tf.enable_eager_execution() + @test_util.run_v1_only("b/120545219") def testRenames(self): with self.cached_session(): self.assertAllClose(1.04719755, tf.acos(0.5)) self.assertAllClose(0.5, tf.rsqrt(4.0)) + @test_util.run_v1_only("b/120545219") def testSerializeSparseTensor(self): sp_input = tf.SparseTensor( indices=tf.constant([[1]], dtype=tf.int64), @@ -44,6 +46,7 @@ class TestUpgrade(test_util.TensorFlowTestCase): self.assertEqual((3,), serialized_sp.shape) self.assertTrue(serialized_sp[0].numpy()) # check non-empty + @test_util.run_v1_only("b/120545219") def testSerializeManySparse(self): sp_input = tf.SparseTensor( indices=tf.constant([[0, 1]], dtype=tf.int64), @@ -55,6 +58,7 @@ class TestUpgrade(test_util.TensorFlowTestCase): sp_input, 'serialize_name', tf.string) self.assertEqual((1, 3), serialized_sp.shape) + @test_util.run_v1_only("b/120545219") def testArgMaxMin(self): self.assertAllClose( [1], -- GitLab From 323f3eb4c2f15c713e992881ea8854069f2433f4 Mon Sep 17 00:00:00 2001 From: Jiri Simsa Date: Fri, 7 Dec 2018 17:41:38 -0800 Subject: [PATCH 256/873] [tf.data] Replace internal mechanism for copying batch elements in MapAndBatchDataset as the current mechanism could result in a segfault if the elements to be batched are DT_VARIANT. PiperOrigin-RevId: 224605099 --- .../data/experimental/map_and_batch_dataset_op.cc | 4 ++-- .../map_and_batch_dataset_serialization_test.py | 14 ++++++++++++++ 2 files changed, 16 insertions(+), 2 deletions(-) diff --git a/tensorflow/core/kernels/data/experimental/map_and_batch_dataset_op.cc b/tensorflow/core/kernels/data/experimental/map_and_batch_dataset_op.cc index d3ae845cfb..d86c3a1a63 100644 --- a/tensorflow/core/kernels/data/experimental/map_and_batch_dataset_op.cc +++ b/tensorflow/core/kernels/data/experimental/map_and_batch_dataset_op.cc @@ -455,8 +455,8 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel { // TODO(mrry): Add a version of DoParallelConcat that allows us // to move `tensor` where possible, to speed up string tensor // batching. - Status copy_status = ::tensorflow::functor::DoParallelConcat( - *dataset()->device_, tensor, offset, batch); + Status copy_status = + batch_util::CopyElementToSlice(tensor, batch, offset); if (!copy_status.ok()) { result->UpdateStatus(copy_status, offset); break; diff --git a/tensorflow/python/data/experimental/kernel_tests/serialization/map_and_batch_dataset_serialization_test.py b/tensorflow/python/data/experimental/kernel_tests/serialization/map_and_batch_dataset_serialization_test.py index 166ffa99ca..8bfe6ce2f3 100644 --- a/tensorflow/python/data/experimental/kernel_tests/serialization/map_and_batch_dataset_serialization_test.py +++ b/tensorflow/python/data/experimental/kernel_tests/serialization/map_and_batch_dataset_serialization_test.py @@ -22,6 +22,7 @@ import math from tensorflow.python.data.experimental.kernel_tests.serialization import dataset_serialization_test_base from tensorflow.python.data.experimental.ops import batching from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops import math_ops from tensorflow.python.platform import test @@ -83,6 +84,19 @@ class MapAndBatchDatasetSerializationTest( self.run_core_tests(lambda: build_ds(10, True), lambda: build_ds(15, True), num_outputs_drop_remainder) + def testSparse(self): + + def build_dataset(): + + def map_fn(i): + return sparse_tensor.SparseTensorValue( + indices=[[0]], values=(i * [1]), dense_shape=[1]) + + return dataset_ops.Dataset.range(10).apply( + batching.map_and_batch(map_fn, 5)) + + self.run_core_tests(build_dataset, None, 2) + if __name__ == "__main__": test.main() -- GitLab From b03cb954ac1e29b4eb8242173902cebda5c95230 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Fri, 7 Dec 2018 17:52:03 -0800 Subject: [PATCH 257/873] Fix test failure on Ubuntu cc Signed-off-by: Yong Tang --- tensorflow/contrib/tensorrt/resources/trt_allocator_test.cc | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/tensorrt/resources/trt_allocator_test.cc b/tensorflow/contrib/tensorrt/resources/trt_allocator_test.cc index 18b983b9ed..55186d5992 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_allocator_test.cc +++ b/tensorflow/contrib/tensorrt/resources/trt_allocator_test.cc @@ -48,11 +48,11 @@ TEST(TRTAllocatorTest, Align) { 513ul, 700ul, 12345ul, 1ul << 32}) { for (uint64_t alignment = 1; alignment <= space * 4; alignment *= 2) { for (const uintptr_t ptr_val : - {1ull, alignment == 1 ? 1ull : alignment - 1, alignment, alignment + 1, + {static_cast(1), alignment == 1 ? static_cast(1) : alignment - 1, alignment, alignment + 1, alignment + (alignment / 2)}) { if (ptr_val % alignment == 0) { for (const uint64_t size : - {1ull, space == 1 ? 1ull : space - 1, space, space + 1}) { + {static_cast(1), space == 1 ? static_cast(1) : space - 1, space, space + 1}) { EXPECT_EQ(space >= size, RunTest(alignment, size, ptr_val, space)); } } else { @@ -62,7 +62,7 @@ TEST(TRTAllocatorTest, Align) { EXPECT_TRUE( RunTest(alignment, space - diff, ptr_val + diff, space - diff)); for (const uint64_t size : - {1ull, space - diff > 1 ? space - diff - 1 : 1ull, space - diff, + {static_cast(1), space - diff > 1 ? space - diff - 1 : static_cast(1), space - diff, space - diff + 1, space - 1}) { EXPECT_EQ(space - diff >= size, RunTest(alignment, size, ptr_val, space)); -- GitLab From b05c71ac5e93b95f0265ed43102aa27f74f5ec26 Mon Sep 17 00:00:00 2001 From: Dan Moldovan Date: Fri, 7 Dec 2018 17:59:49 -0800 Subject: [PATCH 258/873] Use the new Dataset.reduce in `for` loops. PiperOrigin-RevId: 224606535 --- .../autograph/operators/control_flow.py | 45 +++++-------------- 1 file changed, 10 insertions(+), 35 deletions(-) diff --git a/tensorflow/python/autograph/operators/control_flow.py b/tensorflow/python/autograph/operators/control_flow.py index 670897744a..89f7b8522f 100644 --- a/tensorflow/python/autograph/operators/control_flow.py +++ b/tensorflow/python/autograph/operators/control_flow.py @@ -20,7 +20,6 @@ from __future__ import print_function from tensorflow.python.autograph.operators import py_builtins from tensorflow.python.data.ops import dataset_ops -from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import gen_math_ops @@ -100,6 +99,7 @@ def _known_len_for_stmt(iter_, extra_test, body, init_state): extra_deps=(iter_,), opts=dict(maximum_iterations=n)) # Dropping the iteration index because it's not syntactically visible. + # TODO(mdan): Don't. results = results[1:] # TODO(mdan): Remove this special case. @@ -110,40 +110,15 @@ def _known_len_for_stmt(iter_, extra_test, body, init_state): def _dataset_for_stmt(ds, extra_test, body, init_state): """Overload of for_stmt that iterates over TF Datasets.""" - # Because Datsets only expose get_next, in the style of Python iterators, - # we are forced to unpack the loop as: - # - # epoch_number, iterate = ds.get_next() - # while epoch_number < 2: - # - # epoch_number, iterate = ds.get_next() - epoch_numbers = dataset_ops.Dataset.range(2) - def tag_with(ds, tag): - return dataset_ops.Dataset.zip( - (dataset_ops.Dataset.from_tensors(tag).repeat(), ds)) - ds_with_epoch = epoch_numbers.flat_map(lambda i: tag_with(ds, i)) - - iterator = dataset_ops.make_initializable_iterator(ds_with_epoch) - with ops.control_dependencies((iterator.initializer,)): - epoch_number, iterate = iterator.get_next() - - def while_body(epoch_number, iterate, *state): - new_state = body(iterate, *state) - epoch_number, iterate = iterator.get_next() - return (epoch_number, iterate) + new_state - - def while_cond(epoch_number, iterate, *state): - del iterate - return gen_math_ops.logical_and(epoch_number < 1, extra_test(*state)) - - results = while_stmt( - while_cond, - while_body, - init_state=(epoch_number, iterate) + init_state, - extra_deps=()) - # Dropping the epoch number and iterate because they are not syntactically - # visible. - results = results[2:] + if extra_test(*init_state) is not True: + raise NotImplementedError( + 'break statements are not yet supported in for/Dataset loops') + + def reduce_body(state, iterate): + new_state = body(iterate, *state) + return new_state + + results = ds.reduce(init_state, reduce_body) # TODO(mdan): Remove this special case. if len(results) == 1: -- GitLab From 4c27508f8185c3d92f35c665aa02411623a7b165 Mon Sep 17 00:00:00 2001 From: Derek Murray Date: Fri, 7 Dec 2018 18:00:43 -0800 Subject: [PATCH 259/873] [tf.data] Add support for unbatching to the `Structure` class. PiperOrigin-RevId: 224606610 --- .../python/data/experimental/ops/batching.py | 13 ++-- tensorflow/python/data/ops/dataset_ops.py | 73 +++++++------------ tensorflow/python/data/ops/optional_ops.py | 8 ++ tensorflow/python/data/util/BUILD | 1 + tensorflow/python/data/util/structure.py | 66 +++++++++++++++++ tensorflow/python/data/util/structure_test.py | 72 +++++++++++++++++- 6 files changed, 176 insertions(+), 57 deletions(-) diff --git a/tensorflow/python/data/experimental/ops/batching.py b/tensorflow/python/data/experimental/ops/batching.py index f70a4e0537..29df98f4ea 100644 --- a/tensorflow/python/data/experimental/ops/batching.py +++ b/tensorflow/python/data/experimental/ops/batching.py @@ -24,7 +24,6 @@ from tensorflow.python.data.experimental.ops import grouping 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.data.util import structure from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes @@ -406,21 +405,19 @@ 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) - # NOTE(mrry): We must ensure that any SparseTensors in `dataset` # are normalized to the rank-1 dense representation, so that the # sparse-oblivious unbatching logic will slice them # appropriately. This leads to a somewhat inefficient re-encoding step # for all SparseTensor components. - # TODO(mrry): Consider optimizing this in future - # if it turns out to be a bottleneck. + # TODO(mrry): Consider optimizing this in future if it turns out to be + # a bottleneck. def normalize(arg, *rest): + # pylint: disable=protected-access if rest: - return sparse.serialize_many_sparse_tensors((arg,) + rest) + return dataset._element_structure._to_batched_tensor_list((arg,) + rest) else: - return sparse.serialize_many_sparse_tensors(arg) + return dataset._element_structure._to_batched_tensor_list(arg) normalized_dataset = dataset.map(normalize) diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py index e08a3af852..bee04aaef2 100644 --- a/tensorflow/python/data/ops/dataset_ops.py +++ b/tensorflow/python/data/ops/dataset_ops.py @@ -1800,21 +1800,12 @@ class TensorDataset(DatasetSource): t, name="component_%d" % i) for i, t in enumerate(nest.flatten(tensors)) ]) - - self._tensors = sparse.serialize_sparse_tensors(tensors) - output_classes = sparse.get_classes(tensors) - output_shapes = nest.pack_sequence_as( - tensors, [t.get_shape() for t in nest.flatten(tensors)]) - output_types = nest.pack_sequence_as( - tensors, [t.dtype for t in nest.flatten(tensors)]) - self._structure = structure_lib.convert_legacy_structure( - output_types, output_shapes, output_classes) + self._structure = structure_lib.Structure.from_value(tensors) + self._tensors = self._structure._to_tensor_list(tensors) # pylint: disable=protected-access def _as_variant_tensor(self): - # pylint: disable=protected-access return gen_dataset_ops.tensor_dataset( - nest.flatten(self._tensors), - output_shapes=self._structure._flat_shapes) + self._tensors, output_shapes=self._structure._flat_shapes) # pylint: disable=protected-access @property def _element_structure(self): @@ -1834,27 +1825,22 @@ class TensorSliceDataset(DatasetSource): t, name="component_%d" % i) for i, t in enumerate(nest.flatten(tensors)) ]) - flat_tensors = nest.flatten(tensors) + + batched_structure = structure_lib.Structure.from_value(tensors) + # pylint: disable=protected-access + self._tensors = batched_structure._to_batched_tensor_list(tensors) + self._structure = batched_structure._unbatch() + # pylint: enable=protected-access batch_dim = tensor_shape.Dimension(tensor_shape.dimension_value( - flat_tensors[0].get_shape()[0])) - for t in flat_tensors[1:]: + self._tensors[0].get_shape()[0])) + for t in self._tensors[1:]: batch_dim.assert_is_compatible_with(tensor_shape.Dimension( tensor_shape.dimension_value(t.get_shape()[0]))) - self._tensors = sparse.serialize_many_sparse_tensors(tensors) - output_classes = sparse.get_classes(tensors) - output_shapes = nest.pack_sequence_as( - tensors, [t.get_shape()[1:] for t in nest.flatten(tensors)]) - output_types = nest.pack_sequence_as( - tensors, [t.dtype for t in nest.flatten(tensors)]) - self._structure = structure_lib.convert_legacy_structure( - output_types, output_shapes, output_classes) def _as_variant_tensor(self): return gen_dataset_ops.tensor_slice_dataset( - nest.flatten(self._tensors), - output_shapes=nest.flatten( - self._structure._to_legacy_output_shapes())) # pylint: disable=protected-access + self._tensors, output_shapes=self._structure._flat_shapes) # pylint: disable=protected-access @property def _element_structure(self): @@ -1871,17 +1857,13 @@ class SparseTensorSliceDataset(DatasetSource): raise TypeError("`sparse_tensor` must be a `tf.SparseTensor` object.") self._sparse_tensor = sparse_tensor - output_classes = (ops.Tensor, ops.Tensor, ops.Tensor) indices_shape = self._sparse_tensor.indices.get_shape() shape_shape = self._sparse_tensor.dense_shape.get_shape() rank = (indices_shape.dims[1] - 1).merge_with(shape_shape.dims[0] - 1) - num_values = tensor_shape.Dimension(None) - output_shapes = (tensor_shape.TensorShape([num_values, rank]), - tensor_shape.TensorShape([num_values]), - tensor_shape.TensorShape([rank])) - output_types = (dtypes.int64, self._sparse_tensor.dtype, dtypes.int64) - self._structure = structure_lib.convert_legacy_structure( - output_types, output_shapes, output_classes) + self._structure = structure_lib.NestedStructure( + (structure_lib.TensorStructure(dtypes.int64, [None, rank]), + structure_lib.TensorStructure(self._sparse_tensor.dtype, [None]), + structure_lib.TensorStructure(dtypes.int64, [rank]))) def _as_variant_tensor(self): return gen_dataset_ops.sparse_tensor_slice_dataset( @@ -1936,6 +1918,9 @@ class DatasetStructure(structure_lib.Structure): def _to_tensor_list(self, value): return [value._as_variant_tensor()] # pylint: disable=protected-access + def _to_batched_tensor_list(self, value): + raise NotImplementedError("Unbatching for `tf.data.Dataset` objects.") + def _from_tensor_list(self, flat_value): if (len(flat_value) != 1 or flat_value[0].dtype != dtypes.variant or not flat_value[0].shape.is_compatible_with(tensor_shape.scalar())): @@ -1963,6 +1948,9 @@ class DatasetStructure(structure_lib.Structure): def _batch(self, batch_size): raise NotImplementedError("Batching for `tf.data.Dataset` objects.") + def _unbatch(self): + raise NotImplementedError("Unbatching for `tf.data.Dataset` objects.") + # pylint: disable=protected-access structure_lib.Structure._register_custom_converter(DatasetV2, @@ -2153,25 +2141,14 @@ class _GeneratorDataset(DatasetSource): destroyed. The return value is ignored. """ super(_GeneratorDataset, self).__init__() - # These members will be initialized by `tf_init_func`. - self._state_classes = None - self._state_shapes = None - self._state_types = None - self._init_args = init_args - init_args_classes = sparse.get_classes(init_args) - init_args_shapes = nest.pack_sequence_as( - init_args, [t.get_shape() for t in nest.flatten(init_args)]) - init_args_types = nest.pack_sequence_as( - init_args, [t.dtype for t in nest.flatten(init_args)]) + self._init_structure = structure_lib.Structure.from_value(init_args) self._init_func = StructuredFunctionWrapper( init_func, self._transformation_name(), - input_classes=init_args_classes, - input_shapes=init_args_shapes, - input_types=init_args_types) + input_structure=self._init_structure) self._next_func = StructuredFunctionWrapper( next_func, @@ -2185,7 +2162,7 @@ class _GeneratorDataset(DatasetSource): def _as_variant_tensor(self): return gen_dataset_ops.generator_dataset( - nest.flatten(self._init_args) + self._init_structure._to_tensor_list(self._init_args) # pylint: disable=protected-access + self._init_func.function.captured_inputs, self._next_func.function.captured_inputs, self._finalize_func.function.captured_inputs, diff --git a/tensorflow/python/data/ops/optional_ops.py b/tensorflow/python/data/ops/optional_ops.py index 66011d8518..dcb743bee0 100644 --- a/tensorflow/python/data/ops/optional_ops.py +++ b/tensorflow/python/data/ops/optional_ops.py @@ -169,6 +169,10 @@ class OptionalStructure(structure.Structure): def _to_tensor_list(self, value): return [value._variant_tensor] # pylint: disable=protected-access + def _to_batched_tensor_list(self, value): + raise NotImplementedError( + "Unbatching for `tf.data.experimental.Optional` objects.") + def _from_tensor_list(self, flat_value): if (len(flat_value) != 1 or flat_value[0].dtype != dtypes.variant or not flat_value[0].shape.is_compatible_with(tensor_shape.scalar())): @@ -197,6 +201,10 @@ class OptionalStructure(structure.Structure): raise NotImplementedError( "Batching for `tf.data.experimental.Optional` objects.") + def _unbatch(self): + raise NotImplementedError( + "Unbatching for `tf.data.experimental.Optional` objects.") + # pylint: disable=protected-access structure.Structure._register_custom_converter(Optional, diff --git a/tensorflow/python/data/util/BUILD b/tensorflow/python/data/util/BUILD index f15ebc32a8..04e80299e0 100644 --- a/tensorflow/python/data/util/BUILD +++ b/tensorflow/python/data/util/BUILD @@ -93,6 +93,7 @@ py_test( "//tensorflow/python:sparse_tensor", "//tensorflow/python:tensor_shape", "//tensorflow/python:variables", + "//tensorflow/python/data/kernel_tests:test_base", "@absl_py//absl/testing:parameterized", ], ) diff --git a/tensorflow/python/data/util/structure.py b/tensorflow/python/data/util/structure.py index 5e3addacaa..9de0c4da0e 100644 --- a/tensorflow/python/data/util/structure.py +++ b/tensorflow/python/data/util/structure.py @@ -113,6 +113,26 @@ class Structure(object): """ raise NotImplementedError("Structure._to_tensor_list()") + @abc.abstractmethod + def _to_batched_tensor_list(self, value): + """Returns a flat list of rank >= 1 `tf.Tensor` representing `value`. + + This method can be used, along with `self._flat_shapes` and + `self._flat_types` to represent structured values in lower level APIs + (such as plain TensorFlow operations) that do not understand structure, + *and* that require that the plain tensors have a rank of at least one + (e.g. for the purpose of slicing the tensors). + + Requires: `self.is_compatible_with(Structure.from_value(value))`. + + Args: + value: A value with compatible structure. + + Returns: + A flat list of `tf.Tensor` representing `value`. + """ + raise NotImplementedError("Structure._to_batched_tensor_list()") + @abc.abstractmethod def _from_tensor_list(self, flat_value): """Builds a flat list of `tf.Tensor` into a value matching this structure. @@ -159,6 +179,10 @@ class Structure(object): """ raise NotImplementedError("Structure._batch()") + @abc.abstractmethod + def _unbatch(self): + raise NotImplementedError("Structure._unbatch()") + @staticmethod def from_value(value): """Returns a `Structure` that represents the given `value`. @@ -329,6 +353,22 @@ class NestedStructure(Structure): ret.extend(structure._to_tensor_list(sub_value)) return ret + def _to_batched_tensor_list(self, value): + ret = [] + + try: + flat_value = nest.flatten_up_to(self._nested_structure, value) + except (ValueError, TypeError): + raise ValueError("The value %r is not compatible with the nested " + "structure %r." % (value, self._nested_structure)) + + for sub_value, structure in zip(flat_value, self._flat_nested_structure): + if not structure.is_compatible_with(Structure.from_value(sub_value)): + raise ValueError("Component value %r is not compatible with the nested " + "structure %r." % (sub_value, structure)) + ret.extend(structure._to_batched_tensor_list(sub_value)) + return ret + def _from_tensor_list(self, flat_value): if len(flat_value) != len(self._flat_types): raise ValueError("Expected %d flat values in NestedStructure but got %d." @@ -378,6 +418,10 @@ class NestedStructure(Structure): return NestedStructure(nest.map_structure( lambda s: s._batch(batch_size), self._nested_structure)) + def _unbatch(self): + return NestedStructure(nest.map_structure( + lambda s: s._unbatch(), self._nested_structure)) + @tf_export("data.experimental.TensorStructure") class TensorStructure(Structure): @@ -406,6 +450,11 @@ class TensorStructure(Structure): "and shape %s." % (value, self._dtype, self._shape)) return [value] + def _to_batched_tensor_list(self, value): + if self._shape.merge_with(value.shape).ndims == 0: + raise ValueError("Unbatching a tensor is only supported for rank >= 1") + return [value] + def _from_tensor_list(self, flat_value): if len(flat_value) != 1: raise ValueError("TensorStructure corresponds to a single tf.Tensor.") @@ -442,6 +491,11 @@ class TensorStructure(Structure): self._dtype, tensor_shape.TensorShape([batch_size]).concatenate(self._shape)) + def _unbatch(self): + if self._shape.ndims == 0: + raise ValueError("Unbatching a tensor is only supported for rank >= 1") + return TensorStructure(self._dtype, self._shape[1:]) + @tf_export("data.experimental.SparseTensorStructure") class SparseTensorStructure(Structure): @@ -471,6 +525,13 @@ class SparseTensorStructure(Structure): def _to_tensor_list(self, value): return [sparse_ops.serialize_sparse(value, out_type=dtypes.variant)] + def _to_batched_tensor_list(self, value): + if self._dense_shape.merge_with( + tensor_util.constant_value_as_shape(value.dense_shape)).ndims == 0: + raise ValueError( + "Unbatching a sparse tensor is only supported for rank >= 1") + return [sparse_ops.serialize_many_sparse(value, out_type=dtypes.variant)] + def _from_tensor_list(self, flat_value): if (len(flat_value) != 1 or flat_value[0].dtype != dtypes.variant or not flat_value[0].shape.is_compatible_with(tensor_shape.vector(3))): @@ -505,3 +566,8 @@ class SparseTensorStructure(Structure): return SparseTensorStructure( self._dtype, tensor_shape.TensorShape([batch_size]).concatenate(self._dense_shape)) + + def _unbatch(self): + if self._dense_shape.ndims == 0: + raise ValueError("Unbatching a tensor is only supported for rank >= 1") + return SparseTensorStructure(self._dtype, self._dense_shape[1:]) diff --git a/tensorflow/python/data/util/structure_test.py b/tensorflow/python/data/util/structure_test.py index 3425179e42..91dcfa6f60 100644 --- a/tensorflow/python/data/util/structure_test.py +++ b/tensorflow/python/data/util/structure_test.py @@ -21,6 +21,7 @@ from __future__ import print_function from absl.testing import parameterized import numpy as np +from tensorflow.python.data.kernel_tests import test_base from tensorflow.python.data.util import nest from tensorflow.python.data.util import structure from tensorflow.python.framework import constant_op @@ -34,7 +35,7 @@ from tensorflow.python.ops import variables from tensorflow.python.platform import test -class StructureTest(test.TestCase, parameterized.TestCase): +class StructureTest(test_base.DatasetTestBase, parameterized.TestCase): # NOTE(mrry): The arguments must be lifted into lambdas because otherwise they # will be executed before the (eager- or graph-mode) test environment has been @@ -418,6 +419,75 @@ class StructureTest(test.TestCase, parameterized.TestCase): self.assertTrue( expected_batched_structure.is_compatible_with(batched_structure)) + @parameterized.named_parameters( + ("Tensor", structure.TensorStructure(dtypes.float32, [32]), + structure.TensorStructure(dtypes.float32, [])), + ("TensorUnknown", structure.TensorStructure(dtypes.float32, [None]), + structure.TensorStructure(dtypes.float32, [])), + ("SparseTensor", + structure.SparseTensorStructure(dtypes.float32, [32, None]), + structure.SparseTensorStructure(dtypes.float32, [None])), + ("SparseTensorUnknown", + structure.SparseTensorStructure(dtypes.float32, [None, 4]), + structure.SparseTensorStructure(dtypes.float32, [4])), + ("Nest", structure.NestedStructure({ + "a": structure.TensorStructure(dtypes.float32, [128]), + "b": (structure.SparseTensorStructure(dtypes.int32, [128, 2, 2]), + structure.TensorStructure(dtypes.string, [None]))}), + structure.NestedStructure({ + "a": structure.TensorStructure(dtypes.float32, []), + "b": (structure.SparseTensorStructure(dtypes.int32, [2, 2]), + structure.TensorStructure(dtypes.string, []))})), + ) + def testUnbatch(self, element_structure, expected_unbatched_structure): + unbatched_structure = element_structure._unbatch() + self.assertTrue( + unbatched_structure.is_compatible_with(expected_unbatched_structure)) + self.assertTrue( + expected_unbatched_structure.is_compatible_with(unbatched_structure)) + + # pylint: disable=g-long-lambda + @parameterized.named_parameters( + ("Tensor", lambda: constant_op.constant([[1.0, 2.0], [3.0, 4.0]]), + lambda: constant_op.constant([1.0, 2.0])), + ("SparseTensor", lambda: sparse_tensor.SparseTensor( + indices=[[0, 0], [1, 1]], values=[13, 27], dense_shape=[2, 2]), + lambda: sparse_tensor.SparseTensor( + indices=[[0]], values=[13], dense_shape=[2])), + ("Nest", lambda: ( + constant_op.constant([[1.0, 2.0], [3.0, 4.0]]), + sparse_tensor.SparseTensor( + indices=[[0, 0], [1, 1]], values=[13, 27], dense_shape=[2, 2])), + lambda: (constant_op.constant([1.0, 2.0]), sparse_tensor.SparseTensor( + indices=[[0]], values=[13], dense_shape=[2]))), + ) + def testToBatchedTensorList(self, value_fn, element_0_fn): + batched_value = value_fn() + s = structure.Structure.from_value(batched_value) + batched_tensor_list = s._to_batched_tensor_list(batched_value) + + # The batch dimension is 2 for all of the test cases. + # NOTE(mrry): `tf.shape()` does not currently work for the DT_VARIANT + # tensors in which we store sparse tensors. + for t in batched_tensor_list: + if t.dtype != dtypes.variant: + self.assertEqual(2, self.evaluate(array_ops.shape(t)[0])) + + # Test that the 0th element from the unbatched tensor is equal to the + # expected value. + expected_element_0 = self.evaluate(element_0_fn()) + unbatched_s = s._unbatch() + actual_element_0 = unbatched_s._from_tensor_list( + [t[0] for t in batched_tensor_list]) + + for expected, actual in zip( + nest.flatten(expected_element_0), nest.flatten(actual_element_0)): + if sparse_tensor.is_sparse(expected): + self.assertSparseValuesEqual(expected, actual) + else: + self.assertAllEqual(expected, actual) + + # pylint: enable=g-long-lambda if __name__ == "__main__": test.main() -- GitLab From 7f3228ccf0147e73b986e00f4bcc3e915203ea54 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Fri, 7 Dec 2018 18:08:28 -0800 Subject: [PATCH 260/873] Fix `Experimental clang-format Check` failure Signed-off-by: Yong Tang --- .../tensorrt/resources/trt_allocator_test.cc | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/tensorflow/contrib/tensorrt/resources/trt_allocator_test.cc b/tensorflow/contrib/tensorrt/resources/trt_allocator_test.cc index 55186d5992..beb1284208 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_allocator_test.cc +++ b/tensorflow/contrib/tensorrt/resources/trt_allocator_test.cc @@ -48,11 +48,14 @@ TEST(TRTAllocatorTest, Align) { 513ul, 700ul, 12345ul, 1ul << 32}) { for (uint64_t alignment = 1; alignment <= space * 4; alignment *= 2) { for (const uintptr_t ptr_val : - {static_cast(1), alignment == 1 ? static_cast(1) : alignment - 1, alignment, alignment + 1, - alignment + (alignment / 2)}) { + {static_cast(1), + alignment == 1 ? static_cast(1) : alignment - 1, + alignment, alignment + 1, alignment + (alignment / 2)}) { if (ptr_val % alignment == 0) { for (const uint64_t size : - {static_cast(1), space == 1 ? static_cast(1) : space - 1, space, space + 1}) { + {static_cast(1), + space == 1 ? static_cast(1) : space - 1, space, + space + 1}) { EXPECT_EQ(space >= size, RunTest(alignment, size, ptr_val, space)); } } else { @@ -62,8 +65,10 @@ TEST(TRTAllocatorTest, Align) { EXPECT_TRUE( RunTest(alignment, space - diff, ptr_val + diff, space - diff)); for (const uint64_t size : - {static_cast(1), space - diff > 1 ? space - diff - 1 : static_cast(1), space - diff, - space - diff + 1, space - 1}) { + {static_cast(1), + space - diff > 1 ? space - diff - 1 + : static_cast(1), + space - diff, space - diff + 1, space - 1}) { EXPECT_EQ(space - diff >= size, RunTest(alignment, size, ptr_val, space)); } -- GitLab From cc52e9bfc9a30422868826514f41065bf35167ca Mon Sep 17 00:00:00 2001 From: Rachel Lim Date: Fri, 7 Dec 2018 18:02:49 -0800 Subject: [PATCH 261/873] [tf.data] Enable `shuffle_and_repeat_fusion` optimization by default PiperOrigin-RevId: 224606882 --- .../optimization/optimize_dataset_test.py | 14 +++++++++----- .../optimization/shuffle_and_repeat_fusion_test.py | 5 ----- .../data/experimental/ops/optimization_options.py | 5 +++-- 3 files changed, 12 insertions(+), 12 deletions(-) diff --git a/tensorflow/python/data/experimental/kernel_tests/optimization/optimize_dataset_test.py b/tensorflow/python/data/experimental/kernel_tests/optimization/optimize_dataset_test.py index 05d88eb071..230b74e9e8 100644 --- a/tensorflow/python/data/experimental/kernel_tests/optimization/optimize_dataset_test.py +++ b/tensorflow/python/data/experimental/kernel_tests/optimization/optimize_dataset_test.py @@ -230,10 +230,10 @@ class OptimizeDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): self.assertGreaterEqual(len(w), 1) expected = ("tf.data static optimizations are not compatible with " - "tf.Variable. The following optimizations will be disabled: " - "map_and_batch_fusion, noop_elimination. To enable " - "optimizations, use resource variables instead by calling " - "`tf.enable_resource_variables()` at the start of the program.") + "tf.Variable. The following optimizations will be disabled: %s." + " To enable optimizations, use resource variables instead by " + "calling `tf.enable_resource_variables()` at the start of the " + "program." % (", ".join(opt_options._static_optimizations()))) self.assertTrue(any([expected in str(warning) for warning in w])) # Check that outputs are the same in the optimized and unoptimized cases, @@ -256,7 +256,11 @@ class OptimizeDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): def testOptimizationEnabledByDefault(self): """Tests that some optimizations are applied to datasets by default.""" options = dataset_ops.Options() - expected_optimizations = ["noop_elimination", "map_and_batch_fusion"] + expected_optimizations = [ + "map_and_batch_fusion", + "noop_elimination", + "shuffle_and_repeat_fusion", + ] self.assertEqual( set(options._static_optimizations()), set(expected_optimizations)) diff --git a/tensorflow/python/data/experimental/kernel_tests/optimization/shuffle_and_repeat_fusion_test.py b/tensorflow/python/data/experimental/kernel_tests/optimization/shuffle_and_repeat_fusion_test.py index 5f746ec63a..594b59375f 100644 --- a/tensorflow/python/data/experimental/kernel_tests/optimization/shuffle_and_repeat_fusion_test.py +++ b/tensorflow/python/data/experimental/kernel_tests/optimization/shuffle_and_repeat_fusion_test.py @@ -18,7 +18,6 @@ from __future__ import division from __future__ import print_function from tensorflow.python.data.experimental.ops import optimization -from tensorflow.python.data.experimental.ops.optimization_options import OptimizationOptions from tensorflow.python.data.kernel_tests import test_base from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import errors @@ -32,10 +31,6 @@ class ShuffleAndRepeatFusionTest(test_base.DatasetTestBase): def testShuffleAndRepeatFusion(self): dataset = dataset_ops.Dataset.range(10).apply( optimization.assert_next(["ShuffleAndRepeat"])).shuffle(10).repeat(2) - options = dataset_ops.Options() - options.experimental_optimization = OptimizationOptions() - options.experimental_optimization.shuffle_and_repeat_fusion = True - dataset = dataset.with_options(options) get_next = self.getNext(dataset) for _ in range(2): diff --git a/tensorflow/python/data/experimental/ops/optimization_options.py b/tensorflow/python/data/experimental/ops/optimization_options.py index 73849435a9..11b8b86f64 100644 --- a/tensorflow/python/data/experimental/ops/optimization_options.py +++ b/tensorflow/python/data/experimental/ops/optimization_options.py @@ -86,7 +86,8 @@ class OptimizationOptions(options.OptionsBase): shuffle_and_repeat_fusion = options.create_option( name="shuffle_and_repeat_fusion", ty=bool, - docstring="Whether to fuse shuffle and repeat transformations.") + docstring="Whether to fuse shuffle and repeat transformations. If None, " + "defaults to True.") def _static_optimizations(self): """Produces the list of enabled static optimizations.""" @@ -98,7 +99,6 @@ class OptimizationOptions(options.OptionsBase): "map_fusion", "map_parallelization", "map_vectorization", - "shuffle_and_repeat_fusion", ] for optimization in optimizations_to_enable: if getattr(self, optimization): @@ -110,6 +110,7 @@ class OptimizationOptions(options.OptionsBase): optimizations_to_disable = [ "map_and_batch_fusion", "noop_elimination", + "shuffle_and_repeat_fusion", ] for optimization in optimizations_to_disable: if getattr(self, optimization) is not False: -- GitLab From 798543c1e5cfaa6acf56b087fcb56bd0e227db5d Mon Sep 17 00:00:00 2001 From: Eddie Zhou Date: Fri, 7 Dec 2018 18:10:29 -0800 Subject: [PATCH 262/873] Also skip test_session post-setUp(). PiperOrigin-RevId: 224607628 --- tensorflow/python/framework/test_util.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py index 28f25daa6d..df3cebd2e0 100644 --- a/tensorflow/python/framework/test_util.py +++ b/tensorflow/python/framework/test_util.py @@ -1717,6 +1717,8 @@ class TensorFlowTestCase(googletest.TestCase): use_gpu=False, force_gpu=False): """Use cached_session instead.""" + if self.id().endswith(".test_session"): + self.skipTest("Not a test.") if context.executing_eagerly(): yield None else: -- GitLab From 1881396166c56fb04963683998e6a1569ebf3b19 Mon Sep 17 00:00:00 2001 From: Anna R Date: Fri, 7 Dec 2018 18:11:02 -0800 Subject: [PATCH 263/873] Internal change. PiperOrigin-RevId: 224607664 --- tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) 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 2820e466f3..62e1eaa366 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 @@ -123,7 +123,8 @@ fi run_configure_for_cpu_build bazel build --announce_rc --config=opt ${EXTRA_BUILD_FLAGS} \ - tensorflow/tools/pip_package:build_pip_package || exit $? + tensorflow/tools/pip_package:build_pip_package \ + --incompatible_remove_native_http_archive=false || exit $? if [[ "$SKIP_TEST" == 1 ]]; then exit 0 -- GitLab From 237851c57093edd5126561b2651f216d1726487b Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 7 Dec 2018 20:34:57 -0800 Subject: [PATCH 264/873] Internal Change PiperOrigin-RevId: 224616455 --- .../base_api/api_def_RaggedGather.pbtxt | 6 +- .../base_api/api_def_RaggedRange.pbtxt | 2 +- .../api_def_RaggedTensorToSparse.pbtxt | 2 +- tensorflow/core/kernels/unicode_ops.cc | 2 +- .../kernel_tests/unicode_encode_op_test.py | 127 +- tensorflow/python/ops/ragged/BUILD | 56 +- tensorflow/python/ops/ragged/__init__.py | 76 +- ...vert_to_tensor_or_ragged_tensor_op_test.py | 19 +- .../python/ops/ragged/ragged_array_ops.py | 415 ++---- .../ops/ragged/ragged_batch_gather_op_test.py | 59 +- .../ops/ragged/ragged_boolean_mask_op_test.py | 36 +- .../ops/ragged/ragged_concat_op_test.py | 35 +- .../python/ops/ragged/ragged_const_op_test.py | 46 +- .../ragged/ragged_constant_value_op_test.py | 13 +- .../ops/ragged/ragged_conversion_ops.py | 402 +----- .../python/ops/ragged/ragged_dispatch.py | 25 +- .../python/ops/ragged/ragged_dispatch_test.py | 90 +- .../python/ops/ragged/ragged_eager_test.py | 22 +- .../ops/ragged/ragged_expand_dims_op_test.py | 8 +- .../python/ops/ragged/ragged_factory_ops.py | 465 +------ .../ops/ragged/ragged_from_sparse_op_test.py | 87 +- .../ops/ragged/ragged_from_tensor_op_test.py | 60 +- .../ops/ragged/ragged_functional_ops.py | 33 +- .../ops/ragged/ragged_gather_nd_op_test.py | 23 +- .../ops/ragged/ragged_gather_op_test.py | 128 +- .../python/ops/ragged/ragged_getitem.py | 12 +- ...t.py => ragged_map_flat_values_op_test.py} | 60 +- .../ops/ragged/ragged_map_fn_op_test.py | 96 +- .../python/ops/ragged/ragged_map_ops.py | 44 +- .../python/ops/ragged/ragged_math_ops.py | 49 +- .../ops/ragged/ragged_operators_test.py | 88 +- .../python/ops/ragged/ragged_range_op_test.py | 159 +-- .../ops/ragged/ragged_reduce_op_test.py | 27 +- .../ops/ragged/ragged_row_lengths_op_test.py | 81 +- ...agged_row_splits_to_segment_ids_op_test.py | 13 +- ...agged_segment_ids_to_row_splits_op_test.py | 22 +- .../ops/ragged/ragged_segment_op_test.py | 43 +- .../python/ops/ragged/ragged_stack_op_test.py | 13 +- .../python/ops/ragged/ragged_string_ops.py | 19 +- tensorflow/python/ops/ragged/ragged_tensor.py | 1207 +++++++++++++++-- .../ragged_tensor_bounding_shape_op_test.py | 47 +- .../python/ops/ragged/ragged_tensor_shape.py | 44 +- .../ops/ragged/ragged_tensor_shape_test.py | 66 +- .../python/ops/ragged/ragged_tensor_test.py | 623 ++++----- .../python/ops/ragged/ragged_tensor_value.py | 15 +- .../python/ops/ragged/ragged_test_util.py | 95 ++ .../python/ops/ragged/ragged_tile_op_test.py | 22 +- .../ops/ragged/ragged_to_sparse_op_test.py | 225 ++- .../ops/ragged/ragged_to_tensor_op_test.py | 33 +- .../python/ops/ragged/ragged_util_test.py | 11 +- .../python/ops/ragged/ragged_where_op_test.py | 16 +- tensorflow/tools/pip_package/BUILD | 1 + 52 files changed, 2523 insertions(+), 2845 deletions(-) rename tensorflow/python/ops/ragged/{ragged_map_inner_values_op_test.py => ragged_map_flat_values_op_test.py} (79%) create mode 100644 tensorflow/python/ops/ragged/ragged_test_util.py diff --git a/tensorflow/core/api_def/base_api/api_def_RaggedGather.pbtxt b/tensorflow/core/api_def/base_api/api_def_RaggedGather.pbtxt index 240c987dda..9c40332ea2 100644 --- a/tensorflow/core/api_def/base_api/api_def_RaggedGather.pbtxt +++ b/tensorflow/core/api_def/base_api/api_def_RaggedGather.pbtxt @@ -11,8 +11,8 @@ END in_arg { name: "params_dense_values" description: <input(1); const auto input_splits_flat = input_splits.flat(); - // Since we limit to a 2-D input (inner_values of rank 1 and a single splits + // Since we limit to a 2-D input (flat_values of rank 1 and a single splits // tensor), our output dimension will be 1 with it's size equal to the // number of splits (outer dimension or ragged tensor). TensorShape output_shape({input_splits.dim_size(0) - 1}); diff --git a/tensorflow/python/kernel_tests/unicode_encode_op_test.py b/tensorflow/python/kernel_tests/unicode_encode_op_test.py index 72c8a26766..2f3cd8a657 100644 --- a/tensorflow/python/kernel_tests/unicode_encode_op_test.py +++ b/tensorflow/python/kernel_tests/unicode_encode_op_test.py @@ -23,15 +23,25 @@ import numpy as np from tensorflow.python.framework import constant_op from tensorflow.python.framework import errors_impl as errors -from tensorflow.python.framework import ops from tensorflow.python.framework import test_util from tensorflow.python.ops.ragged import ragged_factory_ops from tensorflow.python.ops.ragged import ragged_string_ops +from tensorflow.python.ops.ragged import ragged_tensor +from tensorflow.python.ops.ragged import ragged_tensor_value from tensorflow.python.platform import test class UnicodeEncodeOpTest(test.TestCase, parameterized.TestCase): + def assertRaggedEqual(self, rt, expected): + with self.cached_session() as sess: + value = sess.run(rt) + if isinstance(value, np.ndarray): + value = value.tolist() + elif isinstance(value, ragged_tensor_value.RaggedTensorValue): + value = value.to_list() + self.assertEqual(value, expected) + def testScalar(self): with self.cached_session(): with self.assertRaises(ValueError): @@ -54,9 +64,10 @@ class UnicodeEncodeOpTest(test.TestCase, parameterized.TestCase): @parameterized.parameters("UTF-8", "UTF-16-BE", "UTF-32-BE") def testStrictErrors(self, encoding): test_value = np.array([72, 101, 2147483647, -1, 111], np.int32) - with self.cached_session(): + with self.cached_session() as session: with self.assertRaises(errors.InvalidArgumentError): - ragged_string_ops.unicode_encode(test_value, encoding, "strict").eval() + session.run( + ragged_string_ops.unicode_encode(test_value, encoding, "strict")) @parameterized.parameters("UTF-8", "UTF-16-BE", "UTF-32-BE") @test_util.run_v1_only("b/120545219") @@ -65,8 +76,8 @@ class UnicodeEncodeOpTest(test.TestCase, parameterized.TestCase): expected_value = u"Heo".encode(encoding) unicode_encode_op = ragged_string_ops.unicode_encode(test_value, encoding, "ignore") - with self.cached_session(): - result = unicode_encode_op.eval() + with self.cached_session() as session: + result = session.run(unicode_encode_op) self.assertIsInstance(result, bytes) self.assertAllEqual(result, expected_value) @@ -77,37 +88,27 @@ class UnicodeEncodeOpTest(test.TestCase, parameterized.TestCase): expected_value = u"He\U0000fffd\U0000fffdo".encode(encoding) unicode_encode_op = ragged_string_ops.unicode_encode(test_value, encoding, "replace") - with self.cached_session(): - result = unicode_encode_op.eval() - self.assertIsInstance(result, bytes) - self.assertAllEqual(result, expected_value) + self.assertRaggedEqual(unicode_encode_op, expected_value) # Test custom replacement character test_value = np.array([72, 101, 2147483647, -1, 111], np.int32) expected_value = u"Heooo".encode(encoding) unicode_encode_op = ragged_string_ops.unicode_encode(test_value, encoding, "replace", 111) - with self.cached_session(): - result = unicode_encode_op.eval() - self.assertIsInstance(result, bytes) - self.assertAllEqual(result, expected_value) + self.assertRaggedEqual(unicode_encode_op, expected_value) # Verify "replace" is default test_value = np.array([72, 101, 2147483647, -1, 111], np.int32) expected_value = u"He\U0000fffd\U0000fffdo".encode(encoding) unicode_encode_op = ragged_string_ops.unicode_encode(test_value, encoding) - with self.cached_session(): - result = unicode_encode_op.eval() - self.assertIsInstance(result, bytes) - self.assertAllEqual(result, expected_value) + self.assertRaggedEqual(unicode_encode_op, expected_value) # Replacement_char must be within range test_value = np.array([72, 101, 2147483647, -1, 111], np.int32) unicode_encode_op = ragged_string_ops.unicode_encode(test_value, encoding, "replace", 1114112) - with self.cached_session(): - with self.assertRaises(errors.InvalidArgumentError): - unicode_encode_op.eval() + with self.assertRaises(errors.InvalidArgumentError): + self.evaluate(unicode_encode_op) # -- regular Tensor tests -- # @@ -117,35 +118,23 @@ class UnicodeEncodeOpTest(test.TestCase, parameterized.TestCase): test_value = np.array([72, 101, 108, 108, 111], np.int32) expected_value = u"Hello".encode(encoding) unicode_encode_op = ragged_string_ops.unicode_encode(test_value, encoding) - with self.cached_session(): - result = unicode_encode_op.eval() - self.assertIsInstance(result, bytes) - self.assertAllEqual(result, expected_value) + self.assertRaggedEqual(unicode_encode_op, expected_value) test_value = np.array([72, 101, 195, 195, 128516], np.int32) expected_value = u"He\xc3\xc3\U0001f604".encode(encoding) unicode_encode_op = ragged_string_ops.unicode_encode(test_value, encoding) - with self.cached_session(): - result = unicode_encode_op.eval() - self.assertIsInstance(result, bytes) - self.assertAllEqual(result, expected_value) + self.assertRaggedEqual(unicode_encode_op, expected_value) # Single character string test_value = np.array([72], np.int32) expected_value = u"H".encode(encoding) unicode_encode_op = ragged_string_ops.unicode_encode(test_value, encoding) - with self.cached_session(): - result = unicode_encode_op.eval() - self.assertIsInstance(result, bytes) - self.assertAllEqual(result, expected_value) + self.assertRaggedEqual(unicode_encode_op, expected_value) test_value = np.array([128516], np.int32) expected_value = u"\U0001f604".encode(encoding) unicode_encode_op = ragged_string_ops.unicode_encode(test_value, encoding) - with self.cached_session(): - result = unicode_encode_op.eval() - self.assertIsInstance(result, bytes) - self.assertAllEqual(result, expected_value) + self.assertRaggedEqual(unicode_encode_op, expected_value) @parameterized.parameters("UTF-8", "UTF-16-BE", "UTF-32-BE") @test_util.run_v1_only("b/120545219") @@ -156,10 +145,7 @@ class UnicodeEncodeOpTest(test.TestCase, parameterized.TestCase): u"H\U0001f604llo".encode(encoding), u"W\U0001f604rld".encode(encoding) ] unicode_encode_op = ragged_string_ops.unicode_encode(test_value, encoding) - with self.cached_session(): - result = unicode_encode_op.eval() - self.assertIsInstance(unicode_encode_op, ops.Tensor) - self.assertAllEqual(result, expected_value) + self.assertAllEqual(unicode_encode_op, expected_value) @parameterized.parameters("UTF-8", "UTF-16-BE", "UTF-32-BE") @test_util.run_v1_only("b/120545219") @@ -172,10 +158,7 @@ class UnicodeEncodeOpTest(test.TestCase, parameterized.TestCase): [u"fixed".encode(encoding), u"words".encode(encoding)], [u"Hyper".encode(encoding), u"cube.".encode(encoding)]] unicode_encode_op = ragged_string_ops.unicode_encode(test_value, encoding) - with self.cached_session(): - result = unicode_encode_op.eval() - self.assertIsInstance(unicode_encode_op, ops.Tensor) - self.assertAllEqual(result, expected_value) + self.assertRaggedEqual(unicode_encode_op, expected_value) @parameterized.parameters("UTF-8", "UTF-16-BE", "UTF-32-BE") @test_util.run_v1_only("b/120545219") @@ -191,10 +174,7 @@ class UnicodeEncodeOpTest(test.TestCase, parameterized.TestCase): [[u"Hyper".encode(encoding)], [u"cube.".encode(encoding)]]] unicode_encode_op = ragged_string_ops.unicode_encode(test_value, encoding) - with self.cached_session(): - result = unicode_encode_op.eval() - self.assertIsInstance(unicode_encode_op, ops.Tensor) - self.assertAllEqual(result, expected_value) + self.assertRaggedEqual(unicode_encode_op, expected_value) # -- Ragged Tensor tests -- # @@ -207,10 +187,7 @@ class UnicodeEncodeOpTest(test.TestCase, parameterized.TestCase): u"H\xc3llo".encode(encoding), u"W\U0001f604rld.".encode(encoding) ] unicode_encode_op = ragged_string_ops.unicode_encode(test_value, encoding) - with self.cached_session(): - result = unicode_encode_op.eval() - self.assertIsInstance(unicode_encode_op, ops.Tensor) - self.assertAllEqual(result, expected_value) + self.assertRaggedEqual(unicode_encode_op, expected_value) @parameterized.parameters("UTF-8", "UTF-16-BE", "UTF-32-BE") @test_util.run_v1_only("b/120545219") @@ -227,10 +204,7 @@ class UnicodeEncodeOpTest(test.TestCase, parameterized.TestCase): u"cube.".encode(encoding) ]] unicode_encode_op = ragged_string_ops.unicode_encode(test_value, encoding) - with self.cached_session(): - result = unicode_encode_op.eval() - self.assertEqual(unicode_encode_op.ragged_rank, 1) - self.assertAllEqual(result.tolist(), expected_value) + self.assertRaggedEqual(unicode_encode_op, expected_value) @parameterized.parameters("UTF-8", "UTF-16-BE", "UTF-32-BE") @test_util.run_v1_only("b/120545219") @@ -245,10 +219,7 @@ class UnicodeEncodeOpTest(test.TestCase, parameterized.TestCase): u"w\xc3rry, be".encode(encoding) ], [u"\U0001f604".encode(encoding), u"".encode(encoding)]] unicode_encode_op = ragged_string_ops.unicode_encode(test_value, encoding) - with self.cached_session(): - result = unicode_encode_op.eval() - self.assertEqual(unicode_encode_op.ragged_rank, 1) - self.assertAllEqual(result.tolist(), expected_value) + self.assertRaggedEqual(unicode_encode_op, expected_value) @parameterized.parameters("UTF-8", "UTF-16-BE", "UTF-32-BE") @test_util.run_v1_only("b/120545219") @@ -259,10 +230,7 @@ class UnicodeEncodeOpTest(test.TestCase, parameterized.TestCase): expected_value = [[u"Hello".encode(encoding), u"World.".encode(encoding)], [], [u"\U0001f604".encode(encoding)]] unicode_encode_op = ragged_string_ops.unicode_encode(test_value, encoding) - with self.cached_session(): - result = unicode_encode_op.eval() - self.assertEqual(unicode_encode_op.ragged_rank, 1) - self.assertAllEqual(result.tolist(), expected_value) + self.assertRaggedEqual(unicode_encode_op, expected_value) @parameterized.parameters("UTF-8", "UTF-16-BE", "UTF-32-BE") @test_util.run_v1_only("b/120545219") @@ -273,41 +241,30 @@ class UnicodeEncodeOpTest(test.TestCase, parameterized.TestCase): expected_value = [[[u"Hello".encode(encoding), u"World".encode(encoding)]], [[u"".encode(encoding)], [u"Hype".encode(encoding)]]] unicode_encode_op = ragged_string_ops.unicode_encode(test_value, encoding) - with self.cached_session(): - result = unicode_encode_op.eval() - self.assertEqual(unicode_encode_op.ragged_rank, 2) - self.assertAllEqual(result.tolist(), expected_value) + self.assertRaggedEqual(unicode_encode_op, expected_value) @parameterized.parameters("UTF-8", "UTF-16-BE", "UTF-32-BE") @test_util.run_v1_only("b/120545219") def testRaggedMatrixWithMultiDimensionInnerValues(self, encoding): - test_inner_values = constant_op.constant([[[72, 101, 108, 108, 111], - [87, 111, 114, 108, 100]], - [[102, 105, 120, 101, 100], - [119, 111, 114, 100, 115]], - [[72, 121, 112, 101, 114], - [99, 117, 98, 101, 46]]]) + test_flat_values = constant_op.constant([[[72, 101, 108, 108, 111], + [87, 111, 114, 108, 100]], + [[102, 105, 120, 101, 100], + [119, 111, 114, 100, 115]], + [[72, 121, 112, 101, 114], + [99, 117, 98, 101, 46]]]) test_row_splits = [ constant_op.constant([0, 2, 3], dtype=np.int64), constant_op.constant([0, 1, 1, 3], dtype=np.int64) ] - test_value = ragged_factory_ops.from_nested_row_splits(test_inner_values, - test_row_splits) + test_value = ragged_tensor.RaggedTensor.from_nested_row_splits( + test_flat_values, test_row_splits) expected_value = [[[[u"Hello".encode(encoding), u"World".encode(encoding)]], []], [[[u"fixed".encode(encoding), u"words".encode(encoding)], [u"Hyper".encode(encoding), u"cube.".encode(encoding)]]]] unicode_encode_op = ragged_string_ops.unicode_encode(test_value, encoding) - with self.cached_session(): - result = unicode_encode_op.eval() - self.assertEqual(unicode_encode_op.ragged_rank, 2) - self.assertAllEqual(result.tolist(), expected_value) - # These next two assertions don't necessarily need to be here as they test - # internal representations and we already verified the value is correct. - self.assertAllEqual(len(result.nested_row_splits), len(test_row_splits)) - self.assertEqual(unicode_encode_op.inner_values.shape.ndims, - test_inner_values.shape.ndims - 1) + self.assertRaggedEqual(unicode_encode_op, expected_value) if __name__ == "__main__": diff --git a/tensorflow/python/ops/ragged/BUILD b/tensorflow/python/ops/ragged/BUILD index fcd9adad21..d88543c400 100644 --- a/tensorflow/python/ops/ragged/BUILD +++ b/tensorflow/python/ops/ragged/BUILD @@ -184,6 +184,8 @@ py_library( srcs_version = "PY2AND3", deps = [ ":ragged_tensor_value", + ":ragged_util", + ":segment_id_ops", "//tensorflow/python:framework_ops", "//tensorflow/python:session", "//tensorflow/python:tensor_shape", @@ -295,6 +297,19 @@ py_library( # RaggedTensor Tests #------------------------------------------------------------------------------- +py_library( + name = "ragged_test_util", + srcs = ["ragged_test_util.py"], + srcs_version = "PY2AND3", + deps = [ + ":ragged_tensor", + ":ragged_tensor_value", + "//tensorflow/python:framework_ops", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:platform_test", + ], +) + py_test( name = "ragged_tensor_test", size = "medium", @@ -305,6 +320,7 @@ py_test( ], deps = [ ":ragged", + ":ragged_test_util", "//tensorflow/python:array_ops", "//tensorflow/python:constant_op", "//tensorflow/python:dtypes", @@ -323,6 +339,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":ragged", + ":ragged_test_util", "//tensorflow/python:framework_ops", "//tensorflow/python:framework_test_lib", "//tensorflow/python:platform_test", @@ -336,6 +353,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":ragged", + ":ragged_test_util", "//tensorflow/python:errors", "//tensorflow/python:framework_test_lib", "//tensorflow/python:platform_test", @@ -348,6 +366,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":ragged", + ":ragged_test_util", "//tensorflow/python:framework_test_lib", "//tensorflow/python:platform_test", ], @@ -359,6 +378,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":ragged", + ":ragged_test_util", "//tensorflow/python:framework_test_lib", "//tensorflow/python:platform_test", "@absl_py//absl/testing:parameterized", @@ -371,6 +391,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":ragged", + ":ragged_test_util", "//tensorflow/python:array_ops", "//tensorflow/python:constant_op", "//tensorflow/python:dtypes", @@ -387,6 +408,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":ragged", + ":ragged_test_util", "//tensorflow/python:array_ops", "//tensorflow/python:dtypes", "//tensorflow/python:errors", @@ -402,6 +424,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":ragged", + ":ragged_test_util", "//tensorflow/python:array_ops", "//tensorflow/python:dtypes", "//tensorflow/python:framework_test_lib", @@ -417,6 +440,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":ragged", + ":ragged_test_util", "//tensorflow/python:constant_op", "//tensorflow/python:framework_test_lib", "//tensorflow/python:platform_test", @@ -429,6 +453,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":ragged", + ":ragged_test_util", "//tensorflow/python:constant_op", "//tensorflow/python:framework_test_lib", "//tensorflow/python:platform_test", @@ -441,6 +466,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":ragged", + ":ragged_test_util", "//tensorflow/python:array_ops", "//tensorflow/python:constant_op", "//tensorflow/python:framework_test_lib", @@ -459,6 +485,7 @@ py_test( ], deps = [ ":ragged", + ":ragged_test_util", "//tensorflow/python:array_ops", "//tensorflow/python:dtypes", "//tensorflow/python:errors", @@ -475,6 +502,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":ragged", + ":ragged_test_util", "//tensorflow/python:array_ops", "//tensorflow/python:dtypes", "//tensorflow/python:errors", @@ -490,6 +518,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":ragged", + ":ragged_test_util", "//tensorflow/python:array_ops", "//tensorflow/python:framework_ops", "//tensorflow/python:framework_test_lib", @@ -504,6 +533,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":ragged", + ":ragged_test_util", "//tensorflow/python:array_ops", "//tensorflow/python:constant_op", "//tensorflow/python:errors", @@ -519,6 +549,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":ragged", + ":ragged_test_util", "//tensorflow/python:array_ops", "//tensorflow/python:constant_op", "//tensorflow/python:dtypes", @@ -530,11 +561,12 @@ py_test( ) py_test( - name = "ragged_map_inner_values_op_test", - srcs = ["ragged_map_inner_values_op_test.py"], + name = "ragged_map_flat_values_op_test", + srcs = ["ragged_map_flat_values_op_test.py"], srcs_version = "PY2AND3", deps = [ ":ragged", + ":ragged_test_util", "//tensorflow/python:array_ops", "//tensorflow/python:constant_op", "//tensorflow/python:dtypes", @@ -551,8 +583,8 @@ py_test( srcs = ["ragged_const_op_test.py"], srcs_version = "PY2AND3", deps = [ - ":ragged_factory_ops", - ":ragged_tensor", + ":ragged", + ":ragged_test_util", "//tensorflow/python:dtypes", "//tensorflow/python:framework_test_lib", "//tensorflow/python:platform_test", @@ -569,6 +601,7 @@ py_test( ], deps = [ ":ragged", + ":ragged_test_util", "//tensorflow/python:framework_test_lib", "//tensorflow/python:platform_test", "//third_party/py/numpy", @@ -582,6 +615,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":ragged", + ":ragged_test_util", "//tensorflow/python:constant_op", "//tensorflow/python:dtypes", "//tensorflow/python:framework_test_lib", @@ -597,6 +631,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":ragged", + ":ragged_test_util", "//tensorflow/python:array_ops", "//tensorflow/python:dtypes", "//tensorflow/python:errors", @@ -612,6 +647,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":ragged", + ":ragged_test_util", "//tensorflow/python:array_ops", "//tensorflow/python:constant_op", "//tensorflow/python:dtypes", @@ -628,6 +664,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":ragged", + ":ragged_test_util", "//tensorflow/python:constant_op", "//tensorflow/python:framework_test_lib", "//tensorflow/python:platform_test", @@ -640,8 +677,8 @@ py_test( srcs = ["ragged_tile_op_test.py"], srcs_version = "PY2AND3", deps = [ - ":ragged_array_ops", - ":ragged_factory_ops", + ":ragged", + ":ragged_test_util", "//tensorflow/python:array_ops", "//tensorflow/python:constant_op", "//tensorflow/python:dtypes", @@ -656,6 +693,7 @@ py_test( srcs = ["ragged_util_test.py"], srcs_version = "PY2AND3", deps = [ + ":ragged_test_util", ":ragged_util", "//tensorflow/python:array_ops", "//tensorflow/python:constant_op", @@ -672,6 +710,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":ragged", + ":ragged_test_util", "//tensorflow/python:framework_test_lib", "//tensorflow/python:platform_test", "@absl_py//absl/testing:parameterized", @@ -684,6 +723,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":ragged", + ":ragged_test_util", "//tensorflow/python:framework_test_lib", "//tensorflow/python:platform_test", "@absl_py//absl/testing:parameterized", @@ -696,6 +736,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":ragged", + ":ragged_test_util", "//tensorflow/python:array_ops", "//tensorflow/python:clip_ops", "//tensorflow/python:dtypes", @@ -717,6 +758,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":ragged", + ":ragged_test_util", "//tensorflow/python:framework_test_lib", "//tensorflow/python:platform_test", ], @@ -729,6 +771,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":ragged", + ":ragged_test_util", "//tensorflow/python:array_ops", "//tensorflow/python:dtypes", "//tensorflow/python:framework_test_lib", @@ -747,6 +790,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":ragged", + ":ragged_test_util", "//tensorflow/python:dtypes", "//tensorflow/python:framework_test_lib", "//tensorflow/python:platform_test", diff --git a/tensorflow/python/ops/ragged/__init__.py b/tensorflow/python/ops/ragged/__init__.py index bfcaa366fc..3d915ee269 100644 --- a/tensorflow/python/ops/ragged/__init__.py +++ b/tensorflow/python/ops/ragged/__init__.py @@ -9,47 +9,6 @@ different lengths. For example, the inner (column) dimension of description of ragged tensors, see the `tf.RaggedTensor` class documentation. -## `RaggedTensor` Operations - -### `RaggedTensor` Factory ops - -* `tf.ragged.constant` -* `tf.ragged.from_row_splits` -* `tf.ragged.from_row_splits` -* `tf.ragged.from_row_lengths` -* `tf.ragged.from_row_starts` -* `tf.ragged.from_row_limits` -* `tf.ragged.from_value_rowids` -* `tf.ragged.from_nested_row_splits` -* `tf.ragged.from_nested_value_rowids` - -### `RaggedTensor` Conversion ops - -* `tf.ragged.from_tensor` -* `tf.ragged.to_tensor` -* `tf.ragged.from_sparse` -* `tf.ragged.to_sparse` -* `tf.ragged.from_variant` -* `tf.ragged.to_variant` -* `tf.ragged.convert_to_tensor_or_ragged_tensor` - -### `RaggedTensor` Shape ops - -* `tf.ragged.row_splits` -* `tf.ragged.row_lengths` -* `tf.ragged.row_starts` -* `tf.ragged.row_limits` -* `tf.ragged.value_rowids` -* `tf.ragged.nrows` -* `tf.ragged.nested_row_splits` -* `tf.ragged.row_splits_to_segment_ids` -* `tf.ragged.segment_ids_to_row_splits` -* `tf.ragged.bounding_shape` - -### Functional ops -* `tf.ragged.map_inner_values` - - @@RaggedTensor @@RaggedTensorType @@ -57,15 +16,9 @@ class documentation. @@is_ragged +@@ragged_factory_ops @@constant @@constant_value -@@from_row_splits -@@from_row_lengths -@@from_row_starts -@@from_row_limits -@@from_value_rowids -@@from_nested_row_splits -@@from_nested_value_rowids @@convert_to_tensor_or_ragged_tensor @@ -77,14 +30,6 @@ class documentation. @@segment_ids_to_row_splits -@@row_splits -@@row_lengths -@@row_starts -@@row_limits -@@value_rowids -@@nrows -@@nested_row_splits -@@bounding_shape @@gather @@batch_gather @@gather_nd @@ -114,7 +59,7 @@ class documentation. @@reduce_any -@@map_inner_values +@@map_flat_values @@map_fn @@ -133,18 +78,12 @@ from tensorflow.python.ops.ragged import ragged_string_ops from tensorflow.python.ops.ragged.ragged_array_ops import batch_gather from tensorflow.python.ops.ragged.ragged_array_ops import boolean_mask -from tensorflow.python.ops.ragged.ragged_array_ops import bounding_shape from tensorflow.python.ops.ragged.ragged_array_ops import concat from tensorflow.python.ops.ragged.ragged_array_ops import expand_dims from tensorflow.python.ops.ragged.ragged_array_ops import gather from tensorflow.python.ops.ragged.ragged_array_ops import gather_nd -from tensorflow.python.ops.ragged.ragged_array_ops import nrows -from tensorflow.python.ops.ragged.ragged_array_ops import row_lengths -from tensorflow.python.ops.ragged.ragged_array_ops import row_limits -from tensorflow.python.ops.ragged.ragged_array_ops import row_starts from tensorflow.python.ops.ragged.ragged_array_ops import stack from tensorflow.python.ops.ragged.ragged_array_ops import tile -from tensorflow.python.ops.ragged.ragged_array_ops import value_rowids from tensorflow.python.ops.ragged.ragged_array_ops import where from tensorflow.python.ops.ragged.ragged_conversion_ops import from_sparse @@ -154,16 +93,8 @@ from tensorflow.python.ops.ragged.ragged_conversion_ops import to_tensor from tensorflow.python.ops.ragged.ragged_factory_ops import constant from tensorflow.python.ops.ragged.ragged_factory_ops import constant_value -from tensorflow.python.ops.ragged.ragged_factory_ops import convert_to_tensor_or_ragged_tensor -from tensorflow.python.ops.ragged.ragged_factory_ops import from_nested_row_splits -from tensorflow.python.ops.ragged.ragged_factory_ops import from_nested_value_rowids -from tensorflow.python.ops.ragged.ragged_factory_ops import from_row_lengths -from tensorflow.python.ops.ragged.ragged_factory_ops import from_row_limits -from tensorflow.python.ops.ragged.ragged_factory_ops import from_row_splits -from tensorflow.python.ops.ragged.ragged_factory_ops import from_row_starts -from tensorflow.python.ops.ragged.ragged_factory_ops import from_value_rowids -from tensorflow.python.ops.ragged.ragged_functional_ops import map_inner_values +from tensorflow.python.ops.ragged.ragged_functional_ops import map_flat_values from tensorflow.python.ops.ragged.ragged_map_ops import map_fn @@ -184,6 +115,7 @@ from tensorflow.python.ops.ragged.ragged_math_ops import segment_prod from tensorflow.python.ops.ragged.ragged_math_ops import segment_sqrt_n from tensorflow.python.ops.ragged.ragged_math_ops import segment_sum +from tensorflow.python.ops.ragged.ragged_tensor import convert_to_tensor_or_ragged_tensor from tensorflow.python.ops.ragged.ragged_tensor import is_ragged from tensorflow.python.ops.ragged.ragged_tensor import RaggedTensor from tensorflow.python.ops.ragged.ragged_tensor import RaggedTensorType diff --git a/tensorflow/python/ops/ragged/convert_to_tensor_or_ragged_tensor_op_test.py b/tensorflow/python/ops/ragged/convert_to_tensor_or_ragged_tensor_op_test.py index ef3464f243..b88f18c8b6 100644 --- a/tensorflow/python/ops/ragged/convert_to_tensor_or_ragged_tensor_op_test.py +++ b/tensorflow/python/ops/ragged/convert_to_tensor_or_ragged_tensor_op_test.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for ragged.convert_to_tensor_or_ragged_tensor.""" +"""Tests for ragged.convert_to_tensor_or_ragged.""" from __future__ import absolute_import from __future__ import division @@ -25,11 +25,13 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import test_util from tensorflow.python.ops import ragged +from tensorflow.python.ops.ragged import ragged_test_util from tensorflow.python.platform import googletest -class RaggedConvertToTensorOrRaggedTensorTest(test_util.TensorFlowTestCase, - parameterized.TestCase): +@test_util.run_all_in_graph_and_eager_modes +class RaggedConvertToTensorOrRaggedTensorTest( + ragged_test_util.RaggedTensorTestCase, parameterized.TestCase): #============================================================================= # Tests where the 'value' param is a RaggedTensor @@ -90,7 +92,6 @@ class RaggedConvertToTensorOrRaggedTensorTest(test_util.TensorFlowTestCase, preferred_dtype=dtypes.string, expected_dtype=dtypes.int32), ]) - @test_util.run_deprecated_v1 def testConvertRaggedTensorValue(self, value, dtype=None, @@ -102,8 +103,7 @@ class RaggedConvertToTensorOrRaggedTensorTest(test_util.TensorFlowTestCase, value, dtype, preferred_dtype) self.assertEqual(value.ragged_rank, converted.ragged_rank) self.assertEqual(dtypes.as_dtype(expected_dtype), converted.dtype) - with self.test_session(): - self.assertEqual(value.tolist(), self.evaluate(converted).tolist()) + self.assertEqual(value.to_list(), self.eval_to_list(converted)) @parameterized.parameters([ dict( @@ -131,8 +131,7 @@ class RaggedConvertToTensorOrRaggedTensorTest(test_util.TensorFlowTestCase, tensor = constant_op.constant(pylist) converted = ragged.convert_to_tensor_or_ragged_tensor( tensor, dtype, preferred_dtype) - with self.test_session(): - self.assertIs(tensor, converted) + self.assertIs(tensor, converted) @parameterized.parameters([ dict( @@ -146,7 +145,6 @@ class RaggedConvertToTensorOrRaggedTensorTest(test_util.TensorFlowTestCase, message=('Tensor conversion requested dtype string for ' 'Tensor with dtype int32')), ]) - @test_util.run_deprecated_v1 def testConvertTensorError(self, pylist, message, @@ -189,8 +187,7 @@ class RaggedConvertToTensorOrRaggedTensorTest(test_util.TensorFlowTestCase, converted = ragged.convert_to_tensor_or_ragged_tensor( value, dtype, preferred_dtype) self.assertEqual(dtypes.as_dtype(expected_dtype), converted.dtype) - with self.test_session(): - self.assertAllEqual(value, converted) + self.assertAllEqual(value, converted) @parameterized.parameters([ dict( diff --git a/tensorflow/python/ops/ragged/ragged_array_ops.py b/tensorflow/python/ops/ragged/ragged_array_ops.py index 25317ba93e..b5917bc4ee 100644 --- a/tensorflow/python/ops/ragged/ragged_array_ops.py +++ b/tensorflow/python/ops/ragged/ragged_array_ops.py @@ -27,282 +27,12 @@ from tensorflow.python.ops import check_ops from tensorflow.python.ops import gen_ragged_array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.ragged import ragged_conversion_ops -from tensorflow.python.ops.ragged import ragged_factory_ops from tensorflow.python.ops.ragged import ragged_functional_ops from tensorflow.python.ops.ragged import ragged_math_ops from tensorflow.python.ops.ragged import ragged_tensor from tensorflow.python.ops.ragged import ragged_util from tensorflow.python.ops.ragged import segment_id_ops -#=============================================================================== -# Row Partitioning -#=============================================================================== - - -def value_rowids(rt_input, name=None): - """Returns the row indices for the `values` in the given ragged tensor. - - `value_rowids(rt)` corresponds one-to-one with the outermost dimension of - `rt.values`, and specifies the row containing each value. In particular, - the row `rt[row]` consists of the values `rt.values[j]` where - `value_rowids(rt)[j] == row`. - - Args: - rt_input: The RaggedTensor whose row indices should be returned. - name: A name prefix for the returned tensor (optional). - - Returns: - A 1-D `int64` `Tensor` with shape `self.values.shape[:1]`. - The returned tensor is nonnegative, and is sorted in ascending order. - - #### Example: - ```python - >>> rt = ragged.constant([[3, 1, 4, 1], [], [5, 9, 2], [6], []]) - >>> rt.values.eval() - [3, 1, 4, 1, 5, 9, 2, 6] - >>> ragged.value_rowids(rt).eval() - [0, 0, 0, 0, 2, 2, 2, 3] # corresponds 1:1 with rt.values - ``` - """ - if not ragged_tensor.is_ragged(rt_input): - raise TypeError( - 'rt_input expected RaggedTensor, got %s' % type(rt_input).__name__) - if (isinstance(rt_input, ragged_tensor.RaggedTensor) and - rt_input.cached_value_rowids is not None): - return rt_input.cached_value_rowids - - with ops.name_scope(name, 'RaggedValueRowIds', [rt_input]): - return segment_id_ops.row_splits_to_segment_ids(rt_input.row_splits) - - -def nrows(rt_input, out_type=dtypes.int64, name=None): - """Returns the number of rows in the given potentially ragged tensor. - - I.e., the size of the outermost dimension of the tensor. - - Args: - rt_input: The potentially ragged tensor whose number of rows should be - returned. - out_type: `dtype` for the returned tensor. - name: A name prefix for the returned tensor (optional). - - Returns: - A scalar `Tensor` with dtype `out_type`. - - #### Example: - ```python - >>> rt = ragged.constant([[3, 1, 4, 1], [], [5, 9, 2], [6], []]) - >>> ragged.nrows(rt).eval() # rt has 5 rows. - 5 - ``` - """ - if (isinstance(rt_input, ragged_tensor.RaggedTensor) and - rt_input.cached_nrows is not None): - return rt_input.cached_nrows - - with ops.name_scope(name, 'RaggedNRows', [rt_input]): - if ragged_tensor.is_ragged(rt_input): - return array_ops.shape(rt_input.row_splits, out_type=out_type)[0] - 1 - else: - return array_ops.shape(rt_input, out_type=out_type)[0] - - -def row_starts(rt_input, name=None): - """Returns the start indices for rows in the given ragged tensor. - - These indices specify where the values for each row begin in - `rt_input.values`. `ragged.row_starts(rt_input)` is equal to - `rt_input.row_splits[:-1]`. - - Args: - rt_input: The RaggedTensor whose row starts should be returned. - name: A name prefix for the returned tensor (optional). - - Returns: - A 1-D Tensor of int64 with shape `[nrows]`. - The returned tensor is nonnegative, and is sorted in ascending order. - - #### Example: - ```python - >>> rt = ragged.constant([[3, 1, 4, 1], [], [5, 9, 2], [6], []]) - >>> ragged.values(rt).eval() - [3, 1, 4, 1, 5, 9, 2, 6] - >>> ragged.row_starts(rt).eval() # indices of row starts in ragged.values - [0, 4, 4, 7, 8] - ``` - """ - if not ragged_tensor.is_ragged(rt_input): - raise TypeError( - 'rt_input expected RaggedTensor, got %s' % type(rt_input).__name__) - with ops.name_scope(name, 'RaggedRowStarts', [rt_input]): - return rt_input.row_splits[:-1] - - -def row_limits(rt_input, name=None): - """Returns the limit indices for rows in the given ragged tensor. - - These indices specify where the values for each row end in - `rt_input.values`. `ragged.row_limits(rt_input)` is equal to - `rt_input.row_splits[:-1]`. - - Args: - rt_input: The RaggedTensor whose row limits should be returned. - name: A name prefix for the returned tensor (optional). - - Returns: - A 1-D Tensor of int64 with shape `[nrows]`. - The returned tensor is nonnegative, and is sorted in ascending order. - - #### Example: - ```python - >>> rt = ragged.constant([[3, 1, 4, 1], [], [5, 9, 2], [6], []]) - >>> ragged.values(rt).eval() - [3, 1, 4, 1, 5, 9, 2, 6] - >>> ragged.row_limits(rt).eval() # indices of row limits in ragged.values - [4, 4, 7, 8, 8] - ``` - """ - if not ragged_tensor.is_ragged(rt_input): - raise TypeError( - 'rt_input expected RaggedTensor, got %s' % type(rt_input).__name__) - with ops.name_scope(name, 'RaggedRowLimits', [rt_input]): - return rt_input.row_splits[1:] - - -def row_lengths(rt_input, axis=1, name=None): - """Returns the lengths of the rows in the given potentially ragged tensor. - - `ragged.row_lengths(rt_input)[i]` indicates the number of values in the - `i`th row of `rt_input`. - - Args: - rt_input: The potentially ragged tensor whose row lengths should be - returned. Must have at least `axis+1` dimensions. - axis: An integer constant indicating the axis whose row lengths should be - returned. - name: A name prefix for the returned tensor (optional). - - Returns: - A potentially Tensor of int64 with shape `rt_input.shape[:axis]`. - - Raises: - ValueError: If rt_input is a scalar, or `axis` is out of bounds. - - #### Example: - ```python - >>> rt = ragged.constant([[[3, 1, 4], [1]], [], [[5, 9], [2]], [[6]], []]) - >>> ragged.row_lengths(rt).eval() # lengths of rows in rt - [2, 0, 2, 1, 0] - >>> ragged.row_lengths(rt, axis=2).eval() # lengths of axis=2 rows. - [[3, 1], [], [2, 1], [1], []] - ``` - """ - if (isinstance(rt_input, ragged_tensor.RaggedTensor) and - rt_input.cached_row_lengths is not None): - return rt_input.cached_row_lengths - - with ops.name_scope(name, 'RaggedRowLengths', [rt_input]): - rt_input = ragged_factory_ops.convert_to_tensor_or_ragged_tensor( - rt_input, name='rt_input') - ndims = rt_input.shape.ndims - if ndims is not None: - if ndims == 0: - raise ValueError('rt_input may not be a scalar.') - elif not -ndims <= axis < ndims: - raise ValueError('axis=%d out of bounds: expected %d<=axis<%d.' % - (axis, -ndims, ndims)) - if ragged_tensor.is_ragged(rt_input): - axis = ragged_util.get_positive_axis(axis, rt_input.shape.ndims) - if axis == 0: - return nrows(rt_input) - elif axis == 1: - splits = rt_input.row_splits - return splits[1:] - splits[:-1] - else: - return rt_input.with_values(row_lengths(rt_input.values, axis - 1)) - else: - shape = array_ops.shape(rt_input, out_type=dtypes.int64) - return array_ops.ones(shape[:axis], dtypes.int64) * shape[axis] - - -def nested_row_lengths(rt_input, name=None): - """Returns a tuple containing the row_lengths for all ragged dimensions. - - `nested_row_lengths(rt)` is a tuple containing the `row_lengths` tensors for - all ragged dimensions in `rt`, ordered from outermost to innermost. - - Args: - rt_input: A potentially ragged tensor. - name: A name prefix for the returned tensors (optional). - - Returns: - A `tuple` of 1-D `int64` `Tensors`. The length of the tuple is equal to - `rt_input.ragged_rank`. - """ - with ops.name_scope(name, 'RaggedNestedRowLengths', [rt_input]): - rt_nested_row_lengths = [] - while isinstance(rt_input, ragged_tensor.RaggedTensor): - rt_nested_row_lengths.append(row_lengths(rt_input)) - rt_input = rt_input.values - return tuple(rt_nested_row_lengths) - - -#=============================================================================== -# Bounding Shape -#=============================================================================== -def bounding_shape(rt_input, axis=None, name=None): - """Returns the tight bounding box shape for a potentially ragged tensor. - - Args: - rt_input: A potentially ragged tensor. - axis: An integer scalar or vector indicating which axes to return the - bounding box for. If not specified, then the full bounding box is - returned. - name: A name prefix for the returned tensor (optional). - - Returns: - An int64 `Tensor`. If `axis` is not specified, then `output` - is a vector with `output.shape=[rt_input.shape.ndims]`. If `axis` is a - scalar, then the `output` is a scalar. If `axis` is a vector, then - `output` is a vector, where `output[i]` is the bounding size for - dimension `axis[i]`. - - #### Example: - ```python - >>> rt = ragged.constant([[1, 2, 3, 4], [5], [], [6, 7, 8, 9], [10]]) - >>> ragged.bounding_shape(rt).eval().tolist() - [5, 4] - ``` - """ - with ops.name_scope(name, 'RaggedBoundingBox', [rt_input, axis]): - rt_input = ragged_factory_ops.convert_to_tensor_or_ragged_tensor( - rt_input, name='rt_input') - if not ragged_tensor.is_ragged(rt_input): - bbox = array_ops.shape(rt_input) - return bbox if axis is None else array_ops.gather(bbox, axis) - - nested_splits = rt_input.nested_row_splits - rt_inner_values = rt_input.inner_values - - # Optimized special cases for when axis=0 or axis=1: - if isinstance(axis, int): - if axis == 0: - return array_ops.shape(nested_splits[0], out_type=dtypes.int64)[0] - 1 - elif axis == 1: - return math_ops.maximum(math_ops.reduce_max(row_lengths(rt_input)), 0) - - splits_shape = array_ops.shape(rt_input.row_splits, out_type=dtypes.int64) - inner_values_shape = array_ops.shape(rt_inner_values, out_type=dtypes.int64) - - ragged_dimensions = array_ops.stack([splits_shape[0] - 1] + [ - math_ops.maximum(math_ops.reduce_max(splits[1:] - splits[:-1]), 0) - for splits in nested_splits - ]) - inner_dimensions = inner_values_shape[1:] - - bbox = array_ops.concat([ragged_dimensions, inner_dimensions], axis=0) - return bbox if axis is None else array_ops.gather(bbox, axis) - #=============================================================================== # ragged_gather @@ -331,13 +61,13 @@ def gather(params, indices, validate_indices=None, axis=0, name=None): >>> ragged_params = ragged.constant([['a', 'b', 'c'], ['d'], [], ['e']]) >>> ragged_indices = ragged.constant([[3, 1, 2], [1], [], [0]]) - >>> print ragged.gather(params, ragged_indices).eval().tolist() + >>> print ragged.gather(params, ragged_indices) [['d', 'b', 'c'], ['b'], [], ['a']] - >>> print ragged.gather(ragged_params, indices).eval().tolist() + >>> print ragged.gather(ragged_params, indices) [['e'], ['d'], [], ['d'], ['a', 'b', 'c']] - >>> print ragged.gather(ragged_params, ragged_indices).eval().tolist() + >>> print ragged.gather(ragged_params, ragged_indices) [[['e'], ['d'], []], [['d']], [], [['a', 'b', 'c']]] ``` @@ -363,9 +93,9 @@ def gather(params, indices, validate_indices=None, axis=0, name=None): if not isinstance(axis, int) or axis != 0: raise ValueError('axis>0 is not supported for ragged gather yet.') with ops.name_scope(name, 'RaggedGather', [params, indices]): - params = ragged_factory_ops.convert_to_tensor_or_ragged_tensor( + params = ragged_tensor.convert_to_tensor_or_ragged_tensor( params, name='params') - indices = ragged_factory_ops.convert_to_tensor_or_ragged_tensor( + indices = ragged_tensor.convert_to_tensor_or_ragged_tensor( indices, name='indices') if ragged_tensor.is_ragged(indices): @@ -380,13 +110,13 @@ def gather(params, indices, validate_indices=None, axis=0, name=None): result = gen_ragged_array_ops.ragged_gather( indices=indices, - params_dense_values=params.inner_values, + params_dense_values=params.flat_values, params_nested_splits=params.nested_row_splits, OUTPUT_RAGGED_RANK=indices.shape.ndims + len(params.nested_row_splits) - 1) # Compose the RaggedTensor from splits & values. - return ragged_factory_ops.from_nested_row_splits( + return ragged_tensor.RaggedTensor.from_nested_row_splits( result.output_dense_values, result.output_nested_splits) @@ -429,9 +159,9 @@ def batch_gather(params, indices, name=None): return array_ops.batch_gather(params, indices, name) with ops.name_scope(name, 'RaggedBatchGather', [params, indices]): - params = ragged_factory_ops.convert_to_tensor_or_ragged_tensor( + params = ragged_tensor.convert_to_tensor_or_ragged_tensor( params, name='params') - indices = ragged_factory_ops.convert_to_tensor_or_ragged_tensor( + indices = ragged_tensor.convert_to_tensor_or_ragged_tensor( indices, name='indices') indices_ndims = indices.shape.ndims if indices_ndims is None: @@ -448,7 +178,7 @@ def batch_gather(params, indices, name=None): 'not match params shape') checks = [check_ops.assert_equal(params.row_splits, indices.row_splits)] with ops.control_dependencies(checks): - return ragged_factory_ops.from_row_splits( + return ragged_tensor.RaggedTensor.from_row_splits( batch_gather(params.values, indices.values), indices.row_splits) # Otherwise, indices is a 2D ragged tensor with 1 ragged dimension. @@ -462,11 +192,11 @@ def batch_gather(params, indices, name=None): # Adjust indices from within-batch to global (in params.values), and # then use ragged.gather to gather them. - num_indices = row_lengths(indices) - params_starts = row_starts(params) + num_indices = indices.row_lengths() + params_starts = params.row_starts() adjustments = ragged_util.repeat(params_starts, num_indices, axis=0) adjusted_index_values = math_ops.to_int64(indices.values) + adjustments - return ragged_factory_ops.from_row_splits( + return ragged_tensor.RaggedTensor.from_row_splits( gather(params.values, adjusted_index_values), indices.row_splits) else: # params is a RaggedTensor and indices is a Tensor. @@ -474,7 +204,7 @@ def batch_gather(params, indices, name=None): return gather(params, indices) elif indices_ndims == 2: # Adjust indices from batch-local to global (in params.values) - adjustments = array_ops.expand_dims(row_starts(params), 1) + adjustments = array_ops.expand_dims(params.row_starts(), 1) adjusted_indices = math_ops.to_int64(indices) + adjustments return gather(params.values, adjusted_indices) else: @@ -532,9 +262,9 @@ def gather_nd(params, indices, name=None): with ops.name_scope(name, 'RaggedGatherNd', [params, indices]): - params = ragged_factory_ops.convert_to_tensor_or_ragged_tensor( + params = ragged_tensor.convert_to_tensor_or_ragged_tensor( params, name='params') - indices = ragged_factory_ops.convert_to_tensor_or_ragged_tensor( + indices = ragged_tensor.convert_to_tensor_or_ragged_tensor( indices, name='indices') indices_shape = indices.shape indices_ndims = indices_shape.ndims @@ -548,7 +278,7 @@ def gather_nd(params, indices, name=None): # `index_size` is the "n" in "gather_nd" -- i.e., the number of dimensions # that each index slices into. - index_size = indices_shape[-1].value + index_size = tensor_shape.dimension_value(indices_shape[-1]) if index_size is None: raise ValueError('indices.shape[-1] must be statically known.') @@ -560,8 +290,7 @@ def gather_nd(params, indices, name=None): if indices_is_dense: indices = ragged_conversion_ops.from_tensor( indices, ragged_rank=indices_ndims - 2) - result = indices.with_inner_values( - gather_nd(params, indices.inner_values)) + result = indices.with_flat_values(gather_nd(params, indices.flat_values)) if (indices_is_dense and ragged_tensor.is_ragged(result) and result.ragged_rank == indices_ndims - 2): result = ragged_conversion_ops.to_tensor(result) @@ -575,7 +304,7 @@ def gather_nd(params, indices, name=None): # Handle corner case: An empty index tuple selects the entire `params` # value. So if `index_size` is zero, then tile `params`. if index_size == 0: - params_ndims = params.ragged_rank + array_ops.rank(params.inner_values) + params_ndims = params.ragged_rank + array_ops.rank(params.flat_values) for dim in range(indices_ndims - 1): params = expand_dims(params, axis=0) multiples = array_ops.concat([ @@ -613,7 +342,7 @@ def gather_nd(params, indices, name=None): return array_ops.gather_nd(flattened_params, flattened_index_tuples) flattened_index_tuples = array_ops.gather( - row_starts(flattened_params), flattened_index_tuples) + flattened_params.row_starts(), flattened_index_tuples) flattened_index_tuples += indices[..., dim] flattened_params = flattened_params.values @@ -709,9 +438,8 @@ def boolean_mask(data, mask, keepdims=False, name=None): """ with ops.name_scope(name, 'RaggedMask', [data, mask]): # Convert inputs to tensors. - data = ragged_factory_ops.convert_to_tensor_or_ragged_tensor( - data, name='data') - mask = ragged_factory_ops.convert_to_tensor_or_ragged_tensor( + data = ragged_tensor.convert_to_tensor_or_ragged_tensor(data, name='data') + mask = ragged_tensor.convert_to_tensor_or_ragged_tensor( mask, dtypes.bool, name='mask') # Get static rank of mask. @@ -742,7 +470,7 @@ def boolean_mask(data, mask, keepdims=False, name=None): else: # Count the number of True mask values in each row to find the # lengths of the filtered rows; then convert to splits. - int_mask = ragged_functional_ops.map_inner_values( + int_mask = ragged_functional_ops.map_flat_values( math_ops.cast, mask, dtype=dtypes.int64) masked_row_lengths = ragged_math_ops.reduce_sum(int_mask, axis=1) splits.append(ragged_util.lengths_to_splits(masked_row_lengths)) @@ -754,7 +482,7 @@ def boolean_mask(data, mask, keepdims=False, name=None): # Add the ragged `splits` back to the result. if keepdims: - masked_values = ragged_factory_ops.from_nested_row_splits( + masked_values = ragged_tensor.RaggedTensor.from_nested_row_splits( masked_values, splits) return masked_values @@ -765,7 +493,7 @@ def boolean_mask(data, mask, keepdims=False, name=None): # Get the masked splits: first get the length of each row, then filter # out the rows that we are deleting, and convert that filtered set of # masks back to a splits tensor. - lengths = row_lengths(data) + lengths = data.row_lengths() masked_lengths = array_ops.boolean_mask(lengths, mask) masked_splits = ragged_util.lengths_to_splits(masked_lengths) @@ -777,7 +505,8 @@ def boolean_mask(data, mask, keepdims=False, name=None): segment_mask = array_ops.gather(mask, segment_ids) masked_values = boolean_mask(data.values, segment_mask, keepdims=False) - return ragged_factory_ops.from_row_splits(masked_values, masked_splits) + return ragged_tensor.RaggedTensor.from_row_splits(masked_values, + masked_splits) # If mask is non-ragged and has rank>1, then convert it to be ragged, # with a ragged rank matching data. @@ -798,7 +527,7 @@ def boolean_mask(data, mask, keepdims=False, name=None): # and values to get the innermost ragged tensor. masked_lengths = math_ops.count_nonzero(mask, axis=-1) flattened_masked_lengths = array_ops.reshape(masked_lengths, [-1]) - masked_values = ragged_factory_ops.from_row_lengths( + masked_values = ragged_tensor.RaggedTensor.from_row_lengths( masked_values, flattened_masked_lengths) # Wrap remaining ragged dimensions. @@ -808,7 +537,7 @@ def boolean_mask(data, mask, keepdims=False, name=None): for dim in range(mask.shape.ndims - 3, -1, -1): elt_size = mask_shape[dim + 1] masked_splits = math_ops.range(split_size[dim]) * elt_size - masked_values = ragged_factory_ops.from_row_splits( + masked_values = ragged_tensor.RaggedTensor.from_row_splits( masked_values, masked_splits) return masked_values @@ -919,7 +648,7 @@ def _ragged_stack_concat_helper(rt_inputs, axis, stack_values): # Convert input tensors. rt_inputs = [ - ragged_factory_ops.convert_to_tensor_or_ragged_tensor( + ragged_tensor.convert_to_tensor_or_ragged_tensor( rt_input, name='rt_input') for rt_input in rt_inputs ] @@ -970,7 +699,7 @@ def _ragged_stack_concat_helper(rt_inputs, axis, stack_values): values = [rt.values for rt in rt_inputs] splits = [[rt_input.row_splits] for rt_input in rt_inputs] with ops.control_dependencies(ragged_util.assert_splits_match(splits)): - return ragged_factory_ops.from_row_splits( + return ragged_tensor.RaggedTensor.from_row_splits( _ragged_stack_concat_helper(values, axis - 1, stack_values), splits[0][0]) @@ -987,8 +716,8 @@ def _ragged_stack_concat_axis_0(rt_inputs, stack_values): A RaggedTensor. """ # Concatenate the inner values together. - inner_values = [rt.inner_values for rt in rt_inputs] - concatenated_inner_values = array_ops.concat(inner_values, axis=0) + flat_values = [rt.flat_values for rt in rt_inputs] + concatenated_flat_values = array_ops.concat(flat_values, axis=0) # Concatenate the splits together for each ragged dimension (adjusting # split offsets as necessary). @@ -1002,12 +731,12 @@ def _ragged_stack_concat_axis_0(rt_inputs, stack_values): # If we are performing a stack operation, then add another splits. if stack_values: - stack_lengths = array_ops.stack([nrows(rt) for rt in rt_inputs]) + stack_lengths = array_ops.stack([_nrows(rt) for rt in rt_inputs]) stack_splits = ragged_util.lengths_to_splits(stack_lengths) concatenated_nested_splits.insert(0, stack_splits) - return ragged_factory_ops.from_nested_row_splits(concatenated_inner_values, - concatenated_nested_splits) + return ragged_tensor.RaggedTensor.from_nested_row_splits( + concatenated_flat_values, concatenated_nested_splits) def _ragged_stack_concat_axis_1(rt_inputs, stack_values): @@ -1023,10 +752,10 @@ def _ragged_stack_concat_axis_1(rt_inputs, stack_values): """ num_inputs = len(rt_inputs) - rt_nrows = nrows(rt_inputs[0]) + rt_nrows = _nrows(rt_inputs[0]) nrows_msg = 'Input tensors have incompatible shapes.' nrows_checks = [ - check_ops.assert_equal(nrows(rt), rt_nrows, message=nrows_msg) + check_ops.assert_equal(_nrows(rt), rt_nrows, message=nrows_msg) for rt in rt_inputs[1:] ] @@ -1050,14 +779,15 @@ def _ragged_stack_concat_axis_1(rt_inputs, stack_values): # Add a new splits tensor to group together the values. stack_splits = math_ops.range(0, rt_nrows * num_inputs + 1, num_inputs) _copy_row_shape(rt_inputs, stack_splits) - return ragged_factory_ops.from_row_splits(permuted_rt, stack_splits) + return ragged_tensor.RaggedTensor.from_row_splits(permuted_rt, + stack_splits) else: # Merge together adjacent rows by dropping the row-split indices that # separate them. concat_splits = permuted_rt.row_splits[::num_inputs] _copy_row_shape(rt_inputs, concat_splits) - return ragged_factory_ops.from_row_splits(permuted_rt.values, - concat_splits) + return ragged_tensor.RaggedTensor.from_row_splits(permuted_rt.values, + concat_splits) def _copy_row_shape(rt_inputs, splits): @@ -1090,12 +820,12 @@ def tile(input, multiples, name=None): # pylint: disable=redefined-builtin #### Example: ```python >>> rt = ragged.constant([[1, 2], [3]]) - >>> ragged.tile(rt, [3, 2]).eval().tolist() + >>> ragged.tile(rt, [3, 2]) [[1, 2, 1, 2], [3, 3], [1, 2, 1, 2], [3, 3], [1, 2, 1, 2], [3, 3]] ``` """ with ops.name_scope(name, 'RaggedTile', [input, multiples]): - input = ragged_factory_ops.convert_to_tensor_or_ragged_tensor( + input = ragged_tensor.convert_to_tensor_or_ragged_tensor( input, name='input') multiples = ragged_util.convert_to_int_tensor( multiples, name='multiples', dtype=dtypes.int64) @@ -1107,16 +837,16 @@ def tile(input, multiples, name=None): # pylint: disable=redefined-builtin # to skip tiling dimensions where `multiples=1`. const_multiples = tensor_util.constant_value(multiples) - return ragged_factory_ops.from_nested_row_splits( + return ragged_tensor.RaggedTensor.from_nested_row_splits( _tile_ragged_values(input, multiples, const_multiples), _tile_ragged_splits(input, multiples, const_multiples)) def _tile_ragged_values(rt_input, multiples, const_multiples=None): - """Builds inner_values tensor for a tiled `RaggedTensor`. + """Builds flat_values tensor for a tiled `RaggedTensor`. Returns a tensor that repeats the values in - `rt_input.inner_values` in the + `rt_input.flat_values` in the appropriate pattern to construct a `RaggedTensor` that tiles `rt_input` as specified by `multiples`. @@ -1128,19 +858,19 @@ def _tile_ragged_values(rt_input, multiples, const_multiples=None): dimensions where `multiples=1`. Returns: - A `Tensor` with the same type and rank as `rt_input.inner_values`. + A `Tensor` with the same type and rank as `rt_input.flat_values`. #### Example: ```python >>> rt = ragged.constant([[1, 2], [3]]) - >>> _tile_ragged_values(rt, [3, 2]).eval().tolist() + >>> _tile_ragged_values(rt, [3, 2]) [1, 2, 1, 2, 3, 3, 1, 2, 1, 2, 3, 3, 1, 2, 1, 2, 3, 3] ``` """ ragged_rank = rt_input.ragged_rank nested_splits = rt_input.nested_row_splits - # Pointers to the values in `rt_input.inner_values`. + # Pointers to the values in `rt_input.flat_values`. inner_value_ids = math_ops.range(nested_splits[-1][-1]) # For each ragged dimension (working from the innermost to outermost), @@ -1163,9 +893,9 @@ def _tile_ragged_values(rt_input, multiples, const_multiples=None): prev_splits = splits # Gather the tiled inner values. - ragged_tiled_values = array_ops.gather(rt_input.inner_values, inner_value_ids) + ragged_tiled_values = array_ops.gather(rt_input.flat_values, inner_value_ids) - # Tile the inner_values for the uniform dimensions (i.e., for `axis=0` plus + # Tile the flat_values for the uniform dimensions (i.e., for `axis=0` plus # `axis=range(ragged_rank, rank)`). inner_repeats = array_ops.concat([multiples[:1], multiples[ragged_rank + 1:]], axis=0) @@ -1192,7 +922,7 @@ def _tile_ragged_splits(rt_input, multiples, const_multiples=None): #### Example: ```python >>> rt = ragged.constant([[1, 2], [3]]) - >>> _tile_ragged_splits(rt, [3, 2]).eval().tolist() + >>> _tile_ragged_splits(rt, [3, 2]) [0, 4, 6, 10, 12, 16, 18] ``` """ @@ -1293,20 +1023,20 @@ def expand_dims(input, axis, name=None): # pylint: disable=redefined-builtin TensorShape([2, None]) >>> expanded = ragged.expand_dims(rt, axis=0) - >>> print(expanded.shape, expanded.eval().tolist()) + >>> print(expanded.shape, expanded) TensorShape([1, None, None]) [[[1, 2], [3]]] >>> expanded = ragged.expand_dims(rt, axis=1) - >>> print(expanded.shape, expanded.eval().tolist()) + >>> print(expanded.shape, expanded) TensorShape([2, None, None]) [[[1, 2]], [[3]]] >>> expanded = ragged.expand_dims(rt, axis=2) - >>> print(expanded.shape, expanded.eval().tolist()) + >>> print(expanded.shape, expanded) TensorShape([2, None, 1]) [[[1], [2]], [[3]]] ``` """ with ops.name_scope(name, 'RaggedExpandDims', [input]): - input = ragged_factory_ops.convert_to_tensor_or_ragged_tensor( + input = ragged_tensor.convert_to_tensor_or_ragged_tensor( input, name='input') if not ragged_tensor.is_ragged(input): @@ -1316,15 +1046,15 @@ def expand_dims(input, axis, name=None): # pylint: disable=redefined-builtin axis = ragged_util.get_positive_axis(axis, ndims) if axis == 0: values = input - splits = array_ops.stack([0, nrows(input)]) + splits = array_ops.stack([0, input.nrows()]) elif axis == 1: values = input - splits = math_ops.range(nrows(input) + 1) + splits = math_ops.range(input.nrows() + 1) else: values = expand_dims(input.values, axis - 1) splits = input.row_splits - return ragged_factory_ops.from_row_splits(values, splits) + return ragged_tensor.RaggedTensor.from_row_splits(values, splits) #=============================================================================== @@ -1401,13 +1131,13 @@ def where(condition, x=None, y=None, name=None): if (x is None) != (y is None): raise ValueError('x and y must be either both None or both non-None') with ops.name_scope('RaggedWhere', name, [condition, x, y]): - condition = ragged_factory_ops.convert_to_tensor_or_ragged_tensor( + condition = ragged_tensor.convert_to_tensor_or_ragged_tensor( condition, name='condition') if x is None: return _coordinate_where(condition) else: - x = ragged_factory_ops.convert_to_tensor_or_ragged_tensor(x, name='x') - y = ragged_factory_ops.convert_to_tensor_or_ragged_tensor(y, name='y') + x = ragged_tensor.convert_to_tensor_or_ragged_tensor(x, name='x') + y = ragged_tensor.convert_to_tensor_or_ragged_tensor(y, name='y') return _elementwise_where(condition, x, y) @@ -1421,15 +1151,15 @@ def _elementwise_where(condition, x, y): return array_ops.where(condition, x, y) elif condition_is_ragged and x_is_ragged and y_is_ragged: - return ragged_functional_ops.map_inner_values(array_ops.where, condition, x, - y) + return ragged_functional_ops.map_flat_values(array_ops.where, condition, x, + y) elif not condition_is_ragged: # Concatenate x and y, and then use `gather` to assemble the selected rows. condition.shape.assert_has_rank(1) - x_nrows = nrows(x) + x_nrows = _nrows(x) x_and_y = concat([x, y], axis=0) indices = array_ops.where(condition, math_ops.range(x_nrows), - x_nrows + math_ops.range(nrows(y))) + x_nrows + math_ops.range(_nrows(y))) return gather(x_and_y, indices) else: @@ -1446,7 +1176,7 @@ def _coordinate_where(condition): # Convert the first index in each coordinate to a row index and column index. first_index = selected_coords[:, 0] - selected_rows = array_ops.gather(value_rowids(condition), first_index) + selected_rows = array_ops.gather(condition.value_rowids(), first_index) selected_row_starts = array_ops.gather(condition.row_splits, selected_rows) selected_cols = first_index - selected_row_starts @@ -1482,3 +1212,12 @@ def _concat_ragged_splits(splits_list): pieces.append(splits[1:] + splits_offset) splits_offset += splits[-1] return array_ops.concat(pieces, axis=0) + + +def _nrows(rt_input, out_type=dtypes.int64, name=None): + if isinstance(rt_input, ragged_tensor.RaggedTensor): + return rt_input.nrows(out_type=out_type, name=name) + else: + with ops.name_scope(name, 'RaggedNRows', [rt_input]): + return array_ops.shape(rt_input, out_type=out_type)[0] + diff --git a/tensorflow/python/ops/ragged/ragged_batch_gather_op_test.py b/tensorflow/python/ops/ragged/ragged_batch_gather_op_test.py index 7fe185641f..79f1ae591f 100644 --- a/tensorflow/python/ops/ragged/ragged_batch_gather_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_batch_gather_op_test.py @@ -20,15 +20,18 @@ from __future__ import print_function from absl.testing import parameterized +from tensorflow.python.eager import context from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import ragged +from tensorflow.python.ops.ragged import ragged_test_util from tensorflow.python.platform import googletest -class RaggedBatchGatherOpTest(test_util.TensorFlowTestCase, +@test_util.run_all_in_graph_and_eager_modes +class RaggedBatchGatherOpTest(ragged_test_util.RaggedTensorTestCase, parameterized.TestCase): @parameterized.parameters([ @@ -135,21 +138,16 @@ class RaggedBatchGatherOpTest(test_util.TensorFlowTestCase, expected=ragged.constant_value( [[[[b'c', b'a'], [b'd', b'd']], [[b'f', b'e']]]], ragged_rank=2)), ]) - @test_util.run_v1_only('b/120545219') def testRaggedBatchGather(self, descr, params, indices, expected): result = ragged.batch_gather(params, indices) - self.assertEqual( - getattr(result, 'ragged_rank', 0), getattr(expected, 'ragged_rank', 0)) - with self.test_session(): - if hasattr(expected, 'tolist'): - expected = expected.tolist() - self.assertEqual(result.eval().tolist(), expected) + self.assertRaggedEqual(result, expected) - @test_util.run_deprecated_v1 def testRaggedBatchGatherUnknownRankError(self): + if context.executing_eagerly(): + return params = [['a', 'b'], ['c', 'd']] indices = array_ops.placeholder(dtypes.int32, shape=None) - ragged_indices = ragged.from_row_splits(indices, [0, 2, 4]) + ragged_indices = ragged.RaggedTensor.from_row_splits(indices, [0, 2, 4]) with self.assertRaisesRegexp( ValueError, 'batch_gather does not allow indices with unknown shape.'): @@ -161,38 +159,39 @@ class RaggedBatchGatherOpTest(test_util.TensorFlowTestCase, @parameterized.parameters([ dict( - params=ragged.constant([['a'], ['b'], ['c']]), - indices=ragged.constant([[0], [0]]), + params=ragged.constant_value([['a'], ['b'], ['c']]), + indices=ragged.constant_value([[0], [0]]), message='Dimensions 3 and 2 are not compatible'), dict( params=[[[1, 2], [3, 4]], [[5, 6], [7, 8]]], - indices=ragged.constant([[[0, 0], [0, 0, 0]], [[0]]]), + indices=ragged.constant_value([[[0, 0], [0, 0, 0]], [[0]]]), message='batch shape from indices does not match params shape'), + dict( # rank mismatch + params=ragged.constant_value([[[0, 0], [0, 0, 0]], [[0]]]), + indices=ragged.constant_value([[[0, 0]], [[0, 0, 0]], [[0]]]), + error=(ValueError, errors.InvalidArgumentError)), dict( - params=ragged.constant([[[0, 0], [0, 0, 0]], [[0]]]), - indices=ragged.constant([[[0, 0]], [[0, 0, 0]], [[0]]]), - message='Dimensions must be equal, but are 3 and 4'), - dict( - params=ragged.constant([[[0, 0], [0, 0, 0]], [[0]], [[0]]]), - indices=ragged.constant([[[0, 0]], [[0, 0, 0]], [[0]]]), + params=ragged.constant_value([[[0, 0], [0, 0, 0]], [[0]], [[0]]]), + indices=ragged.constant_value([[[0, 0]], [[0, 0, 0]], [[0]]]), error=errors.InvalidArgumentError, - message='Condition x == y did not hold element-wise'), + message='.*Condition x == y did not hold.*'), + dict( + params=ragged.constant_value(['a', 'b', 'c']), + indices=ragged.constant_value([[0], [0]]), + message='batch shape from indices does not match params shape'), + dict( + params=ragged.constant_value([['a']]), + indices=0, + message='indices.rank must be at least 1.'), dict( - params=ragged.constant(['a', 'b', 'c']), - indices=ragged.constant([[0], [0]]), + params=ragged.constant_value([['a']]), + indices=[[[0]]], message='batch shape from indices does not match params shape'), - dict(params=ragged.constant_value([['a']]), - indices=0, - message='indices.rank must be at least 1.'), - dict(params=ragged.constant_value([['a']]), - indices=[[[0]]], - message='batch shape from indices does not match params shape'), ]) - @test_util.run_v1_only('b/120545219') def testRaggedBatchGatherStaticError(self, params, indices, - message, + message=None, error=ValueError): with self.assertRaisesRegexp(error, message): ragged.batch_gather(params, indices) diff --git a/tensorflow/python/ops/ragged/ragged_boolean_mask_op_test.py b/tensorflow/python/ops/ragged/ragged_boolean_mask_op_test.py index 763b016405..b0f7459322 100644 --- a/tensorflow/python/ops/ragged/ragged_boolean_mask_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_boolean_mask_op_test.py @@ -20,15 +20,18 @@ from __future__ import print_function from absl.testing import parameterized +from tensorflow.python.eager import context from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import ragged +from tensorflow.python.ops.ragged import ragged_test_util from tensorflow.python.platform import googletest -class RaggedBooleanMaskOpTest(test_util.TensorFlowTestCase, +@test_util.run_all_in_graph_and_eager_modes +class RaggedBooleanMaskOpTest(ragged_test_util.RaggedTensorTestCase, parameterized.TestCase): # Define short constants for true & false, so the data & mask can be lined # up in the examples below. This makes it easier to read the examples, to @@ -298,26 +301,18 @@ class RaggedBooleanMaskOpTest(test_util.TensorFlowTestCase, keepdims=True, expected=ragged.constant_value([[[1], [4, 6]], [[7, 9], []]])), ]) # pyformat: disable - @test_util.run_v1_only('b/120545219') def testBooleanMask(self, descr, data, mask, keepdims, expected): actual = ragged.boolean_mask(data, mask, keepdims=keepdims) - self.assertEqual( - getattr(actual, 'ragged_rank', 0), getattr(expected, 'ragged_rank', 0)) - with self.test_session(): - if isinstance(expected, ragged.RaggedTensorValue): - expected = expected.tolist() - self.assertEqual(actual.eval().tolist(), expected) + self.assertRaggedEqual(actual, expected) - @test_util.run_deprecated_v1 def testErrors(self): - self.assertRaisesRegexp(ValueError, - r'mask\.shape\.ndims must be kown statically', - ragged.boolean_mask, [[1, 2]], - array_ops.placeholder(dtypes.bool)) + if not context.executing_eagerly(): + self.assertRaisesRegexp(ValueError, + r'mask\.shape\.ndims must be kown statically', + ragged.boolean_mask, [[1, 2]], + array_ops.placeholder(dtypes.bool)) - self.assertRaisesRegexp(TypeError, - "Expected bool, got 0 of type 'int' instead.", - ragged.boolean_mask, [[1, 2]], [[0, 1]]) + self.assertRaises(TypeError, ragged.boolean_mask, [[1, 2]], [[0, 1]]) self.assertRaisesRegexp( ValueError, 'Tensor conversion requested dtype bool for ' 'RaggedTensor with dtype int32', ragged.boolean_mask, @@ -327,15 +322,6 @@ class RaggedBooleanMaskOpTest(test_util.TensorFlowTestCase, ValueError, r'Shapes \(1, 2\) and \(1, 3\) are incompatible', ragged.boolean_mask, [[1, 2]], [[True, False, True]]) - # self.assertRaisesRegexp(ValueError, - # r'data=.* is non-ragged but mask=.* is ragged', - # ragged.boolean_mask, [[1, 2]], - # ragged.constant([[True, False]])) - - # self.assertRaisesRegexp( - # ValueError, r'data=.* is ragged but mask=.* is non-ragged', - # ragged.boolean_mask, ragged.constant([[1, 2]]), [[True, False]]) - self.assertRaisesRegexp(errors.InvalidArgumentError, r'Inputs must have identical ragged splits', ragged.boolean_mask, ragged.constant([[1, 2]]), diff --git a/tensorflow/python/ops/ragged/ragged_concat_op_test.py b/tensorflow/python/ops/ragged/ragged_concat_op_test.py index ba7867418a..e72afb0448 100644 --- a/tensorflow/python/ops/ragged/ragged_concat_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_concat_op_test.py @@ -20,16 +20,20 @@ from __future__ import print_function from absl.testing import parameterized +from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import ragged +from tensorflow.python.ops.ragged import ragged_test_util from tensorflow.python.platform import googletest -class RaggedConcatOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): +@test_util.run_all_in_graph_and_eager_modes +class RaggedConcatOpTest(ragged_test_util.RaggedTensorTestCase, + parameterized.TestCase): def _rt_inputs_to_tensors(self, rt_inputs, ragged_ranks=None): if ragged_ranks is None: @@ -221,7 +225,6 @@ class RaggedConcatOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): axis=0, expected=[[b'a00', b'a01'], [], [b'a20', b'a21']]), ) # pyformat: disable - @test_util.run_v1_only('b/120545219') def testRaggedConcat(self, descr, rt_inputs, @@ -236,8 +239,7 @@ class RaggedConcatOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): self.assertEqual(concatenated.ragged_rank, expected_ragged_rank) if expected_shape is not None: self.assertEqual(concatenated.shape.as_list(), expected_shape) - with self.test_session(): - self.assertEqual(concatenated.eval().tolist(), expected) + self.assertRaggedEqual(concatenated, expected) @parameterized.parameters( dict( @@ -264,11 +266,14 @@ class RaggedConcatOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): ragged_ranks=(0, 0), rt_inputs=([[1, 2]], [[3, 4], [5, 6]]), axis=1, - error=ValueError, - message='Dimension 0 in both shapes must be equal'), + error=(ValueError, errors.InvalidArgumentError)), ) - @test_util.run_deprecated_v1 - def testStaticError(self, rt_inputs, axis, error, message, ragged_ranks=None): + def testStaticError(self, + rt_inputs, + axis, + error, + message=None, + ragged_ranks=None): rt_inputs = self._rt_inputs_to_tensors(rt_inputs, ragged_ranks) self.assertRaisesRegexp(error, message, ragged.concat, rt_inputs, axis) @@ -280,18 +285,20 @@ class RaggedConcatOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): error=errors.InvalidArgumentError, message='Input tensors have incompatible shapes'), ]) - @test_util.run_v1_only('b/120545219') def testRuntimeError(self, rt_inputs, axis, error, message, ragged_ranks=None): + if context.executing_eagerly(): + return rt_inputs = [ array_ops.placeholder_with_default(rt, shape=None) for rt in rt_inputs ] concatenated = ragged.concat(rt_inputs, axis) - with self.test_session(): - self.assertRaisesRegexp(error, message, concatenated.eval) + with self.assertRaisesRegexp(error, message): + self.evaluate(concatenated) - @test_util.run_deprecated_v1 def testNegativeAxisWithUnknownRankError(self): + if context.executing_eagerly(): + return rt_inputs = [ array_ops.placeholder(dtypes.int64), array_ops.placeholder(dtypes.int64) @@ -300,7 +307,6 @@ class RaggedConcatOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): ValueError, r'axis may only be negative if ndims is statically known.', ragged.concat, rt_inputs, -1) - @test_util.run_deprecated_v1 def testSingleTensorInput(self): """Tests ragged_concat with a single tensor input. @@ -310,8 +316,7 @@ class RaggedConcatOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): """ rt_inputs = ragged.constant([[1, 2], [3, 4]]) concatenated = ragged.concat(rt_inputs, 0) - with self.test_session(): - self.assertEqual(concatenated.eval().tolist(), [[1, 2], [3, 4]]) + self.assertRaggedEqual(concatenated, [[1, 2], [3, 4]]) if __name__ == '__main__': diff --git a/tensorflow/python/ops/ragged/ragged_const_op_test.py b/tensorflow/python/ops/ragged/ragged_const_op_test.py index 2505b23912..c014f71030 100644 --- a/tensorflow/python/ops/ragged/ragged_const_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_const_op_test.py @@ -20,15 +20,16 @@ from __future__ import print_function from absl.testing import parameterized - from tensorflow.python.framework import dtypes from tensorflow.python.framework import test_util -from tensorflow.python.ops.ragged import ragged_factory_ops -from tensorflow.python.ops.ragged import ragged_tensor +from tensorflow.python.ops import ragged +from tensorflow.python.ops.ragged import ragged_test_util from tensorflow.python.platform import googletest -class RaggedConstOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): +@test_util.run_all_in_graph_and_eager_modes +class RaggedConstOpTest(ragged_test_util.RaggedTensorTestCase, + parameterized.TestCase): @parameterized.parameters( #========================================================================= @@ -133,7 +134,6 @@ class RaggedConstOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): dict(pylist=[[b'a', b'b'], [b'c'], [b'd', b'e', b'f']], dtype=dtypes.string), ) - @test_util.run_deprecated_v1 def testRaggedConst(self, pylist, dtype=None, @@ -157,7 +157,7 @@ class RaggedConstOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): expected_dtype: The expected dtype for the resulting ragged tensor (used to test default/inferred types when dtype=None). """ - rt = ragged_factory_ops.constant( + rt = ragged.constant( pylist, dtype=dtype, ragged_rank=ragged_rank, inner_shape=inner_shape) # If dtype was explicitly specified, check it. @@ -168,31 +168,22 @@ class RaggedConstOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): # If ragged_rank was explicitly specified, check it. if ragged_rank is not None: - if isinstance(rt, ragged_tensor.RaggedTensor): + if isinstance(rt, ragged.RaggedTensor): self.assertEqual(rt.ragged_rank, ragged_rank) else: self.assertEqual(0, ragged_rank) # If inner_shape was explicitly specified, check it. if inner_shape is not None: - if isinstance(rt, ragged_tensor.RaggedTensor): - self.assertEqual(rt.inner_values.shape.as_list()[1:], list(inner_shape)) + if isinstance(rt, ragged.RaggedTensor): + self.assertEqual(rt.flat_values.shape.as_list()[1:], list(inner_shape)) else: self.assertEqual(rt.shape.as_list(), list(inner_shape)) if expected_shape is not None: self.assertEqual(tuple(rt.shape.as_list()), expected_shape) - with self.test_session(): - result = self.evaluate(rt) - if rt.shape.ndims > 0: - self.assertEqual(result.tolist(), pylist) - if expected_shape is not None: - self.assertEqual(result.shape, expected_shape) - else: - self.assertEqual(result, pylist) - if expected_shape is not None: - self.assertEqual((), expected_shape) + self.assertRaggedEqual(rt, pylist) @parameterized.parameters( dict( @@ -236,11 +227,7 @@ class RaggedConstOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): exception=ValueError, message='pylist has scalar values depth 2, but ragged_rank=2 ' 'requires scalar value depth greater than 2'), - dict( - pylist=[1, 2, 3], - inner_shape=(1, 1), - exception=TypeError, - message='Expected Tensor\'s shape'), + dict(pylist=[1, 2, 3], inner_shape=(1, 1), exception=TypeError), dict( pylist=[[[1, 2], [3, 4]], [[5, 6], [7, 8]]], inner_shape=(2, 2), @@ -259,7 +246,6 @@ class RaggedConstOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): exception=ValueError, message='inner values have inconsistent shape'), ) - @test_util.run_deprecated_v1 def testRaggedConstError(self, pylist, dtype=None, @@ -271,7 +257,7 @@ class RaggedConstOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): self.assertRaisesRegexp( exception, message, - ragged_factory_ops.constant, + ragged.constant, pylist, dtype=dtype, ragged_rank=ragged_rank, @@ -310,10 +296,10 @@ class RaggedConstOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): if exception is not None: self.assertRaisesRegexp( exception, message, - ragged_factory_ops._find_scalar_and_max_depth, pylist) + ragged.ragged_factory_ops._find_scalar_and_max_depth, pylist) else: self.assertEqual( - ragged_factory_ops._find_scalar_and_max_depth(pylist), + ragged.ragged_factory_ops._find_scalar_and_max_depth(pylist), (scalar_depth, max_depth)) @parameterized.parameters([ @@ -360,11 +346,11 @@ class RaggedConstOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): if exception is not None: self.assertRaisesRegexp( exception, message, - ragged_factory_ops._default_inner_shape_for_pylist, pylist, + ragged.ragged_factory_ops._default_inner_shape_for_pylist, pylist, ragged_rank) else: self.assertEqual( - ragged_factory_ops._default_inner_shape_for_pylist( + ragged.ragged_factory_ops._default_inner_shape_for_pylist( pylist, ragged_rank), inner_shape) diff --git a/tensorflow/python/ops/ragged/ragged_constant_value_op_test.py b/tensorflow/python/ops/ragged/ragged_constant_value_op_test.py index d80518930d..56768a9a47 100644 --- a/tensorflow/python/ops/ragged/ragged_constant_value_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_constant_value_op_test.py @@ -23,10 +23,12 @@ import numpy as np from tensorflow.python.framework import test_util from tensorflow.python.ops import ragged +from tensorflow.python.ops.ragged import ragged_test_util from tensorflow.python.platform import googletest -class RaggedConstantValueOpTest(test_util.TensorFlowTestCase, +@test_util.run_all_in_graph_and_eager_modes +class RaggedConstantValueOpTest(ragged_test_util.RaggedTensorTestCase, parameterized.TestCase): @parameterized.parameters( @@ -144,7 +146,7 @@ class RaggedConstantValueOpTest(test_util.TensorFlowTestCase, inner_shape=None, expected_shape=None, expected_dtype=None): - """Tests that `ragged_value(pylist).tolist() == pylist`.""" + """Tests that `ragged_value(pylist).to_list() == pylist`.""" rt = ragged.constant_value( pylist, dtype=dtype, ragged_rank=ragged_rank, inner_shape=inner_shape) @@ -164,7 +166,7 @@ class RaggedConstantValueOpTest(test_util.TensorFlowTestCase, # If inner_shape was explicitly specified, check it. if inner_shape is not None: if isinstance(rt, ragged.RaggedTensorValue): - self.assertEqual(rt.inner_values.shape[1:], inner_shape) + self.assertEqual(rt.flat_values.shape[1:], inner_shape) else: self.assertEqual(rt.shape, inner_shape) @@ -172,7 +174,10 @@ class RaggedConstantValueOpTest(test_util.TensorFlowTestCase, self.assertEqual(tuple(rt.shape), expected_shape) if rt.shape: - self.assertEqual(rt.tolist(), pylist) + if isinstance(rt, ragged.RaggedTensorValue): + self.assertEqual(rt.to_list(), pylist) + else: + self.assertEqual(rt.tolist(), pylist) if expected_shape is not None: self.assertEqual(rt.shape, expected_shape) else: diff --git a/tensorflow/python/ops/ragged/ragged_conversion_ops.py b/tensorflow/python/ops/ragged/ragged_conversion_ops.py index 83212e49cf..854c5b303c 100644 --- a/tensorflow/python/ops/ragged/ragged_conversion_ops.py +++ b/tensorflow/python/ops/ragged/ragged_conversion_ops.py @@ -18,407 +18,27 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.python.framework import constant_op -from tensorflow.python.framework import dtypes -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 control_flow_ops -from tensorflow.python.ops import gen_ragged_conversion_ops -from tensorflow.python.ops import math_ops -from tensorflow.python.ops.ragged import ragged_factory_ops from tensorflow.python.ops.ragged import ragged_tensor -from tensorflow.python.ops.ragged import ragged_util -#=============================================================================== -# RaggedTensor <-> Tensor conversion -#=============================================================================== def from_tensor(tensor, lengths=None, padding=None, ragged_rank=1, name=None): - """Converts a `Tensor` into a `RaggedTensor`. - - The set of absent/default values may be specified using a vector of lengths - or a padding value (but not both). If `lengths` is specified, then the - output tensor will satisfy `output[row] = tensor[row][:lengths[row]]`. - If `padding` is specified, then any row *suffix* consisting entirely of - `padding` will be excluded from the returned `RaggedTensor`. If neither - `lengths` nor `padding` is specified, then the returned `RaggedTensor` will - have no absent/default values. - - Examples: - - ```python - >>> dt = tf.constant([[5, 7, 0], [0, 3, 0], [6, 0, 0]]) - >>> ragged.from_tensor(dt).eval().tolist() - [[5, 7, 0], [0, 3, 0], [6, 0, 0]] - >>> ragged.from_tensor(dt, lengths=[2, 0, 3]).eval().tolist() - [[5, 7], [], [6, 0, 0]] - >>> ragged.from_tensor(dt, padding=0).eval().tolist() - [[5, 7], [0, 3], [6]] - ``` - - Args: - tensor: The `Tensor` to convert. Must have rank `ragged_rank + 1` or - higher. - lengths: An optional set of row lengths, specified using a 1-D integer - `Tensor` whose length is equal to `tensor.shape[0]` (the number of rows in - `tensor`). If specified, then `output[row]` will contain - `tensor[row][:lengths[row]]`. Negative lengths are treated as zero. - padding: An optional padding value. If specified, then any row suffix - consisting entirely of `padding` will be excluded from the returned - RaggedTensor. `padding` is a `Tensor` with the same dtype as `tensor` - and with `shape=tensor.shape[ragged_rank + 1:]`. - ragged_rank: Integer specifying the ragged rank for the returned - `RaggedTensor`. Must be greater than zero. - name: A name prefix for the returned tensors (optional). - - Returns: - A `RaggedTensor` with the specified `ragged_rank`. The shape of the - returned ragged tensor is compatible with the shape of `tensor`. - Raises: - ValueError: If both `lengths` and `padding` are specified. - """ - if lengths is not None and padding is not None: - raise ValueError('Specify lengths or padding, but not both') - if not isinstance(ragged_rank, int): - raise TypeError('ragged_rank expected int, got %r' % ragged_rank) - if ragged_rank <= 0: - raise ValueError('ragged_rank must be greater than 0; got %s' % ragged_rank) - - with ops.name_scope(name, 'RaggedFromTensor', [tensor, lengths, padding]): - tensor = ops.convert_to_tensor(tensor, name='tensor') - tensor.shape.with_rank_at_least(ragged_rank + 1) - input_shape = array_ops.shape(tensor, out_type=dtypes.int64) - ncols = input_shape[1] - - # Handle ragged_rank>1 via recursion: - # If the output should have multiple ragged dimensions, then first - # flatten the tensor to eliminate all but the last ragged dimension, - # and recursively convert that flattened tensor. Then add on the splits - # for the dimensions that we flattened out. - if ragged_rank > 1: - # Flatten `tensor` to eliminate all but the last ragged dimension. - new_shape = array_ops.concat( - [constant_op.constant([-1], dtypes.int64), input_shape[ragged_rank:]], - axis=0) - flattened = array_ops.reshape(tensor, new_shape) - # Recursively convert the flattened tensor. - values = from_tensor(flattened, lengths, padding) - # The total number of elements in each dimension. E.g., if - # input_shape=[3, 4, 5, 6], then dim[2] has 3*4*5 elements in total. - dim_size = math_ops.cumprod(input_shape) - # Construct splits tensors for the dimensions that were flattened. - new_splits = [ - math_ops.range(0, dim_size[dim - 1] + 1) * input_shape[dim] - for dim in range(1, ragged_rank) - ] - return ragged_factory_ops.from_nested_row_splits(values, new_splits) - - # If padding was specified, then use it to find row lengths. - if padding is not None: - padding = ops.convert_to_tensor( - padding, name='padding', dtype=tensor.dtype) - padding.shape.assert_is_compatible_with(tensor.shape[2:]) - - # Find places where the padding is equal to the tensor. (This will - # broadcast `padding` across the outermost 2 dimensions of `tensor`, - # so `has_default_value.shape = tensor.shape`.) - has_default_value = math_ops.equal(padding, tensor) - - # If the padding isn't a scalar, then require that all values in the - # padding match each item in the tensor. After this block of code, - # `has_default.shape = tensor.shape[:2]`. (Unfortunately, we can't just - # use reduce_all for both cases, becaue when you pass an empty `axis` - # list to reduce_all, it reduces all axes; but we want it to reduce no - # axes -- i.e., to be a no-op.) - tensor_rank = array_ops.rank(tensor) - reduce_axis = math_ops.range(2, tensor_rank) - has_default = control_flow_ops.cond( - tensor_rank > 2, - lambda: math_ops.reduce_all(has_default_value, axis=reduce_axis), - lambda: has_default_value) - has_default.set_shape(tensor_shape.TensorShape([None, None])) - has_default.set_shape(tensor.shape[:2]) - - # Use has_default it to find the length of each row: for each non-default - # item in a row, calculate the length that the row needs to have to - # include that item; and then take the max of those values (across each - # row). - has_nondefault = math_ops.logical_not(has_default) - has_nondefault = math_ops.cast(has_nondefault, dtypes.int64) - length_for_nondefault_value = ( - has_nondefault * array_ops.expand_dims( - math_ops.range(1, ncols + 1), 0)) - lengths = math_ops.reduce_max(length_for_nondefault_value, axis=1) - - # If we have lengths (either directly supplied, or computed from paddings), - # then use those to construct splits; and then use masking to get the - # corresponding values. - if lengths is not None: - lengths = ragged_util.convert_to_int_tensor(lengths, 'lengths', - dtypes.int64) - lengths.shape.assert_has_rank(1) - lengths = math_ops.minimum(lengths, ncols) - lengths = math_ops.maximum(lengths, 0) - limits = math_ops.cumsum(lengths) - splits = array_ops.concat( - [array_ops.zeros([1], dtypes.int64), limits], axis=0) - mask = array_ops.sequence_mask(lengths, maxlen=ncols) - values = array_ops.boolean_mask(tensor, mask) - return ragged_factory_ops.from_row_splits(values, splits) - - # If neither padding nor lengths were specified, then create a splits - # vector that contains no default values, and reshape the input tensor - # to form the values for the RaggedTensor. - nrows = input_shape[0] - nvals = nrows * ncols - splits = math_ops.range(nrows + 1) * ncols - values_shape = array_ops.concat([[nvals], input_shape[2:]], axis=0) - values = array_ops.reshape(tensor, values_shape) - return ragged_factory_ops.from_row_splits(values, splits) + if ragged_tensor.is_ragged(tensor): + return tensor + else: + return ragged_tensor.RaggedTensor.from_tensor(tensor, lengths, padding, + ragged_rank, name) def to_tensor(rt_input, default_value=None, name=None): - """Converts a `RaggedTensor` into a `Tensor`. - - Example: - - ```python - >>> rt = ragged.constant([[9, 8, 7], [], [6, 5], [4]]) - >>> print ragged.to_tensor(rt).eval() - [[9 8 7] - [0 0 0] - [6 5 0] - [4 0 0]] - ``` - - Args: - rt_input: The input `RaggedTensor`. - default_value: Value to set for indices not specified in `rt_input`. - Defaults to zero. `default_value` must be broadcastable to - `rt_input.shape[rt_input.ragged_rank + 1:]`. - name: A name prefix for the returned tensors (optional). - - Returns: - A `Tensor` with shape `ragged.bounding_shape(rt_input)` and the - values specified by the non-empty values in `rt_input`. Empty values are - assigned `default_value`. - """ - with ops.name_scope(name, 'RaggedToTensor', [rt_input, default_value]): - rt_input = ragged_factory_ops.convert_to_tensor_or_ragged_tensor( - rt_input, name='rt_input') - if not ragged_tensor.is_ragged(rt_input): - return rt_input # already dense - if default_value is not None: - default_value = ops.convert_to_tensor( - default_value, name='default_value', dtype=rt_input.dtype) - - # If ragged_rank > 1, then recursively convert the ragged values into a - # `Tensor` before we proceed. - values = rt_input.values - if ragged_tensor.is_ragged(values): - values = to_tensor(values, default_value) - - # Tile the default value, if necessary. - if default_value is not None: - if values.shape.ndims is not None: - default_value.shape.with_rank_at_most(values.shape.ndims - 1) - if (values.shape.ndims is None or default_value.shape.ndims is None or - values.shape.ndims != default_value.shape.ndims + 1): - value_shape = array_ops.shape(values)[1:] - default_value = array_ops.broadcast_to(default_value, value_shape) - default_value.shape.assert_is_compatible_with(values.shape[1:]) - - # Get the expected dense shape ([nrows, ncols] + value_shape). - rt_row_lengths = [rt_input.row_splits[1:] - rt_input.row_splits[:-1]] - nrows = array_ops.shape(rt_input.row_splits, out_type=dtypes.int64)[0] - 1 - ncols = math_ops.maximum(math_ops.reduce_max(rt_row_lengths), 0) - values_shape = array_ops.shape(values, out_type=dtypes.int64) - value_shape = values_shape[1:] - nvals = values_shape[0] - - # Build a default value if none was supplied. - if default_value is None: - default_value = array_ops.zeros(value_shape, dtype=values.dtype) - default_value.shape.assert_is_compatible_with(values.shape[1:]) - default_value.set_shape(values.shape[1:]) - - # Get the row start indices, and expand to shape=[nrows, 1]. - starts = array_ops.expand_dims(rt_input.row_splits[:-1], 1) - - # Get the row limit indices, and expand to shape=[nrows, 1]. - limits = array_ops.expand_dims(rt_input.row_splits[1:], 1) - - # Get the column indices, and expand to shape=[1, ncols]. - columns = array_ops.expand_dims(math_ops.range(0, ncols), 0) - - # Build a list containing the values plus the default value. We will use - # tf.gather to collect values from this list for the `Tensor` (using - # nvals as the index for the default value). - values_and_default = array_ops.concat( - [values, array_ops.stack([default_value])], axis=0) - - # Construct a matrix "indices" pointing into values_and_default. I.e., - # output[r, c] = values_and_default[indices[r, c]. - nondefault_index = starts + columns - has_value = nondefault_index < limits - default_index = array_ops.fill(array_ops.stack([nrows, ncols]), nvals) - indices = array_ops.where(has_value, nondefault_index, default_index) - - # Gather the results into a `Tensor`. - return array_ops.gather(values_and_default, indices) + if ragged_tensor.is_ragged(rt_input): + return rt_input.to_tensor(default_value, name) + else: + return rt_input -#=============================================================================== -# RaggedTensor <-> SparseTensor conversion -#=============================================================================== def to_sparse(rt_input, name=None): - """Converts a `RaggedTensor` into a sparse tensor. - - Example: - - ```python - >>> rt = ragged.constant([[1, 2, 3], [4], [], [5, 6]]) - >>> ragged.to_sparse(rt).eval() - SparseTensorValue(indices=[[0, 0], [0, 1], [0, 2], [1, 0], [3, 0], [3, 1]], - values=[1, 2, 3, 4, 5, 6], - dense_shape=[4, 3]) - ``` - - Args: - rt_input: The input `RaggedTensor`. - name: A name prefix for the returned tensors (optional). - - Returns: - A SparseTensor with the same values as `rt_input`. - """ - if not ragged_tensor.is_ragged(rt_input): - raise TypeError('Expected RaggedTensor, got %s' % type(rt_input).__name__) - with ops.name_scope(name, 'RaggedToSparse', [rt_input]): - rt_input = ragged_factory_ops.convert_to_tensor_or_ragged_tensor( - rt_input, name='rt_input') - result = gen_ragged_conversion_ops.ragged_tensor_to_sparse( - rt_input.nested_row_splits, rt_input.inner_values, name=name) - return sparse_tensor.SparseTensor( - result.sparse_indices, result.sparse_values, result.sparse_dense_shape) - - -@ops.RegisterGradient('RaggedTensorToSparse') -def _ragged_tensor_to_sparse_gradient(op, unused_sparse_indices_grad, - sparse_values_grad, - unused_sparse_shape_grad): - """Gradient for ragged.to_sparse.""" - op_inputs_nested_row_splits = op.inputs[:-1] - op_inputs_inner_values = op.inputs[-1] - - # No gradient for the RaggedTensor's nested_row_splits. - nested_row_splits_gradient = [None] * len(op_inputs_nested_row_splits) - - # Gradient for the RaggedTensor's inner_values is formed by reshaping - # the gradient for the SparseTensor's values. - inner_values_shape = array_ops.shape(op_inputs_inner_values) - inner_values_gradient = array_ops.reshape(sparse_values_grad, - inner_values_shape) - - return nested_row_splits_gradient + [inner_values_gradient] + return rt_input.to_sparse(name) def from_sparse(st_input, name=None): - """Converts a 2D `SparseTensor` to a `RaggedTensor`. - - Each row of the `output` `RaggedTensor` will contain the explicit values from - the same row in `st_input`. `st_input` must be ragged-right. If not it is - not ragged-right, then an error will be generated. - - Example: - - ```python - >>> st = SparseTensor(indices=[[0, 1], [0, 2], [0, 3], [1, 0], [3, 0]], - ... values=[1, 2, 3, 4, 5], - ... dense_shape=[4, 3]) - >>> ragged.from_sparse(st).eval().tolist() - [[1, 2, 3], [4], [], [5]] - ``` - - Currently, only two-dimensional `SparseTensors` are supported. - - Args: - st_input: The sparse tensor to convert. Must have rank 2. - name: A name prefix for the returned tensors (optional). - - Returns: - A `RaggedTensor` with the same values as `st_input`. - `output.ragged_rank = rank(st_input) - 1`. - `output.shape = [st_input.dense_shape[0], None]`. - Raises: - ValueError: If the number of dimensions in `st_input` is not known - statically, or is not two. - """ - if not sparse_tensor.is_sparse(st_input): - raise TypeError('Expected SparseTensor, got %s' % type(st_input).__name__) - with ops.name_scope(name, 'RaggedFromSparse', [st_input]): - st_input = sparse_tensor.convert_to_tensor_or_sparse_tensor( - st_input, name='rt_input') - - static_rank_from_dense_shape = ( - None if st_input.dense_shape.shape.ndims is None - else st_input.dense_shape.shape.dims[0].value) - static_rank_from_indices = ( - None if st_input.indices.shape.ndims is None - else st_input.indices.shape.dims[1].value) - - if static_rank_from_dense_shape != 2 and static_rank_from_indices != 2: - raise ValueError('rank(st_input) must be 2') - - with ops.control_dependencies( - _assert_sparse_indices_are_ragged_right(st_input.indices)): - # Treat sparse row indices as segment ids to generate a splits tensor that - # we can pair with the sparse tensor values. (Ignore sparse column - # indices.) - segment_ids = st_input.indices[:, 0] - num_segments = st_input.dense_shape[0] - return ragged_factory_ops.from_value_rowids(st_input.values, segment_ids, - num_segments) - - -def _assert_sparse_indices_are_ragged_right(indices): - """Checks that the given SparseTensor.indices tensor is ragged-right. - - Example: `indices = [[0, 0], [0, 1], [2, 0], [3, 1]]` is not ragged right - because the entry `[3, 1]` skips a cell. - - Args: - indices: The SparseTensor indices to check. - - Returns: - A list of control dependency op tensors. - """ - index_prefix = indices[:, :-1] - index_suffix = indices[:, -1] - - # Check whether each index is starting a new row in the innermost dimension - # (prefix[i] != prefix[i-1]) or continuing a row (prefix[i] == prefix[i-1]). - # (Note: this skips the first index; we will check that separately below.) - index_prefix_changed = math_ops.reduce_any( - math_ops.not_equal(index_prefix[1:], index_prefix[:-1]), axis=1) - - # Check two cases: - # * For indices that start a new row: index_suffix[i] must be zero. - # * For indices that continue a row: index_suffix[i] must be equal to - # index_suffix[i-1]+1. - index_ok = array_ops.where( - index_prefix_changed, math_ops.equal(index_suffix[1:], 0), - math_ops.equal(index_suffix[1:], index_suffix[:-1] + 1)) - - # Also check that the very first index didn't skip any cells. The first - # index starts a new row (by definition), so its suffix should be zero. - sparse_indices_are_ragged_right = math_ops.logical_and( - math_ops.reduce_all(math_ops.equal(index_suffix[:1], 0)), - math_ops.reduce_all(index_ok)) - - message = [ - 'SparseTensor is not right-ragged', - 'SparseTensor.indices =', indices - ] - return [control_flow_ops.Assert(sparse_indices_are_ragged_right, message)] + return ragged_tensor.RaggedTensor.from_sparse(st_input, name) diff --git a/tensorflow/python/ops/ragged/ragged_dispatch.py b/tensorflow/python/ops/ragged/ragged_dispatch.py index 7f44ac2ec1..7c74f7be62 100644 --- a/tensorflow/python/ops/ragged/ragged_dispatch.py +++ b/tensorflow/python/ops/ragged/ragged_dispatch.py @@ -30,7 +30,6 @@ from tensorflow.python.ops import parsing_ops from tensorflow.python.ops import string_ops from tensorflow.python.ops import variables from tensorflow.python.ops.ragged import ragged_array_ops -from tensorflow.python.ops.ragged import ragged_factory_ops from tensorflow.python.ops.ragged import ragged_math_ops from tensorflow.python.ops.ragged import ragged_tensor from tensorflow.python.ops.ragged import ragged_tensor_shape @@ -122,22 +121,22 @@ class UnaryRaggedElementwiseDispatcher(dispatch.OpDispatcher): nested_splits_lists = [ elt.nested_row_splits for elt in x if ragged_tensor.is_ragged(elt) ] - inner_values = [ - elt.inner_values if ragged_tensor.is_ragged(elt) else elt + flat_values = [ + elt.flat_values if ragged_tensor.is_ragged(elt) else elt for elt in x ] with ops.control_dependencies( ragged_util.assert_splits_match(nested_splits_lists)): - return ragged_factory_ops.from_nested_row_splits( - self._original_op(inner_values, *args, **kwargs), + return ragged_tensor.RaggedTensor.from_nested_row_splits( + self._original_op(flat_values, *args, **kwargs), nested_splits_lists[0]) else: return self.NOT_SUPPORTED else: found_ragged = ragged_tensor.is_ragged(x) if found_ragged: - mapped_values = self._original_op(x.inner_values, *args, **kwargs) - return x.with_inner_values(mapped_values) + mapped_values = self._original_op(x.flat_values, *args, **kwargs) + return x.with_flat_values(mapped_values) else: return self.NOT_SUPPORTED @@ -191,8 +190,8 @@ class BinaryRaggedElementwiseDispatcher(dispatch.OpDispatcher): return self.NOT_SUPPORTED if ((x_is_ragged and y_is_ragged) or - (x_is_ragged and x.inner_values.shape.ndims <= y.shape.ndims) or - (y_is_ragged and y.inner_values.shape.ndims <= x.shape.ndims)): + (x_is_ragged and x.flat_values.shape.ndims <= y.shape.ndims) or + (y_is_ragged and y.flat_values.shape.ndims <= x.shape.ndims)): bcast_shape = ragged_tensor_shape.broadcast_dynamic_shape( ragged_tensor_shape.RaggedTensorDynamicShape.from_tensor(x), ragged_tensor_shape.RaggedTensorDynamicShape.from_tensor(y)) @@ -201,13 +200,13 @@ class BinaryRaggedElementwiseDispatcher(dispatch.OpDispatcher): y = ragged_tensor_shape.broadcast_to( y, bcast_shape, broadcast_inner_dimensions=False) - x_values = x.inner_values if ragged_tensor.is_ragged(x) else x - y_values = y.inner_values if ragged_tensor.is_ragged(y) else y + x_values = x.flat_values if ragged_tensor.is_ragged(x) else x + y_values = y.flat_values if ragged_tensor.is_ragged(y) else y mapped_values = self._original_op(x_values, y_values, *args, **kwargs) if ragged_tensor.is_ragged(x): - return x.with_inner_values(mapped_values) + return x.with_flat_values(mapped_values) else: - return y.with_inner_values(mapped_values) + return y.with_flat_values(mapped_values) class RaggedDispatcher(dispatch.OpDispatcher): diff --git a/tensorflow/python/ops/ragged/ragged_dispatch_test.py b/tensorflow/python/ops/ragged/ragged_dispatch_test.py index 2533c60c4e..82827aa2aa 100644 --- a/tensorflow/python/ops/ragged/ragged_dispatch_test.py +++ b/tensorflow/python/ops/ragged/ragged_dispatch_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 context from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import ops @@ -32,6 +33,7 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops import parsing_ops from tensorflow.python.ops import ragged from tensorflow.python.ops import string_ops +from tensorflow.python.ops.ragged import ragged_test_util from tensorflow.python.platform import googletest # Constants listing various op types to test. Each operation @@ -126,8 +128,8 @@ BINARY_INT_OPS = [ ] -@test_util.run_v1_only('b/120545219') -class RaggedElementwiseOpsTest(test_util.TensorFlowTestCase, +@test_util.run_all_in_graph_and_eager_modes +class RaggedElementwiseOpsTest(ragged_test_util.RaggedTensorTestCase, parameterized.TestCase): def assertSameShape(self, x, y): @@ -138,7 +140,7 @@ class RaggedElementwiseOpsTest(test_util.TensorFlowTestCase, for (x_splits, y_splits) in zip(x.nested_row_splits, y.nested_row_splits): self.assertAllEqual(x_splits, y_splits) self.assertAllEqual( - array_ops.shape(x.inner_values), array_ops.shape(y.inner_values)) + array_ops.shape(x.flat_values), array_ops.shape(y.flat_values)) else: self.assertIsInstance(y, ops.Tensor) self.assertAllEqual(array_ops.shape(x), array_ops.shape(y)) @@ -215,19 +217,18 @@ class RaggedElementwiseOpsTest(test_util.TensorFlowTestCase, result = op(x, **extra_args) # Run the wrapped op on the dense values, for comparison. - dense_x = x.inner_values if isinstance(x, ragged.RaggedTensor) else x + dense_x = x.flat_values if isinstance(x, ragged.RaggedTensor) else x expected_flat_values = array_ops.reshape(op(dense_x, **extra_args), [-1]) - with self.test_session(): - # Check that the result has the expected shape. - self.assertSameShape(x, result) + # Check that the result has the expected shape. + self.assertSameShape(x, result) - # Check that the result has the expected (flattened) values. - if isinstance(result, ragged.RaggedTensor): - result_flat_values = array_ops.reshape(result.inner_values, [-1]) - else: - result_flat_values = array_ops.reshape(result, [-1]) - self.assertAllEqual(expected_flat_values, result_flat_values) + # Check that the result has the expected (flattened) values. + if isinstance(result, ragged.RaggedTensor): + result_flat_values = array_ops.reshape(result.flat_values, [-1]) + else: + result_flat_values = array_ops.reshape(result, [-1]) + self.assertAllEqual(expected_flat_values, result_flat_values) @parameterized.parameters( [ @@ -327,21 +328,20 @@ class RaggedElementwiseOpsTest(test_util.TensorFlowTestCase, result = op(x, y, **extra_args) # Run the wrapped op on the dense values, for comparison. - dense_x = x.inner_values if isinstance(x, ragged.RaggedTensor) else x - dense_y = y.inner_values if isinstance(y, ragged.RaggedTensor) else y + dense_x = x.flat_values if isinstance(x, ragged.RaggedTensor) else x + dense_y = y.flat_values if isinstance(y, ragged.RaggedTensor) else y expected_flat_values = array_ops.reshape( op(dense_x, dense_y, **extra_args), [-1]) - with self.test_session(): - # Check that the result has the expected shape. - self.assertSameShape(y, result) + # Check that the result has the expected shape. + self.assertSameShape(y, result) - # Check that the result has the expected (flattened) values. - if isinstance(result, ragged.RaggedTensor): - result_flat_values = array_ops.reshape(result.inner_values, [-1]) - else: - result_flat_values = array_ops.reshape(result, [-1]) - self.assertAllEqual(expected_flat_values, result_flat_values) + # Check that the result has the expected (flattened) values. + if isinstance(result, ragged.RaggedTensor): + result_flat_values = array_ops.reshape(result.flat_values, [-1]) + else: + result_flat_values = array_ops.reshape(result, [-1]) + self.assertAllEqual(expected_flat_values, result_flat_values) @parameterized.parameters( [ @@ -385,26 +385,27 @@ class RaggedElementwiseOpsTest(test_util.TensorFlowTestCase, # Run the wrapped op on the dense values, for comparison. dense_inputs = [ - x.inner_values if isinstance(x, ragged.RaggedTensor) else x + x.flat_values if isinstance(x, ragged.RaggedTensor) else x for x in inputs ] expected_flat_values = array_ops.reshape( op(dense_inputs, **extra_args), [-1]) - with self.test_session(): - # Check that the result has the expected shape. - self.assertSameShape(inputs[0], result) + # Check that the result has the expected shape. + self.assertSameShape(inputs[0], result) - # Check that the result has the expected (flattened) values. - if isinstance(result, ragged.RaggedTensor): - result_flat_values = array_ops.reshape(result.inner_values, [-1]) - else: - result_flat_values = array_ops.reshape(result, [-1]) - self.assertAllEqual(expected_flat_values, result_flat_values) + # Check that the result has the expected (flattened) values. + if isinstance(result, ragged.RaggedTensor): + result_flat_values = array_ops.reshape(result.flat_values, [-1]) + else: + result_flat_values = array_ops.reshape(result, [-1]) + self.assertAllEqual(expected_flat_values, result_flat_values) def testElementwiseOpUnknownRankError(self): + if context.executing_eagerly(): + return x = ragged.constant([[1, 2], [3]]) - y = ragged.from_row_splits( + y = ragged.RaggedTensor.from_row_splits( array_ops.placeholder_with_default([1, 2, 3], shape=None), x.row_splits) with self.assertRaisesRegexp(ValueError, r'Unable to broadcast: unknown rank'): @@ -428,27 +429,22 @@ class RaggedElementwiseOpsTest(test_util.TensorFlowTestCase, x = ragged.convert_to_tensor_or_ragged_tensor(x, dtype=dtypes.int32) y = ragged.convert_to_tensor_or_ragged_tensor(y, dtype=dtypes.int32) result = x + y - with self.cached_session(): - self.assertEqual(result.eval().tolist(), expected) + self.assertRaggedEqual(result, expected) def testElementwiseOpShapeMismatch(self): x = ragged.constant([[1, 2, 3], [4, 5]]) y = ragged.constant([[1, 2, 3], [4, 5, 6]]) - with self.assertRaisesRegexp(errors.InvalidArgumentError, - 'Incompatible shapes'): - with self.cached_session(): - math_ops.add(x, y).eval() + with self.assertRaises(errors.InvalidArgumentError): + self.evaluate(math_ops.add(x, y)) def testBinaryOpSparseAndRagged(self): x = ragged.constant([[1, 2, 3], [4, 5]]) y = sparse_tensor.SparseTensor([[0, 0], [0, 1], [2, 0]], [1, 2, 3], [3, 2]) - with self.assertRaises(TypeError): - with self.cached_session(): - math_ops.add(x, y).eval() + with self.assertRaises((TypeError, ValueError)): + self.evaluate(math_ops.add(x, y)) - with self.assertRaises(TypeError): - with self.cached_session(): - math_ops.add_n([x, y]).eval() + with self.assertRaises((TypeError, ValueError)): + self.evaluate(math_ops.add_n([x, y])) if __name__ == '__main__': diff --git a/tensorflow/python/ops/ragged/ragged_eager_test.py b/tensorflow/python/ops/ragged/ragged_eager_test.py index 731ff742aa..f1befbf961 100644 --- a/tensorflow/python/ops/ragged/ragged_eager_test.py +++ b/tensorflow/python/ops/ragged/ragged_eager_test.py @@ -17,17 +17,17 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import sys from absl.testing import parameterized from tensorflow.python.framework import ops -from tensorflow.python.framework import test_util from tensorflow.python.ops import ragged +from tensorflow.python.ops.ragged import ragged_test_util from tensorflow.python.platform import googletest -class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): +class RaggedTensorTest(ragged_test_util.RaggedTensorTestCase, + parameterized.TestCase): @parameterized.parameters([ dict(pylist=[[b'a', b'b'], [b'c']]), @@ -36,21 +36,15 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): ]) def testRaggedTensorToList(self, pylist, ragged_rank=None): rt = ragged.constant(pylist, ragged_rank) - self.assertEqual(rt.tolist(), pylist) - - expected = "RaggedTensor([['a', 'b'], ['c']])" - if sys.version_info[0] == 3: - expected = "RaggedTensor([[b'a', b'b'], [b'c']])" + self.assertRaggedEqual(rt, pylist) @parameterized.parameters([ - dict(pylist=[['a', 'b'], ['c']], - expected=expected), - dict(pylist=[[[1, 2], [3]], [[4, 5, 6], [], [7]]], - expected='RaggedTensor([[[1, 2], [3]], [[4, 5, 6], [], [7]]])'), + dict(pylist=[[b'a', b'b'], [b'c']]), + dict(pylist=[[[1, 2], [3]], [[4, 5, 6], [], [7]]]), ]) - def testRaggedTensorStr(self, pylist, expected): + def testRaggedTensorStr(self, pylist): rt = ragged.constant(pylist) - self.assertEqual(str(rt), expected) + self.assertEqual(str(rt), '' % pylist) if __name__ == '__main__': diff --git a/tensorflow/python/ops/ragged/ragged_expand_dims_op_test.py b/tensorflow/python/ops/ragged/ragged_expand_dims_op_test.py index 3ff66973b6..072f330e3c 100644 --- a/tensorflow/python/ops/ragged/ragged_expand_dims_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_expand_dims_op_test.py @@ -22,10 +22,12 @@ from absl.testing import parameterized from tensorflow.python.framework import test_util from tensorflow.python.ops import ragged +from tensorflow.python.ops.ragged import ragged_test_util from tensorflow.python.platform import googletest -class RaggedExpandDimsOpTest(test_util.TensorFlowTestCase, +@test_util.run_all_in_graph_and_eager_modes +class RaggedExpandDimsOpTest(ragged_test_util.RaggedTensorTestCase, parameterized.TestCase): # An example 4-d ragged tensor with shape [3, (D2), (D3), 2], and the @@ -105,7 +107,6 @@ class RaggedExpandDimsOpTest(test_util.TensorFlowTestCase, expected=EXAMPLE4D_EXPAND_AXIS[4], expected_shape=[3, None, None, 2, 1]), ]) # pyformat: disable - @test_util.run_deprecated_v1 def testRaggedExpandDims(self, rt_input, axis, @@ -118,8 +119,7 @@ class RaggedExpandDimsOpTest(test_util.TensorFlowTestCase, if expected_shape is not None: self.assertEqual(expanded.shape.as_list(), expected_shape) - with self.test_session(): - self.assertEqual(expanded.eval().tolist(), expected) + self.assertRaggedEqual(expanded, expected) if __name__ == '__main__': diff --git a/tensorflow/python/ops/ragged/ragged_factory_ops.py b/tensorflow/python/ops/ragged/ragged_factory_ops.py index d1f301bc58..2c63e1c799 100644 --- a/tensorflow/python/ops/ragged/ragged_factory_ops.py +++ b/tensorflow/python/ops/ragged/ragged_factory_ops.py @@ -21,11 +21,7 @@ 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.framework import tensor_util -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import math_ops from tensorflow.python.ops.ragged import ragged_tensor from tensorflow.python.ops.ragged import ragged_tensor_value @@ -56,8 +52,8 @@ def constant(pylist, dtype=None, ragged_rank=None, inner_shape=None, name=None): `pylist`. ragged_rank: An integer specifying the ragged rank of the returned `RaggedTensor`. Must be nonnegative and less than `K`. Defaults to - `max(0, K - 1)` if `inner_shape` is not specified. Defaults to - `max(0, K - 1 - len(inner_shape))` if `inner_shape` is specified. + `max(0, K - 1)` if `inner_shape` is not specified. Defaults to `max(0, K + - 1 - len(inner_shape))` if `inner_shape` is specified. inner_shape: A tuple of integers specifying the shape for individual inner values in the returned `RaggedTensor`. Defaults to `()` if `ragged_rank` is not specified. If `ragged_rank` is specified, then a default is chosen @@ -72,9 +68,10 @@ def constant(pylist, dtype=None, ragged_rank=None, inner_shape=None, name=None): ValueError: If the scalar values in `pylist` have inconsistent nesting depth; or if ragged_rank or inner_shape are incompatible with `pylist`. """ - with ops.name_scope(name, 'RaggedConstant'): - return _constant_value(from_row_splits, constant_op.constant, pylist, dtype, - ragged_rank, inner_shape) + with ops.name_scope(name, "RaggedConstant"): + return _constant_value(ragged_tensor.RaggedTensor.from_row_splits, + constant_op.constant, pylist, dtype, ragged_rank, + inner_shape) def constant_value(pylist, dtype=None, ragged_rank=None, inner_shape=None): @@ -153,29 +150,29 @@ def _constant_value(ragged_factory, inner_factory, pylist, dtype, ragged_rank, depth; or if ragged_rank or inner_shape are incompatible with `pylist`. """ if ragged_tensor.is_ragged(pylist): - raise TypeError('pylist may not be a RaggedTensor or RaggedTensorValue.') + raise TypeError("pylist may not be a RaggedTensor or RaggedTensorValue.") if not isinstance(pylist, (list, tuple)): # Scalar value if ragged_rank is not None and ragged_rank != 0: - raise ValueError('Invalid pylist=%r: incompatible with ragged_rank=%d' % + raise ValueError("Invalid pylist=%r: incompatible with ragged_rank=%d" % (pylist, ragged_rank)) if inner_shape is not None and inner_shape: raise ValueError( - 'Invalid pylist=%r: incompatible with dim(inner_shape)=%d' % + "Invalid pylist=%r: incompatible with dim(inner_shape)=%d" % (pylist, len(inner_shape))) return inner_factory(pylist, dtype, ()) if ragged_rank is not None and ragged_rank < 0: raise ValueError( - 'Invalid ragged_rank=%r: must be nonnegative' % ragged_rank) + "Invalid ragged_rank=%r: must be nonnegative" % ragged_rank) # Find the depth of scalar values in `pylist`. scalar_depth, max_depth = _find_scalar_and_max_depth(pylist) if scalar_depth is not None: if max_depth > scalar_depth: - raise ValueError('Invalid pylist=%r: empty list nesting is greater ' - 'than scalar value nesting' % pylist) + raise ValueError("Invalid pylist=%r: empty list nesting is greater " + "than scalar value nesting" % pylist) # If both inner_shape and ragged_rank were specified, then check that # they are compatible with pylist. @@ -184,8 +181,8 @@ def _constant_value(ragged_factory, inner_factory, pylist, dtype, ragged_rank, if ((scalar_depth is not None and expected_depth != scalar_depth) or (scalar_depth is None and expected_depth < max_depth)): raise ValueError( - 'Invalid pylist=%r: incompatible with ragged_rank=%d ' - 'and dim(inner_shape)=%d' % (pylist, ragged_rank, len(inner_shape))) + "Invalid pylist=%r: incompatible with ragged_rank=%d " + "and dim(inner_shape)=%d" % (pylist, ragged_rank, len(inner_shape))) # Check if the result is a `Tensor`. if (ragged_rank == 0 or @@ -221,7 +218,7 @@ def _constant_value(ragged_factory, inner_factory, pylist, dtype, ragged_rank, values = concatenated_values values = inner_factory( - values, dtype=dtype, shape=(len(values),) + inner_shape, name='values') + values, dtype=dtype, shape=(len(values),) + inner_shape, name="values") for row_splits in reversed(nested_splits): values = ragged_factory(values, row_splits) return values @@ -249,7 +246,7 @@ def _find_scalar_and_max_depth(pylist): child_scalar_depth, child_max_depth = _find_scalar_and_max_depth(child) if child_scalar_depth is not None: if scalar_depth is not None and scalar_depth != child_scalar_depth + 1: - raise ValueError('all scalar values must have the same nesting depth') + raise ValueError("all scalar values must have the same nesting depth") scalar_depth = child_scalar_depth + 1 max_depth = max(max_depth, child_max_depth + 1) return (scalar_depth, max_depth) @@ -273,436 +270,24 @@ def _default_inner_shape_for_pylist(pylist, ragged_rank): """Checks that `item` has a consistent shape matching `shape`.""" is_nested = isinstance(item, (list, tuple)) if is_nested != bool(shape): - raise ValueError('inner values have inconsistent shape') + raise ValueError("inner values have inconsistent shape") if is_nested: if shape[0] != len(item): - raise ValueError('inner values have inconsistent shape') + raise ValueError("inner values have inconsistent shape") for child in item: check_inner_shape(child, shape[1:]) # Collapse the ragged layers to get the list of inner values. - inner_values = pylist + flat_values = pylist for dim in range(ragged_rank): - if not all(isinstance(v, (list, tuple)) for v in inner_values): - raise ValueError('pylist has scalar values depth %d, but ragged_rank=%d ' - 'requires scalar value depth greater than %d' % + if not all(isinstance(v, (list, tuple)) for v in flat_values): + raise ValueError("pylist has scalar values depth %d, but ragged_rank=%d " + "requires scalar value depth greater than %d" % (dim + 1, ragged_rank, ragged_rank)) - inner_values = sum((list(v) for v in inner_values), []) + flat_values = sum((list(v) for v in flat_values), []) # Compute the inner shape looking only at the leftmost elements; and then # use check_inner_shape to verify that other elements have the same shape. - inner_shape = get_inner_shape(inner_values) - check_inner_shape(inner_values, inner_shape) + inner_shape = get_inner_shape(flat_values) + check_inner_shape(flat_values, inner_shape) return inner_shape[1:] - - -#=============================================================================== -# Convert value -> tensor -#=============================================================================== -def convert_to_tensor_or_ragged_tensor(value, - dtype=None, - preferred_dtype=None, - name=None): - """Converts value to a `RaggedTensor` or `Tensor`. - - * If `value` is a `RaggedTensor`, then return it as-is. - * If `value` is a `RaggedTensorValue`, return a corresponding constant - `RaggedTensor`. - * Otherwise, use `convert_to_tensor` to convert `value` to a `Tensor`. - - Args: - value: A `RaggedTensor`, a `RaggedTensorValue`, or an object whose type has - a registered `Tensor` conversion function. - dtype: Optional element type for the returned tensor. If missing the type - is inferred from the type of `value`. - preferred_dtype: Optional element type for the returned tensor, used when - dtype is None. This argument has no effect if `value` is already a - tensor, or when conversion is not possible. - name: Optional name to use if a new `Tensor` is created. - - Returns: - A `Tensor` or `RaggedTensor`. - """ - if isinstance(value, ragged_tensor.RaggedTensor): - if dtype and not dtype.is_compatible_with(value.dtype): - raise ValueError('Tensor conversion requested dtype %s for ' - 'RaggedTensor with dtype %s: %r' % - (dtype.name, value.dtype.name, value)) - return value - elif isinstance(value, ragged_tensor_value.RaggedTensorValue): - with ops.name_scope(name, 'ConvertToTensorOrRaggedTensor', []): - inner_values = ops.convert_to_tensor( - value=value.inner_values, - dtype=dtype, - preferred_dtype=preferred_dtype, - name='inner_values') - return from_nested_row_splits(inner_values, value.nested_row_splits) - else: - return ops.convert_to_tensor( - value=value, dtype=dtype, preferred_dtype=preferred_dtype, name=name) - - -#=============================================================================== -# Ops to construct RaggedTensor from row-partitioned values. -#=============================================================================== - - -def from_value_rowids(values, value_rowids, nrows=None, name=None): - """Creates a `RaggedTensor` with rows partitioned by `value_rowids`. - - The returned `RaggedTensor` corresponds with the python list defined by: - - ```python - result = [[values[i] for i in range(len(values)) if value_rowids[i] == row] - for row in range(nrows)] - ``` - - Warning: currently, this needs to cast value_rowids to int64 before - converting, since `tf.bincount` only supports `int32`. - - Args: - values: A potentially ragged tensor with shape `[nvals, ...]`. - value_rowids: A 1-D int64 tensor with shape `[nvals]`, which corresponds - one-to-one with `values`, and specifies each value's row index. Must be - nonnegative, and must be sorted in ascending order. - nrows: An int64 scalar specifying the number of rows. This should be - specified if the `RaggedTensor` may containing empty training rows. Must - be greater than `value_rowids[-1]` (or zero if `value_rowids` is empty). - Defaults to `value_rowids[-1]` (or zero if `value_rowids` is empty). - name: A name prefix for the RaggedTensor (optional). - - Returns: - A `RaggedTensor`. `result.rank = values.rank + 1`. - `result.ragged_rank = values.ragged_rank + 1`. - - Raises: - ValueError: If `nrows` is incompatible with `value_rowids`. - - #### Example: - ```python - >>> rt = ragged.from_value_rowids( - ... values=[3, 1, 4, 1, 5, 9, 2, 6], - ... value_rowids=[0, 0, 0, 0, 2, 2, 2, 3], - ... nrows=5) - >>> rt.eval().tolist() - [[3, 1, 4, 1], [], [5, 9, 2], [6], []] - ``` - """ - with ops.name_scope(name, 'RaggedFromValueRowIds', - [values, value_rowids, nrows]): - values = convert_to_tensor_or_ragged_tensor(values, name='values') - value_rowids = ops.convert_to_tensor( - value_rowids, dtypes.int64, name='value_rowids') - if nrows is None: - const_rowids = tensor_util.constant_value(value_rowids) - if const_rowids is None: - nrows = array_ops.concat([value_rowids[-1:], [-1]], axis=0)[0] + 1 - const_nrows = None - else: - const_nrows = const_rowids[-1] + 1 if const_rowids.size > 0 else 0 - nrows = ops.convert_to_tensor(const_nrows, dtypes.int64, name='nrows') - else: - nrows = ops.convert_to_tensor(nrows, dtypes.int64, 'nrows') - const_nrows = tensor_util.constant_value(nrows) - if const_nrows is not None: - if const_nrows < 0: - raise ValueError('Expected nrows >= 0; got %d' % const_nrows) - const_rowids = tensor_util.constant_value(value_rowids) - if const_rowids is not None and const_rowids.size > 0: - if not const_nrows >= const_rowids[-1] + 1: - raise ValueError( - 'Expected nrows >= value_rowids[-1] + 1; got nrows=%d, ' - 'value_rowids[-1]=%d' % (const_nrows, const_rowids[-1])) - - value_rowids.shape.assert_has_rank(1) - nrows.shape.assert_has_rank(0) - values.shape[:1].assert_is_compatible_with(value_rowids.shape) - - # Convert value_rowids & nrows to row_splits. - # Note: we don't use segment_ids_to_row_splits() here because we want - # to save the intermediate value `row_lengths`, so we can cache it. - # TODO(b/116708836) Upgrade bincount to accept int64 so we can skip the cast - # (Remove the warning in the docstring when we do.) - value_rowids_int32 = math_ops.cast(value_rowids, dtypes.int32) - nrows_int32 = math_ops.cast(nrows, dtypes.int32) - row_lengths = math_ops.bincount( - value_rowids_int32, - minlength=nrows_int32, - maxlength=nrows_int32, - dtype=dtypes.int64) - row_splits = array_ops.concat([[0], math_ops.cumsum(row_lengths)], axis=0) - if const_nrows is not None: - row_lengths.set_shape([const_nrows]) - row_splits.set_shape([const_nrows + 1]) - - return ragged_tensor.RaggedTensor( - values, - row_splits, - cached_row_lengths=row_lengths, - cached_value_rowids=value_rowids, - cached_nrows=nrows, - internal=True) - - -def from_row_splits(values, row_splits, name=None): - """Creates a `RaggedTensor` with rows partitioned by `row_splits`. - - The returned `RaggedTensor` corresponds with the python list defined by: - - ```python - result = [values[row_splits[i]:row_splits[i + 1]] - for i in range(len(row_splits) - 1)] - ``` - - Args: - values: A potentially ragged tensor with shape `[nvals, ...]`. - row_splits: A 1-D int64 tensor with shape `[nrows+1]`. Must not be empty, - and must be sorted in ascending order. `row_splits[0]` must be zero and - `row_splits[-1]` must be `nvals`. - name: A name prefix for the RaggedTensor (optional). - - Returns: - A `RaggedTensor`. `result.rank = values.rank + 1`. - `result.ragged_rank = values.ragged_rank + 1`. - - Raises: - ValueError: If `row_splits` is an empty list. - - #### Example: - ```python - >>> rt = ragged.from_row_splits( - ... values=[3, 1, 4, 1, 5, 9, 2, 6], - ... row_splits=[0, 4, 4, 7, 8, 8]) - >>> rt.eval().tolist() - [[3, 1, 4, 1], [], [5, 9, 2], [6], []] - ``` - """ - if isinstance(row_splits, (list, tuple)) and not row_splits: - raise ValueError('row_splits tensor may not be empty.') - with ops.name_scope(name, 'RaggedFromRowSplits', [values, row_splits]): - values = convert_to_tensor_or_ragged_tensor(values, name='values') - row_splits = ops.convert_to_tensor(row_splits, dtypes.int64, 'row_splits') - row_splits.shape.assert_has_rank(1) - return ragged_tensor.RaggedTensor( - values=values, row_splits=row_splits, internal=True) - - -def from_row_lengths(values, row_lengths, name=None): - """Creates a `RaggedTensor` with rows partitioned by `row_lengths`. - - The returned `RaggedTensor` corresponds with the python list defined by: - - ```python - result = [[values.pop(0) for i in range(length)] - for length in row_lengths] - ``` - - Args: - values: A potentially ragged tensor with shape `[nvals, ...]`. - row_lengths: A 1-D int64 tensor with shape `[nrows]`. Must be nonnegative. - `sum(row_lengths)` must be `nvals`. - name: A name prefix for the RaggedTensor (optional). - - Returns: - A `RaggedTensor`. `result.rank = values.rank + 1`. - `result.ragged_rank = values.ragged_rank + 1`. - - #### Example: - ```python - >>> rt = ragged.from_row_lengths( - ... values=[3, 1, 4, 1, 5, 9, 2, 6], - ... row_lengths=[4, 0, 3, 1, 0]) - >>> rt.eval().tolist() - [[3, 1, 4, 1], [], [5, 9, 2], [6], []] - ``` - """ - with ops.name_scope(name, 'RaggedFromRowLengths', [values, row_lengths]): - values = convert_to_tensor_or_ragged_tensor(values, name='values') - row_lengths = ops.convert_to_tensor(row_lengths, dtypes.int64, - 'row_lengths') - row_lengths.shape.assert_has_rank(1) - row_limits = math_ops.cumsum(row_lengths) - row_splits = array_ops.concat([[0], row_limits], axis=0) - return ragged_tensor.RaggedTensor( - values=values, - row_splits=row_splits, - cached_row_lengths=row_lengths, - internal=True) - - -def from_row_starts(values, row_starts, name=None): - """Creates a `RaggedTensor` with rows partitioned by `row_starts`. - - Equivalent to: `from_row_splits(values, concat([row_starts, nvals]))`. - - Args: - values: A potentially ragged tensor with shape `[nvals, ...]`. - row_starts: A 1-D int64 tensor with shape `[nrows]`. Must be nonnegative - and sorted in ascending order. If `nrows>0`, then `row_starts[0]` must be - zero. - name: A name prefix for the RaggedTensor (optional). - - Returns: - A `RaggedTensor`. `result.rank = values.rank + 1`. - `result.ragged_rank = values.ragged_rank + 1`. - - #### Example: - ```python - >>> rt = ragged.from_row_starts( - ... values=[3, 1, 4, 1, 5, 9, 2, 6], - ... row_starts=[0, 4, 4, 7, 8]) - >>> rt.eval().tolist() - [[3, 1, 4, 1], [], [5, 9, 2], [6], []] - ``` - """ - with ops.name_scope(name, 'RaggedFromRowStarts', [values, row_starts]): - values = convert_to_tensor_or_ragged_tensor(values, name='values') - row_starts = ops.convert_to_tensor(row_starts, dtypes.int64, 'row_starts') - row_starts.shape.assert_has_rank(1) - nvals = array_ops.shape(values, out_type=dtypes.int64)[:1] - row_splits = array_ops.concat([row_starts, nvals], axis=0) - return ragged_tensor.RaggedTensor( - values=values, row_splits=row_splits, internal=True) - - -def from_row_limits(values, row_limits, name=None): - """Creates a `RaggedTensor` with rows partitioned by `row_limits`. - - Equivalent to: `from_row_splits(values, concat([0, row_limits]))`. - - Args: - values: A potentially ragged tensor with shape `[nvals, ...]`. - row_limits: A 1-D int64 tensor with shape `[nrows]`. Must be sorted in - ascending order. If `nrows>0`, then `row_limits[-1]` must be `nvals`. - name: A name prefix for the RaggedTensor (optional). - - Returns: - A `RaggedTensor`. `result.rank = values.rank + 1`. - `result.ragged_rank = values.ragged_rank + 1`. - - #### Example: - ```python - >>> rt = ragged.from_row_limits( - ... values=[3, 1, 4, 1, 5, 9, 2, 6], - ... row_limits=[4, 4, 7, 8, 8]) - >>> rt.eval().tolist() - [[3, 1, 4, 1], [], [5, 9, 2], [6], []] - ``` - """ - with ops.name_scope(name, 'RaggedFromRowLimits', [values, row_limits]): - values = convert_to_tensor_or_ragged_tensor(values, name='values') - row_limits = ops.convert_to_tensor(row_limits, dtypes.int64, 'row_limits') - row_limits.shape.assert_has_rank(1) - zero = array_ops.zeros([1], dtypes.int64) - row_splits = array_ops.concat([zero, row_limits], axis=0) - return ragged_tensor.RaggedTensor( - values=values, row_splits=row_splits, internal=True) - - -def from_nested_value_rowids(inner_values, - nested_value_rowids, - nested_nrows=None, - name=None): - """Creates a `RaggedTensor` from a nested list of `value_rowids` tensors. - - Equivalent to: - - ```python - result = inner_values - for (value_rowids, nrows) in reversed(zip(nested_value_rowids, nested_nrows)): - result = from_value_rowids(result, value_rowids, nrows) - ``` - - Args: - inner_values: A potentially ragged tensor. - nested_value_rowids: A list of 1-D int64 tensors. The `i`th tensor is used - as the `value_rowids` for the `i`th ragged dimension. - nested_nrows: A list of int64 scalars. The `i`th scalar is used as the - `nrows` for the `i`th ragged dimension. - name: A name prefix for the RaggedTensor (optional). - - Returns: - A `RaggedTensor` (or `inner_values` if `nested_value_rowids` is empty). - - Raises: - ValueError: If `len(nested_values_rowids) != len(nested_nrows)`. - """ - if isinstance(nested_value_rowids, ops.Tensor): - raise TypeError('nested_value_rowids must be a list of Tensors') - if nested_nrows is None: - nested_nrows = [None] * len(nested_value_rowids) - else: - if isinstance(nested_nrows, ops.Tensor): - raise TypeError('nested_nrows must be a list of Tensors') - if len(nested_nrows) != len(nested_value_rowids): - raise ValueError('nested_nrows must have the same length as ' - 'nested_value_rowids') - - with ops.name_scope( - name, 'RaggedFromNestedValueRowIds', - [inner_values] + list(nested_value_rowids) + list(nested_nrows)): - result = inner_values - for value_rowids, nrows in reversed( - list(zip(nested_value_rowids, nested_nrows))): - result = from_value_rowids(result, value_rowids, nrows) - return result - - -def from_nested_row_splits(inner_values, nested_row_splits, name=None): - """Creates a `RaggedTensor` from a nested list of `row_splits` tensors. - - Equivalent to: - - ```python - result = inner_values - for row_splits in reversed(nested_row_splits): - result = from_row_splits(result, row_splits) - ``` - - Args: - inner_values: A potentially ragged tensor. - nested_row_splits: A list of 1-D int64 tensors. The `i`th tensor is used as - the `row_splits` for the `i`th ragged dimension. - name: A name prefix for the RaggedTensor (optional). - - Returns: - A `RaggedTensor` (or `inner_values` if `nested_row_splits` is empty). - """ - if isinstance(nested_row_splits, ops.Tensor): - raise TypeError('nested_row_splits must be a list of Tensors') - with ops.name_scope(name, 'RaggedFromNestedRowSplits', - [inner_values] + list(nested_row_splits)): - result = inner_values - for splits in reversed(nested_row_splits): - result = from_row_splits(result, splits) - return result - - -def from_nested_row_lengths(inner_values, nested_row_lengths, name=None): - """Creates a `RaggedTensor` from a nested list of `row_lengths` tensors. - - Equivalent to: - - ```python - result = inner_values - for row_lengths in reversed(nested_row_lengths): - result = from_row_lengths(result, row_lengths) - ``` - - Args: - inner_values: A potentially ragged tensor. - nested_row_lengths: A list of 1-D int64 tensors. The `i`th tensor is used - as the `row_lengths` for the `i`th ragged dimension. - name: A name prefix for the RaggedTensor (optional). - - Returns: - A `RaggedTensor` (or `inner_values` if `nested_row_lengths` is empty). - """ - if isinstance(nested_row_lengths, ops.Tensor): - raise TypeError('nested_row_lengths must be a list of Tensors') - with ops.name_scope(name, 'RaggedFromNestedRowlengths', - [inner_values] + list(nested_row_lengths)): - result = inner_values - for lengths in reversed(nested_row_lengths): - result = from_row_lengths(result, lengths) - return result diff --git a/tensorflow/python/ops/ragged/ragged_from_sparse_op_test.py b/tensorflow/python/ops/ragged/ragged_from_sparse_op_test.py index 674dbab112..07cf910202 100644 --- a/tensorflow/python/ops/ragged/ragged_from_sparse_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_from_sparse_op_test.py @@ -12,77 +12,77 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for ragged.from_sparse.""" +"""Tests for RaggedTensor.from_sparse.""" 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 dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops -from tensorflow.python.ops import ragged +from tensorflow.python.ops.ragged import ragged_test_util +from tensorflow.python.ops.ragged import RaggedTensor from tensorflow.python.platform import googletest -class RaggedTensorToSparseOpTest(test_util.TensorFlowTestCase): +@test_util.run_all_in_graph_and_eager_modes +class RaggedTensorToSparseOpTest(ragged_test_util.RaggedTensorTestCase): - @test_util.run_v1_only('b/120545219') def testDocStringExample(self): st = sparse_tensor.SparseTensor( indices=[[0, 0], [0, 1], [0, 2], [1, 0], [3, 0]], values=[1, 2, 3, 4, 5], dense_shape=[4, 3]) - rt = ragged.from_sparse(st) + rt = RaggedTensor.from_sparse(st) - with self.test_session(): - self.assertEqual(rt.eval().tolist(), [[1, 2, 3], [4], [], [5]]) + self.assertRaggedEqual(rt, [[1, 2, 3], [4], [], [5]]) - @test_util.run_v1_only('b/120545219') def testEmpty(self): st = sparse_tensor.SparseTensor( indices=array_ops.zeros([0, 2], dtype=dtypes.int64), values=[], dense_shape=[4, 3]) - rt = ragged.from_sparse(st) + rt = RaggedTensor.from_sparse(st) - with self.test_session(): - self.assertEqual(rt.eval().tolist(), [[], [], [], []]) + self.assertRaggedEqual(rt, [[], [], [], []]) - @test_util.run_deprecated_v1 def testBadSparseTensorRank(self): st1 = sparse_tensor.SparseTensor(indices=[[0]], values=[0], dense_shape=[3]) + self.assertRaisesRegexp(ValueError, r'rank\(st_input\) must be 2', + RaggedTensor.from_sparse, st1) + st2 = sparse_tensor.SparseTensor( indices=[[0, 0, 0]], values=[0], dense_shape=[3, 3, 3]) - st3 = sparse_tensor.SparseTensor( - indices=array_ops.placeholder(dtypes.int64), - values=[0], - dense_shape=array_ops.placeholder(dtypes.int64)) - self.assertRaisesRegexp(ValueError, r'rank\(st_input\) must be 2', - ragged.from_sparse, st1) - self.assertRaisesRegexp(ValueError, r'rank\(st_input\) must be 2', - ragged.from_sparse, st2) self.assertRaisesRegexp(ValueError, r'rank\(st_input\) must be 2', - ragged.from_sparse, st3) + RaggedTensor.from_sparse, st2) + + if not context.executing_eagerly(): + st3 = sparse_tensor.SparseTensor( + indices=array_ops.placeholder(dtypes.int64), + values=[0], + dense_shape=array_ops.placeholder(dtypes.int64)) + self.assertRaisesRegexp(ValueError, r'rank\(st_input\) must be 2', + RaggedTensor.from_sparse, st3) - @test_util.run_v1_only('b/120545219') def testGoodPartialSparseTensorRank(self): - st1 = sparse_tensor.SparseTensor( - indices=[[0, 0]], - values=[0], - dense_shape=array_ops.placeholder(dtypes.int64)) - st2 = sparse_tensor.SparseTensor( - indices=array_ops.placeholder(dtypes.int64), - values=[0], - dense_shape=[4, 3]) + if not context.executing_eagerly(): + st1 = sparse_tensor.SparseTensor( + indices=[[0, 0]], + values=[0], + dense_shape=array_ops.placeholder(dtypes.int64)) + st2 = sparse_tensor.SparseTensor( + indices=array_ops.placeholder(dtypes.int64), + values=[0], + dense_shape=[4, 3]) - # Shouldn't throw ValueError - ragged.from_sparse(st1) - ragged.from_sparse(st2) + # Shouldn't throw ValueError + RaggedTensor.from_sparse(st1) + RaggedTensor.from_sparse(st2) - @test_util.run_v1_only('b/120545219') def testNonRaggedSparseTensor(self): # "index_suffix" means the value of the innermost dimension of the index # (i.e., indices[i][-1]). @@ -92,22 +92,21 @@ class RaggedTensorToSparseOpTest(test_util.TensorFlowTestCase): # index_suffix of first index is not zero. st1 = sparse_tensor.SparseTensor( indices=[[0, 1], [0, 2], [2, 0]], values=[1, 2, 3], dense_shape=[3, 3]) + with self.assertRaisesRegexp(errors.InvalidArgumentError, + r'.*SparseTensor is not right-ragged'): + self.evaluate(RaggedTensor.from_sparse(st1)) # index_suffix of an index that starts a new row is not zero. st2 = sparse_tensor.SparseTensor( indices=[[0, 0], [0, 1], [2, 1]], values=[1, 2, 3], dense_shape=[3, 3]) + with self.assertRaisesRegexp(errors.InvalidArgumentError, + r'.*SparseTensor is not right-ragged'): + self.evaluate(RaggedTensor.from_sparse(st2)) # index_suffix of an index that continues a row skips a cell. st3 = sparse_tensor.SparseTensor( indices=[[0, 1], [0, 1], [0, 3]], values=[1, 2, 3], dense_shape=[3, 3]) - rt1 = ragged.from_sparse(st1) - rt2 = ragged.from_sparse(st2) - rt3 = ragged.from_sparse(st3) - with self.test_session(): - self.assertRaisesRegexp(errors.InvalidArgumentError, - r'.*SparseTensor is not right-ragged', rt1.eval) - self.assertRaisesRegexp(errors.InvalidArgumentError, - r'.*SparseTensor is not right-ragged', rt2.eval) - self.assertRaisesRegexp(errors.InvalidArgumentError, - r'.*SparseTensor is not right-ragged', rt3.eval) + with self.assertRaisesRegexp(errors.InvalidArgumentError, + r'.*SparseTensor is not right-ragged'): + self.evaluate(RaggedTensor.from_sparse(st3)) if __name__ == '__main__': diff --git a/tensorflow/python/ops/ragged/ragged_from_tensor_op_test.py b/tensorflow/python/ops/ragged/ragged_from_tensor_op_test.py index 09c17fad61..6a3d639c5e 100644 --- a/tensorflow/python/ops/ragged/ragged_from_tensor_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_from_tensor_op_test.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for ragged.from_tensor.""" +"""Tests for RaggedTensor.from_tensor.""" from __future__ import absolute_import from __future__ import division @@ -24,29 +24,26 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops -from tensorflow.python.ops import ragged +from tensorflow.python.ops.ragged import ragged_test_util +from tensorflow.python.ops.ragged import RaggedTensor from tensorflow.python.platform import googletest -class RaggedFromTensorOpTest(test_util.TensorFlowTestCase, - parameterized.TestCase): +@test_util.run_all_in_graph_and_eager_modes +class RaggedTensorToSparseOpTest(ragged_test_util.RaggedTensorTestCase, + parameterized.TestCase): - @test_util.run_v1_only('b/120545219') def testDocStringExamples(self): - # The examples from ragged.from_tensor.__doc__. + # The examples from RaggedTensor.from_tensor.__doc__. dt = constant_op.constant([[5, 7, 0], [0, 3, 0], [6, 0, 0]]) - with self.test_session(): - self.assertEqual( - ragged.from_tensor(dt).eval().tolist(), - [[5, 7, 0], [0, 3, 0], [6, 0, 0]]) + self.assertRaggedEqual( + RaggedTensor.from_tensor(dt), [[5, 7, 0], [0, 3, 0], [6, 0, 0]]) - self.assertEqual( - ragged.from_tensor(dt, lengths=[1, 0, 3]).eval().tolist(), - [[5], [], [6, 0, 0]]) + self.assertRaggedEqual( + RaggedTensor.from_tensor(dt, lengths=[1, 0, 3]), [[5], [], [6, 0, 0]]) - self.assertEqual( - ragged.from_tensor(dt, padding=0).eval().tolist(), - [[5, 7], [0, 3], [6]]) + self.assertRaggedEqual( + RaggedTensor.from_tensor(dt, padding=0), [[5, 7], [0, 3], [6]]) @parameterized.parameters( # 2D test cases, no length or padding. @@ -263,7 +260,6 @@ class RaggedFromTensorOpTest(test_util.TensorFlowTestCase, [[[5, 6], [7]], [[0, 8], []]]] }, ) # pyformat: disable - @test_util.run_v1_only('b/120545219') def testRaggedFromTensor(self, tensor, expected, @@ -271,30 +267,27 @@ class RaggedFromTensorOpTest(test_util.TensorFlowTestCase, padding=None, ragged_rank=1): dt = constant_op.constant(tensor) - rt = ragged.from_tensor(dt, lengths, padding, ragged_rank) - self.assertEqual(type(rt), ragged.RaggedTensor) + rt = RaggedTensor.from_tensor(dt, lengths, padding, ragged_rank) + self.assertEqual(type(rt), RaggedTensor) self.assertEqual(rt.ragged_rank, ragged_rank) self.assertTrue( dt.shape.is_compatible_with(rt.shape), '%s is incompatible with %s' % (dt.shape, rt.shape)) - with self.test_session(): - self.assertEqual(rt.eval().tolist(), expected) + self.assertRaggedEqual(rt, expected) - @test_util.run_deprecated_v1 def testHighDimensions(self): # Use distinct prime numbers for all dimension shapes in this test, so # we can see any errors that are caused by mixing up dimension sizes. dt = array_ops.reshape( math_ops.range(3 * 5 * 7 * 11 * 13 * 17), [3, 5, 7, 11, 13, 17]) for ragged_rank in range(1, 4): - rt = ragged.from_tensor(dt, ragged_rank=ragged_rank) - self.assertEqual(type(rt), ragged.RaggedTensor) + rt = RaggedTensor.from_tensor(dt, ragged_rank=ragged_rank) + self.assertEqual(type(rt), RaggedTensor) self.assertEqual(rt.ragged_rank, ragged_rank) self.assertTrue( dt.shape.is_compatible_with(rt.shape), '%s is incompatible with %s' % (dt.shape, rt.shape)) - with self.test_session(): - self.assertEqual(rt.eval().tolist(), self.evaluate(dt).tolist()) + self.assertRaggedEqual(rt, self.evaluate(dt).tolist()) @parameterized.parameters( # With no padding or lengths @@ -398,15 +391,13 @@ class RaggedFromTensorOpTest(test_util.TensorFlowTestCase, 'expected': [[], []] }, ) - @test_util.run_v1_only('b/120545219') def testEmpty(self, dt_shape, expected, lengths=None, padding=None): dt = array_ops.zeros(dt_shape) - rt = ragged.from_tensor(dt, lengths, padding) - self.assertEqual(type(rt), ragged.RaggedTensor) + rt = RaggedTensor.from_tensor(dt, lengths, padding) + self.assertEqual(type(rt), RaggedTensor) self.assertEqual(rt.ragged_rank, 1) self.assertTrue(dt.shape.is_compatible_with(rt.shape)) - with self.test_session(): - self.assertEqual(rt.eval().tolist(), expected) + self.assertRaggedEqual(rt, expected) @parameterized.parameters( { @@ -423,7 +414,7 @@ class RaggedFromTensorOpTest(test_util.TensorFlowTestCase, { 'tensor': [[1]], 'padding': 'a', - 'error': (TypeError, "Expected int32, got 'a'.*") + 'error': (TypeError, '.*') }, { 'tensor': [[1]], @@ -451,7 +442,6 @@ class RaggedFromTensorOpTest(test_util.TensorFlowTestCase, 'error': (ValueError, r'ragged_rank must be greater than 0; got -1') }, ) - @test_util.run_deprecated_v1 def testErrors(self, tensor, lengths=None, @@ -459,8 +449,8 @@ class RaggedFromTensorOpTest(test_util.TensorFlowTestCase, ragged_rank=1, error=None): dt = constant_op.constant(tensor) - self.assertRaisesRegexp(error[0], error[1], ragged.from_tensor, dt, lengths, - padding, ragged_rank) + self.assertRaisesRegexp(error[0], error[1], RaggedTensor.from_tensor, dt, + lengths, padding, ragged_rank) if __name__ == '__main__': diff --git a/tensorflow/python/ops/ragged/ragged_functional_ops.py b/tensorflow/python/ops/ragged/ragged_functional_ops.py index 6b71d88435..751f2c7359 100644 --- a/tensorflow/python/ops/ragged/ragged_functional_ops.py +++ b/tensorflow/python/ops/ragged/ragged_functional_ops.py @@ -19,15 +19,14 @@ from __future__ import division from __future__ import print_function from tensorflow.python.framework import ops -from tensorflow.python.ops.ragged import ragged_factory_ops from tensorflow.python.ops.ragged import ragged_tensor from tensorflow.python.ops.ragged import ragged_util -def map_inner_values(op, *args, **kwargs): +def map_flat_values(op, *args, **kwargs): """Applies `op` to the inner values of one or more RaggedTensors. - Replaces any `RaggedTensor` in `args` or `kwargs` with its `inner_values` + Replaces any `RaggedTensor` in `args` or `kwargs` with its `flat_values` tensor, and then calls `op`. Returns a `RaggedTensor` that is constructed from the input `RaggedTensor`s' `splits` and the value returned by the `op`. @@ -39,20 +38,20 @@ def map_inner_values(op, *args, **kwargs): ```python >>> rt = ragged.constant([[1, 2, 3], [], [4, 5], [6]]) - >>> ragged.map_inner_values(tf.ones_like, rt).eval().tolist() + >>> ragged.map_flat_values(tf.ones_like, rt).eval().tolist() [[1, 1, 1], [], [1, 1], [1]] - >>> ragged.map_inner_values(tf.multiply, rt, rt).eval().tolist() + >>> ragged.map_flat_values(tf.multiply, rt, rt).eval().tolist() [[1, 4, 9], [], [16, 25], [36]] - >>> ragged.map_inner_values(tf.add, rt, 5).eval().tolist() + >>> ragged.map_flat_values(tf.add, rt, 5).eval().tolist() [[6, 7, 8], [], [9, 10], [11]] ``` Args: - op: The operation that should be applied to the RaggedTensor `inner_values`. + op: The operation that should be applied to the RaggedTensor `flat_values`. `op` is typically an element-wise operation (such as math_ops.add), but any operation that preserves the size of the outermost dimension can be used. I.e., `shape[0]` of the value returned by `op` must match - `shape[0]` of the `RaggedTensor`s' `inner_values` tensors. + `shape[0]` of the `RaggedTensor`s' `flat_values` tensors. *args: Arguments for `op`. **kwargs: Keyword arguments for `op`. @@ -66,8 +65,8 @@ def map_inner_values(op, *args, **kwargs): # Replace RaggedTensors with their values; and collect the splits tensors # from each RaggedTensor. nested_splits_lists = [] - inner_args = _replace_ragged_with_inner_values(args, nested_splits_lists) - inner_kwargs = _replace_ragged_with_inner_values(kwargs, nested_splits_lists) + inner_args = _replace_ragged_with_flat_values(args, nested_splits_lists) + inner_kwargs = _replace_ragged_with_flat_values(kwargs, nested_splits_lists) if not nested_splits_lists: return op(*args, **kwargs) @@ -75,15 +74,15 @@ def map_inner_values(op, *args, **kwargs): ragged_util.assert_splits_match(nested_splits_lists)): # Delegate to op, and then compose the result from the transformed values # and the splits. - return ragged_factory_ops.from_nested_row_splits( + return ragged_tensor.RaggedTensor.from_nested_row_splits( op(*inner_args, **inner_kwargs), nested_splits_lists[0]) -def _replace_ragged_with_inner_values(value, nested_splits_lists): - """Replace RaggedTensors with their inner_values, and record their splits. +def _replace_ragged_with_flat_values(value, nested_splits_lists): + """Replace RaggedTensors with their flat_values, and record their splits. Returns a copy of `value`, with any nested `RaggedTensor`s replaced by their - `inner_values` tensor. Looks inside lists, tuples, and dicts. + `flat_values` tensor. Looks inside lists, tuples, and dicts. Appends each `RaggedTensor`'s `nested_splits` to `nested_splits_lists`. @@ -97,13 +96,13 @@ def _replace_ragged_with_inner_values(value, nested_splits_lists): """ # Base case if ragged_tensor.is_ragged(value): - value = ragged_factory_ops.convert_to_tensor_or_ragged_tensor(value) + value = ragged_tensor.convert_to_tensor_or_ragged_tensor(value) nested_splits_lists.append(value.nested_row_splits) - return value.inner_values + return value.flat_values # Recursion cases def recurse(v): - return _replace_ragged_with_inner_values(v, nested_splits_lists) + return _replace_ragged_with_flat_values(v, nested_splits_lists) if isinstance(value, list): return [recurse(v) for v in value] diff --git a/tensorflow/python/ops/ragged/ragged_gather_nd_op_test.py b/tensorflow/python/ops/ragged/ragged_gather_nd_op_test.py index b0a4fe9d31..6673192752 100644 --- a/tensorflow/python/ops/ragged/ragged_gather_nd_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_gather_nd_op_test.py @@ -21,15 +21,18 @@ from __future__ import print_function from absl.testing import parameterized import numpy as np +from tensorflow.python.eager import context from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import ragged +from tensorflow.python.ops.ragged import ragged_test_util from tensorflow.python.platform import googletest -@test_util.run_v1_only('b/120545219') -class RaggedGatherNdOpTest(test_util.TensorFlowTestCase, +@test_util.run_all_in_graph_and_eager_modes +class RaggedGatherNdOpTest(ragged_test_util.RaggedTensorTestCase, parameterized.TestCase): DOCSTRING_PARAMS = [[['000', '001'], ['010']], @@ -186,14 +189,11 @@ class RaggedGatherNdOpTest(test_util.TensorFlowTestCase, ]) # pyformat: disable def testRaggedGatherNd(self, descr, params, indices, expected): result = ragged.gather_nd(params, indices) - self.assertEqual( - getattr(result, 'ragged_rank', 0), getattr(expected, 'ragged_rank', 0)) - with self.test_session() as sess: - if hasattr(expected, 'tolist'): - expected = expected.tolist() - self.assertEqual(self.evaluate(result).tolist(), expected) + self.assertRaggedEqual(result, expected) def testRaggedGatherNdUnknownRankError(self): + if context.executing_eagerly(): + return params = ragged.constant([['a', 'b'], ['c', 'd']]) indices1 = array_ops.placeholder(dtypes.int32, shape=None) indices2 = array_ops.placeholder(dtypes.int32, shape=[None]) @@ -209,21 +209,20 @@ class RaggedGatherNdOpTest(test_util.TensorFlowTestCase, dict( params=['a'], indices=0, - message='Shape must be at least rank 1 but is rank 0' - " for 'GatherNd'"), + error=(ValueError, errors.InvalidArgumentError)), dict( params=ragged.constant_value([['a']]), indices=0, message='indices.rank must be at least 1.'), dict( params=['a', 'b', 'c'], - indices=ragged.constant([[0]]), + indices=ragged.constant_value([[0]]), message='The innermost dimension of indices may not be ragged'), ]) def testRaggedGatherNdStaticError(self, params, indices, - message, + message=None, error=ValueError): with self.assertRaisesRegexp(error, message): ragged.gather_nd(params, indices) diff --git a/tensorflow/python/ops/ragged/ragged_gather_op_test.py b/tensorflow/python/ops/ragged/ragged_gather_op_test.py index d371745b73..42efdc8a7d 100644 --- a/tensorflow/python/ops/ragged/ragged_gather_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_gather_op_test.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 dtypes from tensorflow.python.framework import errors @@ -25,90 +26,75 @@ from tensorflow.python.framework import ops from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import ragged +from tensorflow.python.ops.ragged import ragged_test_util from tensorflow.python.platform import googletest -class RaggedTensorOpsTest(test_util.TensorFlowTestCase): +@test_util.run_all_in_graph_and_eager_modes +class RaggedGatherOpTest(ragged_test_util.RaggedTensorTestCase): - @test_util.run_v1_only('b/120545219') def testDocStringExamples(self): params = constant_op.constant(['a', 'b', 'c', 'd', 'e']) indices = constant_op.constant([3, 1, 2, 1, 0]) ragged_params = ragged.constant([['a', 'b', 'c'], ['d'], [], ['e']]) ragged_indices = ragged.constant([[3, 1, 2], [1], [], [0]]) - with self.test_session(): - self.assertEqual( - ragged.gather(params, ragged_indices).eval().tolist(), - [[b'd', b'b', b'c'], [b'b'], [], [b'a']]) - self.assertEqual( - ragged.gather(ragged_params, indices).eval().tolist(), - [[b'e'], [b'd'], [], [b'd'], [b'a', b'b', b'c']]) - self.assertEqual( - ragged.gather(ragged_params, ragged_indices).eval().tolist(), - [[[b'e'], [b'd'], []], [[b'd']], [], [[b'a', b'b', b'c']]]) - - @test_util.run_deprecated_v1 + self.assertRaggedEqual( + ragged.gather(params, ragged_indices), + [[b'd', b'b', b'c'], [b'b'], [], [b'a']]) + self.assertRaggedEqual( + ragged.gather(ragged_params, indices), + [[b'e'], [b'd'], [], [b'd'], [b'a', b'b', b'c']]) + self.assertRaggedEqual( + ragged.gather(ragged_params, ragged_indices), + [[[b'e'], [b'd'], []], [[b'd']], [], [[b'a', b'b', b'c']]]) + def testTensorParamsAndTensorIndices(self): params = ['a', 'b', 'c', 'd', 'e'] indices = [2, 0, 2, 1] - with self.test_session(): - self.assertEqual( - ragged.gather(params, indices).eval().tolist(), - [b'c', b'a', b'c', b'b']) - self.assertEqual(type(ragged.gather(params, indices)), ops.Tensor) + self.assertRaggedEqual( + ragged.gather(params, indices), [b'c', b'a', b'c', b'b']) + self.assertIsInstance(ragged.gather(params, indices), ops.Tensor) - @test_util.run_deprecated_v1 def testRaggedParamsAndTensorIndices(self): params = ragged.constant([['a', 'b'], ['c', 'd', 'e'], ['f'], [], ['g']]) indices = [2, 0, 2, 1] - with self.test_session(): - self.assertEqual( - ragged.gather(params, indices).eval().tolist(), - [[b'f'], [b'a', b'b'], [b'f'], [b'c', b'd', b'e']]) + self.assertRaggedEqual( + ragged.gather(params, indices), + [[b'f'], [b'a', b'b'], [b'f'], [b'c', b'd', b'e']]) - @test_util.run_v1_only('b/120545219') def testTensorParamsAndRaggedIndices(self): params = ['a', 'b', 'c', 'd', 'e'] indices = ragged.constant([[2, 1], [1, 2, 0], [3]]) - with self.test_session(): - self.assertEqual( - ragged.gather(params, indices).eval().tolist(), - [[b'c', b'b'], [b'b', b'c', b'a'], [b'd']]) + self.assertRaggedEqual( + ragged.gather(params, indices), + [[b'c', b'b'], [b'b', b'c', b'a'], [b'd']]) - @test_util.run_v1_only('b/120545219') def testRaggedParamsAndRaggedIndices(self): params = ragged.constant([['a', 'b'], ['c', 'd', 'e'], ['f'], [], ['g']]) indices = ragged.constant([[2, 1], [1, 2, 0], [3]]) - with self.test_session(): - self.assertEqual( - ragged.gather(params, indices).eval().tolist(), - [[[b'f'], [b'c', b'd', b'e']], # [[p[2], p[1] ], - [[b'c', b'd', b'e'], [b'f'], [b'a', b'b']], # [p[1], p[2], p[0]], - [[]]] # [p[3] ]] - ) # pyformat: disable - - @test_util.run_deprecated_v1 + self.assertRaggedEqual( + ragged.gather(params, indices), + [[[b'f'], [b'c', b'd', b'e']], # [[p[2], p[1] ], + [[b'c', b'd', b'e'], [b'f'], [b'a', b'b']], # [p[1], p[2], p[0]], + [[]]] # [p[3] ]] + ) # pyformat: disable + def testRaggedParamsAndScalarIndices(self): params = ragged.constant([['a', 'b'], ['c', 'd', 'e'], ['f'], [], ['g']]) indices = 1 - with self.test_session(): - self.assertEqual( - ragged.gather(params, indices).eval().tolist(), [b'c', b'd', b'e']) + self.assertRaggedEqual(ragged.gather(params, indices), [b'c', b'd', b'e']) - @test_util.run_deprecated_v1 def test3DRaggedParamsAnd2DTensorIndices(self): params = ragged.constant([[['a', 'b'], []], [['c', 'd'], ['e'], ['f']], [['g']]]) indices = [[1, 2], [0, 1], [2, 2]] - with self.test_session(): - self.assertEqual( - ragged.gather(params, indices).eval().tolist(), - [[[[b'c', b'd'], [b'e'], [b'f']], [[b'g']]], # [[p1, p2], - [[[b'a', b'b'], []], [[b'c', b'd'], [b'e'], [b'f']]], # [p0, p1], - [[[b'g']], [[b'g']]]] # [p2, p2]] - ) # pyformat: disable - - @test_util.run_v1_only('b/120545219') + self.assertRaggedEqual( + ragged.gather(params, indices), + [[[[b'c', b'd'], [b'e'], [b'f']], [[b'g']]], # [[p1, p2], + [[[b'a', b'b'], []], [[b'c', b'd'], [b'e'], [b'f']]], # [p0, p1], + [[[b'g']], [[b'g']]]] # [p2, p2]] + ) # pyformat: disable + def testTensorParamsAnd4DRaggedIndices(self): indices = ragged.constant( [[[[3, 4], [0, 6]], []], [[[2, 1], [1, 0]], [[2, 5]], [[2, 3]]], @@ -116,32 +102,30 @@ class RaggedTensorOpsTest(test_util.TensorFlowTestCase): ragged_rank=2, inner_shape=(2,)) params = ['a', 'b', 'c', 'd', 'e', 'f', 'g'] - with self.test_session(): - self.assertEqual( - ragged.gather(params, indices).eval().tolist(), - [[[[b'd', b'e'], [b'a', b'g']], []], - [[[b'c', b'b'], [b'b', b'a']], [[b'c', b'f']], [[b'c', b'd']]], - [[[b'b', b'a']]]]) # pyformat: disable - - @test_util.run_v1_only('b/120545219') + self.assertRaggedEqual( + ragged.gather(params, indices), + [[[[b'd', b'e'], [b'a', b'g']], []], + [[[b'c', b'b'], [b'b', b'a']], [[b'c', b'f']], [[b'c', b'd']]], + [[[b'b', b'a']]]]) # pyformat: disable + def testOutOfBoundsError(self): tensor_params = ['a', 'b', 'c'] tensor_indices = [0, 1, 2] ragged_params = ragged.constant([['a', 'b'], ['c']]) ragged_indices = ragged.constant([[0, 3]]) - with self.test_session(): - self.assertRaisesRegexp(errors.InvalidArgumentError, - r'indices\[1\] = 3 is not in \[0, 3\)', - ragged.gather(tensor_params, ragged_indices).eval) - self.assertRaisesRegexp(errors.InvalidArgumentError, - r'indices\[2\] = 2 is not in \[0, 2\)', - ragged.gather(ragged_params, tensor_indices).eval) - self.assertRaisesRegexp(errors.InvalidArgumentError, - r'indices\[1\] = 3 is not in \[0, 2\)', - ragged.gather(ragged_params, ragged_indices).eval) - - @test_util.run_deprecated_v1 + with self.assertRaisesRegexp(errors.InvalidArgumentError, + r'indices\[1\] = 3 is not in \[0, 3\)'): + self.evaluate(ragged.gather(tensor_params, ragged_indices)) + with self.assertRaisesRegexp(errors.InvalidArgumentError, + r'indices\[2\] = 2 is not in \[0, 2\)'): + self.evaluate(ragged.gather(ragged_params, tensor_indices)) + with self.assertRaisesRegexp(errors.InvalidArgumentError, + r'indices\[1\] = 3 is not in \[0, 2\)'): + self.evaluate(ragged.gather(ragged_params, ragged_indices)) + def testUnknownIndicesRankError(self): + if context.executing_eagerly(): + return params = ragged.constant([], ragged_rank=1) indices = constant_op.constant([0], dtype=dtypes.int64) indices = array_ops.placeholder_with_default(indices, None) diff --git a/tensorflow/python/ops/ragged/ragged_getitem.py b/tensorflow/python/ops/ragged/ragged_getitem.py index 9821695046..0fa72a3658 100644 --- a/tensorflow/python/ops/ragged/ragged_getitem.py +++ b/tensorflow/python/ops/ragged/ragged_getitem.py @@ -24,7 +24,6 @@ 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.ragged import ragged_array_ops -from tensorflow.python.ops.ragged import ragged_factory_ops from tensorflow.python.ops.ragged import ragged_math_ops from tensorflow.python.ops.ragged import ragged_tensor @@ -137,8 +136,8 @@ def _ragged_getitem(rt_input, key_list): if row_key is array_ops.newaxis: inner_rt = _ragged_getitem(rt_input, inner_keys) nsplits = array_ops.shape(inner_rt.row_splits, out_type=dtypes.int64)[0] - return ragged_factory_ops.from_row_splits(inner_rt, - array_ops.stack([0, nsplits - 1])) + return ragged_tensor.RaggedTensor.from_row_splits( + inner_rt, array_ops.stack([0, nsplits - 1])) # Slicing a range of rows: first slice the outer dimension, and then # call `_ragged_getitem_inner_dimensions` to handle the inner keys. @@ -184,7 +183,7 @@ def _slice_ragged_row_dimension(rt_input, row_key): axis=0) values_start = new_splits[0] values_limit = new_splits[-1] - return ragged_factory_ops.from_row_splits( + return ragged_tensor.RaggedTensor.from_row_splits( rt_input.values[values_start:values_limit], new_splits - values_start) # If there is a slice step (aka a strided slice), then use ragged_gather to @@ -225,7 +224,8 @@ def _ragged_getitem_inner_dimensions(rt_input, key_list): if column_key is array_ops.newaxis: inner_rt = _ragged_getitem_inner_dimensions(rt_input, key_list[1:]) nsplits = array_ops.shape(inner_rt.row_splits, out_type=dtypes.int64)[0] - return ragged_factory_ops.from_row_splits(inner_rt, math_ops.range(nsplits)) + return ragged_tensor.RaggedTensor.from_row_splits(inner_rt, + math_ops.range(nsplits)) # Slicing a range of columns in a ragged inner dimension. We use a # recursive call to process the values, and then assemble a RaggedTensor @@ -239,7 +239,7 @@ def _ragged_getitem_inner_dimensions(rt_input, key_list): else: # Nontrivial slice: use ragged_gather to extract the indicated slice as # a new RaggedTensor (inner_rt), and then recursively process its values. - # The splits can be taken from ragged.row_splits(inner_rt). + # The splits can be taken from inner_rt.row_splits(). inner_rt_starts = rt_input.row_splits[:-1] inner_rt_limits = rt_input.row_splits[1:] if column_key.start is not None and column_key.start != 0: diff --git a/tensorflow/python/ops/ragged/ragged_map_inner_values_op_test.py b/tensorflow/python/ops/ragged/ragged_map_flat_values_op_test.py similarity index 79% rename from tensorflow/python/ops/ragged/ragged_map_inner_values_op_test.py rename to tensorflow/python/ops/ragged/ragged_map_flat_values_op_test.py index dc214f4437..8b28cac99d 100644 --- a/tensorflow/python/ops/ragged/ragged_map_inner_values_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_map_flat_values_op_test.py @@ -12,14 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for ragged.map_inner_values.""" +"""Tests for ragged.map_flat_values.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function -from absl.testing import parameterized - from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors @@ -27,11 +25,12 @@ from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import ragged +from tensorflow.python.ops.ragged import ragged_test_util from tensorflow.python.platform import googletest -class RaggedMapInnerValuesOpTest(test_util.TensorFlowTestCase, - parameterized.TestCase): +@test_util.run_all_in_graph_and_eager_modes +class RaggedMapInnerValuesOpTest(ragged_test_util.RaggedTensorTestCase): def assertRaggedMapInnerValuesReturns(self, op, @@ -39,23 +38,21 @@ class RaggedMapInnerValuesOpTest(test_util.TensorFlowTestCase, args=(), kwargs=None): kwargs = kwargs or {} - result = ragged.map_inner_values(op, *args, **kwargs) + result = ragged.map_flat_values(op, *args, **kwargs) with self.test_session(): - self.assertEqual(result.eval().tolist(), expected) + self.assertRaggedEqual(result, expected) - @test_util.run_v1_only('b/120545219') def testDocStringExamples(self): """Test the examples in apply_op_to_ragged_values.__doc__.""" rt = ragged.constant([[1, 2, 3], [], [4, 5], [6]]) - v1 = ragged.map_inner_values(array_ops.ones_like, rt) - v2 = ragged.map_inner_values(math_ops.multiply, rt, rt) - v3 = ragged.map_inner_values(math_ops.add, rt, 5) + v1 = ragged.map_flat_values(array_ops.ones_like, rt) + v2 = ragged.map_flat_values(math_ops.multiply, rt, rt) + v3 = ragged.map_flat_values(math_ops.add, rt, 5) with self.test_session(): - self.assertEqual(v1.eval().tolist(), [[1, 1, 1], [], [1, 1], [1]]) - self.assertEqual(v2.eval().tolist(), [[1, 4, 9], [], [16, 25], [36]]) - self.assertEqual(v3.eval().tolist(), [[6, 7, 8], [], [9, 10], [11]]) + self.assertRaggedEqual(v1, [[1, 1, 1], [], [1, 1], [1]]) + self.assertRaggedEqual(v2, [[1, 4, 9], [], [16, 25], [36]]) + self.assertRaggedEqual(v3, [[6, 7, 8], [], [9, 10], [11]]) - @test_util.run_deprecated_v1 def testOpWithSingleRaggedTensorArg(self): tensor = ragged.constant([[1, 2, 3], [], [4, 5]]) self.assertRaggedMapInnerValuesReturns( @@ -63,20 +60,17 @@ class RaggedMapInnerValuesOpTest(test_util.TensorFlowTestCase, args=(tensor,), expected=[[0, 0, 0], [], [0, 0]]) - @test_util.run_v1_only('b/120545219') def testOpWithTwoRaggedTensorArgs(self): x = ragged.constant([[3, 1, 4], [], [1, 5]]) y = ragged.constant([[1, 2, 3], [], [4, 5]]) self.assertRaggedMapInnerValuesReturns( op=math_ops.multiply, args=(x, y), expected=[[3, 2, 12], [], [4, 25]]) - @test_util.run_deprecated_v1 def testOpWithRaggedTensorAndScalarArgs(self): y = ragged.constant([[1, 2, 3], [], [4, 5]]) self.assertRaggedMapInnerValuesReturns( op=math_ops.multiply, args=(5, y), expected=[[5, 10, 15], [], [20, 25]]) - @test_util.run_v1_only('b/120545219') def testOpWithThreeRaggedTensorArgs(self): condition = ragged.constant( [[True, True, False], [], [True, False]]) # pyformat: disable @@ -87,7 +81,6 @@ class RaggedMapInnerValuesOpTest(test_util.TensorFlowTestCase, args=(condition, x, y), expected=[[b'a', b'b', b'C'], [], [b'd', b'E']]) - @test_util.run_v1_only('b/120545219') def testOpWithRaggedTensorListArg(self): x = ragged.constant([[1, 2, 3], [], [4, 5]]) y = ragged.constant([[10, 20, 30], [], [40, 50]]) @@ -96,7 +89,6 @@ class RaggedMapInnerValuesOpTest(test_util.TensorFlowTestCase, args=([x, y, x],), expected=[[12, 24, 36], [], [48, 60]]) - @test_util.run_v1_only('b/120545219') def testOpWithKeywordArgs(self): x = ragged.constant([[3, 1, 4], [], [1, 5]]) y = ragged.constant([[1, 2, 3], [], [4, 5]]) @@ -105,7 +97,6 @@ class RaggedMapInnerValuesOpTest(test_util.TensorFlowTestCase, kwargs=dict(x=x, y=y), expected=[[3, 2, 12], [], [4, 25]]) - @test_util.run_v1_only('b/120545219') def testOpWithMixedPositionalAndKeywordArgs(self): x = ragged.constant([[3, 1, 4], [], [1, 5]]) y = ragged.constant([[1, 2, 3], [], [4, 5]]) @@ -115,7 +106,6 @@ class RaggedMapInnerValuesOpTest(test_util.TensorFlowTestCase, kwargs=dict(y=y), expected=[[3, 2, 12], [], [4, 25]]) - @test_util.run_deprecated_v1 def testNonElementWiseOp(self): x = ragged.constant( [[[3, 1, 4], [1, 5, 9], [2, 6, 5]], [], [[3, 5, 8], [9, 7, 9]]], @@ -128,15 +118,13 @@ class RaggedMapInnerValuesOpTest(test_util.TensorFlowTestCase, }, expected=[[8, 15, 13], [], [16, 25]]) - @test_util.run_v1_only('b/120545219') def testOpWithRaggedRankGreaterThanOne(self): # ragged_rank=0 x0 = [3, 1, 4, 1, 5, 9, 2, 6, 5] y0 = [1, 2, 3, 4, 5, 6, 7, 8, 9] with self.test_session(): - self.assertEqual( - math_ops.multiply(x0, y0).eval().tolist(), - [3, 2, 12, 4, 25, 54, 14, 48, 45]) + self.assertRaggedEqual( + math_ops.multiply(x0, y0), [3, 2, 12, 4, 25, 54, 14, 48, 45]) # ragged_rank=1 x1 = ragged.constant([[3, 1, 4], [], [1, 5], [9, 2], [6, 5]]) @@ -173,7 +161,6 @@ class RaggedMapInnerValuesOpTest(test_util.TensorFlowTestCase, [[[54, 14], [48, 45]]] # row 3 ]) # pyformat: disable - @test_util.run_v1_only('b/120545219') def testOpWithRaggedRankThree(self): x = ragged.constant([[[3, 1, 4]], [], [[], [1, 5]]]) y = ragged.constant([[[1, 2, 3]], [], [[], [4, 5]]]) @@ -182,7 +169,6 @@ class RaggedMapInnerValuesOpTest(test_util.TensorFlowTestCase, args=(x, y), expected=[[[3, 2, 12]], [], [[], [4, 25]]]) - @test_util.run_deprecated_v1 def testOpWithInnerValuesOnly(self): x = constant_op.constant([[1, 2], [3, 4], [5, 6]]) y = constant_op.constant(2) @@ -194,29 +180,25 @@ class RaggedMapInnerValuesOpTest(test_util.TensorFlowTestCase, y = ragged.constant([[[3, 1, 4], []], [], [[1, 5]]]) self.assertRaisesRegexp(ValueError, r'Inputs must have identical ragged splits.*', - ragged.map_inner_values, math_ops.add, x, y) + ragged.map_flat_values, math_ops.add, x, y) def testRaggedTensorSplitsValueMismatchError(self): x = ragged.constant([[3, 1, 4], [], [1, 5]]) y = ragged.constant([[1], [2, 3], [4, 5]]) self.assertRaisesRegexp(errors.InvalidArgumentError, r'Inputs must have identical ragged splits.*', - ragged.map_inner_values, math_ops.add, x, y) + ragged.map_flat_values, math_ops.add, x, y) - @test_util.run_v1_only('b/120545219') def testRaggedTensorSplitsMismatchErrorAtRuntime(self): splits1 = array_ops.placeholder_with_default( constant_op.constant([0, 3, 3, 5], dtypes.int64), None) splits2 = array_ops.placeholder_with_default( constant_op.constant([0, 1, 3, 5], dtypes.int64), None) - x = ragged.from_row_splits([3, 1, 4, 1, 5], splits1) - y = ragged.from_row_splits([1, 2, 3, 4, 5], splits2) - result = ragged.map_inner_values(math_ops.add, x, y) - with self.test_session(): - self.assertRaisesRegexp( - errors.InvalidArgumentError, - r'\[Inputs must have identical ragged splits\] ' - r'\[Condition x == y did not hold element-wise:\].*', result.eval) + x = ragged.RaggedTensor.from_row_splits([3, 1, 4, 1, 5], splits1) + y = ragged.RaggedTensor.from_row_splits([1, 2, 3, 4, 5], splits2) + with self.assertRaisesRegexp(errors.InvalidArgumentError, + r'.*Inputs must have identical ragged splits'): + self.evaluate(ragged.map_flat_values(math_ops.add, x, y)) if __name__ == '__main__': diff --git a/tensorflow/python/ops/ragged/ragged_map_fn_op_test.py b/tensorflow/python/ops/ragged/ragged_map_fn_op_test.py index ab70d5a123..49c0996b24 100644 --- a/tensorflow/python/ops/ragged/ragged_map_fn_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_map_fn_op_test.py @@ -27,11 +27,14 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops as mo from tensorflow.python.ops import ragged from tensorflow.python.ops import string_ops +from tensorflow.python.ops.ragged import ragged_test_util from tensorflow.python.platform import googletest -@test_util.run_v1_only('b/120545219') -class RaggedMapOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): +@test_util.run_all_in_graph_and_eager_modes +class RaggedMapOpTest(ragged_test_util.RaggedTensorTestCase, + parameterized.TestCase): + @parameterized.parameters([ # The following test sets map over a RaggedTensor and apply a # transformation that returns with shape: @@ -54,6 +57,7 @@ class RaggedMapOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): elems=[[1, 2, 3], [4, 5], [6, 7]], expected_output=[[2, 6], [4.5, 9], [6.5, 13]], dtype=dtypes.float32, + expected_ragged_rank=0, ), # [d1, (d2)] -> [d1, (d2)] dict( @@ -61,8 +65,8 @@ class RaggedMapOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): elems=[[1, 2, 3], [4, 5], [6, 7]], expected_output=[[2, 3, 4], [5, 6], [7, 8]], dtype=dtypes.int64, - result_dtype=ragged.RaggedTensorType(dtype=dtypes.int64, - ragged_rank=1), + result_dtype=ragged.RaggedTensorType( + dtype=dtypes.int64, ragged_rank=1), ), # [d1, (d2), d3] -> [d1, (d2), d3] dict( @@ -70,41 +74,41 @@ class RaggedMapOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): elems=[[[1, 2], [3, 4]], [], [[5, 6], [7, 8], [9, 0]]], elems_ragged_rank=1, expected_ragged_rank=1, - result_dtype=ragged.RaggedTensorType(dtype=dtypes.int64, - ragged_rank=1), + result_dtype=ragged.RaggedTensorType( + dtype=dtypes.int64, ragged_rank=1), expected_output=[[[2, 3], [4, 5]], [], [[6, 7], [8, 9], [10, 1]]], ), # [d1, (d2)] -> [d1, (d2), (d3)] dict( - fn=lambda x: ragged.from_row_starts(x, [0]), + fn=lambda x: ragged.RaggedTensor.from_row_starts(x, [0]), elems=[[1, 2, 3], [4, 5], [6, 7]], expected_output=[[[1, 2, 3]], [[4, 5]], [[6, 7]]], - result_dtype=ragged.RaggedTensorType(dtype=dtypes.int64, - ragged_rank=2), + result_dtype=ragged.RaggedTensorType( + dtype=dtypes.int64, ragged_rank=2), ), # [d1, (d2), (d3)] -> [d1, (d2), (d3)] dict( - fn=lambda x: ragged.map_inner_values(mo.add, x, 1), + fn=lambda x: ragged.map_flat_values(mo.add, x, 1), elems=[[[1, 2, 3]], [[4, 5], [6, 7]]], expected_output=[[[2, 3, 4]], [[5, 6], [7, 8]]], - result_dtype=ragged.RaggedTensorType(dtype=dtypes.int64, - ragged_rank=2), + result_dtype=ragged.RaggedTensorType( + dtype=dtypes.int64, ragged_rank=2), ), # [d1, (d2), (d3)] -> [d1, (d2)] dict( fn=lambda x: ragged.reduce_sum(x, axis=1), elems=[[[1, 2, 3]], [[4, 5], [6, 7]]], expected_output=[[6], [9, 13]], - result_dtype=ragged.RaggedTensorType(dtype=dtypes.int64, - ragged_rank=1), + result_dtype=ragged.RaggedTensorType( + dtype=dtypes.int64, ragged_rank=1), ), # [d1, (d2), (d3)] -> [d1, (d3)] dict( fn=lambda x: ragged.reduce_sum(x, axis=0), elems=[[[1, 2, 3]], [[4, 5], [6, 7]]], expected_output=[[1, 2, 3], [10, 12]], - result_dtype=ragged.RaggedTensorType(dtype=dtypes.int64, - ragged_rank=1), + result_dtype=ragged.RaggedTensorType( + dtype=dtypes.int64, ragged_rank=1), ), # [d1, (d2), (d3)] -> [d1] dict( @@ -118,27 +122,26 @@ class RaggedMapOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): fn=mo.range, elems=[4, 0, 2], expected_output=[[0, 1, 2, 3], [], [0, 1]], - result_dtype=ragged.RaggedTensorType(dtype=dtypes.int64, - ragged_rank=1), + result_dtype=ragged.RaggedTensorType( + dtype=dtypes.int64, ragged_rank=1), ), # [d1] -> [d1, (d2), (d3)] dict( fn=lambda x: ragged.range(mo.range(x)), elems=[5, 0, 3], - expected_output=[ - [[], [0], [0, 1], [0, 1, 2], [0, 1, 2, 3]], [], [[], [0], [0, 1]] - ], - result_dtype=ragged.RaggedTensorType(dtype=dtypes.int64, - ragged_rank=2), + expected_output=[[[], [0], [0, 1], [0, 1, 2], [0, 1, 2, 3]], [], + [[], [0], [0, 1]]], + result_dtype=ragged.RaggedTensorType( + dtype=dtypes.int64, ragged_rank=2), ), # [d1, (d2), (d3), (d4a), (d5)] -> [d1, (d2), (d3), (d4b), (d5)] dict( fn=lambda x: x + np.int64(1), elems=[[[[[1, 2, 3]], [[4], [5]]]], [[[[6, 7]]], [[[8], []]]]], - expected_output=[[[[[2, 3, 4]], [[5], [6]]]], - [[[[7, 8]]], [[[9], []]]]], - result_dtype=ragged.RaggedTensorType(dtype=dtypes.int64, - ragged_rank=4), + expected_output=[[[[[2, 3, 4]], [[5], [6]]]], [[[[7, 8]]], [[[9], + []]]]], + result_dtype=ragged.RaggedTensorType( + dtype=dtypes.int64, ragged_rank=4), ), ]) @@ -160,16 +163,12 @@ class RaggedMapOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): expected_rt = ragged.constant( expected_output, ragged_rank=expected_ragged_rank) - with self.test_session(): - if ragged.is_ragged(expected_output): - self.assertEqual(output.ragged_rank, expected_rt.ragged_rank) - output_values = self.evaluate(output) - self.assertAllEqual(expected_output, output_values.tolist()) + self.assertRaggedEqual(expected_rt, output) def testRaggedMapOnStructure(self): batman = ragged.constant([[1, 2, 3], [4], [5, 6, 7]]) # [[10, 20, 30], [40], [50, 60, 70]] - robin = ragged.map_inner_values(mo.multiply, batman, 10) + robin = ragged.map_flat_values(mo.multiply, batman, 10) features = {'batman': batman, 'robin': robin} @@ -182,14 +181,13 @@ class RaggedMapOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): dtype=dtypes.int32, ) - with self.test_session(): - self.assertAllEqual(output.eval().tolist(), [66, 44, 198]) + self.assertRaggedEqual(output, [66, 44, 198]) # Test mapping over a dict of RTs can produce a dict of RTs. def testRaggedMapOnStructure_RaggedOutputs(self): batman = ragged.constant([[1, 2, 3], [4], [5, 6, 7]]) # [[10, 20, 30], [40], [50, 60, 70]] - robin = ragged.map_inner_values(mo.multiply, batman, 10) + robin = ragged.map_flat_values(mo.multiply, batman, 10) features = {'batman': batman, 'robin': robin} @@ -211,17 +209,13 @@ class RaggedMapOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): }, ) - with self.test_session(): - self.assertAllEqual(output['batman'].eval().tolist(), - [[2, 3, 4], [5], [6, 7, 8]]) - self.assertAllEqual(output['robin'].eval().tolist(), - [[11, 21, 31], [41], [51, 61, 71]]) + self.assertRaggedEqual(output['batman'], [[2, 3, 4], [5], [6, 7, 8]]) + self.assertRaggedEqual(output['robin'], [[11, 21, 31], [41], [51, 61, 71]]) def testZip(self): x = ragged.constant([[10, 20], [30, 40], [50, 60], [70], [80, 90, 100]], dtypes.int64) - y = array_ops.expand_dims( - mo.range(ragged.nrows(x), dtype=dtypes.int64), axis=1) + y = array_ops.expand_dims(mo.range(x.nrows(), dtype=dtypes.int64), axis=1) def _zip(foo): y_val, x_val = foo @@ -233,11 +227,9 @@ class RaggedMapOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): dtype=ragged.RaggedTensorType(dtype=dtypes.int64, ragged_rank=1), infer_shape=False) - with self.test_session(): - result = self.evaluate(output).tolist() - self.assertAllEqual( - result, [[[0, 10], [0, 20]], [[1, 30], [1, 40]], [[2, 50], [2, 60]], - [[3, 70]], [[4, 80], [4, 90], [4, 100]]]) + self.assertRaggedEqual( + output, [[[0, 10], [0, 20]], [[1, 30], [1, 40]], [[2, 50], [2, 60]], + [[3, 70]], [[4, 80], [4, 90], [4, 100]]]) def testBatchGather(self): tokens = ragged.constant([['hello', '.', 'there'], ['merhaba'], @@ -255,10 +247,8 @@ class RaggedMapOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): dtype=ragged.RaggedTensorType(dtype=dtypes.string, ragged_rank=1), infer_shape=False) - with self.test_session(): - self.assertAllEqual( - self.evaluate(out).tolist(), - [[b'hello', b'there'], [b'merhaba'], [b'bonjour', b'ca va']]) + self.assertRaggedEqual( + out, [[b'hello', b'there'], [b'merhaba'], [b'bonjour', b'ca va']]) def testMismatchRaggedRank(self): elems = ragged.constant([[[1, 2, 3]], [[4, 5], [6, 7]]]) @@ -272,7 +262,7 @@ class RaggedMapOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): def testMismatchRaggedRank2(self): elems = ragged.constant([[1, 2, 3], [4, 5], [6, 7]]) - fn = lambda x: ragged.from_row_starts(x, [0]) + fn = lambda x: ragged.RaggedTensor.from_row_starts(x, [0]) with self.assertRaisesWithLiteralMatch( ValueError, r'The declared ragged rank (10) mismatches the result (1)'): _ = ragged.map_fn( diff --git a/tensorflow/python/ops/ragged/ragged_map_ops.py b/tensorflow/python/ops/ragged/ragged_map_ops.py index fafa23b8dc..af40352b1d 100644 --- a/tensorflow/python/ops/ragged/ragged_map_ops.py +++ b/tensorflow/python/ops/ragged/ragged_map_ops.py @@ -27,12 +27,11 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes 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 control_flow_ops from tensorflow.python.ops import tensor_array_ops from tensorflow.python.ops import variable_scope as vs -from tensorflow.python.ops.ragged import ragged_array_ops -from tensorflow.python.ops.ragged import ragged_factory_ops from tensorflow.python.ops.ragged import ragged_tensor from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util import nest @@ -216,8 +215,8 @@ def map_fn(fn, varscope_caching_device_was_none = True elems_flat = [ - ragged_factory_ops.convert_to_tensor_or_ragged_tensor( - elem, name="elem") for elem in elems_flat + ragged_tensor.convert_to_tensor_or_ragged_tensor(elem, name="elem") + for elem in elems_flat ] # We can either infer the output, or we can assume that it will be the same @@ -226,7 +225,7 @@ def map_fn(fn, # Find the number of iterations, n may be known statically. if isinstance(elems_flat[0], ragged_tensor.RaggedTensor): - n = ragged_array_ops.nrows(elems_flat[0], out_type=dtypes.int32) + n = elems_flat[0].nrows(out_type=dtypes.int32) else: static_shape = elems_flat[0].shape if static_shape.ndims is not None and static_shape.ndims < 1: @@ -236,7 +235,8 @@ def map_fn(fn, else: raise ValueError( "elements in elems must be 1+ dimensional Tensors, not scalars") - n = static_shape[0].value or array_ops.shape(elems_flat[0])[0] + n = (tensor_shape.dimension_value(static_shape[0]) or + array_ops.shape(elems_flat[0])[0]) # Create a flat list of TAs. @@ -334,7 +334,7 @@ def map_fn(fn, class _RaggedTensorComponents( collections.namedtuple( "_RaggedTensorComponents", - ["inner_values", "nested_row_lengths", "outer_row_length"])): + ["flat_values", "nested_row_lengths", "outer_row_length"])): """A namedtuple of components which represent a `RaggedTensor`. _RaggedTensorComponents is a list of components which can be used to create a @@ -344,7 +344,7 @@ class _RaggedTensorComponents( The following are a list of components for a `RaggedTensor`: - inner_values: The flat and inner values of a RaggedTensor. This could be + flat_values: The flat and inner values of a RaggedTensor. This could be a `Tensor`, a `TensorArray`, or a data type. nested_row_lengths: a tuple containing the row lengths of each rank. The elements of the tuple could be `Tensor`s or `TensorArray`s. @@ -357,12 +357,12 @@ class _RaggedTensorComponents( def _concat_ragged_tensor_components(rt_ta): - inner_values = rt_ta.inner_values.concat() + flat_values = rt_ta.flat_values.concat() nested_row_lengths = tuple( row_lengths_ta.concat() for row_lengths_ta in rt_ta.nested_row_lengths) outer_row_length = rt_ta.outer_row_length.concat() return _RaggedTensorComponents( - inner_values=inner_values, + flat_values=flat_values, nested_row_lengths=nested_row_lengths, outer_row_length=outer_row_length) @@ -374,17 +374,17 @@ def _maybe_decompose_tensor(rt): # The three component pieces we need: # - inner values - inner_values = rt.inner_values + flat_values = rt.flat_values # - row_splits of the RT splits = rt.nested_row_splits nested_row_lengths = tuple(split[1:] - split[:-1] for split in splits) # - outer row length - outer_row_length = array_ops.expand_dims(ragged_array_ops.nrows(rt), axis=0) + outer_row_length = array_ops.expand_dims(rt.nrows(), axis=0) return _RaggedTensorComponents( - inner_values=inner_values, + flat_values=flat_values, nested_row_lengths=nested_row_lengths, outer_row_length=outer_row_length, ) @@ -395,11 +395,12 @@ def _maybe_recompose_tensor(t): if not isinstance(t, _RaggedTensorComponents): return t - values = t.inner_values + values = t.flat_values nested_row_lengths = tuple(t.nested_row_lengths) for nested_row_length in reversed(nested_row_lengths): - values = ragged_factory_ops.from_row_lengths(values, nested_row_length) - return ragged_factory_ops.from_row_lengths(values, t.outer_row_length) + values = ragged_tensor.RaggedTensor.from_row_lengths( + values, nested_row_length) + return ragged_tensor.RaggedTensor.from_row_lengths(values, t.outer_row_length) def _maybe_decompose_dtype(d): @@ -408,7 +409,7 @@ def _maybe_decompose_dtype(d): return d result = _RaggedTensorComponents( - inner_values=d.dtype, + flat_values=d.dtype, nested_row_lengths=tuple(dtypes.int64 for i in range(d.ragged_rank - 1)), outer_row_length=dtypes.int64, ) @@ -435,10 +436,13 @@ def _convert_declared(fn_output_flat, output_declared): "The declared ragged rank (%d) mismatches the result (1)" % declared.ragged_rank) - row_length = array_ops.expand_dims( - ragged_array_ops.nrows(current), axis=0) + if isinstance(current, ragged_tensor.RaggedTensor): + nrows = current.nrows() + else: + nrows = array_ops.shape(current, out_type=dtypes.int64)[0] + row_length = array_ops.expand_dims(nrows, axis=0) rt = _RaggedTensorComponents( - inner_values=current, + flat_values=current, nested_row_lengths=(), outer_row_length=row_length) yield rt diff --git a/tensorflow/python/ops/ragged/ragged_math_ops.py b/tensorflow/python/ops/ragged/ragged_math_ops.py index d661563a9f..92f82be84a 100644 --- a/tensorflow/python/ops/ragged/ragged_math_ops.py +++ b/tensorflow/python/ops/ragged/ragged_math_ops.py @@ -18,6 +18,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import numpy as np + from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util @@ -25,7 +27,6 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops import gen_ragged_math_ops from tensorflow.python.ops import math_ops -from tensorflow.python.ops.ragged import ragged_factory_ops from tensorflow.python.ops.ragged import ragged_functional_ops from tensorflow.python.ops.ragged import ragged_tensor from tensorflow.python.ops.ragged import ragged_util @@ -97,8 +98,8 @@ def range(starts, limits=None, deltas=1, dtype=None, name=None): [dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64]) result = gen_ragged_math_ops.ragged_range(starts, limits, deltas, name=name) - return ragged_factory_ops.from_row_splits(result.rt_dense_values, - result.rt_nested_splits) + return ragged_tensor.RaggedTensor.from_row_splits(result.rt_dense_values, + result.rt_nested_splits) def _infer_matching_dtype(tensors, dtype_hierarchy): @@ -184,9 +185,8 @@ def _ragged_segment_aggregate(unsorted_segment_op, with ops.name_scope(name, 'RaggedSegment', [data, segment_ids, num_segments]) as name: - data = ragged_factory_ops.convert_to_tensor_or_ragged_tensor( - data, name='data') - segment_ids = ragged_factory_ops.convert_to_tensor_or_ragged_tensor( + data = ragged_tensor.convert_to_tensor_or_ragged_tensor(data, name='data') + segment_ids = ragged_tensor.convert_to_tensor_or_ragged_tensor( segment_ids, name='segment_ids') if ragged_tensor.is_ragged(segment_ids): @@ -236,7 +236,8 @@ def _ragged_segment_aggregate(unsorted_segment_op, output_values = _ragged_segment_aggregate(unsorted_segment_op, data.values, data_val_to_out_val_index, output_splits[-1]) - return ragged_factory_ops.from_row_splits(output_values, output_splits) + return ragged_tensor.RaggedTensor.from_row_splits(output_values, + output_splits) def segment_sum(data, segment_ids, num_segments, name=None): @@ -272,11 +273,11 @@ def segment_mean(data, segment_ids, num_segments, name=None): with ops.name_scope(name, 'RaggedSegmentMean', [data, segment_ids, num_segments]): total = segment_sum(data, segment_ids, num_segments) - ones = ragged_factory_ops.from_nested_row_splits( - array_ops.ones_like(data.inner_values), data.nested_row_splits) + ones = ragged_tensor.RaggedTensor.from_nested_row_splits( + array_ops.ones_like(data.flat_values), data.nested_row_splits) count = segment_sum(ones, segment_ids, num_segments) - return ragged_factory_ops.from_nested_row_splits( - total.inner_values / count.inner_values, total.nested_row_splits) + return ragged_tensor.RaggedTensor.from_nested_row_splits( + total.flat_values / count.flat_values, total.nested_row_splits) def segment_sqrt_n(data, segment_ids, num_segments, name=None): @@ -284,11 +285,11 @@ def segment_sqrt_n(data, segment_ids, num_segments, name=None): with ops.name_scope(name, 'RaggedSegmentSqrtN', [data, segment_ids, num_segments]): total = segment_sum(data, segment_ids, num_segments) - ones = ragged_factory_ops.from_nested_row_splits( - array_ops.ones_like(data.inner_values), data.nested_row_splits) + ones = ragged_tensor.RaggedTensor.from_nested_row_splits( + array_ops.ones_like(data.flat_values), data.nested_row_splits) count = segment_sum(ones, segment_ids, num_segments) - return ragged_factory_ops.from_nested_row_splits( - total.inner_values / math_ops.sqrt(count.inner_values), + return ragged_tensor.RaggedTensor.from_nested_row_splits( + total.flat_values / math_ops.sqrt(count.flat_values), total.nested_row_splits) @@ -440,10 +441,12 @@ def _ragged_reduce_aggregate(reduce_op, axis = tensor_util.constant_value(axis) if axis is None: raise ValueError('axis must be known at graph construction time.') + if isinstance(axis, np.ndarray): + axis = axis.tolist() # When reducing all axes, just ignore splits & reduce the inner values. if axis is None: - return reduce_op(rt_input.inner_values, None, name=name) + return reduce_op(rt_input.flat_values, None, name=name) with ops.name_scope(name, 'RaggedReduce', [rt_input, axis]): if isinstance(axis, (tuple, list)): @@ -464,7 +467,7 @@ def _ragged_reduce_aggregate(reduce_op, axis = ragged_util.get_positive_axis(axis, rt_input.shape.ndims) - rt_input = ragged_factory_ops.convert_to_tensor_or_ragged_tensor( + rt_input = ragged_tensor.convert_to_tensor_or_ragged_tensor( rt_input, name='rt_input') if axis == 0: @@ -522,22 +525,22 @@ def reduce_mean(input_tensor, axis=None, keepdims=None, name=None): with ops.name_scope(name, 'RaggedReduceMean', [input_tensor, axis]): total = reduce_sum(input_tensor, axis, keepdims) if ragged_tensor.is_ragged(input_tensor): - ones = ragged_factory_ops.from_nested_row_splits( - array_ops.ones_like(input_tensor.inner_values), + ones = ragged_tensor.RaggedTensor.from_nested_row_splits( + array_ops.ones_like(input_tensor.flat_values), input_tensor.nested_row_splits) else: ones = array_ops.ones_like(input_tensor) count = reduce_sum(ones, axis, keepdims) if ragged_tensor.is_ragged(total): - return ragged_factory_ops.from_nested_row_splits( - total.inner_values / count.inner_values, total.nested_row_splits) + return ragged_tensor.RaggedTensor.from_nested_row_splits( + total.flat_values / count.flat_values, total.nested_row_splits) else: return total / count def _cast(input_tensor, dtype): - return ragged_functional_ops.map_inner_values(math_ops.cast, input_tensor, - dtype) + return ragged_functional_ops.map_flat_values(math_ops.cast, input_tensor, + dtype) def reduce_all(input_tensor, axis=None, keepdims=None, name=None): diff --git a/tensorflow/python/ops/ragged/ragged_operators_test.py b/tensorflow/python/ops/ragged/ragged_operators_test.py index c1223db307..78bb37c341 100644 --- a/tensorflow/python/ops/ragged/ragged_operators_test.py +++ b/tensorflow/python/ops/ragged/ragged_operators_test.py @@ -20,79 +20,71 @@ from __future__ import print_function from tensorflow.python.framework import test_util from tensorflow.python.ops import ragged +from tensorflow.python.ops.ragged import ragged_test_util from tensorflow.python.platform import googletest -@test_util.run_v1_only('b/120545219') -class RaggedElementwiseOpsTest(test_util.TensorFlowTestCase): - # @TODO(edloper): Test right-handed versions of operators once we add - # broadcasting support for elementwise ops. +@test_util.run_all_in_graph_and_eager_modes +class RaggedElementwiseOpsTest(ragged_test_util.RaggedTensorTestCase): def testOrderingOperators(self): x = ragged.constant([[1, 5], [3]]) y = ragged.constant([[4, 5], [1]]) - with self.test_session(): - self.assertEqual((x > y).eval().tolist(), [[False, False], [True]]) - self.assertEqual((x >= y).eval().tolist(), [[False, True], [True]]) - self.assertEqual((x < y).eval().tolist(), [[True, False], [False]]) - self.assertEqual((x <= y).eval().tolist(), [[True, True], [False]]) - - def assertEqual(self, a, b): - if a != b: - print('%30s %s' % (b, a)) + self.assertRaggedEqual((x > y), [[False, False], [True]]) + self.assertRaggedEqual((x >= y), [[False, True], [True]]) + self.assertRaggedEqual((x < y), [[True, False], [False]]) + self.assertRaggedEqual((x <= y), [[True, True], [False]]) def testArithmeticOperators(self): x = ragged.constant([[1.0, -2.0], [8.0]]) y = ragged.constant([[4.0, 4.0], [2.0]]) - with self.test_session(): - self.assertEqual(abs(x).eval().tolist(), [[1.0, 2.0], [8.0]]) + self.assertRaggedEqual(abs(x), [[1.0, 2.0], [8.0]]) - self.assertEqual((-x).eval().tolist(), [[-1.0, 2.0], [-8.0]]) + self.assertRaggedEqual((-x), [[-1.0, 2.0], [-8.0]]) - self.assertEqual((x + y).eval().tolist(), [[5.0, 2.0], [10.0]]) - self.assertEqual((3.0 + y).eval().tolist(), [[7.0, 7.0], [5.0]]) - self.assertEqual((x + 3.0).eval().tolist(), [[4.0, 1.0], [11.0]]) + self.assertRaggedEqual((x + y), [[5.0, 2.0], [10.0]]) + self.assertRaggedEqual((3.0 + y), [[7.0, 7.0], [5.0]]) + self.assertRaggedEqual((x + 3.0), [[4.0, 1.0], [11.0]]) - self.assertEqual((x - y).eval().tolist(), [[-3.0, -6.0], [6.0]]) - self.assertEqual((3.0 - y).eval().tolist(), [[-1.0, -1.0], [1.0]]) - self.assertEqual((x + 3.0).eval().tolist(), [[4.0, 1.0], [11.0]]) + self.assertRaggedEqual((x - y), [[-3.0, -6.0], [6.0]]) + self.assertRaggedEqual((3.0 - y), [[-1.0, -1.0], [1.0]]) + self.assertRaggedEqual((x + 3.0), [[4.0, 1.0], [11.0]]) - self.assertEqual((x * y).eval().tolist(), [[4.0, -8.0], [16.0]]) - self.assertEqual((3.0 * y).eval().tolist(), [[12.0, 12.0], [6.0]]) - self.assertEqual((x * 3.0).eval().tolist(), [[3.0, -6.0], [24.0]]) + self.assertRaggedEqual((x * y), [[4.0, -8.0], [16.0]]) + self.assertRaggedEqual((3.0 * y), [[12.0, 12.0], [6.0]]) + self.assertRaggedEqual((x * 3.0), [[3.0, -6.0], [24.0]]) - self.assertEqual((x / y).eval().tolist(), [[0.25, -0.5], [4.0]]) - self.assertEqual((y / x).eval().tolist(), [[4.0, -2.0], [0.25]]) - self.assertEqual((2.0 / y).eval().tolist(), [[0.5, 0.5], [1.0]]) - self.assertEqual((x / 2.0).eval().tolist(), [[0.5, -1.0], [4.0]]) + self.assertRaggedEqual((x / y), [[0.25, -0.5], [4.0]]) + self.assertRaggedEqual((y / x), [[4.0, -2.0], [0.25]]) + self.assertRaggedEqual((2.0 / y), [[0.5, 0.5], [1.0]]) + self.assertRaggedEqual((x / 2.0), [[0.5, -1.0], [4.0]]) - self.assertEqual((x // y).eval().tolist(), [[0.0, -1.0], [4.0]]) - self.assertEqual((y // x).eval().tolist(), [[4.0, -2.0], [0.0]]) - self.assertEqual((2.0 // y).eval().tolist(), [[0.0, 0.0], [1.0]]) - self.assertEqual((x // 2.0).eval().tolist(), [[0.0, -1.0], [4.0]]) + self.assertRaggedEqual((x // y), [[0.0, -1.0], [4.0]]) + self.assertRaggedEqual((y // x), [[4.0, -2.0], [0.0]]) + self.assertRaggedEqual((2.0 // y), [[0.0, 0.0], [1.0]]) + self.assertRaggedEqual((x // 2.0), [[0.0, -1.0], [4.0]]) - self.assertEqual((x % y).eval().tolist(), [[1.0, 2.0], [0.0]]) - self.assertEqual((y % x).eval().tolist(), [[0.0, -0.0], [2.0]]) - self.assertEqual((2.0 % y).eval().tolist(), [[2.0, 2.0], [0.0]]) - self.assertEqual((x % 2.0).eval().tolist(), [[1.0, 0.0], [0.0]]) + self.assertRaggedEqual((x % y), [[1.0, 2.0], [0.0]]) + self.assertRaggedEqual((y % x), [[0.0, -0.0], [2.0]]) + self.assertRaggedEqual((2.0 % y), [[2.0, 2.0], [0.0]]) + self.assertRaggedEqual((x % 2.0), [[1.0, 0.0], [0.0]]) def testLogicalOperators(self): a = ragged.constant([[True, True], [False]]) b = ragged.constant([[True, False], [False]]) - with self.test_session(): - self.assertEqual((~a).eval().tolist(), [[False, False], [True]]) + self.assertRaggedEqual((~a), [[False, False], [True]]) - self.assertEqual((a & b).eval().tolist(), [[True, False], [False]]) - self.assertEqual((a & True).eval().tolist(), [[True, True], [False]]) - self.assertEqual((True & b).eval().tolist(), [[True, False], [False]]) + self.assertRaggedEqual((a & b), [[True, False], [False]]) + self.assertRaggedEqual((a & True), [[True, True], [False]]) + self.assertRaggedEqual((True & b), [[True, False], [False]]) - self.assertEqual((a | b).eval().tolist(), [[True, True], [False]]) - self.assertEqual((a | False).eval().tolist(), [[True, True], [False]]) - self.assertEqual((False | b).eval().tolist(), [[True, False], [False]]) + self.assertRaggedEqual((a | b), [[True, True], [False]]) + self.assertRaggedEqual((a | False), [[True, True], [False]]) + self.assertRaggedEqual((False | b), [[True, False], [False]]) - self.assertEqual((a ^ b).eval().tolist(), [[False, True], [False]]) - self.assertEqual((a ^ True).eval().tolist(), [[False, False], [True]]) - self.assertEqual((True ^ b).eval().tolist(), [[False, True], [True]]) + self.assertRaggedEqual((a ^ b), [[False, True], [False]]) + self.assertRaggedEqual((a ^ True), [[False, False], [True]]) + self.assertRaggedEqual((True ^ b), [[False, True], [True]]) def testDummyOperators(self): a = ragged.constant([[True, True], [False]]) diff --git a/tensorflow/python/ops/ragged/ragged_range_op_test.py b/tensorflow/python/ops/ragged/ragged_range_op_test.py index 644423ecb7..5ab3d4abc3 100644 --- a/tensorflow/python/ops/ragged/ragged_range_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_range_op_test.py @@ -21,111 +21,102 @@ from __future__ import print_function from tensorflow.python.framework import errors from tensorflow.python.framework import test_util from tensorflow.python.ops import ragged +from tensorflow.python.ops.ragged import ragged_test_util from tensorflow.python.platform import googletest -class RaggedRangeOpTest(test_util.TensorFlowTestCase): +@test_util.run_all_in_graph_and_eager_modes +class RaggedRangeOpTest(ragged_test_util.RaggedTensorTestCase): - @test_util.run_deprecated_v1 def testDocStringExamples(self): """Examples from ragged_range.__doc__.""" - with self.test_session(): - rt1 = ragged.range([3, 5, 2]).eval().tolist() - self.assertEqual(rt1, [[0, 1, 2], [0, 1, 2, 3, 4], [0, 1]]) + rt1 = ragged.range([3, 5, 2]) + self.assertRaggedEqual(rt1, [[0, 1, 2], [0, 1, 2, 3, 4], [0, 1]]) - rt2 = ragged.range([0, 5, 8], [3, 3, 12]).eval().tolist() - self.assertEqual(rt2, [[0, 1, 2], [], [8, 9, 10, 11]]) + rt2 = ragged.range([0, 5, 8], [3, 3, 12]) + self.assertRaggedEqual(rt2, [[0, 1, 2], [], [8, 9, 10, 11]]) - rt3 = ragged.range([0, 5, 8], [3, 3, 12], 2).eval().tolist() - self.assertEqual(rt3, [[0, 2], [], [8, 10]]) + rt3 = ragged.range([0, 5, 8], [3, 3, 12], 2) + self.assertRaggedEqual(rt3, [[0, 2], [], [8, 10]]) - @test_util.run_deprecated_v1 def testBasicRanges(self): - with self.test_session(): - # Specify limits only. - self.assertEqual( - ragged.range([0, 3, 5]).eval().tolist(), - [list(range(0)), list(range(3)), list(range(5))]) - - # Specify starts and limits. - self.assertEqual( - ragged.range([0, 3, 5], [2, 3, 10]).eval().tolist(), - [list(range(0, 2)), list(range(3, 3)), list(range(5, 10))]) - - # Specify starts, limits, and deltas. - self.assertEqual( - ragged.range([0, 3, 5], [4, 4, 15], [2, 3, 4]).eval().tolist(), - [list(range(0, 4, 2)), list(range(3, 4, 3)), - list(range(5, 15, 4))]) - - @test_util.run_deprecated_v1 + # Specify limits only. + self.assertRaggedEqual( + ragged.range([0, 3, 5]), + [list(range(0)), list(range(3)), + list(range(5))]) + + # Specify starts and limits. + self.assertRaggedEqual( + ragged.range([0, 3, 5], [2, 3, 10]), + [list(range(0, 2)), + list(range(3, 3)), + list(range(5, 10))]) + + # Specify starts, limits, and deltas. + self.assertRaggedEqual( + ragged.range([0, 3, 5], [4, 4, 15], [2, 3, 4]), + [list(range(0, 4, 2)), + list(range(3, 4, 3)), + list(range(5, 15, 4))]) + def testFloatRanges(self): - with self.test_session(): - expected = [[0.0, 0.4, 0.8, 1.2, 1.6, 2.0, 2.4, 2.8, 3.2, 3.6], [3.0], - [5.0, 7.2, 9.4, 11.6, 13.8]] - actual = ragged.range([0.0, 3.0, 5.0], [3.9, 4.0, 15.0], - [0.4, 1.5, 2.2]).eval().tolist() - self.assertEqual(expected, [[round(v, 5) for v in row] for row in actual]) - - @test_util.run_deprecated_v1 + expected = [[0.0, 0.4, 0.8, 1.2, 1.6, 2.0, 2.4, 2.8, 3.2, 3.6], [3.0], + [5.0, 7.2, 9.4, 11.6, 13.8]] + actual = ragged.range([0.0, 3.0, 5.0], [3.9, 4.0, 15.0], [0.4, 1.5, 2.2]) + self.assertEqual( + expected, + [[round(v, 5) for v in row] for row in self.eval_to_list(actual)]) + def testNegativeDeltas(self): - with self.test_session(): - self.assertEqual( - ragged.range([0, 3, 5], limits=0, deltas=-1).eval().tolist(), - [list(range(0, 0, -1)), list(range(3, 0, -1)), - list(range(5, 0, -1))]) - - self.assertEqual( - ragged.range([0, -3, 5], limits=0, deltas=[-1, 1, - -2]).eval().tolist(), - [list(range(0, 0, -1)), list(range(-3, 0, 1)), - list(range(5, 0, -2))]) - - @test_util.run_deprecated_v1 + self.assertRaggedEqual( + ragged.range([0, 3, 5], limits=0, deltas=-1), + [list(range(0, 0, -1)), + list(range(3, 0, -1)), + list(range(5, 0, -1))]) + + self.assertRaggedEqual( + ragged.range([0, -3, 5], limits=0, deltas=[-1, 1, -2]), + [list(range(0, 0, -1)), + list(range(-3, 0, 1)), + list(range(5, 0, -2))]) + def testBroadcast(self): - with self.test_session(): - # Specify starts and limits, broadcast deltas. - self.assertEqual( - ragged.range([0, 3, 5], [4, 4, 15], 3).eval().tolist(), - [list(range(0, 4, 3)), list(range(3, 4, 3)), - list(range(5, 15, 3))]) - - # Broadcast all arguments. - self.assertEqual( - ragged.range(0, 5, 1).eval().tolist(), [list(range(0, 5, 1))]) - - @test_util.run_deprecated_v1 + # Specify starts and limits, broadcast deltas. + self.assertRaggedEqual( + ragged.range([0, 3, 5], [4, 4, 15], 3), + [list(range(0, 4, 3)), + list(range(3, 4, 3)), + list(range(5, 15, 3))]) + + # Broadcast all arguments. + self.assertRaggedEqual(ragged.range(0, 5, 1), [list(range(0, 5, 1))]) + def testEmptyRanges(self): rt1 = ragged.range([0, 5, 3], [0, 3, 5]) rt2 = ragged.range([0, 5, 5], [0, 3, 5], -1) - with self.test_session(): - self.assertEqual(rt1.eval().tolist(), [[], [], [3, 4]]) - self.assertEqual(rt2.eval().tolist(), [[], [5, 4], []]) + self.assertRaggedEqual(rt1, [[], [], [3, 4]]) + self.assertRaggedEqual(rt2, [[], [5, 4], []]) - @test_util.run_deprecated_v1 def testShapeFnErrors(self): - with self.test_session(): - self.assertRaisesRegexp(ValueError, r'Shape must be at most rank 1.*', - ragged.range, [[0]], 5) - self.assertRaisesRegexp(ValueError, r'Shape must be at most rank 1.*', - ragged.range, 0, [[5]]) - self.assertRaisesRegexp(ValueError, r'Shape must be at most rank 1.*', - ragged.range, 0, 5, [[0]]) - self.assertRaisesRegexp(ValueError, r'Dimensions must be equal.*', - ragged.range, [0], [1, 2]) - - @test_util.run_deprecated_v1 + self.assertRaises((ValueError, errors.InvalidArgumentError), ragged.range, + [[0]], 5) + self.assertRaises((ValueError, errors.InvalidArgumentError), ragged.range, + 0, [[5]]) + self.assertRaises((ValueError, errors.InvalidArgumentError), ragged.range, + 0, 5, [[0]]) + self.assertRaises((ValueError, errors.InvalidArgumentError), ragged.range, + [0], [1, 2]) + def testKernelErrors(self): - with self.test_session(): - self.assertRaisesRegexp(errors.InvalidArgumentError, - r'Requires delta != 0', - ragged.range(0, 0, 0).eval) + with self.assertRaisesRegexp(errors.InvalidArgumentError, + r'Requires delta != 0'): + self.evaluate(ragged.range(0, 0, 0)) - @test_util.run_deprecated_v1 def testShape(self): - self.assertEqual(ragged.range(0, 0, 0).shape.as_list(), [1, None]) - self.assertEqual(ragged.range([1, 2, 3]).shape.as_list(), [3, None]) - self.assertEqual( + self.assertRaggedEqual(ragged.range(0, 0, 1).shape.as_list(), [1, None]) + self.assertRaggedEqual(ragged.range([1, 2, 3]).shape.as_list(), [3, None]) + self.assertRaggedEqual( ragged.range([1, 2, 3], [4, 5, 6]).shape.as_list(), [3, None]) diff --git a/tensorflow/python/ops/ragged/ragged_reduce_op_test.py b/tensorflow/python/ops/ragged/ragged_reduce_op_test.py index 2e4db2a423..890460221b 100644 --- a/tensorflow/python/ops/ragged/ragged_reduce_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_reduce_op_test.py @@ -21,11 +21,13 @@ from __future__ import print_function from absl.testing import parameterized import numpy as np +from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import ragged +from tensorflow.python.ops.ragged import ragged_test_util from tensorflow.python.platform import googletest _MAX_INT32 = dtypes.int32.max @@ -37,7 +39,9 @@ def mean(*values): return 1.0 * sum(values) / len(values) -class RaggedReduceOpsTest(test_util.TensorFlowTestCase, parameterized.TestCase): +@test_util.run_all_in_graph_and_eager_modes +class RaggedReduceOpsTest(ragged_test_util.RaggedTensorTestCase, + parameterized.TestCase): @parameterized.parameters( #========================================================================= @@ -300,19 +304,16 @@ class RaggedReduceOpsTest(test_util.TensorFlowTestCase, parameterized.TestCase): axis=2, expected=[[mean(1, 2), mean(3, 4, 5)], [mean(6, 7), 8], [9]]), ) - @test_util.run_v1_only('b/120545219') def testReduce(self, ragged_reduce_op, rt_input, axis, expected): rt_input = ragged.constant(rt_input) reduced = ragged_reduce_op(rt_input, axis) - with self.test_session(): - self.assertEqual(reduced.eval().tolist(), expected) + self.assertRaggedEqual(reduced, expected) def assertEqualWithNan(self, actual, expected): """Like assertEqual, but NaN==NaN.""" self.assertTrue( ((actual == expected) | (np.isnan(actual) & np.isnan(expected))).all()) - @test_util.run_deprecated_v1 def testMeanNan(self): rt_as_list = [[0, 1, 2, 3], [4], [], [5, 6], [7], [8, 9]] expected = ( @@ -320,24 +321,22 @@ class RaggedReduceOpsTest(test_util.TensorFlowTestCase, parameterized.TestCase): [4, 1, 0, 2, 1, 2])) rt_input = ragged.constant(rt_as_list) reduced = ragged.reduce_mean(rt_input, axis=1) - with self.test_session(): - self.assertEqualWithNan(reduced.eval(), expected) + self.assertEqualWithNan(self.evaluate(reduced), expected) - @test_util.run_deprecated_v1 def testMeanWithTensorInputs(self): tensor = [[1.0, 2.0, 3.0], [10.0, 20.0, 30.0]] expected = [2.0, 20.0] reduced = ragged.reduce_mean(tensor, axis=1) - with self.test_session(): - self.assertAllEqual(reduced.eval(), expected) + self.assertRaggedEqual(reduced, expected) - @test_util.run_deprecated_v1 def testErrors(self): rt_input = ragged.constant([[1, 2, 3], [4, 5]]) axis = array_ops.placeholder_with_default(constant_op.constant([0]), None) - self.assertRaisesRegexp(ValueError, - r'axis must be known at graph construction time.', - ragged.reduce_sum, rt_input, axis) + + if not context.executing_eagerly(): + self.assertRaisesRegexp( + ValueError, r'axis must be known at graph construction time.', + ragged.reduce_sum, rt_input, axis) self.assertRaisesRegexp(TypeError, r'axis must be an int; got str.*', ragged.reduce_sum, rt_input, ['x']) diff --git a/tensorflow/python/ops/ragged/ragged_row_lengths_op_test.py b/tensorflow/python/ops/ragged/ragged_row_lengths_op_test.py index 9f4aa1b136..15112d6c9c 100644 --- a/tensorflow/python/ops/ragged/ragged_row_lengths_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_row_lengths_op_test.py @@ -20,12 +20,16 @@ from __future__ import print_function from absl.testing import parameterized +from tensorflow.python.framework import errors from tensorflow.python.framework import test_util from tensorflow.python.ops import ragged +from tensorflow.python.ops.ragged import ragged_test_util from tensorflow.python.platform import googletest -class RaggedRowLengthsOp(test_util.TensorFlowTestCase, parameterized.TestCase): +@test_util.run_all_in_graph_and_eager_modes +class RaggedRowLengthsOp(ragged_test_util.RaggedTensorTestCase, + parameterized.TestCase): @parameterized.parameters([ # Docstring Example @@ -37,24 +41,6 @@ class RaggedRowLengthsOp(test_util.TensorFlowTestCase, parameterized.TestCase): axis=2, expected=[[3, 1], [], [2, 1], [1], []]), - # 1D tensor - dict( - rt_input=[1, 2, 3, 4, 5], - ragged_rank=0, - axis=0, - expected=5), - - # 2D Tensor (0 ragged dimensions) - dict( - rt_input=[[1, 2], [3, 4], [5, 6], [7, 8]], - ragged_rank=0, - expected=[2, 2, 2, 2]), - dict( - rt_input=[[1, 2], [3, 4], [5, 6], [7, 8]], - ragged_rank=0, - axis=0, - expected=4), - # 2D Tensor (1 ragged dimension) dict( rt_input=[['a'], ['b', 'c', 'd'], ['e'], [], ['f']], @@ -79,24 +65,6 @@ class RaggedRowLengthsOp(test_util.TensorFlowTestCase, parameterized.TestCase): axis=0, expected=0), - # 3D Tensor (0 ragged dimensions) - dict( - rt_input=[[[1, 2], [3, 4], [5, 6]], [[7, 8], [9, 10], [11, 12]]], - ragged_rank=0, - axis=0, - expected=2), - dict( - rt_input=[[[1, 2], [3, 4], [5, 6]], [[7, 8], [9, 10], [11, 12]]], - ragged_rank=0, - axis=1, - expected=[3, 3]), - dict( - rt_input=[[[1, 2], [3, 4], [5, 6]], [[7, 8], [9, 10], [11, 12]]], - ragged_rank=0, - axis=2, - expected=[[2, 2, 2], [2, 2, 2]], - expected_ragged_rank=0), - # 3D Tensor (1 ragged dimension) dict( rt_input=[[[1, 2], [3, 4], [5, 6]], [[7, 8], [9, 10]]], @@ -143,7 +111,6 @@ class RaggedRowLengthsOp(test_util.TensorFlowTestCase, parameterized.TestCase): expected=[[2, 3, 0], [4, 1]], expected_ragged_rank=1), ]) # pyformat: disable - @test_util.run_v1_only('b/120545219') def testRowLengths(self, rt_input, expected, @@ -151,34 +118,28 @@ class RaggedRowLengthsOp(test_util.TensorFlowTestCase, parameterized.TestCase): ragged_rank=None, expected_ragged_rank=None): rt = ragged.constant(rt_input, ragged_rank=ragged_rank) - lengths = ragged.row_lengths(rt, axis) - with self.test_session(): - self.assertEqual(lengths.eval().tolist(), expected) - if expected_ragged_rank is not None: - if isinstance(lengths, ragged.RaggedTensor): - self.assertEqual(lengths.ragged_rank, expected_ragged_rank) - else: - self.assertEqual(0, expected_ragged_rank) + lengths = rt.row_lengths(axis) + self.assertRaggedEqual(lengths, expected) + if expected_ragged_rank is not None: + if isinstance(lengths, ragged.RaggedTensor): + self.assertEqual(lengths.ragged_rank, expected_ragged_rank) + else: + self.assertEqual(0, expected_ragged_rank) @parameterized.parameters([ - dict( - rt_input=10, - exception=ValueError, - message='rt_input may not be a scalar.'), - dict( - rt_input=[10, 20], - axis=1, - exception=ValueError, - message='axis=1 out of bounds: expected -1<=axis<1.'), - dict( + dict( # axis=2 out of bounds: expected -2<=axis<2. + rt_input=[[10, 20], [30]], + axis=2, + exception=(ValueError, errors.InvalidArgumentError)), + dict( # axis=-3 out of bounds: expected -2<=axis<2. rt_input=[[2, 3, 0], [4, 1, 2]], axis=-3, - exception=ValueError, - message='axis=-3 out of bounds: expected -2<=axis<2.'), + exception=(ValueError, errors.InvalidArgumentError)), ]) - def testErrors(self, rt_input, exception, message, axis=1): + def testErrors(self, rt_input, exception, message=None, axis=1): + rt = ragged.constant(rt_input) with self.assertRaisesRegexp(exception, message): - ragged.row_lengths(rt_input, axis) + rt.row_lengths(axis) if __name__ == '__main__': diff --git a/tensorflow/python/ops/ragged/ragged_row_splits_to_segment_ids_op_test.py b/tensorflow/python/ops/ragged/ragged_row_splits_to_segment_ids_op_test.py index 7f5f4e91bd..2970540f3e 100644 --- a/tensorflow/python/ops/ragged/ragged_row_splits_to_segment_ids_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_row_splits_to_segment_ids_op_test.py @@ -21,27 +21,24 @@ from __future__ import print_function from tensorflow.python.framework import constant_op from tensorflow.python.framework import test_util from tensorflow.python.ops import ragged +from tensorflow.python.ops.ragged import ragged_test_util from tensorflow.python.platform import googletest -class RaggedSplitsToSegmentIdsOpTest(test_util.TensorFlowTestCase): +@test_util.run_all_in_graph_and_eager_modes +class RaggedSplitsToSegmentIdsOpTest(ragged_test_util.RaggedTensorTestCase): - @test_util.run_deprecated_v1 def testDocStringExample(self): splits = [0, 3, 3, 5, 6, 9] expected = [0, 0, 0, 2, 2, 3, 4, 4, 4] segment_ids = ragged.row_splits_to_segment_ids(splits) - with self.test_session(): - self.assertEqual(segment_ids.eval().tolist(), expected) + self.assertAllEqual(segment_ids, expected) - @test_util.run_deprecated_v1 def testEmptySplits(self): # Note: the splits for an empty ragged tensor contains a single zero. segment_ids = ragged.row_splits_to_segment_ids([0]) - with self.test_session(): - self.assertEqual(segment_ids.eval().tolist(), []) + self.assertAllEqual(segment_ids, []) - @test_util.run_deprecated_v1 def testErrors(self): self.assertRaisesRegexp(ValueError, r'Invalid row_splits: \[\]', ragged.row_splits_to_segment_ids, []) diff --git a/tensorflow/python/ops/ragged/ragged_segment_ids_to_row_splits_op_test.py b/tensorflow/python/ops/ragged/ragged_segment_ids_to_row_splits_op_test.py index 7e52f2d844..4ed9626767 100644 --- a/tensorflow/python/ops/ragged/ragged_segment_ids_to_row_splits_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_segment_ids_to_row_splits_op_test.py @@ -21,25 +21,23 @@ from __future__ import print_function from tensorflow.python.framework import constant_op from tensorflow.python.framework import test_util from tensorflow.python.ops import ragged +from tensorflow.python.ops.ragged import ragged_test_util from tensorflow.python.platform import googletest -class RaggedSplitsToSegmentIdsOpTest(test_util.TensorFlowTestCase): +@test_util.run_all_in_graph_and_eager_modes +class RaggedSplitsToSegmentIdsOpTest(ragged_test_util.RaggedTensorTestCase): - @test_util.run_deprecated_v1 def testDocStringExample(self): segment_ids = [0, 0, 0, 2, 2, 3, 4, 4, 4] expected = [0, 3, 3, 5, 6, 9] splits = ragged.segment_ids_to_row_splits(segment_ids) - with self.test_session(): - self.assertEqual(splits.eval().tolist(), expected) + self.assertAllEqual(splits, expected) - @test_util.run_deprecated_v1 def testEmptySegmentIds(self): # Note: the splits for an empty ragged tensor contains a single zero. segment_ids = ragged.segment_ids_to_row_splits([]) - with self.test_session(): - self.assertEqual(segment_ids.eval().tolist(), [0]) + self.assertAllEqual(segment_ids, [0]) def testErrors(self): self.assertRaisesRegexp(TypeError, @@ -51,16 +49,13 @@ class RaggedSplitsToSegmentIdsOpTest(test_util.TensorFlowTestCase): self.assertRaisesRegexp(ValueError, r'Shape \(1, 1\) must have rank 1', ragged.segment_ids_to_row_splits, [[0]]) - @test_util.run_deprecated_v1 def testNumSegments(self): segment_ids = [0, 0, 0, 2, 2, 3, 4, 4, 4] num_segments = 7 expected = [0, 3, 3, 5, 6, 9, 9, 9] splits = ragged.segment_ids_to_row_splits(segment_ids, num_segments) - with self.test_session(): - self.assertEqual(splits.eval().tolist(), expected) + self.assertAllEqual(splits, expected) - @test_util.run_deprecated_v1 def testUnsortedSegmentIds(self): # Segment ids are not required to be sorted. segment_ids = [0, 4, 3, 2, 4, 4, 2, 0, 0] @@ -69,9 +64,8 @@ class RaggedSplitsToSegmentIdsOpTest(test_util.TensorFlowTestCase): splits2 = ragged.segment_ids_to_row_splits(segment_ids, 7) expected2 = [0, 3, 3, 5, 6, 9, 9, 9] - with self.test_session(): - self.assertEqual(splits1.eval().tolist(), expected1) - self.assertEqual(splits2.eval().tolist(), expected2) + self.assertAllEqual(splits1, expected1) + self.assertAllEqual(splits2, expected2) if __name__ == '__main__': diff --git a/tensorflow/python/ops/ragged/ragged_segment_op_test.py b/tensorflow/python/ops/ragged/ragged_segment_op_test.py index 52fe739342..be1f39afef 100644 --- a/tensorflow/python/ops/ragged/ragged_segment_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_segment_op_test.py @@ -26,6 +26,7 @@ from tensorflow.python.framework import errors from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import ragged +from tensorflow.python.ops.ragged import ragged_test_util from tensorflow.python.platform import googletest @@ -45,21 +46,10 @@ def sqrt_n(values): return 1.0 * sum(values) / math.sqrt(len(values)) -class RaggedSegmentOpsTest(test_util.TensorFlowTestCase, +@test_util.run_all_in_graph_and_eager_modes +class RaggedSegmentOpsTest(ragged_test_util.RaggedTensorTestCase, parameterized.TestCase): - def assertNestedListAmostEqual(self, lhs, rhs, places=7, context='value'): - self.assertEqual(type(lhs), type(rhs)) - if isinstance(lhs, (list, tuple)): - self.assertEqual(len(lhs), len(rhs), 'Length differs for %s' % context) - for i in range(len(lhs)): - self.assertNestedListAmostEqual(lhs[i], rhs[i], places, - '%s[%s]' % (context, i)) - else: - self.assertAlmostEqual( - lhs, rhs, places, - '%s != %s within %s places at %s' % (lhs, rhs, places, context)) - def expected_value(self, data, segment_ids, num_segments, combiner): """Find the expected value for a call to ragged_segment_. @@ -110,7 +100,6 @@ class RaggedSegmentOpsTest(test_util.TensorFlowTestCase, (ragged.segment_mean, mean, [5, 4, 3, 2, 1, 0]), (ragged.segment_mean, mean, [0, 0, 0, 10, 10, 10]), ) - @test_util.run_deprecated_v1 def testRaggedSegment_Int(self, segment_op, combiner, segment_ids): rt_as_list = [[0, 1, 2, 3], [4], [], [5, 6], [7], [8, 9]] rt = ragged.constant(rt_as_list) @@ -119,7 +108,7 @@ class RaggedSegmentOpsTest(test_util.TensorFlowTestCase, combiner) segmented = segment_op(rt, segment_ids, num_segments) - self.assertListEqual(self.evaluate(segmented).tolist(), expected) + self.assertRaggedEqual(segmented, expected) @parameterized.parameters( (ragged.segment_sum, sum, [0, 0, 1, 1, 2, 2]), @@ -147,7 +136,6 @@ class RaggedSegmentOpsTest(test_util.TensorFlowTestCase, (ragged.segment_sqrt_n, sqrt_n, [5, 4, 3, 2, 1, 0]), (ragged.segment_sqrt_n, sqrt_n, [0, 0, 0, 10, 10, 10]), ) - @test_util.run_deprecated_v1 def testRaggedSegment_Float(self, segment_op, combiner, segment_ids): rt_as_list = [[0., 1., 2., 3.], [4.], [], [5., 6.], [7.], [8., 9.]] rt = ragged.constant(rt_as_list) @@ -156,10 +144,8 @@ class RaggedSegmentOpsTest(test_util.TensorFlowTestCase, combiner) segmented = segment_op(rt, segment_ids, num_segments) - self.assertNestedListAmostEqual( - self.evaluate(segmented).tolist(), expected, places=5) + self.assertRaggedAlmostEqual(segmented, expected, places=5) - @test_util.run_deprecated_v1 def testRaggedRankTwo(self): rt = ragged.constant([ [[111, 112, 113, 114], [121],], # row 0 @@ -173,16 +159,15 @@ class RaggedSegmentOpsTest(test_util.TensorFlowTestCase, [], # row 1 [[411, 412], [321, 322], [331]] # row 2 ] # pyformat: disable - self.assertEqual(self.evaluate(segmented1).tolist(), expected1) + self.assertRaggedEqual(segmented1, expected1) segment_ids2 = [1, 2, 1, 1] segmented2 = ragged.segment_sum(rt, segment_ids2, 3) expected2 = [[], [[111+411, 112+412, 113, 114], [121+321, 322], [331]], []] # pyformat: disable - self.assertEqual(self.evaluate(segmented2).tolist(), expected2) + self.assertRaggedEqual(segmented2, expected2) - @test_util.run_v1_only('b/120545219') def testRaggedSegmentIds(self): rt = ragged.constant([ [[111, 112, 113, 114], [121],], # row 0 @@ -195,7 +180,7 @@ class RaggedSegmentOpsTest(test_util.TensorFlowTestCase, expected = [[], [111+321, 112+322, 113, 114], [121+331+411, 412]] # pyformat: disable - self.assertEqual(self.evaluate(segmented).tolist(), expected) + self.assertRaggedEqual(segmented, expected) def testShapeMismatchError1(self): dt = constant_op.constant([1, 2, 3, 4, 5, 6]) @@ -205,7 +190,6 @@ class RaggedSegmentOpsTest(test_util.TensorFlowTestCase, 'but segment_ids is ragged and data is not.', ragged.segment_sum, dt, segment_ids, 3) - @test_util.run_v1_only('b/120545219') def testShapeMismatchError2(self): rt = ragged.constant([ [[111, 112, 113, 114], [121]], # row 0 @@ -222,14 +206,13 @@ class RaggedSegmentOpsTest(test_util.TensorFlowTestCase, ragged.segment_sum, rt, segment_ids, 3) # Otherwise, error is raised when we run the graph. - segment_ids2 = ragged.from_row_splits( + segment_ids2 = ragged.RaggedTensor.from_row_splits( array_ops.placeholder_with_default(segment_ids.values, None), array_ops.placeholder_with_default(segment_ids.row_splits, None)) - segmented2 = ragged.segment_sum(rt, segment_ids2, 3) - with self.cached_session(): - self.assertRaisesRegexp( - errors.InvalidArgumentError, - 'segment_ids.shape must be a prefix of data.shape.*', segmented2.eval) + with self.assertRaisesRegexp( + errors.InvalidArgumentError, + 'segment_ids.shape must be a prefix of data.shape.*'): + self.evaluate(ragged.segment_sum(rt, segment_ids2, 3)) if __name__ == '__main__': diff --git a/tensorflow/python/ops/ragged/ragged_stack_op_test.py b/tensorflow/python/ops/ragged/ragged_stack_op_test.py index ed07aca6d4..17d80b5aad 100644 --- a/tensorflow/python/ops/ragged/ragged_stack_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_stack_op_test.py @@ -23,10 +23,13 @@ from absl.testing import parameterized from tensorflow.python.framework import constant_op from tensorflow.python.framework import test_util from tensorflow.python.ops import ragged +from tensorflow.python.ops.ragged import ragged_test_util from tensorflow.python.platform import googletest -class RaggedStackOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): +@test_util.run_all_in_graph_and_eager_modes +class RaggedStackOpTest(ragged_test_util.RaggedTensorTestCase, + parameterized.TestCase): @parameterized.parameters( dict( @@ -265,7 +268,6 @@ class RaggedStackOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): axis=0, expected=[[[b'a00', b'a01'], [], [b'a20', b'a21']]]), ) # pyformat: disable - @test_util.run_v1_only('b/120545219') def testRaggedStack(self, descr, rt_inputs, @@ -286,8 +288,7 @@ class RaggedStackOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): self.assertEqual(stacked.ragged_rank, expected_ragged_rank) if expected_shape is not None: self.assertEqual(stacked.shape.as_list(), expected_shape) - with self.test_session(): - self.assertEqual(stacked.eval().tolist(), expected) + self.assertRaggedEqual(stacked, expected) @parameterized.parameters( dict( @@ -314,7 +315,6 @@ class RaggedStackOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): def testError(self, rt_inputs, axis, error, message): self.assertRaisesRegexp(error, message, ragged.stack, rt_inputs, axis) - @test_util.run_deprecated_v1 def testSingleTensorInput(self): """Tests ragged_stack with a single tensor input. @@ -324,8 +324,7 @@ class RaggedStackOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): """ rt_inputs = ragged.constant([[1, 2], [3, 4]]) stacked = ragged.stack(rt_inputs, 0) - with self.test_session(): - self.assertEqual(stacked.eval().tolist(), [[[1, 2], [3, 4]]]) + self.assertRaggedEqual(stacked, [[[1, 2], [3, 4]]]) if __name__ == '__main__': diff --git a/tensorflow/python/ops/ragged/ragged_string_ops.py b/tensorflow/python/ops/ragged/ragged_string_ops.py index cdcdbdff07..1f9f0abe4f 100644 --- a/tensorflow/python/ops/ragged/ragged_string_ops.py +++ b/tensorflow/python/ops/ragged/ragged_string_ops.py @@ -23,7 +23,6 @@ from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_string_ops from tensorflow.python.ops.ragged import ragged_conversion_ops -from tensorflow.python.ops.ragged import ragged_factory_ops from tensorflow.python.ops.ragged import ragged_tensor from tensorflow.python.util.tf_export import tf_export @@ -65,16 +64,16 @@ def unicode_encode(input, output_encoding, errors="replace", ``` """ with ops.name_scope(name, "UnicodeEncode", [input]): - input_tensor = ragged_factory_ops.convert_to_tensor_or_ragged_tensor(input) + input_tensor = ragged_tensor.convert_to_tensor_or_ragged_tensor(input) if input_tensor.shape.ndims is None: raise ValueError("Rank of input_tensor must be statically known.") if ragged_tensor.is_ragged(input_tensor): - if input_tensor.inner_values.shape.ndims > 1: - # If the inner_values of our ragged tensor is multi-dimensional, we can + if input_tensor.flat_values.shape.ndims > 1: + # If the flat_values of our ragged tensor is multi-dimensional, we can # process it separately and our output will have the same nested splits # as our input. - return input_tensor.with_inner_values( - unicode_encode(input_tensor.inner_values, output_encoding, errors, + return input_tensor.with_flat_values( + unicode_encode(input_tensor.flat_values, output_encoding, errors, replacement_char)) elif input_tensor.ragged_rank > 1: # Recursively process the values of the ragged tensor. @@ -82,7 +81,7 @@ def unicode_encode(input, output_encoding, errors="replace", unicode_encode(input_tensor.values, output_encoding, errors, replacement_char)) else: - # Our ragged tensor is of the correct shape (rank 1 inner_values tensor + # Our ragged tensor is of the correct shape (rank 1 flat_values tensor # with ragged_rank of 1) so we can process it as normal. return gen_string_ops.unicode_encode( input_values=input_tensor.values, @@ -110,10 +109,10 @@ def unicode_encode(input, output_encoding, errors="replace", # Our input tensor is rank 1, so we create a ragged tensor with an added # dimension to create the correct input shape & type, and then remove # the additional dimension from the output and return the string scalar. - ragged_input_tensor = ragged_factory_ops.from_row_splits( + ragged_input_tensor = ragged_tensor.RaggedTensor.from_row_splits( input_tensor, - array_ops.stack([0, array_ops.shape(input_tensor, - out_type=dtypes.int64)[0]])) + array_ops.stack( + [0, array_ops.shape(input_tensor, out_type=dtypes.int64)[0]])) output_tensor = unicode_encode(ragged_input_tensor, output_encoding, errors, replacement_char) return array_ops.reshape(output_tensor, []) diff --git a/tensorflow/python/ops/ragged/ragged_tensor.py b/tensorflow/python/ops/ragged/ragged_tensor.py index ddeabfb464..567c50203a 100644 --- a/tensorflow/python/ops/ragged/ragged_tensor.py +++ b/tensorflow/python/ops/ragged/ragged_tensor.py @@ -19,9 +19,19 @@ from __future__ import division from __future__ import print_function from tensorflow.python.client import session +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops +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 control_flow_ops +from tensorflow.python.ops import gen_ragged_conversion_ops +from tensorflow.python.ops import math_ops from tensorflow.python.ops.ragged import ragged_tensor_value +from tensorflow.python.ops.ragged import ragged_util +from tensorflow.python.ops.ragged import segment_id_ops # pylint: disable=protected-access _eval_using_default_session = ops._eval_using_default_session @@ -84,10 +94,10 @@ class RaggedTensor(object): Example: ```python - >>> rt = ragged.from_row_splits(values=[3, 1, 4, 1, 5, 9, 2, 6], - ... row_splits=[0, 4, 4, 7, 8, 8]) - >>> rt.tolist() - [[3, 1, 4, 1], [], [5, 9, 2], [6], []] + >>> print(tf.RaggedTensor.from_row_splits( + ... values=[3, 1, 4, 1, 5, 9, 2, 6], + ... row_splits=[0, 4, 4, 7, 8, 8])) + ``` ### Alternative Row-Partitioning Schemes @@ -116,13 +126,12 @@ class RaggedTensor(object): ```python >>> values = [3, 1, 4, 1, 5, 9, 2, 6] - >>> rt1 = ragged.from_row_splits(values, row_splits=[0, 4, 4, 7, 8, 8]) - >>> rt2 = ragged.from_row_lengths(values, row_lengths=[4, 0, 3, 1, 0]) - >>> rt3 = ragged.from_value_rowids(values, - ... value_rowids=[0, 0, 0, 0, 2, 2, 2, 3], - ... nrows=5) - >>> rt4 = ragged.from_row_starts(values, row_starts=[0, 4, 4, 7, 8]) - >>> rt5 = ragged.from_row_limits(values, row_limits=[4, 4, 7, 8, 8]) + >>> rt1 = RaggedTensor.from_row_splits(values, row_splits=[0, 4, 4, 7, 8, 8]) + >>> rt2 = RaggedTensor.from_row_lengths(values, row_lengths=[4, 0, 3, 1, 0]) + >>> rt3 = RaggedTensor.from_value_rowids( + ... values, value_rowids=[0, 0, 0, 0, 2, 2, 2, 3], nrows=5) + >>> rt4 = RaggedTensor.from_row_starts(values, row_starts=[0, 4, 4, 7, 8]) + >>> rt5 = RaggedTensor.from_row_limits(values, row_limits=[4, 4, 7, 8, 8]) ``` ### Multiple Ragged Dimensions @@ -132,24 +141,24 @@ class RaggedTensor(object): adds a single ragged dimension. ```python - >>> inner_rt = ragged.from_row_splits( # =rt1 from above + >>> inner_rt = RaggedTensor.from_row_splits( # =rt1 from above ... values=[3, 1, 4, 1, 5, 9, 2, 6], row_splits=[0, 4, 4, 7, 8, 8]) - >>> outer_rt = ragged.from_row_splits( + >>> outer_rt = RaggedTensor.from_row_splits( ... values=inner_rt, row_splits=[0, 3, 3, 5]) - >>> print outer_rt.tolist() + >>> print outer_rt.to_list() [[[3, 1, 4, 1], [], [5, 9, 2]], [], [[6], []]] >>> print outer_rt.ragged_rank 2 ``` - The factory function `ragged.from_nested_row_splits` may be used to + The factory function `RaggedTensor.from_nested_row_splits` may be used to construct a `RaggedTensor` with multiple ragged dimensions directly, by providing a list of `row_splits` tensors: ```python - >>> ragged.from_nested_row_splits( - ... inner_values=[3, 1, 4, 1, 5, 9, 2, 6], - ... nested_row_splits=([0, 3, 3, 5], [0, 4, 4, 7, 8, 8])).tolist() + >>> RaggedTensor.from_nested_row_splits( + ... flat_values=[3, 1, 4, 1, 5, 9, 2, 6], + ... nested_row_splits=([0, 3, 3, 5], [0, 4, 4, 7, 8, 8])).to_list() [[[3, 1, 4, 1], [], [5, 9, 2]], [], [[6], []]] ``` @@ -159,12 +168,13 @@ class RaggedTensor(object): by using a multidimensional `Tensor` for `values`. ```python - >>> rt = ragged.from_row_splits(values=tf.ones([5, 3]), row_splits=[0, 2, 5]) - >>> print rt.tolist() + >>> rt = RaggedTensor.from_row_splits(values=tf.ones([5, 3]), + .. row_splits=[0, 2, 5]) + >>> print rt.to_list() [[[1, 1, 1], [1, 1, 1]], [[1, 1, 1], [1, 1, 1], [1, 1, 1]]] - >>> print rt.shape.as_list() - [2, None, 3] + >>> print rt.shape + (2, ?, 3) ``` ### RaggedTensor Shape Restrictions @@ -181,31 +191,6 @@ class RaggedTensor(object): dimension followed by a ragged dimension. """ - #============================================================================= - # Implementation notes - #============================================================================= - # Currently, the RaggedTensor class uses a single row-partitioning scheme - # (row_splits). - # - # We are considering adding value_rowids+nvals as a secondary - # row-partitioning scheme. This change would not impact the functional - # interface of the RaggedTensor class, but it would impact the efficiency - # of several operations. In particular: - # - # * The functions `ragged.value_rowids` and `ragged.nrows` would always - # return pre-existing tensors; they would not need to add any ops to - # the graph. - # - # * The `RaggedTensor` constructor would construct all row-partitioning - # tensors (row_splits, value_rowids, and nvals). In eager mode, this - # would mean that conversion operations would occur whenever a - # `RaggedTensor` is constructed. But in graph mode, the converted - # row-partitioning tensors would only be evaluated if they are used. - # - # Since this change impacts efficiency but not functionality, we would like - # to perform additional profiling with real-world use cases before we - # decide whether to make this change. - #============================================================================= # Constructor (private) #============================================================================= @@ -221,13 +206,14 @@ class RaggedTensor(object): This constructor is private -- please use one of the following ops to build `RaggedTensor`s: - * [`ragged.from_row_lengths()`](from_row_lengths.md) - * [`ragged.from_value_rowids()`](from_value_rowids.md) - * [`ragged.from_row_splits()`](from_row_splits.md) - * [`ragged.from_row_starts()`](from_row_starts.md) - * [`ragged.from_row_limits()`](from_row_limits.md) - * [`ragged.from_nested_row_splits()`](from_nested_row_splits.md) - * [`ragged.from_nested_value_rowids()`](from_nested_value_rowids.md) + * `tf.RaggedTensor.from_row_lengths` + * `tf.RaggedTensor.from_value_rowids` + * `tf.RaggedTensor.from_row_splits` + * `tf.RaggedTensor.from_row_starts` + * `tf.RaggedTensor.from_row_limits` + * `tf.RaggedTensor.from_nested_row_splits` + * `tf.RaggedTensor.from_nested_row_lengths` + * `tf.RaggedTensor.from_nested_value_rowids` Args: values: A potentially ragged tensor of any dtype and shape `[nvals, ...]`. @@ -248,7 +234,7 @@ class RaggedTensor(object): if not internal: raise ValueError("RaggedTensor constructor is private; please use one " "of the factory methods instead (e.g., " - "ragged.from_row_lengths())") + "RaggedTensor.from_row_lengths())") # Validate the arguments. if not isinstance(values, (RaggedTensor, ops.Tensor)): @@ -272,6 +258,364 @@ class RaggedTensor(object): self._cached_value_rowids = cached_value_rowids self._cached_nrows = cached_nrows + #============================================================================= + # Factory Methods + #============================================================================= + + @classmethod + def from_value_rowids(cls, values, value_rowids, nrows=None, name=None): + """Creates a `RaggedTensor` with rows partitioned by `value_rowids`. + + The returned `RaggedTensor` corresponds with the python list defined by: + + ```python + result = [[values[i] for i in range(len(values)) if value_rowids[i] == row] + for row in range(nrows)] + ``` + + Warning: currently, this needs to cast value_rowids to int64 before + converting, since `tf.bincount` only supports `int32`. + + Args: + values: A potentially ragged tensor with shape `[nvals, ...]`. + value_rowids: A 1-D int64 tensor with shape `[nvals]`, which corresponds + one-to-one with `values`, and specifies each value's row index. Must be + nonnegative, and must be sorted in ascending order. + nrows: An int64 scalar specifying the number of rows. This should be + specified if the `RaggedTensor` may containing empty training rows. Must + be greater than `value_rowids[-1]` (or zero if `value_rowids` is empty). + Defaults to `value_rowids[-1]` (or zero if `value_rowids` is empty). + name: A name prefix for the RaggedTensor (optional). + + Returns: + A `RaggedTensor`. `result.rank = values.rank + 1`. + `result.ragged_rank = values.ragged_rank + 1`. + + Raises: + ValueError: If `nrows` is incompatible with `value_rowids`. + + #### Example: + ```python + >>> print(tf.RaggedTensor.from_value_rowids( + ... values=[3, 1, 4, 1, 5, 9, 2, 6], + ... value_rowids=[0, 0, 0, 0, 2, 2, 2, 3], + ... nrows=5)) + + ``` + """ + with ops.name_scope(name, "RaggedFromValueRowIds", + [values, value_rowids, nrows]): + values = convert_to_tensor_or_ragged_tensor(values, name="values") + value_rowids = ops.convert_to_tensor( + value_rowids, dtypes.int64, name="value_rowids") + if nrows is None: + const_rowids = tensor_util.constant_value(value_rowids) + if const_rowids is None: + nrows = array_ops.concat([value_rowids[-1:], [-1]], axis=0)[0] + 1 + const_nrows = None + else: + const_nrows = const_rowids[-1] + 1 if const_rowids.size > 0 else 0 + nrows = ops.convert_to_tensor(const_nrows, dtypes.int64, name="nrows") + else: + nrows = ops.convert_to_tensor(nrows, dtypes.int64, "nrows") + const_nrows = tensor_util.constant_value(nrows) + if const_nrows is not None: + if const_nrows < 0: + raise ValueError("Expected nrows >= 0; got %d" % const_nrows) + const_rowids = tensor_util.constant_value(value_rowids) + if const_rowids is not None and const_rowids.size > 0: + if not const_nrows >= const_rowids[-1] + 1: + raise ValueError( + "Expected nrows >= value_rowids[-1] + 1; got nrows=%d, " + "value_rowids[-1]=%d" % (const_nrows, const_rowids[-1])) + + value_rowids.shape.assert_has_rank(1) + nrows.shape.assert_has_rank(0) + values.shape[:1].assert_is_compatible_with(value_rowids.shape) + + # Convert value_rowids & nrows to row_splits. + # Note: we don't use segment_ids_to_row_splits() here because we want + # to save the intermediate value `row_lengths`, so we can cache it. + # TODO(b/116708836) Upgrade bincount to accept int64 so we can skip the + # cast (Remove the warning in the docstring when we do.) + value_rowids_int32 = math_ops.cast(value_rowids, dtypes.int32) + nrows_int32 = math_ops.cast(nrows, dtypes.int32) + row_lengths = math_ops.bincount( + value_rowids_int32, + minlength=nrows_int32, + maxlength=nrows_int32, + dtype=dtypes.int64) + row_splits = array_ops.concat([[0], math_ops.cumsum(row_lengths)], axis=0) + if const_nrows is not None: + row_lengths.set_shape([const_nrows]) + row_splits.set_shape([const_nrows + 1]) + + return cls( + values, + row_splits, + cached_row_lengths=row_lengths, + cached_value_rowids=value_rowids, + cached_nrows=nrows, + internal=True) + + @classmethod + def from_row_splits(cls, values, row_splits, name=None): + """Creates a `RaggedTensor` with rows partitioned by `row_splits`. + + The returned `RaggedTensor` corresponds with the python list defined by: + + ```python + result = [values[row_splits[i]:row_splits[i + 1]] + for i in range(len(row_splits) - 1)] + ``` + + Args: + values: A potentially ragged tensor with shape `[nvals, ...]`. + row_splits: A 1-D int64 tensor with shape `[nrows+1]`. Must not be empty, + and must be sorted in ascending order. `row_splits[0]` must be zero and + `row_splits[-1]` must be `nvals`. + name: A name prefix for the RaggedTensor (optional). + + Returns: + A `RaggedTensor`. `result.rank = values.rank + 1`. + `result.ragged_rank = values.ragged_rank + 1`. + + Raises: + ValueError: If `row_splits` is an empty list. + + #### Example: + ```python + >>> print(tf.RaggedTensor.from_row_splits( + ... values=[3, 1, 4, 1, 5, 9, 2, 6], + ... row_splits=[0, 4, 4, 7, 8, 8])) + + ``` + """ + if isinstance(row_splits, (list, tuple)) and not row_splits: + raise ValueError("row_splits tensor may not be empty.") + with ops.name_scope(name, "RaggedFromRowSplits", [values, row_splits]): + values = convert_to_tensor_or_ragged_tensor(values, name="values") + row_splits = ops.convert_to_tensor(row_splits, dtypes.int64, "row_splits") + row_splits.shape.assert_has_rank(1) + return cls(values=values, row_splits=row_splits, internal=True) + + @classmethod + def from_row_lengths(cls, values, row_lengths, name=None): + """Creates a `RaggedTensor` with rows partitioned by `row_lengths`. + + The returned `RaggedTensor` corresponds with the python list defined by: + + ```python + result = [[values.pop(0) for i in range(length)] + for length in row_lengths] + ``` + + Args: + values: A potentially ragged tensor with shape `[nvals, ...]`. + row_lengths: A 1-D int64 tensor with shape `[nrows]`. Must be + nonnegative. `sum(row_lengths)` must be `nvals`. + name: A name prefix for the RaggedTensor (optional). + + Returns: + A `RaggedTensor`. `result.rank = values.rank + 1`. + `result.ragged_rank = values.ragged_rank + 1`. + + #### Example: + ```python + >>> print(tf.RaggedTensor.from_row_lengths( + ... values=[3, 1, 4, 1, 5, 9, 2, 6], + ... row_lengths=[4, 0, 3, 1, 0])) + + ``` + """ + with ops.name_scope(name, "RaggedFromRowLengths", [values, row_lengths]): + values = convert_to_tensor_or_ragged_tensor(values, name="values") + row_lengths = ops.convert_to_tensor(row_lengths, dtypes.int64, + "row_lengths") + row_lengths.shape.assert_has_rank(1) + row_limits = math_ops.cumsum(row_lengths) + row_splits = array_ops.concat([[0], row_limits], axis=0) + return cls( + values=values, + row_splits=row_splits, + cached_row_lengths=row_lengths, + internal=True) + + @classmethod + def from_row_starts(cls, values, row_starts, name=None): + """Creates a `RaggedTensor` with rows partitioned by `row_starts`. + + Equivalent to: `from_row_splits(values, concat([row_starts, nvals]))`. + + Args: + values: A potentially ragged tensor with shape `[nvals, ...]`. + row_starts: A 1-D int64 tensor with shape `[nrows]`. Must be nonnegative + and sorted in ascending order. If `nrows>0`, then `row_starts[0]` must + be zero. + name: A name prefix for the RaggedTensor (optional). + + Returns: + A `RaggedTensor`. `result.rank = values.rank + 1`. + `result.ragged_rank = values.ragged_rank + 1`. + + #### Example: + ```python + >>> print(tf.RaggedTensor.from_row_starts( + ... values=[3, 1, 4, 1, 5, 9, 2, 6], + ... row_starts=[0, 4, 4, 7, 8])) + + ``` + """ + with ops.name_scope(name, "RaggedFromRowStarts", [values, row_starts]): + values = convert_to_tensor_or_ragged_tensor(values, name="values") + row_starts = ops.convert_to_tensor(row_starts, dtypes.int64, "row_starts") + row_starts.shape.assert_has_rank(1) + nvals = array_ops.shape(values, out_type=dtypes.int64)[:1] + row_splits = array_ops.concat([row_starts, nvals], axis=0) + return cls(values=values, row_splits=row_splits, internal=True) + + @classmethod + def from_row_limits(cls, values, row_limits, name=None): + """Creates a `RaggedTensor` with rows partitioned by `row_limits`. + + Equivalent to: `from_row_splits(values, concat([0, row_limits]))`. + + Args: + values: A potentially ragged tensor with shape `[nvals, ...]`. + row_limits: A 1-D int64 tensor with shape `[nrows]`. Must be sorted in + ascending order. If `nrows>0`, then `row_limits[-1]` must be `nvals`. + name: A name prefix for the RaggedTensor (optional). + + Returns: + A `RaggedTensor`. `result.rank = values.rank + 1`. + `result.ragged_rank = values.ragged_rank + 1`. + + #### Example: + ```python + >>> print(tf.RaggedTensor.from_row_limits( + ... values=[3, 1, 4, 1, 5, 9, 2, 6], + ... row_limits=[4, 4, 7, 8, 8])) + + ``` + """ + with ops.name_scope(name, "RaggedFromRowLimits", [values, row_limits]): + values = convert_to_tensor_or_ragged_tensor(values, name="values") + row_limits = ops.convert_to_tensor(row_limits, dtypes.int64, "row_limits") + row_limits.shape.assert_has_rank(1) + zero = array_ops.zeros([1], dtypes.int64) + row_splits = array_ops.concat([zero, row_limits], axis=0) + return cls(values=values, row_splits=row_splits, internal=True) + + @classmethod + def from_nested_value_rowids(cls, + flat_values, + nested_value_rowids, + nested_nrows=None, + name=None): + """Creates a `RaggedTensor` from a nested list of `value_rowids` tensors. + + Equivalent to: + + ```python + result = flat_values + for (rowids, nrows) in reversed(zip(nested_value_rowids, nested_nrows)): + result = from_value_rowids(result, rowids, nrows) + ``` + + Args: + flat_values: A potentially ragged tensor. + nested_value_rowids: A list of 1-D int64 tensors. The `i`th tensor is + used as the `value_rowids` for the `i`th ragged dimension. + nested_nrows: A list of int64 scalars. The `i`th scalar is used as the + `nrows` for the `i`th ragged dimension. + name: A name prefix for the RaggedTensor (optional). + + Returns: + A `RaggedTensor` (or `flat_values` if `nested_value_rowids` is empty). + + Raises: + ValueError: If `len(nested_values_rowids) != len(nested_nrows)`. + """ + if isinstance(nested_value_rowids, ops.Tensor): + raise TypeError("nested_value_rowids must be a list of Tensors") + if nested_nrows is None: + nested_nrows = [None] * len(nested_value_rowids) + else: + if isinstance(nested_nrows, ops.Tensor): + raise TypeError("nested_nrows must be a list of Tensors") + if len(nested_nrows) != len(nested_value_rowids): + raise ValueError("nested_nrows must have the same length as " + "nested_value_rowids") + + with ops.name_scope( + name, "RaggedFromNestedValueRowIds", + [flat_values] + list(nested_value_rowids) + list(nested_nrows)): + result = flat_values + for value_rowids, nrows in reversed( + list(zip(nested_value_rowids, nested_nrows))): + result = cls.from_value_rowids(result, value_rowids, nrows) + return result + + @classmethod + def from_nested_row_splits(cls, flat_values, nested_row_splits, name=None): + """Creates a `RaggedTensor` from a nested list of `row_splits` tensors. + + Equivalent to: + + ```python + result = flat_values + for row_splits in reversed(nested_row_splits): + result = from_row_splits(result, row_splits) + ``` + + Args: + flat_values: A potentially ragged tensor. + nested_row_splits: A list of 1-D int64 tensors. The `i`th tensor is used + as the `row_splits` for the `i`th ragged dimension. + name: A name prefix for the RaggedTensor (optional). + + Returns: + A `RaggedTensor` (or `flat_values` if `nested_row_splits` is empty). + """ + if isinstance(nested_row_splits, ops.Tensor): + raise TypeError("nested_row_splits must be a list of Tensors") + with ops.name_scope(name, "RaggedFromNestedRowSplits", + [flat_values] + list(nested_row_splits)): + result = flat_values + for splits in reversed(nested_row_splits): + result = cls.from_row_splits(result, splits) + return result + + @classmethod + def from_nested_row_lengths(cls, flat_values, nested_row_lengths, name=None): + """Creates a `RaggedTensor` from a nested list of `row_lengths` tensors. + + Equivalent to: + + ```python + result = flat_values + for row_lengths in reversed(nested_row_lengths): + result = from_row_lengths(result, row_lengths) + ``` + + Args: + flat_values: A potentially ragged tensor. + nested_row_lengths: A list of 1-D int64 tensors. The `i`th tensor is used + as the `row_lengths` for the `i`th ragged dimension. + name: A name prefix for the RaggedTensor (optional). + + Returns: + A `RaggedTensor` (or `flat_values` if `nested_row_lengths` is empty). + """ + if isinstance(nested_row_lengths, ops.Tensor): + raise TypeError("nested_row_lengths must be a list of Tensors") + with ops.name_scope(name, "RaggedFromNestedRowlengths", + [flat_values] + list(nested_row_lengths)): + result = flat_values + for lengths in reversed(nested_row_lengths): + result = cls.from_row_lengths(result, lengths) + return result + #============================================================================= # Accessors #============================================================================= @@ -334,8 +678,8 @@ class RaggedTensor(object): #### Example: ```python >>> rt = ragged.constant([[3, 1, 4, 1], [], [5, 9, 2], [6], []]) - >>> rt.values.eval() - [3, 1, 4, 1, 5, 9, 2, 6] + >>> print rt.values + tf.Tensor([3, 1, 4, 1, 5, 9, 2, 6]) ``` """ return self._values @@ -357,26 +701,24 @@ class RaggedTensor(object): #### Example: ```python >>> rt = ragged.constant([[3, 1, 4, 1], [], [5, 9, 2], [6], []]) - >>> rt.values.eval() - [3, 1, 4, 1, 5, 9, 2, 6] - >>> rt.row_splits.eval() # indices of row splits in ragged.values - [0, 4, 4, 7, 8, 8] + >>> print rt.row_splits # indices of row splits in rt.values + tf.Tensor([0, 4, 4, 7, 8, 8]) ``` """ return self._row_splits @property - def inner_values(self): + def flat_values(self): """The innermost `values` tensor for this ragged tensor. - Concretely, if `rt.values` is a `Tensor`, then `rt.inner_values` is - `rt.values`; otherwise, `rt.inner_values` is `rt.values.inner_values`. + Concretely, if `rt.values` is a `Tensor`, then `rt.flat_values` is + `rt.values`; otherwise, `rt.flat_values` is `rt.values.flat_values`. - Conceptually, `inner_values` is the tensor formed by flattening the + Conceptually, `flat_values` is the tensor formed by flattening the outermost dimension and all of the ragged dimensions into a single dimension. - `rt.inner_values.shape = [nvals] + rt.shape[rt.ragged_rank + 1:]` + `rt.flat_values.shape = [nvals] + rt.shape[rt.ragged_rank + 1:]` (where `nvals` is the number of items in the flattened dimensions). Returns: @@ -386,8 +728,8 @@ class RaggedTensor(object): ```python >>> rt = ragged.constant([[[3, 1, 4, 1], [], [5, 9, 2]], [], [[6], []]]) - >>> ragged.inner_values(rt).eval() - [3, 1, 4, 1, 5, 9, 2, 6] + >>> print rt.flat_values() + tf.Tensor([3, 1, 4, 1, 5, 9, 2, 6]) ``` """ rt_values = self.values @@ -413,8 +755,8 @@ class RaggedTensor(object): ```python >>> rt = ragged.constant([[[[3, 1, 4, 1], [], [5, 9, 2]], [], [[6], []]]]) - >>> for i, splits in enumerate(ragged.nested_row_splits(rt)): - ... print('Splits for dimension %d: %s' % (i+1, splits.eval())) + >>> for i, splits in enumerate(rt.nested_row_splits()): + ... print('Splits for dimension %d: %s' % (i+1, splits)) Splits for dimension 1: [0, 1] Splits for dimension 2: [0, 3, 3, 5] Splits for dimension 3: [0, 4, 4, 7, 8, 8] @@ -428,38 +770,220 @@ class RaggedTensor(object): rt_values = rt_values.values return tuple(rt_nested_splits) - @property - def cached_value_rowids(self): - """The row lengths for this `RaggedTensor`, or `None`. + def value_rowids(self, name=None): + """Returns the row indices for the `values` in this ragged tensor. + + `rt.value_rowids()` corresponds one-to-one with the outermost dimension of + `rt.values`, and specifies the row containing each value. In particular, + the row `rt[row]` consists of the values `rt.values[j]` where + `rt.value_rowids()[j] == row`. + + Args: + name: A name prefix for the returned tensor (optional). Returns: - The `value_rowids` tensor that was used to construct this `RaggedTensor` - if it was constructed using - [`ragged.from_value_rowids`](from_value_rowids.md); or `None` otherwise. + A 1-D `int64` `Tensor` with shape `self.values.shape[:1]`. + The returned tensor is nonnegative, and is sorted in ascending order. + + #### Example: + ```python + >>> rt = ragged.constant([[3, 1, 4, 1], [], [5, 9, 2], [6], []]) + >>> rt.values + tf.Tensor([3, 1, 4, 1, 5, 9, 2, 6]) + >>> rt.value_rowids() + tf.Tensor([0, 0, 0, 0, 2, 2, 2, 3]) # corresponds 1:1 with rt.values + ``` """ - return self._cached_value_rowids + if self._cached_value_rowids is not None: + return self._cached_value_rowids - @property - def cached_nrows(self): - """The row lengths for this `RaggedTensor`, or `None`. + with ops.name_scope(name, "RaggedValueRowIds", [self]): + return segment_id_ops.row_splits_to_segment_ids(self.row_splits) + + def nrows(self, out_type=dtypes.int64, name=None): + """Returns the number of rows in this ragged tensor. + + I.e., the size of the outermost dimension of the tensor. + + Args: + out_type: `dtype` for the returned tensor. + name: A name prefix for the returned tensor (optional). Returns: - The `nrows` tensor that was used to construct this `RaggedTensor` - if it was constructed using - [`ragged.from_value_rowids`](from_value_rowids.md); or `None` otherwise. + A scalar `Tensor` with dtype `out_type`. + + #### Example: + ```python + >>> rt = ragged.constant([[3, 1, 4, 1], [], [5, 9, 2], [6], []]) + >>> rt.nrows() # rt has 5 rows. + 5 + ``` """ - return self._cached_nrows + if self._cached_nrows is not None: + return self._cached_nrows - @property - def cached_row_lengths(self): - """The row lengths for this `RaggedTensor`, or `None`. + with ops.name_scope(name, "RaggedNRows", [self]): + return array_ops.shape(self.row_splits, out_type=out_type)[0] - 1 + + def row_starts(self, name=None): + """Returns the start indices for rows in this ragged tensor. + + These indices specify where the values for each row begin in + `self.values`. `rt.row_starts()` is equal to `rt.row_splits[:-1]`. + + Args: + name: A name prefix for the returned tensor (optional). + + Returns: + A 1-D Tensor of int64 with shape `[nrows]`. + The returned tensor is nonnegative, and is sorted in ascending order. + + #### Example: + ```python + >>> rt = ragged.constant([[3, 1, 4, 1], [], [5, 9, 2], [6], []]) + >>> rt.values + tf.Tensor([3, 1, 4, 1, 5, 9, 2, 6]) + >>> rt.row_starts() # indices of row starts in rt.values + tf.Tensor([0, 4, 4, 7, 8]) + ``` + """ + with ops.name_scope(name, "RaggedRowStarts", [self]): + return self.row_splits[:-1] + + def row_limits(self, name=None): + """Returns the limit indices for rows in this ragged tensor. + + These indices specify where the values for each row end in + `self.values`. `rt.row_limits(self)` is equal to `rt.row_splits[:-1]`. + + Args: + name: A name prefix for the returned tensor (optional). + + Returns: + A 1-D Tensor of int64 with shape `[nrows]`. + The returned tensor is nonnegative, and is sorted in ascending order. + + #### Example: + ```python + >>> rt = ragged.constant([[3, 1, 4, 1], [], [5, 9, 2], [6], []]) + >>> rt.values + tf.Tensor([3, 1, 4, 1, 5, 9, 2, 6]) + >>> rt.row_limits() # indices of row limits in rt.values + tf.Tensor([4, 4, 7, 8, 8]) + ``` + """ + with ops.name_scope(name, "RaggedRowLimits", [self]): + return self.row_splits[1:] + + def row_lengths(self, axis=1, name=None): + """Returns the lengths of the rows in this ragged tensor. + + `rt.row_lengths()[i]` indicates the number of values in the + `i`th row of `rt`. + + Args: + axis: An integer constant indicating the axis whose row lengths should be + returned. + name: A name prefix for the returned tensor (optional). + + Returns: + A potentially ragged Tensor of int64 with shape `self.shape[:axis]`. + + Raises: + ValueError: If `axis` is out of bounds. + + #### Example: + ```python + >>> rt = ragged.constant([[[3, 1, 4], [1]], [], [[5, 9], [2]], [[6]], []]) + >>> rt.row_lengths(rt) # lengths of rows in rt + tf.Tensor([2, 0, 2, 1, 0]) + >>> rt.row_lengths(axis=2) # lengths of axis=2 rows. + + ``` + """ + if self._cached_row_lengths is not None: + return self._cached_row_lengths + + with ops.name_scope(name, "RaggedRowLengths", [self]): + axis = ragged_util.get_positive_axis(axis, self.shape.ndims) + if axis == 0: + return self.nrows() + elif axis == 1: + splits = self.row_splits + return splits[1:] - splits[:-1] + elif isinstance(self.values, RaggedTensor): + return self.with_values(self.values.row_lengths(axis - 1)) + else: + shape = array_ops.shape(self.values, out_type=dtypes.int64) + return self.with_values( + array_ops.ones(shape[:axis - 1], dtypes.int64) * shape[axis - 1]) + + def nested_row_lengths(self, name=None): + """Returns a tuple containing the row_lengths for all ragged dimensions. + + `rtnested_row_lengths()` is a tuple containing the `row_lengths` tensors for + all ragged dimensions in `rt`, ordered from outermost to innermost. + + Args: + name: A name prefix for the returned tensors (optional). + + Returns: + A `tuple` of 1-D `int64` `Tensors`. The length of the tuple is equal to + `self.ragged_rank`. + """ + with ops.name_scope(name, "RaggedNestedRowLengths", [self]): + rt_nested_row_lengths = [] + rt = self + while isinstance(rt, RaggedTensor): + rt_nested_row_lengths.append(rt.row_lengths()) + rt = rt.values + return tuple(rt_nested_row_lengths) + + def bounding_shape(self, axis=None, name=None): + """Returns the tight bounding box shape for this `RaggedTensor`. + + Args: + axis: An integer scalar or vector indicating which axes to return the + bounding box for. If not specified, then the full bounding box is + returned. + name: A name prefix for the returned tensor (optional). Returns: - The `row_lengths` tensor that was used to construct this `RaggedTensor` - if it was constructed using - [`ragged.from_row_lengths`](from_row_lengths.md); or `None` otherwise. + An int64 `Tensor`. If `axis` is not specified, then `output` + is a vector with `output.shape=[self.shape.ndims]`. If `axis` is a + scalar, then the `output` is a scalar. If `axis` is a vector, then + `output` is a vector, where `output[i]` is the bounding size for + dimension `axis[i]`. + + #### Example: + ```python + >>> rt = ragged.constant([[1, 2, 3, 4], [5], [], [6, 7, 8, 9], [10]]) + >>> rt.bounding_shape() + [5, 4] + ``` """ - return self._cached_row_lengths + with ops.name_scope(name, "RaggedBoundingBox", [self, axis]): + nested_splits = self.nested_row_splits + rt_flat_values = self.flat_values + + # Optimized special cases for when axis=0 or axis=1: + if isinstance(axis, int): + if axis == 0: + return array_ops.shape(nested_splits[0], out_type=dtypes.int64)[0] - 1 + elif axis == 1: + return math_ops.maximum(math_ops.reduce_max(self.row_lengths()), 0) + + splits_shape = array_ops.shape(self.row_splits, out_type=dtypes.int64) + flat_values_shape = array_ops.shape(rt_flat_values, out_type=dtypes.int64) + + ragged_dimensions = array_ops.stack([splits_shape[0] - 1] + [ + math_ops.maximum(math_ops.reduce_max(splits[1:] - splits[:-1]), 0) + for splits in nested_splits + ]) + inner_dimensions = flat_values_shape[1:] + + bbox = array_ops.concat([ragged_dimensions, inner_dimensions], axis=0) + return bbox if axis is None else array_ops.gather(bbox, axis) #============================================================================= # Transformation @@ -481,7 +1005,7 @@ class RaggedTensor(object): `result.ragged_rank = 1 + new_values.ragged_rank` """ new_values.shape.with_rank_at_least(1) - self.values.shape[0].assert_is_compatible_with(new_values.shape[0]) + self.values.shape[:1].assert_is_compatible_with(new_values.shape[:1]) return RaggedTensor( new_values, self._row_splits, @@ -490,16 +1014,16 @@ class RaggedTensor(object): self._cached_nrows, internal=True) - def with_inner_values(self, new_values): - """Returns a copy of `self` with `inner_values` replaced by `new_value`. + def with_flat_values(self, new_values): + """Returns a copy of `self` with `flat_values` replaced by `new_value`. Preserves cached row-partitioning tensors such as `self.cached_nrows` and `self.cached_value_rowids` if they have values. Args: new_values: Potentially ragged tensor that should replace - `self.inner_values`. Must have `rank > 0`, and must have the same - number of rows as `self.inner_values`. + `self.flat_values`. Must have `rank > 0`, and must have the same + number of rows as `self.flat_values`. Returns: A `RaggedTensor`. @@ -509,46 +1033,369 @@ class RaggedTensor(object): if isinstance(self._values, ops.Tensor): return self.with_values(new_values) else: - return self.with_values(self.values.with_inner_values(new_values)) + return self.with_values(self.values.with_flat_values(new_values)) + + #============================================================================= + # Tensor Type Conversions + #============================================================================= + + @classmethod + def from_tensor(cls, + tensor, + lengths=None, + padding=None, + ragged_rank=1, + name=None): + """Converts a `tf.Tensor` into a `RaggedTensor`. + + The set of absent/default values may be specified using a vector of lengths + or a padding value (but not both). If `lengths` is specified, then the + output tensor will satisfy `output[row] = tensor[row][:lengths[row]]`. + If `padding` is specified, then any row *suffix* consisting entirely of + `padding` will be excluded from the returned `RaggedTensor`. If neither + `lengths` nor `padding` is specified, then the returned `RaggedTensor` will + have no absent/default values. + + Examples: + + ```python + >>> dt = tf.constant([[5, 7, 0], [0, 3, 0], [6, 0, 0]]) + >>> tf.RaggedTensor.from_tensor(dt) + + >>> tf.RaggedTensor.from_tensor(dt, lengths=[2, 0, 3]) + + >>> tf.RaggedTensor.from_tensor(dt, padding=0) + + ``` + + Args: + tensor: The `Tensor` to convert. Must have rank `ragged_rank + 1` or + higher. + lengths: An optional set of row lengths, specified using a 1-D integer + `Tensor` whose length is equal to `tensor.shape[0]` (the number of rows + in `tensor`). If specified, then `output[row]` will contain + `tensor[row][:lengths[row]]`. Negative lengths are treated as zero. + padding: An optional padding value. If specified, then any row suffix + consisting entirely of `padding` will be excluded from the returned + RaggedTensor. `padding` is a `Tensor` with the same dtype as `tensor` + and with `shape=tensor.shape[ragged_rank + 1:]`. + ragged_rank: Integer specifying the ragged rank for the returned + `RaggedTensor`. Must be greater than zero. + name: A name prefix for the returned tensors (optional). + + Returns: + A `RaggedTensor` with the specified `ragged_rank`. The shape of the + returned ragged tensor is compatible with the shape of `tensor`. + Raises: + ValueError: If both `lengths` and `padding` are specified. + """ + if lengths is not None and padding is not None: + raise ValueError("Specify lengths or padding, but not both") + if not isinstance(ragged_rank, int): + raise TypeError("ragged_rank expected int, got %r" % ragged_rank) + if ragged_rank <= 0: + raise ValueError( + "ragged_rank must be greater than 0; got %s" % ragged_rank) + + with ops.name_scope(name, "RaggedFromTensor", [tensor, lengths, padding]): + tensor = ops.convert_to_tensor(tensor, name="tensor") + tensor.shape.with_rank_at_least(ragged_rank + 1) + input_shape = array_ops.shape(tensor, out_type=dtypes.int64) + ncols = input_shape[1] + + # Handle ragged_rank>1 via recursion: + # If the output should have multiple ragged dimensions, then first + # flatten the tensor to eliminate all but the last ragged dimension, + # and recursively convert that flattened tensor. Then add on the splits + # for the dimensions that we flattened out. + if ragged_rank > 1: + # Flatten `tensor` to eliminate all but the last ragged dimension. + new_shape = array_ops.concat([ + constant_op.constant([-1], dtypes.int64), input_shape[ragged_rank:] + ], + axis=0) + flattened = array_ops.reshape(tensor, new_shape) + # Recursively convert the flattened tensor. + values = cls.from_tensor(flattened, lengths, padding) + # The total number of elements in each dimension. E.g., if + # input_shape=[3, 4, 5, 6], then dim[2] has 3*4*5 elements in total. + dim_size = math_ops.cumprod(input_shape) + # Construct splits tensors for the dimensions that were flattened. + new_splits = [ + math_ops.range(0, dim_size[dim - 1] + 1) * input_shape[dim] + for dim in range(1, ragged_rank) + ] + return cls.from_nested_row_splits(values, new_splits) + + # If padding was specified, then use it to find row lengths. + if padding is not None: + padding = ops.convert_to_tensor( + padding, name="padding", dtype=tensor.dtype) + padding.shape.assert_is_compatible_with(tensor.shape[2:]) + + # Find places where the padding is equal to the tensor. (This will + # broadcast `padding` across the outermost 2 dimensions of `tensor`, + # so `has_default_value.shape = tensor.shape`.) + has_default_value = math_ops.equal(padding, tensor) + + # If the padding isn't a scalar, then require that all values in the + # padding match each item in the tensor. After this block of code, + # `has_default.shape = tensor.shape[:2]`. (Unfortunately, we can't just + # use reduce_all for both cases, becaue when you pass an empty `axis` + # list to reduce_all, it reduces all axes; but we want it to reduce no + # axes -- i.e., to be a no-op.) + tensor_rank = array_ops.rank(tensor) + reduce_axis = math_ops.range(2, tensor_rank) + has_default = control_flow_ops.cond( + tensor_rank > 2, + lambda: math_ops.reduce_all(has_default_value, axis=reduce_axis), + lambda: has_default_value) + has_default.set_shape(tensor_shape.TensorShape([None, None])) + has_default.set_shape(tensor.shape[:2]) + + # Use has_default it to find the length of each row: for each + # non-default item in a row, calculate the length that the row needs to + # have to include that item; and then take the max of those values + # (across each row). + has_nondefault = math_ops.logical_not(has_default) + has_nondefault = math_ops.cast(has_nondefault, dtypes.int64) + length_for_nondefault_value = ( + has_nondefault * array_ops.expand_dims( + math_ops.range(1, ncols + 1), 0)) + lengths = math_ops.reduce_max(length_for_nondefault_value, axis=1) + + # If we have lengths (either directly supplied, or computed from + # paddings), then use those to construct splits; and then use masking + # to get the corresponding values. + if lengths is not None: + lengths = ragged_util.convert_to_int_tensor(lengths, "lengths", + dtypes.int64) + lengths.shape.assert_has_rank(1) + lengths = math_ops.minimum(lengths, ncols) + lengths = math_ops.maximum(lengths, 0) + limits = math_ops.cumsum(lengths) + splits = array_ops.concat([array_ops.zeros([1], dtypes.int64), limits], + axis=0) + mask = array_ops.sequence_mask(lengths, maxlen=ncols) + values = array_ops.boolean_mask(tensor, mask) + return cls.from_row_splits(values, splits) + + # If neither padding nor lengths were specified, then create a splits + # vector that contains no default values, and reshape the input tensor + # to form the values for the RaggedTensor. + nrows = input_shape[0] + nvals = nrows * ncols + splits = math_ops.range(nrows + 1) * ncols + values_shape = array_ops.concat([[nvals], input_shape[2:]], axis=0) + values = array_ops.reshape(tensor, values_shape) + return cls.from_row_splits(values, splits) + + def to_tensor(self, default_value=None, name=None): + """Converts this `RaggedTensor` into a `tf.Tensor`. + + Example: + + ```python + >>> rt = ragged.constant([[9, 8, 7], [], [6, 5], [4]]) + >>> print rt.to_tensor() + [[9 8 7] + [0 0 0] + [6 5 0] + [4 0 0]] + ``` + + Args: + default_value: Value to set for indices not specified in `self`. Defaults + to zero. `default_value` must be broadcastable to + `self.shape[self.ragged_rank + 1:]`. + name: A name prefix for the returned tensors (optional). + + Returns: + A `Tensor` with shape `ragged.bounding_shape(self)` and the + values specified by the non-empty values in `self`. Empty values are + assigned `default_value`. + """ + with ops.name_scope(name, "RaggedToTensor", [self, default_value]): + if default_value is not None: + default_value = ops.convert_to_tensor( + default_value, name="default_value", dtype=self.dtype) + + # If ragged_rank > 1, then recursively convert the ragged values into a + # `Tensor` before we proceed. + values = self.values + if is_ragged(values): + values = values.to_tensor(default_value) + + # Tile the default value, if necessary. + if default_value is not None: + if values.shape.ndims is not None: + default_value.shape.with_rank_at_most(values.shape.ndims - 1) + if (values.shape.ndims is None or default_value.shape.ndims is None or + values.shape.ndims != default_value.shape.ndims + 1): + value_shape = array_ops.shape(values)[1:] + default_value = array_ops.broadcast_to(default_value, value_shape) + default_value.shape.assert_is_compatible_with(values.shape[1:]) + + # Get the expected dense shape ([nrows, ncols] + value_shape). + rt_row_lengths = [self.row_splits[1:] - self.row_splits[:-1]] + nrows = array_ops.shape(self.row_splits, out_type=dtypes.int64)[0] - 1 + ncols = math_ops.maximum(math_ops.reduce_max(rt_row_lengths), 0) + values_shape = array_ops.shape(values, out_type=dtypes.int64) + value_shape = values_shape[1:] + nvals = values_shape[0] + + # Build a default value if none was supplied. + if default_value is None: + default_value = array_ops.zeros(value_shape, dtype=values.dtype) + default_value.shape.assert_is_compatible_with(values.shape[1:]) + default_value.set_shape(values.shape[1:]) + + # Get the row start indices, and expand to shape=[nrows, 1]. + starts = array_ops.expand_dims(self.row_splits[:-1], 1) + + # Get the row limit indices, and expand to shape=[nrows, 1]. + limits = array_ops.expand_dims(self.row_splits[1:], 1) + + # Get the column indices, and expand to shape=[1, ncols]. + columns = array_ops.expand_dims(math_ops.range(0, ncols), 0) + + # Build a list containing the values plus the default value. We will use + # tf.gather to collect values from this list for the `Tensor` (using + # nvals as the index for the default value). + values_and_default = array_ops.concat( + [values, array_ops.stack([default_value])], axis=0) + + # Construct a matrix "indices" pointing into values_and_default. I.e., + # output[r, c] = values_and_default[indices[r, c]. + nondefault_index = starts + columns + has_value = nondefault_index < limits + default_index = array_ops.fill(array_ops.stack([nrows, ncols]), nvals) + indices = array_ops.where(has_value, nondefault_index, default_index) + + # Gather the results into a `Tensor`. + return array_ops.gather(values_and_default, indices) + + @classmethod + def from_sparse(cls, st_input, name=None): + """Converts a 2D `tf.SparseTensor` to a `RaggedTensor`. + + Each row of the `output` `RaggedTensor` will contain the explicit values + from the same row in `st_input`. `st_input` must be ragged-right. If not + it is not ragged-right, then an error will be generated. + + Example: + + ```python + >>> st = SparseTensor(indices=[[0, 1], [0, 2], [0, 3], [1, 0], [3, 0]], + ... values=[1, 2, 3, 4, 5], + ... dense_shape=[4, 3]) + >>> rt.RaggedTensor.from_sparse(st).eval().tolist() + [[1, 2, 3], [4], [], [5]] + ``` + + Currently, only two-dimensional `SparseTensors` are supported. + + Args: + st_input: The sparse tensor to convert. Must have rank 2. + name: A name prefix for the returned tensors (optional). + + Returns: + A `RaggedTensor` with the same values as `st_input`. + `output.ragged_rank = rank(st_input) - 1`. + `output.shape = [st_input.dense_shape[0], None]`. + Raises: + ValueError: If the number of dimensions in `st_input` is not known + statically, or is not two. + """ + if not sparse_tensor.is_sparse(st_input): + raise TypeError("Expected SparseTensor, got %s" % type(st_input).__name__) + with ops.name_scope(name, "RaggedFromSparse", [st_input]): + st_input = sparse_tensor.convert_to_tensor_or_sparse_tensor( + st_input, name="st_input") + + if st_input.dense_shape.shape.ndims is None: + static_rank_from_dense_shape = None + else: + static_rank_from_dense_shape = st_input.dense_shape.shape.dims[0].value + + if st_input.indices.shape.ndims is None: + static_rank_from_indices = None + else: + static_rank_from_indices = st_input.indices.shape.dims[1].value + + if static_rank_from_dense_shape != 2 and static_rank_from_indices != 2: + raise ValueError("rank(st_input) must be 2") + + with ops.control_dependencies( + _assert_sparse_indices_are_ragged_right(st_input.indices)): + # Treat sparse row indices as segment ids to generate a splits tensor + # thta we can pair with the sparse tensor values. (Ignore sparse column + # indices.) + segment_ids = st_input.indices[:, 0] + num_segments = st_input.dense_shape[0] + return cls.from_value_rowids(st_input.values, segment_ids, num_segments) + + def to_sparse(self, name=None): + """Converts this `RaggedTensor` into a `tf.SparseTensor`. + + Example: + + ```python + >>> rt = ragged.constant([[1, 2, 3], [4], [], [5, 6]]) + >>> rt.to_sparse().eval() + SparseTensorValue(indices=[[0, 0], [0, 1], [0, 2], [1, 0], [3, 0], [3, 1]], + values=[1, 2, 3, 4, 5, 6], + dense_shape=[4, 3]) + ``` + + Args: + name: A name prefix for the returned tensors (optional). + + Returns: + A SparseTensor with the same values as `self`. + """ + with ops.name_scope(name, "RaggedToSparse", [self]): + result = gen_ragged_conversion_ops.ragged_tensor_to_sparse( + self.nested_row_splits, self.flat_values, name=name) + return sparse_tensor.SparseTensor(result.sparse_indices, + result.sparse_values, + result.sparse_dense_shape) #============================================================================= # String Encoding #============================================================================= def __str__(self): if self._is_eager(): - return "RaggedTensor(%s)" % self.tolist() + return "" % self.to_list() else: return self.__repr__() def __repr__(self): - return "RaggedTensor(values=%s, row_splits=%s)" % (self._values, - self._row_splits) + return "tf.RaggedTensor(values=%s, row_splits=%s)" % (self._values, + self._row_splits) #============================================================================= # Eager Execution Mode #============================================================================= - def tolist(self): + def to_list(self): """Returns a nested Python `list` with the values for this `RaggedTensor`. - If a `RaggedTensor` `rt` was constructed in graph execution mode, then - `rt.tolist()` is equivalent to `rt.eval().tolist()`. - - If a `RaggedTensor` `rt` was constructed in eager execution mode, then - `rt.tolist()` builds the Python list based on `rt`'s `EagerTensor` - components. + Requires that `rt` was constructed in eager execution mode. Returns: A nested Python `list`. """ if self._is_eager(): - return self._eager_value().tolist() + return self._eager_value().to_list() else: - return self.eval().tolist() + raise ValueError("RaggedTensor.to_list() is only supported in eager " + "mode; in graph mode, evaluate the RaggedTensor first " + "and then use RaggedTensorValue.to_list().") def _eager_value(self): """Returns a RaggedTensorValue for self. Requires self._is_eager()=true.""" - value = self.inner_values.numpy() + value = self.flat_values.numpy() for row_splits in reversed(self.nested_row_splits): value = ragged_tensor_value.RaggedTensorValue(value, row_splits.numpy()) return value @@ -562,24 +1409,6 @@ class RaggedTensor(object): rt = rt.values return isinstance(rt, ops.EagerTensor) - #============================================================================= - # Evaluation - #============================================================================= - def eval(self, feed_dict=None, session=None): # pylint: disable=redefined-outer-name - """Evaluates this ragged tensor in a `Session`. - - Args: - feed_dict: A dictionary that maps `Tensor` objects to feed values. See - `tf.Session.run` for a description of the valid feed values. - session: The `Session` to be used to evaluate this ragged tensor. If none, - the default session will be used. - - Returns: - A `RaggedTensorValue` object. - """ - return _eval_using_default_session(self, feed_dict, - self._as_graph_element().graph, session) - #============================================================================= # Indexing & Slicing #============================================================================= @@ -613,6 +1442,53 @@ def is_ragged(value): (RaggedTensor, ragged_tensor_value.RaggedTensorValue)) +#=============================================================================== +# Convert value -> tensor +#=============================================================================== +def convert_to_tensor_or_ragged_tensor(value, + dtype=None, + preferred_dtype=None, + name=None): + """Converts value to a `RaggedTensor` or `Tensor`. + + * If `value` is a `RaggedTensor`, then return it as-is. + * If `value` is a `RaggedTensorValue`, return a corresponding constant + `RaggedTensor`. + * Otherwise, use `convert_to_tensor` to convert `value` to a `Tensor`. + + Args: + value: A `RaggedTensor`, a `RaggedTensorValue`, or an object whose type has + a registered `Tensor` conversion function. + dtype: Optional element type for the returned tensor. If missing the type + is inferred from the type of `value`. + preferred_dtype: Optional element type for the returned tensor, used when + dtype is None. This argument has no effect if `value` is already a + tensor, or when conversion is not possible. + name: Optional name to use if a new `Tensor` is created. + + Returns: + A `Tensor` or `RaggedTensor`. + """ + if isinstance(value, RaggedTensor): + if dtype and not dtype.is_compatible_with(value.dtype): + raise ValueError("Tensor conversion requested dtype %s for " + "RaggedTensor with dtype %s: %r" % + (dtype.name, value.dtype.name, value)) + return value + elif isinstance(value, ragged_tensor_value.RaggedTensorValue): + with ops.name_scope(name, "ConvertToTensorOrRaggedTensor", []): + flat_values = ops.convert_to_tensor( + value=value.flat_values, + dtype=dtype, + preferred_dtype=preferred_dtype, + name="flat_values") + return RaggedTensor.from_nested_row_splits(flat_values, + value.nested_row_splits) + else: + return ops.convert_to_tensor( + value=value, dtype=dtype, preferred_dtype=preferred_dtype, name=name) + + #=============================================================================== # Register RaggedTensor for use with session.run. #=============================================================================== @@ -625,18 +1501,18 @@ def _ragged_tensor_value_from_components(components): def _ragged_tensor_session_fetch(rt): - components = rt.nested_row_splits + (rt.inner_values,) + components = rt.nested_row_splits + (rt.flat_values,) return (components, _ragged_tensor_value_from_components) def _ragged_tensor_session_feed(feed_key, feed_val): - key_components = feed_key.nested_row_splits + (feed_key.inner_values,) - val_components = feed_val.nested_row_splits + (feed_val.inner_values,) + key_components = feed_key.nested_row_splits + (feed_key.flat_values,) + val_components = feed_val.nested_row_splits + (feed_val.flat_values,) return zip(key_components, val_components) def _ragged_tensor_session_feed_for_partial_run(feed_key): - return feed_key.nested_row_splits + (feed_key.inner_values,) + return feed_key.nested_row_splits + (feed_key.flat_values,) session.register_session_run_conversion_functions( @@ -644,6 +1520,9 @@ session.register_session_run_conversion_functions( _ragged_tensor_session_feed_for_partial_run) +#=============================================================================== +# RaggedTensorType +#=============================================================================== class RaggedTensorType(object): """Encoding of a static type for a `RaggedTensor`. @@ -663,3 +1542,67 @@ class RaggedTensorType(object): dtype = property(lambda self: self._dtype) ragged_rank = property(lambda self: self._ragged_rank) + + +#=============================================================================== +# Helper Functions +#=============================================================================== +def _assert_sparse_indices_are_ragged_right(indices): + """Checks that the given SparseTensor.indices tensor is ragged-right. + + Example: `indices = [[0, 0], [0, 1], [2, 0], [3, 1]]` is not ragged right + because the entry `[3, 1]` skips a cell. + + Args: + indices: The SparseTensor indices to check. + + Returns: + A list of control dependency op tensors. + """ + index_prefix = indices[:, :-1] + index_suffix = indices[:, -1] + + # Check whether each index is starting a new row in the innermost dimension + # (prefix[i] != prefix[i-1]) or continuing a row (prefix[i] == prefix[i-1]). + # (Note: this skips the first index; we will check that separately below.) + index_prefix_changed = math_ops.reduce_any( + math_ops.not_equal(index_prefix[1:], index_prefix[:-1]), axis=1) + + # Check two cases: + # * For indices that start a new row: index_suffix[i] must be zero. + # * For indices that continue a row: index_suffix[i] must be equal to + # index_suffix[i-1]+1. + index_ok = array_ops.where( + index_prefix_changed, math_ops.equal(index_suffix[1:], 0), + math_ops.equal(index_suffix[1:], index_suffix[:-1] + 1)) + + # Also check that the very first index didn't skip any cells. The first + # index starts a new row (by definition), so its suffix should be zero. + sparse_indices_are_ragged_right = math_ops.logical_and( + math_ops.reduce_all(math_ops.equal(index_suffix[:1], 0)), + math_ops.reduce_all(index_ok)) + + message = [ + "SparseTensor is not right-ragged", "SparseTensor.indices =", indices + ] + return [control_flow_ops.Assert(sparse_indices_are_ragged_right, message)] + + +@ops.RegisterGradient("RaggedTensorToSparse") +def _ragged_tensor_to_sparse_gradient(op, unused_sparse_indices_grad, + sparse_values_grad, + unused_sparse_shape_grad): + """Gradient for RaggedTensorToSparse.""" + op_inputs_nested_row_splits = op.inputs[:-1] + op_inputs_flat_values = op.inputs[-1] + + # No gradient for the RaggedTensor's nested_row_splits. + nested_row_splits_gradient = [None] * len(op_inputs_nested_row_splits) + + # Gradient for the RaggedTensor's flat_values is formed by reshaping + # the gradient for the SparseTensor's values. + flat_values_shape = array_ops.shape(op_inputs_flat_values) + flat_values_gradient = array_ops.reshape(sparse_values_grad, + flat_values_shape) + + return nested_row_splits_gradient + [flat_values_gradient] diff --git a/tensorflow/python/ops/ragged/ragged_tensor_bounding_shape_op_test.py b/tensorflow/python/ops/ragged/ragged_tensor_bounding_shape_op_test.py index befe30f0e1..4e6ebdf332 100644 --- a/tensorflow/python/ops/ragged/ragged_tensor_bounding_shape_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_tensor_bounding_shape_op_test.py @@ -20,47 +20,42 @@ from __future__ import print_function from tensorflow.python.framework import test_util from tensorflow.python.ops import ragged +from tensorflow.python.ops.ragged import ragged_test_util from tensorflow.python.platform import googletest -class RaggedTensorBoundingShapeOp(test_util.TensorFlowTestCase): +@test_util.run_all_in_graph_and_eager_modes +class RaggedTensorBoundingShapeOp(ragged_test_util.RaggedTensorTestCase): def testDocStringExample(self): # This is the example from ragged.bounding_shape.__doc__. rt = ragged.constant([[1, 2, 3, 4], [5], [], [6, 7, 8, 9], [10]]) - self.assertEqual(self.evaluate(ragged.bounding_shape(rt)).tolist(), [5, 4]) + self.assertRaggedEqual(rt.bounding_shape(), [5, 4]) def test2DRaggedTensorWithOneRaggedDimension(self): values = ['a', 'b', 'c', 'd', 'e', 'f', 'g'] - rt1 = ragged.from_row_splits(values, [0, 2, 5, 6, 6, 7]) - rt2 = ragged.from_row_splits(values, [0, 7]) - rt3 = ragged.from_row_splits(values, [0, 0, 7, 7]) - self.assertEqual(self.evaluate(ragged.bounding_shape(rt1)).tolist(), [5, 3]) - self.assertEqual(self.evaluate(ragged.bounding_shape(rt2)).tolist(), [1, 7]) - self.assertEqual(self.evaluate(ragged.bounding_shape(rt3)).tolist(), [3, 7]) + rt1 = ragged.RaggedTensor.from_row_splits(values, [0, 2, 5, 6, 6, 7]) + rt2 = ragged.RaggedTensor.from_row_splits(values, [0, 7]) + rt3 = ragged.RaggedTensor.from_row_splits(values, [0, 0, 7, 7]) + self.assertRaggedEqual(rt1.bounding_shape(), [5, 3]) + self.assertRaggedEqual(rt2.bounding_shape(), [1, 7]) + self.assertRaggedEqual(rt3.bounding_shape(), [3, 7]) def test3DRaggedTensorWithOneRaggedDimension(self): values = [[0, 1], [2, 3], [4, 5], [6, 7], [8, 9], [10, 11], [12, 13]] - rt1 = ragged.from_row_splits(values, [0, 2, 5, 6, 6, 7]) - rt2 = ragged.from_row_splits(values, [0, 7]) - rt3 = ragged.from_row_splits(values, [0, 0, 7, 7]) - self.assertEqual( - self.evaluate(ragged.bounding_shape(rt1)).tolist(), [5, 3, 2]) - self.assertEqual( - self.evaluate(ragged.bounding_shape(rt2)).tolist(), [1, 7, 2]) - self.assertEqual( - self.evaluate(ragged.bounding_shape(rt3)).tolist(), [3, 7, 2]) - - def testNonRaggedTensor(self): - dt = [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11]] - self.assertEqual(self.evaluate(ragged.bounding_shape(dt)).tolist(), [4, 3]) + rt1 = ragged.RaggedTensor.from_row_splits(values, [0, 2, 5, 6, 6, 7]) + rt2 = ragged.RaggedTensor.from_row_splits(values, [0, 7]) + rt3 = ragged.RaggedTensor.from_row_splits(values, [0, 0, 7, 7]) + self.assertRaggedEqual(rt1.bounding_shape(), [5, 3, 2]) + self.assertRaggedEqual(rt2.bounding_shape(), [1, 7, 2]) + self.assertRaggedEqual(rt3.bounding_shape(), [3, 7, 2]) def testExplicitAxisOptimizations(self): - rt = ragged.from_row_splits(b'a b c d e f g'.split(), [0, 2, 5, 6, 6, 7]) - self.assertEqual(self.evaluate(ragged.bounding_shape(rt, 0)).tolist(), 5) - self.assertEqual(self.evaluate(ragged.bounding_shape(rt, 1)).tolist(), 3) - self.assertEqual( - self.evaluate(ragged.bounding_shape(rt, [1, 0])).tolist(), [3, 5]) + rt = ragged.RaggedTensor.from_row_splits(b'a b c d e f g'.split(), + [0, 2, 5, 6, 6, 7]) + self.assertRaggedEqual(rt.bounding_shape(0), 5) + self.assertRaggedEqual(rt.bounding_shape(1), 3) + self.assertRaggedEqual(rt.bounding_shape([1, 0]), [3, 5]) if __name__ == '__main__': diff --git a/tensorflow/python/ops/ragged/ragged_tensor_shape.py b/tensorflow/python/ops/ragged/ragged_tensor_shape.py index 9129b4b10b..706881da74 100644 --- a/tensorflow/python/ops/ragged/ragged_tensor_shape.py +++ b/tensorflow/python/ops/ragged/ragged_tensor_shape.py @@ -21,13 +21,13 @@ from __future__ import print_function from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes 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.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.ragged import ragged_array_ops from tensorflow.python.ops.ragged import ragged_conversion_ops -from tensorflow.python.ops.ragged import ragged_factory_ops from tensorflow.python.ops.ragged import ragged_tensor from tensorflow.python.ops.ragged import ragged_util @@ -55,7 +55,7 @@ class RaggedTensorDynamicShape(object): be ragged. * "Inner dimensions" are dimensions that are encoded using a - `RaggedTensor`'s `inner_values`. Inner dimensions are always uniform. + `RaggedTensor`'s `flat_values`. Inner dimensions are always uniform. The sizes of partitioned dimensions are recorded using `partitioned_dim_sizes` and `inner_dim_sizes`: @@ -161,15 +161,15 @@ class RaggedTensorDynamicShape(object): def from_tensor(cls, rt_input): """Constructs a ragged shape for a potentially ragged tensor.""" with ops.name_scope(None, 'RaggedTensorDynamicShapeFromTensor', [rt_input]): - rt_input = ragged_factory_ops.convert_to_tensor_or_ragged_tensor(rt_input) + rt_input = ragged_tensor.convert_to_tensor_or_ragged_tensor(rt_input) if not ragged_tensor.is_ragged(rt_input): return cls([], array_ops.shape(rt_input)) else: - partitioned_dim_sizes = ((ragged_array_ops.nrows(rt_input),) + - ragged_array_ops.nested_row_lengths(rt_input)) + partitioned_dim_sizes = ( + (rt_input.nrows(),) + rt_input.nested_row_lengths()) return RaggedTensorDynamicShape( partitioned_dim_sizes, - array_ops.shape(rt_input.inner_values)[1:]) + array_ops.shape(rt_input.flat_values)[1:]) def dimension_size(self, axis): """Returns the size of slices across the specified dimension.""" @@ -197,7 +197,7 @@ class RaggedTensorDynamicShape(object): @property def rank(self): """The number of dimensions in this shape, or None if unknown.""" - inner_ndims = self._inner_dim_sizes.shape[0].value + inner_ndims = tensor_shape.dimension_value(self._inner_dim_sizes.shape[0]) if inner_ndims is None: return None else: @@ -229,7 +229,7 @@ class RaggedTensorDynamicShape(object): @property def num_inner_dimensions(self): """The number of inner dimensions, or `None` if not statically known.""" - return self._inner_dim_sizes.shape[0].value + return tensor_shape.dimension_value(self._inner_dim_sizes.shape[0]) def broadcast_to_rank(self, rank): """Adds leading size-1 dimensions to broadcast `self` to the given rank. @@ -456,7 +456,7 @@ def broadcast_to(rt_input, shape, broadcast_inner_dimensions=True): """ if not isinstance(shape, RaggedTensorDynamicShape): raise TypeError('shape must be a RaggedTensorDynamicShape') - rt_input = ragged_factory_ops.convert_to_tensor_or_ragged_tensor(rt_input) + rt_input = ragged_tensor.convert_to_tensor_or_ragged_tensor(rt_input) # Broadcasting to a uniform shape. if shape.num_partitioned_dimensions == 0: @@ -497,17 +497,20 @@ def _broadcast_to_ragged_shape(rt_input, dst_shape, broadcast_inner_dimensions): rt_input = array_ops.reshape( rt_input, array_ops.concat([[-1], dst_shape.inner_dim_sizes], axis=0)) for _ in range(dst_shape.rank - rt_input.shape.ndims): - rt_input = ragged_factory_ops.from_row_lengths( - rt_input, [ragged_array_ops.nrows(rt_input)]) + if ragged_tensor.is_ragged(rt_input): + nrows = rt_input.nrows() + else: + nrows = array_ops.shape(rt_input, out_type=dtypes.int64)[0] + rt_input = ragged_tensor.RaggedTensor.from_row_lengths(rt_input, [nrows]) # Add ragged dimensions to match dst_shape. if ragged_tensor.is_ragged(rt_input): inner_rank_diff = ( - rt_input.inner_values.shape.ndims - 1 - dst_shape.num_inner_dimensions) + rt_input.flat_values.shape.ndims - 1 - dst_shape.num_inner_dimensions) if inner_rank_diff > 0: - rt_input = rt_input.with_inner_values( + rt_input = rt_input.with_flat_values( ragged_conversion_ops.from_tensor( - rt_input.inner_values, ragged_rank=inner_rank_diff)) + rt_input.flat_values, ragged_rank=inner_rank_diff)) else: rt_input = ragged_conversion_ops.from_tensor( rt_input, ragged_rank=dst_shape.num_partitioned_dimensions - 1) @@ -528,9 +531,9 @@ def _broadcast_to_ragged_shape(rt_input, dst_shape, broadcast_inner_dimensions): rt_input = ragged_array_ops.tile(rt_input, multiples) if broadcast_inner_dimensions: - rt_input = rt_input.with_inner_values( + rt_input = rt_input.with_flat_values( array_ops.reshape( - rt_input.inner_values, + rt_input.flat_values, array_ops.concat([[-1], dst_shape.inner_dim_sizes], axis=0))) # Do broadcasting for dimensions that become ragged. We must do these from @@ -555,7 +558,7 @@ def _ragged_tile_axis(rt_input, axis, repeats): _ragged_tile_axis(rt_input.values, axis - 1, repeats)) else: src_row_splits = rt_input.nested_row_splits - src_row_lengths = ragged_array_ops.nested_row_lengths(rt_input) + src_row_lengths = rt_input.nested_row_lengths() splits = src_row_splits[0] dst_row_lengths = [repeats] @@ -563,8 +566,7 @@ def _ragged_tile_axis(rt_input, axis, repeats): dst_row_lengths.append( ragged_util.repeat_ranges(src_row_lengths[i], splits, repeats)) splits = array_ops.gather(src_row_splits[i], splits) - dst_values = ragged_util.repeat_ranges(rt_input.inner_values, splits, + dst_values = ragged_util.repeat_ranges(rt_input.flat_values, splits, repeats) - return ragged_factory_ops.from_nested_row_lengths(dst_values, - dst_row_lengths) - + return ragged_tensor.RaggedTensor.from_nested_row_lengths( + dst_values, dst_row_lengths) diff --git a/tensorflow/python/ops/ragged/ragged_tensor_shape_test.py b/tensorflow/python/ops/ragged/ragged_tensor_shape_test.py index 699dcc2bdb..ec06aeaea5 100644 --- a/tensorflow/python/ops/ragged/ragged_tensor_shape_test.py +++ b/tensorflow/python/ops/ragged/ragged_tensor_shape_test.py @@ -24,26 +24,27 @@ import numpy as np from tensorflow.python.framework import dtypes from tensorflow.python.framework import test_util from tensorflow.python.ops import ragged +from tensorflow.python.ops.ragged import ragged_test_util from tensorflow.python.platform import googletest -class RaggedTensorShapeTest(test_util.TensorFlowTestCase, - parameterized.TestCase): +@test_util.run_all_in_graph_and_eager_modes +class RaggedTensorBoundingShapeOp(ragged_test_util.RaggedTensorTestCase, + parameterized.TestCase): def assertShapeEq(self, x, y): assert isinstance(x, ragged.RaggedTensorDynamicShape) assert isinstance(y, ragged.RaggedTensorDynamicShape) x_partitioned_dim_sizes = [ - splits.eval().tolist() # + self.eval_to_list(splits) # for splits in x.partitioned_dim_sizes ] y_partitioned_dim_sizes = [ - splits.eval().tolist() # + self.eval_to_list(splits) # for splits in y.partitioned_dim_sizes ] self.assertEqual(x_partitioned_dim_sizes, y_partitioned_dim_sizes) - self.assertEqual(x.inner_dim_sizes.eval().tolist(), - y.inner_dim_sizes.eval().tolist()) + self.assertAllEqual(x.inner_dim_sizes, y.inner_dim_sizes) @parameterized.parameters([ dict(value='x', expected_dim_sizes=[]), @@ -82,13 +83,11 @@ class RaggedTensorShapeTest(test_util.TensorFlowTestCase, value=ragged.constant_value([[[1, 2], [3]], [[4, 5]]]), expected_dim_sizes=[2, [2, 1], [2, 1, 2]]), ]) - @test_util.run_v1_only('b/120545219') def testFromTensor(self, value, expected_dim_sizes): shape = ragged.RaggedTensorDynamicShape.from_tensor(value) expected = ragged.RaggedTensorDynamicShape.from_dim_sizes( expected_dim_sizes) - with self.cached_session(): - self.assertShapeEq(shape, expected) + self.assertShapeEq(shape, expected) @parameterized.parameters([ dict(dim_sizes=[], rank=0, expected_dim_sizes=[]), @@ -106,15 +105,13 @@ class RaggedTensorShapeTest(test_util.TensorFlowTestCase, rank=5, expected_dim_sizes=[1, 3, [3, 2, 4], 2, 3]), ]) - @test_util.run_v1_only('b/120545219') def testBroadcastToRank(self, dim_sizes, rank, expected_dim_sizes): shape = ragged.RaggedTensorDynamicShape.from_dim_sizes(dim_sizes) expected = ragged.RaggedTensorDynamicShape.from_dim_sizes( expected_dim_sizes) broadcasted_shape = shape.broadcast_to_rank(rank) - with self.cached_session(): - self.assertShapeEq(broadcasted_shape, expected) - self.assertEqual(broadcasted_shape.rank, rank) + self.assertShapeEq(broadcasted_shape, expected) + self.assertEqual(broadcasted_shape.rank, rank) @parameterized.parameters([ #========================================================================= @@ -283,7 +280,6 @@ class RaggedTensorShapeTest(test_util.TensorFlowTestCase, original_dim_sizes=[2, (2, 1), 2, 1], broadcast_dim_sizes=[2, (2, 1), 2, (2, 1, 2, 1, 2, 1)]), ]) # pyformat: disable - @test_util.run_v1_only('b/120545219') def testBroadcastDimension(self, axis, row_length, original_dim_sizes, broadcast_dim_sizes): """Tests for the broadcast_dimension method. @@ -306,17 +302,16 @@ class RaggedTensorShapeTest(test_util.TensorFlowTestCase, broadcast_shape = ragged.RaggedTensorDynamicShape.from_dim_sizes( broadcast_dim_sizes) self.assertEqual(original_shape.rank, broadcast_shape.rank) - with self.cached_session(): - # shape[axis].value == 1 and row_length > 1: - bcast1 = original_shape.broadcast_dimension(axis, row_length) - # shape[axis].value > 1 and row_length == shape[axis].value: - bcast2 = broadcast_shape.broadcast_dimension(axis, row_length) - # shape[axis].value > 1 and row_length == 1: - bcast3 = broadcast_shape.broadcast_dimension(axis, 1) - - self.assertShapeEq(bcast1, broadcast_shape) - self.assertShapeEq(bcast2, broadcast_shape) - self.assertShapeEq(bcast3, broadcast_shape) + # shape[axis].value == 1 and row_length > 1: + bcast1 = original_shape.broadcast_dimension(axis, row_length) + # shape[axis].value > 1 and row_length == shape[axis].value: + bcast2 = broadcast_shape.broadcast_dimension(axis, row_length) + # shape[axis].value > 1 and row_length == 1: + bcast3 = broadcast_shape.broadcast_dimension(axis, 1) + + self.assertShapeEq(bcast1, broadcast_shape) + self.assertShapeEq(bcast2, broadcast_shape) + self.assertShapeEq(bcast3, broadcast_shape) @parameterized.parameters( [ @@ -373,16 +368,14 @@ class RaggedTensorShapeTest(test_util.TensorFlowTestCase, y_dims=[1, 1, 2, (2, 1)], expected_dims=[2, (2, 1), 2, (2, 1, 2, 1, 2, 1)]), ]) - @test_util.run_v1_only('b/120545219') def testBroadcastDynamicShape(self, x_dims, y_dims, expected_dims): x_shape = ragged.RaggedTensorDynamicShape.from_dim_sizes(x_dims) y_shape = ragged.RaggedTensorDynamicShape.from_dim_sizes(y_dims) expected = ragged.RaggedTensorDynamicShape.from_dim_sizes(expected_dims) result1 = ragged.broadcast_dynamic_shape(x_shape, y_shape) result2 = ragged.broadcast_dynamic_shape(y_shape, x_shape) - with self.cached_session(): - self.assertShapeEq(expected, result1) - self.assertShapeEq(expected, result2) + self.assertShapeEq(expected, result1) + self.assertShapeEq(expected, result2) def testRepr(self): shape = ragged.RaggedTensorDynamicShape.from_dim_sizes([2, (2, 1), 2, 1]) @@ -420,17 +413,12 @@ class RaggedTensorShapeTest(test_util.TensorFlowTestCase, dim_sizes=[3, [3, 0, 2]], expected=ragged.constant_value([[10, 10, 10], [], [10, 10]])), ]) - @test_util.run_v1_only('b/120545219') def testRaggedBroadcastTo(self, x, dim_sizes, expected): shape = ragged.RaggedTensorDynamicShape.from_dim_sizes(dim_sizes) result = ragged.broadcast_to(x, shape) - with self.cached_session(): - self.assertEqual( - getattr(result, 'ragged_rank', 0), getattr(expected, 'ragged_rank', - 0)) - if hasattr(expected, 'tolist'): - expected = expected.tolist() - self.assertEqual(result.eval().tolist(), expected) + self.assertEqual( + getattr(result, 'ragged_rank', 0), getattr(expected, 'ragged_rank', 0)) + self.assertRaggedEqual(result, expected) @parameterized.parameters([ dict( @@ -475,7 +463,6 @@ class RaggedTensorShapeTest(test_util.TensorFlowTestCase, [[[[11, 21], [32]], [[13, 23], [34]]], [[[15, 25], [36]]]])), ]) - @test_util.run_v1_only('b/120545219') def testRaggedAddWithBroadcasting(self, x, y, expected, doc): expected_rrank = getattr(expected, 'ragged_rank', 0) x = ragged.convert_to_tensor_or_ragged_tensor(x, dtype=dtypes.int32) @@ -485,8 +472,7 @@ class RaggedTensorShapeTest(test_util.TensorFlowTestCase, self.assertEqual(expected_rrank, result_rrank) if hasattr(expected, 'tolist'): expected = expected.tolist() - with self.cached_session(): - self.assertEqual(result.eval().tolist(), expected) + self.assertRaggedEqual(result, expected) if __name__ == '__main__': diff --git a/tensorflow/python/ops/ragged/ragged_tensor_test.py b/tensorflow/python/ops/ragged/ragged_tensor_test.py index e86676f70a..b8f1d97137 100644 --- a/tensorflow/python/ops/ragged/ragged_tensor_test.py +++ b/tensorflow/python/ops/ragged/ragged_tensor_test.py @@ -19,17 +19,20 @@ from __future__ import division from __future__ import print_function import re -import sys from absl.testing import parameterized import numpy as np +from tensorflow.python.eager import context 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 test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import ragged +from tensorflow.python.ops.ragged import ragged_test_util +from tensorflow.python.ops.ragged import RaggedTensor from tensorflow.python.platform import googletest @@ -103,63 +106,62 @@ EXAMPLE_RAGGED_TENSOR_4D = [ EXAMPLE_RAGGED_TENSOR_4D_SPLITS1 = [0, 2, 2, 3, 4] EXAMPLE_RAGGED_TENSOR_4D_SPLITS2 = [0, 3, 6, 9, 10] EXAMPLE_RAGGED_TENSOR_4D_VALUES = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], - [11, 12], [13, 14], [15, 16], [17, - 18], [19, 20]] + [11, 12], [13, 14], [15, 16], [17, 18], + [19, 20]] -class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): +@test_util.run_all_in_graph_and_eager_modes +class RaggedTensorTest(ragged_test_util.RaggedTensorTestCase, + parameterized.TestCase): longMessage = True # Property in unittest.Testcase. pylint: disable=invalid-name #============================================================================= # RaggedTensor class docstring examples #============================================================================= - @test_util.run_deprecated_v1 def testClassDocStringExamples(self): # From section: "Component Tensors" - rt = ragged.from_row_splits( + rt = RaggedTensor.from_row_splits( values=[3, 1, 4, 1, 5, 9, 2, 6], row_splits=[0, 4, 4, 7, 8, 8]) - self.assertEqual( - self.evaluate(rt).tolist(), [[3, 1, 4, 1], [], [5, 9, 2], [6], []]) + self.assertRaggedEqual(rt, [[3, 1, 4, 1], [], [5, 9, 2], [6], []]) del rt # From section: "Alternative Row-Partitioning Schemes" values = [3, 1, 4, 1, 5, 9, 2, 6] - rt1 = ragged.from_row_splits(values, row_splits=[0, 4, 4, 7, 8, 8]) - rt2 = ragged.from_row_lengths(values, row_lengths=[4, 0, 3, 1, 0]) - rt3 = ragged.from_value_rowids( + rt1 = RaggedTensor.from_row_splits(values, row_splits=[0, 4, 4, 7, 8, 8]) + rt2 = RaggedTensor.from_row_lengths(values, row_lengths=[4, 0, 3, 1, 0]) + rt3 = RaggedTensor.from_value_rowids( values, value_rowids=[0, 0, 0, 0, 2, 2, 2, 3], nrows=5) - rt4 = ragged.from_row_starts(values, row_starts=[0, 4, 4, 7, 8]) - rt5 = ragged.from_row_limits(values, row_limits=[4, 4, 7, 8, 8]) + rt4 = RaggedTensor.from_row_starts(values, row_starts=[0, 4, 4, 7, 8]) + rt5 = RaggedTensor.from_row_limits(values, row_limits=[4, 4, 7, 8, 8]) for rt in (rt1, rt2, rt3, rt4, rt5): - self.assertEqual( - self.evaluate(rt).tolist(), [[3, 1, 4, 1], [], [5, 9, 2], [6], []]) + self.assertRaggedEqual(rt, [[3, 1, 4, 1], [], [5, 9, 2], [6], []]) del rt1, rt2, rt3, rt4, rt5 # From section: "Multiple Ragged Dimensions" - inner_rt = ragged.from_row_splits( + inner_rt = RaggedTensor.from_row_splits( values=[3, 1, 4, 1, 5, 9, 2, 6], row_splits=[0, 4, 4, 7, 8, 8]) - outer_rt = ragged.from_row_splits(values=inner_rt, row_splits=[0, 3, 3, 5]) + outer_rt = RaggedTensor.from_row_splits( + values=inner_rt, row_splits=[0, 3, 3, 5]) self.assertEqual(outer_rt.ragged_rank, 2) self.assertEqual( - self.evaluate(outer_rt).tolist(), + self.eval_to_list(outer_rt), [[[3, 1, 4, 1], [], [5, 9, 2]], [], [[6], []]]) del inner_rt, outer_rt # From section: "Multiple Ragged Dimensions" - rt = ragged.from_nested_row_splits( - inner_values=[3, 1, 4, 1, 5, 9, 2, 6], + rt = RaggedTensor.from_nested_row_splits( + flat_values=[3, 1, 4, 1, 5, 9, 2, 6], nested_row_splits=([0, 3, 3, 5], [0, 4, 4, 7, 8, 8])) self.assertEqual( - self.evaluate(rt).tolist(), - [[[3, 1, 4, 1], [], [5, 9, 2]], [], [[6], []]]) + self.eval_to_list(rt), [[[3, 1, 4, 1], [], [5, 9, 2]], [], [[6], []]]) del rt # From section: "Uniform Inner Dimensions" - rt = ragged.from_row_splits( + rt = RaggedTensor.from_row_splits( values=array_ops.ones([5, 3]), row_splits=[0, 2, 5]) self.assertEqual( - self.evaluate(rt).tolist(), + self.eval_to_list(rt), [[[1, 1, 1], [1, 1, 1]], [[1, 1, 1], [1, 1, 1], [1, 1, 1]]]) self.assertEqual(rt.shape.as_list(), [2, None, 3]) del rt @@ -181,7 +183,7 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): self.assertAllEqual(splits, rt_value.row_splits) self.assertAllEqual(values, rt_value.values) self.assertAllEqual(splits, rt_value.nested_row_splits[0]) - self.assertAllEqual(values, rt_value.inner_values) + self.assertAllEqual(values, rt_value.flat_values) # Test construction of a RaggedTensorValue with ragged_rank=2. rt_value = ragged.RaggedTensorValue( @@ -194,21 +196,19 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): self.assertAllEqual(splits2, rt_value.nested_row_splits[0]) self.assertAllEqual(splits, rt_value.nested_row_splits[1]) self.assertAllEqual(values, rt_value.values.values) - self.assertAllEqual(values, rt_value.inner_values) + self.assertAllEqual(values, rt_value.flat_values) #============================================================================= # RaggedTensor Constructor (private) #============================================================================= - @test_util.run_deprecated_v1 def testRaggedTensorConstruction(self): values = constant_op.constant(['a', 'b', 'c', 'd', 'e', 'f', 'g']) row_splits = constant_op.constant([0, 2, 2, 5, 6, 7], dtypes.int64) - rt = ragged.RaggedTensor( - values=values, row_splits=row_splits, internal=True) + rt = RaggedTensor(values=values, row_splits=row_splits, internal=True) self.assertEqual( - self.evaluate(rt).tolist(), + self.eval_to_list(rt), [[b'a', b'b'], [], [b'c', b'd', b'e'], [b'f'], [b'g']]) def testRaggedTensorConstructionErrors(self): @@ -217,117 +217,118 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): with self.assertRaisesRegexp(ValueError, 'RaggedTensor constructor is private'): - ragged.RaggedTensor(values=values, row_splits=row_splits) + RaggedTensor(values=values, row_splits=row_splits) with self.assertRaisesRegexp(TypeError, 'values must be a Tensor or RaggedTensor'): - ragged.RaggedTensor(values=range(7), row_splits=row_splits, internal=True) + RaggedTensor(values=range(7), row_splits=row_splits, internal=True) with self.assertRaisesRegexp(TypeError, 'Row-partitioning argument must be a Tensor'): - ragged.RaggedTensor( - values=values, row_splits=[0, 2, 2, 5, 6, 7], internal=True) + RaggedTensor(values=values, row_splits=[0, 2, 2, 5, 6, 7], internal=True) with self.assertRaisesRegexp(ValueError, r'Shape \(6, 1\) must have rank 1'): - ragged.RaggedTensor( + RaggedTensor( values=values, row_splits=array_ops.expand_dims(row_splits, 1), internal=True) with self.assertRaisesRegexp(TypeError, 'Cached value must be a Tensor or None.'): - ragged.RaggedTensor(values=values, row_splits=row_splits, - cached_row_lengths=[2, 3, 4], internal=True) + RaggedTensor( + values=values, + row_splits=row_splits, + cached_row_lengths=[2, 3, 4], + internal=True) #============================================================================= # RaggedTensor Factory Ops #============================================================================= - @test_util.run_deprecated_v1 def testFromValueRowIdsWithDerivedNRows(self): # nrows is known at graph creation time. values = constant_op.constant(['a', 'b', 'c', 'd', 'e', 'f', 'g']) value_rowids = constant_op.constant([0, 0, 2, 2, 2, 3, 4], dtypes.int64) - rt = ragged.from_value_rowids(values, value_rowids) + rt = RaggedTensor.from_value_rowids(values, value_rowids) self.assertEqual(rt.dtype, dtypes.string) self.assertEqual(rt.shape.as_list(), [5, None]) self.assertEqual(rt.ragged_rank, 1) rt_values = rt.values - rt_value_rowids = ragged.value_rowids(rt) - rt_nrows = ragged.nrows(rt) + rt_value_rowids = rt.value_rowids() + rt_nrows = rt.nrows() self.assertIs(rt_values, values) self.assertIs(rt_value_rowids, value_rowids) # cached_value_rowids self.assertAllEqual(rt_value_rowids, value_rowids) - self.assertEqual(self.evaluate(rt_nrows), 5) + self.assertEqual(self.eval_to_list(rt_nrows), 5) self.assertEqual( - self.evaluate(rt).tolist(), + self.eval_to_list(rt), [[b'a', b'b'], [], [b'c', b'd', b'e'], [b'f'], [b'g']]) - @test_util.run_deprecated_v1 def testFromValueRowIdsWithDerivedNRowsDynamic(self): # nrows is not known at graph creation time. values = constant_op.constant(['a', 'b', 'c', 'd', 'e', 'f', 'g']) value_rowids = constant_op.constant([0, 0, 2, 2, 2, 3, 4], dtypes.int64) value_rowids = array_ops.placeholder_with_default(value_rowids, shape=None) - rt = ragged.from_value_rowids(values, value_rowids) + rt = RaggedTensor.from_value_rowids(values, value_rowids) self.assertEqual(rt.dtype, dtypes.string) - self.assertEqual(rt.shape.as_list(), [None, None]) + if context.executing_eagerly(): + self.assertEqual(rt.shape.as_list(), [5, None]) + else: + self.assertEqual(rt.shape.as_list(), [None, None]) self.assertEqual(rt.ragged_rank, 1) rt_values = rt.values - rt_value_rowids = ragged.value_rowids(rt) - rt_nrows = ragged.nrows(rt) + rt_value_rowids = rt.value_rowids() + rt_nrows = rt.nrows() self.assertIs(rt_values, values) self.assertIs(rt_value_rowids, value_rowids) # cached_value_rowids self.assertAllEqual(rt_value_rowids, value_rowids) - self.assertEqual(self.evaluate(rt_nrows), 5) + self.assertEqual(self.eval_to_list(rt_nrows), 5) self.assertEqual( - self.evaluate(rt).tolist(), + self.eval_to_list(rt), [[b'a', b'b'], [], [b'c', b'd', b'e'], [b'f'], [b'g']]) - @test_util.run_deprecated_v1 def testFromValueRowIdsWithExplicitNRows(self): values = constant_op.constant(['a', 'b', 'c', 'd', 'e', 'f', 'g']) value_rowids = constant_op.constant([0, 0, 2, 2, 2, 3, 4], dtypes.int64) nrows = constant_op.constant(7, dtypes.int64) - rt = ragged.from_value_rowids(values, value_rowids, nrows) + rt = RaggedTensor.from_value_rowids(values, value_rowids, nrows) self.assertEqual(rt.dtype, dtypes.string) self.assertEqual(rt.shape.as_list(), [7, None]) self.assertEqual(rt.ragged_rank, 1) rt_values = rt.values - rt_value_rowids = ragged.value_rowids(rt) - rt_nrows = ragged.nrows(rt) + rt_value_rowids = rt.value_rowids() + rt_nrows = rt.nrows() self.assertIs(rt_values, values) self.assertIs(rt_value_rowids, value_rowids) # cached_value_rowids self.assertIs(rt_nrows, nrows) # cached_nrows self.assertEqual( - self.evaluate(rt).tolist(), + self.eval_to_list(rt), [[b'a', b'b'], [], [b'c', b'd', b'e'], [b'f'], [b'g'], [], []]) - @test_util.run_deprecated_v1 def testFromValueRowIdsWithExplicitNRowsEqualToDefault(self): values = constant_op.constant(['a', 'b', 'c', 'd', 'e', 'f', 'g']) value_rowids = constant_op.constant([0, 0, 2, 2, 2, 3, 4], dtypes.int64) nrows = constant_op.constant(5, dtypes.int64) - rt = ragged.from_value_rowids(values, value_rowids, nrows) + rt = RaggedTensor.from_value_rowids(values, value_rowids, nrows) self.assertEqual(rt.dtype, dtypes.string) self.assertEqual(rt.shape.as_list(), [5, None]) self.assertEqual(rt.ragged_rank, 1) rt_values = rt.values - rt_value_rowids = ragged.value_rowids(rt) - rt_nrows = ragged.nrows(rt) + rt_value_rowids = rt.value_rowids() + rt_nrows = rt.nrows() self.assertIs(rt_values, values) self.assertIs(rt_value_rowids, value_rowids) # cached_value_rowids @@ -335,112 +336,106 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): self.assertAllEqual(rt_value_rowids, value_rowids) self.assertAllEqual(rt_nrows, nrows) self.assertEqual( - self.evaluate(rt).tolist(), + self.eval_to_list(rt), [[b'a', b'b'], [], [b'c', b'd', b'e'], [b'f'], [b'g']]) - @test_util.run_deprecated_v1 def testFromValueRowIdsWithEmptyValues(self): - rt = ragged.from_value_rowids([], []) - rt_nrows = ragged.nrows(rt) + rt = RaggedTensor.from_value_rowids([], []) + rt_nrows = rt.nrows() self.assertEqual(rt.dtype, dtypes.float32) self.assertEqual(rt.shape.as_list(), [0, None]) self.assertEqual(rt.ragged_rank, 1) self.assertEqual(rt.values.shape.as_list(), [0]) - self.assertEqual(ragged.value_rowids(rt).shape.as_list(), [0]) - self.assertEqual(self.evaluate(rt_nrows).tolist(), 0) - self.assertEqual(self.evaluate(rt).tolist(), []) + self.assertEqual(rt.value_rowids().shape.as_list(), [0]) + self.assertEqual(self.eval_to_list(rt_nrows), 0) + self.assertEqual(self.eval_to_list(rt), []) - @test_util.run_deprecated_v1 def testFromRowSplits(self): values = constant_op.constant(['a', 'b', 'c', 'd', 'e', 'f', 'g']) row_splits = constant_op.constant([0, 2, 2, 5, 6, 7], dtypes.int64) - rt = ragged.from_row_splits(values, row_splits) + rt = RaggedTensor.from_row_splits(values, row_splits) self.assertEqual(rt.dtype, dtypes.string) self.assertEqual(rt.shape.as_list(), [5, None]) self.assertEqual(rt.ragged_rank, 1) rt_values = rt.values rt_row_splits = rt.row_splits - rt_nrows = ragged.nrows(rt) + rt_nrows = rt.nrows() self.assertIs(rt_values, values) self.assertIs(rt_row_splits, row_splits) - self.assertEqual(self.evaluate(rt_nrows), 5) + self.assertEqual(self.eval_to_list(rt_nrows), 5) self.assertEqual( - self.evaluate(rt).tolist(), + self.eval_to_list(rt), [[b'a', b'b'], [], [b'c', b'd', b'e'], [b'f'], [b'g']]) def testFromRowSplitsWithEmptySplits(self): err_msg = 'row_splits tensor may not be empty' with self.assertRaisesRegexp(ValueError, err_msg): - ragged.from_row_splits([], []) + RaggedTensor.from_row_splits([], []) - @test_util.run_deprecated_v1 def testFromRowStarts(self): values = constant_op.constant(['a', 'b', 'c', 'd', 'e', 'f', 'g']) row_starts = constant_op.constant([0, 2, 2, 5, 6], dtypes.int64) - rt = ragged.from_row_starts(values, row_starts) + rt = RaggedTensor.from_row_starts(values, row_starts) self.assertEqual(rt.dtype, dtypes.string) self.assertEqual(rt.shape.as_list(), [5, None]) self.assertEqual(rt.ragged_rank, 1) rt_values = rt.values - rt_row_starts = ragged.row_starts(rt) - rt_nrows = ragged.nrows(rt) + rt_row_starts = rt.row_starts() + rt_nrows = rt.nrows() self.assertIs(rt_values, values) - self.assertEqual(self.evaluate(rt_nrows), 5) + self.assertEqual(self.eval_to_list(rt_nrows), 5) self.assertAllEqual(rt_row_starts, row_starts) self.assertEqual( - self.evaluate(rt).tolist(), + self.eval_to_list(rt), [[b'a', b'b'], [], [b'c', b'd', b'e'], [b'f'], [b'g']]) - @test_util.run_deprecated_v1 def testFromRowLimits(self): values = constant_op.constant(['a', 'b', 'c', 'd', 'e', 'f', 'g']) row_limits = constant_op.constant([2, 2, 5, 6, 7], dtypes.int64) - rt = ragged.from_row_limits(values, row_limits) + rt = RaggedTensor.from_row_limits(values, row_limits) self.assertEqual(rt.dtype, dtypes.string) self.assertEqual(rt.shape.as_list(), [5, None]) self.assertEqual(rt.ragged_rank, 1) rt_values = rt.values - rt_row_limits = ragged.row_limits(rt) - rt_nrows = ragged.nrows(rt) + rt_row_limits = rt.row_limits() + rt_nrows = rt.nrows() self.assertIs(rt_values, values) - self.assertEqual(self.evaluate(rt_nrows), 5) + self.assertEqual(self.eval_to_list(rt_nrows), 5) self.assertAllEqual(rt_row_limits, row_limits) self.assertEqual( - self.evaluate(rt).tolist(), + self.eval_to_list(rt), [[b'a', b'b'], [], [b'c', b'd', b'e'], [b'f'], [b'g']]) - @test_util.run_deprecated_v1 def testFromRowLengths(self): values = constant_op.constant(['a', 'b', 'c', 'd', 'e', 'f', 'g']) row_lengths = constant_op.constant([2, 0, 3, 1, 1], dtypes.int64) - rt = ragged.from_row_lengths(values, row_lengths) + rt = RaggedTensor.from_row_lengths(values, row_lengths) self.assertEqual(rt.dtype, dtypes.string) self.assertEqual(rt.shape.as_list(), [5, None]) self.assertEqual(rt.ragged_rank, 1) rt_values = rt.values - rt_row_lengths = ragged.row_lengths(rt) - rt_nrows = ragged.nrows(rt) + rt_row_lengths = rt.row_lengths() + rt_nrows = rt.nrows() self.assertIs(rt_values, values) self.assertIs(rt_row_lengths, row_lengths) # cached_nrows - self.assertEqual(self.evaluate(rt_nrows), 5) + self.assertEqual(self.eval_to_list(rt_nrows), 5) self.assertAllEqual(rt_row_lengths, row_lengths) self.assertEqual( - self.evaluate(rt).tolist(), + self.eval_to_list(rt), [[b'a', b'b'], [], [b'c', b'd', b'e'], [b'f'], [b'g']]) - @test_util.run_deprecated_v1 def testFromNestedValueRowIdsWithDerivedNRows(self): values = constant_op.constant(['a', 'b', 'c', 'd', 'e', 'f', 'g']) nested_value_rowids = [ @@ -448,24 +443,23 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): constant_op.constant([0, 0, 2, 2, 2, 3, 4], dtypes.int64) ] - rt = ragged.from_nested_value_rowids(values, nested_value_rowids) + rt = RaggedTensor.from_nested_value_rowids(values, nested_value_rowids) self.assertEqual(rt.dtype, dtypes.string) self.assertEqual(rt.shape.as_list(), [4, None, None]) self.assertEqual(rt.ragged_rank, 2) rt_values = rt.values - rt_value_rowids = ragged.value_rowids(rt) + rt_value_rowids = rt.value_rowids() rt_values_values = rt_values.values - rt_values_value_rowids = ragged.value_rowids(rt_values) + rt_values_value_rowids = rt_values.value_rowids() self.assertIs(rt_values_values, values) self.assertAllEqual(rt_value_rowids, nested_value_rowids[0]) self.assertAllEqual(rt_values_value_rowids, nested_value_rowids[1]) self.assertEqual( - self.evaluate(rt).tolist(), + self.eval_to_list(rt), [[[b'a', b'b'], []], [[b'c', b'd', b'e']], [], [[b'f'], [b'g']]]) - @test_util.run_deprecated_v1 def testFromNestedValueRowIdsWithExplicitNRows(self): values = constant_op.constant(['a', 'b', 'c', 'd', 'e', 'f', 'g']) nested_value_rowids = [ @@ -477,17 +471,18 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): constant_op.constant(6, dtypes.int64) ] - rt = ragged.from_nested_value_rowids(values, nested_value_rowids, nrows) + rt = RaggedTensor.from_nested_value_rowids(values, nested_value_rowids, + nrows) self.assertEqual(rt.dtype, dtypes.string) self.assertEqual(rt.shape.as_list(), [6, None, None]) self.assertEqual(rt.ragged_rank, 2) rt_values = rt.values - rt_value_rowids = ragged.value_rowids(rt) - rt_nrows = ragged.nrows(rt) + rt_value_rowids = rt.value_rowids() + rt_nrows = rt.nrows() rt_values_values = rt_values.values - rt_values_value_rowids = ragged.value_rowids(rt_values) - rt_values_nrows = ragged.nrows(rt_values) + rt_values_value_rowids = rt_values.value_rowids() + rt_values_nrows = rt_values.nrows() self.assertIs(rt_values_values, values) self.assertAllEqual(rt_value_rowids, nested_value_rowids[0]) @@ -495,9 +490,8 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): self.assertAllEqual(rt_nrows, nrows[0]) self.assertAllEqual(rt_values_nrows, nrows[1]) self.assertEqual( - self.evaluate(rt).tolist(), - [[[b'a', b'b'], []], [[b'c', b'd', b'e']], [], [[b'f'], [b'g'], []], [], - []]) + self.eval_to_list(rt), [[[b'a', b'b'], []], [[b'c', b'd', b'e']], [], + [[b'f'], [b'g'], []], [], []]) def testFromNestedValueRowIdsWithExplicitNRowsMismatch(self): values = constant_op.constant(['a', 'b', 'c', 'd', 'e', 'f', 'g']) @@ -509,28 +503,26 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): with self.assertRaisesRegexp( ValueError, 'nested_nrows must have the same ' 'length as nested_value_rowids'): - ragged.from_nested_value_rowids(values, nested_value_rowids, nrows) + RaggedTensor.from_nested_value_rowids(values, nested_value_rowids, nrows) def testFromNestedValueRowIdsWithNonListInput(self): with self.assertRaisesRegexp( TypeError, 'nested_value_rowids must be a list of Tensors'): - ragged.from_nested_value_rowids([1, 2, 3], - constant_op.constant( - [[0, 1, 2], [0, 1, 2]], dtypes.int64)) + RaggedTensor.from_nested_value_rowids( + [1, 2, 3], constant_op.constant([[0, 1, 2], [0, 1, 2]], dtypes.int64)) with self.assertRaisesRegexp(TypeError, 'nested_nrows must be a list of Tensors'): - ragged.from_nested_value_rowids([1, 2, 3], [[0, 1, 2], [0, 1, 2]], - constant_op.constant([3, 3])) + RaggedTensor.from_nested_value_rowids([1, 2, 3], [[0, 1, 2], [0, 1, 2]], + constant_op.constant([3, 3])) - @test_util.run_deprecated_v1 def testFromNestedRowSplits(self): - inner_values = constant_op.constant(['a', 'b', 'c', 'd', 'e', 'f', 'g']) + flat_values = constant_op.constant(['a', 'b', 'c', 'd', 'e', 'f', 'g']) nested_row_splits = [ constant_op.constant([0, 2, 3, 3, 5], dtypes.int64), constant_op.constant([0, 2, 2, 5, 6, 7], dtypes.int64) ] - rt = ragged.from_nested_row_splits(inner_values, nested_row_splits) + rt = RaggedTensor.from_nested_row_splits(flat_values, nested_row_splits) self.assertEqual(rt.dtype, dtypes.string) self.assertEqual(rt.shape.as_list(), [4, None, None]) self.assertEqual(rt.ragged_rank, 2) @@ -540,19 +532,18 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): rt_values_values = rt_values.values rt_values_row_splits = rt_values.row_splits - self.assertIs(rt_values_values, inner_values) + self.assertIs(rt_values_values, flat_values) self.assertIs(rt_row_splits, nested_row_splits[0]) self.assertIs(rt_values_row_splits, nested_row_splits[1]) self.assertEqual( - self.evaluate(rt).tolist(), + self.eval_to_list(rt), [[[b'a', b'b'], []], [[b'c', b'd', b'e']], [], [[b'f'], [b'g']]]) def testFromNestedRowSplitsWithNonListInput(self): with self.assertRaisesRegexp(TypeError, 'nested_row_splits must be a list of Tensors'): - ragged.from_nested_row_splits([1, 2], - constant_op.constant([[0, 1, 2], [0, 1, 2]], - dtypes.int64)) + RaggedTensor.from_nested_row_splits( + [1, 2], constant_op.constant([[0, 1, 2], [0, 1, 2]], dtypes.int64)) def testFromValueRowIdsWithBadNRows(self): values = constant_op.constant(['a', 'b', 'c', 'd', 'e', 'f', 'g']) @@ -560,7 +551,7 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): nrows = constant_op.constant(5, dtypes.int64) with self.assertRaisesRegexp(ValueError, r'Expected nrows >= 0; got -2'): - ragged.from_value_rowids( + RaggedTensor.from_value_rowids( values=values, value_rowids=array_ops.placeholder_with_default(value_rowids, None), nrows=-2) @@ -568,113 +559,94 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): with self.assertRaisesRegexp( ValueError, r'Expected nrows >= value_rowids\[-1\] \+ 1; got nrows=2, ' r'value_rowids\[-1\]=4'): - ragged.from_value_rowids( + RaggedTensor.from_value_rowids( values=values, value_rowids=value_rowids, nrows=2) with self.assertRaisesRegexp( ValueError, r'Expected nrows >= value_rowids\[-1\] \+ 1; got nrows=4, ' r'value_rowids\[-1\]=4'): - ragged.from_value_rowids( + RaggedTensor.from_value_rowids( values=values, value_rowids=value_rowids, nrows=4) with self.assertRaisesRegexp(ValueError, r'Shape \(7, 1\) must have rank 1'): - ragged.from_value_rowids( + RaggedTensor.from_value_rowids( values=values, value_rowids=array_ops.expand_dims(value_rowids, 1), nrows=nrows) with self.assertRaisesRegexp(ValueError, r'Shape \(1,\) must have rank 0'): - ragged.from_value_rowids( + RaggedTensor.from_value_rowids( values=values, value_rowids=value_rowids, nrows=array_ops.expand_dims(nrows, 0)) - @test_util.run_deprecated_v1 def testGraphMismatch(self): - with ops.Graph().as_default(): - values = constant_op.constant([1, 2, 3]) - with ops.Graph().as_default(): - splits = constant_op.constant([0, 2, 3]) - self.assertRaisesRegexp(ValueError, '.* must be from the same graph as .*', - ragged.from_row_splits, values, splits) + if not context.executing_eagerly(): + with ops.Graph().as_default(): + values = constant_op.constant([1, 2, 3], dtypes.int64) + with ops.Graph().as_default(): + splits = constant_op.constant([0, 2, 3], dtypes.int64) + self.assertRaisesRegexp(ValueError, + '.* must be from the same graph as .*', + RaggedTensor.from_row_splits, values, splits) #============================================================================= # Ragged Value & Row-Partitioning Tensor Accessors #============================================================================= - @test_util.run_deprecated_v1 def testRaggedTensorAccessors_2d(self): values = constant_op.constant(['a', 'b', 'c', 'd', 'e', 'f', 'g']) row_splits = constant_op.constant([0, 2, 2, 5, 6, 7], dtypes.int64) value_rowids = constant_op.constant([0, 0, 2, 2, 2, 3, 4], dtypes.int64) - rt1 = ragged.from_row_splits(values, row_splits) - rt2 = ragged.from_value_rowids(values, value_rowids) + rt1 = RaggedTensor.from_row_splits(values, row_splits) + rt2 = RaggedTensor.from_value_rowids(values, value_rowids) for rt in [rt1, rt2]: - self.assertEqual( - self.evaluate(rt).tolist(), - [[b'a', b'b'], [], [b'c', b'd', b'e'], [b'f'], [b'g']]) - self.assertEqual( - self.evaluate(rt.values).tolist(), - [b'a', b'b', b'c', b'd', b'e', b'f', b'g']) + self.assertRaggedEqual( + rt, [[b'a', b'b'], [], [b'c', b'd', b'e'], [b'f'], [b'g']]) + self.assertAllEqual(rt.values, [b'a', b'b', b'c', b'd', b'e', b'f', b'g']) self.assertEqual(rt.values.shape.dims[0].value, 7) - self.assertEqual( - self.evaluate(ragged.value_rowids(rt)).tolist(), - [0, 0, 2, 2, 2, 3, 4]) - self.assertEqual(self.evaluate(ragged.nrows(rt)).tolist(), 5) - self.assertEqual( - self.evaluate(rt.row_splits).tolist(), [0, 2, 2, 5, 6, 7]) - self.assertEqual( - self.evaluate(ragged.row_starts(rt)).tolist(), [0, 2, 2, 5, 6]) - self.assertEqual( - self.evaluate(ragged.row_limits(rt)).tolist(), [2, 2, 5, 6, 7]) - self.assertEqual( - self.evaluate(ragged.row_lengths(rt)).tolist(), [2, 0, 3, 1, 1]) - self.assertEqual( - self.evaluate(rt.inner_values).tolist(), - [b'a', b'b', b'c', b'd', b'e', b'f', b'g']) - self.assertEqual( - [self.evaluate(s).tolist() for s in rt.nested_row_splits], - [[0, 2, 2, 5, 6, 7]]) + self.assertAllEqual(rt.value_rowids(), [0, 0, 2, 2, 2, 3, 4]) + self.assertAllEqual(rt.nrows(), 5) + self.assertAllEqual(rt.row_splits, [0, 2, 2, 5, 6, 7]) + self.assertAllEqual(rt.row_starts(), [0, 2, 2, 5, 6]) + self.assertAllEqual(rt.row_limits(), [2, 2, 5, 6, 7]) + self.assertAllEqual(rt.row_lengths(), [2, 0, 3, 1, 1]) + self.assertAllEqual(rt.flat_values, + [b'a', b'b', b'c', b'd', b'e', b'f', b'g']) + self.assertLen(rt.nested_row_splits, 1) + self.assertAllEqual(rt.nested_row_splits[0], [0, 2, 2, 5, 6, 7]) - @test_util.run_deprecated_v1 def testRaggedTensorAccessors_3d_with_ragged_rank_1(self): values = [[0, 1], [2, 3], [4, 5], [6, 7], [8, 9], [10, 11], [12, 13]] row_splits = constant_op.constant([0, 2, 2, 5, 6, 7], dtypes.int64) value_rowids = constant_op.constant([0, 0, 2, 2, 2, 3, 4], dtypes.int64) - rt1 = ragged.from_row_splits(values, row_splits) - rt2 = ragged.from_value_rowids(values, value_rowids) + rt1 = RaggedTensor.from_row_splits(values, row_splits) + rt2 = RaggedTensor.from_value_rowids(values, value_rowids) for rt in [rt1, rt2]: self.assertEqual( - self.evaluate(rt).tolist(), + self.eval_to_list(rt), [[[0, 1], [2, 3]], [], [[4, 5], [6, 7], [8, 9]], [[10, 11]], [[12, 13]]]) self.assertEqual( - self.evaluate(rt.values).tolist(), + self.eval_to_list(rt.values), [[0, 1], [2, 3], [4, 5], [6, 7], [8, 9], [10, 11], [12, 13]]) self.assertEqual(rt.values.shape.dims[0].value, 7) self.assertEqual( - self.evaluate(ragged.value_rowids(rt)).tolist(), - [0, 0, 2, 2, 2, 3, 4]) - self.assertEqual(self.evaluate(ragged.nrows(rt)).tolist(), 5) - self.assertEqual( - self.evaluate(rt.row_splits).tolist(), [0, 2, 2, 5, 6, 7]) - self.assertEqual( - self.evaluate(ragged.row_starts(rt)).tolist(), [0, 2, 2, 5, 6]) - self.assertEqual( - self.evaluate(ragged.row_limits(rt)).tolist(), [2, 2, 5, 6, 7]) + self.eval_to_list(rt.value_rowids()), [0, 0, 2, 2, 2, 3, 4]) + self.assertEqual(self.eval_to_list(rt.nrows()), 5) + self.assertEqual(self.eval_to_list(rt.row_splits), [0, 2, 2, 5, 6, 7]) + self.assertEqual(self.eval_to_list(rt.row_starts()), [0, 2, 2, 5, 6]) + self.assertEqual(self.eval_to_list(rt.row_limits()), [2, 2, 5, 6, 7]) + self.assertEqual(self.eval_to_list(rt.row_lengths()), [2, 0, 3, 1, 1]) self.assertEqual( - self.evaluate(ragged.row_lengths(rt)).tolist(), [2, 0, 3, 1, 1]) - self.assertEqual( - self.evaluate(rt.inner_values).tolist(), + self.eval_to_list(rt.flat_values), [[0, 1], [2, 3], [4, 5], [6, 7], [8, 9], [10, 11], [12, 13]]) - self.assertEqual( - [self.evaluate(s).tolist() for s in rt.nested_row_splits], - [[0, 2, 2, 5, 6, 7]]) + self.assertEqual([self.eval_to_list(s) for s in rt.nested_row_splits], + [[0, 2, 2, 5, 6, 7]]) - @test_util.run_deprecated_v1 def testRaggedTensorAccessors_3d_with_ragged_rank_2(self): values = constant_op.constant(['a', 'b', 'c', 'd', 'e', 'f', 'g']) nested_row_splits = [ @@ -685,73 +657,59 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): constant_op.constant([0, 0, 1, 3, 3], dtypes.int64), constant_op.constant([0, 0, 2, 2, 2, 3, 4], dtypes.int64) ] - rt1 = ragged.from_nested_row_splits(values, nested_row_splits) - rt2 = ragged.from_nested_value_rowids(values, nested_value_rowids) + rt1 = RaggedTensor.from_nested_row_splits(values, nested_row_splits) + rt2 = RaggedTensor.from_nested_value_rowids(values, nested_value_rowids) for rt in [rt1, rt2]: self.assertEqual( - self.evaluate(rt).tolist(), + self.eval_to_list(rt), [[[b'a', b'b'], []], [[b'c', b'd', b'e']], [], [[b'f'], [b'g']]]) self.assertEqual( - self.evaluate(rt.values).tolist(), + self.eval_to_list(rt.values), [[b'a', b'b'], [], [b'c', b'd', b'e'], [b'f'], [b'g']]) self.assertEqual(rt.values.shape.dims[0].value, 5) + self.assertEqual(self.eval_to_list(rt.value_rowids()), [0, 0, 1, 3, 3]) + self.assertEqual(self.eval_to_list(rt.nrows()), 4) + self.assertEqual(self.eval_to_list(rt.row_splits), [0, 2, 3, 3, 5]) + self.assertEqual(self.eval_to_list(rt.row_starts()), [0, 2, 3, 3]) + self.assertEqual(self.eval_to_list(rt.row_limits()), [2, 3, 3, 5]) + self.assertEqual(self.eval_to_list(rt.row_lengths()), [2, 1, 0, 2]) self.assertEqual( - self.evaluate(ragged.value_rowids(rt)).tolist(), [0, 0, 1, 3, 3]) - self.assertEqual(self.evaluate(ragged.nrows(rt)).tolist(), 4) - self.assertEqual(self.evaluate(rt.row_splits).tolist(), [0, 2, 3, 3, 5]) - self.assertEqual( - self.evaluate(ragged.row_starts(rt)).tolist(), [0, 2, 3, 3]) - self.assertEqual( - self.evaluate(ragged.row_limits(rt)).tolist(), [2, 3, 3, 5]) - self.assertEqual( - self.evaluate(ragged.row_lengths(rt)).tolist(), [2, 1, 0, 2]) - self.assertEqual( - self.evaluate(rt.inner_values).tolist(), + self.eval_to_list(rt.flat_values), [b'a', b'b', b'c', b'd', b'e', b'f', b'g']) - self.assertEqual( - [self.evaluate(s).tolist() for s in rt.nested_row_splits], - [[0, 2, 3, 3, 5], [0, 2, 2, 5, 6, 7]]) - - def testNRowsWithTensorInput(self): - dt = constant_op.constant([[1, 2, 3], [4, 5, 6]]) - nrows = ragged.nrows(dt) - self.assertEqual(self.evaluate(nrows), 2) - - def testRowLengthsWithTensorInput(self): - dt = constant_op.constant([[1, 2, 3], [4, 5, 6]]) - row_lengths = ragged.row_lengths(dt) - self.assertEqual(self.evaluate(row_lengths).tolist(), [3, 3]) + self.assertEqual([self.eval_to_list(s) for s in rt.nested_row_splits], + [[0, 2, 3, 3, 5], [0, 2, 2, 5, 6, 7]]) #============================================================================= # RaggedTensor.shape #============================================================================= - @test_util.run_deprecated_v1 def testShape(self): """Tests for RaggedTensor.shape.""" - rt1 = ragged.from_row_splits(b'a b c d e f g'.split(), [0, 2, 5, 6, 6, 7]) + rt1 = RaggedTensor.from_row_splits(b'a b c d e f g'.split(), + [0, 2, 5, 6, 6, 7]) self.assertEqual(rt1.shape.as_list(), [5, None]) - rt2 = ragged.from_row_splits( + rt2 = RaggedTensor.from_row_splits( [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14]], [0, 2, 5, 6, 6, 7]) self.assertEqual(rt2.shape.as_list(), [5, None, 2]) - rt3 = ragged.from_row_splits( + rt3 = RaggedTensor.from_row_splits( [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], [0, 2, 2, 3]) self.assertEqual(rt3.shape.as_list(), [3, None, 2, 2]) - rt4 = ragged.from_row_splits(rt3, [0, 1, 3, 3]) + rt4 = RaggedTensor.from_row_splits(rt3, [0, 1, 3, 3]) self.assertEqual(rt4.shape.as_list(), [3, None, None, 2, 2]) - rt5 = ragged.from_row_splits( - array_ops.placeholder(dtype=dtypes.string), [0, 2, 3, 5]) - self.assertEqual(rt5.shape.ndims, None) + if not context.executing_eagerly(): + rt5 = RaggedTensor.from_row_splits( + array_ops.placeholder(dtype=dtypes.string), [0, 2, 3, 5]) + self.assertEqual(rt5.shape.ndims, None) - rt6 = ragged.from_row_splits([1, 2, 3], - array_ops.placeholder(dtype=dtypes.int64)) - self.assertEqual(rt6.shape.as_list(), [None, None]) + rt6 = RaggedTensor.from_row_splits( + [1, 2, 3], array_ops.placeholder(dtype=dtypes.int64)) + self.assertEqual(rt6.shape.as_list(), [None, None]) #============================================================================= # RaggedTensor.__getitem__ @@ -777,15 +735,9 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): """ tensor_slice_spec1 = _make_tensor_slice_spec(slice_spec, True) tensor_slice_spec2 = _make_tensor_slice_spec(slice_spec, False) - value1 = self.evaluate(rt.__getitem__(slice_spec)) - value2 = self.evaluate(rt.__getitem__(tensor_slice_spec1)) - value3 = self.evaluate(rt.__getitem__(tensor_slice_spec2)) - if hasattr(value1, 'tolist'): - value1 = value1.tolist() - if hasattr(value2, 'tolist'): - value2 = value2.tolist() - if hasattr(value3, 'tolist'): - value3 = value3.tolist() + value1 = self.eval_to_list(rt.__getitem__(slice_spec)) + value2 = self.eval_to_list(rt.__getitem__(tensor_slice_spec1)) + value3 = self.eval_to_list(rt.__getitem__(tensor_slice_spec2)) self.assertEqual(value1, expected, 'slice_spec=%s' % (slice_spec,)) self.assertEqual(value2, expected, 'slice_spec=%s' % (slice_spec,)) self.assertEqual(value3, expected, 'slice_spec=%s' % (slice_spec,)) @@ -861,23 +813,26 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): (SLICE_BUILDER[:, -2:], [row[-2:] for row in EXAMPLE_RAGGED_TENSOR_2D]), # TODO(edloper): Add tests for strided slices, once support is added. ) - @test_util.run_v1_only('b/120545219') def testRaggedTensorGetItemWithRaggedRank1(self, slice_spec, expected): """Test that rt.__getitem__(slice_spec) == expected.""" # Ragged tensor - rt = ragged.from_row_splits(EXAMPLE_RAGGED_TENSOR_2D_VALUES, - EXAMPLE_RAGGED_TENSOR_2D_SPLITS) + rt = RaggedTensor.from_row_splits(EXAMPLE_RAGGED_TENSOR_2D_VALUES, + EXAMPLE_RAGGED_TENSOR_2D_SPLITS) - self.assertEqual(self.evaluate(rt).tolist(), EXAMPLE_RAGGED_TENSOR_2D) + self.assertEqual(self.eval_to_list(rt), EXAMPLE_RAGGED_TENSOR_2D) self._TestGetItem(rt, slice_spec, expected) # pylint: disable=invalid-slice-index @parameterized.parameters( # Tests for out-of-bound errors - (SLICE_BUILDER[5], ValueError, '.*out of bounds.*'), - (SLICE_BUILDER[-6], ValueError, '.*out of bounds.*'), - (SLICE_BUILDER[0, 2], ValueError, '.*out of bounds.*'), - (SLICE_BUILDER[3, 0], ValueError, '.*out of bounds.*'), + (SLICE_BUILDER[5], + (ValueError, errors.InvalidArgumentError), '.*out of bounds.*'), + (SLICE_BUILDER[-6], + (ValueError, errors.InvalidArgumentError), '.*out of bounds.*'), + (SLICE_BUILDER[0, 2], + (ValueError, errors.InvalidArgumentError), '.*out of bounds.*'), + (SLICE_BUILDER[3, 0], + (ValueError, errors.InvalidArgumentError), '.*out of bounds.*'), # Indexing into an inner ragged dimension (SLICE_BUILDER[:, 3], ValueError, @@ -889,8 +844,8 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): # Tests for type errors (SLICE_BUILDER[0.5], TypeError, re.escape(array_ops._SLICE_TYPE_ERROR)), - (SLICE_BUILDER[1:3:0.5], TypeError, - re.escape(array_ops._SLICE_TYPE_ERROR)), + (SLICE_BUILDER[1:3:0.5], TypeError, re.escape( + array_ops._SLICE_TYPE_ERROR)), (SLICE_BUILDER[:, 1:3:0.5], TypeError, 'slice strides must be integers or None'), (SLICE_BUILDER[:, 0.5:1.5], TypeError, @@ -903,17 +858,14 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): (SLICE_BUILDER[..., 0, 0, 0], IndexError, 'Too many indices for RaggedTensor'), ) - @test_util.run_v1_only('b/120545219') def testRaggedTensorGetItemErrorsWithRaggedRank1(self, slice_spec, expected, message): """Test that rt.__getitem__(slice_spec) == expected.""" # Ragged tensor - rt = ragged.from_row_splits(EXAMPLE_RAGGED_TENSOR_2D_VALUES, - EXAMPLE_RAGGED_TENSOR_2D_SPLITS) - # if sys.version_info[0] == 3: - # message = 'must be str, not int' + rt = RaggedTensor.from_row_splits(EXAMPLE_RAGGED_TENSOR_2D_VALUES, + EXAMPLE_RAGGED_TENSOR_2D_SPLITS) - self.assertEqual(self.evaluate(rt).tolist(), EXAMPLE_RAGGED_TENSOR_2D) + self.assertEqual(self.eval_to_list(rt), EXAMPLE_RAGGED_TENSOR_2D) self._TestGetItemException(rt, slice_spec, expected, message) @parameterized.parameters( @@ -982,13 +934,12 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): # TODO(edloper): Add tests slicing inner ragged dimensions, one support # is added. ) - @test_util.run_v1_only('b/120545219') def testRaggedTensorGetItemWithRaggedRank2(self, slice_spec, expected): """Test that rt.__getitem__(slice_spec) == expected.""" - rt = ragged.from_nested_row_splits( + rt = RaggedTensor.from_nested_row_splits( EXAMPLE_RAGGED_TENSOR_4D_VALUES, [EXAMPLE_RAGGED_TENSOR_4D_SPLITS1, EXAMPLE_RAGGED_TENSOR_4D_SPLITS2]) - self.assertEqual(self.evaluate(rt).tolist(), EXAMPLE_RAGGED_TENSOR_4D) + self.assertEqual(self.eval_to_list(rt), EXAMPLE_RAGGED_TENSOR_4D) self._TestGetItem(rt, slice_spec, expected) @parameterized.parameters( @@ -999,19 +950,22 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): 'Cannot index into an inner ragged dimension.'), # Test for out-of-bounds errors. - (SLICE_BUILDER[1, 0], ValueError, '.*out of bounds.*'), - (SLICE_BUILDER[0, 0, 3], ValueError, '.*out of bounds.*'), - (SLICE_BUILDER[5], ValueError, '.*out of bounds.*'), - (SLICE_BUILDER[0, 5], ValueError, '.*out of bounds.*'), + (SLICE_BUILDER[1, 0], + (ValueError, errors.InvalidArgumentError), '.*out of bounds.*'), + (SLICE_BUILDER[0, 0, 3], + (ValueError, errors.InvalidArgumentError), '.*out of bounds.*'), + (SLICE_BUILDER[5], + (ValueError, errors.InvalidArgumentError), '.*out of bounds.*'), + (SLICE_BUILDER[0, 5], + (ValueError, errors.InvalidArgumentError), '.*out of bounds.*'), ) - @test_util.run_deprecated_v1 def testRaggedTensorGetItemErrorsWithRaggedRank2(self, slice_spec, expected, message): """Test that rt.__getitem__(slice_spec) == expected.""" - rt = ragged.from_nested_row_splits( + rt = RaggedTensor.from_nested_row_splits( EXAMPLE_RAGGED_TENSOR_4D_VALUES, [EXAMPLE_RAGGED_TENSOR_4D_SPLITS1, EXAMPLE_RAGGED_TENSOR_4D_SPLITS2]) - self.assertEqual(self.evaluate(rt).tolist(), EXAMPLE_RAGGED_TENSOR_4D) + self.assertEqual(self.eval_to_list(rt), EXAMPLE_RAGGED_TENSOR_4D) self._TestGetItemException(rt, slice_spec, expected, message) @parameterized.parameters( @@ -1019,21 +973,21 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): (SLICE_BUILDER[2:], []), (SLICE_BUILDER[:-3], []), ) - @test_util.run_deprecated_v1 def testRaggedTensorGetItemWithEmptyTensor(self, slice_spec, expected): """Test that rt.__getitem__(slice_spec) == expected.""" - rt = ragged.from_row_splits([], [0]) + rt = RaggedTensor.from_row_splits([], [0]) self._TestGetItem(rt, slice_spec, expected) @parameterized.parameters( - (SLICE_BUILDER[0], ValueError, '.*out of bounds.*'), - (SLICE_BUILDER[-1], ValueError, '.*out of bounds.*'), + (SLICE_BUILDER[0], + (ValueError, errors.InvalidArgumentError), '.*out of bounds.*'), + (SLICE_BUILDER[-1], + (ValueError, errors.InvalidArgumentError), '.*out of bounds.*'), ) - @test_util.run_deprecated_v1 def testRaggedTensorGetItemErrorsWithEmptyTensor(self, slice_spec, expected, message): """Test that rt.__getitem__(slice_spec) == expected.""" - rt = ragged.from_row_splits([], [0]) + rt = RaggedTensor.from_row_splits([], [0]) self._TestGetItemException(rt, slice_spec, expected, message) @parameterized.parameters( @@ -1045,7 +999,6 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): (SLICE_BUILDER[0, 1], EXAMPLE_RAGGED_TENSOR_2D[0][1]), (SLICE_BUILDER[-3, 0], EXAMPLE_RAGGED_TENSOR_2D[-3][0]), ) - @test_util.run_deprecated_v1 def testRaggedTensorGetItemWithPlaceholderShapes(self, slice_spec, expected): """Test that rt.__getitem__(slice_spec) == expected.""" # Intentionally use an unknown shape for `splits`, to force the code path @@ -1053,29 +1006,28 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): splits = constant_op.constant( EXAMPLE_RAGGED_TENSOR_2D_SPLITS, dtype=dtypes.int64) splits = array_ops.placeholder_with_default(splits, None) - rt = ragged.from_row_splits(EXAMPLE_RAGGED_TENSOR_2D_VALUES, splits) - self.assertEqual(self.evaluate(rt).tolist(), EXAMPLE_RAGGED_TENSOR_2D) + rt = RaggedTensor.from_row_splits(EXAMPLE_RAGGED_TENSOR_2D_VALUES, splits) + self.assertEqual(self.eval_to_list(rt), EXAMPLE_RAGGED_TENSOR_2D) self._TestGetItem(rt, slice_spec, expected) @parameterized.parameters( (SLICE_BUILDER[..., 2], ValueError, 'Ellipsis not supported for unknown shape RaggedTensors'),) - @test_util.run_deprecated_v1 def testRaggedTensorGetItemErrorsWithPlaceholderShapes( self, slice_spec, expected, message): """Test that rt.__getitem__(slice_spec) == expected.""" - # Intentionally use an unknown shape for `values`. - values = array_ops.placeholder_with_default([0], None) - rt = ragged.from_row_splits(values, [0, 1]) - self._TestGetItemException(rt, slice_spec, expected, message) + if not context.executing_eagerly(): + # Intentionally use an unknown shape for `values`. + values = array_ops.placeholder_with_default([0], None) + rt = RaggedTensor.from_row_splits(values, [0, 1]) + self._TestGetItemException(rt, slice_spec, expected, message) - @test_util.run_v1_only('b/120545219') def testGetItemNewAxis(self): # rt: [[[['a', 'b'], ['c', 'd']], [], [['e', 'f']]], []] splits1 = [0, 3, 3] splits2 = [0, 2, 2, 3] values = constant_op.constant([['a', 'b'], ['c', 'd'], ['e', 'f']]) - rt = ragged.from_nested_row_splits(values, [splits1, splits2]) + rt = RaggedTensor.from_nested_row_splits(values, [splits1, splits2]) rt_newaxis0 = rt[array_ops.newaxis] rt_newaxis1 = rt[:, array_ops.newaxis] rt_newaxis2 = rt[:, :, array_ops.newaxis] @@ -1083,22 +1035,22 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): rt_newaxis4 = rt[:, :, :, :, array_ops.newaxis] self.assertEqual( - self.evaluate(rt).tolist(), + self.eval_to_list(rt), [[[[b'a', b'b'], [b'c', b'd']], [], [[b'e', b'f']]], []]) self.assertEqual( - self.evaluate(rt_newaxis0).tolist(), + self.eval_to_list(rt_newaxis0), [[[[[b'a', b'b'], [b'c', b'd']], [], [[b'e', b'f']]], []]]) self.assertEqual( - self.evaluate(rt_newaxis1).tolist(), + self.eval_to_list(rt_newaxis1), [[[[[b'a', b'b'], [b'c', b'd']], [], [[b'e', b'f']]]], [[]]]) self.assertEqual( - self.evaluate(rt_newaxis2).tolist(), + self.eval_to_list(rt_newaxis2), [[[[[b'a', b'b'], [b'c', b'd']]], [[]], [[[b'e', b'f']]]], []]) self.assertEqual( - self.evaluate(rt_newaxis3).tolist(), + self.eval_to_list(rt_newaxis3), [[[[[b'a', b'b']], [[b'c', b'd']]], [], [[[b'e', b'f']]]], []]) self.assertEqual( - self.evaluate(rt_newaxis4).tolist(), + self.eval_to_list(rt_newaxis4), [[[[[b'a'], [b'b']], [[b'c'], [b'd']]], [], [[[b'e'], [b'f']]]], []]) self.assertEqual(rt.ragged_rank, 2) @@ -1117,104 +1069,118 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): #============================================================================= # RaggedTensor.__str__ #============================================================================= - @test_util.run_deprecated_v1 def testRaggedTensorStr(self): - rt1 = ragged.from_row_splits(b'a b c d e f g'.split(), [0, 2, 5, 6, 6, 7]) - expected1 = ('RaggedTensor(values=Tensor("RaggedFromRowSplits/values:0", ' - 'shape=(7,), dtype=string), row_splits=' - 'Tensor("RaggedFromRowSplits/row_splits:0", ' - 'shape=(6,), dtype=int64))') - self.assertEqual(str(rt1), expected1) - self.assertEqual(repr(rt1), expected1) + values = [b'a', b'b', b'c', b'd', b'e', b'f', b'g'] + row_splits = [0, 2, 5, 6, 6, 7] + rt = RaggedTensor.from_row_splits(values, row_splits) + if context.executing_eagerly(): + expected_str = ''.format([[b'a', b'b'], + [b'c', b'd', b'e'], [b'f'], + [], [b'g']]) + expected_repr = ( + 'tf.RaggedTensor(values=tf.Tensor([{}], shape=(7,), dtype=string), ' + 'row_splits=tf.Tensor([{}], shape=(6,), dtype=int64))'.format( + ' '.join(repr(x) for x in values), ' '.join( + repr(x) for x in row_splits))) + self.assertEqual(str(rt), expected_str) + self.assertEqual(repr(rt), expected_repr) + else: + expected_repr = ( + 'tf.RaggedTensor(values=Tensor("RaggedFromRowSplits/values:0", ' + 'shape=(7,), dtype=string), row_splits=' + 'Tensor("RaggedFromRowSplits/row_splits:0", ' + 'shape=(6,), dtype=int64))') + self.assertEqual(repr(rt), expected_repr) + self.assertEqual(str(rt), expected_repr) def testRaggedTensorValueStr(self): + values = [b'a', b'b', b'c', b'd', b'e', b'f', b'g'] + row_splits = [0, 2, 5, 6, 6, 7] rt = ragged.RaggedTensorValue( - values=np.array(b'a b c d e f g'.split()), - row_splits=np.array([0, 2, 5, 6, 6, 7], dtype=np.int64)) - if sys.version_info[0] == 2: - self.assertEqual(' '.join(str(rt).split()), - (r"")) - self.assertEqual( - ' '.join(repr(rt).split()), - (r"RaggedTensorValue(values=array(['a', 'b', 'c', 'd', " - "'e', 'f', 'g'], dtype='|S1'), row_splits=array([0, 2, 5," - ' 6, 6, 7]))')) - else: - self.assertEqual( - ' '.join(str(rt).split()), - (r"")) - self.assertEqual( - ' '.join(repr(rt).split()), - (r"RaggedTensorValue(values=array([b'a', b'b', b'c', b'd', " - "b'e', b'f', b'g'], dtype='|S1'), row_splits=array([0, 2, 5," - ' 6, 6, 7]))')) + np.array(values), np.array(row_splits, dtype=np.int64)) + expected_str = ''.format([[b'a', b'b'], + [b'c', b'd', b'e'], + [b'f'], [], [b'g']]) + expected_repr = ("tf.RaggedTensorValue(values=array({}, dtype='|S1'), " + 'row_splits=array({}))'.format(values, row_splits)) + self.assertEqual(' '.join(str(rt).split()), expected_str) + self.assertEqual(' '.join(repr(rt).split()), expected_repr) #============================================================================= - # RaggedTensor.with_values() and RaggedTensor.with_inner_values(). + # RaggedTensor.with_values() and RaggedTensor.with_flat_values(). #============================================================================= - @test_util.run_v1_only('b/120545219') def testWithValues(self): rt1 = ragged.constant([[1, 2], [3, 4, 5], [6], [], [7]]) rt2 = ragged.constant([[[1, 2], [3, 4, 5]], [[6]], [], [[], [7]]]) rt1_plus_10 = rt1.with_values(rt1.values + 10) - rt2_times_10 = rt2.with_inner_values(rt2.inner_values * 10) + rt2_times_10 = rt2.with_flat_values(rt2.flat_values * 10) rt1_expanded = rt1.with_values(array_ops.expand_dims(rt1.values, axis=1)) self.assertEqual( - self.evaluate(rt1_plus_10).tolist(), + self.eval_to_list(rt1_plus_10), [[11, 12], [13, 14, 15], [16], [], [17]]) self.assertEqual( - self.evaluate(rt2_times_10).tolist(), + self.eval_to_list(rt2_times_10), [[[10, 20], [30, 40, 50]], [[60]], [], [[], [70]]]) self.assertEqual( - self.evaluate(rt1_expanded).tolist(), + self.eval_to_list(rt1_expanded), [[[1], [2]], [[3], [4], [5]], [[6]], [], [[7]]]) #============================================================================= # Session.run #============================================================================= - @test_util.run_deprecated_v1 def testSessionRun(self): + if context.executing_eagerly(): + return + rt1 = ragged.constant([[1, 2, 3], [4]]) rt2 = ragged.constant([[[], [1, 2]], [[3]]]) with self.test_session() as session: result = session.run({'rt1': rt1, 'rt2': rt2}) self.assertCountEqual(sorted(result.keys()), ['rt1', 'rt2']) - self.assertEqual(result['rt1'].tolist(), [[1, 2, 3], [4]]) - self.assertEqual(result['rt2'].tolist(), [[[], [1, 2]], [[3]]]) + self.assertEqual(result['rt1'].to_list(), [[1, 2, 3], [4]]) + self.assertEqual(result['rt2'].to_list(), [[[], [1, 2]], [[3]]]) - @test_util.run_deprecated_v1 def testSessionRunFeed(self): - rt1 = ragged.from_row_splits( + if context.executing_eagerly(): + return + + rt1 = RaggedTensor.from_row_splits( array_ops.placeholder(dtypes.int32), array_ops.placeholder(dtypes.int64)) - rt2 = ragged.from_nested_row_splits( - array_ops.placeholder(dtypes.int32), - [array_ops.placeholder(dtypes.int64), - array_ops.placeholder(dtypes.int64)]) + rt2 = RaggedTensor.from_nested_row_splits( + array_ops.placeholder(dtypes.int32), [ + array_ops.placeholder(dtypes.int64), + array_ops.placeholder(dtypes.int64) + ]) rt1_feed_val = ragged.constant_value([[1, 2, 3], [4]]) rt2_feed_val = ragged.constant_value([[[], [1, 2]], [[3]]]) with self.test_session() as session: - result = session.run({'rt1': rt1, 'rt2': rt2}, - feed_dict={rt1: rt1_feed_val, - rt2: rt2_feed_val}) + result = session.run({ + 'rt1': rt1, + 'rt2': rt2 + }, + feed_dict={ + rt1: rt1_feed_val, + rt2: rt2_feed_val + }) self.assertCountEqual(sorted(result.keys()), ['rt1', 'rt2']) - self.assertEqual(result['rt1'].tolist(), [[1, 2, 3], [4]]) - self.assertEqual(result['rt2'].tolist(), [[[], [1, 2]], [[3]]]) + self.assertEqual(result['rt1'].to_list(), [[1, 2, 3], [4]]) + self.assertEqual(result['rt2'].to_list(), [[[], [1, 2]], [[3]]]) - @test_util.run_v1_only('b/120545219') def testSessionPartialRunFeed(self): + if context.executing_eagerly(): + return + # Placeholder inputs. - a = ragged.from_row_splits( + a = RaggedTensor.from_row_splits( array_ops.placeholder(dtypes.int32, shape=[None], name='a.values'), array_ops.placeholder(dtypes.int64, name='a.row_splits')) - b = ragged.from_row_splits( + b = RaggedTensor.from_row_splits( array_ops.placeholder(dtypes.int32, shape=[None], name='b.values'), array_ops.placeholder(dtypes.int64, name='b.row_splits')) c = array_ops.placeholder(dtypes.int32, shape=[], name='c') @@ -1232,11 +1198,10 @@ class RaggedTensorTest(test_util.TensorFlowTestCase, parameterized.TestCase): handle = session.partial_run_setup([r1, r2], [a, b, c]) res1 = session.partial_run(handle, r1, feed_dict={a: a_val, b: b_val}) - self.assertEqual(res1.tolist(), [22, 8]) + self.assertAllEqual(res1, [22, 8]) res2 = session.partial_run(handle, r2, feed_dict={c: c_val}) - self.assertEqual(res2.tolist(), [15, 7]) - + self.assertAllEqual(res2, [15, 7]) if __name__ == '__main__': googletest.main() diff --git a/tensorflow/python/ops/ragged/ragged_tensor_value.py b/tensorflow/python/ops/ragged/ragged_tensor_value.py index 39d3249c99..bf0ac4482a 100644 --- a/tensorflow/python/ops/ragged/ragged_tensor_value.py +++ b/tensorflow/python/ops/ragged/ragged_tensor_value.py @@ -53,7 +53,7 @@ class RaggedTensorValue(object): doc="""The numpy dtype of values in this tensor.""") @property - def inner_values(self): + def flat_values(self): """The innermost `values` array for this ragged tensor value.""" rt_values = self.values while isinstance(rt_values, RaggedTensorValue): @@ -82,15 +82,18 @@ class RaggedTensorValue(object): return (self._row_splits.shape[0] - 1,) + (None,) + self._values.shape[1:] def __str__(self): - return "" % self.tolist() + return "" % self.to_list() def __repr__(self): - return "RaggedTensorValue(values=%r, row_splits=%r)" % (self._values, - self._row_splits) + return "tf.RaggedTensorValue(values=%r, row_splits=%r)" % (self._values, + self._row_splits) - def tolist(self): + def to_list(self): """Returns this ragged tensor value as a nested Python list.""" - values_as_list = self._values.tolist() + if isinstance(self._values, RaggedTensorValue): + values_as_list = self._values.to_list() + else: + values_as_list = self._values.tolist() return [ values_as_list[self._row_splits[i]:self._row_splits[i + 1]] for i in range(len(self._row_splits) - 1) diff --git a/tensorflow/python/ops/ragged/ragged_test_util.py b/tensorflow/python/ops/ragged/ragged_test_util.py new file mode 100644 index 0000000000..027417664d --- /dev/null +++ b/tensorflow/python/ops/ragged/ragged_test_util.py @@ -0,0 +1,95 @@ +# 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. +# ============================================================================== + +# pylint: disable=invalid-name +"""Test utils for tensorflow RaggedTensors.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.python.framework import ops +from tensorflow.python.framework import test_util +from tensorflow.python.ops import ragged + + +class RaggedTensorTestCase(test_util.TensorFlowTestCase): + """Base class for RaggedTensor test cases.""" + + def _GetPyList(self, a): + """Converts a to a nested python list.""" + if isinstance(a, ragged.RaggedTensor): + return self.evaluate(a).to_list() + elif isinstance(a, ops.Tensor): + a = self.evaluate(a) + return a.tolist() if isinstance(a, np.ndarray) else a + elif isinstance(a, np.ndarray): + return a.tolist() + elif isinstance(a, ragged.RaggedTensorValue): + return a.to_list() + else: + return np.array(a).tolist() + + def assertRaggedEqual(self, a, b): + """Asserts that two potentially ragged tensors are equal.""" + a_list = self._GetPyList(a) + b_list = self._GetPyList(b) + self.assertEqual(a_list, b_list) + + if not (isinstance(a, (list, tuple)) or isinstance(b, (list, tuple))): + a_ragged_rank = a.ragged_rank if ragged.is_ragged(a) else 0 + b_ragged_rank = b.ragged_rank if ragged.is_ragged(b) else 0 + self.assertEqual(a_ragged_rank, b_ragged_rank) + + def assertRaggedAlmostEqual(self, a, b, places=7): + a_list = self._GetPyList(a) + b_list = self._GetPyList(b) + self.assertNestedListAlmostEqual(a_list, b_list, places, context='value') + + if not (isinstance(a, (list, tuple)) or isinstance(b, (list, tuple))): + a_ragged_rank = a.ragged_rank if ragged.is_ragged(a) else 0 + b_ragged_rank = b.ragged_rank if ragged.is_ragged(b) else 0 + self.assertEqual(a_ragged_rank, b_ragged_rank) + + def assertNestedListAlmostEqual(self, a, b, places=7, context='value'): + self.assertEqual(type(a), type(b)) + if isinstance(a, (list, tuple)): + self.assertLen(a, len(b), 'Length differs for %s' % context) + for i in range(len(a)): + self.assertNestedListAlmostEqual(a[i], b[i], places, + '%s[%s]' % (context, i)) + else: + self.assertAlmostEqual( + a, b, places, + '%s != %s within %s places at %s' % (a, b, places, context)) + + def eval_to_list(self, tensor): + value = self.evaluate(tensor) + if ragged.is_ragged(value): + return value.to_list() + elif isinstance(value, np.ndarray): + return value.tolist() + else: + return value + + def _eval_tensor(self, tensor): + if ragged.is_ragged(tensor): + return ragged.RaggedTensorValue( + self._eval_tensor(tensor.values), + self._eval_tensor(tensor.row_splits)) + else: + return test_util.TensorFlowTestCase._eval_tensor(self, tensor) diff --git a/tensorflow/python/ops/ragged/ragged_tile_op_test.py b/tensorflow/python/ops/ragged/ragged_tile_op_test.py index f335b15dd1..d3445571bf 100644 --- a/tensorflow/python/ops/ragged/ragged_tile_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_tile_op_test.py @@ -24,12 +24,14 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops -from tensorflow.python.ops.ragged import ragged_array_ops -from tensorflow.python.ops.ragged import ragged_factory_ops +from tensorflow.python.ops import ragged +from tensorflow.python.ops.ragged import ragged_test_util from tensorflow.python.platform import googletest -class RaggedTileOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): +@test_util.run_all_in_graph_and_eager_modes +class RaggedTileOpTest(ragged_test_util.RaggedTensorTestCase, + parameterized.TestCase): @parameterized.parameters([ #========================================================================= @@ -181,14 +183,13 @@ class RaggedTileOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): [[[5], [6]]]]), ]) # pyformat: disable - @test_util.run_deprecated_v1 def testRaggedTile(self, descr, rt_input, multiples, expected, ragged_rank=None): - rt = ragged_factory_ops.constant(rt_input, ragged_rank) + rt = ragged.constant(rt_input, ragged_rank) expected_shape = [ None if dim is None else dim * multiple @@ -202,24 +203,21 @@ class RaggedTileOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): const_multiples, shape=[len(multiples)]) for multiples_tensor in (const_multiples, non_const_multiples): - tiled = ragged_array_ops.tile(rt, multiples_tensor) + tiled = ragged.tile(rt, multiples_tensor) self.assertEqual(tiled.ragged_rank, rt.ragged_rank) self.assertEqual(tiled.shape.ndims, rt.shape.ndims) if multiples_tensor is const_multiples: self.assertEqual(tiled.shape.as_list(), expected_shape) - with self.test_session(): - self.assertEqual(tiled.eval().tolist(), expected) + self.assertRaggedEqual(tiled, expected) - @test_util.run_deprecated_v1 def testRaggedTileWithTensorInput(self): # When the input is a `Tensor`, ragged_tile just delegates to tf.tile. dt = constant_op.constant([[1, 2], [3, 4]]) - tiled = ragged_array_ops.tile(dt, [3, 2]) + tiled = ragged.tile(dt, [3, 2]) expected = [[1, 2, 1, 2], [3, 4, 3, 4], [1, 2, 1, 2], [3, 4, 3, 4], [1, 2, 1, 2], [3, 4, 3, 4]] # pyformat: disable - with self.test_session(): - self.assertEqual(tiled.eval().tolist(), expected) + self.assertRaggedEqual(tiled, expected) if __name__ == '__main__': diff --git a/tensorflow/python/ops/ragged/ragged_to_sparse_op_test.py b/tensorflow/python/ops/ragged/ragged_to_sparse_op_test.py index 9863e3b583..46d7a56a7c 100644 --- a/tensorflow/python/ops/ragged/ragged_to_sparse_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_to_sparse_op_test.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 dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import test_util @@ -25,176 +26,168 @@ 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 ragged +from tensorflow.python.ops.ragged import ragged_test_util from tensorflow.python.platform import googletest -class RaggedTensorToSparseOpTest(test_util.TensorFlowTestCase): +@test_util.run_all_in_graph_and_eager_modes +class RaggedTensorToSparseOpTest(ragged_test_util.RaggedTensorTestCase): - @test_util.run_deprecated_v1 def testDocStringExample(self): rt = ragged.constant([[1, 2, 3], [4], [], [5, 6]]) - st = ragged.to_sparse(rt) - expected = ('SparseTensorValue(indices=' - 'array([[0, 0], [0, 1], [0, 2], [1, 0], [3, 0], [3, 1]]), ' - 'values=array([1, 2, 3, 4, 5, 6], dtype=int32), ' - 'dense_shape=array([4, 3]))') - with self.test_session(): - self.assertEqual(' '.join(repr(st.eval()).split()), expected) - - @test_util.run_deprecated_v1 + st = self.evaluate(rt.to_sparse()) + self.assertAllEqual(st.indices, + [[0, 0], [0, 1], [0, 2], [1, 0], [3, 0], [3, 1]]) + self.assertAllEqual(st.values, [1, 2, 3, 4, 5, 6]) + self.assertAllEqual(st.dense_shape, [4, 3]) + def test2DRaggedTensorWithOneRaggedDimension(self): rt = ragged.constant([['a', 'b'], ['c', 'd', 'e'], ['f'], [], ['g']]) - with self.test_session(): - st = ragged.to_sparse(rt).eval() - self.assertAllEqual( - st.indices, [[0, 0], [0, 1], [1, 0], [1, 1], [1, 2], [2, 0], [4, 0]]) - self.assertAllEqual(st.values, b'a b c d e f g'.split()) - self.assertAllEqual(st.dense_shape, [5, 3]) - - @test_util.run_deprecated_v1 + st = self.evaluate(rt.to_sparse()) + self.assertAllEqual( + st.indices, [[0, 0], [0, 1], [1, 0], [1, 1], [1, 2], [2, 0], [4, 0]]) + self.assertAllEqual(st.values, b'a b c d e f g'.split()) + self.assertAllEqual(st.dense_shape, [5, 3]) + def test3DRaggedTensorWithOneRaggedDimension(self): rt = ragged.constant([[[1, 2], [3, 4]], [[5, 6], [7, 8], [9, 10]], [[11, 12]], [], [[13, 14]]], ragged_rank=1) - with self.test_session(): - st = ragged.to_sparse(rt).eval() - self.assertAllEqual( - st.indices, [[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 1, 1], [1, 0, 0], - [1, 0, 1], [1, 1, 0], [1, 1, 1], [1, 2, 0], [1, 2, 1], - [2, 0, 0], [2, 0, 1], [4, 0, 0], [4, 0, 1]]) - self.assertAllEqual(st.values, - [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]) - self.assertAllEqual(st.dense_shape, [5, 3, 2]) - - @test_util.run_deprecated_v1 + st = self.evaluate(rt.to_sparse()) + self.assertAllEqual(st.indices, + [[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 1, 1], [1, 0, 0], + [1, 0, 1], [1, 1, 0], [1, 1, 1], [1, 2, 0], [1, 2, 1], + [2, 0, 0], [2, 0, 1], [4, 0, 0], [4, 0, 1]]) + self.assertAllEqual(st.values, + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]) + self.assertAllEqual(st.dense_shape, [5, 3, 2]) + def test4DRaggedTensorWithOneRaggedDimension(self): rt = ragged.constant( [[[[1, 2], [3, 4]], [[5, 6], [7, 8]]], [], [[[9, 10], [11, 12]]]], ragged_rank=1) - with self.test_session(): - st = ragged.to_sparse(rt).eval() - self.assertAllEqual(st.values, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]) - self.assertAllEqual( - st.indices, - [ - [0, 0, 0, 0], # index for value=1 - [0, 0, 0, 1], # index for value=2 - [0, 0, 1, 0], # index for value=3 - [0, 0, 1, 1], # index for value=4 - [0, 1, 0, 0], # index for value=5 - [0, 1, 0, 1], # index for value=6 - [0, 1, 1, 0], # index for value=7 - [0, 1, 1, 1], # index for value=8 - [2, 0, 0, 0], # index for value=9 - [2, 0, 0, 1], # index for value=10 - [2, 0, 1, 0], # index for value=11 - [2, 0, 1, 1], # index for value=12 - ]) - self.assertAllEqual(st.dense_shape, [3, 2, 2, 2]) - - @test_util.run_deprecated_v1 + st = self.evaluate(rt.to_sparse()) + self.assertAllEqual(st.values, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]) + self.assertAllEqual( + st.indices, + [ + [0, 0, 0, 0], # index for value=1 + [0, 0, 0, 1], # index for value=2 + [0, 0, 1, 0], # index for value=3 + [0, 0, 1, 1], # index for value=4 + [0, 1, 0, 0], # index for value=5 + [0, 1, 0, 1], # index for value=6 + [0, 1, 1, 0], # index for value=7 + [0, 1, 1, 1], # index for value=8 + [2, 0, 0, 0], # index for value=9 + [2, 0, 0, 1], # index for value=10 + [2, 0, 1, 0], # index for value=11 + [2, 0, 1, 1], # index for value=12 + ]) + self.assertAllEqual(st.dense_shape, [3, 2, 2, 2]) + def test4DRaggedTensorWithTwoRaggedDimensions(self): rt = ragged.constant([[[[1, 2], [3, 4]], [[5, 6], [7, 8], [9, 10]]], [[[11, 12]], [], [[13, 14]]], []], ragged_rank=2) - with self.test_session(): - st = ragged.to_sparse(rt).eval() - self.assertAllEqual( - st.indices, - [ - [0, 0, 0, 0], # index for value=1 - [0, 0, 0, 1], # index for value=2 - [0, 0, 1, 0], # index for value=3 - [0, 0, 1, 1], # index for value=4 - [0, 1, 0, 0], # index for value=5 - [0, 1, 0, 1], # index for value=6 - [0, 1, 1, 0], # index for value=7 - [0, 1, 1, 1], # index for value=8 - [0, 1, 2, 0], # index for value=9 - [0, 1, 2, 1], # index for value=10 - [1, 0, 0, 0], # index for value=11 - [1, 0, 0, 1], # index for value=12 - [1, 2, 0, 0], # index for value=13 - [1, 2, 0, 1], # index for value=14 - ]) - self.assertAllEqual(st.values, - [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]) - self.assertAllEqual(st.dense_shape, [3, 3, 3, 2]) + st = self.evaluate(rt.to_sparse()) + self.assertAllEqual( + st.indices, + [ + [0, 0, 0, 0], # index for value=1 + [0, 0, 0, 1], # index for value=2 + [0, 0, 1, 0], # index for value=3 + [0, 0, 1, 1], # index for value=4 + [0, 1, 0, 0], # index for value=5 + [0, 1, 0, 1], # index for value=6 + [0, 1, 1, 0], # index for value=7 + [0, 1, 1, 1], # index for value=8 + [0, 1, 2, 0], # index for value=9 + [0, 1, 2, 1], # index for value=10 + [1, 0, 0, 0], # index for value=11 + [1, 0, 0, 1], # index for value=12 + [1, 2, 0, 0], # index for value=13 + [1, 2, 0, 1], # index for value=14 + ]) + self.assertAllEqual(st.values, + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]) + self.assertAllEqual(st.dense_shape, [3, 3, 3, 2]) def testShape(self): rt = ragged.constant([[1, 2], [3, 4, 5], [6], [], [7]]) - st = ragged.to_sparse(rt) + st = rt.to_sparse() self.assertEqual(st.indices.shape.as_list(), [7, 2]) self.assertEqual(st.values.shape.as_list(), [7]) self.assertEqual(st.dense_shape.shape.as_list(), [2]) rt = ragged.constant([[[1, 2]], [], [[3, 4]], []], ragged_rank=1) - st = ragged.to_sparse(rt) + st = rt.to_sparse() self.assertEqual(st.indices.shape.as_list(), [4, 3]) self.assertEqual(st.values.shape.as_list(), [4]) self.assertEqual(st.dense_shape.shape.as_list(), [3]) rt = ragged.constant([[[1], [2, 3, 4, 5, 6, 7]], [[]]]) - st = ragged.to_sparse(rt) + st = rt.to_sparse() self.assertEqual(st.indices.shape.as_list(), [7, 3]) self.assertEqual(st.values.shape.as_list(), [7]) self.assertEqual(st.dense_shape.shape.as_list(), [3]) - @test_util.run_v1_only('b/120545219') def testKernelErrors(self): # An empty vector, defined using a placeholder to ensure that we can't # determine that it's invalid at graph-construction time. empty_vector = array_ops.placeholder_with_default( array_ops.zeros([0], dtypes.int64), shape=None) - bad_rt1 = ragged.from_row_splits(row_splits=[2, 3], values=[1, 2, 3]) - with self.test_session(): - bad_split0_error = r'First value of ragged splits must be 0.*' - self.assertRaisesRegexp(errors.InvalidArgumentError, bad_split0_error, - ragged.to_sparse(bad_rt1).eval) + bad_rt1 = ragged.RaggedTensor.from_row_splits( + row_splits=[2, 3], values=[1, 2, 3]) + bad_split0 = r'First value of ragged splits must be 0.*' + with self.assertRaisesRegexp(errors.InvalidArgumentError, bad_split0): + self.evaluate(bad_rt1.to_sparse()) - bad_rt2 = ragged.from_row_splits(row_splits=[0, 5], values=empty_vector) - bad_rt3 = ragged.from_row_splits( + bad_rt2 = ragged.RaggedTensor.from_row_splits( + row_splits=[0, 5], values=empty_vector) + bad_rt3 = ragged.RaggedTensor.from_row_splits( row_splits=[0, 1], - values=ragged.from_row_splits(row_splits=[0, 5], values=empty_vector)) - with self.test_session(): - split_mismatch1_error = r'Final value of ragged splits must match.*' - for rt in [bad_rt2, bad_rt3]: - self.assertRaisesRegexp(errors.InvalidArgumentError, - split_mismatch1_error, - ragged.to_sparse(rt).eval) - - bad_rt4 = ragged.from_row_splits( + values=ragged.RaggedTensor.from_row_splits( + row_splits=[0, 5], values=empty_vector)) + split_mismatch1_error = r'Final value of ragged splits must match.*' + for rt in [bad_rt2, bad_rt3]: + with self.assertRaisesRegexp(errors.InvalidArgumentError, + split_mismatch1_error): + self.evaluate(rt.to_sparse()) + + bad_rt4 = ragged.RaggedTensor.from_row_splits( row_splits=[0, 5], - values=ragged.from_row_splits(row_splits=[0], values=empty_vector)) - with self.test_session(): - split_mismatch2_error = r'Final value of ragged splits must match.*' - self.assertRaisesRegexp(errors.InvalidArgumentError, - split_mismatch2_error, - ragged.to_sparse(bad_rt4).eval) - - bad_rt5 = ragged.from_row_splits(row_splits=empty_vector, values=[]) - with self.test_session(): - empty_splits_error = (r'ragged splits may not be empty.*') - self.assertRaisesRegexp(errors.InvalidArgumentError, empty_splits_error, - ragged.to_sparse(bad_rt5).eval) - - @test_util.run_v1_only('b/120545219') + values=ragged.RaggedTensor.from_row_splits( + row_splits=[0], values=empty_vector)) + split_mismatch2_error = r'Final value of ragged splits must match.*' + with self.assertRaisesRegexp(errors.InvalidArgumentError, + split_mismatch2_error): + self.evaluate(bad_rt4.to_sparse()) + + bad_rt5 = ragged.RaggedTensor.from_row_splits( + row_splits=empty_vector, values=[]) + empty_splits_error = (r'ragged splits may not be empty.*') + with self.assertRaisesRegexp(errors.InvalidArgumentError, + empty_splits_error): + self.evaluate(bad_rt5.to_sparse()) + def testGradient(self): + if context.executing_eagerly(): + return # rt1.shape == rt2.shape == [2, (D2), (D3), 2]. rt1 = ragged.constant([[[[1.0, 2.0], [3.0, 4.0]], [[5.0, 6.0]]]], ragged_rank=2) rt2 = ragged.constant([[[[9.0, 8.0], [7.0, 6.0]], [[5.0, 4.0]]]], ragged_rank=2) - rt = ragged.map_inner_values(math_ops.add, rt1, rt2 * 2.0) - st = ragged.to_sparse(rt) + rt = ragged.map_flat_values(math_ops.add, rt1, rt2 * 2.0) + st = rt.to_sparse() - g1, g2 = gradients_impl.gradients(st.values, [rt1.inner_values, - rt2.inner_values]) + g1, g2 = gradients_impl.gradients(st.values, + [rt1.flat_values, rt2.flat_values]) print(g1, g2) - with self.test_session(): - self.assertEqual(g1.eval().tolist(), [[1.0, 1.0], [1.0, 1.0], [1.0, 1.0]]) - self.assertEqual(g2.eval().tolist(), [[2.0, 2.0], [2.0, 2.0], [2.0, 2.0]]) + self.assertRaggedEqual(g1, [[1.0, 1.0], [1.0, 1.0], [1.0, 1.0]]) + self.assertRaggedEqual(g2, [[2.0, 2.0], [2.0, 2.0], [2.0, 2.0]]) if __name__ == '__main__': diff --git a/tensorflow/python/ops/ragged/ragged_to_tensor_op_test.py b/tensorflow/python/ops/ragged/ragged_to_tensor_op_test.py index 77499b9cb3..ffcc2be52e 100644 --- a/tensorflow/python/ops/ragged/ragged_to_tensor_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_to_tensor_op_test.py @@ -24,23 +24,19 @@ from tensorflow.python.framework import ops from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import ragged +from tensorflow.python.ops.ragged import ragged_test_util from tensorflow.python.platform import googletest -class RaggedTensorToTensorOpTest(test_util.TensorFlowTestCase, +@test_util.run_all_in_graph_and_eager_modes +class RaggedTensorToTensorOpTest(ragged_test_util.RaggedTensorTestCase, parameterized.TestCase): - @test_util.run_deprecated_v1 def testDocStringExamples(self): """Example from ragged_to_tensor.__doc__.""" rt = ragged.constant([[9, 8, 7], [], [6, 5], [4]]) - dt = ragged.to_tensor(rt) - with self.test_session(): - self.assertEqual(str(dt.eval()), - '[[9 8 7]\n' - ' [0 0 0]\n' - ' [6 5 0]\n' - ' [4 0 0]]') # pyformat: disable + dt = rt.to_tensor() + self.assertAllEqual(dt, [[9, 8, 7], [0, 0, 0], [6, 5, 0], [4, 0, 0]]) @parameterized.parameters( { @@ -98,7 +94,6 @@ class RaggedTensorToTensorOpTest(test_util.TensorFlowTestCase, 'expected': [[[[1], [2]], [[9], [9]], [[3], [9]]]], }, ) - @test_util.run_deprecated_v1 def testRaggedTensorToTensor(self, rt_input, expected, @@ -106,15 +101,14 @@ class RaggedTensorToTensorOpTest(test_util.TensorFlowTestCase, default=None, expected_shape=None): rt = ragged.constant(rt_input, ragged_rank=ragged_rank) - dt = ragged.to_tensor(rt, default) - self.assertEqual(type(dt), ops.Tensor) + dt = rt.to_tensor(default) + self.assertIsInstance(dt, ops.Tensor) self.assertEqual(rt.dtype, dt.dtype) self.assertTrue(dt.shape.is_compatible_with(rt.shape)) - with self.test_session(): - self.assertEqual(dt.eval().tolist(), expected) - if expected_shape is not None: - dt_shape = array_ops.shape(dt) - self.assertEqual(dt_shape.eval().tolist(), expected_shape) + self.assertAllEqual(self.eval_to_list(dt), expected) + if expected_shape is not None: + dt_shape = array_ops.shape(dt) + self.assertAllEqual(dt_shape, expected_shape) @parameterized.parameters( { @@ -131,14 +125,13 @@ class RaggedTensorToTensorOpTest(test_util.TensorFlowTestCase, { 'rt_input': [[1, 2, 3]], 'default': 'a', - 'error': (TypeError, "Expected int32, got 'a' of type 'str' instead"), + 'error': (TypeError, '.*'), }, ) - @test_util.run_deprecated_v1 def testError(self, rt_input, default, error, ragged_rank=None): rt = ragged.constant(rt_input, ragged_rank=ragged_rank) with self.assertRaisesRegexp(error[0], error[1]): - ragged.to_tensor(rt, default) + rt.to_tensor(default) if __name__ == '__main__': diff --git a/tensorflow/python/ops/ragged/ragged_util_test.py b/tensorflow/python/ops/ragged/ragged_util_test.py index 69c605dbf9..72a4155930 100644 --- a/tensorflow/python/ops/ragged/ragged_util_test.py +++ b/tensorflow/python/ops/ragged/ragged_util_test.py @@ -24,6 +24,7 @@ import numpy as np from tensorflow.python.framework import constant_op from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops +from tensorflow.python.ops.ragged import ragged_test_util from tensorflow.python.ops.ragged import ragged_util from tensorflow.python.platform import googletest @@ -41,7 +42,9 @@ TENSOR_4D = [[[[('%d%d%d%d' % (i, j, k, l)).encode('utf-8') for i in range(4)] -class RaggedRepeatTest(test_util.TensorFlowTestCase, parameterized.TestCase): +@test_util.run_all_in_graph_and_eager_modes +class RaggedUtilTest(ragged_test_util.RaggedTensorTestCase, + parameterized.TestCase): @parameterized.parameters([ # Docstring examples @@ -87,11 +90,10 @@ class RaggedRepeatTest(test_util.TensorFlowTestCase, parameterized.TestCase): # Empty tensor dict(data=[], repeats=[], axis=0, expected=[]), ]) - @test_util.run_v1_only('b/120545219') def testRepeat(self, data, repeats, expected, axis=None): result = ragged_util.repeat(data, repeats, axis) with self.test_session(): - self.assertEqual(result.eval().tolist(), expected) + self.assertAllEqual(result, expected) @parameterized.parameters([ dict(mode=mode, **args) @@ -136,7 +138,6 @@ class RaggedRepeatTest(test_util.TensorFlowTestCase, parameterized.TestCase): dict(data=TENSOR_4D, repeats=[1, 3, 0, 0, 2], axis=3), ] ]) - @test_util.run_v1_only('b/120545219') def testValuesMatchesNumpy(self, mode, data, repeats, axis): # Exception: we can't handle negative axis if data.ndims is unknown. if axis < 0 and mode == 'unknown_shape': @@ -158,7 +159,7 @@ class RaggedRepeatTest(test_util.TensorFlowTestCase, parameterized.TestCase): result = ragged_util.repeat(data, repeats, axis) with self.test_session(): - self.assertEqual(result.eval().tolist(), expected.tolist()) + self.assertAllEqual(result, expected) @parameterized.parameters([ dict( diff --git a/tensorflow/python/ops/ragged/ragged_where_op_test.py b/tensorflow/python/ops/ragged/ragged_where_op_test.py index 6d645eefac..b3cd5a2deb 100644 --- a/tensorflow/python/ops/ragged/ragged_where_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_where_op_test.py @@ -22,10 +22,13 @@ from absl.testing import parameterized from tensorflow.python.framework import test_util from tensorflow.python.ops import ragged +from tensorflow.python.ops.ragged import ragged_test_util from tensorflow.python.platform import googletest -class RaggedWhereOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): +@test_util.run_all_in_graph_and_eager_modes +class RaggedWhereOpTest(ragged_test_util.RaggedTensorTestCase, + parameterized.TestCase): @parameterized.parameters([ #========================================================================= @@ -165,18 +168,9 @@ class RaggedWhereOpTest(test_util.TensorFlowTestCase, parameterized.TestCase): y=ragged.constant_value([[[['a']]], [[['b']]]]), expected=ragged.constant_value([[[[], [b'A']]], [[[b'b']]]])), ]) # pyformat: disable - @test_util.run_v1_only('b/120545219') def testRaggedWhere(self, condition, expected, x=None, y=None): result = ragged.where(condition, x, y) - self.assertEqual( - getattr(result, 'ragged_rank', 0), getattr(expected, 'ragged_rank', 0)) - with self.test_session(): - result_value = self.evaluate(result) - if hasattr(result_value, 'tolist'): - result_value = result_value.tolist() - if hasattr(expected, 'tolist'): - expected = expected.tolist() - self.assertEqual(result_value, expected) + self.assertRaggedEqual(result, expected) @parameterized.parameters([ dict( diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD index f34ac4c3e8..baacb87239 100644 --- a/tensorflow/tools/pip_package/BUILD +++ b/tensorflow/tools/pip_package/BUILD @@ -113,6 +113,7 @@ COMMON_PIP_DEPS = [ "//tensorflow/python/eager:eager_pip", "//tensorflow/python/kernel_tests/signal:test_util", "//tensorflow/python/kernel_tests/testdata:self_adjoint_eig_op_test_files", + "//tensorflow/python/ops/ragged:ragged_test_util", "//tensorflow/python/saved_model:saved_model", "//tensorflow/python/tools:tools_pip", "//tensorflow/python/tools/api/generator:create_python_api", -- GitLab From 49123b01d3c18ad82cb9f754b8e130baa691a409 Mon Sep 17 00:00:00 2001 From: Suharsh Sivakumar Date: Fri, 7 Dec 2018 22:47:27 -0800 Subject: [PATCH 265/873] Fix flexbuffer byte type and annotation. PiperOrigin-RevId: 224623431 --- tensorflow/lite/schema/schema.fbs | 2 +- tensorflow/lite/schema/schema_generated.h | 14 +++++++------- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/tensorflow/lite/schema/schema.fbs b/tensorflow/lite/schema/schema.fbs index 6436167303..980f13b19b 100644 --- a/tensorflow/lite/schema/schema.fbs +++ b/tensorflow/lite/schema/schema.fbs @@ -45,7 +45,7 @@ enum TensorType : byte { // Custom quantization parameters for experimenting with new quantization // techniques. table CustomQuantization { - custom:[byte]; + custom:[ubyte] (force_align: 16); } // Represents a specific quantization technique's parameters. diff --git a/tensorflow/lite/schema/schema_generated.h b/tensorflow/lite/schema/schema_generated.h index af8b143364..637cbafabd 100755 --- a/tensorflow/lite/schema/schema_generated.h +++ b/tensorflow/lite/schema/schema_generated.h @@ -2247,7 +2247,7 @@ inline const char *EnumNameCustomOptionsFormat(CustomOptionsFormat e) { struct CustomQuantizationT : public flatbuffers::NativeTable { typedef CustomQuantization TableType; - std::vector custom; + std::vector custom; CustomQuantizationT() { } }; @@ -2257,8 +2257,8 @@ struct CustomQuantization FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { enum { VT_CUSTOM = 4 }; - const flatbuffers::Vector *custom() const { - return GetPointer *>(VT_CUSTOM); + const flatbuffers::Vector *custom() const { + return GetPointer *>(VT_CUSTOM); } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && @@ -2274,7 +2274,7 @@ struct CustomQuantization FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { struct CustomQuantizationBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; - void add_custom(flatbuffers::Offset> custom) { + void add_custom(flatbuffers::Offset> custom) { fbb_.AddOffset(CustomQuantization::VT_CUSTOM, custom); } explicit CustomQuantizationBuilder(flatbuffers::FlatBufferBuilder &_fbb) @@ -2291,7 +2291,7 @@ struct CustomQuantizationBuilder { inline flatbuffers::Offset CreateCustomQuantization( flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> custom = 0) { + flatbuffers::Offset> custom = 0) { CustomQuantizationBuilder builder_(_fbb); builder_.add_custom(custom); return builder_.Finish(); @@ -2299,10 +2299,10 @@ inline flatbuffers::Offset CreateCustomQuantization( inline flatbuffers::Offset CreateCustomQuantizationDirect( flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *custom = nullptr) { + const std::vector *custom = nullptr) { return tflite::CreateCustomQuantization( _fbb, - custom ? _fbb.CreateVector(*custom) : 0); + custom ? _fbb.CreateVector(*custom) : 0); } flatbuffers::Offset CreateCustomQuantization(flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -- GitLab From 8ca891f53de9145113d889f876ab0cc9efee3c34 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 7 Dec 2018 23:09:51 -0800 Subject: [PATCH 266/873] Deflake optimize_dataset_test in tsan mode. PiperOrigin-RevId: 224624656 --- .../python/data/experimental/kernel_tests/optimization/BUILD | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/data/experimental/kernel_tests/optimization/BUILD b/tensorflow/python/data/experimental/kernel_tests/optimization/BUILD index 2fc243aa13..bf868ebe79 100644 --- a/tensorflow/python/data/experimental/kernel_tests/optimization/BUILD +++ b/tensorflow/python/data/experimental/kernel_tests/optimization/BUILD @@ -286,7 +286,7 @@ py_test( py_test( name = "optimize_dataset_test", - size = "small", + size = "medium", srcs = ["optimize_dataset_test.py"], srcs_version = "PY2AND3", tags = [ -- GitLab From d7fd24856d77b14021524b5cd3833cfd322e665d Mon Sep 17 00:00:00 2001 From: Rick Chao Date: Fri, 7 Dec 2018 23:10:18 -0800 Subject: [PATCH 267/873] Add tf.estimator as a valid v1 api prefix in testAllAPIV1 check in tf_upgrade_v2_test.py. PiperOrigin-RevId: 224624681 --- tensorflow/tools/compatibility/tf_upgrade_v2_test.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/tools/compatibility/tf_upgrade_v2_test.py b/tensorflow/tools/compatibility/tf_upgrade_v2_test.py index 63aa5f0c6b..0fc7a18734 100644 --- a/tensorflow/tools/compatibility/tf_upgrade_v2_test.py +++ b/tensorflow/tools/compatibility/tf_upgrade_v2_test.py @@ -198,6 +198,7 @@ class TestUpgrade(test_util.TensorFlowTestCase): _, _, _, text = self._upgrade("tf." + name) if (text and not text.startswith("tf.compat.v1") and + not text.startswith("tf.estimator") and text not in v1_symbols): self.assertFalse( True, "Symbol %s generated from %s not in v1 API" % ( -- GitLab From a3f24855d6697c37f1842e097a4563d6613d906b Mon Sep 17 00:00:00 2001 From: Andrew Selle Date: Fri, 7 Dec 2018 23:37:13 -0800 Subject: [PATCH 268/873] Allow TensorFlow demo app to switch between devices Please approve this CL. It will be submitted automatically, and its GitHub pull request will be marked as merged. - ListView gui for model (float and quant) - ListView gui for devices (optional gpu, nnapi and cpu) - Add a few AAR options. PiperOrigin-RevId: 224626109 --- tensorflow/lite/java/demo/app/build.gradle | 12 ++ .../Camera2BasicFragment.java | 164 ++++++++++++++---- .../tflitecamerademo/GpuDelegateHelper.java | 49 ++++++ .../tflitecamerademo/ImageClassifier.java | 14 ++ .../ImageClassifierFloatMobileNet.java | 94 ++++++++++ .../src/main/res/drawable/item_selector.xml | 11 ++ .../res/layout-v26/fragment_camera2_basic.xml | 98 +++++++---- .../res/layout/fragment_camera2_basic.xml | 88 +++++++--- .../app/src/main/res/layout/listview_row.xml | 17 ++ .../demo/app/src/main/res/values/colors.xml | 3 + .../demo/app/src/main/res/values/strings.xml | 6 + 11 files changed, 463 insertions(+), 93 deletions(-) create mode 100644 tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/GpuDelegateHelper.java create mode 100644 tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierFloatMobileNet.java create mode 100644 tensorflow/lite/java/demo/app/src/main/res/drawable/item_selector.xml create mode 100644 tensorflow/lite/java/demo/app/src/main/res/layout/listview_row.xml diff --git a/tensorflow/lite/java/demo/app/build.gradle b/tensorflow/lite/java/demo/app/build.gradle index 5e50ed4b94..b8fc282cb1 100644 --- a/tensorflow/lite/java/demo/app/build.gradle +++ b/tensorflow/lite/java/demo/app/build.gradle @@ -40,6 +40,15 @@ repositories { url 'https://google.bintray.com/tensorflow' } } +allprojects { + repositories { + // Uncomment if you want to use a local repo. + // mavenLocal() + jcenter() + } +} + + dependencies { compile fileTree(dir: 'libs', include: ['*.jar']) @@ -49,7 +58,10 @@ dependencies { compile 'com.android.support:support-annotations:25.3.1' compile 'com.android.support:support-v13:25.2.0' + // Build off of nightly TensorFlow Lite compile 'org.tensorflow:tensorflow-lite:0.0.0-nightly' + // Use local TensorFlow library + // compile 'org.tensorflow:tensorflow-lite-local:0.0.0' } def targetFolder = "src/main/assets" diff --git a/tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/Camera2BasicFragment.java b/tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/Camera2BasicFragment.java index 20e96f586a..165d335101 100644 --- a/tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/Camera2BasicFragment.java +++ b/tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/Camera2BasicFragment.java @@ -56,8 +56,10 @@ import android.view.Surface; import android.view.TextureView; import android.view.View; import android.view.ViewGroup; +import android.widget.AdapterView; +import android.widget.ArrayAdapter; +import android.widget.ListView; import android.widget.NumberPicker; -import android.widget.RadioButton; import android.widget.TextView; import android.widget.Toast; import java.io.IOException; @@ -69,6 +71,7 @@ import java.util.List; import java.util.concurrent.Semaphore; import java.util.concurrent.TimeUnit; + /** Basic fragments for the Camera. */ public class Camera2BasicFragment extends Fragment implements FragmentCompat.OnRequestPermissionsResultCallback { @@ -88,9 +91,9 @@ public class Camera2BasicFragment extends Fragment private TextView textView; private NumberPicker np; private ImageClassifier classifier; + private ListView deviceView; + private ListView modelView; - enum InferenceEngine { CPU, NNAPI }; - private InferenceEngine inferenceEngine = InferenceEngine.CPU; /** Max preview width that is guaranteed by Camera2 API */ private static final int MAX_PREVIEW_WIDTH = 1920; @@ -124,6 +127,15 @@ public class Camera2BasicFragment extends Fragment public void onSurfaceTextureUpdated(SurfaceTexture texture) {} }; + // Model parameter constants. + private String gpu; + private String cpu; + private String nnApi; + private String mobilenetV1Quant; + private String mobilenetV1Float; + + + /** ID of the current {@link CameraDevice}. */ private String cameraId; @@ -170,6 +182,14 @@ public class Camera2BasicFragment extends Fragment } }; + private ArrayList deviceStrings = new ArrayList(); + private ArrayList modelStrings = new ArrayList(); + + /** Current indices of device and model. */ + int currentDevice = -1; + + int currentModel = -1; + /** An additional thread for running tasks that shouldn't block the UI. */ private HandlerThread backgroundThread; @@ -299,11 +319,115 @@ public class Camera2BasicFragment extends Fragment return inflater.inflate(R.layout.fragment_camera2_basic, container, false); } + private void updateActiveModel() { + // Get UI information before delegating to background + final int modelIndex = modelView.getCheckedItemPosition(); + final int deviceIndex = deviceView.getCheckedItemPosition(); + + backgroundHandler.post(() -> { + if (modelIndex == currentModel && deviceIndex == currentDevice) { + return; + } + currentModel = modelIndex; + currentDevice = deviceIndex; + + // Disable classifier while updating + if (classifier != null) { + classifier.close(); + classifier = null; + } + + // Lookup names of parameters. + String model = modelStrings.get(modelIndex); + String device = deviceStrings.get(deviceIndex); + + Log.i(TAG, "Changing model to " + model + " device " + device); + + // Try to load model. + try { + if (model.equals(mobilenetV1Quant)) { + classifier = new ImageClassifierQuantizedMobileNet(getActivity()); + } else if (model.equals(mobilenetV1Float)) { + classifier = new ImageClassifierFloatMobileNet(getActivity()); + } else { + showToast("Failed to load model"); + } + } catch (IOException e) { + Log.d(TAG, "Failed to load", e); + classifier = null; + } + + // Customzie the interpreter to the type of device we want to use. + if (device.equals(cpu)) { + } else if (device.equals(gpu)) { + if (!GpuDelegateHelper.isGpuDelegateAvailable()) { + showToast("gpu not in this build."); + classifier = null; + } else if (model.equals(mobilenetV1Quant)) { + showToast("gpu requires float model."); + classifier = null; + } else { + classifier.useGpu(); + } + } else if (device.equals(nnApi)) { + classifier.useNNAPI(); + } + }); + } + /** Connect the buttons to their event handler. */ @Override public void onViewCreated(final View view, Bundle savedInstanceState) { + gpu = getString(R.string.gpu); + cpu = getString(R.string.cpu); + nnApi = getString(R.string.nnapi); + mobilenetV1Quant = getString(R.string.mobilenetV1Quant); + mobilenetV1Float = getString(R.string.mobilenetV1Float); + + // Get references to widgets. textureView = (AutoFitTextureView) view.findViewById(R.id.texture); textView = (TextView) view.findViewById(R.id.text); + deviceView = (ListView) view.findViewById(R.id.device); + modelView = (ListView) view.findViewById(R.id.model); + + // Build list of models + modelStrings.add(mobilenetV1Quant); + modelStrings.add(mobilenetV1Float); + + // Build list of devices + int defaultModelIndex = 0; + deviceStrings.add(cpu); + if (GpuDelegateHelper.isGpuDelegateAvailable()) { + deviceStrings.add(gpu); + } + deviceStrings.add(nnApi); + + deviceView.setAdapter( + new ArrayAdapter( + getContext(), R.layout.listview_row, R.id.listview_row_text, deviceStrings)); + deviceView.setChoiceMode(ListView.CHOICE_MODE_SINGLE); + deviceView.setOnItemClickListener( + new AdapterView.OnItemClickListener() { + @Override + public void onItemClick(AdapterView parent, View view, int position, long id) { + updateActiveModel(); + } + }); + deviceView.setItemChecked(0, true); + + modelView.setChoiceMode(ListView.CHOICE_MODE_SINGLE); + ArrayAdapter modelAdapter = + new ArrayAdapter<>( + getContext(), R.layout.listview_row, R.id.listview_row_text, modelStrings); + modelView.setAdapter(modelAdapter); + modelView.setItemChecked(defaultModelIndex, true); + modelView.setOnItemClickListener( + new AdapterView.OnItemClickListener() { + @Override + public void onItemClick(AdapterView parent, View view, int position, long id) { + updateActiveModel(); + } + }); np = (NumberPicker) view.findViewById(R.id.np); np.setMinValue(1); @@ -317,43 +441,13 @@ public class Camera2BasicFragment extends Fragment } }); - RadioButton cpuButton = (RadioButton) view.findViewById(R.id.radio_cpu); - cpuButton.setChecked(true); // TFLite runs on CPU by default. - cpuButton.setOnClickListener( - new View.OnClickListener() { - @Override - public void onClick(View view) { - if (inferenceEngine == InferenceEngine.CPU) { - return; - } - inferenceEngine = InferenceEngine.CPU; - backgroundHandler.post(() -> classifier.useCPU()); - } - }); - - ((RadioButton) view.findViewById(R.id.radio_nnapi)).setOnClickListener( - new View.OnClickListener() { - @Override - public void onClick(View view) { - if (inferenceEngine == InferenceEngine.NNAPI) { - return; - } - inferenceEngine = InferenceEngine.NNAPI; - backgroundHandler.post(() -> classifier.useNNAPI()); - } - }); + // Start initial model. } /** Load the model and labels. */ @Override public void onActivityCreated(Bundle savedInstanceState) { super.onActivityCreated(savedInstanceState); - try { - // create either a new ImageClassifierQuantizedMobileNet or an ImageClassifierFloatInception - classifier = new ImageClassifierQuantizedMobileNet(getActivity()); - } catch (IOException e) { - Log.e(TAG, "Failed to initialize an image classifier.", e); - } startBackgroundThread(); } @@ -581,10 +675,12 @@ public class Camera2BasicFragment extends Fragment backgroundThread = new HandlerThread(HANDLE_THREAD_NAME); backgroundThread.start(); backgroundHandler = new Handler(backgroundThread.getLooper()); + // Start the classification train & load an initial model. synchronized (lock) { runClassifier = true; } backgroundHandler.post(periodicClassify); + updateActiveModel(); } /** Stops the background thread and its {@link Handler}. */ diff --git a/tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/GpuDelegateHelper.java b/tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/GpuDelegateHelper.java new file mode 100644 index 0000000000..8dca17744e --- /dev/null +++ b/tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/GpuDelegateHelper.java @@ -0,0 +1,49 @@ +/* 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 com.example.android.tflitecamerademo; + +import org.tensorflow.lite.Delegate; + +/** + * Helper class for {@code GpuDelegate}. + * + *

WARNING: This is an experimental API and subject to change. + */ +public class GpuDelegateHelper { + private GpuDelegateHelper() {} + + /** Checks whether {@code GpuDelegate} is available. */ + public static boolean isGpuDelegateAvailable() { + try { + Class.forName("org.tensorflow.lite.experimental.GpuDelegate"); + return true; + } catch (Exception e) { + return false; + } + } + + /** Returns an instance of {@code GpuDelegate} if available. */ + public static Delegate createGpuDelegate() { + try { + return Class.forName("org.tensorflow.lite.experimental.GpuDelegate") + .asSubclass(Delegate.class) + .getDeclaredConstructor() + .newInstance(); + } catch (Exception e) { + throw new IllegalStateException(e); + } + } +} diff --git a/tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifier.java b/tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifier.java index 700efc1c1a..512f8b64db 100644 --- a/tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifier.java +++ b/tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifier.java @@ -38,6 +38,7 @@ import java.util.Comparator; import java.util.List; import java.util.Map; import java.util.PriorityQueue; +import org.tensorflow.lite.Delegate; import org.tensorflow.lite.Interpreter; /** @@ -93,6 +94,9 @@ public abstract class ImageClassifier { } }); + /** holds a gpu delegate */ + Delegate gpuDelegate = null; + /** Initializes an {@code ImageClassifier}. */ ImageClassifier(Activity activity) throws IOException { tfliteModel = loadModelFile(activity); @@ -159,10 +163,20 @@ public abstract class ImageClassifier { private void recreateInterpreter() { if (tflite != null) { tflite.close(); + // TODO(b/120679982) + // gpuDelegate.close(); tflite = new Interpreter(tfliteModel, tfliteOptions); } } + public void useGpu() { + if (gpuDelegate == null && GpuDelegateHelper.isGpuDelegateAvailable()) { + gpuDelegate = GpuDelegateHelper.createGpuDelegate(); + tfliteOptions.addDelegate(gpuDelegate); + recreateInterpreter(); + } + } + public void useCPU() { tfliteOptions.setUseNNAPI(false); recreateInterpreter(); diff --git a/tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierFloatMobileNet.java b/tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierFloatMobileNet.java new file mode 100644 index 0000000000..c87ffff8f6 --- /dev/null +++ b/tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierFloatMobileNet.java @@ -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. +==============================================================================*/ + +package com.example.android.tflitecamerademo; + +import android.app.Activity; +import java.io.IOException; + +/** This classifier works with the float MobileNet model. */ +public class ImageClassifierFloatMobileNet extends ImageClassifier { + + /** + * An array to hold inference results, to be feed into Tensorflow Lite as outputs. This isn't part + * of the super class, because we need a primitive array here. + */ + private float[][] labelProbArray = null; + + /** + * Initializes an {@code ImageClassifierFloatMobileNet}. + * + * @param activity + */ + ImageClassifierFloatMobileNet(Activity activity) throws IOException { + super(activity); + labelProbArray = new float[1][getNumLabels()]; + } + + @Override + protected String getModelPath() { + // you can download this file from + // see build.gradle for where to obtain this file. It should be auto + // downloaded into assets. + return "mobilenet_v1_1.0_224.tflite"; + } + + @Override + protected String getLabelPath() { + return "labels_mobilenet_quant_v1_224.txt"; + } + + @Override + protected int getImageSizeX() { + return 224; + } + + @Override + protected int getImageSizeY() { + return 224; + } + + @Override + protected int getNumBytesPerChannel() { + return 4; // Float.SIZE / Byte.SIZE; + } + + @Override + protected void addPixelValue(int pixelValue) { + imgData.putFloat(((pixelValue >> 16) & 0xFF) / 255.f); + imgData.putFloat(((pixelValue >> 8) & 0xFF) / 255.f); + imgData.putFloat((pixelValue & 0xFF) / 255.f); + } + + @Override + protected float getProbability(int labelIndex) { + return labelProbArray[0][labelIndex]; + } + + @Override + protected void setProbability(int labelIndex, Number value) { + labelProbArray[0][labelIndex] = value.floatValue(); + } + + @Override + protected float getNormalizedProbability(int labelIndex) { + return labelProbArray[0][labelIndex]; + } + + @Override + protected void runInference() { + tflite.run(imgData, labelProbArray); + } +} diff --git a/tensorflow/lite/java/demo/app/src/main/res/drawable/item_selector.xml b/tensorflow/lite/java/demo/app/src/main/res/drawable/item_selector.xml new file mode 100644 index 0000000000..202c900769 --- /dev/null +++ b/tensorflow/lite/java/demo/app/src/main/res/drawable/item_selector.xml @@ -0,0 +1,11 @@ + + + + + + + + + + + diff --git a/tensorflow/lite/java/demo/app/src/main/res/layout-v26/fragment_camera2_basic.xml b/tensorflow/lite/java/demo/app/src/main/res/layout-v26/fragment_camera2_basic.xml index 19e0a9bab4..70eedfdd02 100644 --- a/tensorflow/lite/java/demo/app/src/main/res/layout-v26/fragment_camera2_basic.xml +++ b/tensorflow/lite/java/demo/app/src/main/res/layout-v26/fragment_camera2_basic.xml @@ -57,49 +57,83 @@ android:textStyle="bold" /> - - - + + - - + + - + + + + + + - + + + + + + + + + + + + + + diff --git a/tensorflow/lite/java/demo/app/src/main/res/layout/fragment_camera2_basic.xml b/tensorflow/lite/java/demo/app/src/main/res/layout/fragment_camera2_basic.xml index be66eeac75..f8312cc0f7 100644 --- a/tensorflow/lite/java/demo/app/src/main/res/layout/fragment_camera2_basic.xml +++ b/tensorflow/lite/java/demo/app/src/main/res/layout/fragment_camera2_basic.xml @@ -57,22 +57,30 @@ android:textStyle="bold" /> - - + + + + + android:text="Threads" + android:textAlignment="center" + android:textColor="@android:color/white" /> + - + + - + + - + + + + + + + - + + + + + diff --git a/tensorflow/lite/java/demo/app/src/main/res/layout/listview_row.xml b/tensorflow/lite/java/demo/app/src/main/res/layout/listview_row.xml new file mode 100644 index 0000000000..349b0f63b4 --- /dev/null +++ b/tensorflow/lite/java/demo/app/src/main/res/layout/listview_row.xml @@ -0,0 +1,17 @@ + + + + + + + \ No newline at end of file diff --git a/tensorflow/lite/java/demo/app/src/main/res/values/colors.xml b/tensorflow/lite/java/demo/app/src/main/res/values/colors.xml index 4b75d2b2bd..c30f1dc3ac 100644 --- a/tensorflow/lite/java/demo/app/src/main/res/values/colors.xml +++ b/tensorflow/lite/java/demo/app/src/main/res/values/colors.xml @@ -16,4 +16,7 @@ --> #cc4285f4 + #aaaaaa + #eeaa55 + #eeeeee diff --git a/tensorflow/lite/java/demo/app/src/main/res/values/strings.xml b/tensorflow/lite/java/demo/app/src/main/res/values/strings.xml index 45b12850e5..8cc88f2565 100644 --- a/tensorflow/lite/java/demo/app/src/main/res/values/strings.xml +++ b/tensorflow/lite/java/demo/app/src/main/res/values/strings.xml @@ -23,5 +23,11 @@ Use NNAPI tflite NNAPI + GPU CPU + Model + Device + mobilenet v1 quant; + mobilenet v1 float;; + -- GitLab From ee0f5b839d47ef35b1c42321ffb7ddecc3c8a4ba Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Sat, 8 Dec 2018 01:02:37 -0800 Subject: [PATCH 269/873] compat: Update forward compatibility horizon to 2018-12-08 PiperOrigin-RevId: 224631588 --- tensorflow/python/compat/compat.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/compat/compat.py b/tensorflow/python/compat/compat.py index e66c29ae39..1fcd0501d7 100644 --- a/tensorflow/python/compat/compat.py +++ b/tensorflow/python/compat/compat.py @@ -32,7 +32,7 @@ from tensorflow.python.ops import variable_scope from tensorflow.python.util import tf_contextlib from tensorflow.python.util.tf_export import tf_export -_FORWARD_COMPATIBILITY_HORIZON = datetime.date(2018, 12, 7) +_FORWARD_COMPATIBILITY_HORIZON = datetime.date(2018, 12, 8) @tf_export("compat.forward_compatible") -- GitLab From e5bb37762ea4597b1d855e89009041b94dcc30dc Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Sat, 8 Dec 2018 04:40:13 -0800 Subject: [PATCH 270/873] Internal Change PiperOrigin-RevId: 224642874 --- tensorflow/python/ops/ragged/ragged_tensor_value.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/tensorflow/python/ops/ragged/ragged_tensor_value.py b/tensorflow/python/ops/ragged/ragged_tensor_value.py index bf0ac4482a..e94ca4afac 100644 --- a/tensorflow/python/ops/ragged/ragged_tensor_value.py +++ b/tensorflow/python/ops/ragged/ragged_tensor_value.py @@ -98,3 +98,10 @@ class RaggedTensorValue(object): values_as_list[self._row_splits[i]:self._row_splits[i + 1]] for i in range(len(self._row_splits) - 1) ] + + def value_rowids(self, name=None): + del name + row_lengths = self._row_splits[1:] - self._row_splits[:-1] + nrows = self._row_splits.shape[-1] - 1 + indices = np.arange(nrows) + return np.repeat(indices, repeats=row_lengths, axis=0) -- GitLab From 01ecd5b53121be09de84f9e3e0a09f033bd2a3ca Mon Sep 17 00:00:00 2001 From: Rick Chao Date: Sat, 8 Dec 2018 10:59:06 -0800 Subject: [PATCH 271/873] Export tf.train.* hooks or hooks-related classes to tf.estimator.* (exporting to both v1 and v2). Keep the existing only in v1. PiperOrigin-RevId: 224658867 --- .../training/basic_session_run_hooks.py | 18 +++---- ...ow.estimator.-checkpoint-saver-hook.pbtxt} | 2 +- ...estimator.-checkpoint-saver-listener.pbtxt | 24 +++++++++ .../tensorflow.estimator.-feed-fn-hook.pbtxt} | 2 +- ...ensorflow.estimator.-final-ops-hook.pbtxt} | 2 +- ....estimator.-global-step-waiter-hook.pbtxt} | 2 +- ...flow.estimator.-logging-tensor-hook.pbtxt} | 2 +- ...ator.-nan-loss-during-training-error.pbtxt | 12 +++++ ...nsorflow.estimator.-nan-tensor-hook.pbtxt} | 2 +- .../tensorflow.estimator.-profiler-hook.pbtxt | 30 +++++++++++ ...flow.estimator.-second-or-step-timer.pbtxt | 26 ++++++++++ ...orflow.estimator.-step-counter-hook.pbtxt} | 2 +- ...orflow.estimator.-stop-at-step-hook.pbtxt} | 2 +- ...rflow.estimator.-summary-saver-hook.pbtxt} | 2 +- ...perimental.-in-memory-evaluator-hook.pbtxt | 2 +- .../api/golden/v1/tensorflow.estimator.pbtxt | 52 +++++++++++++++++++ ...low.estimator.-checkpoint-saver-hook.pbtxt | 30 +++++++++++ ...estimator.-checkpoint-saver-listener.pbtxt | 24 +++++++++ .../tensorflow.estimator.-feed-fn-hook.pbtxt | 30 +++++++++++ ...tensorflow.estimator.-final-ops-hook.pbtxt | 34 ++++++++++++ ...w.estimator.-global-step-waiter-hook.pbtxt | 30 +++++++++++ ...rflow.estimator.-logging-tensor-hook.pbtxt | 30 +++++++++++ ...ator.-nan-loss-during-training-error.pbtxt | 12 +++++ ...ensorflow.estimator.-nan-tensor-hook.pbtxt | 30 +++++++++++ .../tensorflow.estimator.-profiler-hook.pbtxt | 30 +++++++++++ ...flow.estimator.-second-or-step-timer.pbtxt | 26 ++++++++++ ...sorflow.estimator.-step-counter-hook.pbtxt | 30 +++++++++++ ...sorflow.estimator.-stop-at-step-hook.pbtxt | 30 +++++++++++ ...orflow.estimator.-summary-saver-hook.pbtxt | 30 +++++++++++ ...perimental.-in-memory-evaluator-hook.pbtxt | 2 +- .../api/golden/v2/tensorflow.estimator.pbtxt | 52 +++++++++++++++++++ .../api/golden/v2/tensorflow.train.pbtxt | 36 ------------- .../api/lib/python_object_to_proto_visitor.py | 3 ++ tensorflow/tools/compatibility/renames_v2.py | 17 ++++-- 34 files changed, 598 insertions(+), 60 deletions(-) rename tensorflow/tools/api/golden/{v2/tensorflow.train.-checkpoint-saver-hook.pbtxt => v1/tensorflow.estimator.-checkpoint-saver-hook.pbtxt} (96%) create mode 100644 tensorflow/tools/api/golden/v1/tensorflow.estimator.-checkpoint-saver-listener.pbtxt rename tensorflow/tools/api/golden/{v2/tensorflow.train.-feed-fn-hook.pbtxt => v1/tensorflow.estimator.-feed-fn-hook.pbtxt} (96%) rename tensorflow/tools/api/golden/{v2/tensorflow.train.-final-ops-hook.pbtxt => v1/tensorflow.estimator.-final-ops-hook.pbtxt} (96%) rename tensorflow/tools/api/golden/{v2/tensorflow.train.-global-step-waiter-hook.pbtxt => v1/tensorflow.estimator.-global-step-waiter-hook.pbtxt} (95%) rename tensorflow/tools/api/golden/{v2/tensorflow.train.-logging-tensor-hook.pbtxt => v1/tensorflow.estimator.-logging-tensor-hook.pbtxt} (96%) create mode 100644 tensorflow/tools/api/golden/v1/tensorflow.estimator.-nan-loss-during-training-error.pbtxt rename tensorflow/tools/api/golden/{v2/tensorflow.train.-nan-tensor-hook.pbtxt => v1/tensorflow.estimator.-nan-tensor-hook.pbtxt} (96%) create mode 100644 tensorflow/tools/api/golden/v1/tensorflow.estimator.-profiler-hook.pbtxt create mode 100644 tensorflow/tools/api/golden/v1/tensorflow.estimator.-second-or-step-timer.pbtxt rename tensorflow/tools/api/golden/{v2/tensorflow.train.-step-counter-hook.pbtxt => v1/tensorflow.estimator.-step-counter-hook.pbtxt} (96%) rename tensorflow/tools/api/golden/{v2/tensorflow.train.-stop-at-step-hook.pbtxt => v1/tensorflow.estimator.-stop-at-step-hook.pbtxt} (96%) rename tensorflow/tools/api/golden/{v2/tensorflow.train.-summary-saver-hook.pbtxt => v1/tensorflow.estimator.-summary-saver-hook.pbtxt} (96%) create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.estimator.-checkpoint-saver-hook.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.estimator.-checkpoint-saver-listener.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.estimator.-feed-fn-hook.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.estimator.-final-ops-hook.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.estimator.-global-step-waiter-hook.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.estimator.-logging-tensor-hook.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.estimator.-nan-loss-during-training-error.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.estimator.-nan-tensor-hook.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.estimator.-profiler-hook.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.estimator.-second-or-step-timer.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.estimator.-step-counter-hook.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.estimator.-stop-at-step-hook.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.estimator.-summary-saver-hook.pbtxt diff --git a/tensorflow/python/training/basic_session_run_hooks.py b/tensorflow/python/training/basic_session_run_hooks.py index b64c7ada62..86718ab45f 100644 --- a/tensorflow/python/training/basic_session_run_hooks.py +++ b/tensorflow/python/training/basic_session_run_hooks.py @@ -163,7 +163,7 @@ class NeverTriggerTimer(_HookTimer): return None -@tf_export("train.LoggingTensorHook") +@tf_export(v1=["train.LoggingTensorHook"]) class LoggingTensorHook(session_run_hook.SessionRunHook): """Prints the given tensors every N local steps, every N seconds, or at end. @@ -373,7 +373,7 @@ class _MultiStepStopAtStepHook(session_run_hook.SessionRunHook): self._update_steps_per_run_variable(global_step, run_context.session) -@tf_export("train.StopAtStepHook") +@tf_export(v1=["train.StopAtStepHook"]) class StopAtStepHook(session_run_hook.SessionRunHook): """Hook that requests stop at a specified step.""" @@ -495,7 +495,7 @@ class CheckpointSaverListener(object): pass -@tf_export("train.CheckpointSaverHook") +@tf_export(v1=["train.CheckpointSaverHook"]) class CheckpointSaverHook(session_run_hook.SessionRunHook): """Saves checkpoints every N steps or seconds.""" @@ -634,7 +634,7 @@ class CheckpointSaverHook(session_run_hook.SessionRunHook): return savers[0] -@tf_export("train.StepCounterHook") +@tf_export(v1=["train.StepCounterHook"]) class StepCounterHook(session_run_hook.SessionRunHook): """Hook that counts steps per second.""" @@ -725,7 +725,7 @@ class NanLossDuringTrainingError(RuntimeError): return "NaN loss during training." -@tf_export("train.NanTensorHook") +@tf_export(v1=["train.NanTensorHook"]) class NanTensorHook(session_run_hook.SessionRunHook): """Monitors the loss tensor and stops training if loss is NaN. @@ -757,7 +757,7 @@ class NanTensorHook(session_run_hook.SessionRunHook): run_context.request_stop() -@tf_export("train.SummarySaverHook") +@tf_export(v1=["train.SummarySaverHook"]) class SummarySaverHook(session_run_hook.SessionRunHook): """Saves summaries every N steps.""" @@ -866,7 +866,7 @@ class SummarySaverHook(session_run_hook.SessionRunHook): return summary_op -@tf_export("train.GlobalStepWaiterHook") +@tf_export(v1=["train.GlobalStepWaiterHook"]) class GlobalStepWaiterHook(session_run_hook.SessionRunHook): """Delays execution until global step reaches `wait_until_step`. @@ -914,7 +914,7 @@ class GlobalStepWaiterHook(session_run_hook.SessionRunHook): time.sleep(0.5) -@tf_export("train.FinalOpsHook") +@tf_export(v1=["train.FinalOpsHook"]) class FinalOpsHook(session_run_hook.SessionRunHook): """A hook which evaluates `Tensors` at the end of a session.""" @@ -958,7 +958,7 @@ class FinalOpsHook(session_run_hook.SessionRunHook): raise e -@tf_export("train.FeedFnHook") +@tf_export(v1=["train.FeedFnHook"]) class FeedFnHook(session_run_hook.SessionRunHook): """Runs `feed_fn` and sets the `feed_dict` accordingly.""" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-checkpoint-saver-hook.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-checkpoint-saver-hook.pbtxt similarity index 96% rename from tensorflow/tools/api/golden/v2/tensorflow.train.-checkpoint-saver-hook.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-checkpoint-saver-hook.pbtxt index c3037baa8c..f9e1504b49 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.train.-checkpoint-saver-hook.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-checkpoint-saver-hook.pbtxt @@ -1,4 +1,4 @@ -path: "tensorflow.train.CheckpointSaverHook" +path: "tensorflow.estimator.CheckpointSaverHook" tf_class { is_instance: "" is_instance: "" diff --git a/tensorflow/tools/api/golden/v1/tensorflow.estimator.-checkpoint-saver-listener.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-checkpoint-saver-listener.pbtxt new file mode 100644 index 0000000000..111b7583f2 --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-checkpoint-saver-listener.pbtxt @@ -0,0 +1,24 @@ +path: "tensorflow.estimator.CheckpointSaverListener" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "after_save" + argspec: "args=[\'self\', \'session\', \'global_step_value\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "before_save" + argspec: "args=[\'self\', \'session\', \'global_step_value\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "begin" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "end" + argspec: "args=[\'self\', \'session\', \'global_step_value\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-feed-fn-hook.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-feed-fn-hook.pbtxt similarity index 96% rename from tensorflow/tools/api/golden/v2/tensorflow.train.-feed-fn-hook.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-feed-fn-hook.pbtxt index 7bec4d032c..f24de493f2 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.train.-feed-fn-hook.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-feed-fn-hook.pbtxt @@ -1,4 +1,4 @@ -path: "tensorflow.train.FeedFnHook" +path: "tensorflow.estimator.FeedFnHook" tf_class { is_instance: "" is_instance: "" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-final-ops-hook.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-final-ops-hook.pbtxt similarity index 96% rename from tensorflow/tools/api/golden/v2/tensorflow.train.-final-ops-hook.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-final-ops-hook.pbtxt index 31cf9aaeb2..6651170ba3 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.train.-final-ops-hook.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-final-ops-hook.pbtxt @@ -1,4 +1,4 @@ -path: "tensorflow.train.FinalOpsHook" +path: "tensorflow.estimator.FinalOpsHook" tf_class { is_instance: "" is_instance: "" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-global-step-waiter-hook.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-global-step-waiter-hook.pbtxt similarity index 95% rename from tensorflow/tools/api/golden/v2/tensorflow.train.-global-step-waiter-hook.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-global-step-waiter-hook.pbtxt index 147448618e..37db48bc64 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.train.-global-step-waiter-hook.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-global-step-waiter-hook.pbtxt @@ -1,4 +1,4 @@ -path: "tensorflow.train.GlobalStepWaiterHook" +path: "tensorflow.estimator.GlobalStepWaiterHook" tf_class { is_instance: "" is_instance: "" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-logging-tensor-hook.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-logging-tensor-hook.pbtxt similarity index 96% rename from tensorflow/tools/api/golden/v2/tensorflow.train.-logging-tensor-hook.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-logging-tensor-hook.pbtxt index 9801c05df1..425f0167a1 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.train.-logging-tensor-hook.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-logging-tensor-hook.pbtxt @@ -1,4 +1,4 @@ -path: "tensorflow.train.LoggingTensorHook" +path: "tensorflow.estimator.LoggingTensorHook" tf_class { is_instance: "" is_instance: "" diff --git a/tensorflow/tools/api/golden/v1/tensorflow.estimator.-nan-loss-during-training-error.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-nan-loss-during-training-error.pbtxt new file mode 100644 index 0000000000..6cf6e17e43 --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-nan-loss-during-training-error.pbtxt @@ -0,0 +1,12 @@ +path: "tensorflow.estimator.NanLossDuringTrainingError" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "args" + mtype: "" + } + member_method { + name: "__init__" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-nan-tensor-hook.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-nan-tensor-hook.pbtxt similarity index 96% rename from tensorflow/tools/api/golden/v2/tensorflow.train.-nan-tensor-hook.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-nan-tensor-hook.pbtxt index 7d1c89f9b3..82293c2c0c 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.train.-nan-tensor-hook.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-nan-tensor-hook.pbtxt @@ -1,4 +1,4 @@ -path: "tensorflow.train.NanTensorHook" +path: "tensorflow.estimator.NanTensorHook" tf_class { is_instance: "" is_instance: "" diff --git a/tensorflow/tools/api/golden/v1/tensorflow.estimator.-profiler-hook.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-profiler-hook.pbtxt new file mode 100644 index 0000000000..65b5fb16b0 --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-profiler-hook.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.estimator.ProfilerHook" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'save_steps\', \'save_secs\', \'output_dir\', \'show_dataflow\', \'show_memory\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'\', \'True\', \'False\'], " + } + member_method { + name: "after_create_session" + argspec: "args=[\'self\', \'session\', \'coord\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "after_run" + argspec: "args=[\'self\', \'run_context\', \'run_values\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "before_run" + argspec: "args=[\'self\', \'run_context\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "begin" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "end" + argspec: "args=[\'self\', \'session\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v1/tensorflow.estimator.-second-or-step-timer.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-second-or-step-timer.pbtxt new file mode 100644 index 0000000000..64051d2bd6 --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-second-or-step-timer.pbtxt @@ -0,0 +1,26 @@ +path: "tensorflow.estimator.SecondOrStepTimer" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'every_secs\', \'every_steps\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "last_triggered_step" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "reset" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "should_trigger_for_step" + argspec: "args=[\'self\', \'step\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "update_last_triggered_step" + argspec: "args=[\'self\', \'step\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-step-counter-hook.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-step-counter-hook.pbtxt similarity index 96% rename from tensorflow/tools/api/golden/v2/tensorflow.train.-step-counter-hook.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-step-counter-hook.pbtxt index 13261f6dde..4368e04df3 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.train.-step-counter-hook.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-step-counter-hook.pbtxt @@ -1,4 +1,4 @@ -path: "tensorflow.train.StepCounterHook" +path: "tensorflow.estimator.StepCounterHook" tf_class { is_instance: "" is_instance: "" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-stop-at-step-hook.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-stop-at-step-hook.pbtxt similarity index 96% rename from tensorflow/tools/api/golden/v2/tensorflow.train.-stop-at-step-hook.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-stop-at-step-hook.pbtxt index e388599b0b..938b189a8c 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.train.-stop-at-step-hook.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-stop-at-step-hook.pbtxt @@ -1,4 +1,4 @@ -path: "tensorflow.train.StopAtStepHook" +path: "tensorflow.estimator.StopAtStepHook" tf_class { is_instance: "" is_instance: "" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-summary-saver-hook.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-summary-saver-hook.pbtxt similarity index 96% rename from tensorflow/tools/api/golden/v2/tensorflow.train.-summary-saver-hook.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-summary-saver-hook.pbtxt index 697c3667b0..104157315f 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.train.-summary-saver-hook.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-summary-saver-hook.pbtxt @@ -1,4 +1,4 @@ -path: "tensorflow.train.SummarySaverHook" +path: "tensorflow.estimator.SummarySaverHook" tf_class { is_instance: "" is_instance: "" diff --git a/tensorflow/tools/api/golden/v1/tensorflow.estimator.experimental.-in-memory-evaluator-hook.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.experimental.-in-memory-evaluator-hook.pbtxt index aba120218c..5a2a01cd53 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.estimator.experimental.-in-memory-evaluator-hook.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.estimator.experimental.-in-memory-evaluator-hook.pbtxt @@ -1,6 +1,6 @@ path: "tensorflow.estimator.experimental.InMemoryEvaluatorHook" tf_class { - is_instance: "" + is_instance: "" is_instance: "" is_instance: "" member_method { diff --git a/tensorflow/tools/api/golden/v1/tensorflow.estimator.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.pbtxt index c5b0085b8d..d3656ae045 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.estimator.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.estimator.pbtxt @@ -24,6 +24,14 @@ tf_module { name: "BoostedTreesRegressor" mtype: "" } + member { + name: "CheckpointSaverHook" + mtype: "" + } + member { + name: "CheckpointSaverListener" + mtype: "" + } member { name: "DNNClassifier" mtype: "" @@ -64,10 +72,22 @@ tf_module { name: "Exporter" mtype: "" } + member { + name: "FeedFnHook" + mtype: "" + } member { name: "FinalExporter" mtype: "" } + member { + name: "FinalOpsHook" + mtype: "" + } + member { + name: "GlobalStepWaiterHook" + mtype: "" + } member { name: "LatestExporter" mtype: "" @@ -84,14 +104,46 @@ tf_module { name: "LinearRegressor" mtype: "" } + member { + name: "LoggingTensorHook" + mtype: "" + } member { name: "ModeKeys" mtype: "" } + member { + name: "NanLossDuringTrainingError" + mtype: "" + } + member { + name: "NanTensorHook" + mtype: "" + } + member { + name: "ProfilerHook" + mtype: "" + } member { name: "RunConfig" mtype: "" } + member { + name: "SecondOrStepTimer" + mtype: "" + } + member { + name: "StepCounterHook" + mtype: "" + } + member { + name: "StopAtStepHook" + mtype: "" + } + member { + name: "SummarySaverHook" + mtype: "" + } member { name: "TrainSpec" mtype: "" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-checkpoint-saver-hook.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-checkpoint-saver-hook.pbtxt new file mode 100644 index 0000000000..f9e1504b49 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-checkpoint-saver-hook.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.estimator.CheckpointSaverHook" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'checkpoint_dir\', \'save_secs\', \'save_steps\', \'saver\', \'checkpoint_basename\', \'scaffold\', \'listeners\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'model.ckpt\', \'None\', \'None\'], " + } + member_method { + name: "after_create_session" + argspec: "args=[\'self\', \'session\', \'coord\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "after_run" + argspec: "args=[\'self\', \'run_context\', \'run_values\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "before_run" + argspec: "args=[\'self\', \'run_context\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "begin" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "end" + argspec: "args=[\'self\', \'session\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-checkpoint-saver-listener.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-checkpoint-saver-listener.pbtxt new file mode 100644 index 0000000000..111b7583f2 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-checkpoint-saver-listener.pbtxt @@ -0,0 +1,24 @@ +path: "tensorflow.estimator.CheckpointSaverListener" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "after_save" + argspec: "args=[\'self\', \'session\', \'global_step_value\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "before_save" + argspec: "args=[\'self\', \'session\', \'global_step_value\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "begin" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "end" + argspec: "args=[\'self\', \'session\', \'global_step_value\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-feed-fn-hook.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-feed-fn-hook.pbtxt new file mode 100644 index 0000000000..f24de493f2 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-feed-fn-hook.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.estimator.FeedFnHook" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'feed_fn\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "after_create_session" + argspec: "args=[\'self\', \'session\', \'coord\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "after_run" + argspec: "args=[\'self\', \'run_context\', \'run_values\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "before_run" + argspec: "args=[\'self\', \'run_context\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "begin" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "end" + argspec: "args=[\'self\', \'session\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-final-ops-hook.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-final-ops-hook.pbtxt new file mode 100644 index 0000000000..6651170ba3 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-final-ops-hook.pbtxt @@ -0,0 +1,34 @@ +path: "tensorflow.estimator.FinalOpsHook" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "final_ops_values" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'final_ops\', \'final_ops_feed_dict\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "after_create_session" + argspec: "args=[\'self\', \'session\', \'coord\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "after_run" + argspec: "args=[\'self\', \'run_context\', \'run_values\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "before_run" + argspec: "args=[\'self\', \'run_context\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "begin" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "end" + argspec: "args=[\'self\', \'session\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-global-step-waiter-hook.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-global-step-waiter-hook.pbtxt new file mode 100644 index 0000000000..37db48bc64 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-global-step-waiter-hook.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.estimator.GlobalStepWaiterHook" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'wait_until_step\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "after_create_session" + argspec: "args=[\'self\', \'session\', \'coord\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "after_run" + argspec: "args=[\'self\', \'run_context\', \'run_values\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "before_run" + argspec: "args=[\'self\', \'run_context\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "begin" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "end" + argspec: "args=[\'self\', \'session\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-logging-tensor-hook.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-logging-tensor-hook.pbtxt new file mode 100644 index 0000000000..425f0167a1 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-logging-tensor-hook.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.estimator.LoggingTensorHook" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'tensors\', \'every_n_iter\', \'every_n_secs\', \'at_end\', \'formatter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'False\', \'None\'], " + } + member_method { + name: "after_create_session" + argspec: "args=[\'self\', \'session\', \'coord\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "after_run" + argspec: "args=[\'self\', \'run_context\', \'run_values\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "before_run" + argspec: "args=[\'self\', \'run_context\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "begin" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "end" + argspec: "args=[\'self\', \'session\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-nan-loss-during-training-error.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-nan-loss-during-training-error.pbtxt new file mode 100644 index 0000000000..6cf6e17e43 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-nan-loss-during-training-error.pbtxt @@ -0,0 +1,12 @@ +path: "tensorflow.estimator.NanLossDuringTrainingError" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "args" + mtype: "" + } + member_method { + name: "__init__" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-nan-tensor-hook.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-nan-tensor-hook.pbtxt new file mode 100644 index 0000000000..82293c2c0c --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-nan-tensor-hook.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.estimator.NanTensorHook" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'loss_tensor\', \'fail_on_nan_loss\'], varargs=None, keywords=None, defaults=[\'True\'], " + } + member_method { + name: "after_create_session" + argspec: "args=[\'self\', \'session\', \'coord\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "after_run" + argspec: "args=[\'self\', \'run_context\', \'run_values\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "before_run" + argspec: "args=[\'self\', \'run_context\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "begin" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "end" + argspec: "args=[\'self\', \'session\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-profiler-hook.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-profiler-hook.pbtxt new file mode 100644 index 0000000000..65b5fb16b0 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-profiler-hook.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.estimator.ProfilerHook" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'save_steps\', \'save_secs\', \'output_dir\', \'show_dataflow\', \'show_memory\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'\', \'True\', \'False\'], " + } + member_method { + name: "after_create_session" + argspec: "args=[\'self\', \'session\', \'coord\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "after_run" + argspec: "args=[\'self\', \'run_context\', \'run_values\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "before_run" + argspec: "args=[\'self\', \'run_context\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "begin" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "end" + argspec: "args=[\'self\', \'session\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-second-or-step-timer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-second-or-step-timer.pbtxt new file mode 100644 index 0000000000..64051d2bd6 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-second-or-step-timer.pbtxt @@ -0,0 +1,26 @@ +path: "tensorflow.estimator.SecondOrStepTimer" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'every_secs\', \'every_steps\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "last_triggered_step" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "reset" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "should_trigger_for_step" + argspec: "args=[\'self\', \'step\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "update_last_triggered_step" + argspec: "args=[\'self\', \'step\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-step-counter-hook.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-step-counter-hook.pbtxt new file mode 100644 index 0000000000..4368e04df3 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-step-counter-hook.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.estimator.StepCounterHook" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'every_n_steps\', \'every_n_secs\', \'output_dir\', \'summary_writer\'], varargs=None, keywords=None, defaults=[\'100\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "after_create_session" + argspec: "args=[\'self\', \'session\', \'coord\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "after_run" + argspec: "args=[\'self\', \'run_context\', \'run_values\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "before_run" + argspec: "args=[\'self\', \'run_context\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "begin" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "end" + argspec: "args=[\'self\', \'session\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-stop-at-step-hook.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-stop-at-step-hook.pbtxt new file mode 100644 index 0000000000..938b189a8c --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-stop-at-step-hook.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.estimator.StopAtStepHook" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'num_steps\', \'last_step\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "after_create_session" + argspec: "args=[\'self\', \'session\', \'coord\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "after_run" + argspec: "args=[\'self\', \'run_context\', \'run_values\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "before_run" + argspec: "args=[\'self\', \'run_context\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "begin" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "end" + argspec: "args=[\'self\', \'session\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-summary-saver-hook.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-summary-saver-hook.pbtxt new file mode 100644 index 0000000000..104157315f --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-summary-saver-hook.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.estimator.SummarySaverHook" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'save_steps\', \'save_secs\', \'output_dir\', \'summary_writer\', \'scaffold\', \'summary_op\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "after_create_session" + argspec: "args=[\'self\', \'session\', \'coord\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "after_run" + argspec: "args=[\'self\', \'run_context\', \'run_values\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "before_run" + argspec: "args=[\'self\', \'run_context\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "begin" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "end" + argspec: "args=[\'self\', \'session\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.experimental.-in-memory-evaluator-hook.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.experimental.-in-memory-evaluator-hook.pbtxt index aba120218c..5a2a01cd53 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.estimator.experimental.-in-memory-evaluator-hook.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.experimental.-in-memory-evaluator-hook.pbtxt @@ -1,6 +1,6 @@ path: "tensorflow.estimator.experimental.InMemoryEvaluatorHook" tf_class { - is_instance: "" + is_instance: "" is_instance: "" is_instance: "" member_method { diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.pbtxt index c5b0085b8d..d3656ae045 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.estimator.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.pbtxt @@ -24,6 +24,14 @@ tf_module { name: "BoostedTreesRegressor" mtype: "" } + member { + name: "CheckpointSaverHook" + mtype: "" + } + member { + name: "CheckpointSaverListener" + mtype: "" + } member { name: "DNNClassifier" mtype: "" @@ -64,10 +72,22 @@ tf_module { name: "Exporter" mtype: "" } + member { + name: "FeedFnHook" + mtype: "" + } member { name: "FinalExporter" mtype: "" } + member { + name: "FinalOpsHook" + mtype: "" + } + member { + name: "GlobalStepWaiterHook" + mtype: "" + } member { name: "LatestExporter" mtype: "" @@ -84,14 +104,46 @@ tf_module { name: "LinearRegressor" mtype: "" } + member { + name: "LoggingTensorHook" + mtype: "" + } member { name: "ModeKeys" mtype: "" } + member { + name: "NanLossDuringTrainingError" + mtype: "" + } + member { + name: "NanTensorHook" + mtype: "" + } + member { + name: "ProfilerHook" + mtype: "" + } member { name: "RunConfig" mtype: "" } + member { + name: "SecondOrStepTimer" + mtype: "" + } + member { + name: "StepCounterHook" + mtype: "" + } + member { + name: "StopAtStepHook" + mtype: "" + } + member { + name: "SummarySaverHook" + mtype: "" + } member { name: "TrainSpec" mtype: "" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.pbtxt index 3ff4b69d39..8c327f88f3 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.train.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.pbtxt @@ -12,10 +12,6 @@ tf_module { name: "CheckpointManager" mtype: "" } - member { - name: "CheckpointSaverHook" - mtype: "" - } member { name: "ClusterDef" mtype: "" @@ -52,22 +48,10 @@ tf_module { name: "Features" mtype: "" } - member { - name: "FeedFnHook" - mtype: "" - } - member { - name: "FinalOpsHook" - mtype: "" - } member { name: "FloatList" mtype: "" } - member { - name: "GlobalStepWaiterHook" - mtype: "" - } member { name: "Int64List" mtype: "" @@ -76,14 +60,6 @@ tf_module { name: "JobDef" mtype: "" } - member { - name: "LoggingTensorHook" - mtype: "" - } - member { - name: "NanTensorHook" - mtype: "" - } member { name: "ProximalGradientDescentOptimizer" mtype: "" @@ -104,18 +80,6 @@ tf_module { name: "SessionRunHook" mtype: "" } - member { - name: "StepCounterHook" - mtype: "" - } - member { - name: "StopAtStepHook" - mtype: "" - } - member { - name: "SummarySaverHook" - mtype: "" - } member_method { name: "cosine_decay" argspec: "args=[\'learning_rate\', \'global_step\', \'decay_steps\', \'alpha\', \'name\'], varargs=None, keywords=None, defaults=[\'0.0\', \'None\'], " diff --git a/tensorflow/tools/api/lib/python_object_to_proto_visitor.py b/tensorflow/tools/api/lib/python_object_to_proto_visitor.py index 70df38ba8b..5102066730 100644 --- a/tensorflow/tools/api/lib/python_object_to_proto_visitor.py +++ b/tensorflow/tools/api/lib/python_object_to_proto_visitor.py @@ -37,6 +37,9 @@ _CORNER_CASES = { 'train.NanLossDuringTrainingError': { 'message': {} }, + 'estimator.NanLossDuringTrainingError': { + 'message': {} + }, } # Python 2 vs. 3 differences diff --git a/tensorflow/tools/compatibility/renames_v2.py b/tensorflow/tools/compatibility/renames_v2.py index e4ef8eb528..b757ad4647 100644 --- a/tensorflow/tools/compatibility/renames_v2.py +++ b/tensorflow/tools/compatibility/renames_v2.py @@ -590,31 +590,40 @@ renames = { 'tf.train.AdagradDAOptimizer': 'tf.compat.v1.train.AdagradDAOptimizer', 'tf.train.AdagradOptimizer': 'tf.compat.v1.train.AdagradOptimizer', 'tf.train.AdamOptimizer': 'tf.compat.v1.train.AdamOptimizer', - 'tf.train.CheckpointSaverListener': 'tf.compat.v1.train.CheckpointSaverListener', + 'tf.train.CheckpointSaverHook': 'tf.estimator.CheckpointSaverHook', + 'tf.train.CheckpointSaverListener': 'tf.estimator.CheckpointSaverListener', 'tf.train.ChiefSessionCreator': 'tf.compat.v1.train.ChiefSessionCreator', + 'tf.train.FeedFnHook': 'tf.estimator.FeedFnHook', + 'tf.train.FinalOpsHook': 'tf.estimator.FinalOpsHook', 'tf.train.FtrlOptimizer': 'tf.compat.v1.train.FtrlOptimizer', + 'tf.train.GlobalStepWaiterHook': 'tf.estimator.GlobalStepWaiterHook', 'tf.train.GradientDescentOptimizer': 'tf.compat.v1.train.GradientDescentOptimizer', + 'tf.train.LoggingTensorHook': 'tf.estimator.LoggingTensorHook', 'tf.train.LooperThread': 'tf.compat.v1.train.LooperThread', 'tf.train.MomentumOptimizer': 'tf.compat.v1.train.MomentumOptimizer', 'tf.train.MonitoredSession': 'tf.compat.v1.train.MonitoredSession', 'tf.train.MonitoredTrainingSession': 'tf.compat.v1.train.MonitoredTrainingSession', - 'tf.train.NanLossDuringTrainingError': 'tf.compat.v1.train.NanLossDuringTrainingError', + 'tf.train.NanLossDuringTrainingError': 'tf.estimator.NanLossDuringTrainingError', + 'tf.train.NanTensorHook': 'tf.estimator.NanTensorHook', 'tf.train.NewCheckpointReader': 'tf.compat.v1.train.NewCheckpointReader', 'tf.train.Optimizer': 'tf.compat.v1.train.Optimizer', - 'tf.train.ProfilerHook': 'tf.compat.v1.train.ProfilerHook', + 'tf.train.ProfilerHook': 'tf.estimator.ProfilerHook', 'tf.train.ProximalAdagradOptimizer': 'tf.compat.v1.train.ProximalAdagradOptimizer', 'tf.train.QueueRunner': 'tf.compat.v1.train.QueueRunner', 'tf.train.RMSPropOptimizer': 'tf.compat.v1.train.RMSPropOptimizer', 'tf.train.Saver': 'tf.compat.v1.train.Saver', 'tf.train.SaverDef': 'tf.compat.v1.train.SaverDef', 'tf.train.Scaffold': 'tf.compat.v1.train.Scaffold', - 'tf.train.SecondOrStepTimer': 'tf.compat.v1.train.SecondOrStepTimer', + 'tf.train.SecondOrStepTimer': 'tf.estimator.SecondOrStepTimer', 'tf.train.SessionCreator': 'tf.compat.v1.train.SessionCreator', 'tf.train.SessionManager': 'tf.compat.v1.train.SessionManager', 'tf.train.SessionRunArgs': 'tf.compat.v1.train.SessionRunArgs', 'tf.train.SessionRunContext': 'tf.compat.v1.train.SessionRunContext', 'tf.train.SessionRunValues': 'tf.compat.v1.train.SessionRunValues', 'tf.train.SingularMonitoredSession': 'tf.compat.v1.train.SingularMonitoredSession', + 'tf.train.StepCounterHook': 'tf.estimator.StepCounterHook', + 'tf.train.StopAtStepHook': 'tf.estimator.StopAtStepHook', + 'tf.train.SummarySaverHook': 'tf.estimator.SummarySaverHook', 'tf.train.Supervisor': 'tf.compat.v1.train.Supervisor', 'tf.train.SyncReplicasOptimizer': 'tf.compat.v1.train.SyncReplicasOptimizer', 'tf.train.VocabInfo': 'tf.estimator.VocabInfo', -- GitLab From 7f88af511429354e67318b80d8478e6eddf9bfd1 Mon Sep 17 00:00:00 2001 From: Niranjan Hasabnis Date: Sat, 8 Dec 2018 15:12:52 -0800 Subject: [PATCH 272/873] Addressing review comments - v2 --- tensorflow/core/kernels/mkl_fused_ops_test.cc | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/tensorflow/core/kernels/mkl_fused_ops_test.cc b/tensorflow/core/kernels/mkl_fused_ops_test.cc index 4beb70f74f..1214711edc 100644 --- a/tensorflow/core/kernels/mkl_fused_ops_test.cc +++ b/tensorflow/core/kernels/mkl_fused_ops_test.cc @@ -295,8 +295,10 @@ TYPED_TEST_P(MklFusedConv2DWithBiasOpTest, SpatialConvolutionAndRelu) { this->VerifyConv2DWithBiasAndRelu(filter_size, filter_count); } -REGISTER_TYPED_TEST_CASE_P(MklFusedConv2DWithBiasOpTest, OneByOneConvolution, - SpatialConvolution, OneByOneConvolutionAndRelu, +REGISTER_TYPED_TEST_CASE_P(MklFusedConv2DWithBiasOpTest, + OneByOneConvolution, // + SpatialConvolution, // + OneByOneConvolutionAndRelu, // SpatialConvolutionAndRelu); using MklFusedBiasAddDataTypes = ::testing::Types; -- GitLab From 766eb63f2f3e43cd8b23c1cbb05fe63dd918ffa3 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Sun, 9 Dec 2018 01:02:35 -0800 Subject: [PATCH 273/873] compat: Update forward compatibility horizon to 2018-12-09 PiperOrigin-RevId: 224696976 --- tensorflow/python/compat/compat.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/compat/compat.py b/tensorflow/python/compat/compat.py index 1fcd0501d7..51cd68436a 100644 --- a/tensorflow/python/compat/compat.py +++ b/tensorflow/python/compat/compat.py @@ -32,7 +32,7 @@ from tensorflow.python.ops import variable_scope from tensorflow.python.util import tf_contextlib from tensorflow.python.util.tf_export import tf_export -_FORWARD_COMPATIBILITY_HORIZON = datetime.date(2018, 12, 8) +_FORWARD_COMPATIBILITY_HORIZON = datetime.date(2018, 12, 9) @tf_export("compat.forward_compatible") -- GitLab From 38ac3c811b346117a854be9875d7ea21f94d9eae Mon Sep 17 00:00:00 2001 From: jcf94 Date: Sun, 9 Dec 2018 22:49:19 +0800 Subject: [PATCH 274/873] Bug Fix for verbs --- tensorflow/contrib/verbs/rdma_mgr.cc | 27 ++++++++++++++++--- .../core/common_runtime/process_state.cc | 9 +++++++ .../core/common_runtime/process_state.h | 3 +++ 3 files changed, 35 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/verbs/rdma_mgr.cc b/tensorflow/contrib/verbs/rdma_mgr.cc index 2784bf124c..7e821d6be2 100644 --- a/tensorflow/contrib/verbs/rdma_mgr.cc +++ b/tensorflow/contrib/verbs/rdma_mgr.cc @@ -20,6 +20,7 @@ limitations under the License. #include #include "tensorflow/contrib/verbs/grpc_verbs_client.h" #include "tensorflow/contrib/verbs/verbs_service.pb.h" +#include "tensorflow/core/common_runtime/bfc_allocator.h" #include "tensorflow/core/common_runtime/gpu/gpu_process_state.h" #include "tensorflow/core/common_runtime/gpu/gpu_util.h" #include "tensorflow/core/common_runtime/pool_allocator.h" @@ -256,6 +257,21 @@ void MRDeleter(ibv_mr* mr) { } } +// TODO: This is to fix the bug of "local protection error when doing rdma send" +// Bug caused by commit 33170cc. The new design of Allocator/SubAllocator is +// good but not working correctly with this part. +// Waiting to migrate all the "cpu_allocator()" to "ProcessState::singleton()", +// and this patch will nolonger be needed. +class BFCRdmaAllocatorFactory : public AllocatorFactory { + public: + Allocator* CreateAllocator() { return ProcessState::singleton()->GetCPUAllocator(port::kNUMANoAffinity); } + + SubAllocator* CreateSubAllocator(int numa_node) { + return new BasicCPUAllocator(numa_node, ProcessState::singleton()->GetCPUAllocatorVisitor(), ProcessState::singleton()->GetCPUFreeVisitor()); + } +}; +REGISTER_MEM_ALLOCATOR("BFCRdmaAllocator", 101, BFCRdmaAllocatorFactory); + void RdmaMgr::InitAllocators() { static std::once_flag flag; std::call_once( @@ -277,9 +293,15 @@ void RdmaMgr::InitAllocators() { ProcessState::singleton()->AddCPUFreeVisitor(free_visitor); #if GOOGLE_CUDA + GPUProcessState::singleton()->AddCUDAHostAllocVisitor(0, alloc_visitor); + GPUProcessState::singleton()->AddCUDAHostFreeVisitor(0, free_visitor); if (IsGDRAvailable()) { // Note we don't free allocated GPU memory so there is no free visitor - int32_t bus_id = TryToReadNumaNode(rdma_adapter_->context_->device) + 1; + + // TODO: This is to fix the 'invalid use of member in static member function bug'. + // Waiting for better implementation. + // int32_t bus_id = TryToReadNumaNode(rdma_adapter_->context_->device) + 1; + int32_t bus_id = 0; SubAllocator::Visitor cuda_alloc_visitor = [](void* ptr, int gpu_id, size_t num_bytes) { @@ -288,9 +310,6 @@ void RdmaMgr::InitAllocators() { }; GPUProcessState::singleton()->AddGPUAllocVisitor(bus_id, cuda_alloc_visitor); - GPUProcessState::singleton()->AddCUDAHostAllocVisitor(bus_id, - alloc_visitor); - GPUProcessState::singleton()->AddCUDAHostFreeVisitor(bus_id, free_visitor); LOG(INFO) << "Instrumenting GPU allocator with bus_id " << bus_id; } #endif // GOOGLE_CUDA diff --git a/tensorflow/core/common_runtime/process_state.cc b/tensorflow/core/common_runtime/process_state.cc index 3d8ac9b134..a33d6e0830 100644 --- a/tensorflow/core/common_runtime/process_state.cc +++ b/tensorflow/core/common_runtime/process_state.cc @@ -147,4 +147,13 @@ void ProcessState::TestOnlyReset() { gtl::STLDeleteElements(&cpu_al_); } +const std::vector& ProcessState::GetCPUAllocatorVisitor() +{ + return cpu_alloc_visitors_; +} +const std::vector& ProcessState::GetCPUFreeVisitor() +{ + return cpu_free_visitors_; +} + } // namespace tensorflow diff --git a/tensorflow/core/common_runtime/process_state.h b/tensorflow/core/common_runtime/process_state.h index 6849d305b3..fc9d60bdc3 100644 --- a/tensorflow/core/common_runtime/process_state.h +++ b/tensorflow/core/common_runtime/process_state.h @@ -74,6 +74,9 @@ class ProcessState { // REQUIRES: must be called before GetCPUAllocator. void AddCPUFreeVisitor(SubAllocator::Visitor v); + const std::vector& GetCPUAllocatorVisitor(); + const std::vector& GetCPUFreeVisitor(); + typedef std::unordered_map MDMap; protected: -- GitLab From 36999ac8c0d3854e1637381e689c7c0016c11364 Mon Sep 17 00:00:00 2001 From: Zhenyu Tan Date: Sun, 9 Dec 2018 19:52:58 -0800 Subject: [PATCH 275/873] Remove some op test. PiperOrigin-RevId: 224750894 --- tensorflow/core/kernels/training_ops_test.cc | 34 -------------------- 1 file changed, 34 deletions(-) diff --git a/tensorflow/core/kernels/training_ops_test.cc b/tensorflow/core/kernels/training_ops_test.cc index 1ec57b4522..09804f95dc 100644 --- a/tensorflow/core/kernels/training_ops_test.cc +++ b/tensorflow/core/kernels/training_ops_test.cc @@ -151,40 +151,6 @@ static void BM_Momentum(int iters, int params) { } BENCHMARK(BM_Momentum)->Arg(128 << 10)->Arg(256 << 10); -static void KerasMomentum(int32 n, Graph** init_g, Graph** train_g) { - TensorShape shape({n}); - { - Graph* g = new Graph(OpRegistry::Global()); - auto var = Var(g, n); - auto accum = Var(g, n); - auto zero = Zeros(g, n); - test::graph::Assign(g, var, zero); - test::graph::Assign(g, accum, zero); - *init_g = g; - } - { - Graph* g = new Graph(OpRegistry::Global()); - auto var = Var(g, n); - auto accum = Var(g, n); - auto lr = Scalar(g, 0.01); - auto grad = Random(g, n); - auto mom = Scalar(g, 0.01); - test::graph::Multi(g, "ApplyKerasMomentum", {var, accum, lr, grad, mom}); - *train_g = g; - } -} - -static void BM_KerasMomentum(int iters, int params) { - const int64 tot = static_cast(iters) * params; - testing::ItemsProcessed(tot); - testing::BytesProcessed(tot * sizeof(float)); - Graph* init; - Graph* train; - KerasMomentum(params, &init, &train); - test::Benchmark("cpu", train, GetOptions(), init).Run(iters); -} -BENCHMARK(BM_KerasMomentum)->Arg(128 << 10)->Arg(256 << 10); - static void Adam(int32 n, Graph** init_g, Graph** train_g) { TensorShape shape({n}); { -- GitLab From 54b110ae4369f86518f3950f11be749df2507c29 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Sun, 9 Dec 2018 22:01:10 -0800 Subject: [PATCH 276/873] Internal Change PiperOrigin-RevId: 224757952 --- tensorflow/python/ops/ragged/ragged_tensor_value.py | 7 ------- 1 file changed, 7 deletions(-) diff --git a/tensorflow/python/ops/ragged/ragged_tensor_value.py b/tensorflow/python/ops/ragged/ragged_tensor_value.py index e94ca4afac..bf0ac4482a 100644 --- a/tensorflow/python/ops/ragged/ragged_tensor_value.py +++ b/tensorflow/python/ops/ragged/ragged_tensor_value.py @@ -98,10 +98,3 @@ class RaggedTensorValue(object): values_as_list[self._row_splits[i]:self._row_splits[i + 1]] for i in range(len(self._row_splits) - 1) ] - - def value_rowids(self, name=None): - del name - row_lengths = self._row_splits[1:] - self._row_splits[:-1] - nrows = self._row_splits.shape[-1] - 1 - indices = np.arange(nrows) - return np.repeat(indices, repeats=row_lengths, axis=0) -- GitLab From 2ae0b450a741c37959a9fb9322f79e4ad476e8b7 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 10 Dec 2018 01:02:37 -0800 Subject: [PATCH 277/873] compat: Update forward compatibility horizon to 2018-12-10 PiperOrigin-RevId: 224771346 --- tensorflow/python/compat/compat.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/compat/compat.py b/tensorflow/python/compat/compat.py index 51cd68436a..f11e97b211 100644 --- a/tensorflow/python/compat/compat.py +++ b/tensorflow/python/compat/compat.py @@ -32,7 +32,7 @@ from tensorflow.python.ops import variable_scope from tensorflow.python.util import tf_contextlib from tensorflow.python.util.tf_export import tf_export -_FORWARD_COMPATIBILITY_HORIZON = datetime.date(2018, 12, 9) +_FORWARD_COMPATIBILITY_HORIZON = datetime.date(2018, 12, 10) @tf_export("compat.forward_compatible") -- GitLab From 18e98e57a7b9db07017d2f4f953e3b820b2e01e6 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 10 Dec 2018 04:37:56 -0800 Subject: [PATCH 278/873] Continue conversion of opensource-only files to opensource_only.files. PiperOrigin-RevId: 224792124 --- tensorflow/opensource_only.files | 35 +++++++++++++++++++++++++++++++- 1 file changed, 34 insertions(+), 1 deletion(-) diff --git a/tensorflow/opensource_only.files b/tensorflow/opensource_only.files index 688a837dac..347dc9fc6b 100644 --- a/tensorflow/opensource_only.files +++ b/tensorflow/opensource_only.files @@ -1,3 +1,31 @@ +tensorflow/contrib/tpu/profiler/pip_package/BUILD +tensorflow/contrib/tpu/profiler/pip_package/setup.py +tensorflow/contrib/tpu/profiler/pip_package/README +tensorflow/contrib/tpu/profiler/pip_package/build_pip_package.sh +tensorflow/contrib/tpu/profiler/pip_package/cloud_tpu_profiler/main.py +tensorflow/contrib/tpu/profiler/pip_package/cloud_tpu_profiler/__init__.py +tensorflow/contrib/mpi/BUILD +tensorflow/tools/ci_build/remote/BUILD +tensorflow/tools/pip_package/README +tensorflow/tools/pip_package/MANIFEST.in +tensorflow/tools/pip_package/simple_console.py +tensorflow/tools/pip_package/build_pip_package.sh +tensorflow/tools/pip_package/check_load_py_test.py +tensorflow/tools/pip_package/pip_smoke_test.py +tensorflow/tools/pip_package/simple_console_for_windows.py +tensorflow/tools/pip_package/setup.py +tensorflow/tools/pip_package/BUILD +tensorflow/tools/lib_package/concat_licenses.sh +tensorflow/tools/lib_package/libtensorflow_test.c +tensorflow/tools/lib_package/LibTensorFlowTest.java +tensorflow/tools/lib_package/BUILD +tensorflow/tools/lib_package/libtensorflow_test.sh +tensorflow/tools/lib_package/README.md +tensorflow/tools/lib_package/libtensorflow_java_test.sh +tensorflow/tools/def_file_filter/def_file_filter_configure.bzl +tensorflow/tools/def_file_filter/BUILD +tensorflow/tools/def_file_filter/BUILD.tpl +tensorflow/tools/def_file_filter/def_file_filter.py.tpl tensorflow/third_party/mkl/MKL_LICENSE tensorflow/third_party/mkl/LICENSE tensorflow/third_party/mkl/BUILD @@ -207,4 +235,9 @@ tensorflow/third_party/jsoncpp.BUILD tensorflow/third_party/tflite_ovic_testdata.BUILD tensorflow/third_party/libxsmm.BUILD tensorflow/third_party/zlib.BUILD -tensorflow/third_party/eigen.BUILD \ No newline at end of file +tensorflow/third_party/eigen.BUILD +tensorflow/stream_executor/BUILD +tensorflow/api_template_v1.__init__.py +tensorflow/compat_template_v1.__init__.py +tensorflow/api_template.__init__.py +tensorflow/__init__.py \ No newline at end of file -- GitLab From 8855358cff24b8b29296a01eabf3bf9bbff3509c Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 10 Dec 2018 06:09:33 -0800 Subject: [PATCH 279/873] Put arm_compiler.BUILD into the right spot. PiperOrigin-RevId: 224800160 --- .../opensource_only/arm_compiler.BUILD => arm_compiler.BUILD | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename tensorflow/opensource_only/arm_compiler.BUILD => arm_compiler.BUILD (100%) diff --git a/tensorflow/opensource_only/arm_compiler.BUILD b/arm_compiler.BUILD similarity index 100% rename from tensorflow/opensource_only/arm_compiler.BUILD rename to arm_compiler.BUILD -- GitLab From 41cfa5da49577fb04908997c70946a4881c85430 Mon Sep 17 00:00:00 2001 From: Dan Moldovan Date: Mon, 10 Dec 2018 09:30:43 -0800 Subject: [PATCH 280/873] Actually restrict parse_expression to expression nodes. PiperOrigin-RevId: 224826621 --- tensorflow/python/autograph/pyct/parser.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/autograph/pyct/parser.py b/tensorflow/python/autograph/pyct/parser.py index 39fc1a7ed0..d04a40157e 100644 --- a/tensorflow/python/autograph/pyct/parser.py +++ b/tensorflow/python/autograph/pyct/parser.py @@ -117,7 +117,7 @@ def parse_expression(src): """ node = parse_str(src) assert isinstance(node, gast.Module) - if len(node.body) != 1 and not isinstance(node.body[0], gast.Expr): + if len(node.body) != 1 or not isinstance(node.body[0], gast.Expr): raise ValueError( 'Expected a single expression, found instead %s' % node.body) return node.body[0].value -- GitLab From e6432106f41facbca0cf2d51a2bf6dec72ad8961 Mon Sep 17 00:00:00 2001 From: Dan Moldovan Date: Mon, 10 Dec 2018 09:32:43 -0800 Subject: [PATCH 281/873] Strengthen the checks in side_effect_guards a bit. This is still not fully robust, but the converter is about to be deprecated anyway. PiperOrigin-RevId: 224827008 --- .../converters/side_effect_guards.py | 25 +++++++++++++++---- 1 file changed, 20 insertions(+), 5 deletions(-) diff --git a/tensorflow/python/autograph/converters/side_effect_guards.py b/tensorflow/python/autograph/converters/side_effect_guards.py index 98e29ec8e1..d7c0951fcc 100644 --- a/tensorflow/python/autograph/converters/side_effect_guards.py +++ b/tensorflow/python/autograph/converters/side_effect_guards.py @@ -85,11 +85,26 @@ class SideEffectGuardTransformer(converter.Base): new_alias_map.update(alias_map) alias_map = new_alias_map current_dest = new_dest - if reindent_requested and not current_dest: - # TODO(mdan): There may still be something that could be done. - raise ValueError('Unable to insert statement into the computation flow: ' - 'it is not followed by any computation which ' - 'the statement could gate.') + + if reindent_requested: + no_controls_to_gate = False + if not current_dest: + no_controls_to_gate = True + if len(current_dest) == 1: + if ast_util.matches(current_dest[0], 'return'): + no_controls_to_gate = True + if ast_util.matches(current_dest[0], 'return ()'): + no_controls_to_gate = True + if ast_util.matches(current_dest[0], 'return []'): + no_controls_to_gate = True + if ast_util.matches(current_dest[0], 'return {}'): + no_controls_to_gate = True + if no_controls_to_gate: + # TODO(mdan): There may still be something that could be done. + raise ValueError( + 'Unable to insert statement into the computation flow: it is not' + ' followed by any computation which the statement could gate.') + return new_nodes def visit_FunctionDef(self, node): -- GitLab From 1d850778ac31dfe4a6fdb0846739f3294e47b8c4 Mon Sep 17 00:00:00 2001 From: Dan Moldovan Date: Mon, 10 Dec 2018 09:38:06 -0800 Subject: [PATCH 282/873] Allow non-expressions in the pattern matcher. PiperOrigin-RevId: 224828066 --- tensorflow/python/autograph/converters/call_trees.py | 4 ++-- tensorflow/python/autograph/pyct/ast_util.py | 3 ++- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/tensorflow/python/autograph/converters/call_trees.py b/tensorflow/python/autograph/converters/call_trees.py index 9b85fc8367..3e0b40290f 100644 --- a/tensorflow/python/autograph/converters/call_trees.py +++ b/tensorflow/python/autograph/converters/call_trees.py @@ -323,12 +323,12 @@ class CallTreeTransformer(converter.Base): # 1. super() calls - these are preserved. The class conversion mechanism # will ensure that they return the correct value. - if ast_util.matches(node, 'super(_)'): + if ast_util.matches(node, parser.parse_expression('super(_)')): return node # 2. super().method calls - these are preserved as well, when the # conversion processes the entire class. - if (ast_util.matches(node, 'super(_)._(_)') and + if (ast_util.matches(node, parser.parse_expression('super(_)._(_)')) and self.ctx.info.owner_type is not None): return node diff --git a/tensorflow/python/autograph/pyct/ast_util.py b/tensorflow/python/autograph/pyct/ast_util.py index ea7eca6463..3dc10cf349 100644 --- a/tensorflow/python/autograph/pyct/ast_util.py +++ b/tensorflow/python/autograph/pyct/ast_util.py @@ -200,7 +200,8 @@ def matches(node, pattern): bool """ if isinstance(pattern, str): - pattern = parser.parse_expression(pattern) + pattern, = parser.parse_str(pattern).body + matcher = PatternMatcher(pattern) matcher.visit(node) return matcher.matches -- GitLab From e88a87c89195a820c62433784bcde063ab568cbc Mon Sep 17 00:00:00 2001 From: Pooya Davoodi Date: Mon, 10 Dec 2018 09:56:11 -0800 Subject: [PATCH 283/873] Update README.md Tensorflow -> TensorFlow uring -> using --- tensorflow/contrib/tensorrt/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/tensorrt/README.md b/tensorflow/contrib/tensorrt/README.md index dedac2c748..1310b3cd27 100644 --- a/tensorflow/contrib/tensorrt/README.md +++ b/tensorflow/contrib/tensorrt/README.md @@ -6,13 +6,13 @@ This module is under active development. ## Installing TF-TRT -Currently Tensorflow nightly builds include TF-TRT by default, +Currently TensorFlow nightly builds include TF-TRT by default, which means you don't need to install TF-TRT separately. You can pull the latest TF containers from docker hub or install the latest TF pip package to get access to the latest TF-TRT. If you want to use TF-TRT on NVIDIA Jetson platform, you can find -the download links for the relevant Tensorflow pip packages here: +the download links for the relevant TensorFlow pip packages here: https://docs.nvidia.com/deeplearning/dgx/index.html#installing-frameworks-for-jetson ## Installing TensorRT @@ -42,7 +42,7 @@ and verified models, explains best practices with troubleshooting guides. TF-TRT includes both Python tests and C++ unit tests. Most of Python tests are located in the test directory -and they can be executed uring `bazel test` or directly +and they can be executed using `bazel test` or directly with the Python command. Most of the C++ unit tests are used to test the conversion functions that convert each TF op to a number of TensorRT layers. -- GitLab From 97164413d009aa6506f269eff7fb78411419146d Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 10 Dec 2018 09:54:09 -0800 Subject: [PATCH 284/873] Internal Change PiperOrigin-RevId: 224830708 --- tensorflow/python/ops/array_ops.py | 2 + .../python/ops/ragged/ragged_array_ops.py | 2 +- .../python/ops/ragged/ragged_dispatch.py | 20 ++- .../python/ops/ragged/ragged_dispatch_test.py | 133 ++++++++++++++++++ .../python/ops/ragged/ragged_math_ops.py | 22 +-- 5 files changed, 167 insertions(+), 12 deletions(-) diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py index 9dabbffb13..e10d9036cd 100644 --- a/tensorflow/python/ops/array_ops.py +++ b/tensorflow/python/ops/array_ops.py @@ -87,6 +87,7 @@ def identity(input, name=None): # pylint: disable=redefined-builtin # pylint: disable=redefined-builtin,protected-access @tf_export(v1=["expand_dims"]) +@dispatch.add_dispatch_support @deprecation.deprecated_args(None, "Use the `axis` argument instead", "dim") def expand_dims(input, axis=None, name=None, dim=None): """Inserts a dimension of 1 into a tensor's shape. @@ -3256,6 +3257,7 @@ reverse_sequence_v2.__doc__ = deprecation.rewrite_argument_docstring( @tf_export(v1=["gather"]) +@dispatch.add_dispatch_support def gather(params, indices, validate_indices=None, name=None, axis=0): del validate_indices if axis != 0: diff --git a/tensorflow/python/ops/ragged/ragged_array_ops.py b/tensorflow/python/ops/ragged/ragged_array_ops.py index b5917bc4ee..dfa9790cd8 100644 --- a/tensorflow/python/ops/ragged/ragged_array_ops.py +++ b/tensorflow/python/ops/ragged/ragged_array_ops.py @@ -587,7 +587,7 @@ def concat(values, axis, name=None): return _ragged_stack_concat_helper(values, axis, stack_values=False) -def stack(values, axis, name=None): +def stack(values, axis=0, name=None): """Stacks potentially ragged tensors along one dimension. Given a list of tensors with the same rank `K` (`K >= axis`), returns a diff --git a/tensorflow/python/ops/ragged/ragged_dispatch.py b/tensorflow/python/ops/ragged/ragged_dispatch.py index 7c74f7be62..f334f1fc8e 100644 --- a/tensorflow/python/ops/ragged/ragged_dispatch.py +++ b/tensorflow/python/ops/ragged/ragged_dispatch.py @@ -374,15 +374,31 @@ _BINARY_ELEMENTWISE_OPS = [ math_ops.truncatemod, ] + +def _ragged_gather_v1(params, indices, validate_indices=None, name=None, + axis=0): + return ragged_array_ops.gather(params=params, indices=indices, + validate_indices=validate_indices, + axis=axis, name=name) + + +def _ragged_expand_dims_v1(input, axis=None, name=None, dim=None): # pylint: disable=redefined-builtin + if dim is not None: + axis = dim + return ragged_array_ops.expand_dims(input=input, axis=axis, name=name) + + # (original_op, ragged_op, ragged_args) _RAGGED_DISPATCH_OPS = [ (array_ops.batch_gather, ragged_array_ops.batch_gather, ['params', 'indices']), - (array_ops.concat, ragged_array_ops.concat, ['values']), + (array_ops.concat, ragged_array_ops.concat, ['[values]']), + (array_ops.expand_dims, _ragged_expand_dims_v1, ['input']), (array_ops.expand_dims_v2, ragged_array_ops.expand_dims, ['input']), + (array_ops.gather, _ragged_gather_v1, ['params', 'indices']), (array_ops.gather_v2, ragged_array_ops.gather, ['params', 'indices']), (array_ops.gather_nd, ragged_array_ops.gather_nd, ['params', 'indices']), - (array_ops.stack, ragged_array_ops.stack, ['values']), + (array_ops.stack, ragged_array_ops.stack, ['[values]']), (array_ops.tile, ragged_array_ops.tile, ['input']), (array_ops.where, ragged_array_ops.where, ['condition', 'x', 'y']), (math_ops.unsorted_segment_sum, ragged_math_ops.segment_sum, diff --git a/tensorflow/python/ops/ragged/ragged_dispatch_test.py b/tensorflow/python/ops/ragged/ragged_dispatch_test.py index 82827aa2aa..9d63dcf7c4 100644 --- a/tensorflow/python/ops/ragged/ragged_dispatch_test.py +++ b/tensorflow/python/ops/ragged/ragged_dispatch_test.py @@ -446,6 +446,139 @@ class RaggedElementwiseOpsTest(ragged_test_util.RaggedTensorTestCase, with self.assertRaises((TypeError, ValueError)): self.evaluate(math_ops.add_n([x, y])) + @parameterized.parameters([ + dict( + op=array_ops.batch_gather, + args=(ragged.constant_value([[5, 6, 7], [8, 9]]), + ragged.constant_value([[2, 1, 0], [1]])), + expected=ragged.constant_value([[7, 6, 5], [9]])), + dict( + op=array_ops.concat, + args=([ragged.constant_value([[1, 2, 3], [4]], dtype=np.int32), + np.array([[5, 6]], dtype=np.int32)],), + kwargs={'axis': 0}, + expected=ragged.constant_value([[1, 2, 3], [4], [5, 6]])), + dict( + op=array_ops.expand_dims, + kwargs={'input': ragged.constant_value([[1, 2], [3]]), + 'axis': 0}, + expected=ragged.constant_value([[[1, 2], [3]]])), + dict( + op=array_ops.expand_dims_v2, + kwargs={'input': ragged.constant_value([[1, 2], [3]]), + 'axis': -1}, + expected=ragged.constant_value([[[1], [2]], [[3]]], + ragged_rank=1),), + dict( + op=array_ops.gather, + kwargs={'params': ragged.constant_value([[1, 2], [3]]), + 'indices': [1, 0, 1]}, + expected=ragged.constant_value([[3], [1, 2], [3]])), + dict( + op=array_ops.gather_v2, + kwargs={'params': ragged.constant_value([[1, 2], [3]]), + 'indices': ragged.constant_value([[1, 0], [1]])}, + expected=ragged.constant_value([[[3], [1, 2]], [[3]]])), + dict( + op=array_ops.gather_nd, + kwargs={'params': ragged.constant_value([[7, 8], [9]]), + 'indices': [[0, 1], [1, 0], [0, 0]]}, + expected=ragged.constant_value([8, 9, 7])), + dict( + op=array_ops.stack, + args=([ragged.constant_value([[1, 2, 3], [4]], dtype=np.int32), + np.array([[5, 6]], dtype=np.int32)],), + expected=ragged.constant_value([[[1, 2, 3], [4]], [[5, 6]]])), + dict( + op=array_ops.tile, + args=([ragged.constant_value([[1, 2], [3]], dtype=np.int32), [2, 3]]), + expected=ragged.constant_value([[1, 2, 1, 2, 1, 2], [3, 3, 3], + [1, 2, 1, 2, 1, 2], [3, 3, 3]])), + dict( + op=array_ops.where, + args=(ragged.constant_value([[True, False], [True]]), + ragged.constant_value([[b'A', b'B'], [b'C']]), + ragged.constant_value([[b'a', b'b'], [b'c']])), + expected=ragged.constant_value([[b'A', b'b'], [b'C']])), + dict( + op=math_ops.unsorted_segment_sum, + kwargs={'data': ragged.constant_value([[1, 2], [3]]), + 'segment_ids': ragged.constant_value([[0, 2], [0]]), + 'num_segments': 3}, + expected=[4, 0, 2]), + dict( + op=math_ops.unsorted_segment_prod, + kwargs={'data': ragged.constant_value([[1, 2], [3]]), + 'segment_ids': ragged.constant_value([[0, 2], [0]]), + 'num_segments': 3}, + expected=[3, 1, 2]), + dict( + op=math_ops.unsorted_segment_min, + kwargs={'data': ragged.constant_value([[1, 2], [3]]), + 'segment_ids': ragged.constant_value([[0, 1], [0]]), + 'num_segments': 2}, + expected=[1, 2]), + dict( + op=math_ops.unsorted_segment_max, + kwargs={'data': ragged.constant_value([[1, 2], [3]]), + 'segment_ids': ragged.constant_value([[0, 1], [0]]), + 'num_segments': 2}, + expected=[3, 2]), + dict( + op=math_ops.unsorted_segment_mean, + kwargs={'data': ragged.constant_value([[1, 2], [3]]), + 'segment_ids': ragged.constant_value([[0, 1], [0]]), + 'num_segments': 2}, + expected=[2, 2]), + dict( + op=math_ops.unsorted_segment_sqrt_n, + kwargs={'data': ragged.constant_value([[1.0, 2.0], [3.0, 4.0, 6.0]]), + 'segment_ids': ragged.constant_value([[0, 1], [0, 0, 0]]), + 'num_segments': 2}, + expected=[7.0, 2.0]), + dict( + op=math_ops.reduce_sum, + kwargs={'input_tensor': ragged.constant_value([[1, 2], [3, 4, 5]]), + 'axis': 1}, + expected=[3, 12]), + dict( + op=math_ops.reduce_prod, + kwargs={'input_tensor': ragged.constant_value([[1, 2], [3, 4, 5]]), + 'axis': 1}, + expected=[2, 60]), + dict( + op=math_ops.reduce_min, + kwargs={'input_tensor': ragged.constant_value([[1, 2], [3, 4, 5]]), + 'axis': 1}, + expected=[1, 3]), + dict( + op=math_ops.reduce_max, + kwargs={'input_tensor': ragged.constant_value([[1, 2], [3, 4, 5]]), + 'axis': 1}, + expected=[2, 5]), + dict( + op=math_ops.reduce_mean, + kwargs={'input_tensor': ragged.constant_value([[1, 3], [3, 4, 5]]), + 'axis': 1}, + expected=[2, 4]), + dict( + op=math_ops.reduce_any, + kwargs={'input_tensor': ragged.constant_value([[True, False], + [True, True, True]]), + 'axis': 1}, + expected=[True, True]), + dict( + op=math_ops.reduce_all, + kwargs={'input_tensor': ragged.constant_value([[True, False], + [True, True, True]]), + 'axis': 1}, + expected=[False, True]), + ]) + def testRaggedDispatch(self, op, expected, args=(), kwargs=None): + if kwargs is None: kwargs = {} + result = op(*args, **kwargs) + self.assertRaggedEqual(result, expected) + if __name__ == '__main__': googletest.main() diff --git a/tensorflow/python/ops/ragged/ragged_math_ops.py b/tensorflow/python/ops/ragged/ragged_math_ops.py index 92f82be84a..f774c1eb58 100644 --- a/tensorflow/python/ops/ragged/ragged_math_ops.py +++ b/tensorflow/python/ops/ragged/ragged_math_ops.py @@ -269,28 +269,32 @@ def segment_max(data, segment_ids, num_segments, name=None): def segment_mean(data, segment_ids, num_segments, name=None): - # For docs, see: _RAGGED_SEGMENT_DOCSTRING + """For docs, see: _RAGGED_SEGMENT_DOCSTRING.""" with ops.name_scope(name, 'RaggedSegmentMean', [data, segment_ids, num_segments]): total = segment_sum(data, segment_ids, num_segments) ones = ragged_tensor.RaggedTensor.from_nested_row_splits( array_ops.ones_like(data.flat_values), data.nested_row_splits) count = segment_sum(ones, segment_ids, num_segments) - return ragged_tensor.RaggedTensor.from_nested_row_splits( - total.flat_values / count.flat_values, total.nested_row_splits) + if ragged_tensor.is_ragged(total): + return total.with_flat_values(total.flat_values / count.flat_values) + else: + return total / count def segment_sqrt_n(data, segment_ids, num_segments, name=None): - # For docs, see: _RAGGED_SEGMENT_DOCSTRING + """For docs, see: _RAGGED_SEGMENT_DOCSTRING.""" with ops.name_scope(name, 'RaggedSegmentSqrtN', [data, segment_ids, num_segments]): total = segment_sum(data, segment_ids, num_segments) ones = ragged_tensor.RaggedTensor.from_nested_row_splits( array_ops.ones_like(data.flat_values), data.nested_row_splits) count = segment_sum(ones, segment_ids, num_segments) - return ragged_tensor.RaggedTensor.from_nested_row_splits( - total.flat_values / math_ops.sqrt(count.flat_values), - total.nested_row_splits) + if ragged_tensor.is_ragged(total): + return total.with_flat_values( + total.flat_values / math_ops.sqrt(count.flat_values)) + else: + return total / math_ops.sqrt(count) def _set_ragged_segment_docstring(func, combination, combined): @@ -465,11 +469,11 @@ def _ragged_reduce_aggregate(reduce_op, return _ragged_reduce_aggregate(reduce_op, unsorted_segment_op, inner_reduced, axis[:-1], keepdims) - axis = ragged_util.get_positive_axis(axis, rt_input.shape.ndims) - rt_input = ragged_tensor.convert_to_tensor_or_ragged_tensor( rt_input, name='rt_input') + axis = ragged_util.get_positive_axis(axis, rt_input.shape.ndims) + if axis == 0: # out[i_1, i_2, ..., i_N] = sum_{j} rt_input[j, i_1, i_2, ..., i_N] row_lengths = rt_input.row_splits[1:] - rt_input.row_splits[:-1] -- GitLab From 95b5a2c831fbe71e6a1202a7b8585f74bb74ee0c Mon Sep 17 00:00:00 2001 From: Pooya Davoodi Date: Mon, 10 Dec 2018 10:01:30 -0800 Subject: [PATCH 285/873] Update README.md Remove NVIDIA link encouraging users to start from https://www.tensorflow.org/install/gpu for installing TensorRT to reduce the confusion about which TensorRT version to install. --- tensorflow/contrib/tensorrt/README.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/tensorrt/README.md b/tensorflow/contrib/tensorrt/README.md index 1310b3cd27..cb827c35d1 100644 --- a/tensorflow/contrib/tensorrt/README.md +++ b/tensorflow/contrib/tensorrt/README.md @@ -18,9 +18,8 @@ https://docs.nvidia.com/deeplearning/dgx/index.html#installing-frameworks-for-je ## Installing TensorRT In order to make use of TF-TRT, you will need a local installation -of TensorRT from the -[NVIDIA Developer website](https://developer.nvidia.com/tensorrt). -Installation instructions for compatibility with TensorFlow are provided on the +of TensorRT. Installation instructions for compatibility with TensorFlow +are provided on the [TensorFlow GPU support](https://www.tensorflow.org/install/gpu) guide. ## Examples -- GitLab From c07297759059a953351f1d5e531b6e6af878365c Mon Sep 17 00:00:00 2001 From: Peter Hawkins Date: Mon, 10 Dec 2018 10:04:47 -0800 Subject: [PATCH 286/873] [XLA:CPU] Add missing intrinsics on Mac OS X. Fixes crashes seen in JAX test suite on Mac OS. PiperOrigin-RevId: 224832861 --- tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc index efccadedf2..bd6868d397 100644 --- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc +++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc @@ -296,6 +296,9 @@ bool RegisterKnownJITSymbols() { REGISTER_LIBM_SYMBOL(sin, double (*)(double)); #ifdef __APPLE__ REGISTER_LIBM_SYMBOL(__sincos, void (*)(double, double*, double*)); + registry->Register("__sincosf_stret", + reinterpret_cast(__sincosf_stret)); + registry->Register("__sincos_stret", reinterpret_cast(__sincos_stret)); #else REGISTER_LIBM_SYMBOL(sincos, void (*)(double, double*, double*)); #endif @@ -311,6 +314,12 @@ bool RegisterKnownJITSymbols() { registry->Register("memcpy", reinterpret_cast(memcpy)); registry->Register("memmove", reinterpret_cast(memmove)); registry->Register("memset", reinterpret_cast(memset)); + +#ifdef __APPLE__ + registry->Register("memset_pattern16", + reinterpret_cast(memset_pattern16)); +#endif + return true; } -- GitLab From a73776b102701792d1464042ec0c61f5142e9c18 Mon Sep 17 00:00:00 2001 From: Gaurav Jain Date: Mon, 10 Dec 2018 10:09:27 -0800 Subject: [PATCH 287/873] Annotate additional tests with @run_v1_only PiperOrigin-RevId: 224833857 --- tensorflow/python/eager/ops_test.py | 1 + tensorflow/python/keras/layers/core_test.py | 2 ++ tensorflow/python/keras/layers/local_test.py | 2 +- tensorflow/python/layers/core_test.py | 7 +------ 4 files changed, 5 insertions(+), 7 deletions(-) diff --git a/tensorflow/python/eager/ops_test.py b/tensorflow/python/eager/ops_test.py index 17a090d526..91d0d5c6f0 100644 --- a/tensorflow/python/eager/ops_test.py +++ b/tensorflow/python/eager/ops_test.py @@ -330,6 +330,7 @@ class OpsTest(test_util.TensorFlowTestCase): self.assertEquals(t, dtypes.string) self.assertEquals(r[0].dtype, dtypes.string) + @test_util.run_v1_only('b/120545219') def testFlattenLayer(self): flatten_layer = core.Flatten() x = constant_op.constant([[[-10, -20], [-30, -40]], [[10, 20], [30, 40]]]) diff --git a/tensorflow/python/keras/layers/core_test.py b/tensorflow/python/keras/layers/core_test.py index f138adf760..b8def07190 100644 --- a/tensorflow/python/keras/layers/core_test.py +++ b/tensorflow/python/keras/layers/core_test.py @@ -135,6 +135,7 @@ class CoreLayersTest(test.TestCase): kwargs={'dims': (1, 4, 2)}, input_shape=(3, 2, 4)) @tf_test_util.run_in_graph_and_eager_modes + @tf_test_util.run_v1_only('b/120545219') def test_flatten(self): testing_utils.layer_test( keras.layers.Flatten, kwargs={}, input_shape=(3, 2, 4)) @@ -150,6 +151,7 @@ class CoreLayersTest(test.TestCase): self.assertAllClose(outputs, target_outputs) @tf_test_util.run_in_graph_and_eager_modes + @tf_test_util.run_v1_only('b/120545219') def test_flatten_scalar_channels(self): testing_utils.layer_test( keras.layers.Flatten, kwargs={}, input_shape=(3,)) diff --git a/tensorflow/python/keras/layers/local_test.py b/tensorflow/python/keras/layers/local_test.py index e4f4d0a639..6db5bf385e 100644 --- a/tensorflow/python/keras/layers/local_test.py +++ b/tensorflow/python/keras/layers/local_test.py @@ -235,7 +235,7 @@ class LocallyConnected2DLayersTest(test.TestCase): class LocallyConnectedImplementationModeTest(test.TestCase): - @tf_test_util.run_deprecated_v1 + @tf_test_util.run_v1_only('b/120545219') def test_locallyconnected_implementation(self): with self.cached_session(): num_samples = 4 diff --git a/tensorflow/python/layers/core_test.py b/tensorflow/python/layers/core_test.py index b40a268238..3338e55f82 100644 --- a/tensorflow/python/layers/core_test.py +++ b/tensorflow/python/layers/core_test.py @@ -463,9 +463,9 @@ class DropoutTest(test.TestCase): self.assertAllClose(np.ones((5, 5)), np_output) +@test_util.run_v1_only('b/120545219') class FlattenTest(test.TestCase): - @test_util.run_deprecated_v1 def testCreateFlatten(self): with self.cached_session() as sess: x = array_ops.placeholder(shape=(None, 2, 3), dtype='float32') @@ -490,7 +490,6 @@ class FlattenTest(test.TestCase): shape = core_layers.Flatten().compute_output_shape((None, 3, None)) self.assertEqual(shape.as_list(), [None, None]) - @test_util.run_deprecated_v1 def testDataFormat5d(self): np_input_channels_last = np.arange( 120, dtype='float32').reshape([1, 5, 4, 3, 2]) @@ -508,7 +507,6 @@ class FlattenTest(test.TestCase): self.assertAllEqual(np_output_cl, np_output_cf) - @test_util.run_deprecated_v1 def testDataFormat4d(self): np_input_channels_last = np.arange( 24, dtype='float32').reshape([1, 4, 3, 2]) @@ -526,13 +524,11 @@ class FlattenTest(test.TestCase): self.assertAllEqual(np_output_cl, np_output_cf) - @test_util.run_deprecated_v1 def testFunctionalFlatten(self): x = array_ops.placeholder(shape=(None, 2, 3), dtype='float32') y = core_layers.flatten(x, name='flatten') self.assertEqual(y.get_shape().as_list(), [None, 6]) - @test_util.run_deprecated_v1 def testFlatten0D(self): x = array_ops.placeholder(shape=(None,), dtype='float32') y = core_layers.Flatten()(x) @@ -541,7 +537,6 @@ class FlattenTest(test.TestCase): self.assertEqual(list(np_output.shape), [5, 1]) self.assertEqual(y.shape.as_list(), [None, 1]) - @test_util.run_deprecated_v1 def testFlattenUnknownAxes(self): with self.cached_session() as sess: x = array_ops.placeholder(shape=(5, None, None), dtype='float32') -- GitLab From 929ae05b8c98d1885ceff2f6cf07db66d1bdb737 Mon Sep 17 00:00:00 2001 From: Nupur Garg Date: Mon, 10 Dec 2018 10:09:39 -0800 Subject: [PATCH 288/873] Internal change. PiperOrigin-RevId: 224833908 --- tensorflow/lite/toco/python/BUILD | 17 +++++++++++++++-- 1 file changed, 15 insertions(+), 2 deletions(-) diff --git a/tensorflow/lite/toco/python/BUILD b/tensorflow/lite/toco/python/BUILD index 07056f66c3..8a6e82ec46 100644 --- a/tensorflow/lite/toco/python/BUILD +++ b/tensorflow/lite/toco/python/BUILD @@ -1,4 +1,8 @@ -package(default_visibility = ["//visibility:public"]) +package(default_visibility = [ + "//tensorflow/contrib/lite:__subpackages__", + "//tensorflow/lite:__subpackages__", + "//tensorflow/tools/pip_package:__subpackages__", +]) licenses(["notice"]) # Apache 2.0 @@ -9,7 +13,10 @@ load("//tensorflow:tensorflow.bzl", "py_binary") config_setting( name = "tflite_convert_with_select_tf_ops", define_values = {"tflite_convert_with_select_tf_ops": "true"}, - visibility = ["//visibility:public"], + visibility = [ + "//tensorflow/contrib/lite:__subpackages__", + "//tensorflow/lite:__subpackages__", + ], ) cc_library( @@ -37,6 +44,12 @@ cc_library( tf_py_wrap_cc( name = "tensorflow_wrap_toco", srcs = ["toco.i"], + visibility = [ + "//learning/expander/pod/deep_pod/utils:__subpackages__", + "//research/handwriting/converters/tflite:__subpackages__", + "//tensorflow/contrib/lite:__subpackages__", + "//tensorflow/lite:__subpackages__", + ], deps = [ ":toco_python_api", "//tensorflow/lite/toco:model_flags_proto_cc", -- GitLab From 7250f8531a1f35cd22899fa3b124bcbe252281c5 Mon Sep 17 00:00:00 2001 From: Niranjan Hasabnis Date: Mon, 10 Dec 2018 10:25:19 -0800 Subject: [PATCH 289/873] Addressing review comments - V3 --- tensorflow/core/kernels/mkl_fused_ops_test.cc | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/tensorflow/core/kernels/mkl_fused_ops_test.cc b/tensorflow/core/kernels/mkl_fused_ops_test.cc index 1214711edc..1003fa5e4f 100644 --- a/tensorflow/core/kernels/mkl_fused_ops_test.cc +++ b/tensorflow/core/kernels/mkl_fused_ops_test.cc @@ -32,7 +32,7 @@ limitations under the License. namespace tensorflow { -// Helper class for converting MKL tesnors to TF tensors and comparing to +// Helper class for converting MKL tensors to TF tensors and comparing to // expected values static const uint8 dummy_tensor[] = {0, 0, 0, 0, 0, 0, 0, 0}; @@ -295,10 +295,10 @@ TYPED_TEST_P(MklFusedConv2DWithBiasOpTest, SpatialConvolutionAndRelu) { this->VerifyConv2DWithBiasAndRelu(filter_size, filter_count); } -REGISTER_TYPED_TEST_CASE_P(MklFusedConv2DWithBiasOpTest, - OneByOneConvolution, // - SpatialConvolution, // - OneByOneConvolutionAndRelu, // +REGISTER_TYPED_TEST_CASE_P(MklFusedConv2DWithBiasOpTest, // + OneByOneConvolution, // + SpatialConvolution, // + OneByOneConvolutionAndRelu, // SpatialConvolutionAndRelu); using MklFusedBiasAddDataTypes = ::testing::Types; -- GitLab From 3d5b131ab82e0ea065ea2705b1aa251711850562 Mon Sep 17 00:00:00 2001 From: Pooya Davoodi Date: Mon, 10 Dec 2018 10:31:08 -0800 Subject: [PATCH 290/873] Update README.md documentaion --> documentation --- tensorflow/contrib/tensorrt/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/tensorrt/README.md b/tensorflow/contrib/tensorrt/README.md index cb827c35d1..79b4886cce 100644 --- a/tensorflow/contrib/tensorrt/README.md +++ b/tensorflow/contrib/tensorrt/README.md @@ -33,7 +33,7 @@ performance of TF-TRT. For more information see ## Documentation -[TF-TRT documentaion](https://docs.nvidia.com/deeplearning/dgx/integrate-tf-trt/index.html) +[TF-TRT documentation](https://docs.nvidia.com/deeplearning/dgx/integrate-tf-trt/index.html) gives an overview of the supported functionalities, provides tutorials and verified models, explains best practices with troubleshooting guides. -- GitLab From c9d6b87d824378c0076542ab337beeb33d9dff50 Mon Sep 17 00:00:00 2001 From: Tong Shen Date: Mon, 10 Dec 2018 10:44:47 -0800 Subject: [PATCH 291/873] Build `node_name_to_cost_id_map_` after graph optimization passes. Graph optimization passes might overwrite feed/fetch nodes. PiperOrigin-RevId: 224840744 --- tensorflow/core/common_runtime/graph_execution_state.cc | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/tensorflow/core/common_runtime/graph_execution_state.cc b/tensorflow/core/common_runtime/graph_execution_state.cc index 880806f120..04d658f047 100644 --- a/tensorflow/core/common_runtime/graph_execution_state.cc +++ b/tensorflow/core/common_runtime/graph_execution_state.cc @@ -546,10 +546,6 @@ Status GraphExecutionState::InitBaseGraph(const BuildGraphOptions& options) { std::unique_ptr new_graph(new Graph(OpRegistry::Global())); GraphConstructorOptions opts; TF_RETURN_IF_ERROR(ConvertGraphDefToGraph(opts, *graph_def, new_graph.get())); - for (const Node* n : new_graph->nodes()) { - VLOG(2) << "Mapping " << n->name() << " to " << n->cost_id(); - node_name_to_cost_id_map_[n->name()] = n->cost_id(); - } if (session_options_ && session_options_->config.graph_options().place_pruned_graph()) { // Rewrite the graph before placement. @@ -578,6 +574,11 @@ Status GraphExecutionState::InitBaseGraph(const BuildGraphOptions& options) { TF_RETURN_IF_ERROR(OptimizationPassRegistry::Global()->RunGrouping( OptimizationPassRegistry::POST_PLACEMENT, optimization_options)); + for (const Node* n : new_graph->nodes()) { + VLOG(2) << "Mapping " << n->name() << " to " << n->cost_id(); + node_name_to_cost_id_map_[n->name()] = n->cost_id(); + } + SaveStatefulNodes(new_graph.get()); graph_ = new_graph.release(); return Status::OK(); -- GitLab From c5fe1e476b651877022b6d43a851f0ad9ed6880a Mon Sep 17 00:00:00 2001 From: Tom Hennigan Date: Mon, 10 Dec 2018 10:47:29 -0800 Subject: [PATCH 292/873] Make execution callback an enum. PiperOrigin-RevId: 224841335 --- tensorflow/contrib/eager/python/tfe.py | 4 + .../python/eager/execution_callbacks.py | 77 +++++++++---------- .../python/eager/execution_callbacks_test.py | 11 ++- tensorflow/python/ops/math_grad_test.py | 4 +- 4 files changed, 52 insertions(+), 44 deletions(-) diff --git a/tensorflow/contrib/eager/python/tfe.py b/tensorflow/contrib/eager/python/tfe.py index 33c988fd90..8882a863c3 100644 --- a/tensorflow/contrib/eager/python/tfe.py +++ b/tensorflow/contrib/eager/python/tfe.py @@ -41,6 +41,8 @@ To use, at program startup, call `tf.enable_eager_execution()`. @@add_execution_callback @@clear_execution_callbacks +@@errstate +@@ExecutionCallback @@inf_callback @@inf_nan_callback @@nan_callback @@ -119,6 +121,8 @@ from tensorflow.python.eager.context import set_server_def from tensorflow.python.eager.def_function import function from tensorflow.python.eager.execution_callbacks import add_execution_callback from tensorflow.python.eager.execution_callbacks import clear_execution_callbacks +from tensorflow.python.eager.execution_callbacks import errstate +from tensorflow.python.eager.execution_callbacks import ExecutionCallback from tensorflow.python.eager.execution_callbacks import inf_callback from tensorflow.python.eager.execution_callbacks import inf_nan_callback from tensorflow.python.eager.execution_callbacks import nan_callback diff --git a/tensorflow/python/eager/execution_callbacks.py b/tensorflow/python/eager/execution_callbacks.py index 28b6b84a82..af1afa3454 100644 --- a/tensorflow/python/eager/execution_callbacks.py +++ b/tensorflow/python/eager/execution_callbacks.py @@ -20,6 +20,7 @@ from __future__ import print_function import contextlib import functools +import enum # pylint: disable=g-bad-import-order import numpy as np @@ -29,13 +30,25 @@ from tensorflow.python.eager import core from tensorflow.python.eager import execute from tensorflow.python.platform import tf_logging as logging -IGNORE = "ignore" -PRINT = "print" -RAISE = "raise" -WARN = "warn" -_DEFAULT_CALLBACK_ACTION = RAISE -_VALID_CALLBACK_ACTIONS = (None, IGNORE, PRINT, RAISE, WARN) +class ExecutionCallback(enum.Enum): + """Valid callback actions. + + These can be passed to `seterr` or `errstate` to create callbacks when + specific events occur (e.g. an operation produces `NaN`s). + + IGNORE: take no action. + PRINT: print a warning to `stdout`. + RAISE: raise an error (e.g. `InfOrNanError`). + WARN: print a warning using `tf.logging.warn`. + """ + + IGNORE = "ignore" + PRINT = "print" + RAISE = "raise" + WARN = "warn" + +_DEFAULT_CALLBACK_ACTION = ExecutionCallback.RAISE # TODO(cais): Consider moving this exception class to errors_impl.py. @@ -139,11 +152,8 @@ def inf_nan_callback(op_type, the output tensor values. check_nan: (`bool`) Whether this callback should check for `nan` values in the output tensor values. - action: (`str`) Action to be taken by the callback when `inf` or `nan` - values are detected. Possible values {"raise", "warn", "print"} - `"raise"`: Raise a `InfOrNanError`. - `"warn"`: Log a warning using `tf.logging.warn`. - `"print"`: Print a message to `sys.stdout`. + action: (`ExecutionCallback`) Action to be taken by the callback when + `inf` or `nan` values are detected. Raises: InfOrNanError: iff `inf` or `nan` values are seen in any of `outputs` and @@ -152,6 +162,7 @@ def inf_nan_callback(op_type, """ del attrs, inputs # Not used. + action = ExecutionCallback(action) ctx = context.context() for index, output in enumerate(outputs): @@ -180,16 +191,16 @@ def inf_nan_callback(op_type, continue error = InfOrNanError(op_type, op_name, index, len(outputs), value) - if action == "print": + if action == ExecutionCallback.PRINT: print("Warning: %s" % str(error)) - elif action == "warn": + elif action == ExecutionCallback.WARN: logging.warn(str(error)) - elif action == "raise": + elif action == ExecutionCallback.RAISE: raise error else: raise ValueError( "Invalid action for inf_nan_callback: %s. Valid actions are: " - "{print | warn | raise}" % action) + "{PRINT | WARN | RAISE}" % action) def inf_callback(op_type, @@ -282,7 +293,7 @@ def seterr(inf_or_nan=None): Example: ```python - tfe.seterr(inf_or_nan="raise") + tfe.seterr(inf_or_nan=ExecutionCallback.RAISE) a = tf.constant(10.0) b = tf.constant(0.0) try: @@ -290,18 +301,14 @@ def seterr(inf_or_nan=None): except Exception as e: print("Caught Exception: %s" % e) - tfe.seterr(inf_or_nan="ignore") + tfe.seterr(inf_or_nan=ExecutionCallback.IGNORE) c = a / b # <-- Does NOT raise exception anymore. ``` Args: - inf_or_nan: Set action for infinity (`inf`) and NaN (`nan`) values. - Possible values: `{"ignore", "print", "raise", "warn"}`. - `"ignore"`: take no action when `inf` values appear. - `"print"`: print a warning to `stdout`. - `"raise"`: raise an `InfOrNanError`. - `"warn"`: print a warning using `tf.logging.warn`. - A value of `None` leads to no change in the action of the condition. + inf_or_nan: An `ExecutionCallback` determining the action for infinity + (`inf`) and NaN (`nan`) values. A value of `None` leads to no change in + the action of the condition. Returns: A dictionary of old actions. @@ -309,12 +316,8 @@ def seterr(inf_or_nan=None): Raises: ValueError: If the value of any keyword arguments is invalid. """ - if inf_or_nan not in _VALID_CALLBACK_ACTIONS: - raise ValueError( - "Invalid action value for inf_or_nan: %s. " - "Valid actions are %s." % (inf_or_nan, _VALID_CALLBACK_ACTIONS)) - - old_settings = {"inf_or_nan": "ignore"} + inf_or_nan = ExecutionCallback(inf_or_nan) if inf_or_nan is not None else None + old_settings = {"inf_or_nan": ExecutionCallback.IGNORE} default_context = context.context() carryover_callbacks = [] @@ -336,7 +339,7 @@ def seterr(inf_or_nan=None): default_context.clear_post_execution_callbacks() for callback in carryover_callbacks: default_context.add_post_execution_callback(callback) - if inf_or_nan != "ignore": + if inf_or_nan != ExecutionCallback.IGNORE: default_context.add_post_execution_callback( functools.partial(inf_nan_callback, action=inf_or_nan)) @@ -351,18 +354,14 @@ def errstate(inf_or_nan=None): ``` c = tf.log(0.) # -inf - with errstate(inf_or_nan="raise"): + with errstate(inf_or_nan=ExecutionCallback.RAISE): tf.log(0.) # <-- Raises InfOrNanError. ``` Args: - inf_or_nan: Set action for infinity (`inf`) and NaN (`nan`) values. - Possible values: `{IGNORE, PRINT, RAISE, WARN}`. - `IGNORE`: take no action when `inf` values appear. - `PRINT`: print a warning to `stdout`. - `RAISE`: raise an `InfOrNanError`. - `WARN`: print a warning using `tf.logging.warn`. - A value of `None` leads to no change in the action of the condition. + inf_or_nan: An `ExecutionCallback` determining the action for infinity + (`inf`) and NaN (`nan`) values. A value of `None` leads to no change in + the action of the condition. Yields: None. diff --git a/tensorflow/python/eager/execution_callbacks_test.py b/tensorflow/python/eager/execution_callbacks_test.py index 5594ab5f12..b8b786ad2e 100644 --- a/tensorflow/python/eager/execution_callbacks_test.py +++ b/tensorflow/python/eager/execution_callbacks_test.py @@ -24,6 +24,9 @@ from tensorflow.python.framework import ops from tensorflow.python.ops import math_ops from tensorflow.python.platform import test +RAISE = execution_callbacks.ExecutionCallback.RAISE +IGNORE = execution_callbacks.ExecutionCallback.IGNORE + def log_zero(): """Computes `log(0.0)`.""" @@ -33,17 +36,17 @@ def log_zero(): class ExecutionCallbacksTest(test.TestCase): def test_errstate_inf_raise(self): - with execution_callbacks.errstate(inf_or_nan=execution_callbacks.RAISE): + with execution_callbacks.errstate(inf_or_nan=RAISE): with self.assertRaises(execution_callbacks.InfOrNanError): log_zero() def test_errstate_inf_ignore(self): - with execution_callbacks.errstate(inf_or_nan=execution_callbacks.IGNORE): + with execution_callbacks.errstate(inf_or_nan=IGNORE): self.assertEqual(-float("inf"), log_zero().numpy()) def test_errstate_nesting(self): - with execution_callbacks.errstate(inf_or_nan=execution_callbacks.RAISE): - with execution_callbacks.errstate(inf_or_nan=execution_callbacks.IGNORE): + with execution_callbacks.errstate(inf_or_nan=RAISE): + with execution_callbacks.errstate(inf_or_nan=IGNORE): self.assertEqual(-float("inf"), log_zero().numpy()) with self.assertRaises(execution_callbacks.InfOrNanError): diff --git a/tensorflow/python/ops/math_grad_test.py b/tensorflow/python/ops/math_grad_test.py index 822f89768c..f415e65787 100644 --- a/tensorflow/python/ops/math_grad_test.py +++ b/tensorflow/python/ops/math_grad_test.py @@ -33,6 +33,8 @@ from tensorflow.python.ops import gradients from tensorflow.python.ops import math_ops from tensorflow.python.platform import test +RAISE = execution_callbacks.ExecutionCallback.RAISE + class SquaredDifferenceOpTest(test.TestCase): @@ -385,7 +387,7 @@ class PowGradTest(test.TestCase): self.assertAllClose([-2., 0., 2.], g) def test_zero_grad_tape(self): - with execution_callbacks.errstate(inf_or_nan=execution_callbacks.RAISE): + with execution_callbacks.errstate(inf_or_nan=RAISE): x = constant_op.constant([-1, 0., 1.]) with backprop.GradientTape() as tape: tape.watch(x) -- GitLab From 8eb8217c58edd2f6e7b7bd398ce6495ec29099af Mon Sep 17 00:00:00 2001 From: Eugene Zhulenev Date: Mon, 10 Dec 2018 10:53:48 -0800 Subject: [PATCH 293/873] Optimize gemm_pack_rhs for row stride != 1 PiperOrigin-RevId: 224842783 --- tensorflow/core/kernels/eigen_spatial_convolutions.h | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/tensorflow/core/kernels/eigen_spatial_convolutions.h b/tensorflow/core/kernels/eigen_spatial_convolutions.h index 25c735d080..86d8c98ee6 100644 --- a/tensorflow/core/kernels/eigen_spatial_convolutions.h +++ b/tensorflow/core/kernels/eigen_spatial_convolutions.h @@ -871,11 +871,9 @@ struct gemm_pack_rhs< const bool pad_col2 = dm2.padCol(c); const bool pad_col3 = dm3.padCol(c); - // We can squeeze reads along the `row` and `depth` dimensions if - // the row stride is `1`, which means that `row` and `depth` - // dimensions are contiguous (two innermost dimensions). - if (rhs.rowStride() == 1 && // - !pad_col0 && !pad_col1 && !pad_col2 && !pad_col3 && // + // Check if we can squeeze reads along the `row` and `depth` + // dimensions (two innermost dimensions). + if (!pad_col0 && !pad_col1 && !pad_col2 && !pad_col3 && // !dm0.padRow(start_row) && !dm0.padRow(max_row - 1) && // !dm1.padRow(start_row) && !dm1.padRow(max_row - 1) && // !dm2.padRow(start_row) && !dm2.padRow(max_row - 1) && // -- GitLab From ea02fb88d2abe11b1a7779abb0a7d50e07f9d7b8 Mon Sep 17 00:00:00 2001 From: Frank Chen Date: Mon, 10 Dec 2018 10:58:15 -0800 Subject: [PATCH 294/873] Unify num_accelerators for all Cluster Resolvers PiperOrigin-RevId: 224843723 --- .../cluster_resolver/cluster_resolver.py | 15 +++++- .../cluster_resolver/cluster_resolver_test.py | 53 +++++++++++++++++++ .../cluster_resolver/gce_cluster_resolver.py | 13 ----- .../kubernetes_cluster_resolver.py | 14 ----- .../tfconfig_cluster_resolver.py | 19 +------ .../tfconfig_cluster_resolver_test.py | 4 +- .../cluster_resolver/tpu_cluster_resolver.py | 14 +++-- 7 files changed, 74 insertions(+), 58 deletions(-) diff --git a/tensorflow/python/distribute/cluster_resolver/cluster_resolver.py b/tensorflow/python/distribute/cluster_resolver/cluster_resolver.py index ca40e60a55..73188bd7ca 100644 --- a/tensorflow/python/distribute/cluster_resolver/cluster_resolver.py +++ b/tensorflow/python/distribute/cluster_resolver/cluster_resolver.py @@ -22,6 +22,8 @@ import abc import six +from tensorflow.python.client import session +from tensorflow.python.framework import ops from tensorflow.python.training.server_lib import ClusterSpec @@ -32,6 +34,14 @@ def format_master_url(master, rpc_layer=None): return master +def get_accelerator_devices(master, config_proto): + # TODO(frankchn): Add support for eager mode as well as graph mode. + with ops.Graph().as_default(): + with session.Session(master, config=config_proto) as s: + devices = s.list_devices() + return devices + + @six.add_metaclass(abc.ABCMeta) class ClusterResolver(object): """Abstract class for all implementations of ClusterResolvers. @@ -91,7 +101,6 @@ class ClusterResolver(object): """ raise NotImplementedError() - @abc.abstractmethod def num_accelerators(self, task_type=None, task_index=None, @@ -119,7 +128,9 @@ class ClusterResolver(object): config_proto: (Optional) Configuration for starting a new session to query how many accelerator cores it has. """ - raise NotImplementedError() + master = self.master(task_type, task_index) + devices = get_accelerator_devices(master, config_proto) + return sum(1 for d in devices if d.device_type == accelerator_type) @abc.abstractproperty def environment(self): diff --git a/tensorflow/python/distribute/cluster_resolver/cluster_resolver_test.py b/tensorflow/python/distribute/cluster_resolver/cluster_resolver_test.py index 3f7b469727..0ff6b6be62 100644 --- a/tensorflow/python/distribute/cluster_resolver/cluster_resolver_test.py +++ b/tensorflow/python/distribute/cluster_resolver/cluster_resolver_test.py @@ -18,11 +18,64 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.python.client import session +from tensorflow.python.distribute.cluster_resolver import ClusterResolver from tensorflow.python.distribute.cluster_resolver import SimpleClusterResolver from tensorflow.python.distribute.cluster_resolver import UnionClusterResolver from tensorflow.python.platform import test from tensorflow.python.training import server_lib +mock = test.mock + + +class MockBaseClusterResolver(ClusterResolver): + + def cluster_spec(self): + return None + + def master(self, task_type=None, task_index=None, rpc_layer=None): + return "" + + def environment(self): + return "" + + +class BaseClusterResolverTest(test.TestCase): + + @mock.patch.object(session.BaseSession, "list_devices") + def testNumAcceleratorsSuccess(self, mock_list_devices): + device_names = [ + "/job:worker/task:0/device:GPU:0", + "/job:worker/task:0/device:GPU:1", + "/job:worker/task:0/device:GPU:2", + "/job:worker/task:0/device:GPU:3", + ] + device_list = [ + session._DeviceAttributes( + name, "GPU", 1024, 0) for name in device_names + ] + mock_list_devices.return_value = device_list + + resolver = MockBaseClusterResolver() + self.assertEqual(resolver.num_accelerators(), 4) + + @mock.patch.object(session.BaseSession, "list_devices") + def testNumAcceleratorsFilterSuccess(self, mock_list_devices): + device_names = [ + "/job:worker/task:0/device:TPU:0", + "/job:worker/task:0/device:TPU:1", + "/job:worker/task:0/device:TPU:2", + "/job:worker/task:0/device:TPU:3", + ] + device_list = [ + session._DeviceAttributes( + name, "TPU", 1024, 0) for name in device_names + ] + mock_list_devices.return_value = device_list + + resolver = MockBaseClusterResolver() + self.assertEqual(resolver.num_accelerators(), 0) + class UnionClusterResolverTest(test.TestCase): # TODO(frankchn): Transform to parameterized test after it is included in the diff --git a/tensorflow/python/distribute/cluster_resolver/gce_cluster_resolver.py b/tensorflow/python/distribute/cluster_resolver/gce_cluster_resolver.py index 2412f6dad0..06512613cb 100644 --- a/tensorflow/python/distribute/cluster_resolver/gce_cluster_resolver.py +++ b/tensorflow/python/distribute/cluster_resolver/gce_cluster_resolver.py @@ -51,7 +51,6 @@ class GceClusterResolver(ClusterResolver): task_type='worker', task_index=0, rpc_layer='grpc', - num_accelerators=0, credentials='default', service=None): """Creates a new GceClusterResolver object. @@ -73,8 +72,6 @@ class GceClusterResolver(ClusterResolver): can be distinguished from each other. rpc_layer: The RPC layer TensorFlow should use to communicate across instances. - num_accelerators: Number of accelerators (GPUs) present per - instance. credentials: GCE Credentials. If nothing is specified, this defaults to GoogleCredentials.get_application_default(). service: The GCE API object returned by the googleapiclient.discovery @@ -90,7 +87,6 @@ class GceClusterResolver(ClusterResolver): self._task_type = task_type self._task_index = task_index self._rpc_layer = rpc_layer - self._num_accelerators = num_accelerators self._port = port self._credentials = credentials @@ -201,12 +197,3 @@ class GceClusterResolver(ClusterResolver): @rpc_layer.setter def rpc_layer(self, rpc_layer): self._rpc_layer = rpc_layer - - def num_accelerators(self, - task_type=None, - task_index=None, - accelerator_type='GPU', - config_proto=None): - # Unused - del task_type, task_index, accelerator_type, config_proto - return self._num_accelerators diff --git a/tensorflow/python/distribute/cluster_resolver/kubernetes_cluster_resolver.py b/tensorflow/python/distribute/cluster_resolver/kubernetes_cluster_resolver.py index b21c3676be..88625a5542 100644 --- a/tensorflow/python/distribute/cluster_resolver/kubernetes_cluster_resolver.py +++ b/tensorflow/python/distribute/cluster_resolver/kubernetes_cluster_resolver.py @@ -18,7 +18,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.python.client import device_lib from tensorflow.python.distribute.cluster_resolver.cluster_resolver import ClusterResolver from tensorflow.python.distribute.cluster_resolver.cluster_resolver import format_master_url from tensorflow.python.training import server_lib @@ -167,16 +166,3 @@ class KubernetesClusterResolver(ClusterResolver): on internal systems. """ return '' - - def num_accelerators(self, - task_type=None, - task_index=None, - accelerator_type='GPU', - config_proto=None): - # TODO(frankchn): Make querying non-local accelerators work - if task_type is not None or task_index is not None: - raise NotImplementedError('Querying non-local accelerators is not yet' - 'implemented.') - - local_devices = device_lib.list_local_devices(config_proto) - return sum(d.device_type == accelerator_type for d in local_devices) diff --git a/tensorflow/python/distribute/cluster_resolver/tfconfig_cluster_resolver.py b/tensorflow/python/distribute/cluster_resolver/tfconfig_cluster_resolver.py index b4465714b2..8d530cc15a 100644 --- a/tensorflow/python/distribute/cluster_resolver/tfconfig_cluster_resolver.py +++ b/tensorflow/python/distribute/cluster_resolver/tfconfig_cluster_resolver.py @@ -54,8 +54,7 @@ class TFConfigClusterResolver(ClusterResolver): task_type=None, task_index=None, rpc_layer=None, - environment=None, - num_accelerators=0): + environment=None): """Creates a new TFConfigClusterResolver. Args: @@ -66,17 +65,11 @@ class TFConfigClusterResolver(ClusterResolver): rpc_layer: (String, optional) Overrides the rpc layer TensorFlow uses. environment: (String, optional) Overrides the environment TensorFlow operates in. - num_accelerators: (Integer, optional) Specifies the number of - accelerators (e.g. GPUs, TPUs, others) that each node has. """ - # TODO(frankchn): num_accelerators is a stop-gap and will be removed - # in favor of autodetection of devices soon. - self._task_type = task_type self._task_index = task_index self._rpc_layer = rpc_layer self._environment = environment - self._num_accelerators = num_accelerators @property def task_type(self): @@ -117,16 +110,6 @@ class TFConfigClusterResolver(ClusterResolver): def rpc_layer(self, rpc_layer): self._rpc_layer = rpc_layer - def num_accelerators(self, - task_type=None, - task_index=None, - accelerator_type='GPU', - config_proto=None): - # TODO(frankchn): Connect to server (w/ session_config) in the future. - # Unused, we do not connect to another server here right now. - del task_type, task_index, accelerator_type, config_proto - return self._num_accelerators - def cluster_spec(self): """Returns a ClusterSpec based on the TF_CONFIG environment variable. diff --git a/tensorflow/python/distribute/cluster_resolver/tfconfig_cluster_resolver_test.py b/tensorflow/python/distribute/cluster_resolver/tfconfig_cluster_resolver_test.py index 197eba1739..36b3bb9c1e 100644 --- a/tensorflow/python/distribute/cluster_resolver/tfconfig_cluster_resolver_test.py +++ b/tensorflow/python/distribute/cluster_resolver/tfconfig_cluster_resolver_test.py @@ -168,13 +168,11 @@ class TFConfigClusterResolverTest(test.TestCase): } """ - cluster_resolver = TFConfigClusterResolver(task_type='ps', task_index=0, - num_accelerators=8) + cluster_resolver = TFConfigClusterResolver(task_type='ps', task_index=0) self.assertEqual('grpc://ps0:2222', cluster_resolver.master()) self.assertEqual('ps', cluster_resolver.task_type) self.assertEqual(0, cluster_resolver.task_index) - self.assertEqual(8, cluster_resolver.num_accelerators()) cluster_resolver.task_type = 'worker' cluster_resolver.task_index = 1 diff --git a/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver.py b/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver.py index e907d6fde4..72a27b915c 100644 --- a/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver.py +++ b/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver.py @@ -25,11 +25,10 @@ import re from six.moves.urllib.request import Request from six.moves.urllib.request import urlopen -from tensorflow.python.client import session from tensorflow.python.distribute.cluster_resolver.cluster_resolver import ClusterResolver from tensorflow.python.distribute.cluster_resolver.cluster_resolver import format_master_url +from tensorflow.python.distribute.cluster_resolver.cluster_resolver import get_accelerator_devices from tensorflow.python.framework import errors -from tensorflow.python.framework import ops from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import server_lib from tensorflow.python.util import compat @@ -451,17 +450,16 @@ class TPUClusterResolver(ClusterResolver): retrieve the system metadata. Raises: - RuntimeError: If this is used with a non-TPU accelerator_type. + RuntimeError: If we cannot talk to a TPU worker after retrying or if the + number of TPU devices per host is different. """ retry_count = 1 # TODO(b/120564445): Replace with standard library for retries. while True: try: - with ops.Graph().as_default(): - with session.Session(self.master(), config=config_proto) as s: - devices = s.list_devices() - device_details = _get_device_dict_and_cores(devices) - break + device_details = _get_device_dict_and_cores( + get_accelerator_devices(self.master(), config_proto=config_proto)) + break except errors.DeadlineExceededError: error_message = ('Failed to connect to master. The TPU might not be ' 'ready (e.g. still scheduling) or the master ' -- GitLab From a9c129a66c6ec4328f16aac6a66f0d3d31f88581 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 10 Dec 2018 11:16:09 -0800 Subject: [PATCH 295/873] Automated rollback of commit 3640da49c3731807a3dbc27d813e8ab68a86328a PiperOrigin-RevId: 224847522 --- tensorflow/contrib/tpu/python/tpu/tpu_estimator.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index 96b9556e13..84816d70d0 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -2234,7 +2234,7 @@ class TPUEstimator(estimator_lib.Estimator): def computation(): """Compute tpu tensors used in export_outputs. - Passed to rewrite so that model_fn will be called under + Passed to rewrite_for_inference so that model_fn will be called under the rewriting contexts. Only tpu tensors are returned, but export_outputs and scaffold are captured. @@ -2243,7 +2243,7 @@ class TPUEstimator(estimator_lib.Estimator): outside_compilation. """ # We should only call model fn once and it should be inside `computation` - # so that building the graph will happen under `rewrite`. + # so that building the graph will happen under `rewrite_for_inference`. mode = model_fn_lib.ModeKeys.PREDICT estimator_spec = self._call_model_fn(features, labels, mode, config) @@ -2260,7 +2260,7 @@ class TPUEstimator(estimator_lib.Estimator): capture.capture((estimator_spec, tensors_dict, tensors)) return tpu_tensors - tpu_tensors_on_cpu = tpu.rewrite(computation) + tpu_tensors_on_cpu = tpu.rewrite_for_inference(computation) estimator_spec, tensors_dict, tensors = capture.get() # Reconstruct `tensors`, but with `tpu_tensors` replaced with -- GitLab From 16bd4eb5f2f58111a55f8b223f161f0ce1c07be5 Mon Sep 17 00:00:00 2001 From: Pete Warden Date: Mon, 10 Dec 2018 11:42:52 -0800 Subject: [PATCH 296/873] Add extra tests to the speech example's makefile PiperOrigin-RevId: 224852926 --- .../micro/examples/micro_speech/Makefile.inc | 153 ++++++++++++++++++ .../experimental/micro/tools/make/Makefile | 80 +-------- 2 files changed, 156 insertions(+), 77 deletions(-) create mode 100644 tensorflow/lite/experimental/micro/examples/micro_speech/Makefile.inc diff --git a/tensorflow/lite/experimental/micro/examples/micro_speech/Makefile.inc b/tensorflow/lite/experimental/micro/examples/micro_speech/Makefile.inc new file mode 100644 index 0000000000..0e42329cad --- /dev/null +++ b/tensorflow/lite/experimental/micro/examples/micro_speech/Makefile.inc @@ -0,0 +1,153 @@ + +# Tests loading and running a speech model. +MICRO_SPEECH_TEST_SRCS := \ +tensorflow/lite/experimental/micro/examples/micro_speech/micro_speech_test.cc \ +tensorflow/lite/experimental/micro/examples/micro_speech/tiny_conv_model_data.cc \ +tensorflow/lite/experimental/micro/examples/micro_speech/no_features_data.cc \ +tensorflow/lite/experimental/micro/examples/micro_speech/yes_features_data.cc +ALL_SRCS += $(MICRO_SPEECH_TEST_SRCS) +MICRO_SPEECH_TEST_OBJS := $(addprefix $(OBJDIR), \ +$(patsubst %.cc,%.o,$(patsubst %.c,%.o,$(MICRO_SPEECH_TEST_SRCS)))) +MICRO_SPEECH_TEST_BINARY := $(BINDIR)micro_speech_test +ALL_BINARIES += $(MICRO_SPEECH_TEST_BINARY) +$(MICRO_SPEECH_TEST_BINARY): $(MICRO_SPEECH_TEST_OBJS) $(MICROLITE_LIB_PATH) + @mkdir -p $(dir $@) + $(CXX) $(CXXFLAGS) $(INCLUDES) \ + -o $(MICRO_SPEECH_TEST_BINARY) $(MICRO_SPEECH_TEST_OBJS) \ + $(LIBFLAGS) $(MICROLITE_LIB_PATH) $(LDFLAGS) $(MICROLITE_LIBS) +micro_speech_test: $(MICRO_SPEECH_TEST_BINARY) +micro_speech_test_bin: $(MICRO_SPEECH_TEST_BINARY).bin +test_micro_speech: $(MICRO_SPEECH_TEST_BINARY) + $(TEST_SCRIPT) $(MICRO_SPEECH_TEST_BINARY) '~~~ALL TESTS PASSED~~~' + +# Source files that are used by multiple preprocessor tests. +PREPROCESSOR_TEST_SHARED_SRCS := \ +tensorflow/lite/experimental/micro/examples/micro_speech/preprocessor_test.cc \ +tensorflow/lite/experimental/micro/examples/micro_speech/no_30ms_sample_data.cc \ +tensorflow/lite/experimental/micro/examples/micro_speech/yes_30ms_sample_data.cc \ +tensorflow/lite/experimental/micro/examples/micro_speech/no_power_spectrum_data.cc \ +tensorflow/lite/experimental/micro/examples/micro_speech/yes_power_spectrum_data.cc + +# Test the float reference code for feature generation. +PREPROCESSOR_REFERENCE_TEST_SRCS = \ +$(PREPROCESSOR_TEST_SHARED_SRCS) \ +tensorflow/lite/experimental/micro/examples/micro_speech/preprocessor.cc +ALL_SRCS += $(PREPROCESSOR_REFERENCE_TEST_SRCS) +PREPROCESSOR_REFERENCE_TEST_OBJS := $(addprefix $(OBJDIR), \ +$(patsubst %.cc,%.o,$(patsubst %.c,%.o,$(PREPROCESSOR_REFERENCE_TEST_SRCS)))) +PREPROCESSOR_REFERENCE_TEST_BINARY := $(BINDIR)preprocessor_reference_test +ALL_BINARIES += $(PREPROCESSOR_REFERENCE_TEST_BINARY) +$(PREPROCESSOR_REFERENCE_TEST_BINARY): $(PREPROCESSOR_REFERENCE_TEST_OBJS) $(MICROLITE_LIB_PATH) + @mkdir -p $(dir $@) + $(CXX) $(CXXFLAGS) $(INCLUDES) \ + -o $(PREPROCESSOR_REFERENCE_TEST_BINARY) $(PREPROCESSOR_REFERENCE_TEST_OBJS) \ + $(LIBFLAGS) $(MICROLITE_LIB_PATH) $(LDFLAGS) $(MICROLITE_LIBS) +preprocessor_reference_test: $(PREPROCESSOR_REFERENCE_TEST_BINARY) +preprocessor_reference_test_bin: $(PREPROCESSOR_REFERENCE_TEST_BINARY).bin +test_preprocessor_reference: $(PREPROCESSOR_REFERENCE_TEST_BINARY) + $(TEST_SCRIPT) $(PREPROCESSOR_REFERENCE_TEST_BINARY) '~~~ALL TESTS PASSED~~~' + +# Test the fixed point reference code for feature generation. +PREPROCESSOR_FIXED_TEST_SRCS = \ +$(PREPROCESSOR_TEST_SHARED_SRCS) \ +tensorflow/lite/experimental/micro/examples/micro_speech/fixed_point/preprocessor.cc +ALL_SRCS += $(PREPROCESSOR_FIXED_TEST_SRCS) +PREPROCESSOR_FIXED_TEST_OBJS := $(addprefix $(OBJDIR), \ +$(patsubst %.cc,%.o,$(patsubst %.c,%.o,$(PREPROCESSOR_FIXED_TEST_SRCS)))) +PREPROCESSOR_FIXED_TEST_BINARY := $(BINDIR)preprocessor_fixed_test +ALL_BINARIES += $(PREPROCESSOR_FIXED_TEST_BINARY) +$(PREPROCESSOR_FIXED_TEST_BINARY): $(PREPROCESSOR_FIXED_TEST_OBJS) $(MICROLITE_LIB_PATH) + @mkdir -p $(dir $@) + $(CXX) $(CXXFLAGS) $(INCLUDES) \ + -o $(PREPROCESSOR_FIXED_TEST_BINARY) $(PREPROCESSOR_FIXED_TEST_OBJS) \ + $(LIBFLAGS) $(MICROLITE_LIB_PATH) $(LDFLAGS) $(MICROLITE_LIBS) +preprocessor_fixed_test: $(PREPROCESSOR_FIXED_TEST_BINARY) +preprocessor_fixed_test_bin: $(PREPROCESSOR_FIXED_TEST_BINARY).bin +test_preprocessor_fixed: $(PREPROCESSOR_FIXED_TEST_BINARY) + $(TEST_SCRIPT) $(PREPROCESSOR_FIXED_TEST_BINARY) '~~~ALL TESTS PASSED~~~' + +# Tests the audio provider module. +AUDIO_PROVIDER_TEST_SRCS := \ +tensorflow/lite/experimental/micro/examples/micro_speech/audio_provider_test.cc \ +tensorflow/lite/experimental/micro/examples/micro_speech/model_settings.cc \ +tensorflow/lite/experimental/micro/examples/micro_speech/audio_provider.cc +ALL_SRCS += $(AUDIO_PROVIDER_TEST_SRCS) +AUDIO_PROVIDER_TEST_OBJS := $(addprefix $(OBJDIR), \ +$(patsubst %.cc,%.o,$(patsubst %.c,%.o,$(AUDIO_PROVIDER_TEST_SRCS)))) +AUDIO_PROVIDER_TEST_BINARY := $(BINDIR)audio_provider_test +ALL_BINARIES += $(AUDIO_PROVIDER_TEST_BINARY) +$(AUDIO_PROVIDER_TEST_BINARY): $(AUDIO_PROVIDER_TEST_OBJS) $(MICROLITE_LIB_PATH) + @mkdir -p $(dir $@) + $(CXX) $(CXXFLAGS) $(INCLUDES) \ + -o $(AUDIO_PROVIDER_TEST_BINARY) $(AUDIO_PROVIDER_TEST_OBJS) \ + $(LIBFLAGS) $(MICROLITE_LIB_PATH) $(LDFLAGS) $(MICROLITE_LIBS) +audio_provider_test: $(AUDIO_PROVIDER_TEST_BINARY) +audio_provider_test_bin: $(AUDIO_PROVIDER_TEST_BINARY).bin +test_audio_provider: $(AUDIO_PROVIDER_TEST_BINARY) + $(TEST_SCRIPT) $(AUDIO_PROVIDER_TEST_BINARY) '~~~ALL TESTS PASSED~~~' + +# Tests the feature provider module. +FEATURE_PROVIDER_TEST_SRCS := \ +tensorflow/lite/experimental/micro/examples/micro_speech/feature_provider_test.cc \ +tensorflow/lite/experimental/micro/examples/micro_speech/model_settings.cc \ +tensorflow/lite/experimental/micro/examples/micro_speech/audio_provider.cc \ +tensorflow/lite/experimental/micro/examples/micro_speech/timer.cc \ +tensorflow/lite/experimental/micro/examples/micro_speech/preprocessor.cc \ +tensorflow/lite/experimental/micro/examples/micro_speech/feature_provider.cc +ALL_SRCS += $(FEATURE_PROVIDER_TEST_SRCS) +FEATURE_PROVIDER_TEST_OBJS := $(addprefix $(OBJDIR), \ +$(patsubst %.cc,%.o,$(patsubst %.c,%.o,$(FEATURE_PROVIDER_TEST_SRCS)))) +FEATURE_PROVIDER_TEST_BINARY := $(BINDIR)feature_provider_test +ALL_BINARIES += $(FEATURE_PROVIDER_TEST_BINARY) +$(FEATURE_PROVIDER_TEST_BINARY): $(FEATURE_PROVIDER_TEST_OBJS) $(MICROLITE_LIB_PATH) + @mkdir -p $(dir $@) + $(CXX) $(CXXFLAGS) $(INCLUDES) \ + -o $(FEATURE_PROVIDER_TEST_BINARY) $(FEATURE_PROVIDER_TEST_OBJS) \ + $(LIBFLAGS) $(MICROLITE_LIB_PATH) $(LDFLAGS) $(MICROLITE_LIBS) +feature_provider_test: $(FEATURE_PROVIDER_TEST_BINARY) +feature_provider_test_bin: $(FEATURE_PROVIDER_TEST_BINARY).bin +test_feature_provider: $(FEATURE_PROVIDER_TEST_BINARY) + $(TEST_SCRIPT) $(FEATURE_PROVIDER_TEST_BINARY) '~~~ALL TESTS PASSED~~~' + +# Tests the timer module. +TIMER_TEST_SRCS := \ +tensorflow/lite/experimental/micro/examples/micro_speech/timer_test.cc \ +tensorflow/lite/experimental/micro/examples/micro_speech/timer.cc +ALL_SRCS += $(TIMER_TEST_SRCS) +TIMER_TEST_OBJS := $(addprefix $(OBJDIR), \ +$(patsubst %.cc,%.o,$(patsubst %.c,%.o,$(TIMER_TEST_SRCS)))) +TIMER_TEST_BINARY := $(BINDIR)timer_test +ALL_BINARIES += $(TIMER_TEST_BINARY) +$(TIMER_TEST_BINARY): $(TIMER_TEST_OBJS) $(MICROLITE_LIB_PATH) + @mkdir -p $(dir $@) + $(CXX) $(CXXFLAGS) $(INCLUDES) \ + -o $(TIMER_TEST_BINARY) $(TIMER_TEST_OBJS) \ + $(LIBFLAGS) $(MICROLITE_LIB_PATH) $(LDFLAGS) $(MICROLITE_LIBS) +timer_test: $(TIMER_TEST_BINARY) +timer_test_bin: $(TIMER_TEST_BINARY).bin +test_timer: $(TIMER_TEST_BINARY) + $(TEST_SCRIPT) $(TIMER_TEST_BINARY) '~~~ALL TESTS PASSED~~~' + +# Builds a standalone speech command recognizer binary. +MICRO_SPEECH_SRCS := \ +tensorflow/lite/experimental/micro/examples/micro_speech/main.cc \ +tensorflow/lite/experimental/micro/examples/micro_speech/model_settings.cc \ +tensorflow/lite/experimental/micro/examples/micro_speech/audio_provider.cc \ +tensorflow/lite/experimental/micro/examples/micro_speech/feature_provider.cc \ +tensorflow/lite/experimental/micro/examples/micro_speech/timer.cc \ +tensorflow/lite/experimental/micro/examples/micro_speech/preprocessor.cc \ +tensorflow/lite/experimental/micro/examples/micro_speech/no_features_data.cc \ +tensorflow/lite/experimental/micro/examples/micro_speech/yes_features_data.cc \ +tensorflow/lite/experimental/micro/examples/micro_speech/tiny_conv_model_data.cc +ALL_SRCS += $(MICRO_SPEECH_SRCS) +MICRO_SPEECH_OBJS := $(addprefix $(OBJDIR), \ +$(patsubst %.cc,%.o,$(patsubst %.c,%.o,$(MICRO_SPEECH_SRCS)))) +MICRO_SPEECH_BINARY := $(BINDIR)micro_speech +ALL_BINARIES += $(MICRO_SPEECH_BINARY) +$(MICRO_SPEECH_BINARY): $(MICRO_SPEECH_OBJS) $(MICROLITE_LIB_PATH) + @mkdir -p $(dir $@) + $(CXX) $(CXXFLAGS) $(INCLUDES) \ + -o $(MICRO_SPEECH_BINARY) $(MICRO_SPEECH_OBJS) \ + $(LIBFLAGS) $(MICROLITE_LIB_PATH) $(LDFLAGS) $(MICROLITE_LIBS) +micro_speech: $(MICRO_SPEECH_BINARY) +micro_speech_bin: $(MICRO_SPEECH_BINARY).bin diff --git a/tensorflow/lite/experimental/micro/tools/make/Makefile b/tensorflow/lite/experimental/micro/tools/make/Makefile index 0caf0ca099..20307e2b21 100644 --- a/tensorflow/lite/experimental/micro/tools/make/Makefile +++ b/tensorflow/lite/experimental/micro/tools/make/Makefile @@ -52,29 +52,6 @@ CC_PREFIX := # runtime that can be linked in to other programs. MICROLITE_LIB_NAME := libtensorflow-microlite.a -# Test binary for the microcontroller speech model. -MICRO_SPEECH_TEST_SRCS := \ -tensorflow/lite/experimental/micro/examples/micro_speech/micro_speech_test.cc \ -tensorflow/lite/experimental/micro/examples/micro_speech/tiny_conv_model_data.cc \ -tensorflow/lite/experimental/micro/examples/micro_speech/no_features_data.cc \ -tensorflow/lite/experimental/micro/examples/micro_speech/yes_features_data.cc - -# Test binary for the microcontroller speech model. -PREPROCESSOR_TEST_SRCS := \ -tensorflow/lite/experimental/micro/examples/micro_speech/preprocessor_test.cc \ -tensorflow/lite/experimental/micro/examples/micro_speech/no_30ms_sample_data.cc \ -tensorflow/lite/experimental/micro/examples/micro_speech/yes_30ms_sample_data.cc \ -tensorflow/lite/experimental/micro/examples/micro_speech/no_power_spectrum_data.cc \ -tensorflow/lite/experimental/micro/examples/micro_speech/yes_power_spectrum_data.cc - -PREPROCESSOR_REFERENCE_TEST_SRCS = \ -$(PREPROCESSOR_TEST_SRCS) \ -tensorflow/lite/experimental/micro/examples/micro_speech/preprocessor.cc - -PREPROCESSOR_FIXED_TEST_SRCS += \ -$(PREPROCESSOR_TEST_SRCS) \ -tensorflow/lite/experimental/micro/examples/micro_speech/fixed_point/preprocessor.cc - MICROLITE_TEST_SRCS := \ $(wildcard tensorflow/lite/experimental/micro/*test.cc) \ $(wildcard tensorflow/lite/experimental/micro/kernels/*test.cc) @@ -97,9 +74,6 @@ MICROLITE_CC_SRCS := $(filter-out $(MICROLITE_TEST_SRCS), $(MICROLITE_CC_BASE_SR include $(wildcard $(MAKEFILE_DIR)/targets/*_makefile.inc) ALL_SRCS := \ - $(MICRO_SPEECH_TEST_SRCS) \ - $(PREPROCESSOR_REFERENCE_TEST_SRCS) \ - $(PREPROCESSOR_FIXED_TEST_SRCS) \ $(MICROLITE_CC_SRCS) \ $(MICROLITE_TEST_SRCS) @@ -111,22 +85,12 @@ LIBDIR := $(GENDIR)lib/ MICROLITE_LIB_PATH := $(LIBDIR)$(MICROLITE_LIB_NAME) -MICRO_SPEECH_TEST_BINARY := $(BINDIR)micro_speech_test -PREPROCESSOR_REFERENCE_TEST_BINARY := $(BINDIR)preprocessor_reference_test -PREPROCESSOR_FIXED_TEST_BINARY := $(BINDIR)preprocessor_fixed_test - CXX := $(CC_PREFIX)${TARGET_TOOLCHAIN_PREFIX}g++ CC := $(CC_PREFIX)${TARGET_TOOLCHAIN_PREFIX}gcc AR := $(CC_PREFIX)${TARGET_TOOLCHAIN_PREFIX}ar -MICRO_SPEECH_TEST_OBJS := $(addprefix $(OBJDIR), \ -$(patsubst %.cc,%.o,$(patsubst %.c,%.o,$(MICRO_SPEECH_TEST_SRCS)))) - -PREPROCESSOR_REFERENCE_TEST_OBJS := $(addprefix $(OBJDIR), \ -$(patsubst %.cc,%.o,$(patsubst %.c,%.o,$(PREPROCESSOR_REFERENCE_TEST_SRCS)))) - -PREPROCESSOR_FIXED_TEST_OBJS := $(addprefix $(OBJDIR), \ -$(patsubst %.cc,%.o,$(patsubst %.c,%.o,$(PREPROCESSOR_FIXED_TEST_SRCS)))) +# Load the examples. +include $(wildcard tensorflow/lite/experimental/micro/examples/*/Makefile.inc) MICROLITE_LIB_OBJS := $(addprefix $(OBJDIR), \ $(patsubst %.cc,%.o,$(patsubst %.c,%.o,$(MICROLITE_CC_SRCS)))) @@ -145,7 +109,7 @@ $(OBJDIR)%.o: %.c $(CC) $(CCFLAGS) $(INCLUDES) -c $< -o $@ # The target that's compiled if there's no command-line arguments. -all: $(MICROLITE_LIB_PATH) $(MICRO_SPEECH_TEST_BINARY) $(PREPROCESSOR_TEST_BINARY) +all: $(MICROLITE_LIB_PATH) $(ALL_BINARIES) microlite: $(MICROLITE_LIB_PATH) @@ -158,42 +122,6 @@ $(MICROLITE_LIB_PATH): tensorflow/lite/schema/schema_generated.h $(MICROLITE_LIB @mkdir -p $(dir $@) $(AR) $(ARFLAGS) $(MICROLITE_LIB_PATH) $(MICROLITE_LIB_OBJS) -$(MICRO_SPEECH_TEST_BINARY): $(MICRO_SPEECH_TEST_OBJS) $(MICROLITE_LIB_PATH) - @mkdir -p $(dir $@) - $(CXX) $(CXXFLAGS) $(INCLUDES) \ - -o $(MICRO_SPEECH_TEST_BINARY) $(MICRO_SPEECH_TEST_OBJS) \ - $(LIBFLAGS) $(MICROLITE_LIB_PATH) $(LDFLAGS) $(MICROLITE_LIBS) - -micro_speech_test: $(MICRO_SPEECH_TEST_BINARY) -micro_speech_test_bin: $(MICRO_SPEECH_TEST_BINARY).bin - -test_micro_speech: $(MICRO_SPEECH_TEST_BINARY) - $(TEST_SCRIPT) $(MICRO_SPEECH_TEST_BINARY) '~~~ALL TESTS PASSED~~~' - -$(PREPROCESSOR_REFERENCE_TEST_BINARY): $(PREPROCESSOR_REFERENCE_TEST_OBJS) $(MICROLITE_LIB_PATH) - @mkdir -p $(dir $@) - $(CXX) $(CXXFLAGS) $(INCLUDES) \ - -o $(PREPROCESSOR_REFERENCE_TEST_BINARY) $(PREPROCESSOR_REFERENCE_TEST_OBJS) \ - $(LIBFLAGS) $(MICROLITE_LIB_PATH) $(LDFLAGS) $(MICROLITE_LIBS) - -preprocessor_reference_test: $(PREPROCESSOR_REFERENCE_TEST_BINARY) -preprocessor_reference_test_bin: $(PREPROCESSOR_REFERENCE_TEST_BINARY).bin - -test_preprocessor_reference: $(PREPROCESSOR_REFERENCE_TEST_BINARY) - $(TEST_SCRIPT) $(PREPROCESSOR_REFERENCE_TEST_BINARY) '~~~ALL TESTS PASSED~~~' - -$(PREPROCESSOR_FIXED_TEST_BINARY): $(PREPROCESSOR_FIXED_TEST_OBJS) $(MICROLITE_LIB_PATH) - @mkdir -p $(dir $@) - $(CXX) $(CXXFLAGS) $(INCLUDES) \ - -o $(PREPROCESSOR_FIXED_TEST_BINARY) $(PREPROCESSOR_FIXED_TEST_OBJS) \ - $(LIBFLAGS) $(MICROLITE_LIB_PATH) $(LDFLAGS) $(MICROLITE_LIBS) - -preprocessor_fixed_test: $(PREPROCESSOR_FIXED_TEST_BINARY) -preprocessor_fixed_test_bin: $(PREPROCESSOR_FIXED_TEST_BINARY).bin - -test_preprocessor_fixed: $(PREPROCESSOR_FIXED_TEST_BINARY) - $(TEST_SCRIPT) $(PREPROCESSOR_FIXED_TEST_BINARY) '~~~ALL TESTS PASSED~~~' - $(BINDIR)%_test : $(OBJDIR)%_test.o $(MICROLITE_LIB_PATH) @mkdir -p $(dir $@) $(CXX) $(CXXFLAGS) $(INCLUDES) \ @@ -203,8 +131,6 @@ $(BINDIR)%_test : $(OBJDIR)%_test.o $(MICROLITE_LIB_PATH) $(BINDIR)%.test_target: $(BINDIR)%_test $(TEST_SCRIPT) $< '~~~ALL TESTS PASSED~~~' -$(info $(MICROLITE_TEST_TARGETS)) - test: test_micro_speech $(MICROLITE_TEST_TARGETS) # Gets rid of all generated files. -- GitLab From 6c4622385b762da1537a83e21b67d135c2890640 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 10 Dec 2018 11:48:32 -0800 Subject: [PATCH 297/873] Treat a threshold of None as invalid in Keras metrics._assert_thresholds_range. PiperOrigin-RevId: 224853938 --- tensorflow/python/keras/metrics.py | 26 ++++++++++++------------- tensorflow/python/keras/metrics_test.py | 6 ++++++ 2 files changed, 19 insertions(+), 13 deletions(-) diff --git a/tensorflow/python/keras/metrics.py b/tensorflow/python/keras/metrics.py index 331a8636d1..1d1f3b4586 100644 --- a/tensorflow/python/keras/metrics.py +++ b/tensorflow/python/keras/metrics.py @@ -171,8 +171,8 @@ class _ConfusionMatrix(Enum): def _assert_thresholds_range(thresholds): - invalid_thresholds = [t for t in thresholds if t < 0 or t > 1] - if any(invalid_thresholds): + invalid_thresholds = [t for t in thresholds if t is None or t < 0 or t > 1] + if invalid_thresholds: raise ValueError('Threshold values must be in [0, 1]. Invalid values: {}' .format(invalid_thresholds)) @@ -870,11 +870,11 @@ class _ConfusionMatrixConditionCount(Metric): super(_ConfusionMatrixConditionCount, self).__init__(name=name, dtype=dtype) self._confusion_matrix_cond = confusion_matrix_cond self.thresholds = 0.5 if thresholds is None else thresholds - thresholds = to_list(thresholds) - _assert_thresholds_range(thresholds) + thresholds_list = to_list(self.thresholds) + _assert_thresholds_range(thresholds_list) self.accumulator = self.add_weight( 'accumulator', - shape=(len(thresholds),), + shape=(len(thresholds_list),), initializer=init_ops.zeros_initializer) def update_state(self, y_true, y_pred, sample_weight=None): @@ -1153,15 +1153,15 @@ class Precision(Metric): """ super(Precision, self).__init__(name=name, dtype=dtype) self.thresholds = 0.5 if thresholds is None else thresholds - thresholds = to_list(thresholds) - _assert_thresholds_range(thresholds) + thresholds_list = to_list(self.thresholds) + _assert_thresholds_range(thresholds_list) self.tp = self.add_weight( 'true_positives', - shape=(len(thresholds),), + shape=(len(thresholds_list),), initializer=init_ops.zeros_initializer) self.fp = self.add_weight( 'false_positives', - shape=(len(thresholds),), + shape=(len(thresholds_list),), initializer=init_ops.zeros_initializer) def update_state(self, y_true, y_pred, sample_weight=None): @@ -1238,15 +1238,15 @@ class Recall(Metric): """ super(Recall, self).__init__(name=name, dtype=dtype) self.thresholds = 0.5 if thresholds is None else thresholds - thresholds = to_list(thresholds) - _assert_thresholds_range(thresholds) + thresholds_list = to_list(self.thresholds) + _assert_thresholds_range(thresholds_list) self.tp = self.add_weight( 'true_positives', - shape=(len(thresholds),), + shape=(len(thresholds_list),), initializer=init_ops.zeros_initializer) self.fn = self.add_weight( 'false_negatives', - shape=(len(thresholds),), + shape=(len(thresholds_list),), initializer=init_ops.zeros_initializer) def update_state(self, y_true, y_pred, sample_weight=None): diff --git a/tensorflow/python/keras/metrics_test.py b/tensorflow/python/keras/metrics_test.py index 92398acd8e..9cad948966 100644 --- a/tensorflow/python/keras/metrics_test.py +++ b/tensorflow/python/keras/metrics_test.py @@ -369,6 +369,12 @@ class KerasMetricsTest(test.TestCase): result = self.evaluate(result_t) self.assertAlmostEqual(result, 0.93, 2) # 2.5/2.7 + def test_assert_thresholds_range(self): + with self.assertRaisesRegexp( + ValueError, + r'Threshold values must be in \[0, 1\]. Invalid values: \[None\]'): + metrics._assert_thresholds_range([None, 0.5]) + def _get_simple_sequential_model(compile_metrics): model = Sequential() -- GitLab From 4e7564ef05c456a7961e37eb0a6a77a04ca028c5 Mon Sep 17 00:00:00 2001 From: Eugene Zhulenev Date: Mon, 10 Dec 2018 11:52:35 -0800 Subject: [PATCH 298/873] Add new flag to GrapplerItem::AllowedOptimizations PiperOrigin-RevId: 224854657 --- tensorflow/core/grappler/grappler_item.h | 13 ++++++------- .../core/grappler/optimizers/function_optimizer.cc | 2 +- .../core/grappler/optimizers/meta_optimizer.cc | 7 ++++--- 3 files changed, 11 insertions(+), 11 deletions(-) diff --git a/tensorflow/core/grappler/grappler_item.h b/tensorflow/core/grappler/grappler_item.h index 9051542988..1ae551f5ac 100644 --- a/tensorflow/core/grappler/grappler_item.h +++ b/tensorflow/core/grappler/grappler_item.h @@ -86,13 +86,12 @@ struct GrapplerItem { // Is it allowed to add nodes to the graph that do not have registered // gradient function. bool non_differentiable_rewrites = true; - // By default we are not allowed to inline ops with side effects into the - // main graph, because we can't guarantee that after pruning these ops will - // be executed. However if we are optimizing a function library (see - // meta_optimizer.cc) and a graph was instantiated by a function definition, - // we can do that, because functions guarantee that all side effects will be - // executed (see function_optimizer.cc for details). - bool inline_ops_with_side_effects = false; + + // By default we are allowed to prune ops with side-effects from the main + // graph if they are not in transitive fanin of the fetch nodes. If we are + // optimizing a graph that was instantiated by a function definition, we + // must keep all side effects intact. + bool prune_ops_with_side_effects = true; }; const std::unordered_set& devices() const; diff --git a/tensorflow/core/grappler/optimizers/function_optimizer.cc b/tensorflow/core/grappler/optimizers/function_optimizer.cc index 8beebb9049..7069e5ea20 100644 --- a/tensorflow/core/grappler/optimizers/function_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/function_optimizer.cc @@ -1472,7 +1472,7 @@ Status InlineIndirectFunctionCall(const NodeDef& func_node, // for the function body, because functions have strict semantics. if (num_fanouts == 0 && happens_after.empty() && - !ctx->allowed_optimizations().inline_ops_with_side_effects) { + ctx->allowed_optimizations().prune_ops_with_side_effects) { return errors::Internal( "Can't inline a function with a side-effectful op with empty " "fanouts and empty output control edge set. Function body node: ", diff --git a/tensorflow/core/grappler/optimizers/meta_optimizer.cc b/tensorflow/core/grappler/optimizers/meta_optimizer.cc index 572cc41d76..7b788c613c 100644 --- a/tensorflow/core/grappler/optimizers/meta_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/meta_optimizer.cc @@ -533,9 +533,10 @@ Status MetaOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item, VLOG(3) << added_devices.error_message(); } - // We can safely inline nested function calls with side-effectful ops into - // the function body (see function_optimizer.cc for details). - func_item.allowed_optimizations().inline_ops_with_side_effects = true; + // We are not allowed to prune side effects from the graph instantiated + // by the function definition, because we must guarantee function + // execution semantics wrt side effects (see function_optimizer.cc). + func_item.allowed_optimizations().prune_ops_with_side_effects = false; // Optimize function body graph. GraphDef optimized_func_graph; -- GitLab From 95358d2da35254bd0bcef84faf5094522178f4ea Mon Sep 17 00:00:00 2001 From: Alexandre Passos Date: Mon, 10 Dec 2018 11:59:26 -0800 Subject: [PATCH 299/873] Changing the copy-on-write semantics of resource variables. A variable now has a bit which can be turned on which, when turned on, makes that variable act as copy-on-read instead of copy-on-write. This allows sparse writes to happen concurrently while only holding a shared lock, mimicking the use_locking behavior of ref variables. PiperOrigin-RevId: 224855851 --- tensorflow/compiler/jit/xla_device_context.cc | 7 + tensorflow/compiler/jit/xla_device_context.h | 3 + .../gpu/gpu_util_platform_specific.cc | 8 + .../core/common_runtime/gpu_device_context.h | 4 + tensorflow/core/framework/device_base.h | 7 + tensorflow/core/framework/rendezvous_test.cc | 6 + tensorflow/core/framework/resource_var.h | 50 ++- tensorflow/core/framework/tensor.h | 8 +- tensorflow/core/kernels/BUILD | 1 + .../core/kernels/resource_variable_ops.cc | 93 +++++- tensorflow/core/kernels/scatter_nd_op.cc | 2 +- tensorflow/core/kernels/strided_slice_op.cc | 4 +- .../core/kernels/training_op_helpers.cc | 64 ---- tensorflow/core/kernels/training_op_helpers.h | 178 +++++++++-- tensorflow/core/kernels/training_ops.cc | 292 ++++++++++-------- .../resource_variable_ops_test.py | 14 + 16 files changed, 505 insertions(+), 236 deletions(-) diff --git a/tensorflow/compiler/jit/xla_device_context.cc b/tensorflow/compiler/jit/xla_device_context.cc index 6e6532731e..1f3afe8822 100644 --- a/tensorflow/compiler/jit/xla_device_context.cc +++ b/tensorflow/compiler/jit/xla_device_context.cc @@ -79,6 +79,13 @@ XlaDeviceContext::XlaDeviceContext( } } +void XlaDeviceContext::CopyTensorInSameDevice(const Tensor* input_tensor, + Device* device, + Tensor* output_tensor, + StatusCallback done) const { + done(errors::Unimplemented("XLA->XLA same-device copies not implemented.")); +} + void XlaDeviceContext::CopyCPUTensorToDevice(const Tensor* cpu_tensor, Device* device, Tensor* device_tensor, diff --git a/tensorflow/compiler/jit/xla_device_context.h b/tensorflow/compiler/jit/xla_device_context.h index 1e18df197a..e45db989fa 100644 --- a/tensorflow/compiler/jit/xla_device_context.h +++ b/tensorflow/compiler/jit/xla_device_context.h @@ -62,6 +62,9 @@ class XlaDeviceContext : public DeviceContext { void CopyDeviceTensorToCPU(const Tensor* device_tensor, absl::string_view tensor_name, Device* device, Tensor* cpu_tensor, StatusCallback done) override; + void CopyTensorInSameDevice(const Tensor* input_tensor, Device* device, + Tensor* output_tensor, + StatusCallback done) const override; xla::LocalClient* client() const { return client_; } se::Stream* stream() const { return stream_.get(); } diff --git a/tensorflow/core/common_runtime/gpu/gpu_util_platform_specific.cc b/tensorflow/core/common_runtime/gpu/gpu_util_platform_specific.cc index 4bc88ffc8c..0ef39fb3d7 100644 --- a/tensorflow/core/common_runtime/gpu/gpu_util_platform_specific.cc +++ b/tensorflow/core/common_runtime/gpu/gpu_util_platform_specific.cc @@ -37,6 +37,14 @@ void GPUDeviceContext::CopyDeviceTensorToCPU(const Tensor* device_tensor, GPUUtil::CopyGPUTensorToCPU(device, this, device_tensor, cpu_tensor, done); } +void GPUDeviceContext::CopyTensorInSameDevice(const Tensor* input_tensor, + Device* device, + Tensor* output_tensor, + StatusCallback done) const { + GPUUtil::CopyGPUTensorToSameGPU(device, this, input_tensor, output_tensor, + done); +} + Status GPUDeviceContext::ThenExecute(Device* device, se::Stream* stream, std::function func) { const DeviceBase::GpuDeviceInfo* gpu_info = diff --git a/tensorflow/core/common_runtime/gpu_device_context.h b/tensorflow/core/common_runtime/gpu_device_context.h index 3603808152..f513526724 100644 --- a/tensorflow/core/common_runtime/gpu_device_context.h +++ b/tensorflow/core/common_runtime/gpu_device_context.h @@ -57,6 +57,10 @@ class GPUDeviceContext : public DeviceContext { Device* device, Tensor* cpu_tensor, StatusCallback done) override; + void CopyTensorInSameDevice(const Tensor* input_tensor, Device* device, + Tensor* output_tensor, + StatusCallback done) const override; + void MaintainLifetimeOnStream(const Tensor* t, se::Stream* stream) const override {} diff --git a/tensorflow/core/framework/device_base.h b/tensorflow/core/framework/device_base.h index 446c31b17f..321947aca8 100644 --- a/tensorflow/core/framework/device_base.h +++ b/tensorflow/core/framework/device_base.h @@ -82,6 +82,13 @@ class DeviceContext : public core::RefCounted { done(errors::Internal("Unrecognized device type in CPU-to-device Copy")); } + // Copies a tensor in this device. + virtual void CopyTensorInSameDevice(const Tensor* input_tensor, + Device* device, Tensor* output_tensor, + StatusCallback done) const { + done(errors::Unimplemented("Copy in same device not implemented.")); + } + // "device_tensor" is a tensor on a non-CPU device. Copies // device_tensor into "cpu_tensor". "cpu_tensor" must be allocated // to be of the same size as "device_tensor". diff --git a/tensorflow/core/framework/rendezvous_test.cc b/tensorflow/core/framework/rendezvous_test.cc index de148f0bd3..7a777f064c 100644 --- a/tensorflow/core/framework/rendezvous_test.cc +++ b/tensorflow/core/framework/rendezvous_test.cc @@ -278,6 +278,12 @@ class DummyDeviceContext : public DeviceContext { ~DummyDeviceContext() override {} int stream_id() const { return stream_id_; } + void CopyTensorInSameDevice(const Tensor* input_tensor, Device* device, + Tensor* output_tensor, + StatusCallback done) const override { + done(Status::OK()); + } + private: const int stream_id_; }; diff --git a/tensorflow/core/framework/resource_var.h b/tensorflow/core/framework/resource_var.h index ff7b3e78a7..f5de5dba88 100644 --- a/tensorflow/core/framework/resource_var.h +++ b/tensorflow/core/framework/resource_var.h @@ -20,14 +20,46 @@ limitations under the License. namespace tensorflow { -// Resource stored by variables in the resource manager -// (new, resource-style version). +// Resource stored by variables in the resource manager (new, resource-style +// version). +// +// These variables have a mixed access mode: they can operate on copy-on-write +// mode (the default) or copy-on-read mode (used only for sparse access). +// +// When copy-on-write mode is enabled reading the value of the variable involves +// grabbing its mutex in shared mode and aliasing the internal tensor as the +// output of the read operation, increasing its reference count. Writing, +// conversely, works by, under an exclusive lock, detecting whether there are +// outstanding aliases of the tensor, using the reference count, copying the +// tensor if they exist, and writing to either the original or a copy with no +// outstanding aliases. Sparse operations are not supported in copy-on-write +// mode. +// +// When a variable is accessed sparsely it switches to copy-on-read mode. To +// switch we need to grab an exclusive lock and might (if there are aliases) +// need to copy the entire tensor. Once copy-on-read mode is enabled, no tensor +// is allowed to alias the variable's internal tensor. This means dense reads +// must return a copy of the variable, done while holding a shared lock. Dense +// writes do not need to check whether aliases exist, and can always write +// directly to the buffer without making a copy, while holding an exclusive +// lock. Sparse reads and sparse writes, on the other hand, can be done under a +// shared or exclusive mutex (the damage from writes under a shared mutex is +// limited since no other buffer is allowed to alias the variable's +// buffer). Using an exclusive mutex disallows concurrent writes and concurrent +// sparse reads, providing some extra safety at the expense of performance, +// while shared mutex allow for "hogwild" behavior. Doing sparse writes under a +// shared mutex prevents them from overlapping with dense writes, which is +// necessary as dense writes can change the shape the of the tensor. +// +// Transitioning a variable from copy-on-read mode to copy-on-write mode is +// currently not supported. To upgrade a variable from copy-on-write to +// copy-on-read use `EnsureSparseVariableAccess()`, and then grab the variable's +// mutex as desired. To access the variable in dense mode grab the mutex either +// directly or via `MaybeLockVariableInputMutexesInOrder` on all variables being +// modified and then call `PrepareToUpdateVariable` on them in any order. class Var : public ResourceBase { public: explicit Var(DataType dtype) : tensor_(dtype) {} - // Not copyable or movable. - Var(const Var&) = delete; - Var& operator=(const Var&) = delete; // When locking multiple variables, the locks must be acquired in order of // increasing mu() address. @@ -48,11 +80,19 @@ class Var : public ResourceBase { bool is_initialized = false; // GUARDED_BY(mu_) but annotalysis doesn't like // it. + // Also fake-guarded by mu_. Should be set to True whenever any sparse + // operation uses the variable. Once this is true no tensor is allowed to + // alias the memory of the variable, and we always copy the variable on + // reads. This allows sparse operations to happen with only a shared lock if + // so desired. + std::atomic copy_on_read_mode{false}; + private: mutex mu_; Tensor tensor_; ~Var() override {} + TF_DISALLOW_COPY_AND_ASSIGN(Var); }; } // end namespace tensorflow diff --git a/tensorflow/core/framework/tensor.h b/tensorflow/core/framework/tensor.h index 6e03cf9f6f..009dd0846d 100644 --- a/tensorflow/core/framework/tensor.h +++ b/tensorflow/core/framework/tensor.h @@ -45,6 +45,7 @@ class TensorBuffer; class TensorCApi; class TensorDescription; class TensorProto; +class Var; namespace batch_util { Status CopyElementToSlice(Tensor element, Tensor* parent, int64 index); @@ -581,11 +582,16 @@ class Tensor { friend class XlaTensor; // For access to RefCountIsOne(). friend class XlaTensorBuffer; // For access to the private constructor taking // the buffer + friend class Var; template friend class AssignVariableOp; // For access to RefCountIsOne(). template friend Status PrepareToUpdateVariable( - OpKernelContext* ctx, Tensor* tensor); // For access to RefCountIsOne(). + OpKernelContext* ctx, Tensor* tensor, + bool copy_on_read_mode); // For access to RefCountIsOne(). + template + friend Status EnsureSparseVariableAccess( + OpKernelContext* ctx, Var* var); // For access to RefCountIsOne(). friend Status batch_util::CopyElementToSlice( Tensor element, Tensor* parent, int64 index); // For access to RefCountIsOne(). diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD index 0e5d8d765a..e8b1dd270f 100644 --- a/tensorflow/core/kernels/BUILD +++ b/tensorflow/core/kernels/BUILD @@ -2196,6 +2196,7 @@ tf_kernel_library( ":state", ":training_op_helpers", ":variable_ops", + "//tensorflow/core:core_cpu_lib", "//tensorflow/core:framework", "//tensorflow/core:lib", "//tensorflow/core:lib_internal", diff --git a/tensorflow/core/kernels/resource_variable_ops.cc b/tensorflow/core/kernels/resource_variable_ops.cc index 170b08b4b7..4167b60051 100644 --- a/tensorflow/core/kernels/resource_variable_ops.cc +++ b/tensorflow/core/kernels/resource_variable_ops.cc @@ -55,6 +55,7 @@ limitations under the License. #include #include "absl/strings/str_join.h" +#include "tensorflow/core/common_runtime/device.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/resource_mgr.h" @@ -84,6 +85,47 @@ ReadVariableOp::ReadVariableOp(OpKernelConstruction* c) : OpKernel(c) { OP_REQUIRES_OK(c, c->GetAttr("dtype", &dtype_)); } +namespace { +Status CopyVariable(int output_idx, OpKernelContext* ctx, const Tensor* t) { + Tensor* output; + Notification n; + Status status; + AllocatorAttributes attr; + if (t->dtype() == DT_VARIANT) { + attr.set_on_host(true); + } + TF_RETURN_IF_ERROR( + ctx->allocate_output(output_idx, t->shape(), &output, attr)); + if (t->dtype() == DT_VARIANT) { + output->flat() = t->flat(); + } else if (ctx->op_device_context() != nullptr) { + // TODO(apassos): remove the down_cast by just returning Device* from + // OpKernelContext + Device* device = static_cast(ctx->device()); + ctx->op_device_context()->CopyTensorInSameDevice( + t, device, output, [&n, &status](const Status& s) { + status = s; + n.Notify(); + }); + n.WaitForNotification(); + return status; + } else { + switch (t->dtype()) { +#define HANDLER(type) \ + case DataTypeToEnum::value: \ + output->flat() = t->flat(); \ + break; + TF_CALL_ALL_TYPES(HANDLER); +#undef HANDLER + default: + return errors::Internal("Unsupported dtype", t->dtype()); + } + } + return Status::OK(); +} + +} // namespace + void ReadVariableOp::Compute(OpKernelContext* ctx) { Var* variable = nullptr; const ResourceHandle& handle = HandleFromInput(ctx, 0); @@ -100,12 +142,16 @@ void ReadVariableOp::Compute(OpKernelContext* ctx) { // holding a shared lock to guarantee ordering of reads and // writes. tf_shared_lock ml(*variable->mu()); - const Tensor& t = *variable->tensor(); - OP_REQUIRES(ctx, dtype_ == t.dtype(), + const Tensor* t = variable->tensor(); + OP_REQUIRES(ctx, dtype_ == t->dtype(), errors::InvalidArgument( "Trying to read variable with wrong dtype. Expected ", - DataTypeString(dtype_), " got ", DataTypeString(t.dtype()))); - ctx->set_output(0, t); + DataTypeString(dtype_), " got ", DataTypeString(t->dtype()))); + if (variable->copy_on_read_mode.load()) { + OP_REQUIRES_OK(ctx, CopyVariable(0, ctx, t)); + } else { + ctx->set_output(0, *t); + } } ReadVariablesOp::ReadVariablesOp(OpKernelConstruction* c) : OpKernel(c) { @@ -146,14 +192,18 @@ void ReadVariablesOp::Compute(OpKernelContext* ctx) { // holding a shared lock to guarantee ordering of reads and // writes. tf_shared_lock ml(*variables[i]->mu()); - const Tensor& t = *variables[i]->tensor(); - OP_REQUIRES(ctx, dtypes_[i] == t.dtype(), + OP_REQUIRES(ctx, dtypes_[i] == variables[i]->tensor()->dtype(), errors::InvalidArgument( "Trying to read variable ", handles[i]->name(), " from Container: ", handles[i]->container(), " with wrong dtype. Expected ", DataTypeString(dtypes_[i]), - " got ", DataTypeString(t.dtype()))); - ctx->set_output(i, t); + " got ", DataTypeString(variables[i]->tensor()->dtype()))); + if (variables[i]->copy_on_read_mode.load()) { + OP_REQUIRES_OK(ctx, CopyVariable(i, ctx, variables[i]->tensor())); + } else { + const Tensor& t = *variables[i]->tensor(); + ctx->set_output(i, t); + } } } @@ -308,8 +358,23 @@ class AssignVariableOp : public OpKernel { "Trying to assign variable with wrong dtype. Expected ", DataTypeString(variable->tensor()->dtype()), " got ", DataTypeString(dtype_))); + if (variable->copy_on_read_mode.load()) { + PersistentTensor unused; + Tensor* tmp; + AllocatorAttributes attr; + attr.set_gpu_compatible(true); + attr.set_nic_compatible(true); + OP_REQUIRES_OK(context, + context->allocate_persistent(value.dtype(), value.shape(), + &unused, &tmp, attr)); + functor::DenseUpdate copy_functor; + copy_functor(context->eigen_device(), tmp->flat(), + value.flat()); + *variable->tensor() = *tmp; + } else { + *variable->tensor() = value; + } variable->is_initialized = true; - *variable->tensor() = value; } private: @@ -442,8 +507,9 @@ class AssignUpdateVariableOp : public OpKernel { " using a Tensor with shape ", value.shape().DebugString(), ", shapes must be equal.")); - OP_REQUIRES_OK(context, - PrepareToUpdateVariable(context, var_tensor)); + OP_REQUIRES_OK( + context, PrepareToUpdateVariable( + context, var_tensor, variable->copy_on_read_mode.load())); functor::DenseUpdate update_functor; update_functor(context->eigen_device(), var_tensor->flat(), value.flat()); @@ -524,6 +590,7 @@ class ResourceGatherOp : public OpKernel { Var* v = nullptr; OP_REQUIRES_OK(c, LookupResource(c, HandleFromInput(c, 0), &v)); core::ScopedUnref su(v); + OP_REQUIRES_OK(c, EnsureSparseVariableAccess(c, v)); // NOTE: We hold the lock for the whole gather operation instead // of increasing the reference count of v->tensor() to avoid a // situation where a write to the same variable will see a @@ -639,9 +706,9 @@ class ResourceScatterUpdateOp : public OpKernel { Var* v = nullptr; OP_REQUIRES_OK(c, LookupResource(c, HandleFromInput(c, 0), &v)); core::ScopedUnref unref_v(v); - mutex_lock ml(*v->mu()); + OP_REQUIRES_OK(c, EnsureSparseVariableAccess(c, v)); + tf_shared_lock ml(*v->mu()); Tensor* params = v->tensor(); - OP_REQUIRES_OK(c, PrepareToUpdateVariable(c, params)); const Tensor& indices = c->input(1); const Tensor& updates = c->input(2); diff --git a/tensorflow/core/kernels/scatter_nd_op.cc b/tensorflow/core/kernels/scatter_nd_op.cc index 63bb793fdc..b466e57249 100644 --- a/tensorflow/core/kernels/scatter_nd_op.cc +++ b/tensorflow/core/kernels/scatter_nd_op.cc @@ -231,6 +231,7 @@ class ScatterNdUpdateOp : public OpKernel { Var* v; OP_REQUIRES_OK(c, LookupResource(c, HandleFromInput(c, 0), &v)); core::ScopedUnref scoped_unref(v); + OP_REQUIRES_OK(c, EnsureSparseVariableAccess(c, v)); mutex_lock m(*v->mu()); DoCompute(c); } else if (use_exclusive_lock_) { @@ -258,7 +259,6 @@ class ScatterNdUpdateOp : public OpKernel { Var* v; OP_REQUIRES_OK(c, LookupResource(c, HandleFromInput(c, 0), &v)); Tensor* t = v->tensor(); - OP_REQUIRES_OK(c, PrepareToUpdateVariable(c, t)); params = *t; params_shape = params.shape(); } else if (IsRefType(c->input_dtype(0))) { diff --git a/tensorflow/core/kernels/strided_slice_op.cc b/tensorflow/core/kernels/strided_slice_op.cc index 70a7ddbd06..6db68f937d 100644 --- a/tensorflow/core/kernels/strided_slice_op.cc +++ b/tensorflow/core/kernels/strided_slice_op.cc @@ -307,9 +307,9 @@ class StridedSliceAssignOp : public OpKernel { OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0), &v)); core::ScopedUnref scoped_unref(v); - mutex_lock ml(*v->mu()); OP_REQUIRES_OK(context, - PrepareToUpdateVariable(context, v->tensor())); + EnsureSparseVariableAccess(context, v)); + mutex_lock ml(*v->mu()); old_lhs = v->tensor(); OP_REQUIRES(context, old_lhs->dtype() == DataTypeToEnum::value, errors::InvalidArgument( diff --git a/tensorflow/core/kernels/training_op_helpers.cc b/tensorflow/core/kernels/training_op_helpers.cc index 4262a5404b..20c08cf8fb 100644 --- a/tensorflow/core/kernels/training_op_helpers.cc +++ b/tensorflow/core/kernels/training_op_helpers.cc @@ -19,70 +19,6 @@ limitations under the License. namespace tensorflow { -mutex* GetTrainingVariableMutex(OpKernelContext* ctx, int input, - Var** maybe_resource) { - *maybe_resource = nullptr; - if (ctx->input_dtype(input) == DT_RESOURCE) { - if (LookupResource(ctx, HandleFromInput(ctx, input), maybe_resource).ok()) { - return (*maybe_resource)->mu(); - } else { - ctx->CtxFailureWithWarning( - errors::Internal("Invalid variable reference.")); - return nullptr; - } - } - return ctx->input_ref_mutex(input); -} - -// MaybeLockVariableInputMutexesInOrder is a helper function to acquire mutexes -// in address order to mitigate deadlock. Returns a structure that, when -// deleted, will release the acquired mutexes. Safe to pass duplicates - will -// only lock each distinct mutex once. If do_lock is false, returns -// immediately. Note that this silently doesn't lock mutexes for invalid -// variable references; in all usages this is followed by GetInputTensor which -// will signal a failure. -VariableInputLockHolder MaybeLockVariableInputMutexesInOrder( - OpKernelContext* ctx, bool do_lock, const std::vector& input_ids) { - bool any_resource = false; - for (auto i : input_ids) { - if (ctx->input_dtype(i) == DT_RESOURCE) { - any_resource = true; - break; - } - } - if (!do_lock && !any_resource) { - return VariableInputLockHolder({}, {}); - } - std::vector vars; - std::vector mutexes; - std::vector acquire_order; - for (auto input : input_ids) { - Var* var; - mutex* mutex = GetTrainingVariableMutex(ctx, input, &var); - if (var) vars.push_back(var); - // Only lock each mutex once if duplicates exist (n^2 but n is 2 or 3). - if (std::find(mutexes.begin(), mutexes.end(), mutex) == mutexes.end()) { - acquire_order.push_back(mutexes.size()); - mutexes.push_back(mutex); - } - } - std::sort(acquire_order.begin(), acquire_order.end(), - [&mutexes](int a, int b) { return mutexes[a] < mutexes[b]; }); - - std::unique_ptr> locks = - MakeUnique>(); - locks->reserve(acquire_order.size()); - - for (auto input : acquire_order) { - Var* var; - mutex* mu = GetTrainingVariableMutex(ctx, input, &var); - core::ScopedUnref scoped_unref(var); - if (mu != nullptr) { - locks->emplace_back(*mu); - } - } - return VariableInputLockHolder(std::move(vars), std::move(locks)); -} void MaybeForwardRefInputToRefOutput(OpKernelContext* ctx, int input, int output) { diff --git a/tensorflow/core/kernels/training_op_helpers.h b/tensorflow/core/kernels/training_op_helpers.h index 9f173a80f7..e96cd023fc 100644 --- a/tensorflow/core/kernels/training_op_helpers.h +++ b/tensorflow/core/kernels/training_op_helpers.h @@ -17,30 +17,72 @@ limitations under the License. #define TENSORFLOW_CORE_KERNELS_TRAINING_OP_HELPERS_H_ #include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/variant_op_registry.h" #include "tensorflow/core/kernels/dense_update_functor.h" #include "tensorflow/core/kernels/variable_ops.h" namespace tensorflow { -// Returns a borrowed pointer to the mutex for the variable `input` in `ctx`. -// -// If `input` corresponds to a `DT_RESOURCE`-type variable input, -// `*maybe_resource` will be updated to contain the underlying resource, and the -// caller will be responsible for calling `Unref()` on that resource. -mutex* GetTrainingVariableMutex(OpKernelContext* ctx, int input, - Var** maybe_resource); +// Must be called before performing a sparse operation on a variable. Ensures +// that no concurrent dense operations can happen while holding the variable's +// lock. +template +Status EnsureSparseVariableAccess(OpKernelContext* ctx, Var* var) { + if (var->copy_on_read_mode.load()) { + return Status::OK(); + } + mutex_lock ml(*var->mu()); + // Once copy-on-read mode is True the refcount is guaranteed to be 1. This can + // also happen if there are no concurrent reads of the variable and + // copy-on-read mode is false. + if (var->tensor()->RefCountIsOne()) { + var->copy_on_read_mode.store(true); + return Status::OK(); + } + PersistentTensor unused; + Tensor* tmp; + if (std::is_same::value) { + AllocatorAttributes attr; + attr.set_on_host(true); + TF_RETURN_IF_ERROR(ctx->allocate_persistent( + var->tensor()->dtype(), var->tensor()->shape(), &unused, &tmp, attr)); + + const auto elements_in = var->tensor()->flat(); + auto elements_out = tmp->flat(); + for (int64 i = 0; i < elements_in.size(); ++i) { + elements_out(i) = elements_in(i); + } + } else { + AllocatorAttributes attr; + attr.set_gpu_compatible(true); + attr.set_nic_compatible(true); + TF_RETURN_IF_ERROR(ctx->allocate_persistent( + var->tensor()->dtype(), var->tensor()->shape(), &unused, &tmp, attr)); + functor::DenseUpdate copy_functor; + copy_functor(ctx->eigen_device(), tmp->flat(), + const_cast(var->tensor())->flat()); + } + *var->tensor() = *tmp; + var->copy_on_read_mode.store(true); + return Status::OK(); +} // Utility structure that releases a sequence of borrowed mutexes when it is // deleted. struct VariableInputLockHolder { public: - VariableInputLockHolder(std::vector vars, - std::unique_ptr> locks) - : vars_(std::move(vars)), locks_(std::move(locks)) {} + VariableInputLockHolder( + std::vector vars, std::unique_ptr> locks, + std::unique_ptr> shared_locks) + : vars_(std::move(vars)), + locks_(std::move(locks)), + shared_locks_(std::move(shared_locks)) {} VariableInputLockHolder(VariableInputLockHolder&& other) - : vars_(std::move(other.vars_)), locks_(std::move(other.locks_)) {} + : vars_(std::move(other.vars_)), + locks_(std::move(other.locks_)), + shared_locks_(std::move(other.shared_locks_)) {} ~VariableInputLockHolder() { // Release the locks before unreffing the Vars, because each lock @@ -56,10 +98,95 @@ struct VariableInputLockHolder { // NOTE: Use a `std::unique_ptr` instead of moving in a vector directly, // because a `std::vector` is not movable on all platforms. std::unique_ptr> locks_; + std::unique_ptr> shared_locks_; }; +// Returns a borrowed pointer to the mutex for the variable `input` in `ctx`. +// +// If `input` corresponds to a `DT_RESOURCE`-type variable input, +// `*maybe_resource` will be updated to contain the underlying resource, and the +// caller will be responsible for calling `Unref()` on that resource. +template +mutex* GetTrainingVariableMutex(OpKernelContext* ctx, int input, bool sparse, + Var** maybe_resource) { + *maybe_resource = nullptr; + if (ctx->input_dtype(input) == DT_RESOURCE) { + if (LookupResource(ctx, HandleFromInput(ctx, input), maybe_resource).ok()) { + if (sparse) { + EnsureSparseVariableAccess(ctx, *maybe_resource); + } + return (*maybe_resource)->mu(); + } else { + ctx->CtxFailureWithWarning( + errors::Internal("Invalid variable reference.")); + return nullptr; + } + } + return ctx->input_ref_mutex(input); +} + +// MaybeLockVariableInputMutexesInOrder is a helper function to acquire mutexes +// in address order to mitigate deadlock. Returns a structure that, when +// deleted, will release the acquired mutexes. Safe to pass duplicates - will +// only lock each distinct mutex once. If sparse is true will ensure the +// variable gets switched to copy-on-read mode before trying to acquire the +// locks. If do_lock is false, returns immediately for reference variables. For +// resource variables in copy-on-read-mode it will grab a shared lock if do_lock +// is false, exclusive lock otherwise. Note that this silently doesn't lock +// mutexes for invalid variable references; in all usages this is followed by +// GetInputTensor which will signal a failure. +template VariableInputLockHolder MaybeLockVariableInputMutexesInOrder( - OpKernelContext* ctx, bool do_lock, const std::vector& input_ids); + OpKernelContext* ctx, bool do_lock, bool sparse, + const std::vector& input_ids) { + bool any_resource = false; + for (auto i : input_ids) { + if (ctx->input_dtype(i) == DT_RESOURCE) { + any_resource = true; + break; + } + } + if (!do_lock && !any_resource) { + return VariableInputLockHolder({}, {}, {}); + } + std::vector vars; + std::vector mutexes; + std::vector acquire_order; + for (auto input : input_ids) { + Var* var; + mutex* mutex = + GetTrainingVariableMutex(ctx, input, sparse, &var); + if (var) vars.push_back(var); + // Only lock each mutex once if duplicates exist (n^2 but n is 2 or 3). + if (std::find(mutexes.begin(), mutexes.end(), mutex) == mutexes.end()) { + acquire_order.push_back(mutexes.size()); + mutexes.push_back(mutex); + } + } + std::sort(acquire_order.begin(), acquire_order.end(), + [&mutexes](int a, int b) { return mutexes[a] < mutexes[b]; }); + + std::unique_ptr> locks = + absl::make_unique>(); + std::unique_ptr> shared_locks = + absl::make_unique>(); + locks->reserve(acquire_order.size()); + + for (auto input : acquire_order) { + Var* var; + mutex* mu = GetTrainingVariableMutex(ctx, input, sparse, &var); + core::ScopedUnref scoped_unref(var); + if (mu != nullptr) { + if (do_lock) { + locks->emplace_back(*mu); + } else { + shared_locks->emplace_back(*mu); + } + } + } + return VariableInputLockHolder(std::move(vars), std::move(locks), + std::move(shared_locks)); +} void MaybeForwardRefInputToRefOutput(OpKernelContext* ctx, int input, int output); @@ -68,8 +195,9 @@ void MaybeForwardRefInputToRefOutput(OpKernelContext* ctx, int input, // reference count of 1 before you update it. // REQUIRES: If you pass in variable->tensor(), *variable->mu() must be held. template -Status PrepareToUpdateVariable(OpKernelContext* ctx, Tensor* tensor) { - if (!tensor->RefCountIsOne()) { +Status PrepareToUpdateVariable(OpKernelContext* ctx, Tensor* tensor, + bool copy_on_read_mode) { + if (copy_on_read_mode || !tensor->RefCountIsOne()) { // Tensor's buffer is in use by some read, so we need to copy before // updating. PersistentTensor unused; @@ -100,12 +228,14 @@ Status PrepareToUpdateVariable(OpKernelContext* ctx, Tensor* tensor) { return Status::OK(); } -// This gives you `*out`, a tensor you can update, corresponding to a -// variable passed as input index `input`. This handles the -// differences between reference and resource variables. For resource -// variables, we ensure `*out` has a reference count of 1 (using -// PrepareToUpdateVariable() to copy if necessary) unless -// sparse && !lock_held, in which case it never copies. +// This gives you `*out`, a tensor you can update, corresponding to a variable +// passed as input index `input`. This handles the differences between +// reference and resource variables. For reference variables we can just grab +// the tensor, grabbing the lock if lock_held is False. +// +// For resource variables we, if sparse is true, ensure it's in copy-on-read +// mode, and then, regardless of the value of sparse, ensure its refcount is 1 +// (by potentially copying its contents). In this case lock_held is ignored. template Status GetInputTensorFromVariable(OpKernelContext* ctx, int input, bool lock_held, bool sparse, Tensor* out) { @@ -113,7 +243,13 @@ Status GetInputTensorFromVariable(OpKernelContext* ctx, int input, Var* var; TF_RETURN_IF_ERROR(LookupResource(ctx, HandleFromInput(ctx, input), &var)); core::ScopedUnref unref_var(var); - TF_RETURN_IF_ERROR(PrepareToUpdateVariable(ctx, var->tensor())); + if (sparse) { + TF_RETURN_IF_ERROR(EnsureSparseVariableAccess(ctx, var)); + *out = *var->tensor(); + return Status::OK(); + } + TF_RETURN_IF_ERROR(PrepareToUpdateVariable( + ctx, var->tensor(), var->copy_on_read_mode.load())); *out = *var->tensor(); return Status::OK(); } diff --git a/tensorflow/core/kernels/training_ops.cc b/tensorflow/core/kernels/training_ops.cc index 6504ad1b09..b2239ab5c3 100644 --- a/tensorflow/core/kernels/training_ops.cc +++ b/tensorflow/core/kernels/training_ops.cc @@ -465,11 +465,12 @@ class ApplyGradientDescentOp : public OpKernel { } void Compute(OpKernelContext* ctx) override { - auto locks = - MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, {0}); + const bool sparse = false; + auto locks = MaybeLockVariableInputMutexesInOrder( + ctx, use_exclusive_lock_, sparse, {0}); Tensor var; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, false, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); OP_REQUIRES( ctx, var.IsInitialized(), @@ -506,11 +507,12 @@ class ApplyGradientDescentOp : public OpKernel { } void Compute(OpKernelContext* ctx) override { - auto locks = - MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, {0}); + const bool sparse = false; + auto locks = MaybeLockVariableInputMutexesInOrder( + ctx, use_exclusive_lock_, sparse, {0}); Tensor var; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, false, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); OP_REQUIRES( ctx, var.IsInitialized(), @@ -600,7 +602,8 @@ class ApplyAdadeltaOp : public OpKernel { void Compute(OpKernelContext* ctx) override { Var* resource; - mutex* mu = GetTrainingVariableMutex(ctx, 0, &resource); + const bool sparse = false; + mutex* mu = GetTrainingVariableMutex(ctx, 0, sparse, &resource); core::ScopedUnref scoped_unref(resource); if (use_exclusive_lock_ && mu != nullptr) { mutex_lock l1(*mu); @@ -624,14 +627,16 @@ class ApplyAdadeltaOp : public OpKernel { void DoValidate(OpKernelContext* ctx) { Tensor var; + const bool sparse = false; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, false, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); Tensor accum; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 1, use_exclusive_lock_, false, &accum)); + ctx, 1, use_exclusive_lock_, sparse, &accum)); Tensor accum_update; - OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 2, use_exclusive_lock_, false, &accum_update)); + OP_REQUIRES_OK( + ctx, GetInputTensorFromVariable(ctx, 2, use_exclusive_lock_, + sparse, &accum_update)); OP_REQUIRES( ctx, var.IsInitialized(), @@ -678,14 +683,16 @@ class ApplyAdadeltaOp : public OpKernel { void DoCompute(OpKernelContext* ctx) { const Device& device = ctx->template eigen_device(); Tensor var; + const bool sparse = false; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, false, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); Tensor accum; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 1, use_exclusive_lock_, false, &accum)); + ctx, 1, use_exclusive_lock_, sparse, &accum)); Tensor accum_update; - OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 2, use_exclusive_lock_, false, &accum_update)); + OP_REQUIRES_OK( + ctx, GetInputTensorFromVariable(ctx, 2, use_exclusive_lock_, + sparse, &accum_update)); const Tensor& lr = ctx->input(3); const Tensor& rho = ctx->input(4); @@ -751,7 +758,8 @@ class SparseApplyAdadeltaOp : public OpKernel { void Compute(OpKernelContext* ctx) override { Var* var; - mutex* mu = GetTrainingVariableMutex(ctx, 0, &var); + const bool sparse = true; + mutex* mu = GetTrainingVariableMutex(ctx, 0, sparse, &var); core::ScopedUnref scoped_unref(var); // mu_accum is actually the same mutex as mu_var since currently we use a // global mutex. @@ -767,14 +775,16 @@ class SparseApplyAdadeltaOp : public OpKernel { void DoCompute(OpKernelContext* ctx) { Tensor var; + const bool sparse = true; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, true, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); Tensor accum_grad; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 1, use_exclusive_lock_, true, &accum_grad)); + ctx, 1, use_exclusive_lock_, sparse, &accum_grad)); Tensor accum_update; - OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 2, use_exclusive_lock_, true, &accum_update)); + OP_REQUIRES_OK(ctx, + GetInputTensorFromVariable( + ctx, 2, use_exclusive_lock_, sparse, &accum_update)); OP_REQUIRES( ctx, var.IsInitialized(), errors::FailedPrecondition( @@ -907,11 +917,12 @@ class ApplyProximalGradientDescentOp : public OpKernel { } void Compute(OpKernelContext* ctx) override { - auto locks = - MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, {0}); + const bool sparse = false; + auto locks = MaybeLockVariableInputMutexesInOrder( + ctx, use_exclusive_lock_, sparse, {0}); Tensor var; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, false, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); OP_REQUIRES( ctx, var.IsInitialized(), @@ -976,11 +987,12 @@ class SparseApplyProximalGradientDescentOp : public OpKernel { } void Compute(OpKernelContext* ctx) override NO_THREAD_SAFETY_ANALYSIS { - auto locks = - MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, {0}); + const bool sparse = true; + auto locks = MaybeLockVariableInputMutexesInOrder( + ctx, use_exclusive_lock_, sparse, {0}); Tensor var; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, true, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); OP_REQUIRES(ctx, TensorShapeUtils::IsVectorOrHigher(var.shape()), errors::InvalidArgument("var must be at least 1 dimensional")); @@ -1121,14 +1133,15 @@ class ApplyAdagradOp : public OpKernel { } void Compute(OpKernelContext* ctx) override { - auto locks = - MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, {0, 1}); + const bool sparse = false; + auto locks = MaybeLockVariableInputMutexesInOrder( + ctx, use_exclusive_lock_, sparse, {0, 1}); Tensor var; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, false, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); Tensor accum; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 1, use_exclusive_lock_, false, &accum)); + ctx, 1, use_exclusive_lock_, sparse, &accum)); OP_REQUIRES( ctx, var.IsInitialized(), errors::FailedPrecondition( @@ -1214,14 +1227,15 @@ class ApplyProximalAdagradOp : public OpKernel { } void Compute(OpKernelContext* ctx) override { - auto locks = - MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, {0, 1}); + const bool sparse = false; + auto locks = MaybeLockVariableInputMutexesInOrder( + ctx, use_exclusive_lock_, sparse, {0, 1}); Tensor var; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, false, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); Tensor accum; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 1, use_exclusive_lock_, false, &accum)); + ctx, 1, use_exclusive_lock_, sparse, &accum)); OP_REQUIRES( ctx, var.IsInitialized(), errors::FailedPrecondition( @@ -1316,14 +1330,15 @@ class SparseApplyAdagradOp : public OpKernel { } void Compute(OpKernelContext* ctx) override NO_THREAD_SAFETY_ANALYSIS { - auto locks = - MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, {0, 1}); + const bool sparse = true; + auto locks = MaybeLockVariableInputMutexesInOrder( + ctx, use_exclusive_lock_, sparse, {0, 1}); Tensor var; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, true, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); Tensor accum; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 1, use_exclusive_lock_, true, &accum)); + ctx, 1, use_exclusive_lock_, sparse, &accum)); OP_REQUIRES( ctx, var.IsInitialized(), errors::FailedPrecondition( @@ -1456,14 +1471,15 @@ class SparseApplyProximalAdagradOp : public OpKernel { } void Compute(OpKernelContext* ctx) override NO_THREAD_SAFETY_ANALYSIS { - auto locks = - MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, {0, 1}); + const bool sparse = true; + auto locks = MaybeLockVariableInputMutexesInOrder( + ctx, use_exclusive_lock_, sparse, {0, 1}); Tensor var; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, true, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); Tensor accum; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 1, use_exclusive_lock_, true, &accum)); + ctx, 1, use_exclusive_lock_, sparse, &accum)); OP_REQUIRES( ctx, var.IsInitialized(), errors::FailedPrecondition( @@ -1628,19 +1644,20 @@ class ApplyAdagradDAOp : public OpKernel { } void Compute(OpKernelContext* ctx) override { - auto locks = MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, - {0, 1, 2}); + const bool sparse = false; + auto locks = MaybeLockVariableInputMutexesInOrder( + ctx, use_exclusive_lock_, sparse, {0, 1, 2}); Tensor var; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, false, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); Tensor gradient_accum; OP_REQUIRES_OK( ctx, GetInputTensorFromVariable(ctx, 1, use_exclusive_lock_, - false, &gradient_accum)); + sparse, &gradient_accum)); Tensor gradient_squared_accum; OP_REQUIRES_OK( ctx, GetInputTensorFromVariable( - ctx, 2, use_exclusive_lock_, false, &gradient_squared_accum)); + ctx, 2, use_exclusive_lock_, sparse, &gradient_squared_accum)); OP_REQUIRES( ctx, var.IsInitialized(), errors::FailedPrecondition( @@ -1729,19 +1746,20 @@ class SparseApplyAdagradDAOp : public OpKernel { } void Compute(OpKernelContext* ctx) override NO_THREAD_SAFETY_ANALYSIS { - auto locks = MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, - {0, 1, 2}); + const bool sparse = true; + auto locks = MaybeLockVariableInputMutexesInOrder( + ctx, use_exclusive_lock_, sparse, {0, 1, 2}); Tensor var; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, true, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); Tensor gradient_accum; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 1, use_exclusive_lock_, true, &gradient_accum)); + ctx, 1, use_exclusive_lock_, sparse, &gradient_accum)); Tensor gradient_squared_accum; OP_REQUIRES_OK( ctx, GetInputTensorFromVariable( - ctx, 2, use_exclusive_lock_, true, &gradient_squared_accum)); + ctx, 2, use_exclusive_lock_, sparse, &gradient_squared_accum)); OP_REQUIRES( ctx, var.IsInitialized(), errors::FailedPrecondition( @@ -1927,18 +1945,19 @@ class ApplyFtrlOp : public OpKernel { } void Compute(OpKernelContext* ctx) override { - auto locks = MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, - {0, 1, 2}); + const bool sparse = false; + auto locks = MaybeLockVariableInputMutexesInOrder( + ctx, use_exclusive_lock_, sparse, {0, 1, 2}); Tensor var; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, false, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); Tensor accum; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 1, use_exclusive_lock_, false, &accum)); + ctx, 1, use_exclusive_lock_, sparse, &accum)); Tensor linear; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 2, use_exclusive_lock_, false, &linear)); + ctx, 2, use_exclusive_lock_, sparse, &linear)); OP_REQUIRES( ctx, var.IsInitialized(), errors::FailedPrecondition( @@ -2079,17 +2098,18 @@ class SparseApplyFtrlOp : public OpKernel { } void Compute(OpKernelContext* ctx) override NO_THREAD_SAFETY_ANALYSIS { - auto locks = MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, - {0, 1, 2}); + const bool sparse = true; + auto locks = MaybeLockVariableInputMutexesInOrder( + ctx, use_exclusive_lock_, sparse, {0, 1, 2}); Tensor var; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, true, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); Tensor accum; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 1, use_exclusive_lock_, true, &accum)); + ctx, 1, use_exclusive_lock_, sparse, &accum)); Tensor linear; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 2, use_exclusive_lock_, true, &linear)); + ctx, 2, use_exclusive_lock_, sparse, &linear)); OP_REQUIRES( ctx, var.IsInitialized(), errors::FailedPrecondition( @@ -2353,15 +2373,16 @@ class ApplyMomentumOp : public OpKernel { } void Compute(OpKernelContext* ctx) override { - auto locks = - MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, {0, 1}); + const bool sparse = false; + auto locks = MaybeLockVariableInputMutexesInOrder( + ctx, use_exclusive_lock_, sparse, {0, 1}); Tensor var; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, false, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); Tensor accum; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 1, use_exclusive_lock_, false, &accum)); + ctx, 1, use_exclusive_lock_, sparse, &accum)); OP_REQUIRES( ctx, var.IsInitialized(), errors::FailedPrecondition( @@ -2454,15 +2475,16 @@ class SparseApplyMomentumOp : public OpKernel { } void Compute(OpKernelContext* ctx) override NO_THREAD_SAFETY_ANALYSIS { - auto locks = - MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, {0, 1}); + const bool sparse = true; + auto locks = MaybeLockVariableInputMutexesInOrder( + ctx, use_exclusive_lock_, sparse, {0, 1}); Tensor var; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, true, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); Tensor accum; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 1, use_exclusive_lock_, true, &accum)); + ctx, 1, use_exclusive_lock_, sparse, &accum)); OP_REQUIRES( ctx, var.IsInitialized(), errors::FailedPrecondition( @@ -2572,15 +2594,16 @@ class ApplyKerasMomentumOp : public OpKernel { } void Compute(OpKernelContext* ctx) override { - auto locks = - MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, {0, 1}); + const bool sparse = false; + auto locks = MaybeLockVariableInputMutexesInOrder( + ctx, use_exclusive_lock_, sparse, {0, 1}); Tensor var; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, false, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); Tensor accum; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 1, use_exclusive_lock_, false, &accum)); + ctx, 1, use_exclusive_lock_, sparse, &accum)); OP_REQUIRES( ctx, var.IsInitialized(), errors::FailedPrecondition( @@ -2671,15 +2694,16 @@ class SparseApplyKerasMomentumOp : public OpKernel { } void Compute(OpKernelContext* ctx) override NO_THREAD_SAFETY_ANALYSIS { - auto locks = - MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, {0, 1}); + const bool sparse = true; + auto locks = MaybeLockVariableInputMutexesInOrder( + ctx, use_exclusive_lock_, sparse, {0, 1}); Tensor var; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, true, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); Tensor accum; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 1, use_exclusive_lock_, true, &accum)); + ctx, 1, use_exclusive_lock_, sparse, &accum)); OP_REQUIRES( ctx, var.IsInitialized(), errors::FailedPrecondition( @@ -2783,18 +2807,19 @@ class ApplyAdamOp : public OpKernel { } void Compute(OpKernelContext* ctx) override { - auto locks = MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, - {0, 1, 2}); + const bool sparse = false; + auto locks = MaybeLockVariableInputMutexesInOrder( + ctx, use_exclusive_lock_, sparse, {0, 1, 2}); Tensor var; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, false, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); Tensor m; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 1, use_exclusive_lock_, false, &m)); + ctx, 1, use_exclusive_lock_, sparse, &m)); Tensor v; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 2, use_exclusive_lock_, false, &v)); + ctx, 2, use_exclusive_lock_, sparse, &v)); OP_REQUIRES( ctx, var.IsInitialized(), errors::FailedPrecondition( @@ -2873,18 +2898,19 @@ class ApplyAdamOp : public OpKernel { } void Compute(OpKernelContext* ctx) override { - auto locks = MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, - {0, 1, 2}); + const bool sparse = false; + auto locks = MaybeLockVariableInputMutexesInOrder( + ctx, use_exclusive_lock_, sparse, {0, 1, 2}); Tensor var; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, false, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); Tensor m; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 1, use_exclusive_lock_, false, &m)); + ctx, 1, use_exclusive_lock_, sparse, &m)); Tensor v; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 2, use_exclusive_lock_, false, &v)); + ctx, 2, use_exclusive_lock_, sparse, &v)); OP_REQUIRES( ctx, var.IsInitialized(), errors::FailedPrecondition( @@ -3043,21 +3069,22 @@ class ApplyAdamWithAmsgradOp : public OpKernel { } void Compute(OpKernelContext* ctx) override { - auto locks = MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, - {0, 1, 2}); + const bool sparse = false; + auto locks = MaybeLockVariableInputMutexesInOrder( + ctx, use_exclusive_lock_, sparse, {0, 1, 2}); Tensor var; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, false, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); Tensor m; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 1, use_exclusive_lock_, false, &m)); + ctx, 1, use_exclusive_lock_, sparse, &m)); Tensor v; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 2, use_exclusive_lock_, false, &v)); + ctx, 2, use_exclusive_lock_, sparse, &v)); Tensor vhat; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 3, use_exclusive_lock_, false, &vhat)); + ctx, 3, use_exclusive_lock_, sparse, &vhat)); OP_REQUIRES( ctx, var.IsInitialized(), errors::FailedPrecondition( @@ -3184,18 +3211,19 @@ class ApplyAdaMaxOp : public OpKernel { } void Compute(OpKernelContext* ctx) override { - auto locks = MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, - {0, 1, 2}); + const bool sparse = false; + auto locks = MaybeLockVariableInputMutexesInOrder( + ctx, use_exclusive_lock_, sparse, {0, 1, 2}); Tensor var; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, false, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); Tensor m; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 1, use_exclusive_lock_, false, &m)); + ctx, 1, use_exclusive_lock_, sparse, &m)); Tensor v; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 2, use_exclusive_lock_, false, &v)); + ctx, 2, use_exclusive_lock_, sparse, &v)); OP_REQUIRES( ctx, var.IsInitialized(), errors::FailedPrecondition( @@ -3312,18 +3340,19 @@ class ApplyRMSPropOp : public OpKernel { } void Compute(OpKernelContext* ctx) override { - auto locks = MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, - {0, 1, 2}); + const bool sparse = false; + auto locks = MaybeLockVariableInputMutexesInOrder( + ctx, use_exclusive_lock_, sparse, {0, 1, 2}); Tensor var; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, false, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); Tensor ms; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 1, use_exclusive_lock_, false, &ms)); + ctx, 1, use_exclusive_lock_, sparse, &ms)); Tensor mom; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 2, use_exclusive_lock_, false, &mom)); + ctx, 2, use_exclusive_lock_, sparse, &mom)); OP_REQUIRES( ctx, var.IsInitialized(), @@ -3394,21 +3423,22 @@ class ApplyCenteredRMSPropOp : public OpKernel { } void Compute(OpKernelContext* ctx) override { - auto locks = MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, - {0, 1, 2, 3}); + const bool sparse = false; + auto locks = MaybeLockVariableInputMutexesInOrder( + ctx, use_exclusive_lock_, sparse, {0, 1, 2, 3}); Tensor var; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, false, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); Tensor mg; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 1, use_exclusive_lock_, false, &mg)); + ctx, 1, use_exclusive_lock_, sparse, &mg)); Tensor ms; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 2, use_exclusive_lock_, false, &ms)); + ctx, 2, use_exclusive_lock_, sparse, &ms)); Tensor mom; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 3, use_exclusive_lock_, false, &mom)); + ctx, 3, use_exclusive_lock_, sparse, &mom)); OP_REQUIRES( ctx, var.IsInitialized(), @@ -3553,18 +3583,19 @@ class SparseApplyRMSPropOp : public OpKernel { } void Compute(OpKernelContext* ctx) override NO_THREAD_SAFETY_ANALYSIS { - auto locks = MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, - {0, 1, 2}); + const bool sparse = true; + auto locks = MaybeLockVariableInputMutexesInOrder( + ctx, use_exclusive_lock_, sparse, {0, 1, 2}); Tensor var; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, true, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); Tensor ms; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 1, use_exclusive_lock_, true, &ms)); + ctx, 1, use_exclusive_lock_, sparse, &ms)); Tensor mom; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 2, use_exclusive_lock_, true, &mom)); + ctx, 2, use_exclusive_lock_, sparse, &mom)); OP_REQUIRES( ctx, var.IsInitialized(), @@ -3682,21 +3713,22 @@ class SparseApplyCenteredRMSPropOp : public OpKernel { } void Compute(OpKernelContext* ctx) override NO_THREAD_SAFETY_ANALYSIS { - auto locks = MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, - {0, 1, 2, 3}); + const bool sparse = true; + auto locks = MaybeLockVariableInputMutexesInOrder( + ctx, use_exclusive_lock_, sparse, {0, 1, 2, 3}); Tensor var; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, true, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); Tensor mg; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 1, use_exclusive_lock_, true, &mg)); + ctx, 1, use_exclusive_lock_, sparse, &mg)); Tensor ms; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 2, use_exclusive_lock_, true, &ms)); + ctx, 2, use_exclusive_lock_, sparse, &ms)); Tensor mom; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 3, use_exclusive_lock_, true, &mom)); + ctx, 3, use_exclusive_lock_, sparse, &mom)); OP_REQUIRES( ctx, var.IsInitialized(), @@ -3852,15 +3884,16 @@ class ApplyAddSignOp : public OpKernel { } void Compute(OpKernelContext* ctx) override { - auto locks = - MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, {0, 1}); + const bool sparse = false; + auto locks = MaybeLockVariableInputMutexesInOrder( + ctx, use_exclusive_lock_, sparse, {0, 1}); Tensor var; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, false, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); Tensor m; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 1, use_exclusive_lock_, false, &m)); + ctx, 1, use_exclusive_lock_, sparse, &m)); OP_REQUIRES( ctx, var.IsInitialized(), errors::FailedPrecondition( @@ -3958,15 +3991,16 @@ class ApplyPowerSignOp : public OpKernel { } void Compute(OpKernelContext* ctx) override { - auto locks = - MaybeLockVariableInputMutexesInOrder(ctx, use_exclusive_lock_, {0, 1}); + const bool sparse = false; + auto locks = MaybeLockVariableInputMutexesInOrder( + ctx, use_exclusive_lock_, sparse, {0, 1}); Tensor var; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 0, use_exclusive_lock_, false, &var)); + ctx, 0, use_exclusive_lock_, sparse, &var)); Tensor m; OP_REQUIRES_OK(ctx, GetInputTensorFromVariable( - ctx, 1, use_exclusive_lock_, false, &m)); + ctx, 1, use_exclusive_lock_, sparse, &m)); OP_REQUIRES( ctx, var.IsInitialized(), errors::FailedPrecondition( diff --git a/tensorflow/python/kernel_tests/resource_variable_ops_test.py b/tensorflow/python/kernel_tests/resource_variable_ops_test.py index 433957fd1d..1dabcbb5c3 100644 --- a/tensorflow/python/kernel_tests/resource_variable_ops_test.py +++ b/tensorflow/python/kernel_tests/resource_variable_ops_test.py @@ -36,6 +36,7 @@ from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import custom_gradient from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import init_ops +from tensorflow.python.ops import list_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import state_ops @@ -953,6 +954,19 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): state_ops.scatter_sub(v, [1], [3]) self.assertAllEqual([1.0, -1.0], v.numpy()) + def testScatterUpdateVariant(self): + with context.eager_mode(): + v = resource_variable_ops.ResourceVariable([ + list_ops.empty_tensor_list( + element_dtype=dtypes.float32, element_shape=[]) + ]) + v.scatter_update( + ops.IndexedSlices( + list_ops.tensor_list_from_tensor([1., 2.], element_shape=[]), 0)) + self.assertAllEqual( + list_ops.tensor_list_get_item(v[0], 0, element_dtype=dtypes.float32), + 1.) + def testScatterNdAddStateOps(self): with context.eager_mode(): v = resource_variable_ops.ResourceVariable( -- GitLab From 841f5d9fc9fac4433ea57ee61fc4b4286cec5c2b Mon Sep 17 00:00:00 2001 From: Eugene Zhulenev Date: Mon, 10 Dec 2018 12:02:47 -0800 Subject: [PATCH 300/873] Do not fail PartitionedCallOp kernel if Grappler failed PiperOrigin-RevId: 224856604 --- tensorflow/core/kernels/partitioned_function_ops.cc | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/tensorflow/core/kernels/partitioned_function_ops.cc b/tensorflow/core/kernels/partitioned_function_ops.cc index ba51db219e..fbecd909be 100644 --- a/tensorflow/core/kernels/partitioned_function_ops.cc +++ b/tensorflow/core/kernels/partitioned_function_ops.cc @@ -191,10 +191,12 @@ class PartitionedCallOp : public AsyncOpKernel { // Run grappler passes on the graph. It is possible that these are // optimized by the graph executor already. - OP_REQUIRES_OK_ASYNC(ctx, - OptimizeGraph(ctx, fbody->ret_nodes, overlay_lib, - device_set, cpu_device, &graph), - done); + Status optimized = OptimizeGraph(ctx, fbody->ret_nodes, overlay_lib, + device_set, cpu_device, &graph); + if (!optimized.ok()) { + LOG(WARNING) << "Grappler optimization failed. Error: " + << optimized.error_message(); + } OP_REQUIRES_OK_ASYNC( ctx, -- GitLab From ee418c8ee26a4e816e6acf1954748aac4418e558 Mon Sep 17 00:00:00 2001 From: Katherine Wu Date: Mon, 10 Dec 2018 12:28:38 -0800 Subject: [PATCH 301/873] Add attribute to Keras model which generates an exportable tf.function. SaveModel save now looks for this attribute when searching for a function to export. PiperOrigin-RevId: 224861089 --- tensorflow/python/eager/def_function.py | 4 + tensorflow/python/keras/BUILD | 1 + tensorflow/python/keras/engine/training.py | 7 +- .../python/keras/engine/training_utils.py | 60 +++++++ .../keras/engine/training_utils_test.py | 157 ++++++++++++++++++ tensorflow/python/saved_model/save.py | 26 +-- tensorflow/python/saved_model/save_test.py | 82 +++------ 7 files changed, 254 insertions(+), 83 deletions(-) diff --git a/tensorflow/python/eager/def_function.py b/tensorflow/python/eager/def_function.py index 6bacd7a962..3663d72999 100644 --- a/tensorflow/python/eager/def_function.py +++ b/tensorflow/python/eager/def_function.py @@ -342,6 +342,10 @@ class PolymorphicFunction(object): """The python function wrapped in this tf.function.""" return self._python_function + @property + def input_signature(self): + return self._input_signature + def get_initialization_function(self, *args, **kwargs): """Returns a `Function` object which initializes this function's variables. diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD index 36fea36389..faf58e0d93 100755 --- a/tensorflow/python/keras/BUILD +++ b/tensorflow/python/keras/BUILD @@ -848,6 +848,7 @@ py_test( deps = [ ":keras", "//tensorflow/python:client_testlib", + "//tensorflow/python/saved_model:save_test", "//third_party/py/numpy", "@absl_py//absl/testing:parameterized", ], diff --git a/tensorflow/python/keras/engine/training.py b/tensorflow/python/keras/engine/training.py index 462694fda6..fe44bc20a1 100644 --- a/tensorflow/python/keras/engine/training.py +++ b/tensorflow/python/keras/engine/training.py @@ -1539,8 +1539,7 @@ class Model(Network): outputs = nest.flatten(outputs) self.outputs = outputs - self.output_names = [ - 'output_%d' % (i + 1) for i in range(len(self.outputs))] + self.output_names = training_utils.generic_output_names(outputs) self.built = True def fit(self, @@ -2580,6 +2579,10 @@ class Model(Network): batch_size = 32 return batch_size + @property + def _default_save_signature(self): + return training_utils.trace_model_call(self) + class DistributedCallbackModel(Model): """Model that is used for callbacks with DistributionStrategy.""" diff --git a/tensorflow/python/keras/engine/training_utils.py b/tensorflow/python/keras/engine/training_utils.py index 01a09eb031..ec6b39704a 100644 --- a/tensorflow/python/keras/engine/training_utils.py +++ b/tensorflow/python/keras/engine/training_utils.py @@ -27,9 +27,11 @@ import six from tensorflow.python.data.ops import iterator_ops from tensorflow.python.eager import context +from tensorflow.python.eager import def_function from tensorflow.python.framework import constant_op from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape +from tensorflow.python.framework import tensor_spec from tensorflow.python.framework import tensor_util from tensorflow.python.keras import backend as K from tensorflow.python.keras import callbacks as cbks @@ -1191,3 +1193,61 @@ def get_static_batch_size(layer): if batch_input_shape is not None: return tensor_shape.as_dimension(batch_input_shape[0]).value return None + + +def generic_output_names(outputs_list): + return ['output_%d' % (i + 1) for i in range(len(outputs_list))] + + +def trace_model_call(model, input_signature=None): + """Trace the model call to create a tf.function for exporting a Keras model. + + Args: + model: A Keras model. + input_signature: optional, a list of tf.TensorSpec objects specifying the + inputs to the model. + + Returns: + A tf.function wrapping the model's call function with input signatures set. + + Raises: + ValueError: if input signature cannot be inferred from the model. + """ + if input_signature is None: + if isinstance(model.call, def_function.PolymorphicFunction): + input_signature = model.call.input_signature + + if input_signature is None: + try: + inputs = model.inputs + input_names = model.input_names + except AttributeError: + raise ValueError( + 'Model {} cannot be saved because the input shapes have not been ' + 'set. Usually, input shapes are automatically determined from calling' + ' .fit() or .predict(). To manually set the shapes, call ' + 'model._set_inputs(inputs).'.format(model)) + input_specs = [] + for input_tensor, input_name in zip(inputs, input_names): + input_specs.append(tensor_spec.TensorSpec( + shape=input_tensor.shape, dtype=input_tensor.dtype, + name=input_name)) + # The input signature of the call function is a list with one element, since + # all tensor inputs must be passed in as the first argument. + input_signature = [input_specs] if len(input_specs) > 1 else input_specs + + @def_function.function(input_signature=input_signature) + def _wrapped_model(*args): + """A concrete tf.function that wraps the model's call function.""" + # When given a single input, Keras models will call the model on the tensor + # rather than a list consisting of the single tensor. + inputs = args[0] if len(input_signature) == 1 else list(args) + outputs_list = nest.flatten(model(inputs=inputs)) + try: + output_names = model.output_names + except AttributeError: + output_names = generic_output_names(outputs_list) + return {name: output for name, output in zip(output_names, outputs_list)} + + return _wrapped_model + diff --git a/tensorflow/python/keras/engine/training_utils_test.py b/tensorflow/python/keras/engine/training_utils_test.py index 44ea23998f..0250e60426 100644 --- a/tensorflow/python/keras/engine/training_utils_test.py +++ b/tensorflow/python/keras/engine/training_utils_test.py @@ -18,13 +18,25 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import os + import numpy as np +from tensorflow.python import keras from tensorflow.python.eager import context +from tensorflow.python.eager import def_function +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import tensor_spec from tensorflow.python.framework import tensor_util +from tensorflow.python.keras import backend as K +from tensorflow.python.keras import keras_parameterized +from tensorflow.python.keras import testing_utils from tensorflow.python.keras.engine import training_utils from tensorflow.python.keras.utils import tf_utils +from tensorflow.python.ops import array_ops from tensorflow.python.platform import test +from tensorflow.python.saved_model import save as save_lib +from tensorflow.python.saved_model import save_test class ModelInputsTest(test.TestCase): @@ -85,5 +97,150 @@ class ModelInputsTest(test.TestCase): self.assertTrue(tf_utils.is_symbolic_tensor(vals['b'])) +class TraceModelCallTest(keras_parameterized.TestCase): + + def _assert_all_close(self, expected, actual): + if not context.executing_eagerly(): + with self.cached_session() as sess: + K._initialize_variables(sess) + self.assertAllClose(expected, actual) + else: + self.assertAllClose(expected, actual) + + @keras_parameterized.run_with_all_model_types + @keras_parameterized.run_all_keras_modes + def test_trace_model_outputs(self): + input_dim = 5 if testing_utils.get_model_type() == 'functional' else None + model = testing_utils.get_small_mlp(10, 3, input_dim) + inputs = array_ops.ones((8, 5)) + + if input_dim is None: + with self.assertRaisesRegexp(ValueError, + 'input shapes have not been set'): + training_utils.trace_model_call(model) + model._set_inputs(inputs) + + fn = training_utils.trace_model_call(model) + signature_outputs = fn(inputs) + expected_outputs = {model.output_names[0]: model(inputs)} + + self._assert_all_close(expected_outputs, signature_outputs) + + @keras_parameterized.run_with_all_model_types + @keras_parameterized.run_all_keras_modes + def test_trace_model_outputs_after_fitting(self): + input_dim = 5 if testing_utils.get_model_type() == 'functional' else None + model = testing_utils.get_small_mlp(10, 3, input_dim) + model.compile(optimizer='sgd', loss='mse') + model.fit(x=np.random.random((8, 5)), + y=np.random.random((8, 3)), epochs=2) + + inputs = array_ops.ones((8, 5)) + + fn = training_utils.trace_model_call(model) + signature_outputs = fn(inputs) + expected_outputs = {model.output_names[0]: model(inputs)} + + self._assert_all_close(expected_outputs, signature_outputs) + + @keras_parameterized.run_with_all_model_types(exclude_models='sequential') + @keras_parameterized.run_all_keras_modes + def test_trace_multi_io_model_outputs(self): + input_dim = 5 + num_classes = 3 + num_classes_b = 4 + input_a = keras.layers.Input(shape=(input_dim,), name='input_a') + input_b = keras.layers.Input(shape=(input_dim,), name='input_b') + + dense = keras.layers.Dense(num_classes, name='dense') + dense2 = keras.layers.Dense(num_classes_b, name='dense2') + dropout = keras.layers.Dropout(0.5, name='dropout') + branch_a = [input_a, dense] + branch_b = [input_b, dense, dense2, dropout] + + model = testing_utils.get_multi_io_model(branch_a, branch_b) + + input_a_np = np.random.random((10, input_dim)).astype(np.float32) + input_b_np = np.random.random((10, input_dim)).astype(np.float32) + + if testing_utils.get_model_type() == 'subclass': + with self.assertRaisesRegexp(ValueError, + 'input shapes have not been set'): + training_utils.trace_model_call(model) + + model.compile(optimizer='sgd', loss='mse') + model.fit(x=[np.random.random((8, input_dim)).astype(np.float32), + np.random.random((8, input_dim)).astype(np.float32)], + y=[np.random.random((8, num_classes)).astype(np.float32), + np.random.random((8, num_classes_b)).astype(np.float32)], + epochs=2) + + fn = training_utils.trace_model_call(model) + signature_outputs = fn([input_a_np, input_b_np]) + outputs = model([input_a_np, input_b_np]) + expected_outputs = {model.output_names[0]: outputs[0], + model.output_names[1]: outputs[1]} + + self._assert_all_close(expected_outputs, signature_outputs) + + @keras_parameterized.run_all_keras_modes + def test_specify_input_signature(self): + model = testing_utils.get_small_sequential_mlp(10, 3, None) + inputs = array_ops.ones((8, 5)) + + with self.assertRaisesRegexp(ValueError, 'input shapes have not been set'): + training_utils.trace_model_call(model) + + fn = training_utils.trace_model_call( + model, [tensor_spec.TensorSpec(shape=[None, 5], dtype=dtypes.float32)]) + signature_outputs = fn(inputs) + expected_outputs = {model.output_names[0]: model(inputs)} + self._assert_all_close(expected_outputs, signature_outputs) + + @keras_parameterized.run_all_keras_modes + def test_subclassed_model_with_input_signature(self): + + class Model(keras.Model): + + def __init__(self): + super(Model, self).__init__() + self.dense = keras.layers.Dense(3, name='dense') + + @def_function.function( + input_signature=[[tensor_spec.TensorSpec([None, 5], dtypes.float32), + tensor_spec.TensorSpec([None], dtypes.float32)]],) + def call(self, inputs, *args): + x, y = inputs + return self.dense(x) + y + + model = Model() + fn = training_utils.trace_model_call(model) + x = array_ops.ones((8, 5), dtype=dtypes.float32) + y = array_ops.ones((3,), dtype=dtypes.float32) + expected_outputs = {'output_1': model([x, y])} + signature_outputs = fn([x, y]) + self._assert_all_close(expected_outputs, signature_outputs) + + +class ModelSaveTest(keras_parameterized.TestCase): + + @keras_parameterized.run_with_all_model_types + @keras_parameterized.run_all_keras_modes(always_skip_v1=True) + def test_model_save(self): + input_dim = 5 + model = testing_utils.get_small_mlp(10, 3, input_dim) + inputs = array_ops.ones((8, 5)) + + if testing_utils.get_model_type() == 'subclass': + model._set_inputs(inputs) + + save_dir = os.path.join(self.get_temp_dir(), 'saved_model') + save_lib.save(model, save_dir) + + self.assertAllClose( + {model.output_names[0]: model.predict_on_batch(inputs)}, + save_test._import_and_infer(save_dir, + {model.input_names[0]: np.ones((8, 5))})) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/saved_model/save.py b/tensorflow/python/saved_model/save.py index ab6fcb7196..e2726087a5 100644 --- a/tensorflow/python/saved_model/save.py +++ b/tensorflow/python/saved_model/save.py @@ -31,7 +31,6 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import meta_graph from tensorflow.python.framework import ops -from tensorflow.python.framework import tensor_spec from tensorflow.python.lib.io import file_io from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops @@ -50,28 +49,7 @@ from tensorflow.python.util import compat from tensorflow.python.util import nest from tensorflow.python.util.tf_export import tf_export - -def _check_for_functional_keras_model(root): - """Makes an export signature for `root` if it's a functional Keras Model.""" - # If nothing is decorated yet but this is a functional Keras Model (duck - # typed), we'll try to make a signature ourselves. - try: - inputs = root.inputs - input_names = root.input_names - except AttributeError: - return None - input_signature = [] - for input_tensor, input_name in zip(inputs, input_names): - input_signature.append(tensor_spec.TensorSpec( - shape=input_tensor.shape, dtype=input_tensor.dtype, - name=input_name)) - - @def_function.function(input_signature=input_signature) - def _wrapped_model(*args): - outputs_list = nest.flatten(root(inputs=list(args))) - return {name: output for name, output - in zip(root.output_names, outputs_list)} - return _wrapped_model +DEFAULT_SIGNATURE_ATTR = "_default_save_signature" def _find_function_to_export(root): @@ -93,7 +71,7 @@ def _find_function_to_export(root): exported_function = attribute_value previous_attribute_name = attribute_name if exported_function is None: - exported_function = _check_for_functional_keras_model(root) + exported_function = getattr(root, DEFAULT_SIGNATURE_ATTR, None) if exported_function is None: raise ValueError( ("Exporting an object with no tf.saved_model.save(..., signatures=...) " diff --git a/tensorflow/python/saved_model/save_test.py b/tensorflow/python/saved_model/save_test.py index 97218a98ea..1c6eb1b538 100644 --- a/tensorflow/python/saved_model/save_test.py +++ b/tensorflow/python/saved_model/save_test.py @@ -21,8 +21,6 @@ from __future__ import print_function import os import sys -import numpy - from tensorflow.python.client import session as session_lib from tensorflow.python.eager import backprop from tensorflow.python.eager import def_function @@ -32,12 +30,8 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_spec from tensorflow.python.framework import test_util -from tensorflow.python.keras.engine import input_layer -from tensorflow.python.keras.engine import training from tensorflow.python.keras.layers import core -from tensorflow.python.keras.layers import merge from tensorflow.python.lib.io import file_io -from tensorflow.python.ops import array_ops from tensorflow.python.ops import lookup_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import variables @@ -50,10 +44,9 @@ from tensorflow.python.training.checkpointable import tracking from tensorflow.python.training.checkpointable import util -class _ModelWithOptimizer(training.Model): +class _ModelWithOptimizer(util.Checkpoint): def __init__(self): - super(_ModelWithOptimizer, self).__init__() self.dense = core.Dense(1) self.optimizer = adam.AdamOptimizer(0.01) @@ -63,7 +56,7 @@ class _ModelWithOptimizer(training.Model): def call(self, x, y): with backprop.GradientTape() as tape: loss = math_ops.reduce_mean((self.dense(x) - y) ** 2.) - trainable_variables = self.trainable_variables + trainable_variables = self.dense.trainable_variables gradients = tape.gradient(loss, trainable_variables) self.optimizer.apply_gradients(zip(gradients, trainable_variables)) return {"loss": loss} @@ -179,10 +172,10 @@ class SaveTest(test.TestCase): x = constant_op.constant([[3., 4.]]) y = constant_op.constant([2.]) model = _ModelWithOptimizer() - first_loss = model(x, y) + first_loss = model.call(x, y) save_dir = os.path.join(self.get_temp_dir(), "saved_model") save.save(model, save_dir, model.call) - second_loss = model(x, y) + second_loss = model.call(x, y) self.assertNotEqual(first_loss, second_loss) self.assertAllClose( second_loss, @@ -197,7 +190,7 @@ class SaveTest(test.TestCase): model = _ModelWithOptimizer() x = constant_op.constant([[3., 4.]]) y = constant_op.constant([2.]) - model(x, y) + model.call(x, y) save_dir = os.path.join(self.get_temp_dir(), "saved_model") save.save(model, save_dir) self.assertIn("loss", @@ -217,25 +210,40 @@ class SaveTest(test.TestCase): model = _ModelWithOptimizer() x = constant_op.constant([[3., 4.]]) y = constant_op.constant([2.]) - model(x, y) + model.call(x, y) model.second_function = def_function.function(lambda: 1.) save_dir = os.path.join(self.get_temp_dir(), "saved_model") with self.assertRaisesRegexp(ValueError, "call.*second_function"): save.save(model, save_dir) - def test_subclassed_no_signature(self): + def test_no_signature(self): - class Subclassed(training.Model): + class Model(util.Checkpoint): def call(self, inputs): return inputs * 2. save_dir = os.path.join(self.get_temp_dir(), "saved_model") - model = Subclassed() + model = Model() with self.assertRaisesRegexp( ValueError, "no @tf.function-decorated methods"): save.save(model, save_dir) + def test_find_default_save_function(self): + + class ObjWithDefaultSignature(util.Checkpoint): + + @def_function.function(input_signature=[tensor_spec.TensorSpec( + shape=None, dtype=dtypes.float32)]) + def _default_save_signature(self, x): + return x + x + 1 + + obj = ObjWithDefaultSignature() + save_dir = os.path.join(self.get_temp_dir(), "saved_model") + save.save(obj, save_dir) + self.assertAllClose( + {"output_0": 7.}, _import_and_infer(save_dir, {"x": 3.})) + def test_docstring(self): class Adder(util.Checkpoint): @@ -276,46 +284,6 @@ class SaveTest(test.TestCase): self.assertNotIn("T", complex_node.attr) self.assertNotIn("Tout", complex_node.attr) - def test_export_functional_keras_model(self): - x = input_layer.Input((4,), name="x") - y = core.Dense(4, name="out")(x) - model = training.Model(x, y) - save_dir = os.path.join(self.get_temp_dir(), "saved_model") - save.save(model, save_dir) - self.assertAllClose( - {"out": model(array_ops.ones([1, 4]))}, - _import_and_infer(save_dir, {"x": [[1., 1., 1., 1.]]})) - - @test_util.run_v1_only("b/120545219") - def test_export_functional_keras_model_after_fit(self): - x = input_layer.Input((1,)) - y = core.Dense(1, name="y")(x) - model = training.Model(x, y) - model.compile(optimizer="sgd", loss="mse") - model.fit(x=numpy.array([[1.]]), - y=numpy.array([2.]), epochs=2) - save_dir = os.path.join(self.get_temp_dir(), "saved_model") - save.save(model, save_dir) - self.assertAllClose( - {"y": model(constant_op.constant([[1.], [2.]]))}, - _import_and_infer(save_dir, {"input_1": [[1.], [2.]]})) - - def test_export_multi_input_functional_keras_model(self): - x1 = input_layer.Input((2,), name="x1") - x2 = input_layer.Input((2,), name="x2") - y1 = core.Dense(4)(merge.Add()([x1, x2])) - y2 = core.Dense(4)(merge.Multiply()([x1, x2])) - model = training.Model([x1, x2], [y1, y2]) - save_dir = os.path.join(self.get_temp_dir(), "saved_model") - save.save(model, save_dir) - outputs = model([array_ops.ones([1, 2]), 2. * array_ops.ones([1, 2])]) - self.assertAllClose( - {"dense": outputs[0], "dense_1": outputs[1]}, - _import_and_infer( - save_dir, - {"x1": [[1., 1.]], - "x2": [[2., 2.]]})) - class AssetTests(test.TestCase): @@ -376,7 +344,7 @@ class MemoryTests(test.TestCase): def test_no_reference_cycles(self): x = constant_op.constant([[3., 4.]]) y = constant_op.constant([2.]) - self._model(x, y) + self._model.call(x, y) if sys.version_info[0] < 3: # TODO(allenl): debug reference cycles in Python 2.x self.skipTest("This test only works in Python 3+. Reference cycles are " -- GitLab From 1d54cbf4a2252215c5d2ce9accb5e498a7c2a704 Mon Sep 17 00:00:00 2001 From: Skye Wanderman-Milne Date: Mon, 10 Dec 2018 12:35:24 -0800 Subject: [PATCH 302/873] Introduce consolidated ENABLE_CONTROL_FLOW_V2 flag. The new toggle replaces ENABLE_COND_V2, ENABLE_WHILE_V2, and ENABLE_TENSOR_ARRAY_V2. This means that these can't be toggled independently anymore, notably that v1 TensorArrays can only be run with v1 loops, and v2 TensorArrays with v2 loops. This also introduces a corresponding environment variable TF_ENABLE_CONTROL_FLOW_V2. I kept the old env vars as well in case people are using them. They all flip the new single toggle now. In addition, this change removes some while_v2 code for dealing with v1 TensorArrays, since this is no longer a supported configuration. PiperOrigin-RevId: 224862245 --- .../kernel_tests/map_and_batch_test.py | 7 ++- tensorflow/python/framework/test_util.py | 41 ++----------- .../kernel_tests/control_flow_ops_py_test.py | 22 +++---- .../kernel_tests/control_flow_util_v2_test.py | 10 ++-- .../kernel_tests/tensor_array_ops_test.py | 42 +++++--------- tensorflow/python/ops/control_flow_ops.py | 9 +-- .../python/ops/control_flow_ops_benchmark.py | 25 ++++---- tensorflow/python/ops/control_flow_util.py | 8 +++ tensorflow/python/ops/tensor_array_ops.py | 9 +-- tensorflow/python/ops/while_v2.py | 57 ++++--------------- 10 files changed, 75 insertions(+), 155 deletions(-) diff --git a/tensorflow/python/data/experimental/kernel_tests/map_and_batch_test.py b/tensorflow/python/data/experimental/kernel_tests/map_and_batch_test.py index 5c115f7ae3..a8a65dde13 100644 --- a/tensorflow/python/data/experimental/kernel_tests/map_and_batch_test.py +++ b/tensorflow/python/data/experimental/kernel_tests/map_and_batch_test.py @@ -32,6 +32,7 @@ from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import control_flow_util from tensorflow.python.ops import math_ops from tensorflow.python.ops import script_ops from tensorflow.python.platform import test @@ -500,10 +501,10 @@ class MapAndBatchTest(test_base.DatasetTestBase, parameterized.TestCase): def testMapAndBatchControlFlow(self, numa_aware): def map_fn(x): - previous_cond_v2_value = control_flow_ops.ENABLE_COND_V2 - control_flow_ops.ENABLE_COND_V2 = True + previous_control_flow_v2_value = control_flow_util.ENABLE_CONTROL_FLOW_V2 + control_flow_util.ENABLE_CONTROL_FLOW_V2 = True return_value = control_flow_ops.cond(x < 50, lambda: x + 1, lambda: x * x) - control_flow_ops.ENABLE_COND_V2 = previous_cond_v2_value + control_flow_util.ENABLE_CONTROL_FLOW_V2 = previous_control_flow_v2_value return return_value dataset = dataset_ops.Dataset.range(100).apply( diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py index df3cebd2e0..0e48d3c875 100644 --- a/tensorflow/python/framework/test_util.py +++ b/tensorflow/python/framework/test_util.py @@ -67,9 +67,8 @@ from tensorflow.python.framework import sparse_tensor 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 control_flow_ops +from tensorflow.python.ops import control_flow_util from tensorflow.python.ops import script_ops -from tensorflow.python.ops import tensor_array_ops from tensorflow.python.ops import variables from tensorflow.python.platform import googletest from tensorflow.python.platform import tf_logging as logging @@ -409,42 +408,12 @@ def enable_control_flow_v2(fn): """ def wrapper(*args, **kwargs): - enable_cond_v2_old = control_flow_ops.ENABLE_COND_V2 - enable_while_v2_old = control_flow_ops.ENABLE_WHILE_V2 - enable_tensor_array_v2_old = tensor_array_ops.ENABLE_TENSOR_ARRAY_V2 - control_flow_ops.ENABLE_COND_V2 = True - control_flow_ops.ENABLE_WHILE_V2 = True - tensor_array_ops.ENABLE_TENSOR_ARRAY_V2 = True + enable_control_flow_v2_old = control_flow_util.ENABLE_CONTROL_FLOW_V2 + control_flow_util.ENABLE_CONTROL_FLOW_V2 = True try: fn(*args, **kwargs) finally: - control_flow_ops.ENABLE_COND_V2 = enable_cond_v2_old - control_flow_ops.ENABLE_WHILE_V2 = enable_while_v2_old - tensor_array_ops.ENABLE_TENSOR_ARRAY_V2 = enable_tensor_array_v2_old - - return wrapper - - -def enable_tensor_array_v2(fn): - """Decorator for enabling _GraphTensorArrayV2 on a test. - - Note this enables _GraphTensorArrayV2 after running the test class's - setup/teardown methods. - - Args: - fn: the function to be wrapped - - Returns: - The wrapped function - """ - - def wrapper(*args, **kwargs): - enable_tensor_array_v2_old = tensor_array_ops.ENABLE_TENSOR_ARRAY_V2 - tensor_array_ops.ENABLE_TENSOR_ARRAY_V2 = True - try: - fn(*args, **kwargs) - finally: - tensor_array_ops.ENABLE_TENSOR_ARRAY_V2 = enable_tensor_array_v2_old + control_flow_util.ENABLE_CONTROL_FLOW_V2 = enable_control_flow_v2_old return wrapper @@ -493,7 +462,7 @@ def with_control_flow_v2(cls): Returns: cls with new test methods added """ - if control_flow_ops.ENABLE_WHILE_V2 and control_flow_ops.ENABLE_COND_V2: + if control_flow_util.ENABLE_CONTROL_FLOW_V2: return cls for name, value in cls.__dict__.copy().items(): diff --git a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py index 0fd293ebba..21ded25a11 100644 --- a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py +++ b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py @@ -43,6 +43,7 @@ from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import control_flow_util from tensorflow.python.ops import data_flow_ops from tensorflow.python.ops import functional_ops from tensorflow.python.ops import gen_array_ops @@ -700,7 +701,8 @@ class ControlFlowTest(test.TestCase): v1_msg = "The two structures don't have the same nested structure" v2_msg = "Outputs of true_fn and false_fn must have the same structure" with self.assertRaisesRegexp( - ValueError, v2_msg if control_flow_ops.ENABLE_COND_V2 else v1_msg): + ValueError, + v2_msg if control_flow_util.ENABLE_CONTROL_FLOW_V2 else v1_msg): r = control_flow_ops.cond(pred, fn1, fn2) self.evaluate(r) @@ -859,7 +861,7 @@ class ControlFlowTest(test.TestCase): self.assertEqual(sess.run(grad, {pred: False, x: 1.0, y: 2.0}), 0.0) # v1 control flow gets None second derivative for some reason. - if not control_flow_ops.ENABLE_COND_V2: + if not control_flow_util.ENABLE_CONTROL_FLOW_V2: self.assertIsNone(grad_grad) return @@ -949,7 +951,7 @@ class ControlFlowTest(test.TestCase): # In defuns, all prints should execute in program order. # This doesn't work with legacy control flow. - if control_flow_ops.ENABLE_COND_V2: + if control_flow_util.ENABLE_CONTROL_FLOW_V2: @eager_function.defun def cond(): @@ -1003,7 +1005,7 @@ class ControlFlowTest(test.TestCase): # In defuns, all prints should execute in program order. # This doesn't work with legacy control flow. - if control_flow_ops.ENABLE_WHILE_V2: + if control_flow_util.ENABLE_CONTROL_FLOW_V2: @eager_function.defun def while_loop(): @@ -1161,7 +1163,7 @@ class ControlFlowTest(test.TestCase): gs = gradients_impl.gradients(loop_no_xla, v) self.evaluate(gs) # This should execute without error. - if control_flow_ops.ENABLE_WHILE_V2: + if control_flow_util.ENABLE_CONTROL_FLOW_V2: xla_context = control_flow_ops.XLAControlFlowContext() xla_context.Enter() with self.assertRaisesRegexp( @@ -1219,7 +1221,7 @@ class ControlFlowTest(test.TestCase): lambda i, x: (i + 1, v * x), (0, 1.0), maximum_iterations=max_iter_holder[0]) - if control_flow_ops.ENABLE_WHILE_V2: + if control_flow_util.ENABLE_CONTROL_FLOW_V2: xla_context = control_flow_ops.XLAControlFlowContext() xla_context.Enter() with self.assertRaisesRegexp( @@ -1863,7 +1865,7 @@ class ControlFlowTest(test.TestCase): self.assertEqual(sess.run(grad, {pred: True}), 8.0) self.assertEqual(sess.run(grad, {pred: False}), 0.0) - if not control_flow_ops.ENABLE_WHILE_V2: + if not control_flow_util.ENABLE_CONTROL_FLOW_V2: return self.assertEqual(sess.run(grad_grad, {pred: True}), 0.0) @@ -2399,7 +2401,7 @@ class ControlFlowTest(test.TestCase): # outer_loop(x) = g(g(x)) = 4x + 81 # outer_loop'(x) = 4 # Note that v1 control flow gets 4.0 as well if the cond is removed. - if control_flow_ops.ENABLE_WHILE_V2 and control_flow_ops.ENABLE_COND_V2: + if control_flow_util.ENABLE_CONTROL_FLOW_V2: self.assertEqual(grad, 4.0) def testWhile_NestedInput(self): @@ -2982,7 +2984,7 @@ class ControlFlowTest(test.TestCase): result = functional_ops.scan(fn, np.array([1., 2., 3.], dtype=np.float32)) grad_theta = gradients_impl.gradients(result, theta) - if not control_flow_ops.ENABLE_WHILE_V2: + if not control_flow_util.ENABLE_CONTROL_FLOW_V2: with self.assertRaisesRegexp(TypeError, "Second-order gradient"): gradients_impl.gradients(grad_theta, theta) grad_theta_stopped = array_ops.stop_gradient(grad_theta) @@ -3514,7 +3516,7 @@ class ControlFlowTest(test.TestCase): self.assertEqual(r[1].eval(), 65536.0) self.assertEqual(grad.eval(), 524288.0) # while_v2 does not have stacks. - if not control_flow_ops.ENABLE_WHILE_V2: + if not control_flow_util.ENABLE_CONTROL_FLOW_V2: self.assertEqual( len([op for op in x.graph.get_operations() if op.type == "StackV2" ]), 1) diff --git a/tensorflow/python/kernel_tests/control_flow_util_v2_test.py b/tensorflow/python/kernel_tests/control_flow_util_v2_test.py index d0374a7700..08d3214e28 100644 --- a/tensorflow/python/kernel_tests/control_flow_util_v2_test.py +++ b/tensorflow/python/kernel_tests/control_flow_util_v2_test.py @@ -23,6 +23,7 @@ from tensorflow.python.eager import function from tensorflow.python.framework import constant_op from tensorflow.python.framework import test_util from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import control_flow_util from tensorflow.python.ops import control_flow_util_v2 from tensorflow.python.platform import test @@ -30,14 +31,11 @@ from tensorflow.python.platform import test class ControlFlowUtilV2Test(test.TestCase): def setUp(self): - self._enable_cond_v2_old = control_flow_ops.ENABLE_COND_V2 - self._enable_while_v2_old = control_flow_ops.ENABLE_WHILE_V2 - control_flow_ops.ENABLE_COND_V2 = True - control_flow_ops.ENABLE_WHILE_V2 = True + self._enable_control_flow_v2_old = control_flow_util.ENABLE_CONTROL_FLOW_V2 + control_flow_util.ENABLE_CONTROL_FLOW_V2 = True def tearDown(self): - control_flow_ops.ENABLE_COND_V2 = self._enable_cond_v2_old - control_flow_ops.ENABLE_WHILE_V2 = self._enable_while_v2_old + control_flow_util.ENABLE_CONTROL_FLOW_V2 = self._enable_control_flow_v2_old def _create_control_flow(self, expect_in_defun): """Helper method for testInDefun.""" diff --git a/tensorflow/python/kernel_tests/tensor_array_ops_test.py b/tensorflow/python/kernel_tests/tensor_array_ops_test.py index 88625841bc..6d8e3e8356 100644 --- a/tensorflow/python/kernel_tests/tensor_array_ops_test.py +++ b/tensorflow/python/kernel_tests/tensor_array_ops_test.py @@ -32,6 +32,7 @@ from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import control_flow_util from tensorflow.python.ops import data_flow_ops from tensorflow.python.ops import gen_data_flow_ops from tensorflow.python.ops import gradients_impl @@ -345,7 +346,7 @@ class TensorArrayTest(test.TestCase): @test_util.run_deprecated_v1 def testSkipEagerTensorArrayGradGrad(self): - if not tensor_array_ops.ENABLE_TENSOR_ARRAY_V2: + if not control_flow_util.ENABLE_CONTROL_FLOW_V2: self.skipTest("Legacy TensorArray does not support double derivatives.") with self.test_session(use_gpu=True) as session: x = constant_op.constant(4.0) @@ -429,7 +430,7 @@ class TensorArrayTest(test.TestCase): with self.session(use_gpu=True): ta = _make_ta(3, "foo", dtype=dtypes.float32) # Test writing the wrong datatype - if (tensor_array_ops.ENABLE_TENSOR_ARRAY_V2 and + if (control_flow_util.ENABLE_CONTROL_FLOW_V2 and not context.executing_eagerly()): error_msg = ("Invalid data types; op elements string but list elements " "float") @@ -440,7 +441,7 @@ class TensorArrayTest(test.TestCase): with self.assertRaisesOpError(error_msg): self.evaluate(ta.write(0, "wrong_type_scalar").flow) - if (tensor_array_ops.ENABLE_TENSOR_ARRAY_V2 and + if (control_flow_util.ENABLE_CONTROL_FLOW_V2 and not context.executing_eagerly()): error_msg = "Trying to modify element -1 in a list with 3 elements." else: @@ -448,7 +449,7 @@ class TensorArrayTest(test.TestCase): with self.assertRaisesOpError(error_msg): self.evaluate(ta.write(-1, 3.0).flow) - if (tensor_array_ops.ENABLE_TENSOR_ARRAY_V2 and + if (control_flow_util.ENABLE_CONTROL_FLOW_V2 and not context.executing_eagerly()): error_msg = "Trying to modify element 3 in a list with 3 elements" else: @@ -467,14 +468,14 @@ class TensorArrayTest(test.TestCase): # Test reading wrong datatype (only possible when constructing graphs). if (not context.executing_eagerly() and - not tensor_array_ops.ENABLE_TENSOR_ARRAY_V2): + not control_flow_util.ENABLE_CONTROL_FLOW_V2): r0_bad = gen_data_flow_ops.tensor_array_read_v3( handle=w0.handle, index=0, dtype=dtypes.float64, flow_in=w0.flow) with self.assertRaisesOpError( "TensorArray dtype is float but Op requested dtype double."): self.evaluate(r0_bad) - if (tensor_array_ops.ENABLE_TENSOR_ARRAY_V2 and + if (control_flow_util.ENABLE_CONTROL_FLOW_V2 and not context.executing_eagerly()): error_msg = "Trying to access element -1 in a list with 3 elements." else: @@ -483,7 +484,7 @@ class TensorArrayTest(test.TestCase): with self.assertRaisesOpError(error_msg): self.evaluate(ta.read(-1)) - if (tensor_array_ops.ENABLE_TENSOR_ARRAY_V2 and + if (control_flow_util.ENABLE_CONTROL_FLOW_V2 and not context.executing_eagerly()): error_msg = "Trying to access element 3 in a list with 3 elements." else: @@ -550,7 +551,7 @@ class TensorArrayTest(test.TestCase): ta.split([1.0, 2.0, 3.0], lengths).flow.eval(feed_dict={lengths: 1}) error_msg = ("Unused values in tensor. Length of tensor: 3 Values used: 1" - if tensor_array_ops.ENABLE_TENSOR_ARRAY_V2 and + if control_flow_util.ENABLE_CONTROL_FLOW_V2 and not in_eager_mode else r"Expected sum of lengths to be equal to values.shape\[0\], " r"but sum of lengths is 1 and value's shape is: \[3\]") @@ -558,7 +559,7 @@ class TensorArrayTest(test.TestCase): self.evaluate(ta.split([1.0, 2.0, 3.0], [1]).flow) ta = _make_ta(1, "baz") - if tensor_array_ops.ENABLE_TENSOR_ARRAY_V2 and not in_eager_mode: + if control_flow_util.ENABLE_CONTROL_FLOW_V2 and not in_eager_mode: with self.assertRaisesRegexp( ValueError, "Shape must be at least rank 1 but is rank 0"): self.evaluate(ta.split(1.0, [1]).flow) @@ -568,7 +569,7 @@ class TensorArrayTest(test.TestCase): ): self.evaluate(ta.split(1.0, [1]).flow) - if not tensor_array_ops.ENABLE_TENSOR_ARRAY_V2 or in_eager_mode: + if not control_flow_util.ENABLE_CONTROL_FLOW_V2 or in_eager_mode: ta = _make_ta(2, "buz") with self.assertRaisesOpError( r"TensorArray's size is not equal to the size of lengths " @@ -1003,21 +1004,6 @@ class TensorArrayTest(test.TestCase): # self._testWhileLoopWritePackGradients( # dynamic_size=False, dtype=tf.int64) - @test_util.disable_control_flow_v2("Testing v1 while_loop with v2 TA") - @test_util.enable_tensor_array_v2 - def testWhileLoopV1WithTensorArrayV2(self): - size = 3 - ta = tensor_array_ops.TensorArray( - dtype=dtypes.int32, size=size, element_shape=tensor_shape.scalar()) - - def Body(counter, ta): - return counter + 1, ta.write(counter, counter) - - _, ta = control_flow_ops.while_loop(lambda i, _: i < size, Body, [0, ta]) - - for i in range(size): - self.assertEqual(self.evaluate(ta.read(i)), i) - @test_util.disable_control_flow_v2("b/117943489 (dynamic_size)") @test_util.run_v1_only("b/117943489") def testSkipEagerWhileLoopDynamicWritePackGradients(self): @@ -1270,7 +1256,7 @@ class TensorArrayTest(test.TestCase): self.assertEqual((2, 2), w0.read(1).get_shape()) else: self.assertEqual(r0.get_shape().ndims, None) - if not tensor_array_ops.ENABLE_TENSOR_ARRAY_V2: + if not control_flow_util.ENABLE_CONTROL_FLOW_V2: self.assertEqual( tensor_shape.TensorShape( ta1.handle.op.get_attr("element_shape")).ndims, None) @@ -1347,8 +1333,8 @@ class TensorArrayTest(test.TestCase): "TensorArray has size zero, but element shape is not " "fully defined. Currently only static shapes are supported when " "packing zero-size TensorArrays.") - with self.assertRaisesOpError(v2_msg if tensor_array_ops - .ENABLE_TENSOR_ARRAY_V2 else v1_msg): + with self.assertRaisesOpError( + v2_msg if control_flow_util.ENABLE_CONTROL_FLOW_V2 else v1_msg): ta.stack().eval() @test_util.run_v1_only("b/120545219") diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py index b7e50c1dae..99216d7fb1 100644 --- a/tensorflow/python/ops/control_flow_ops.py +++ b/tensorflow/python/ops/control_flow_ops.py @@ -24,13 +24,11 @@ from __future__ import print_function import abc import collections import functools -import os import six from tensorflow.core.framework import attr_value_pb2 from tensorflow.core.protobuf import control_flow_pb2 -from tensorflow.python import tf2 from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes @@ -71,9 +69,6 @@ cond_v2 = LazyLoader("cond_v2", globals(), while_v2 = LazyLoader("while_v2", globals(), "tensorflow.python.ops.while_v2") -ENABLE_COND_V2 = tf2.enabled() or os.getenv("TF_ENABLE_COND_V2", "0") != "0" -ENABLE_WHILE_V2 = tf2.enabled() or os.getenv("TF_ENABLE_WHILE_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 @@ -2052,7 +2047,7 @@ def cond(pred, ``` """ - if ENABLE_COND_V2 and not context.executing_eagerly(): + if util.ENABLE_CONTROL_FLOW_V2 and not context.executing_eagerly(): return cond_v2.cond_v2(pred, true_fn, false_fn, name) # We needed to make true_fn/false_fn keyword arguments for @@ -3487,7 +3482,7 @@ def while_loop(cond, ``` """ - if ENABLE_WHILE_V2 and not context.executing_eagerly(): + if util.ENABLE_CONTROL_FLOW_V2 and not context.executing_eagerly(): return while_v2.while_loop( cond, body, diff --git a/tensorflow/python/ops/control_flow_ops_benchmark.py b/tensorflow/python/ops/control_flow_ops_benchmark.py index 9ba5ff2c0f..9dd1e6673b 100644 --- a/tensorflow/python/ops/control_flow_ops_benchmark.py +++ b/tensorflow/python/ops/control_flow_ops_benchmark.py @@ -27,6 +27,7 @@ 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 control_flow_util from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops from tensorflow.python.platform import test @@ -94,28 +95,28 @@ class CondWithManyIntermediatesBenchmark(test.Benchmark): iters=self.NUM_ITERS) def benchmark_cond_v1_defun(self): - old_val = control_flow_ops.ENABLE_COND_V2 - control_flow_ops.ENABLE_COND_V2 = False + old_val = control_flow_util.ENABLE_CONTROL_FLOW_V2 + control_flow_util.ENABLE_CONTROL_FLOW_V2 = False self._benchmark_defun() - control_flow_ops.ENABLE_COND_V2 = old_val + control_flow_util.ENABLE_CONTROL_FLOW_V2 = old_val def benchmark_cond_v2_defun(self): - old_val = control_flow_ops.ENABLE_COND_V2 - control_flow_ops.ENABLE_COND_V2 = True + old_val = control_flow_util.ENABLE_CONTROL_FLOW_V2 + control_flow_util.ENABLE_CONTROL_FLOW_V2 = True self._benchmark_defun() - control_flow_ops.ENABLE_COND_V2 = old_val + control_flow_util.ENABLE_CONTROL_FLOW_V2 = old_val def benchmark_cond_v1_graph(self): - old_val = control_flow_ops.ENABLE_COND_V2 - control_flow_ops.ENABLE_COND_V2 = False + old_val = control_flow_util.ENABLE_CONTROL_FLOW_V2 + control_flow_util.ENABLE_CONTROL_FLOW_V2 = False self._benchmark_graph() - control_flow_ops.ENABLE_COND_V2 = old_val + control_flow_util.ENABLE_CONTROL_FLOW_V2 = old_val def benchmark_cond_v2_graph(self): - old_val = control_flow_ops.ENABLE_COND_V2 - control_flow_ops.ENABLE_COND_V2 = True + old_val = control_flow_util.ENABLE_CONTROL_FLOW_V2 + control_flow_util.ENABLE_CONTROL_FLOW_V2 = True self._benchmark_graph() - control_flow_ops.ENABLE_COND_V2 = old_val + control_flow_util.ENABLE_CONTROL_FLOW_V2 = old_val if __name__ == "__main__": ops.enable_eager_execution() diff --git a/tensorflow/python/ops/control_flow_util.py b/tensorflow/python/ops/control_flow_util.py index cb628f4aa6..1747f06109 100644 --- a/tensorflow/python/ops/control_flow_util.py +++ b/tensorflow/python/ops/control_flow_util.py @@ -23,10 +23,18 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import os import traceback +from tensorflow.python import tf2 from tensorflow.python.platform import tf_logging as logging +ENABLE_CONTROL_FLOW_V2 = (tf2.enabled() or + os.getenv("TF_ENABLE_CONTROL_FLOW_V2", "0") != "0" or + os.getenv("TF_ENABLE_COND_V2", "0") != "0" or + os.getenv("TF_ENABLE_WHILE_V2", "0") != "0" or + os.getenv("TF_ENABLE_TENSOR_ARRAY_V2", "0") != "0") + def IsInXLAContext(op): try: diff --git a/tensorflow/python/ops/tensor_array_ops.py b/tensorflow/python/ops/tensor_array_ops.py index d151694951..85333ee6b5 100644 --- a/tensorflow/python/ops/tensor_array_ops.py +++ b/tensorflow/python/ops/tensor_array_ops.py @@ -20,10 +20,8 @@ from __future__ import division from __future__ import print_function import contextlib -import os import weakref -from tensorflow.python import tf2 from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes @@ -32,6 +30,7 @@ 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.ops import control_flow_util from tensorflow.python.ops import gen_control_flow_ops from tensorflow.python.ops import gen_data_flow_ops from tensorflow.python.ops import list_ops @@ -40,10 +39,6 @@ from tensorflow.python.util import tf_should_use from tensorflow.python.util.tf_export import tf_export -ENABLE_TENSOR_ARRAY_V2 = ( - tf2.enabled() or os.getenv("TF_ENABLE_TENSOR_ARRAY_V2") is not None) - - # _GraphTensorArray accesses many of the hidden generated ops, but is in # fact built to wrap these methods. # pylint: disable=protected-access @@ -1013,7 +1008,7 @@ class TensorArray(object): if context.executing_eagerly(): implementation = _EagerTensorArray else: - if ENABLE_TENSOR_ARRAY_V2: + if control_flow_util.ENABLE_CONTROL_FLOW_V2: implementation = _GraphTensorArrayV2 else: implementation = _GraphTensorArray diff --git a/tensorflow/python/ops/while_v2.py b/tensorflow/python/ops/while_v2.py index d00c158d15..f7566bac9b 100644 --- a/tensorflow/python/ops/while_v2.py +++ b/tensorflow/python/ops/while_v2.py @@ -52,13 +52,6 @@ from tensorflow.python.util import nest # to them and then pass those in as data inputs. This should probably be # handled in the CapturingGraph itself. -# Op types that output a resource tensor representing a TensorArray handle. -TENSOR_ARRAY_HANDLE_OPS = ( - "TensorArrayV3", - "TensorArrayGradV3", - "TensorArrayGradWithShape", -) - def while_loop(cond, body, @@ -257,24 +250,19 @@ def _WhileGrad(op, *grads): # pylint: disable=invalid-name "_maximum_iterations") if _is_in_xla_context() else None assert not _is_in_xla_context() or maximum_iterations is not None - # Set the incoming gradient of TensorArray handles to None. The gradient - # implementation currently assumes all resource tensors correspond to float32 - # ResourceVariables, which can lead to runtime shape errors when used with a - # TensorArray. This is a workaround until TensorArrays are reimplemented with - # TensorLists instead of resources. - # Also set the incoming gradient of non-trainable inputs to None. It is - # possible that we receive non-None gradients for non-trainable types in - # nested while loops because we accumulate outputs of the inner while as - # variant tensors which are trainable and hence receive zeros_like tensors in - # the gradient pass. The non-trainable tensors then receive the popped zeros - # tensor from this zeros variant. The gradient for the loop vars corresponding - # to these tensors is None or zeros (this happens only if the loop var is - # accumulated as well) in _grad_fn so we reset these. + # Set the incoming gradient of non-trainable inputs to None. It is possible + # that we receive non-None gradients for non-trainable types in nested while + # loops because we accumulate outputs of the inner while as variant tensors + # which are trainable and hence receive zeros_like tensors in the gradient + # pass. The non-trainable tensors then receive the popped zeros tensor from + # this zeros variant. The gradient for the loop vars corresponding to these + # tensors is None or zeros (this happens only if the loop var is accumulated + # as well) in _grad_fn so we reset these. # TODO(b/118712257): Remove the IsTrainable filter once we can handle None # output grads in _grad_fn. grads = [ - None if _is_tensor_array_handle(output) or not _is_trainable(output) - else grad for grad, output in zip(grads, body_graph.outputs) + None if not _is_trainable(output) else grad + for grad, output in zip(grads, body_graph.outputs) ] # Ensure that all non-resource trainable outputs have incoming gradients. @@ -339,8 +327,7 @@ def _WhileGrad(op, *grads): # pylint: disable=invalid-name # See comment in while_loop. outputs = [array_ops.identity(t) for t in outputs] - # Set None as the output gradient for tensors with None input gradient - # e.g. TensorArray handles. + # Set None as the output gradient for tensors with None input gradient. # outputs[0] is the loop counter. # outputs[1] is the total number of loop iterations. index = 2 @@ -853,28 +840,6 @@ def _graph_name(graph): return "Base" -def _is_tensor_array_handle(tensor): - """Returns whether tensor is a TensorArray handle.""" - if tensor.dtype != dtypes.resource: - return False - - if tensor.op.type == "While": - # We assume that any resource outputs of a While op correspond to a captured - # resource input (as opposed to a loop variable specified by the user). - # NOTE(skyewm): we could actually check this, but I can't think of when you - # would have a resource loop variable. - tensor = tensor.op.inputs[tensor.value_index] - - # TODO(b/118452219): add test coverage for this. - tensor = func_graph_module.maybe_captured(tensor) - - if isinstance(tensor, ops.EagerTensor): - # Eager execution doesn't quite support legacy tensorarray - return False - - return tensor.op.type in TENSOR_ARRAY_HANDLE_OPS - - def _pack_sequence_as(structure_with_tas, loop_vars): """Like `nest.pack_sequence_as` but also replaces flows with TensorArrays.""" -- GitLab From b51d81f87f5de3c26b2db59ae6ec6b5f963acd7d Mon Sep 17 00:00:00 2001 From: Scott Zhu Date: Mon, 10 Dec 2018 12:42:26 -0800 Subject: [PATCH 303/873] Update the default activation function for unified LSTM to 'sigmoid'. I believe for historical reason, the activation function for LSTM is hard_sigmoid because it is faster compare to sigmoid. With the new LSTM, the performance issue should be fixed with grappler swapping the backend. PiperOrigin-RevId: 224863406 --- tensorflow/python/keras/layers/recurrent.py | 17 ++++++------ .../python/keras/layers/unified_lstm_test.py | 27 ++++++++++--------- 2 files changed, 23 insertions(+), 21 deletions(-) diff --git a/tensorflow/python/keras/layers/recurrent.py b/tensorflow/python/keras/layers/recurrent.py index 86a69e45d9..fb4c1736b1 100644 --- a/tensorflow/python/keras/layers/recurrent.py +++ b/tensorflow/python/keras/layers/recurrent.py @@ -2546,13 +2546,11 @@ class UnifiedLSTM(LSTM): Arguments: units: Positive integer, dimensionality of the output space. activation: Activation function to use. - Default: hyperbolic tangent (`tanh`). If you pass `None`, no activation - is applied - (ie. "linear" activation: `a(x) = x`). + Default: hyperbolic tangent (`tanh`). If you pass `None`, no activation + is applied (ie. "linear" activation: `a(x) = x`). recurrent_activation: Activation function to use for the recurrent step. - Default: hard sigmoid (`hard_sigmoid`). If you pass `None`, no - activation is applied - (ie. "linear" activation: `a(x) = x`). + Default: sigmoid (`sigmoid`). If you pass `None`, no activation is + applied (ie. "linear" activation: `a(x) = x`). use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs.. @@ -2602,7 +2600,7 @@ class UnifiedLSTM(LSTM): def __init__(self, units, activation='tanh', - recurrent_activation='hard_sigmoid', + recurrent_activation='sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', @@ -2663,8 +2661,9 @@ class UnifiedLSTM(LSTM): self._num_inputs = None self._dropout_mask = None self.could_use_cudnn = ( - activation == 'tanh' and recurrent_dropout == 0 and - not unroll and use_bias and bias_regularizer is None) + activation == 'tanh' and recurrent_activation == 'sigmoid' and + recurrent_dropout == 0 and not unroll and use_bias and + bias_regularizer is None) def call(self, inputs, mask=None, training=None, initial_state=None): # LSTM does not support constants. Ignore it during process. diff --git a/tensorflow/python/keras/layers/unified_lstm_test.py b/tensorflow/python/keras/layers/unified_lstm_test.py index 932b2d331d..a2b523b00e 100644 --- a/tensorflow/python/keras/layers/unified_lstm_test.py +++ b/tensorflow/python/keras/layers/unified_lstm_test.py @@ -161,17 +161,20 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): existing_loss = loss_value @parameterized.named_parameters( - ('_non_tan_activation', 'relu', 0, False, True, None), - ('_use_recurrent_dropout', 'tanh', 0.1, False, True, None), - ('_unroll', 'tanh', 0, True, True, None), - ('_not_use_bias', 'tanh', 0, False, False, None), - ('_use_bias_regularizer', 'tanh', 0, False, True, 'l2') + ('non_tan_activation', 'relu', 'sigmoid', 0, False, True, None), + ('non_sigmoid_recur_activation', 'tanh', 'relu', 0, False, True, None), + ('use_recurrent_dropout', 'tanh', 'sigmoid', 0.1, False, True, None), + ('unroll', 'tanh', 'sigmoid', 0, True, True, None), + ('not_use_bias', 'tanh', 'sigmoid', 0, False, False, None), + ('use_bias_regularizer', 'tanh', 'sigmoid', 0, False, True, 'l2') ) @test_util.run_in_graph_and_eager_modes(config=_config) - def test_could_use_defun_backend(self, activation, recurrent_dropout, - unroll, use_bias, bias_regularizer): + def test_could_use_defun_backend(self, activation, recurrent_activation, + recurrent_dropout, unroll, use_bias, + bias_regularizer): layer = UnifiedLSTM(1, activation=activation, + recurrent_activation=recurrent_activation, recurrent_dropout=recurrent_dropout, unroll=unroll, use_bias=use_bias, @@ -270,22 +273,22 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): inputs = keras.layers.Input( shape=[timestep, input_shape], dtype=dtypes.float32) with test_util.device(use_gpu=False): - # Note that CuDNN use 'sigmoid' as activation. Force the CPU - # implementation to use 'sigmoid' so that it will generate same output as - # CuDNN implementation. - layer = UnifiedLSTM(rnn_state_size, recurrent_activation='sigmoid') + layer = UnifiedLSTM(rnn_state_size) output = layer(inputs) cpu_model = keras.models.Model(inputs, output) weights = cpu_model.get_weights() y_1 = cpu_model.predict(x_train) with test_util.device(use_gpu=True): - layer = UnifiedLSTM(rnn_state_size, recurrent_activation='sigmoid') + layer = UnifiedLSTM(rnn_state_size) output = layer(inputs) gpu_model = keras.models.Model(inputs, output) gpu_model.set_weights(weights) y_2 = gpu_model.predict(x_train) + # Note that CuDNN uses 'sigmoid' as activation, so the unified LSTM uses + # 'sigmoid' as default. Construct the canonical LSTM with sigmoid to achieve + # the same output. with test_util.device(use_gpu=True): layer = keras.layers.LSTM(rnn_state_size, recurrent_activation='sigmoid') output = layer(inputs) -- GitLab From 4bc66cd75aa040b05f744a3ed805afe6032f1848 Mon Sep 17 00:00:00 2001 From: James Ring Date: Mon, 10 Dec 2018 12:44:23 -0800 Subject: [PATCH 304/873] Add TF_DefaultThreadOptions, TF_StartThread and TF_JoinThread. PiperOrigin-RevId: 224863771 --- tensorflow/c/env.cc | 22 ++++++++++++++++++++++ tensorflow/c/env.h | 37 +++++++++++++++++++++++++++++++++++++ tensorflow/c/env_test.cc | 27 +++++++++++++++++++++++++++ 3 files changed, 86 insertions(+) diff --git a/tensorflow/c/env.cc b/tensorflow/c/env.cc index 07b9e8b940..1c35ff9001 100644 --- a/tensorflow/c/env.cc +++ b/tensorflow/c/env.cc @@ -159,3 +159,25 @@ TF_CAPI_EXPORT extern uint64_t TF_NowMicros(void) { TF_CAPI_EXPORT extern uint64_t TF_NowSeconds(void) { return ::tensorflow::Env::Default()->NowSeconds(); } + +void TF_DefaultThreadOptions(TF_ThreadOptions* options) { + options->stack_size = 0; + options->guard_size = 0; + options->numa_node = -1; +} + +TF_Thread* TF_StartThread(const TF_ThreadOptions* options, + const char* thread_name, void (*work_func)(void*), + void* param) { + ::tensorflow::ThreadOptions cc_options; + cc_options.stack_size = options->stack_size; + cc_options.guard_size = options->guard_size; + cc_options.numa_node = options->numa_node; + return reinterpret_cast(::tensorflow::Env::Default()->StartThread( + cc_options, thread_name, [=]() { (*work_func)(param); })); +} + +void TF_JoinThread(TF_Thread* thread) { + // ::tensorflow::Thread joins on destruction + delete reinterpret_cast<::tensorflow::Thread*>(thread); +} diff --git a/tensorflow/c/env.h b/tensorflow/c/env.h index 9d27c5da37..15652353cd 100644 --- a/tensorflow/c/env.h +++ b/tensorflow/c/env.h @@ -13,6 +13,9 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +#include +#include + #ifndef TENSORFLOW_C_ENV_H_ #define TENSORFLOW_C_ENV_H_ @@ -23,6 +26,7 @@ limitations under the License. struct TF_WritableFileHandle; struct TF_StringStream; +struct TF_Thread; #ifdef __cplusplus extern "C" { @@ -37,6 +41,20 @@ typedef struct TF_FileStatistics { bool is_directory; } TF_FileStatistics; +typedef struct TF_ThreadOptions { + // Thread stack size to use (in bytes), zero implies that the system default + // will be used. + size_t stack_size; + + // Guard area size to use near thread stacks to use (in bytes), zero implies + // that the system default will be used. + size_t guard_size; + + // The NUMA node to use, -1 implies that there should be no NUMA affinity for + // this thread. + int numa_node; +} TF_ThreadOptions; + // Creates the specified directory. Typical status code are: // * TF_OK - successfully created the directory // * TF_ALREADY_EXISTS - directory already exists @@ -150,6 +168,25 @@ TF_CAPI_EXPORT extern uint64_t TF_NowMicros(void); // Returns the number of seconds since the Unix epoch. TF_CAPI_EXPORT extern uint64_t TF_NowSeconds(void); +// Populates a TF_ThreadOptions struct with system-default values. +TF_CAPI_EXPORT extern void TF_DefaultThreadOptions(TF_ThreadOptions* options); + +// Returns a new thread that is running work_func and is identified +// (for debugging/performance-analysis) by thread_name. +// +// The given param (which may be null) is passed to work_func when the thread +// starts. In this way, data may be passed from the thread back to the caller. +// +// Caller takes ownership of the result and must call TF_JoinThread on it +// eventually. +TF_CAPI_EXPORT extern TF_Thread* TF_StartThread(const TF_ThreadOptions* options, + const char* thread_name, + void (*work_func)(void*), + void* param); + +// Waits for the given thread to finish execution, then deletes it. +TF_CAPI_EXPORT extern void TF_JoinThread(TF_Thread* thread); + #ifdef __cplusplus } #endif diff --git a/tensorflow/c/env_test.cc b/tensorflow/c/env_test.cc index e2206c6bef..687ad02413 100644 --- a/tensorflow/c/env_test.cc +++ b/tensorflow/c/env_test.cc @@ -17,6 +17,7 @@ limitations under the License. #include "tensorflow/c/c_api.h" #include "tensorflow/core/lib/io/path.h" +#include "tensorflow/core/platform/mutex.h" #include "tensorflow/core/platform/test.h" #include "tensorflow/core/platform/types.h" @@ -98,3 +99,29 @@ TEST(TestEnv, TestTimeFunctions) { ASSERT_GE(TF_NowMicros(), 946684800 * 1e6); ASSERT_GE(TF_NowNanos(), 946684800 * 1e9); } + +namespace { + +struct SomeThreadData { + ::tensorflow::mutex mu; + bool did_work = false; +}; + +void SomeThreadFunc(void* data) { + auto* real_data = static_cast(data); + ::tensorflow::mutex_lock l(real_data->mu); + real_data->did_work = true; +} + +} // namespace + +TEST(TestEnv, TestThreads) { + TF_ThreadOptions options; + TF_DefaultThreadOptions(&options); + SomeThreadData data; + TF_Thread* thread = + TF_StartThread(&options, "SomeThreadName", &SomeThreadFunc, &data); + TF_JoinThread(thread); + ::tensorflow::mutex_lock l(data.mu); + ASSERT_TRUE(data.did_work); +} -- GitLab From 51a86aae7cd98e6b09cf548ce4e57406d7e3314c Mon Sep 17 00:00:00 2001 From: Shivani Agrawal Date: Mon, 10 Dec 2018 12:53:22 -0800 Subject: [PATCH 305/873] Remaining core kernel tests coverage. PiperOrigin-RevId: 224865488 --- .../data/kernel_tests/from_generator_test.py | 358 +++----- .../python/data/kernel_tests/map_test.py | 803 ++++++++---------- 2 files changed, 473 insertions(+), 688 deletions(-) diff --git a/tensorflow/python/data/kernel_tests/from_generator_test.py b/tensorflow/python/data/kernel_tests/from_generator_test.py index a6625534e7..11919bdaee 100644 --- a/tensorflow/python/data/kernel_tests/from_generator_test.py +++ b/tensorflow/python/data/kernel_tests/from_generator_test.py @@ -21,7 +21,6 @@ import threading import numpy as np -from tensorflow.python.client import session from tensorflow.python.data.kernel_tests import test_base from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import constant_op @@ -32,43 +31,27 @@ from tensorflow.python.ops import script_ops from tensorflow.python.platform import test -class FromGeneratorTest(test_base.DatasetTestBase): +@test_util.run_all_in_graph_and_eager_modes +class DatasetConstructorTest(test_base.DatasetTestBase): def _testFromGenerator(self, generator, elem_sequence, num_repeats, output_types=None): if output_types is None: output_types = dtypes.int64 - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.from_generator(generator, output_types=output_types) - .repeat(num_repeats) - .prefetch(5)) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.cached_session() as sess: - for _ in range(2): # Run twice to test reinitialization. - sess.run(init_op) - for _ in range(num_repeats): - for elem in elem_sequence: - self.assertAllEqual(elem, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + dataset = dataset_ops.Dataset.from_generator( + generator, output_types=output_types).repeat(num_repeats).prefetch(5) + self.assertDatasetProduces( + dataset, + elem_sequence * num_repeats, + requires_initialization=True, + num_test_iterations=2) def _testFromGeneratorOneShot(self, generator, elem_sequence, num_repeats): - iterator = dataset_ops.make_one_shot_iterator( - dataset_ops.Dataset.from_generator(generator, output_types=dtypes.int64) - .repeat(num_repeats) - .prefetch(5)) - get_next = iterator.get_next() - - with self.cached_session() as sess: - for _ in range(num_repeats): - for elem in elem_sequence: - self.assertAllEqual(elem, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + dataset = dataset_ops.Dataset.from_generator( + generator, output_types=dtypes.int64).repeat(num_repeats).prefetch(5) + self.assertDatasetProduces( + dataset, elem_sequence * num_repeats, num_test_iterations=2) - @test_util.run_deprecated_v1 def testFromGeneratorUsingFunction(self): def generator(): for i in range(1, 100): @@ -79,21 +62,18 @@ class FromGeneratorTest(test_base.DatasetTestBase): self._testFromGeneratorOneShot(generator, elem_sequence, 1) self._testFromGeneratorOneShot(generator, elem_sequence, 5) - @test_util.run_deprecated_v1 def testFromGeneratorUsingList(self): generator = lambda: [[i] * i for i in range(1, 100)] elem_sequence = list(generator()) self._testFromGenerator(generator, elem_sequence, 1) self._testFromGenerator(generator, elem_sequence, 5) - @test_util.run_deprecated_v1 def testFromGeneratorUsingNdarray(self): generator = lambda: np.arange(100, dtype=np.int64) elem_sequence = list(generator()) self._testFromGenerator(generator, elem_sequence, 1, output_types=np.int64) self._testFromGenerator(generator, elem_sequence, 5, output_types=np.int64) - @test_util.run_deprecated_v1 def testFromGeneratorUsingGeneratorExpression(self): # NOTE(mrry): Generator *expressions* are not repeatable (or in # general reusable), because they eagerly evaluate the `for` @@ -105,7 +85,6 @@ class FromGeneratorTest(test_base.DatasetTestBase): self._testFromGenerator(generator, elem_sequence, 1) self._testFromGenerator(generator, elem_sequence, 5) - @test_util.run_deprecated_v1 def testFromMultipleConcurrentGenerators(self): num_inner_repeats = 5 num_outer_repeats = 100 @@ -128,22 +107,16 @@ class FromGeneratorTest(test_base.DatasetTestBase): output_shapes=([None], [3])) .repeat(num_inner_repeats).prefetch(5)) - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.range(num_outer_repeats) - .interleave(interleave_fn, cycle_length=10, - block_length=len(input_list))) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.cached_session() as sess: - sess.run(init_op) - for _ in range(num_inner_repeats * num_outer_repeats): - for elem in input_list: - val0, val1 = sess.run(get_next) - self.assertAllEqual(elem[0], val0) - self.assertAllEqual(elem[1], val1) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + dataset = dataset_ops.Dataset.range(num_outer_repeats).interleave( + interleave_fn, cycle_length=10, block_length=len(input_list)) + get_next = self.getNext(dataset) + for _ in range(num_inner_repeats * num_outer_repeats): + for elem in input_list: + val0, val1 = self.evaluate(get_next()) + self.assertAllEqual(elem[0], val0) + self.assertAllEqual(elem[1], val1) + with self.assertRaises(errors.OutOfRangeError): + self.evaluate(get_next()) # TODO(b/67868766): Reenable this when the source of flakiness is discovered. def _testFromGeneratorsRunningInParallel(self): @@ -186,22 +159,16 @@ class FromGeneratorTest(test_base.DatasetTestBase): return dataset_ops.Dataset.from_generator( generator, output_types=dtypes.int64, output_shapes=[]).prefetch(2) - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.range(num_parallel_iterators) - .interleave( - interleave_fn, cycle_length=num_parallel_iterators, block_length=1)) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.cached_session() as sess: - sess.run(init_op) - for elem in [0, 1]: - for _ in range(num_parallel_iterators): - self.assertAllEqual(elem, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + dataset = dataset_ops.Dataset.range(num_parallel_iterators).interleave( + interleave_fn, cycle_length=num_parallel_iterators, block_length=1) + get_next = self.getNext(dataset) + + for elem in [0, 1]: + for _ in range(num_parallel_iterators): + self.assertAllEqual(elem, self.evaluate(get_next())) + with self.assertRaises(errors.OutOfRangeError): + self.evaluate(get_next()) - @test_util.run_deprecated_v1 def testFromGeneratorImplicitConversion(self): def generator(): yield [1] @@ -209,45 +176,28 @@ class FromGeneratorTest(test_base.DatasetTestBase): yield [3] for dtype in [dtypes.int8, dtypes.int32, dtypes.int64]: - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.from_generator( - generator, output_types=dtype, output_shapes=[1])) - init_op = iterator.initializer - get_next = iterator.get_next() - - self.assertEqual(dtype, get_next.dtype) - - with self.cached_session() as sess: - sess.run(init_op) - for expected in [[1], [2], [3]]: - next_val = sess.run(get_next) - self.assertEqual(dtype.as_numpy_dtype, next_val.dtype) - self.assertAllEqual(expected, next_val) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - @test_util.run_deprecated_v1 + dataset = dataset_ops.Dataset.from_generator( + generator, output_types=dtype, output_shapes=[1]) + get_next = self.getNext(dataset) + + for expected in [[1], [2], [3]]: + next_val = self.evaluate(get_next()) + self.assertEqual(dtype.as_numpy_dtype, next_val.dtype) + self.assertAllEqual(expected, next_val) + with self.assertRaises(errors.OutOfRangeError): + self.evaluate(get_next()) + def testFromGeneratorString(self): def generator(): yield "foo" yield b"bar" yield u"baz" - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.from_generator( - generator, output_types=dtypes.string, output_shapes=[])) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.cached_session() as sess: - sess.run(init_op) - for expected in [b"foo", b"bar", b"baz"]: - next_val = sess.run(get_next) - self.assertAllEqual(expected, next_val) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + dataset = dataset_ops.Dataset.from_generator( + generator, output_types=dtypes.string, output_shapes=[]) + self.assertDatasetProduces( + dataset, expected_output=[b"foo", b"bar", b"baz"]) - @test_util.run_deprecated_v1 def testFromGeneratorTypeError(self): def generator(): yield np.array([1, 2, 3], dtype=np.int64) @@ -255,23 +205,19 @@ class FromGeneratorTest(test_base.DatasetTestBase): yield "ERROR" yield np.array([7, 8, 9], dtype=np.int64) - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.from_generator( - generator, output_types=dtypes.int64, output_shapes=[3])) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.cached_session() as sess: - sess.run(init_op) - self.assertAllEqual([1, 2, 3], sess.run(get_next)) - self.assertAllEqual([4, 5, 6], sess.run(get_next)) - with self.assertRaisesOpError("The expected type was int64"): - sess.run(get_next) - self.assertAllEqual([7, 8, 9], sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + dataset = dataset_ops.Dataset.from_generator( + generator, output_types=dtypes.int64, output_shapes=[3]) + + get_next = self.getNext(dataset) + + self.assertAllEqual([1, 2, 3], self.evaluate(get_next())) + self.assertAllEqual([4, 5, 6], self.evaluate(get_next())) + with self.assertRaisesOpError("The expected type was int64"): + self.evaluate(get_next()) + self.assertAllEqual([7, 8, 9], self.evaluate(get_next())) + with self.assertRaises(errors.OutOfRangeError): + self.evaluate(get_next()) - @test_util.run_deprecated_v1 def testFromGeneratorShapeError(self): def generator(): yield np.array([1, 2, 3], dtype=np.int64) @@ -279,23 +225,18 @@ class FromGeneratorTest(test_base.DatasetTestBase): yield np.array([7, 8, 9, 10], dtype=np.int64) yield np.array([11, 12, 13], dtype=np.int64) - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.from_generator( - generator, output_types=dtypes.int64, output_shapes=[3])) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.cached_session() as sess: - sess.run(init_op) - self.assertAllEqual([1, 2, 3], sess.run(get_next)) - self.assertAllEqual([4, 5, 6], sess.run(get_next)) - with self.assertRaisesOpError(r"element of shape \(3,\) was expected"): - sess.run(get_next) - self.assertAllEqual([11, 12, 13], sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + dataset = dataset_ops.Dataset.from_generator( + generator, output_types=dtypes.int64, output_shapes=[3]) + get_next = self.getNext(dataset) + + self.assertAllEqual([1, 2, 3], self.evaluate(get_next())) + self.assertAllEqual([4, 5, 6], self.evaluate(get_next())) + with self.assertRaisesOpError(r"element of shape \(3,\) was expected"): + self.evaluate(get_next()) + self.assertAllEqual([11, 12, 13], self.evaluate(get_next())) + with self.assertRaises(errors.OutOfRangeError): + self.evaluate(get_next()) - @test_util.run_deprecated_v1 def testFromGeneratorStructureError(self): def generator(): yield 1, 2 @@ -304,46 +245,31 @@ class FromGeneratorTest(test_base.DatasetTestBase): yield 6, 7, 8 yield 9, 10 - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.from_generator( - generator, output_types=(dtypes.int64, dtypes.int64))) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.cached_session() as sess: - sess.run(init_op) - self.assertEqual((1, 2), sess.run(get_next)) - self.assertEqual((3, 4), sess.run(get_next)) - with self.assertRaisesOpError( - r"The expected structure was \(tf\.int64, tf\.int64\)"): - sess.run(get_next) - with self.assertRaisesOpError( - r"The expected structure was \(tf\.int64, tf\.int64\)"): - sess.run(get_next) - self.assertEqual((9, 10), sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + dataset = dataset_ops.Dataset.from_generator( + generator, output_types=(dtypes.int64, dtypes.int64)) + get_next = self.getNext(dataset) + + self.assertEqual((1, 2), self.evaluate(get_next())) + self.assertEqual((3, 4), self.evaluate(get_next())) + with self.assertRaisesOpError( + r"The expected structure was \(tf\.int64, tf\.int64\)"): + self.evaluate(get_next()) + with self.assertRaisesOpError( + r"The expected structure was \(tf\.int64, tf\.int64\)"): + self.evaluate(get_next()) + self.assertEqual((9, 10), self.evaluate(get_next())) + with self.assertRaises(errors.OutOfRangeError): + self.evaluate(get_next()) - @test_util.run_deprecated_v1 def testFromGeneratorHeterogeneous(self): def generator(): yield 1 yield [2, 3] - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.from_generator( - generator, output_types=dtypes.int64)) - init_op = iterator.initializer - get_next = iterator.get_next() + dataset = dataset_ops.Dataset.from_generator( + generator, output_types=dtypes.int64) + self.assertDatasetProduces(dataset, expected_output=[1, [2, 3]]) - with self.cached_session() as sess: - sess.run(init_op) - self.assertAllEqual(1, sess.run(get_next)) - self.assertAllEqual([2, 3], sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - @test_util.run_deprecated_v1 def testFromGeneratorStopShort(self): def generator(): @@ -351,18 +277,12 @@ class FromGeneratorTest(test_base.DatasetTestBase): yield 1 yield 2 - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.from_generator( - generator, output_types=dtypes.int64)) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.cached_session() as sess: - sess.run(init_op) - self.assertAllEqual(0, sess.run(get_next)) - self.assertAllEqual(1, sess.run(get_next)) + dataset = dataset_ops.Dataset.from_generator( + generator, output_types=dtypes.int64) + get_next = self.getNext(dataset) + self.assertAllEqual(0, self.evaluate(get_next())) + self.assertAllEqual(1, self.evaluate(get_next())) - @test_util.run_deprecated_v1 def testFromGeneratorDestructorCalled(self): # Use an `Event` to signal that the generator has been deleted. event = threading.Event() @@ -381,23 +301,18 @@ class FromGeneratorTest(test_base.DatasetTestBase): def __del__(self): event.set() - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.from_generator( - GeneratorWrapper, output_types=dtypes.int64).take(2)) - init_op = iterator.initializer - get_next = iterator.get_next() + dataset = dataset_ops.Dataset.from_generator( + GeneratorWrapper, output_types=dtypes.int64).take(2) + get_next = self.getNext(dataset) - with session.Session() as sess: - sess.run(init_op) - self.assertAllEqual(42, sess.run(get_next)) - self.assertAllEqual(42, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - # Test that `GeneratorWrapper` object is destroyed when the - # iterator terminates (and the generator iterator is deleted). - self.assertTrue(event.is_set()) + self.assertAllEqual(42, self.evaluate(get_next())) + self.assertAllEqual(42, self.evaluate(get_next())) + with self.assertRaises(errors.OutOfRangeError): + self.evaluate(get_next()) + # Test that `GeneratorWrapper` object is destroyed when the + # iterator terminates (and the generator iterator is deleted). + self.assertTrue(event.is_set()) - @test_util.run_deprecated_v1 def testFromGeneratorWithArgs(self): def flat_map_fn(elem): @@ -410,20 +325,10 @@ class FromGeneratorTest(test_base.DatasetTestBase): generator_with_arg, output_types=dtypes.int64, output_shapes=(), args=(elem,)) - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.range(5).flat_map(flat_map_fn)) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.cached_session() as sess: - sess.run(init_op) - expected = [1, 2, 2, 3, 3, 3, 4, 4, 4, 4] - for x in expected: - self.assertEqual(x, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + dataset = dataset_ops.Dataset.range(5).flat_map(flat_map_fn) + self.assertDatasetProduces( + dataset, expected_output=[1, 2, 2, 3, 3, 3, 4, 4, 4, 4]) - @test_util.run_deprecated_v1 def testFromGeneratorWithTwoArgs(self): def flat_map_fn(elem, message): @@ -436,26 +341,17 @@ class FromGeneratorTest(test_base.DatasetTestBase): generator_with_arg, output_types=(dtypes.int64, dtypes.string), output_shapes=((), ()), args=(elem, message)) - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.zip( - (dataset_ops.Dataset.range(5), - dataset_ops.Dataset.from_tensors("Hi!").repeat(None))) - .flat_map(flat_map_fn)) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.cached_session() as sess: - sess.run(init_op) - expected = [(0, b"Hi!"), - (0, b"Hi!"), (1, b"Hi!"), - (0, b"Hi!"), (1, b"Hi!"), (2, b"Hi!"), - (0, b"Hi!"), (1, b"Hi!"), (2, b"Hi!"), (3, b"Hi!")] - for x in expected: - self.assertEqual(x, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + dataset = dataset_ops.Dataset.zip( + (dataset_ops.Dataset.range(5), + dataset_ops.Dataset.from_tensors("Hi!").repeat(None) + )).flat_map(flat_map_fn) + + self.assertDatasetProduces( + dataset, + expected_output=[(0, b"Hi!"), (0, b"Hi!"), (1, b"Hi!"), (0, b"Hi!"), + (1, b"Hi!"), (2, b"Hi!"), (0, b"Hi!"), (1, b"Hi!"), + (2, b"Hi!"), (3, b"Hi!")]) - @test_util.run_deprecated_v1 def testGeneratorDatasetFinalizeFunctionCalled(self): # NOTE(mrry): This test tests the internal `_GeneratorDataset`, # which affords more control over what the finalize function can do than @@ -472,19 +368,15 @@ class FromGeneratorTest(test_base.DatasetTestBase): stateful=True) dummy = constant_op.constant(37) - iterator = dataset_ops.make_initializable_iterator( - dataset_ops._GeneratorDataset( - dummy, lambda x: x, lambda x: x, finalize_fn).take(2)) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.cached_session() as sess: - sess.run(init_op) - self.assertAllEqual(37, sess.run(get_next)) - self.assertAllEqual(37, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - self.assertTrue(event.is_set()) + dataset = dataset_ops._GeneratorDataset(dummy, lambda x: x, lambda x: x, + finalize_fn).take(2) + get_next = self.getNext(dataset) + + self.assertAllEqual(37, self.evaluate(get_next())) + self.assertAllEqual(37, self.evaluate(get_next())) + with self.assertRaises(errors.OutOfRangeError): + self.evaluate(get_next()) + self.assertTrue(event.is_set()) if __name__ == "__main__": diff --git a/tensorflow/python/data/kernel_tests/map_test.py b/tensorflow/python/data/kernel_tests/map_test.py index e07706413d..67ef98f9fe 100644 --- a/tensorflow/python/data/kernel_tests/map_test.py +++ b/tensorflow/python/data/kernel_tests/map_test.py @@ -28,6 +28,7 @@ from tensorflow.core.framework import attr_value_pb2 from tensorflow.core.protobuf import config_pb2 from tensorflow.python.data.kernel_tests import test_base from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors @@ -46,6 +47,7 @@ from tensorflow.python.ops import script_ops from tensorflow.python.ops import sparse_ops from tensorflow.python.ops import string_ops from tensorflow.python.ops import variable_scope +from tensorflow.python.ops import variables from tensorflow.python.platform import test @@ -83,14 +85,19 @@ def _make_coordinated_sloppy_dataset(num_elements, num_parallel_calls): return next_element, coordination_events -@test_util.run_v1_only("b/120545219") +@test_util.run_all_in_graph_and_eager_modes class MapDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): def _buildMapDataset(self, components, count): + 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(count)) + + dataset = dataset_ops.Dataset.from_tensor_slices(components).map( + _map_fn).repeat(count) + self.assertEqual([c.shape[1:] for c in components], + [shape for shape in dataset.output_shapes]) + return dataset def testMapDataset(self): """Test an dataset that maps a TF function across its input elements.""" @@ -99,34 +106,32 @@ class MapDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): components = (np.arange(7), np.array([[1, 2, 3]]) * np.arange(7)[:, np.newaxis], np.array(37.0) * np.arange(7)) - count = array_ops.placeholder(dtypes.int64, shape=[]) - dataset = self._buildMapDataset(components, count) - iterator = dataset_ops.make_initializable_iterator(dataset) - init_op = iterator.initializer - get_next = iterator.get_next() - - self.assertEqual([c.shape[1:] for c in components], - [t.shape for t in get_next]) + # Test single-threaded access to the iterator. + get_next = self.getNext(self._buildMapDataset(components, 14)) + for _ in range(14): + for i in range(7): + result = self.evaluate(get_next()) + for component, result_component in zip(components, result): + self.assertAllEqual(component[i]**2, result_component) + with self.assertRaises(errors.OutOfRangeError): + self.evaluate(get_next()) + # TODO(b/117581999): add eager coverage, different threads run in graph + # context. + @test_util.run_v1_only("b/120545219") + def testSkipEagerMapDatasetMultithreaded(self): + # Test multi-threaded access to the same iterator. + components = (np.arange(7), + np.array([[1, 2, 3]]) * np.arange(7)[:, np.newaxis], + np.array(37.0) * np.arange(7)) + get_next = self.getNext(self._buildMapDataset(components, 18)) + results = [] with self.cached_session() as sess: - # Test single-threaded access to the iterator. - sess.run(init_op, feed_dict={count: 14}) - for _ in range(14): - for i in range(7): - result = sess.run(get_next) - for component, result_component in zip(components, result): - self.assertAllEqual(component[i]**2, result_component) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Test multi-threaded access to the same iterator. - sess.run(init_op, feed_dict={count: 18}) - results = [] def iterator_thread(): while True: try: - results.append(sess.run(get_next)) + results.append(sess.run(get_next())) except errors.OutOfRangeError: return threads = [self.checkedThread(target=iterator_thread) for _ in range(8)] @@ -148,59 +153,66 @@ class MapDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): def _buildParallelMapDataset(self, components, count, num_parallel_calls, output_buffer_size): + 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=num_parallel_calls) - .prefetch(output_buffer_size) - .repeat(count)) + + dataset = dataset_ops.Dataset.from_tensor_slices(components).map( + _map_fn, num_parallel_calls=num_parallel_calls).prefetch( + output_buffer_size).repeat(count) + + self.assertEqual([c.shape[1:] for c in components], + [shape for shape in dataset.output_shapes]) + return dataset def testParallelMapDataset(self): """Test an dataset that maps a TF function across its input elements.""" + # The pipeline is TensorSliceDataset -> ParallelMapDataset(square_3) -> # RepeatDataset(count). - components = (np.arange(7), - np.array([[1, 2, 3]]) * np.arange(7)[:, np.newaxis], - np.array(37.0) * np.arange(7)) - count = array_ops.placeholder(dtypes.int64, shape=[]) - num_parallel_calls = array_ops.placeholder(dtypes.int32, shape=[]) - output_buffer_size = array_ops.placeholder(dtypes.int64, shape=[]) + def do_test(num_parallel_calls, output_buffer_size): + + components = (np.arange(7), + np.array([[1, 2, 3]]) * np.arange(7)[:, np.newaxis], + np.array(37.0) * np.arange(7)) + # Test single-threaded access to the iterator. + get_next = self.getNext( + self._buildParallelMapDataset(components, 14, num_parallel_calls, + output_buffer_size)) + for _ in range(14): + for i in range(7): + result = self.evaluate(get_next()) + for component, result_component in zip(components, result): + self.assertAllEqual(component[i]**2, result_component) + with self.assertRaises(errors.OutOfRangeError): + self.evaluate(get_next()) - dataset = self._buildParallelMapDataset( - components, count, num_parallel_calls, output_buffer_size) - iterator = dataset_ops.make_initializable_iterator(dataset) - init_op = iterator.initializer - get_next = iterator.get_next() + for num_parallel_calls_val, output_buffer_size_val in [(1, 1), (1, 2), (2, + 2), + (2, 4), (8, 8), + (8, 16)]: + do_test(num_parallel_calls_val, output_buffer_size_val) - self.assertEqual([c.shape[1:] for c in components], - [t.shape for t in get_next]) + # TODO(b/117581999): add eager coverage, different threads run in graph + # context. + @test_util.run_v1_only("b/120545219") + def testSkipEagerParallelMapDatasetMultithreaded(self): - with self.cached_session() as sess: + def do_test(num_parallel_calls, output_buffer_size): + # Test multi-threaded access to the same iterator. + components = (np.arange(7), + np.array([[1, 2, 3]]) * np.arange(7)[:, np.newaxis], + np.array(37.0) * np.arange(7)) + get_next = self.getNext( + self._buildParallelMapDataset(components, 18, num_parallel_calls, + output_buffer_size)) + results = [] + with self.cached_session() as sess: - def do_test(num_parallel_calls_val, output_buffer_size_val): - # Test single-threaded access to the iterator. - sess.run(init_op, feed_dict={ - count: 14, - num_parallel_calls: num_parallel_calls_val, - output_buffer_size: output_buffer_size_val}) - for _ in range(14): - for i in range(7): - result = sess.run(get_next) - for component, result_component in zip(components, result): - self.assertAllEqual(component[i]**2, result_component) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Test multi-threaded access to the same iterator. - sess.run(init_op, feed_dict={ - count: 18, - num_parallel_calls: num_parallel_calls_val, - output_buffer_size: output_buffer_size_val}) - results = [] def iterator_thread(): while True: try: - results.append(sess.run(get_next)) + results.append(sess.run(get_next())) except errors.OutOfRangeError: return threads = [self.checkedThread(target=iterator_thread) @@ -237,14 +249,10 @@ class MapDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): dataset = self._buildParallelMapDataset(components, 1000, 100, 100) # NOTE(mrry): Also test that the prefetching thread is cancelled correctly. dataset = dataset.prefetch(100) - iterator = dataset_ops.make_initializable_iterator(dataset) - init_op = iterator.initializer - get_next = iterator.get_next() + get_next = self.getNext(dataset) - with self.cached_session() as sess: - sess.run(init_op) - for _ in range(3): - sess.run(get_next) + for _ in range(3): + self.evaluate(get_next()) def testParallelMapUnspecifiedOutputSize(self): components = np.array([1., 2., 3., np.nan, 5.]).astype(np.float32) @@ -252,14 +260,10 @@ class MapDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): dataset = (dataset_ops.Dataset.from_tensor_slices(components) .map(lambda x: array_ops.check_numerics(x, "message"), num_parallel_calls=2)) - iterator = dataset_ops.make_initializable_iterator(dataset) - init_op = iterator.initializer - get_next = iterator.get_next() + get_next = self.getNext(dataset) - with self.cached_session() as sess: - sess.run(init_op) - for _ in range(3): - sess.run(get_next) + for _ in range(3): + self.evaluate(get_next()) def testParallelMapError(self): components = np.array([1., 2., 3., np.nan, 5.]).astype(np.float32) @@ -267,20 +271,16 @@ class MapDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): dataset = (dataset_ops.Dataset.from_tensor_slices(components) .map(lambda x: array_ops.check_numerics(x, "message"), num_parallel_calls=2)) - iterator = dataset_ops.make_initializable_iterator(dataset) - init_op = iterator.initializer - get_next = iterator.get_next() + get_next = self.getNext(dataset) - with self.cached_session() as sess: - sess.run(init_op) - for _ in range(3): - sess.run(get_next) - # The 4th element is NaN, so `array_ops.check_numerics()` should fail. - with self.assertRaises(errors.InvalidArgumentError): - sess.run(get_next) - sess.run(get_next) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + for _ in range(3): + self.evaluate(get_next()) + # The 4th element is NaN, so `array_ops.check_numerics()` should fail. + with self.assertRaises(errors.InvalidArgumentError): + self.evaluate(get_next()) + self.evaluate(get_next()) + with self.assertRaises(errors.OutOfRangeError): + self.evaluate(get_next()) def testPrefetchError(self): components = np.array([1., 2., 3., np.nan, 5.]).astype(np.float32) @@ -288,20 +288,17 @@ class MapDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): dataset = (dataset_ops.Dataset.from_tensor_slices(components) .map(lambda x: array_ops.check_numerics(x, "message")) .prefetch(2)) - iterator = dataset_ops.make_initializable_iterator(dataset) - init_op = iterator.initializer - get_next = iterator.get_next() - with self.cached_session() as sess: - sess.run(init_op) - for _ in range(3): - sess.run(get_next) - # The 4th element is NaN, so `array_ops.check_numerics()` should fail. - with self.assertRaises(errors.InvalidArgumentError): - sess.run(get_next) - sess.run(get_next) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + get_next = self.getNext(dataset) + + for _ in range(3): + self.evaluate(get_next()) + # The 4th element is NaN, so `array_ops.check_numerics()` should fail. + with self.assertRaises(errors.InvalidArgumentError): + self.evaluate(get_next()) + self.evaluate(get_next()) + with self.assertRaises(errors.OutOfRangeError): + self.evaluate(get_next()) def testCaptureIterator(self): @@ -314,23 +311,22 @@ class MapDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): return dataset_ops.Dataset.range(10).map(_map_fn) def _build_graph(): - captured_iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.range(10)) + if context.executing_eagerly(): + captured_iterator = iter(dataset_ops.Dataset.range(10)) + else: + captured_iterator = dataset_ops.Dataset.range( + 10).make_initializable_iterator() ds = _build_ds(captured_iterator) - iterator = ds.make_initializable_iterator() - init_op = iterator.initializer - get_next = iterator.get_next() - return captured_iterator.initializer, init_op, get_next - - with ops.Graph().as_default() as g: - captured_init_op, init_op, get_next = _build_graph() - with self.session(graph=g) as sess: - sess.run(captured_init_op) - sess.run(init_op) - for i in range(10): - self.assertEqual(i * i, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + return captured_iterator, ds + + captured_iter, ds = _build_graph() + if not context.executing_eagerly(): + self.evaluate(captured_iter.initializer) + get_next = self.getNext(ds, requires_initialization=True) + for i in range(10): + self.assertEqual(i * i, self.evaluate(get_next())) + with self.assertRaises(errors.OutOfRangeError): + self.evaluate(get_next()) def testCaptureHashTable(self): # NOTE(mrry): We must use the V2 variants of `HashTable` @@ -345,41 +341,37 @@ class MapDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): input_sentences = dataset_ops.Dataset.from_tensor_slices( ["brain brain tank salad surgery", "surgery brain"]) - iterator = dataset_ops.make_initializable_iterator( - input_sentences - .map(lambda x: string_ops.string_split([x]).values).map(table.lookup)) - init_op = iterator.initializer - get_next = iterator.get_next() + dataset = input_sentences.map(lambda x: string_ops.string_split([x]).values + ).map(table.lookup) - with self.cached_session() as sess: - sess.run(table.initializer) - sess.run(init_op) - sess.run(get_next) - sess.run(get_next) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + get_next = self.getNext(dataset, requires_initialization=True) + + self.evaluate(table.initializer) + self.evaluate(get_next()) + self.evaluate(get_next()) + with self.assertRaises(errors.OutOfRangeError): + self.evaluate(get_next()) def testCaptureQueue(self): elements = np.random.randint(100, size=[200]) queue = data_flow_ops.FIFOQueue(200, dtypes.int64, shapes=[]) enqueue_op = queue.enqueue_many(elements) close_op = queue.close() - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.from_tensors(0).repeat(-1) - .map(lambda _: queue.dequeue())) - init_op = iterator.initializer - get_next = iterator.get_next() + dataset = dataset_ops.Dataset.from_tensors(0).repeat( + -1).map(lambda _: queue.dequeue()) - with self.cached_session() as sess: - sess.run(enqueue_op) - sess.run(close_op) - sess.run(init_op) - for element in elements: - self.assertEqual(element, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + get_next = self.getNext(dataset, requires_initialization=True) + self.evaluate(enqueue_op) + self.evaluate(close_op) + + for element in elements: + self.assertEqual(element, self.evaluate(get_next())) + with self.assertRaises(errors.OutOfRangeError): + self.evaluate(get_next()) - def testCaptureSameResourceMultipleTimes(self): + # TODO(b/117581999): Possible deadlock in eager mode, debug. + @test_util.run_v1_only("b/120545219") + def testSkipEagerCaptureSameResourceMultipleTimes(self): elements = np.random.randint(100, size=[200]) queue = data_flow_ops.FIFOQueue( 200, dtypes.int64, shapes=[], shared_name="shared_queue") @@ -389,101 +381,84 @@ class MapDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): enqueue_op = queue.enqueue_many(elements) close_op = queue.close() - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.from_tensors(0).repeat(-1) - .map(lambda _: (queue.dequeue(), queue_2.dequeue()))) - init_op = iterator.initializer - get_next = iterator.get_next() + dataset = dataset_ops.Dataset.from_tensors(0).repeat( + -1).map(lambda _: (queue.dequeue(), queue_2.dequeue())) - with self.cached_session() as sess: - sess.run(enqueue_op) - sess.run(close_op) - sess.run(init_op) - for i in range(100): - self.assertEqual(sorted([elements[i * 2], elements[i * 2 + 1]]), - sorted(sess.run(get_next))) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + self.evaluate(enqueue_op) + self.evaluate(close_op) + get_next = self.getNext(dataset, requires_initialization=True) + for i in range(100): + self.assertCountEqual([elements[i * 2], elements[i * 2 + 1]], + self.evaluate(get_next())) + with self.assertRaises(errors.OutOfRangeError): + self.evaluate(get_next()) def testCaptureVariable(self): counter_var = variable_scope.get_variable( "counter", (), dtypes.int32, use_resource=True) - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.from_tensors(0).repeat(10) - .map(lambda _: counter_var.assign_add(1))) - init_op = iterator.initializer - get_next = iterator.get_next() + dataset = dataset_ops.Dataset.from_tensors(0).repeat( + 10).map(lambda _: counter_var.assign_add(1)) + get_next = self.getNext(dataset, requires_initialization=True) - with self.cached_session() as sess: - sess.run(counter_var.initializer) - sess.run(init_op) - for i in range(10): - self.assertEqual(i, sess.run(counter_var)) - self.assertEqual(i + 1, sess.run(get_next)) - self.assertEqual(10, sess.run(counter_var)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - self.assertEqual(10, sess.run(counter_var)) + self.evaluate(counter_var.initializer) - def testCaptureUninitializedVariableError(self): + for i in range(10): + self.assertEqual(i, self.evaluate(counter_var)) + self.assertEqual(i + 1, self.evaluate(get_next())) + self.assertEqual(10, self.evaluate(counter_var)) + with self.assertRaises(errors.OutOfRangeError): + self.evaluate(get_next()) + self.assertEqual(10, self.evaluate(counter_var)) + + # TODO(b/117581999): error not captured for eager mode, debug. + @test_util.run_v1_only("b/120545219") + def testSkipEagerCaptureUninitializedVariableError(self): counter_var = variable_scope.get_variable( "counter", (), dtypes.int32, use_resource=True) - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.from_tensors(0).repeat(10) - .map(lambda _: counter_var.assign_add(1))) - init_op = iterator.initializer - get_next = iterator.get_next() + dataset = dataset_ops.Dataset.from_tensors(0).repeat( + 10).map(lambda _: counter_var.assign_add(1)) - with self.cached_session() as sess: - sess.run(init_op) - with self.assertRaises(errors.NotFoundError): - sess.run(get_next) - - def testSeededStatefulOperatorIsProperlyStateful(self): - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.from_tensors(0).repeat(10) - .map(lambda _: random_ops.random_uniform((), seed=11)).batch(2)) - init_op = iterator.initializer - get_next = iterator.get_next() + get_next = self.getNext(dataset, requires_initialization=True) - with self.cached_session() as sess: - sess.run(init_op) - random_values = [] - with self.assertRaises(errors.OutOfRangeError): - while True: - random_values.extend(sess.run(get_next)) - self.assertEqual(10, len(random_values)) - self.assertGreater(np.abs(np.diff(random_values)).max(), 1e-6) - sess.run(init_op) - random_values_2 = [] - with self.assertRaises(errors.OutOfRangeError): - while True: - random_values_2.extend(sess.run(get_next)) + with self.assertRaises(errors.NotFoundError): + self.evaluate(get_next()) - # Randomness is repeatable given same seed - self.assertAllClose(random_values, random_values_2) + def testSeededStatefulOperatorIsProperlyStateful(self): + dataset = dataset_ops.Dataset.from_tensors(0).repeat( + 10).map(lambda _: random_ops.random_uniform((), seed=11)).batch(2) + + get_next = self.getNext(dataset, requires_initialization=True) + random_values = [] + with self.assertRaises(errors.OutOfRangeError): + while True: + random_values.extend(self.evaluate(get_next())) + self.assertLen(random_values, 10) + self.assertGreater(np.abs(np.diff(random_values)).max(), 1e-6) + + get_next = self.getNext(dataset, requires_initialization=True) + random_values_2 = [] + with self.assertRaises(errors.OutOfRangeError): + while True: + random_values_2.extend(self.evaluate(get_next())) + + # Randomness is repeatable given same seed + self.assertAllClose(random_values, random_values_2) def testStatefulMapKeepsStateAcrossIterators(self): - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.from_tensors(0).repeat(10) - .map(lambda _: random_ops.random_uniform((), seed=11)) - .repeat(1000) - .batch(10)) - init_op = iterator.initializer - get_next = iterator.get_next() + dataset = dataset_ops.Dataset.from_tensors(0).repeat(10).map( + lambda _: random_ops.random_uniform((), seed=11)).repeat(1000).batch(10) - with self.cached_session() as sess: - sess.run(init_op) - random_values = sess.run(get_next) - - # Assert that one of the next 99 batches yielded by the iterator is - # different from the first. - i = 0 - while i < 99: - if np.any(random_values != sess.run(get_next)): - break - i += 1 - self.assertLess(i, 99) + get_next = self.getNext(dataset) + random_values = self.evaluate(get_next()) + + # Assert that one of the next 99 batches yielded by the iterator is + # different from the first. + i = 0 + while i < 99: + if np.any(random_values != self.evaluate(get_next())): + break + i += 1 + self.assertLess(i, 99) def testStatefulOperationInShortCircuit(self): counter_var = variable_scope.get_variable( @@ -493,36 +468,25 @@ class MapDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): counter_var.assign_add(1) return x - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.range(10).map(increment_fn)) - init_op = iterator.initializer - get_next = iterator.get_next() + dataset = dataset_ops.Dataset.range(10).map(increment_fn) - with self.cached_session() as sess: - sess.run(counter_var.initializer) - sess.run(init_op) - for i in range(10): - self.assertEqual(i, sess.run(counter_var)) - self.assertEqual(i, sess.run(get_next)) - self.assertEqual(10, sess.run(counter_var)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - self.assertEqual(10, sess.run(counter_var)) + get_next = self.getNext(dataset, requires_initialization=True) - def testMapDict(self): - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.range(10) - .map(lambda x: {"foo": x * 2, "bar": x ** 2}) - .map(lambda d: d["foo"] + d["bar"])) - init_op = iterator.initializer - get_next = iterator.get_next() + self.evaluate(counter_var.initializer) + for i in range(10): + self.assertEqual(i, self.evaluate(counter_var)) + self.assertEqual(i, self.evaluate(get_next())) + self.assertEqual(10, self.evaluate(counter_var)) + with self.assertRaises(errors.OutOfRangeError): + self.evaluate(get_next()) + self.assertEqual(10, self.evaluate(counter_var)) - with self.cached_session() as sess: - sess.run(init_op) - for i in range(10): - self.assertEqual(i * 2 + i**2, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + def testMapDict(self): + dataset = dataset_ops.Dataset.range(10).map( + lambda x: {"foo": x * 2, "bar": x**2}).map( + lambda d: d["foo"] + d["bar"]) + self.assertDatasetProduces( + dataset, expected_output=[i * 2 + i**2 for i in range(10)]) def testMapNamedtuple(self, count=10): # construct dataset of tuples @@ -545,33 +509,23 @@ class MapDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): dataset_tuple = dataset_tuple.map(preprocess_tuple) dataset_namedtuple = dataset_namedtuple.map(preprocess_namedtuple) - next_tuple = dataset_ops.make_one_shot_iterator(dataset_tuple).get_next() - next_namedtuple = dataset_ops.make_one_shot_iterator( - dataset_namedtuple).get_next() + next_tuple = self.getNext(dataset_tuple) + next_namedtuple = self.getNext(dataset_namedtuple) # make sure both datasets contain the same data - with self.cached_session() as sess: - for i in range(count): - tuple_, namedtuple_ = sess.run([next_tuple, next_namedtuple]) - self.assertEqual(tuple_, namedtuple_) - self.assertEqual(tuple_, (i, -2 * i)) + for i in range(count): + tuple_, namedtuple_ = self.evaluate([next_tuple(), next_namedtuple()]) + self.assertEqual(tuple_, namedtuple_) + self.assertEqual(tuple_, (i, -2 * i)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(next_namedtuple) + with self.assertRaises(errors.OutOfRangeError): + self.evaluate(next_namedtuple()) def testUseStepContainerInMap(self): row = np.arange(6) - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.from_tensors(row) - .map(lambda elems: functional_ops.map_fn(lambda x: x * x, elems))) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.cached_session() as sess: - sess.run(init_op) - self.assertAllEqual(row**2, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + dataset = dataset_ops.Dataset.from_tensors( + row).map(lambda elems: functional_ops.map_fn(lambda x: x * x, elems)) + self.assertDatasetProduces(dataset, expected_output=[row**2]) def testCaseAndCondInMap(self): @@ -599,24 +553,19 @@ class MapDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): pred_fn_pairs, default=multiply, exclusive=True) def build_dataset(row, num): - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.from_tensor_slices(row).map( - lambda x: control_map_fn(x, num))) - init_op = iterator.initializer - get_next = iterator.get_next() - return init_op, get_next + dataset = dataset_ops.Dataset.from_tensor_slices( + row).map(lambda x: control_map_fn(x, num)) + return self.getNext(dataset) - with self.cached_session() as sess: - row = np.arange(6) - for num in [2, 3, 4]: - init_op, get_next = build_dataset(row, num) - sess.run(init_op) - for i in range(6): - self.assertEqual( - (i // 2 if i % 2 else i * 2) if (num == 2 or num == 3) else i * 2, - sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + row = np.arange(6) + for num in [2, 3, 4]: + get_next = build_dataset(row, num) + for i in range(6): + self.assertEqual( + (i // 2 if i % 2 else i * 2) if (num == 2 or num == 3) else i * 2, + self.evaluate(get_next())) + with self.assertRaises(errors.OutOfRangeError): + self.evaluate(get_next()) def testCaseInWhileInMap(self): @@ -638,24 +587,19 @@ class MapDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): def build_dataset(row, num): # pylint: disable=g-long-lambda - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.from_tensors(row).map( - lambda elems: functional_ops.map_fn( - lambda x: control_map_fn(x, num), elems))) - init_op = iterator.initializer - get_next = iterator.get_next() - return init_op, get_next + dataset = dataset_ops.Dataset.from_tensors( + row).map(lambda elems: functional_ops.map_fn( + lambda x: control_map_fn(x, num), elems)) + return self.getNext(dataset) - with self.cached_session() as sess: - row = np.arange(6) - for num in [2, 3, 4]: - init_op, get_next = build_dataset(row, num) - sess.run(init_op) - self.assertAllEqual( - [x // 2 if (num == 2 or num == 3) else x * 2 for x in row], - sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + row = np.arange(6) + for num in [2, 3, 4]: + get_next = build_dataset(row, num) + self.assertAllEqual( + [x // 2 if (num == 2 or num == 3) else x * 2 for x in row], + self.evaluate(get_next())) + with self.assertRaises(errors.OutOfRangeError): + self.evaluate(get_next()) def testCaseAndCondInWhileInMap(self): @@ -685,21 +629,17 @@ class MapDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): row = np.arange(6) num = 2 # pylint: disable=g-long-lambda - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.from_tensors(row).map( - lambda elems: functional_ops.map_fn( - lambda x: control_map_fn(x, num), elems))) + dataset = dataset_ops.Dataset.from_tensors( + row).map(lambda elems: functional_ops.map_fn( + lambda x: control_map_fn(x, num), elems)) # pylint: enable=g-long-lambda - init_op = iterator.initializer - get_next = iterator.get_next() + get_next = self.getNext(dataset) - with self.cached_session() as sess: - sess.run(init_op) - self.assertAllEqual([(x // 2 if x % 2 else x * 2) if - (num == 2 or num == 3) else x * 2 for x in row], - sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + self.assertAllEqual([(x // 2 if x % 2 else x * 2) if + (num == 2 or num == 3) else x * 2 for x in row], + self.evaluate(get_next())) + with self.assertRaises(errors.OutOfRangeError): + self.evaluate(get_next()) def testPrefetch(self): # We will use this event to test that `_map_py_func()` has been @@ -717,58 +657,54 @@ class MapDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): def _map_fn(x): return script_ops.py_func(_map_py_func, [x], x.dtype) - buffer_size_placeholder = array_ops.placeholder(dtypes.int64, shape=[]) - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.range(100) - .map(_map_fn) - .prefetch(buffer_size_placeholder)) - init_op = iterator.initializer - get_next = iterator.get_next() + def do_test(buffer_size): + dataset = dataset_ops.Dataset.range(100).map(_map_fn).prefetch( + buffer_size) - with self.cached_session() as sess: + get_next = self.getNext(dataset) # Simple test that prefetch yields the expected values in the # expected order. - for buffer_size in [1, 10, 100, 1000]: - sess.run(init_op, feed_dict={buffer_size_placeholder: buffer_size}) - for i in range(100): - self.assertEqual(i * i, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # We can indirectly observe that varying the buffer size has the - # intended effect by observing when `ev` is set (on the 6th - # invocation of `_map_py_func()`). - # NOTE(mrry): We do not test with `buffer_size == - # set_event_during_invocation`, because we must consume at least - # one element to start the prefetching. - for buffer_size in range(1, set_event_during_invocation): - event_will_be_set_after_consuming = ( - set_event_during_invocation - buffer_size + 1) - - ev.clear() - sess.run(init_op, feed_dict={buffer_size_placeholder: buffer_size}) - for i in range(event_will_be_set_after_consuming): - self.assertFalse(ev.is_set()) - self.assertEqual(i * i, sess.run(get_next)) - ev.wait() - for i in range(event_will_be_set_after_consuming, 100): - self.assertEqual(i * i, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + for i in range(100): + self.assertEqual(i * i, self.evaluate(get_next())) + with self.assertRaises(errors.OutOfRangeError): + self.evaluate(get_next()) + + for buffer_size in [1, 10, 100, 1000]: + do_test(buffer_size) + + # We can indirectly observe that varying the buffer size has the + # intended effect by observing when `ev` is set (on the 6th + # invocation of `_map_py_func()`). + # NOTE(mrry): We do not test with `buffer_size == + # set_event_during_invocation`, because we must consume at least + # one element to start the prefetching. + def do_test_ev(buffer_size): + dataset = dataset_ops.Dataset.range(100).map(_map_fn).prefetch( + buffer_size) + + get_next = self.getNext(dataset) + + event_will_be_set_after_consuming = ( + set_event_during_invocation - buffer_size + 1) + + ev.clear() + for i in range(event_will_be_set_after_consuming): + self.assertFalse(ev.is_set()) + self.assertEqual(i * i, self.evaluate(get_next())) + ev.wait() + for i in range(event_will_be_set_after_consuming, 100): + self.assertEqual(i * i, self.evaluate(get_next())) + with self.assertRaises(errors.OutOfRangeError): + self.evaluate(get_next()) - def testReturnList(self): - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.range(10) - .map(lambda x: [x, constant_op.constant(37.0)])) - init_op = iterator.initializer - get_next = iterator.get_next() + for buffer_size in range(1, set_event_during_invocation): + do_test_ev(buffer_size) - with self.cached_session() as sess: - sess.run(init_op) - for i in range(10): - self.assertEqual((i, 37.0), sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + def testReturnList(self): + dataset = dataset_ops.Dataset.range( + 10).map(lambda x: [x, constant_op.constant(37.0)]) + self.assertDatasetProduces( + dataset, expected_output=[(i, 37.0) for i in range(10)]) def testMultiOutputPyFunc(self): # The `tf.py_func()` op returns a list of tensors for its outputs. @@ -778,17 +714,9 @@ class MapDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): return script_ops.py_func( _map_py_func, [x_tensor], [dtypes.int64, dtypes.float64]) - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.range(10).map(_map_fn)) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.cached_session() as sess: - sess.run(init_op) - for i in range(10): - self.assertEqual((i, 37.0), sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + dataset = dataset_ops.Dataset.range(10).map(_map_fn) + self.assertDatasetProduces( + dataset, expected_output=[(i, 37.0) for i in range(10)]) def testSparse(self): @@ -798,19 +726,9 @@ class MapDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): values=(i * np.array([1])), dense_shape=np.array([1, 1])) - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.range(10).map(_sparse)) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.cached_session() as sess: - sess.run(init_op) - for i in range(10): - actual = sess.run(get_next) - self.assertIsInstance(actual, sparse_tensor.SparseTensorValue) - self.assertSparseValuesEqual(actual, _sparse(i)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + dataset = dataset_ops.Dataset.range(10).map(_sparse) + self.assertDatasetProduces( + dataset, expected_output=[_sparse(i) for i in range(10)]) def testSparseChain(self): @@ -824,19 +742,11 @@ class MapDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): self.assertTrue(sparse_tensor.is_sparse(i)) return sparse_ops.sparse_concat(0, [i, i]) - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.range(10).map(_sparse).map(_check)) - init_op = iterator.initializer - get_next = iterator.get_next() + dataset = dataset_ops.Dataset.range(10).map(_sparse).map(_check) - with self.cached_session() as sess: - sess.run(init_op) - for i in range(10): - actual = sess.run(get_next) - self.assertIsInstance(actual, sparse_tensor.SparseTensorValue) - self.assertSparseValuesEqual(actual, _check(_sparse(i)).eval()) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + self.assertDatasetProduces( + dataset, + expected_output=[self.evaluate(_check(_sparse(i))) for i in range(10)]) def testParallelMapOutOfRangeError(self): def raising_py_func(i): @@ -845,32 +755,18 @@ class MapDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): else: return i - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.range(105) - .map(lambda x: script_ops.py_func(raising_py_func, [x], dtypes.int64), - num_parallel_calls=2)) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.cached_session() as sess: - sess.run(init_op) - for i in range(100): - self.assertEqual(i, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + dataset = dataset_ops.Dataset.range(105).map( + lambda x: script_ops.py_func(raising_py_func, [x], dtypes.int64), + num_parallel_calls=2) + get_next = self.getNext(dataset) + for i in range(100): + self.assertEqual(i, self.evaluate(get_next())) + with self.assertRaises(errors.OutOfRangeError): + self.evaluate(get_next()) def testConstantOutput(self): - iterator = dataset_ops.make_initializable_iterator( - dataset_ops.Dataset.range(10).map(lambda x: [x, "hello", 10])) - init_op = iterator.initializer - get_next = iterator.get_next() - - with self.cached_session() as sess: - sess.run(init_op) - for i in range(10): - self.assertEqual((i, b"hello", 10), sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + dataset = dataset_ops.Dataset.range(10).map(lambda x: [x, "hello", 10]) + self.assertDatasetProduces(dataset, [(i, b"hello", 10) for i in range(10)]) def testWarnOnLookupTable(self): def collecting_function(x): @@ -899,7 +795,7 @@ class MapDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): dataset_ops.Dataset.from_tensor_slices).map( lambda ds: ds.batch(3)).flat_map(lambda x: x) - self.assertDatasetProduces(dataset, [[1.0, 2.0, 3.0]]) + self.assertDatasetProduces(dataset, expected_output=[[1.0, 2.0, 3.0]]) def testReturnValueError(self): dataset = dataset_ops.Dataset.from_tensors([1.0, 2.0, 3.0]) @@ -932,11 +828,8 @@ class MapDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): return const_tensor dataset = dataset.map(broken_function) - iterator = dataset_ops.make_initializable_iterator(dataset) - - with self.cached_session() as sess: - with self.assertRaisesRegexp(errors.InvalidArgumentError, "BrokenConst"): - sess.run(iterator.initializer) + self.assertDatasetProduces( + dataset, expected_error=(errors.InvalidArgumentError, "BrokenConst")) # pylint: disable=g-long-lambda @parameterized.named_parameters( @@ -959,12 +852,10 @@ class MapDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): return tids dataset = make_dataset_fn(dataset, _map_fn) - iterator = dataset_ops.make_one_shot_iterator(dataset) - get_next = iterator.get_next() + get_next = self.getNext(dataset) - with self.cached_session() as sess: - tids = sess.run(get_next) - self.assertTrue(all(tids[0] == tid for tid in tids)) + tids = self.evaluate(get_next()) + self.assertTrue(all(tids[0] == tid for tid in tids)) # pylint: enable=g-long-lambda @parameterized.named_parameters( @@ -980,30 +871,28 @@ class MapDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): def testShortCircuit(self, structure, map_fn, num_parallel_calls): dataset = self.structuredDataset(structure).repeat().map( map_fn, num_parallel_calls=num_parallel_calls) - get_next = dataset_ops.make_one_shot_iterator(dataset).get_next() + get_next = self.getNext(dataset) - with self.cached_session() as sess: - if isinstance(structure, tuple): - expected = map_fn(*sess.run(self.structuredElement(structure))) - else: - expected = map_fn(sess.run(self.structuredElement(structure))) - self.assertEqual(expected, sess.run(get_next)) + if isinstance(structure, tuple): + expected = map_fn(*self.evaluate(self.structuredElement(structure))) + else: + expected = map_fn(self.evaluate(self.structuredElement(structure))) + self.assertEqual(expected, self.evaluate(get_next())) @parameterized.named_parameters( ("Sequential", None), ("Parallel", 10), ) def testShortCircuitCapturedInput(self, num_parallel_calls): - captured_t = array_ops.placeholder(dtypes.int64, shape=[]) + captured_t = variables.Variable(42) dataset = self.structuredDataset(None).repeat().map( lambda x: captured_t, num_parallel_calls=num_parallel_calls) - iterator = dataset_ops.make_initializable_iterator(dataset) - get_next = iterator.get_next() + self.evaluate(variables.global_variables_initializer()) + get_next = self.getNext(dataset, requires_initialization=True) - with self.cached_session() as sess: - sess.run(iterator.initializer, feed_dict={captured_t: 42}) - self.assertEqual(42, sess.run(get_next)) + self.assertEqual(42, self.evaluate(get_next())) + # TODO(b/117581999): Add eager coverage. @parameterized.named_parameters( ("1", 1, 1), ("2", 10, 1), @@ -1012,7 +901,9 @@ class MapDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): ("5", 100, 10), ("6", 100, 100), ) - def testSloppyInterleaveInOrder(self, num_elements, num_parallel_calls): + @test_util.run_v1_only("b/120545219") + def testSkipEagerSloppyInterleaveInOrder(self, num_elements, + num_parallel_calls): get_next, coordination_events = _make_coordinated_sloppy_dataset( num_elements, num_parallel_calls) config = config_pb2.ConfigProto( @@ -1025,12 +916,15 @@ class MapDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) + # TODO(b/117581999): Add eager coverage. @parameterized.named_parameters( ("1", 10, 10), ("2", 100, 10), ("3", 100, 100), ) - def testSloppyInterleaveOutOfOrder(self, num_elements, num_parallel_calls): + @test_util.run_v1_only("b/120545219") + def testSkipEagerSloppyInterleaveOutOfOrder(self, num_elements, + num_parallel_calls): get_next, coordination_events = _make_coordinated_sloppy_dataset( num_elements, num_parallel_calls) config = config_pb2.ConfigProto( @@ -1064,6 +958,5 @@ class MapDatasetTest(test_base.DatasetTestBase, parameterized.TestCase): self.evaluate(get_next()) - if __name__ == "__main__": test.main() -- GitLab From 542ec6d4282b9b43f8a1468b466e672bc8f7e32c Mon Sep 17 00:00:00 2001 From: Eugene Brevdo Date: Mon, 10 Dec 2018 13:13:12 -0800 Subject: [PATCH 306/873] [TF optimizers (v1)] Non-slot variables are ResourceVariables iff the input vars are. This fixes a bug where Adam beta*_power variables were always created as RefVars even if the optimizer acts on ResourceVars. This broke certain defun + Adam use cases. Also fixed the unit tests, which *always* created ResourceVariables (ever since variables.Variable() constructor became aliased to ResourceVariables). PiperOrigin-RevId: 224869338 --- tensorflow/python/training/adam_test.py | 19 +++++++++++++++---- tensorflow/python/training/optimizer.py | 5 ++++- 2 files changed, 19 insertions(+), 5 deletions(-) diff --git a/tensorflow/python/training/adam_test.py b/tensorflow/python/training/adam_test.py index b0bae27577..15958112bd 100644 --- a/tensorflow/python/training/adam_test.py +++ b/tensorflow/python/training/adam_test.py @@ -68,8 +68,8 @@ class AdamOptimizerTest(test.TestCase): var0 = resource_variable_ops.ResourceVariable(var0_np) var1 = resource_variable_ops.ResourceVariable(var1_np) else: - var0 = variables.Variable(var0_np) - var1 = variables.Variable(var1_np) + var0 = variables.RefVariable(var0_np) + var1 = variables.RefVariable(var1_np) grads0_np_indices = np.array([0, 1], dtype=np.int32) grads0 = ops.IndexedSlices( constant_op.constant(grads0_np), @@ -156,6 +156,9 @@ class AdamOptimizerTest(test.TestCase): self.evaluate(repeated_index_update_var)) def doTestBasic(self, use_resource=False, use_callable_params=False): + if context.executing_eagerly() and not use_resource: + self.skipTest( + "Skipping test with use_resource=False and executing eagerly.") for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): with self.session(graph=ops.Graph()): # Initialize variables for numpy implementation. @@ -171,8 +174,8 @@ class AdamOptimizerTest(test.TestCase): var1 = resource_variable_ops.ResourceVariable( var1_np, name="var1_%d" % i) else: - var0 = variables.Variable(var0_np) - var1 = variables.Variable(var1_np) + var0 = variables.RefVariable(var0_np) + var1 = variables.RefVariable(var1_np) grads0 = constant_op.constant(grads0_np) grads1 = constant_op.constant(grads1_np) @@ -194,6 +197,14 @@ class AdamOptimizerTest(test.TestCase): self.assertTrue(beta2_power is not None) self.assertIn(beta1_power, opt_variables) self.assertIn(beta2_power, opt_variables) + # Ensure that non-slot variables are the same type as the requested + # variables. + self.assertEqual( + use_resource, + resource_variable_ops.is_resource_variable(beta1_power)) + self.assertEqual( + use_resource, + resource_variable_ops.is_resource_variable(beta2_power)) if not context.executing_eagerly(): with ops.Graph().as_default(): diff --git a/tensorflow/python/training/optimizer.py b/tensorflow/python/training/optimizer.py index d9ebdcad1f..eaa563e84a 100644 --- a/tensorflow/python/training/optimizer.py +++ b/tensorflow/python/training/optimizer.py @@ -822,7 +822,10 @@ class Optimizer( name=name, shape=None) if restored_initial_value is not None: initial_value = restored_initial_value - v = variable_scope.variable(initial_value, name=name, trainable=False) + v = variable_scope.variable( + initial_value, name=name, trainable=False, + use_resource=resource_variable_ops.is_resource_variable( + colocate_with)) # Restore this variable by name if necessary, but don't add a # Checkpointable dependency. Optimizers return the current graph's # non-slot variables from _checkpoint_dependencies explicitly rather -- GitLab From d9ab4a8fedd67b5b4944a4033acfdee5f5001492 Mon Sep 17 00:00:00 2001 From: Anna R Date: Mon, 10 Dec 2018 13:20:49 -0800 Subject: [PATCH 307/873] Internal change. PiperOrigin-RevId: 224870669 --- tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh | 2 +- tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh | 2 +- 2 files changed, 2 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 62e1eaa366..4c4e8ba1ca 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 @@ -133,7 +133,7 @@ fi # Create a python test directory to avoid package name conflict create_python_test_dir "${PY_TEST_DIR}" -./bazel-bin/tensorflow/tools/pip_package/build_pip_package "$PWD/${PY_TEST_DIR}" "${EXTRA_PIP_FLAGS}" +./bazel-bin/tensorflow/tools/pip_package/build_pip_package "$PWD/${PY_TEST_DIR}" ${EXTRA_PIP_FLAGS} if [[ "$TF_NIGHTLY" == 1 ]]; then exit 0 diff --git a/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh b/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh index acafd9ebce..070235fcb2 100644 --- a/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh +++ b/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh @@ -136,7 +136,7 @@ fi create_python_test_dir "${PY_TEST_DIR}" ./bazel-bin/tensorflow/tools/pip_package/build_pip_package "$PWD/${PY_TEST_DIR}" \ - --gpu "${EXTRA_PIP_FLAGS}" + --gpu ${EXTRA_PIP_FLAGS} if [[ "$TF_NIGHTLY" == 1 ]]; then exit 0 -- GitLab From 2d86af34dede8ff45fbec5373e991f1259f5f447 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 10 Dec 2018 13:43:23 -0800 Subject: [PATCH 308/873] Map backprop filter convolutions to depthwise convolutions in cases where the filter is depthwise. PiperOrigin-RevId: 224874845 --- .../compiler/tests/depthwise_conv_op_test.py | 4 +- .../tf2xla/kernels/conv_op_helpers.cc | 149 ++++++++++++++---- 2 files changed, 118 insertions(+), 35 deletions(-) diff --git a/tensorflow/compiler/tests/depthwise_conv_op_test.py b/tensorflow/compiler/tests/depthwise_conv_op_test.py index 174bfa9efb..6183d3ed5b 100644 --- a/tensorflow/compiler/tests/depthwise_conv_op_test.py +++ b/tensorflow/compiler/tests/depthwise_conv_op_test.py @@ -379,8 +379,8 @@ class DepthwiseConv2DTest(xla_test.XLATestCase): for index, (input_size, filter_size, output_size, stride, padding) in enumerate(ConfigsToTest()): print("Testing DepthwiseConv2DFilterGradCompare,", index, "th config:", - input_size, "*", filter_size, "stride:", stride, "padding:", - padding) + input_size, "*", filter_size, "producing output", output_size, + "stride:", stride, "padding:", padding) self._CompareBackpropFilter(input_size, filter_size, output_size, stride, padding) diff --git a/tensorflow/compiler/tf2xla/kernels/conv_op_helpers.cc b/tensorflow/compiler/tf2xla/kernels/conv_op_helpers.cc index 641fefafb3..399e6e1187 100644 --- a/tensorflow/compiler/tf2xla/kernels/conv_op_helpers.cc +++ b/tensorflow/compiler/tf2xla/kernels/conv_op_helpers.cc @@ -392,23 +392,31 @@ xla::StatusOr MakeXlaBackpropFilterConvOp( builder->GetShape(activations)); TF_ASSIGN_OR_RETURN(xla::Shape out_backprop_shape, builder->GetShape(gradients)); + xla::XlaOp filter_backprop; + + xla::Shape input_shape = activations_shape; + xla::Shape output_shape = out_backprop_shape; + + TensorShape input_tensor_shape, filter_tensor_shape, output_tensor_shape; + TF_RETURN_IF_ERROR(XLAShapeToTensorShape(filter_shape, &filter_tensor_shape)); + TF_RETURN_IF_ERROR(XLAShapeToTensorShape(input_shape, &input_tensor_shape)); + TF_RETURN_IF_ERROR(XLAShapeToTensorShape(output_shape, &output_tensor_shape)); + const xla::Shape expanded_filter_shape = attrs.depthwise ? ExpandedFilterShapeForDepthwiseConvolution(filter_shape) : filter_shape; - // Reuse dimension computation logic from conv_grad_ops.cc. ConvBackpropDimensions dims; - TF_RETURN_IF_ERROR(ConvBackpropComputeDimensionsV2XlaShapes( - type_string, attrs.num_spatial_dims, activations_shape, - expanded_filter_shape, out_backprop_shape, attrs.dilations, attrs.strides, - attrs.padding, attrs.data_format, &dims)); - // The filter gradients are computed by a convolution of the input // activations and the output gradients, with some appropriate padding. // See the comment at the top of conv_grad_ops.h for details. - xla::ConvolutionDimensionNumbers dnums; + TF_RETURN_IF_ERROR(ConvBackpropComputeDimensionsV2XlaShapes( + type_string, attrs.num_spatial_dims, activations_shape, + expanded_filter_shape, out_backprop_shape, attrs.dilations, attrs.strides, + attrs.padding, attrs.data_format, &dims)); + // The activations (inputs) form the LHS of the convolution. // Activations have shape: [batch, in_rows, in_cols, ..., in_depth] // For the gradient computation, we flip the roles of the batch and @@ -420,29 +428,97 @@ xla::StatusOr MakeXlaBackpropFilterConvOp( int n_dim = GetTensorBatchDimIndex(num_dims, attrs.data_format); int c_dim = GetTensorFeatureDimIndex(num_dims, attrs.data_format); - // Swap n_dim and c_dim in the activations. - dnums.set_input_batch_dimension(c_dim); - dnums.set_input_feature_dimension(n_dim); + int64 total_spatial_size = 1; + for (int i = 0; i < attrs.num_spatial_dims; ++i) { + total_spatial_size *= dims.input_size(i); + } - // The gradients become the RHS of the convolution. - // The gradients have shape [batch, out_rows, out_cols, ..., out_depth] - // where the batch becomes the input feature for the convolution. - dnums.set_kernel_input_feature_dimension(n_dim); - dnums.set_kernel_output_feature_dimension(c_dim); + // We use this approach only for depthwise convolutions where feature counts + // are large but space dimensions are small. + bool should_perform_depthwise_conv = + (total_spatial_size < dims.in_depth) && + filter_tensor_shape.dim_size(num_dims - 1) == 1 && attrs.depthwise; + + int64 num_spatial_dims = + attrs.num_spatial_dims + (should_perform_depthwise_conv ? 1 : 0); + + std::vector> padding(num_spatial_dims); + std::vector rhs_dilation(num_spatial_dims); + std::vector window_strides(num_spatial_dims); + std::vector ones(num_spatial_dims, 1); + + if (should_perform_depthwise_conv) { + // This approach is similar to handling of grouped convolutions in + // the convolution_feature_group_converter.cc. Please refer to it for + // details. + + // Add spatial dimension to the activation, and reshape. + std::vector activations_reshape_sizes, gradients_reshape_sizes; + + activations_reshape_sizes.push_back(dims.batch_size); + gradients_reshape_sizes.push_back(dims.batch_size); + for (int i = 0; i < attrs.num_spatial_dims; i++) { + activations_reshape_sizes.push_back(dims.input_size(i)); + gradients_reshape_sizes.push_back(dims.output_size(i)); + } + activations_reshape_sizes.push_back(dims.in_depth); + activations_reshape_sizes.push_back(1); + gradients_reshape_sizes.push_back(dims.out_depth); + gradients_reshape_sizes.push_back(1); + + activations = xla::Reshape(activations, activations_reshape_sizes); + gradients = xla::Reshape(gradients, gradients_reshape_sizes); + + int64 new_spatial_dim = activations_reshape_sizes.size() - 1; + + // Set the newly added dimension to be the batch. + dnums.set_input_batch_dimension(new_spatial_dim); + dnums.set_input_feature_dimension(c_dim); + + // The gradients become the RHS of the convolution. + // The gradients have shape [batch, out_rows, out_cols, ..., out_depth, 1] + // where the batch becomes a spatial dimension, and 1 becomes + // the input feature for the convolution. + dnums.set_kernel_input_feature_dimension(new_spatial_dim); + dnums.set_kernel_output_feature_dimension(c_dim); + + // Treat original batch dimension as a spatial dimension. + dnums.add_input_spatial_dimensions(n_dim); + dnums.add_kernel_spatial_dimensions(n_dim); + } else { + // The activations (inputs) form the LHS of the convolution. + // Activations have shape: [batch, in_rows, in_cols, ..., in_depth] + // For the gradient computation, we flip the roles of the batch and + // feature dimensions. + // Each spatial entry has size in_depth * batch + + // Swap n_dim and c_dim in the activations. + dnums.set_input_batch_dimension(c_dim); + dnums.set_input_feature_dimension(n_dim); + + // The gradients become the RHS of the convolution. + // The gradients have shape [batch, out_rows, out_cols, ..., out_depth] + // where the batch becomes the input feature for the convolution. + dnums.set_kernel_input_feature_dimension(n_dim); + dnums.set_kernel_output_feature_dimension(c_dim); + } - std::vector> padding(attrs.num_spatial_dims); - std::vector rhs_dilation(attrs.num_spatial_dims); - std::vector window_strides(attrs.num_spatial_dims); - std::vector ones(attrs.num_spatial_dims, 1); + dnums.set_output_batch_dimension(num_spatial_dims); + dnums.set_output_feature_dimension(num_spatial_dims + 1); // Tensorflow filter shape is [ H, W, ..., inC, outC ]. - for (int i = 0; i < attrs.num_spatial_dims; ++i) { + for (int i = 0; i < num_spatial_dims; ++i) { dnums.add_output_spatial_dimensions(i); } - dnums.set_output_batch_dimension(attrs.num_spatial_dims); - dnums.set_output_feature_dimension(attrs.num_spatial_dims + 1); - for (int i = 0; i < attrs.num_spatial_dims; ++i) { + if (should_perform_depthwise_conv) { + // Set the right parameters for the newly created spatial dimension. + padding[0] = {0, 0}; + rhs_dilation[0] = 1; + window_strides[0] = 1; + } + + for (int64 i = 0; i < attrs.num_spatial_dims; ++i) { int64 dim = GetTensorSpatialDimIndex(num_dims, attrs.data_format, i); dnums.add_input_spatial_dimensions(dim); dnums.add_kernel_spatial_dimensions(dim); @@ -483,9 +559,10 @@ xla::StatusOr MakeXlaBackpropFilterConvOp( const int64 pad_before = attrs.padding == Padding::SAME ? std::max(pad_total / 2, 0) : 0; - padding[i] = {pad_before, pad_total - pad_before}; - rhs_dilation[i] = dims.spatial_dims[i].stride; - window_strides[i] = attrs.dilations[dim]; + int64 dim_being_operated = should_perform_depthwise_conv ? i + 1 : i; + padding[dim_being_operated] = {pad_before, pad_total - pad_before}; + rhs_dilation[dim_being_operated] = dims.spatial_dims[i].stride; + window_strides[dim_being_operated] = attrs.dilations[dim]; } // Besides padding the input, we will also expand output_rows to @@ -496,13 +573,19 @@ xla::StatusOr MakeXlaBackpropFilterConvOp( // // This is done by specifying the window dilation factors in the // convolution HLO below. - auto filter_backprop = - xla::ConvGeneralDilated(activations, gradients, window_strides, padding, - /*lhs_dilation=*/ones, rhs_dilation, dnums); - - if (attrs.depthwise) { - filter_backprop = ContractFilterForDepthwiseBackprop( - filter_shape, filter_backprop, activations.builder()); + filter_backprop = xla::ConvGeneralDilated( + activations, gradients, window_strides, padding, + /*lhs_dilation=*/ones, rhs_dilation, dnums, + /*feature_group_count=*/ + should_perform_depthwise_conv ? dims.in_depth : 1); + + if (should_perform_depthwise_conv) { + filter_backprop = xla::Reshape(filter_backprop, filter_shape.dimensions()); + } else { + if (attrs.depthwise) { + filter_backprop = ContractFilterForDepthwiseBackprop( + filter_shape, filter_backprop, activations.builder()); + } } return filter_backprop; -- GitLab From fddc04ba0780c7fb9211e6b4b9e7b0de3f7de957 Mon Sep 17 00:00:00 2001 From: Daniel Ingram Date: Mon, 10 Dec 2018 16:50:55 -0500 Subject: [PATCH 309/873] Add raise statement before NotImplementedError --- tensorflow/python/keras/engine/training_utils.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tensorflow/python/keras/engine/training_utils.py b/tensorflow/python/keras/engine/training_utils.py index ec6b39704a..39b5b85ece 100644 --- a/tensorflow/python/keras/engine/training_utils.py +++ b/tensorflow/python/keras/engine/training_utils.py @@ -68,7 +68,7 @@ class Aggregator(object): Arguments: batch_outs: A list of batch-level outputs. """ - NotImplementedError('Must be implemented in subclasses.') + raise NotImplementedError('Must be implemented in subclasses.') @abc.abstractmethod def aggregate(self, batch_outs, batch_start=None, batch_end=None): @@ -81,12 +81,12 @@ class Aggregator(object): batch_end: The end index of this batch. Always `None` if `use_steps` is `True`. """ - NotImplementedError('Must be implemented in subclasses.') + raise NotImplementedError('Must be implemented in subclasses.') @abc.abstractmethod def finalize(self): """Prepares the total results to be returned.""" - NotImplementedError('Must be implemented in subclasses.') + raise NotImplementedError('Must be implemented in subclasses.') class MetricsAggregator(Aggregator): -- GitLab From a404d2edf57ac71034e93665454d238045786ae9 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 10 Dec 2018 13:49:07 -0800 Subject: [PATCH 310/873] Internal Change PiperOrigin-RevId: 224875931 --- tensorflow/python/framework/test_util.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py index 0e48d3c875..d06e1f574b 100644 --- a/tensorflow/python/framework/test_util.py +++ b/tensorflow/python/framework/test_util.py @@ -1866,7 +1866,7 @@ class TensorFlowTestCase(googletest.TestCase): # If a is a tensor then convert it to ndarray if isinstance(a, ops.Tensor): if isinstance(a, ops._EagerTensorBase): - return a.numpy() + a = a.numpy() else: a = self.evaluate(a) if not isinstance(a, np.ndarray): -- GitLab From d19f1e45fcb7418fe07333fc99d102214129be3e Mon Sep 17 00:00:00 2001 From: Dan Moldovan Date: Mon, 10 Dec 2018 13:49:51 -0800 Subject: [PATCH 311/873] Allow completely stateless(i.e., with no outputs) loops. Simplify the handling of stateless conditionals. This change will still not support stateless loops pre-v2 until we add auto deps. However, it works properly in tf.function. PiperOrigin-RevId: 224876064 --- .../autograph/converters/control_flow.py | 143 +++++++++++------- .../autograph/operators/control_flow.py | 19 ++- tensorflow/python/autograph/pyct/templates.py | 3 + 3 files changed, 107 insertions(+), 58 deletions(-) diff --git a/tensorflow/python/autograph/converters/control_flow.py b/tensorflow/python/autograph/converters/control_flow.py index bef6cae1bb..a39a0b0cdb 100644 --- a/tensorflow/python/autograph/converters/control_flow.py +++ b/tensorflow/python/autograph/converters/control_flow.py @@ -49,7 +49,13 @@ class ControlFlowTransformer(converter.Base): def _create_cond_branch(self, body_name, aliased_orig_names, aliased_new_names, body, returns): - if len(returns) == 1: + if not returns: + # TODO(b/110167197): Replace with a plain return. + template = """ + return 1 + """ + return_stmt = templates.replace(template) + elif len(returns) == 1: template = """ return retval """ @@ -220,7 +226,7 @@ class ControlFlowTransformer(converter.Base): # branch functions will return a dummy value that ensures cond # actually has some return value as well. cond_results = None - # TODO(mdan): This doesn't belong here; it's specific to the operator. + # TODO(mdan): Replace with None once side_effect_guards is retired. returned_from_body = (templates.replace_as_expression( 'ag__.match_staging_level(1, cond_var_name)', cond_var_name=cond_var_name),) @@ -278,14 +284,6 @@ class ControlFlowTransformer(converter.Base): ' these symbols before the loop'.format( self._fmt_symbols(live_defs_in_loop))) - if not loop_state: - # TODO(mdan): Implement this properly. - # We need to check whether any variable created inside the body scope - # is used before being modified outside the scope. This should be done - # during activity analysis, and in general should cover the case where - # variables may not be initialized. - raise ValueError('cannot convert loop: no outputs') - return loop_state, reserved_symbols def _state_constructs(self, loop_state, reserved_symbols): @@ -337,26 +335,44 @@ class ControlFlowTransformer(converter.Base): node_body = ast_util.rename_symbols(node.body, ssf_map) test = ast_util.rename_symbols(node.test, ssf_map) - template = """ - def test_name(state_ssf): - return test - def body_name(state_ssf): - body - return state_ssf, - state_ast_tuple = ag__.while_stmt( - test_name, body_name, (state,), (extra_deps,)) - """ - node = templates.replace( - template, - state=loop_state, - state_ssf=state_ssf, - state_ast_tuple=state_ast_tuple, - test_name=self.ctx.namer.new_symbol('loop_test', reserved_symbols), - test=test, - body_name=self.ctx.namer.new_symbol('loop_body', reserved_symbols), - body=node_body, - extra_deps=tuple(s.ast() for s in cond_closure), - ) + if loop_state: + template = """ + def test_name(state_ssf): + return test + def body_name(state_ssf): + body + return state_ssf, + state_ast_tuple = ag__.while_stmt( + test_name, body_name, (state,), (extra_deps,)) + """ + node = templates.replace( + template, + state=loop_state, + state_ssf=state_ssf, + state_ast_tuple=state_ast_tuple, + test_name=self.ctx.namer.new_symbol('loop_test', reserved_symbols), + test=test, + body_name=self.ctx.namer.new_symbol('loop_body', reserved_symbols), + body=node_body, + extra_deps=tuple(s.ast() for s in cond_closure), + ) + else: + template = """ + def test_name(): + return test + def body_name(): + body + return () + ag__.while_stmt(test_name, body_name, (), (extra_deps,)) + """ + node = templates.replace( + template, + test_name=self.ctx.namer.new_symbol('loop_test', reserved_symbols), + test=test, + body_name=self.ctx.namer.new_symbol('loop_body', reserved_symbols), + body=node_body, + extra_deps=tuple(s.ast() for s in cond_closure), + ) return node @@ -373,29 +389,50 @@ class ControlFlowTransformer(converter.Base): else: extra_test = parser.parse_expression('True') - template = """ - def extra_test_name(state_ssf): - return extra_test_expr - def body_name(loop_vars, state_ssf): - # Workaround for PEP-3113 - iterate = loop_vars - body - return state_ssf, - state_ast_tuple = ag__.for_stmt( - iter_, extra_test_name, body_name, (state,)) - """ - node = templates.replace( - template, - state=loop_state, - state_ssf=state_ssf, - state_ast_tuple=state_ast_tuple, - iter_=node.iter, - iterate=node.target, - extra_test_name=self.ctx.namer.new_symbol('extra_test', - reserved_symbols), - extra_test_expr=extra_test, - body_name=self.ctx.namer.new_symbol('loop_body', reserved_symbols), - body=node_body) + if loop_state: + template = """ + def extra_test_name(state_ssf): + return extra_test_expr + def body_name(loop_vars, state_ssf): + # Workaround for PEP-3113 + iterate = loop_vars + body + return state_ssf, + state_ast_tuple = ag__.for_stmt( + iter_, extra_test_name, body_name, (state,)) + """ + node = templates.replace( + template, + state=loop_state, + state_ssf=state_ssf, + state_ast_tuple=state_ast_tuple, + iter_=node.iter, + iterate=node.target, + extra_test_name=self.ctx.namer.new_symbol('extra_test', + reserved_symbols), + extra_test_expr=extra_test, + body_name=self.ctx.namer.new_symbol('loop_body', reserved_symbols), + body=node_body) + else: + template = """ + def extra_test_name(): + return extra_test_expr + def body_name(loop_vars): + # Workaround for PEP-3113 + iterate = loop_vars + body + return () + ag__.for_stmt(iter_, extra_test_name, body_name, ()) + """ + node = templates.replace( + template, + iter_=node.iter, + iterate=node.target, + extra_test_name=self.ctx.namer.new_symbol('extra_test', + reserved_symbols), + extra_test_expr=extra_test, + body_name=self.ctx.namer.new_symbol('loop_body', reserved_symbols), + body=node_body) return node diff --git a/tensorflow/python/autograph/operators/control_flow.py b/tensorflow/python/autograph/operators/control_flow.py index 89f7b8522f..afa3787d42 100644 --- a/tensorflow/python/autograph/operators/control_flow.py +++ b/tensorflow/python/autograph/operators/control_flow.py @@ -87,7 +87,10 @@ def _known_len_for_stmt(iter_, extra_test, body, init_state): def while_body(iterate_index, *state): iterate = iter_[iterate_index] new_state = body(iterate, *state) - return (iterate_index + 1,) + new_state + if new_state: + return (iterate_index + 1,) + new_state + else: + return iterate_index + 1 def while_cond(iterate_index, *state): return gen_math_ops.logical_and(iterate_index < n, extra_test(*state)) @@ -98,13 +101,19 @@ def _known_len_for_stmt(iter_, extra_test, body, init_state): init_state=(0,) + init_state, extra_deps=(iter_,), opts=dict(maximum_iterations=n)) + # Dropping the iteration index because it's not syntactically visible. # TODO(mdan): Don't. - results = results[1:] + if isinstance(results, (tuple, list)): + assert len(results) >= 1 # Has at least the iterate. + if len(results) > 1: + results = results[1:] + if len(results) == 1: + # TODO(mdan): Remove this special case. + results, = results + else: + results = () - # TODO(mdan): Remove this special case. - if len(results) == 1: - return results[0] return results diff --git a/tensorflow/python/autograph/pyct/templates.py b/tensorflow/python/autograph/pyct/templates.py index 2272ea4208..43279b3ca0 100644 --- a/tensorflow/python/autograph/pyct/templates.py +++ b/tensorflow/python/autograph/pyct/templates.py @@ -184,6 +184,9 @@ class ReplaceTransformer(gast.NodeTransformer): new_nodes = self._prepare_replacement(node, node.id) + if not new_nodes: + return new_nodes + # Preserve the target context. adjuster = ContextAdjuster(type(node.ctx)) for n in new_nodes: -- GitLab From 341452772c51cf66fc8785081437cddc38ce1081 Mon Sep 17 00:00:00 2001 From: Frank Chen Date: Mon, 10 Dec 2018 13:57:40 -0800 Subject: [PATCH 312/873] Use format_master_url for Kubernetes and Slurm Cluster Resolvers PiperOrigin-RevId: 224877586 --- .../cluster_resolver/kubernetes_cluster_resolver.py | 8 +++----- .../cluster_resolver/slurm_cluster_resolver.py | 10 +++++++--- 2 files changed, 10 insertions(+), 8 deletions(-) diff --git a/tensorflow/python/distribute/cluster_resolver/kubernetes_cluster_resolver.py b/tensorflow/python/distribute/cluster_resolver/kubernetes_cluster_resolver.py index 88625a5542..7ff6ec0f2d 100644 --- a/tensorflow/python/distribute/cluster_resolver/kubernetes_cluster_resolver.py +++ b/tensorflow/python/distribute/cluster_resolver/kubernetes_cluster_resolver.py @@ -107,16 +107,14 @@ class KubernetesClusterResolver(ClusterResolver): Returns: The name or URL of the session master. """ + task_type = task_type if task_type is not None else self.task_type + task_index = task_index if task_index is not None else self.task_index + if task_type is not None and task_index is not None: return format_master_url( self.cluster_spec().task_address(task_type, task_index), rpc_layer or self.rpc_layer) - if self.task_type is not None and self.task_index is not None: - return format_master_url( - self.cluster_spec().task_address(self.task_type, self.task_index), - rpc_layer or self.rpc_layer) - return '' def cluster_spec(self): diff --git a/tensorflow/python/distribute/cluster_resolver/slurm_cluster_resolver.py b/tensorflow/python/distribute/cluster_resolver/slurm_cluster_resolver.py index 1ab81731b7..9dbe25b613 100644 --- a/tensorflow/python/distribute/cluster_resolver/slurm_cluster_resolver.py +++ b/tensorflow/python/distribute/cluster_resolver/slurm_cluster_resolver.py @@ -23,6 +23,7 @@ import os import subprocess from tensorflow.python.distribute.cluster_resolver.cluster_resolver import ClusterResolver +from tensorflow.python.distribute.cluster_resolver.cluster_resolver import format_master_url from tensorflow.python.training.server_lib import ClusterSpec @@ -206,10 +207,13 @@ class SlurmClusterResolver(ClusterResolver): """ task_type = task_type if task_type is not None else self.task_type task_index = task_index if task_index is not None else self.task_index - rpc_layer = rpc_layer or self.rpc_layer - master = self.cluster_spec().task_address(task_type, task_index) - return '%s://%s' % (rpc_layer, master) if rpc_layer else master + if task_type is not None and task_index is not None: + return format_master_url( + self.cluster_spec().task_address(task_type, task_index), + rpc_layer or self.rpc_layer) + + return '' @property def environment(self): -- GitLab From c25282f9e610479586c36b8435c984ceb2530d87 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 10 Dec 2018 14:43:34 -0800 Subject: [PATCH 313/873] Adds support for arbitrarily nested `inputs` and `outputs` in `keras.backend.function`. PiperOrigin-RevId: 224886577 --- tensorflow/python/keras/backend.py | 32 +++++++++--------------- tensorflow/python/keras/backend_test.py | 33 +++++++++++++++++++++++++ 2 files changed, 45 insertions(+), 20 deletions(-) diff --git a/tensorflow/python/keras/backend.py b/tensorflow/python/keras/backend.py index 420c457a0c..381e0ae3e3 100644 --- a/tensorflow/python/keras/backend.py +++ b/tensorflow/python/keras/backend.py @@ -2926,17 +2926,12 @@ class GraphExecutionFunction(object): def __init__(self, inputs, outputs, updates=None, name=None, **session_kwargs): updates = updates or [] - if not isinstance(inputs, (list, tuple)): - raise TypeError('`inputs` to a Keras backend function ' - 'should be a list or tuple.') - if not isinstance(outputs, (list, tuple)): - raise TypeError('`outputs` of a Keras backend function ' - 'should be a list or tuple.') if not isinstance(updates, (list, tuple)): raise TypeError('`updates` in a Keras backend function ' 'should be a list or tuple.') - self.inputs = list(inputs) - self.outputs = list(outputs) + self.inputs = nest.flatten(inputs) + self._outputs_structure = outputs + self.outputs = nest.flatten(outputs) with ops.control_dependencies(self.outputs): updates_ops = [] for update in updates: @@ -3033,8 +3028,7 @@ class GraphExecutionFunction(object): self.fetch_callbacks[fetch](output) def __call__(self, inputs): - if not isinstance(inputs, (list, tuple)): - raise TypeError('`inputs` should be a list or tuple.') + inputs = nest.flatten(inputs) session = get_session() feed_arrays = [] @@ -3077,7 +3071,8 @@ class GraphExecutionFunction(object): fetched = self._callable_fn(*array_vals, run_metadata=self.run_metadata) self._call_fetch_callbacks(fetched[-len(self._fetches):]) - return fetched[:len(self.outputs)] + return nest.pack_sequence_as(self._outputs_structure, + fetched[:len(self.outputs)]) class EagerExecutionFunction(object): @@ -3093,17 +3088,12 @@ class EagerExecutionFunction(object): def __init__(self, inputs, outputs, updates=None, name=None): updates = updates or [] - if not isinstance(inputs, (list, tuple)): - raise TypeError('`inputs` to a Keras backend function ' - 'should be a list or tuple.') - if not isinstance(outputs, (list, tuple)): - raise TypeError('`outputs` of a Keras backend function ' - 'should be a list or tuple.') if not isinstance(updates, (list, tuple)): raise TypeError('`updates` in a Keras backend function ' 'should be a list or tuple.') - self.inputs = list(inputs) - self.outputs = list(outputs) + self.inputs = nest.flatten(inputs) + self._outputs_structure = outputs + self.outputs = nest.flatten(outputs) self.name = name graph = get_graph() @@ -3153,6 +3143,7 @@ class EagerExecutionFunction(object): x.op.inputs[0]) def __call__(self, inputs): + inputs = nest.flatten(inputs) converted_inputs = [] for tensor, value in zip(self.inputs, inputs): if value is None: @@ -3169,7 +3160,8 @@ class EagerExecutionFunction(object): value = math_ops.cast(value, tensor.dtype) converted_inputs.append(value) outputs = self._graph_fn(*converted_inputs) - return [x.numpy() for x in outputs] + return nest.pack_sequence_as(self._outputs_structure, + [x.numpy() for x in outputs]) @tf_export('keras.backend.function') diff --git a/tensorflow/python/keras/backend_test.py b/tensorflow/python/keras/backend_test.py index af01b46fa9..4b83f0bf66 100644 --- a/tensorflow/python/keras/backend_test.py +++ b/tensorflow/python/keras/backend_test.py @@ -1695,6 +1695,39 @@ class BackendGraphTests(test.TestCase): self.assertEqual(callback.times_called, 1) self.assertEqual(callback.callback_result, 200) + @test_util.run_in_graph_and_eager_modes + def test_function_dict_outputs(self): + x_ph = keras.backend.placeholder(shape=(), name='x') + y_ph = keras.backend.placeholder(shape=(), name='y') + outputs = {'x*y': y_ph * x_ph, 'x*x': x_ph * x_ph} + + f = keras.backend.function(inputs=[x_ph, y_ph], outputs=outputs) + x, y = 2., 5. + results = f([x, y]) + + self.assertEqual(results['x*y'], 10.) + self.assertEqual(results['x*x'], 4) + + @test_util.run_in_graph_and_eager_modes + def test_function_dict_inputs(self): + placeholders = { + 'x': keras.backend.placeholder(shape=()), + 'y': keras.backend.placeholder(shape=()) + } + outputs = [placeholders['x'] * placeholders['y']] + + f = keras.backend.function(inputs=placeholders, outputs=outputs) + results = f({'x': 2., 'y': 3.}) + self.assertEqual(results[0], 6.) + + @test_util.run_in_graph_and_eager_modes + def test_function_single_input_output(self): + x_ph = keras.backend.placeholder(shape=(), name='x') + output = x_ph * x_ph + f = keras.backend.function(x_ph, output) + result = f(2.) + self.assertEqual(result, 4.) + def test_placeholder(self): x = keras.backend.placeholder(shape=(3, 4)) self.assertEqual(x.get_shape().as_list(), [3, 4]) -- GitLab From a571aba264f9cc2e8273a4411b193229efce34cb Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 10 Dec 2018 15:07:05 -0800 Subject: [PATCH 314/873] Internal Change PiperOrigin-RevId: 224891138 --- .../ops/ragged/ragged_map_flat_values_op_test.py | 15 ++++++--------- tensorflow/python/ops/ragged/ragged_util_test.py | 6 ++---- 2 files changed, 8 insertions(+), 13 deletions(-) diff --git a/tensorflow/python/ops/ragged/ragged_map_flat_values_op_test.py b/tensorflow/python/ops/ragged/ragged_map_flat_values_op_test.py index 8b28cac99d..45e60ff492 100644 --- a/tensorflow/python/ops/ragged/ragged_map_flat_values_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_map_flat_values_op_test.py @@ -39,8 +39,7 @@ class RaggedMapInnerValuesOpTest(ragged_test_util.RaggedTensorTestCase): kwargs=None): kwargs = kwargs or {} result = ragged.map_flat_values(op, *args, **kwargs) - with self.test_session(): - self.assertRaggedEqual(result, expected) + self.assertRaggedEqual(result, expected) def testDocStringExamples(self): """Test the examples in apply_op_to_ragged_values.__doc__.""" @@ -48,10 +47,9 @@ class RaggedMapInnerValuesOpTest(ragged_test_util.RaggedTensorTestCase): v1 = ragged.map_flat_values(array_ops.ones_like, rt) v2 = ragged.map_flat_values(math_ops.multiply, rt, rt) v3 = ragged.map_flat_values(math_ops.add, rt, 5) - with self.test_session(): - self.assertRaggedEqual(v1, [[1, 1, 1], [], [1, 1], [1]]) - self.assertRaggedEqual(v2, [[1, 4, 9], [], [16, 25], [36]]) - self.assertRaggedEqual(v3, [[6, 7, 8], [], [9, 10], [11]]) + self.assertRaggedEqual(v1, [[1, 1, 1], [], [1, 1], [1]]) + self.assertRaggedEqual(v2, [[1, 4, 9], [], [16, 25], [36]]) + self.assertRaggedEqual(v3, [[6, 7, 8], [], [9, 10], [11]]) def testOpWithSingleRaggedTensorArg(self): tensor = ragged.constant([[1, 2, 3], [], [4, 5]]) @@ -122,9 +120,8 @@ class RaggedMapInnerValuesOpTest(ragged_test_util.RaggedTensorTestCase): # ragged_rank=0 x0 = [3, 1, 4, 1, 5, 9, 2, 6, 5] y0 = [1, 2, 3, 4, 5, 6, 7, 8, 9] - with self.test_session(): - self.assertRaggedEqual( - math_ops.multiply(x0, y0), [3, 2, 12, 4, 25, 54, 14, 48, 45]) + self.assertRaggedEqual( + math_ops.multiply(x0, y0), [3, 2, 12, 4, 25, 54, 14, 48, 45]) # ragged_rank=1 x1 = ragged.constant([[3, 1, 4], [], [1, 5], [9, 2], [6, 5]]) diff --git a/tensorflow/python/ops/ragged/ragged_util_test.py b/tensorflow/python/ops/ragged/ragged_util_test.py index 72a4155930..ab5436a91c 100644 --- a/tensorflow/python/ops/ragged/ragged_util_test.py +++ b/tensorflow/python/ops/ragged/ragged_util_test.py @@ -92,8 +92,7 @@ class RaggedUtilTest(ragged_test_util.RaggedTensorTestCase, ]) def testRepeat(self, data, repeats, expected, axis=None): result = ragged_util.repeat(data, repeats, axis) - with self.test_session(): - self.assertAllEqual(result, expected) + self.assertAllEqual(result, expected) @parameterized.parameters([ dict(mode=mode, **args) @@ -158,8 +157,7 @@ class RaggedUtilTest(ragged_test_util.RaggedTensorTestCase, repeats = array_ops.placeholder_with_default(repeats, None) result = ragged_util.repeat(data, repeats, axis) - with self.test_session(): - self.assertAllEqual(result, expected) + self.assertAllEqual(result, expected) @parameterized.parameters([ dict( -- GitLab From f1ad9aa9a1a01130577190611f73f23478f6563a Mon Sep 17 00:00:00 2001 From: Alexandre Passos Date: Mon, 10 Dec 2018 15:21:57 -0800 Subject: [PATCH 315/873] Fixes build broken on mac compilers PiperOrigin-RevId: 224893836 --- tensorflow/core/kernels/training_op_helpers.h | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/core/kernels/training_op_helpers.h b/tensorflow/core/kernels/training_op_helpers.h index e96cd023fc..98e2b3c0f2 100644 --- a/tensorflow/core/kernels/training_op_helpers.h +++ b/tensorflow/core/kernels/training_op_helpers.h @@ -113,7 +113,8 @@ mutex* GetTrainingVariableMutex(OpKernelContext* ctx, int input, bool sparse, if (ctx->input_dtype(input) == DT_RESOURCE) { if (LookupResource(ctx, HandleFromInput(ctx, input), maybe_resource).ok()) { if (sparse) { - EnsureSparseVariableAccess(ctx, *maybe_resource); + EnsureSparseVariableAccess(ctx, *maybe_resource) + .IgnoreError(); } return (*maybe_resource)->mu(); } else { -- GitLab From 512f0fa92d146b712df9551c7ab507c488abd033 Mon Sep 17 00:00:00 2001 From: Russell Power Date: Mon, 10 Dec 2018 15:23:13 -0800 Subject: [PATCH 316/873] Add bfloat16 support to TileOp. PiperOrigin-RevId: 224894043 --- tensorflow/core/kernels/tile_ops.cc | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/core/kernels/tile_ops.cc b/tensorflow/core/kernels/tile_ops.cc index d714876bda..b9b37612ad 100644 --- a/tensorflow/core/kernels/tile_ops.cc +++ b/tensorflow/core/kernels/tile_ops.cc @@ -325,6 +325,7 @@ class TileGradientOp : public OpKernel { TF_CALL_int16(HANDLE_TYPE_NAME); TF_CALL_int64(HANDLE_TYPE_NAME); TF_CALL_half(HANDLE_TYPE_NAME); + TF_CALL_bfloat16(HANDLE_TYPE_NAME); TF_CALL_complex64(HANDLE_TYPE_NAME); TF_CALL_complex128(HANDLE_TYPE_NAME); -- GitLab From e943b2a6b2bffc925db1d37217696793da6131b2 Mon Sep 17 00:00:00 2001 From: Alexandre Passos Date: Mon, 10 Dec 2018 15:28:19 -0800 Subject: [PATCH 317/873] Automated rollback of commit df74b804064bd16e1fe4aed2940c5f536c993dfc PiperOrigin-RevId: 224894987 --- tensorflow/python/eager/function_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/eager/function_test.py b/tensorflow/python/eager/function_test.py index 50d1b4b6f7..8d1f8c21d9 100644 --- a/tensorflow/python/eager/function_test.py +++ b/tensorflow/python/eager/function_test.py @@ -544,7 +544,7 @@ class FunctionTest(test.TestCase, parameterized.TestCase): self.assertIsInstance( self.v, resource_variable_ops.ResourceVariable) - def disabled_testRunMetadata(self): + def testRunMetadata(self): @def_function.function def f(x): -- GitLab From 7ad28a7ee82f93ff1dd53b60798e603125ae541a Mon Sep 17 00:00:00 2001 From: Trevor Morris Date: Mon, 10 Dec 2018 15:40:06 -0800 Subject: [PATCH 318/873] Add validators for pooling, BN, Conv, Pad, Concat ops --- .../contrib/tensorrt/convert/convert_nodes.cc | 107 ++++++++++++------ 1 file changed, 72 insertions(+), 35 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 777a80bbc4..18e8599a01 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -1533,6 +1533,24 @@ enum class ConvolutionType { DEFAULT, DEPTHWISE_CONV }; tensorflow::Status ConvertConv2DHelper(OpConverterParams* params, int group) { const auto& inputs = params->inputs; const auto& node_def = params->node_def; + if (inputs.at(0).is_weights()) { + return tensorflow::errors::Unimplemented( + node_def.op(), " is only implemented for tensors, not weights, at ", + node_def.name()); + } + if (inputs.at(1).is_tensor()) { + return tensorflow::errors::Unimplemented( + "Kernel for ", node_def.op(), " must be constant weights, at ", + node_def.name()); + } + TRT_ShapedWeights weights_rsck = inputs.at(1).weights(); + VLOG(2) << "weight shape: " << weights_rsck.DebugString(); + if (weights_rsck.shape_.nbDims != 4) { + return tensorflow::errors::Internal( + "Conv2D expects kernel of dimension 4, at: " + node_def.name()); + } + if (params->validation_only) return tensorflow::Status::OK(); + const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); TFAttrs attrs(node_def); @@ -1554,12 +1572,6 @@ tensorflow::Status ConvertConv2DHelper(OpConverterParams* params, int group) { if (num_groups == 0) num_groups = tensor_dim.d[0]; // depthwise convolution VLOG(2) << "groups count: " << num_groups; - TRT_ShapedWeights weights_rsck = inputs.at(1).weights(); - VLOG(2) << "weight shape: " << weights_rsck.DebugString(); - if (weights_rsck.shape_.nbDims != 4) { - return tensorflow::errors::Internal( - "Conv2D expects kernel of dimension 4, at: " + node_def.name()); - } if (params->converter->precision_mode() == FP16MODE) { weights_rsck = ConvertFP32ToFP16(params->weight_store, inputs.at(1).weights()); @@ -2027,9 +2039,31 @@ tensorflow::Status ConvertConv2DDepthwise(OpConverterParams* params) { tensorflow::Status ConvertPool(OpConverterParams* params) { const auto& inputs = params->inputs; const auto& node_def = params->node_def; - const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); + if (inputs.at(0).is_weights()) { + return tensorflow::errors::Unimplemented( + node_def.op(), " is only implemented for tensors, not weights, at ", + node_def.name()); + } + nvinfer1::PoolingType type; + if (node_def.op() == "MaxPool") { + type = nvinfer1::PoolingType::kMAX; + } else if (node_def.op() == "AvgPool") { + type = nvinfer1::PoolingType::kAVERAGE; + } else { + return tensorflow::errors::Unimplemented("Unsupported pooling type: ", + node_def.op(), ", at ", + node_def.name()); + } TFAttrs attrs(node_def); + const string padding_type = attrs.get("padding"); + if ((padding_type != "SAME") && (padding_type != "VALID")) { + return tensorflow::errors::Unimplemented("Unsupported padding type: ", + padding_type, ", at ", + node_def.name()); + } + if (params->validation_only) return Status::OK(); + const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); int h_index = 2; int w_index = 3; const auto data_format = attrs.get("data_format"); @@ -2040,16 +2074,6 @@ tensorflow::Status ConvertPool(OpConverterParams* params) { const_cast(tensor), {0, 3, 1, 2}, &tensor)); } - nvinfer1::PoolingType type; - if (node_def.op() == "MaxPool") { - type = nvinfer1::PoolingType::kMAX; - } else if (node_def.op() == "AvgPool") { - type = nvinfer1::PoolingType::kAVERAGE; - } else { - return tensorflow::errors::Unimplemented("Unsupported pool type: ", - node_def.op()); - } - const auto tf_stride = attrs.get>("strides"); const nvinfer1::DimsHW stride(tf_stride[h_index], tf_stride[w_index]); @@ -2058,7 +2082,6 @@ tensorflow::Status ConvertPool(OpConverterParams* params) { auto tensor_dim = tensor->getDimensions(); std::vector> padding; - const string padding_type = attrs.get("padding"); if (padding_type == "SAME") { // This is NCHW tensor with no batch dimension. // 1 -> h @@ -2068,9 +2091,6 @@ tensorflow::Status ConvertPool(OpConverterParams* params) { {static_cast(tensor_dim.d[1]), static_cast(tensor_dim.d[2])}); } else if (padding_type == "VALID") { padding = {{0, 0}, {0, 0}}; - } else { - return tensorflow::errors::Unimplemented("Unsupported padding type: ", - padding_type); } if (padding[0].first != padding[0].second || @@ -2837,6 +2857,7 @@ tensorflow::Status ConvertPad(OpConverterParams* params) { return tensorflow::errors::Unimplemented( "Padding layer does not support padding on dimension 1 and 3 yet"); } + if (params->validation_only) return Status::OK(); bool legit_pad = true; nvinfer1::DimsHW pre_padding(0, 0); @@ -2940,6 +2961,7 @@ tensorflow::Status ConvertConcat(OpConverterParams* params) { inputs_vec.push_back(tensor_i); } + if (params->validation_only) return tensorflow::Status::OK(); // nvinfer1::ITensor const* tensor = inputs.at(0).tensor(); nvinfer1::IConcatenationLayer* layer = @@ -2961,12 +2983,26 @@ tensorflow::Status ConvertFusedBatchNorm(OpConverterParams* params) { auto data_format = attrs.get("data_format"); if (data_format != "NCHW") { return tensorflow::errors::Unimplemented( - "only data_format=NCHW is supported, at " + node_def.name()); + node_def.op(), " only supports data_format=NCHW, at ", node_def.name()); } bool is_training = attrs.get("is_training"); if (is_training) { return tensorflow::errors::Unimplemented( - "only is_training=false is supported, at " + node_def.name()); + node_def.op(), " only supports is_training=false. If you are using " + "Keras, please use keras.backend.set_learning_phase(0). At ", + node_def.name()); + } + if (inputs.at(0).is_weights()) { + return tensorflow::errors::Unimplemented( + node_def.op(), " is only implemented for tensor inputs, not weights, " + "at ", node_def.name()); + } + for (int i = 1; i < 5; i++) { + if (inputs.at(i).is_tensor()) { + return tensorflow::errors::Unimplemented( + node_def.op(), " must have constant inputs for scale, offset, mean " + "and variance, at ", node_def.name()); + } } nvinfer1::ITensor const* tensor = inputs.at(0).tensor(); @@ -2981,7 +3017,7 @@ tensorflow::Status ConvertFusedBatchNorm(OpConverterParams* params) { for (int i = 1; i < 5; i++) { if (inputs.at(i).weights().type_ != parameter_type) { return tensorflow::errors::Unimplemented( - "Inconsistent parameter type for batchnormis not supported, at: " + + "Inconsistent parameter type for batchnorm is not supported, at: " + node_def.name()); } } @@ -3001,6 +3037,8 @@ tensorflow::Status ConvertFusedBatchNorm(OpConverterParams* params) { "Inconsistent batchnorm parameter count, at: " + node_def.name()); } } + if (params->validation_only) return Status::OK(); + // We could technically have two weights with different shape. // that requires two addScale op, arguably less performant TRT_ShapedWeights combined_scale_weights = @@ -3286,10 +3324,14 @@ static void RegisterValidatableOpConverters( std::unordered_map* registration) { // TODO(laigd): support all op types. (*registration)["BiasAdd"] = ConvertBiasAdd; + (*registration)["ConcatV2"] = ConvertConcat; (*registration)["Const"] = ConvertConst; + (*registration)["Conv2D"] = ConvertConv2D; + (*registration)["DepthwiseConv2dNative"] = ConvertConv2DDepthwise; (*registration)["Transpose"] = ConvertTranspose; (*registration)["Reshape"] = ConvertReshape; (*registration)["MatMul"] = ConvertMatMul; + (*registration)["Pad"] = ConvertPad; (*registration)["Relu6"] = ConvertRelu6; (*registration)["Square"] = ConvertSquare; (*registration)["ExpandDims"] = ConvertExpandDims; @@ -3307,6 +3349,12 @@ static void RegisterValidatableOpConverters( for (auto activation_op_type : {"Relu", "Sigmoid", "Tanh"}) { (*registration)[activation_op_type] = ConvertActivation; } + for (auto pool_op_type : {"AvgPool", "MaxPool"}) { + (*registration)[pool_op_type] = ConvertPool; + } + for (auto normalization_op_type : {"FusedBatchNorm", "FusedBatchNormV2"}) { + (*registration)[normalization_op_type] = ConvertFusedBatchNorm; + } } void TrtNodeValidator::RegisterOpValidators() { @@ -3315,21 +3363,10 @@ void TrtNodeValidator::RegisterOpValidators() { void Converter::RegisterOpConverters() { RegisterValidatableOpConverters(&op_registry_); - - op_registry_["Conv2D"] = ConvertConv2D; - op_registry_["DepthwiseConv2dNative"] = ConvertConv2DDepthwise; - op_registry_["MaxPool"] = ConvertPool; - op_registry_["AvgPool"] = ConvertPool; // TODO(ben,jie): this is a temp hack. op_registry_["Identity"] = ConvertIdentity; // Identity should be removed op_registry_["Snapshot"] = ConvertIdentity; // Snapshot should be removed - op_registry_["Pad"] = ConvertPad; - - op_registry_["ConcatV2"] = ConvertConcat; - op_registry_["FusedBatchNorm"] = ConvertFusedBatchNorm; - op_registry_["FusedBatchNormV2"] = ConvertFusedBatchNorm; - op_registry_["Rsqrt"] = ConvertUnary; op_registry_["Reciprocal"] = ConvertUnary; op_registry_["Exp"] = ConvertUnary; -- GitLab From e330f959df527156a40c86360151ec555c08f4ba Mon Sep 17 00:00:00 2001 From: Trevor Morris Date: Mon, 10 Dec 2018 15:40:27 -0800 Subject: [PATCH 319/873] Fix test --- tensorflow/contrib/tensorrt/test/quantization_mnist_test.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/tensorrt/test/quantization_mnist_test.py b/tensorflow/contrib/tensorrt/test/quantization_mnist_test.py index 31cbef89e2..b96d965bad 100644 --- a/tensorflow/contrib/tensorrt/test/quantization_mnist_test.py +++ b/tensorflow/contrib/tensorrt/test/quantization_mnist_test.py @@ -24,6 +24,7 @@ from tensorflow.contrib.tensorrt.python.ops import trt_engine_op # pylint: enable=unused-import from tensorflow.core.protobuf import config_pb2 from tensorflow.python import data +#from tensorflow.python.data.ops import dataset_ops from tensorflow.python import keras from tensorflow.python.estimator.estimator import Estimator from tensorflow.python.estimator.model_fn import EstimatorSpec @@ -191,7 +192,7 @@ class QuantizationAwareTrainingMNISTTest(test_util.TensorFlowTestCase): batch_size=batch_size, num_parallel_calls=8)) dataset = dataset.repeat(count=1) - iterator = data.make_one_shot_iterator(dataset) + iterator = dataset.make_one_shot_iterator() features, labels = iterator.get_next() return features, labels @@ -205,7 +206,7 @@ class QuantizationAwareTrainingMNISTTest(test_util.TensorFlowTestCase): batch_size=batch_size, num_parallel_calls=8)) dataset = dataset.repeat(count=num_epochs) - iterator = data.make_one_shot_iterator(dataset) + iterator = dataset.make_one_shot_iterator() features, labels = iterator.get_next() return features, labels -- GitLab From 2ab06b48d160fcd41d4d51d38b7d2cf7902790bc Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 10 Dec 2018 15:39:17 -0800 Subject: [PATCH 320/873] Fix GitHub link. PiperOrigin-RevId: 224897071 --- tensorflow/lite/g3doc/convert/cmdline_examples.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/tensorflow/lite/g3doc/convert/cmdline_examples.md b/tensorflow/lite/g3doc/convert/cmdline_examples.md index de81e2cfdd..169f2d91d8 100644 --- a/tensorflow/lite/g3doc/convert/cmdline_examples.md +++ b/tensorflow/lite/g3doc/convert/cmdline_examples.md @@ -95,11 +95,11 @@ tflite_convert \ The TensorFlow Lite Converter is compatible with fixed point quantization models described -[here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/q -uantize/README.md). These are float models with `FakeQuant*` ops inserted at the -boundaries of fused layers to record min-max range information. This generates a -quantized inference workload that reproduces the quantization behavior that was -used during training. +[here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/quantize/README.md). +These are float models with `FakeQuant*` ops inserted at the boundaries of fused +layers to record min-max range information. This generates a quantized inference +workload that reproduces the quantization behavior that was used during +training. The following command generates a quantized TensorFlow Lite FlatBuffer from a "quantized" TensorFlow GraphDef. -- GitLab From cb3cb1ef838ddea1fcfc259b51b3702e80743277 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Mon, 10 Dec 2018 15:39:48 -0800 Subject: [PATCH 321/873] Rename {For|If}ReturnVoid to {For|If} Returning void is more common than returning Status so pick the longer name for the less common variant. PiperOrigin-RevId: 224897169 --- .../xla/service/cpu/dot_op_emitter.cc | 57 +++-- .../xla/service/gpu/ir_emitter_unnested.cc | 74 +++---- .../service/llvm_ir/kernel_support_library.cc | 23 +- .../service/llvm_ir/kernel_support_library.h | 205 +++++++++--------- .../compiler/xla/service/llvm_ir/sort_util.cc | 25 +-- 5 files changed, 193 insertions(+), 191 deletions(-) diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc index 97f9b85a60..a33035ad10 100644 --- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc @@ -323,11 +323,11 @@ void ColumnMajorMatrixVectorProductEmitter::Emit() { int64 column_remainder = k() % tile_cols(); int64 column_limit = k() - column_remainder; - ksl_.ForReturnVoid("dot.outer.tiled", - /*start=*/0, /*end=*/column_limit, /*step=*/tile_cols(), - [&](llvm::Value* column, bool is_first_column) { - EmitOuterLoopBody(column, tile_cols(), is_first_column); - }); + ksl_.For("dot.outer.tiled", + /*start=*/0, /*end=*/column_limit, /*step=*/tile_cols(), + [&](llvm::Value* column, bool is_first_column) { + EmitOuterLoopBody(column, tile_cols(), is_first_column); + }); if (column_remainder != 0) { EmitOuterLoopBody(b_->getInt64(column_limit), column_remainder, @@ -340,7 +340,7 @@ void ColumnMajorMatrixVectorProductEmitter::EmitInnerLoopTiled( int64 columns, bool is_first_column) { int64 row_limit = m() - (m() % tile_rows()); - ksl_.ForReturnVoid( + ksl_.For( "dot.inner.tiled", /*start=*/0, /*end=*/row_limit, /*step=*/tile_rows(), [&](llvm::Value* row) { std::vector lhs_tile = @@ -372,7 +372,7 @@ void ColumnMajorMatrixVectorProductEmitter::EmitInnerLoopEpilogue( // // initialized. // } - ksl_.ForReturnVoid( + ksl_.For( "dot.inner.epilg.outer", /*start=*/current_tile_col, /*end=*/b_->CreateAdd(columns_llvm, current_tile_col), /*step=*/1, /*peel_first_iteration=*/false, @@ -381,14 +381,14 @@ void ColumnMajorMatrixVectorProductEmitter::EmitInnerLoopEpilogue( llvm::Value* total_offset = b_->CreateMul(col, b_->getInt64(m())); llvm::Value* lhs_base_pointer = vsl_.ComputeOffsetPointer(lhs_, total_offset); - ksl_.ForReturnVoid( + ksl_.For( "dot.inner.epilg.inner", /*start=*/row_start, /*end=*/m(), /*step=*/1, [&](llvm::Value* scalar_row) { llvm::Value* product = vsl_.Mul( vsl_.LoadScalar(lhs_base_pointer, scalar_row), rhs_element); llvm::Value* setting_result_first_time = b_->CreateAnd( is_first_scalar_col, b_->getInt1(is_first_tiled_column)); - ksl_.IfReturnVoid( + ksl_.If( setting_result_first_time, /*true_block_generator=*/ [&]() { @@ -568,10 +568,9 @@ void RowMajorMatrixVectorProductEmitter::Emit() { int64 row_remainder = m() % tile_rows(); int64 row_limit = m() - row_remainder; - ksl_.ForReturnVoid( - "dot.outer.tiled", - /*start=*/0, /*end=*/row_limit, /*step=*/tile_rows(), - [&](llvm::Value* row) { EmitOuterLoopBody(row, tile_rows()); }); + ksl_.For("dot.outer.tiled", + /*start=*/0, /*end=*/row_limit, /*step=*/tile_rows(), + [&](llvm::Value* row) { EmitOuterLoopBody(row, tile_rows()); }); if (row_remainder != 0) { EmitOuterLoopBody(b_->getInt64(row_limit), row_remainder); @@ -583,17 +582,17 @@ void RowMajorMatrixVectorProductEmitter::EmitInnerLoopTiled( std::vector* vector_accumulators) { int64 column_limit = k() - (k() % tile_cols()); - ksl_.ForReturnVoid("dot.inner.tiled", /*start=*/0, /*end=*/column_limit, - /*step=*/tile_cols(), [&](llvm::Value* col) { - std::vector lhs_tile = - lhs_memory_tile->LoadTile(/*minor_dim_offset=*/col); - llvm::Value* rhs_value = vsl_.LoadVector(rhs_, col); - for (int i = 0; i < rows; i++) { - llvm::Value* old_sum = (*vector_accumulators)[i].Get(); - (*vector_accumulators)[i].Set(vsl_.Add( - old_sum, vsl_.Mul(rhs_value, lhs_tile[i]))); - } - }); + ksl_.For("dot.inner.tiled", /*start=*/0, /*end=*/column_limit, + /*step=*/tile_cols(), [&](llvm::Value* col) { + std::vector lhs_tile = + lhs_memory_tile->LoadTile(/*minor_dim_offset=*/col); + llvm::Value* rhs_value = vsl_.LoadVector(rhs_, col); + for (int i = 0; i < rows; i++) { + llvm::Value* old_sum = (*vector_accumulators)[i].Get(); + (*vector_accumulators)[i].Set( + vsl_.Add(old_sum, vsl_.Mul(rhs_value, lhs_tile[i]))); + } + }); } void RowMajorMatrixVectorProductEmitter::EmitInnerLoopEpilogue( @@ -609,7 +608,7 @@ void RowMajorMatrixVectorProductEmitter::EmitInnerLoopEpilogue( b_->CreateAdd(b_->getInt64(r), current_tile_row), b_->getInt64(k())); llvm::Value* lhs_base_pointer = vsl_.ComputeOffsetPointer(lhs_, total_offset); - ksl_.ForReturnVoid( + ksl_.For( "dot.inner.epilg.inner", /*start=*/column_start, /*end=*/k(), /*step=*/1, [&](llvm::Value* scalar_col) { llvm::Value* product = @@ -813,7 +812,7 @@ void TiledSmallGemmEmitter::HandleResiduesOnN() { if (n_start != dims().n()) { VectorSupportLibrary vsl(scalar_type(), 1, b_, "gemm"); - ksl_.ForReturnVoid("epi.n", n_start, dims().n(), 1, [&](llvm::Value* n_i) { + ksl_.For("epi.n", n_start, dims().n(), 1, [&](llvm::Value* n_i) { llvm::Value* n_i_next = b_->CreateAdd(n_i, b_->getInt64(1)); HandleResiduesOnK(&vsl, n_i, n_i_next); }); @@ -924,7 +923,7 @@ void TiledSmallGemmEmitter::EmitTiledGemm( VectorSupportLibrary* vsl, int64 tile_size_k, llvm::Value* k_start, llvm::Value* k_end, llvm::Value* n_start, llvm::Value* n_end, int64 tile_size_m, llvm::Value* m_start, llvm::Value* m_end) { - ksl_.ForReturnVoid( + ksl_.For( "dot.m", m_start, m_end, tile_size_m, [&](llvm::Value* m_i) { MemoryTile result_memory_tile( vsl, b_, /*matrix=*/result_, @@ -935,11 +934,11 @@ void TiledSmallGemmEmitter::EmitTiledGemm( /*matrix_size_along_minor_dim=*/dims().k(), /*major_dim_offset=*/m_i, /*tile_size_along_major_dim=*/tile_size_m); - ksl_.ForReturnVoid( + ksl_.For( "dot.n", n_start, n_end, vsl->vector_size(), [&](llvm::Value* n_i) { TileVariable result_tile_var(vsl, result_memory_tile.LoadTile(n_i)); - ksl_.ForReturnVoid( + ksl_.For( "dot.k", k_start, k_end, tile_size_k, [&](llvm::Value* k_i) { MemoryTile rhs_memory_tile(vsl, b_, rhs_, dims().n(), k_i, tile_size_k); diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index fb040aff30..c8b5343e61 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -1389,7 +1389,7 @@ Status IrEmitterUnnested::EmitRowReduction( auto emit_z_tile_element_loop = [&](llvm::Value* z_indvar) -> Status { llvm::Value* z = NSWAdd(z_indvar, NSWMul(index_typed_constant(z_tile_size), z_tile)); - TF_RETURN_IF_ERROR(ksl.For( + TF_RETURN_IF_ERROR(ksl.ForWithStatus( "x_tile", /*start=*/index_typed_constant(0), /*end=*/index_typed_constant(x_tile_loop_bound), @@ -1461,29 +1461,29 @@ Status IrEmitterUnnested::EmitRowReduction( return Status::OK(); }; - return ksl.For("z_tile", - /*start=*/index_typed_constant(0), - /*end=*/index_typed_constant(z_tile_size), - /*step=*/1, emit_z_tile_element_loop); + return ksl.ForWithStatus("z_tile", + /*start=*/index_typed_constant(0), + /*end=*/index_typed_constant(z_tile_size), + /*step=*/1, emit_z_tile_element_loop); }; llvm::Value* tile_in_bounds = Or(b_.getInt1(width % (x_tile_size * kWarpSize) == 0), ICmpULT(last_x, index_typed_constant(width))); - TF_RETURN_IF_ERROR( - ksl.If(tile_in_bounds, - /*true_block_generator=*/ - [&]() -> Status { - return emit_z_x_tile_element_loop(/*x_tile_in_bounds=*/true, - x_tile_size); - }, - /*false_block_generator=*/ - [&]() -> Status { - return emit_z_x_tile_element_loop( - /*x_tile_in_bounds=*/false, - CeilOfRatio(width % (x_tile_size * kWarpSize), kWarpSize)); - })); + TF_RETURN_IF_ERROR(ksl.IfWithStatus( + tile_in_bounds, + /*true_block_generator=*/ + [&]() -> Status { + return emit_z_x_tile_element_loop(/*x_tile_in_bounds=*/true, + x_tile_size); + }, + /*false_block_generator=*/ + [&]() -> Status { + return emit_z_x_tile_element_loop( + /*x_tile_in_bounds=*/false, + CeilOfRatio(width % (x_tile_size * kWarpSize), kWarpSize)); + })); // After accumulating the elements of the z_x_tile, emit calls to // shfl_down that accumulate the partial reduction results of all @@ -3121,11 +3121,9 @@ Status IrEmitterUnnested::EmitTargetElementLoopInThunk( // pressure, since we touch threadIdx.x and blockIdx.x at the beginning of the // kernel *anyway*. std::vector output_arrays = ConstructIrArrayForOutputs(hlo); - TF_RETURN_IF_ERROR( - KernelSupportLibrary(&b_).If("emit_mof_tuple", IsBlock0Thread0(&b_), [&] { - llvm_ir::EmitTuple(GetIrArray(hlo, hlo), output_arrays, &b_, module_); - return Status::OK(); - })); + KernelSupportLibrary{&b_}.If("emit_mof_tuple", IsBlock0Thread0(&b_), [&] { + llvm_ir::EmitTuple(GetIrArray(hlo, hlo), output_arrays, &b_, module_); + }); // For multioutput fusion, we need to emit each operand and the root. TF_RETURN_IF_ERROR( @@ -3241,7 +3239,7 @@ void EmitPartialTile( llvm::Value* x_loc = builder->CreateAdd(llvm::ConstantInt::get(index_ty, j), x); - ksl->IfReturnVoid( + ksl->If( "x_in_tile", builder->CreateICmpULT(x_loc, tile_width), [&] { // tile_height_bound = // ceil(tile_height / num_threads_y) * num_threads_y @@ -3252,13 +3250,13 @@ void EmitPartialTile( llvm::Value* tile_height_bound = builder->CreateMul( ceiling_of_ratio, llvm::ConstantInt::get(index_ty, num_threads_y)); - ksl->ForReturnVoid( + ksl->For( loop_name, /*start=*/llvm::ConstantInt::get(index_ty, 0), /*end=*/tile_height_bound, /*step=*/llvm::ConstantInt::get(index_ty, num_threads_y), [&](llvm::Value* y_indvar) { llvm::Value* y_loc = builder->CreateAdd(y_indvar, y); - ksl->IfReturnVoid( + ksl->If( "y_in_tile", builder->CreateICmpULT(y_loc, tile_height), [&] { emit_elem_function( @@ -3290,7 +3288,7 @@ void EmitTiledElementalCodeWithBoundsCheck( int64 tile_size_y = mapping_scheme->GetTileSizeForDimensionY(); llvm::Type* index_ty = tile_width->getType(); - ksl->IfReturnVoid( + ksl->If( "full_tile", builder->CreateAnd( builder->CreateICmpEQ(llvm::ConstantInt::get(index_ty, tile_size_x), @@ -3419,15 +3417,14 @@ void IrEmitterUnnested::EmitBlock(const TileGenerator& emit_one_tile, Select(ICmpEQ(last_block_for_dim, block_id_for_dim), last_block_size_for_dim, block_size_for_dim); - ksl.ForReturnVoid( - loop_name, - /*start=*/index_typed_constant(0), - /*end=*/num_tiles_in_block, - /*step=*/1, [&](llvm::Value* block_dim_induction_var) { - IrArray::Index tile_index = starting_tile.AddOffsetToDim( - block_dim_induction_var, dim_id, &b_); - emit_next_block_dim(tile_index); - }); + ksl.For(loop_name, + /*start=*/index_typed_constant(0), + /*end=*/num_tiles_in_block, + /*step=*/1, [&](llvm::Value* block_dim_induction_var) { + IrArray::Index tile_index = starting_tile.AddOffsetToDim( + block_dim_induction_var, dim_id, &b_); + emit_next_block_dim(tile_index); + }); } }; @@ -3524,13 +3521,12 @@ LaunchDimensions IrEmitterUnnested::EmitKernel( // since we touch threadIdx.x and blockIdx.x at the beginning of the kernel // *anyway*. if (unnested_hlo->IsMultiOutputFusion()) { - TF_CHECK_OK(KernelSupportLibrary(&b_).If( + KernelSupportLibrary{&b_}.If( "emit_mof_tuple", IsBlock0Thread0(&b_), [&] { llvm_ir::EmitTuple(GetIrArray(*unnested_hlo, *unnested_hlo), ConstructIrArrayForOutputs(*unnested_hlo), &b_, module_); - return Status::OK(); - })); + }); } // For each tiled parameter, cast its input IrArray to the corresponding diff --git a/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.cc b/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.cc index bd0139f85b..5eeb29c478 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.cc @@ -18,28 +18,29 @@ limitations under the License. #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" namespace xla { -Status KernelSupportLibrary::For( +Status KernelSupportLibrary::ForWithStatus( absl::string_view name, llvm::Value* start, llvm::Value* end, llvm::Value* step, const std::function& for_body_generator) { - return If(b_->CreateICmpSLT(start, end), [&]() -> Status { + return IfWithStatus(b_->CreateICmpSLT(start, end), [&]() -> Status { TF_RETURN_IF_ERROR(for_body_generator(start, /*is_first_iteration=*/true)); - return For(name, b_->CreateAdd(start, step), end, step, - [&](llvm::Value* iv) { return for_body_generator(iv, false); }); + return ForWithStatus( + name, b_->CreateAdd(start, step), end, step, + [&](llvm::Value* iv) { return for_body_generator(iv, false); }); }); } -Status KernelSupportLibrary::For( +Status KernelSupportLibrary::ForWithStatus( absl::string_view name, llvm::Value* start, llvm::Value* end, llvm::Value* step, bool peel_first_iteration, const std::function& for_body_generator) { if (peel_first_iteration) { - return For(name, start, end, step, true, - [&](llvm::Value* indvar, bool is_first_iteration) -> Status { - return for_body_generator(indvar, - b_->getInt1(is_first_iteration)); - }); + return ForWithStatus( + name, start, end, step, true, + [&](llvm::Value* indvar, bool is_first_iteration) -> Status { + return for_body_generator(indvar, b_->getInt1(is_first_iteration)); + }); } else { std::unique_ptr loop = llvm_ir::ForLoop::EmitForLoop( name, start, end, step, b_, @@ -55,7 +56,7 @@ Status KernelSupportLibrary::For( } } -Status KernelSupportLibrary::If( +Status KernelSupportLibrary::IfWithStatus( absl::string_view name, llvm::Value* condition, const std::function& true_block_generator, const std::function& false_block_generator) { 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 43fec311f1..612b839cfa 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.h +++ b/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.h @@ -48,41 +48,42 @@ class KernelSupportLibrary { // for (i64 i = `start` + `step`; i s< `end`; i += `step`) // `for_body_generator(/*ind_var=*/,i, /*is_first_iteration=*/false)`; // } - Status For( + Status ForWithStatus( absl::string_view name, llvm::Value* start, llvm::Value* end, llvm::Value* step, const std::function& for_body_generator); - void ForReturnVoid( + void For( absl::string_view name, llvm::Value* start, llvm::Value* end, llvm::Value* step, const std::function& for_body_generator) { CHECK_EQ(Status::OK(), - For(name, start, end, step, + ForWithStatus( + name, start, end, step, [&](llvm::Value* ind_var, bool is_first_iteration) -> Status { for_body_generator(ind_var, is_first_iteration); return Status::OK(); })); } - Status For(absl::string_view name, int64 start, int64 end, int64 step, - const std::function& - for_body_generator) { - return For(name, /*start=*/b_->getInt64(start), - /*end=*/b_->getInt64(end), - /*step=*/b_->getInt64(step), for_body_generator); + Status ForWithStatus( + absl::string_view name, int64 start, int64 end, int64 step, + const std::function& for_body_generator) { + return ForWithStatus(name, /*start=*/b_->getInt64(start), + /*end=*/b_->getInt64(end), + /*step=*/b_->getInt64(step), for_body_generator); } - void ForReturnVoid( + void For( absl::string_view name, int64 start, int64 end, int64 step, const std::function& for_body_generator) { - ForReturnVoid(name, /*start=*/b_->getInt64(start), - /*end=*/b_->getInt64(end), - /*step=*/b_->getInt64(step), for_body_generator); + For(name, /*start=*/b_->getInt64(start), + /*end=*/b_->getInt64(end), + /*step=*/b_->getInt64(step), for_body_generator); } // Generates the following control flow structure if `peel_first_iteration` is @@ -99,19 +100,19 @@ class KernelSupportLibrary { // for (i64 i = `start`; i s< `end`; i += `step`) // `for_body_generator(/*ind_var=*/,i, // /*is_first_iteration=*/,(i != `start`))`; - Status For(absl::string_view name, llvm::Value* start, llvm::Value* end, - llvm::Value* step, bool peel_first_iteration, - const std::function& - for_body_generator); + Status ForWithStatus( + absl::string_view name, llvm::Value* start, llvm::Value* end, + llvm::Value* step, bool peel_first_iteration, + const std::function& + for_body_generator); - void ForReturnVoid(absl::string_view name, llvm::Value* start, - llvm::Value* end, llvm::Value* step, - bool peel_first_iteration, - const std::function& - for_body_generator) { - TF_CHECK_OK(For( + void For(absl::string_view name, llvm::Value* start, llvm::Value* end, + llvm::Value* step, bool peel_first_iteration, + const std::function& + for_body_generator) { + TF_CHECK_OK(ForWithStatus( name, start, end, step, peel_first_iteration, [&](llvm::Value* ind_var, llvm::Value* is_first_iteration) -> Status { for_body_generator(ind_var, is_first_iteration); @@ -119,80 +120,81 @@ class KernelSupportLibrary { })); } - Status For(absl::string_view name, llvm::Value* start, llvm::Value* end, - int64 step, bool peel_first_iteration, - const std::function& - for_body_generator) { - return For(name, /*start=*/start, /*end=*/end, - /*step=*/llvm::ConstantInt::get(start->getType(), step), - peel_first_iteration, for_body_generator); + Status ForWithStatus( + absl::string_view name, llvm::Value* start, llvm::Value* end, int64 step, + bool peel_first_iteration, + const std::function& + for_body_generator) { + return ForWithStatus( + name, /*start=*/start, /*end=*/end, + /*step=*/llvm::ConstantInt::get(start->getType(), step), + peel_first_iteration, for_body_generator); } - void ForReturnVoid(absl::string_view name, llvm::Value* start, - llvm::Value* end, int64 step, bool peel_first_iteration, - const std::function& - for_body_generator) { - ForReturnVoid(name, /*start=*/start, /*end=*/end, - /*step=*/llvm::ConstantInt::get(start->getType(), step), - peel_first_iteration, for_body_generator); + void For(absl::string_view name, llvm::Value* start, llvm::Value* end, + int64 step, bool peel_first_iteration, + const std::function& + for_body_generator) { + For(name, /*start=*/start, /*end=*/end, + /*step=*/llvm::ConstantInt::get(start->getType(), step), + peel_first_iteration, for_body_generator); } - Status For( + Status ForWithStatus( absl::string_view name, llvm::Value* start, llvm::Value* end, llvm::Value* step, const std::function& for_body_generator) { - return For(name, start, end, step, - /*peel_first_iteration=*/false, - [&](llvm::Value* indvar, llvm::Value*) -> Status { - return for_body_generator(indvar); - }); + return ForWithStatus(name, start, end, step, + /*peel_first_iteration=*/false, + [&](llvm::Value* indvar, llvm::Value*) -> Status { + return for_body_generator(indvar); + }); } - void ForReturnVoid( + void For( absl::string_view name, llvm::Value* start, llvm::Value* end, llvm::Value* step, const std::function& for_body_generator) { - ForReturnVoid(name, start, end, step, - /*peel_first_iteration=*/false, - [&](llvm::Value* indvar, llvm::Value*) { - return for_body_generator(indvar); - }); + For(name, start, end, step, + /*peel_first_iteration=*/false, [&](llvm::Value* indvar, llvm::Value*) { + return for_body_generator(indvar); + }); } - Status For( + Status ForWithStatus( absl::string_view name, llvm::Value* start, llvm::Value* end, int64 step, const std::function& for_body_generator) { - 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); - }); + return ForWithStatus(name, start, end, + llvm::ConstantInt::get(start->getType(), step), + /*peel_first_iteration=*/false, + [&](llvm::Value* indvar, llvm::Value*) -> Status { + return for_body_generator(indvar); + }); } - void ForReturnVoid( + void For( absl::string_view name, llvm::Value* start, llvm::Value* end, int64 step, const std::function& for_body_generator) { - ForReturnVoid(name, start, end, - llvm::ConstantInt::get(start->getType(), step), - for_body_generator); + For(name, start, end, llvm::ConstantInt::get(start->getType(), step), + for_body_generator); } - Status For( + Status ForWithStatus( absl::string_view name, int64 start, int64 end, int64 step, const std::function& for_body_generator) { - return For(name, /*start=*/b_->getInt64(start), - /*end=*/b_->getInt64(end), - /*step=*/b_->getInt64(step), for_body_generator); + return ForWithStatus(name, /*start=*/b_->getInt64(start), + /*end=*/b_->getInt64(end), + /*step=*/b_->getInt64(step), for_body_generator); } - void ForReturnVoid( + void For( absl::string_view name, int64 start, int64 end, int64 step, const std::function& for_body_generator) { - ForReturnVoid(name, /*start=*/b_->getInt64(start), - /*end=*/b_->getInt64(end), - /*step=*/b_->getInt64(step), for_body_generator); + For(name, /*start=*/b_->getInt64(start), + /*end=*/b_->getInt64(end), + /*step=*/b_->getInt64(step), for_body_generator); } // Generates the following control flow structure: @@ -201,38 +203,43 @@ class KernelSupportLibrary { // `true_block_generator()`; // else // `false_block_generator()`; - Status If(absl::string_view name, llvm::Value* condition, - const std::function& true_block_generator, - const std::function& false_block_generator = - []() -> Status { return Status::OK(); }); + Status IfWithStatus( + absl::string_view name, llvm::Value* condition, + const std::function& true_block_generator, + const std::function& false_block_generator = []() -> Status { + return Status::OK(); + }); - Status If(llvm::Value* condition, - const std::function& true_block_generator, - const std::function& false_block_generator = - []() -> Status { return Status::OK(); }) { - return If("", condition, true_block_generator, false_block_generator); + Status IfWithStatus( + llvm::Value* condition, + const std::function& true_block_generator, + const std::function& false_block_generator = []() -> Status { + return Status::OK(); + }) { + return IfWithStatus("", condition, true_block_generator, + false_block_generator); } - void IfReturnVoid(llvm::Value* condition, - const std::function& true_block_generator, - const std::function& false_block_generator = []() { - }) { - IfReturnVoid("", condition, true_block_generator, false_block_generator); + void If( + llvm::Value* condition, const std::function& true_block_generator, + const std::function& false_block_generator = []() {}) { + If("", condition, true_block_generator, false_block_generator); } - void IfReturnVoid(absl::string_view name, llvm::Value* condition, - const std::function& true_block_generator, - const std::function& false_block_generator = []() { - }) { - TF_CHECK_OK(If(name, condition, - [&]() { - true_block_generator(); - return Status::OK(); - }, - [&]() { - false_block_generator(); - return Status::OK(); - })); + void If( + absl::string_view name, llvm::Value* condition, + const std::function& true_block_generator, + const std::function& false_block_generator = []() {}) { + TF_CHECK_OK(IfWithStatus( + name, condition, + [&]() { + true_block_generator(); + return Status::OK(); + }, + [&]() { + false_block_generator(); + return Status::OK(); + })); } using ArgumentVector = absl::Span; diff --git a/tensorflow/compiler/xla/service/llvm_ir/sort_util.cc b/tensorflow/compiler/xla/service/llvm_ir/sort_util.cc index e22c2173c2..6a9406bfeb 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/sort_util.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/sort_util.cc @@ -108,7 +108,7 @@ void EmitCompareLoopBody( // if (is_smaller_index && index_is_inbounds) KernelSupportLibrary ksl(b); - ksl.IfReturnVoid("smaller_comparison_index", do_comparison, [&]() { + ksl.If("smaller_comparison_index", do_comparison, [&]() { auto key1 = read_element(0, current_keys_index); auto key2 = read_element(0, compare_keys_index); auto compare_key1 = key1; @@ -155,7 +155,7 @@ void EmitCompareLoopBody( is_smaller_than = b->CreateOr( is_smaller_than, b->CreateAnd(keys_equal, index_is_smaller_than)); } - ksl.IfReturnVoid("is_smaller_than", is_smaller_than, [&]() { + ksl.If("is_smaller_than", is_smaller_than, [&]() { // Swap key1 with key2. write_element(0, current_keys_index, key2); write_element(0, compare_keys_index, key1); @@ -192,7 +192,7 @@ void EmitTiledCompareLoop( b->CreateShl(tiled_keys_index[dimension_to_sort], value_one); // We want to copy two adjacent elements. We first check whether the // first index position is within bounds. - ksl.IfReturnVoid( + ksl.If( "smaller_keys_index", b->CreateICmpSLT(current_keys_index, tiled_keys_index.GetConstantWithIndexType( @@ -203,15 +203,14 @@ void EmitTiledCompareLoop( // Increment to go the next index position. current_keys_index = b->CreateAdd(current_keys_index, value_one); // Here we check whether the next index position is within bounds. - ksl.IfReturnVoid( - "inner_smaller_keys_index", - b->CreateICmpSLT(current_keys_index, - tiled_keys_index.GetConstantWithIndexType( - dimension_to_sort_bound)), - [&]() { - cache_index = b->CreateAdd(cache_index, value_one); - read_or_write(cache_index, current_keys_index); - }); + ksl.If("inner_smaller_keys_index", + b->CreateICmpSLT(current_keys_index, + tiled_keys_index.GetConstantWithIndexType( + dimension_to_sort_bound)), + [&]() { + cache_index = b->CreateAdd(cache_index, value_one); + read_or_write(cache_index, current_keys_index); + }); }); }; @@ -253,7 +252,7 @@ void EmitTiledCompareLoop( if (dimension_to_sort_bound % tile_size) { // Otherwise we need a bounds check for the last tile. The last tile has // size 'dimension_to_sort_bound' % 'tile_size'. - ksl.IfReturnVoid( + ksl.If( "is_last_tile", b->CreateICmpUGE( b->CreateMul(tiled_keys_index[dimension_to_sort], -- GitLab From 97ea1e6c5e9c1881edc7f2c1aa25d4f66ea46be9 Mon Sep 17 00:00:00 2001 From: Scott Zhu Date: Mon, 10 Dec 2018 15:58:18 -0800 Subject: [PATCH 322/873] Make UnifiedLSTM the default LSTM layer in tf 2.0. Also stop exporting CuDNNLSTM since its all covered by unified LSTM. PiperOrigin-RevId: 224900214 --- .../python/keras/layers/cudnn_recurrent.py | 2 +- tensorflow/python/keras/layers/recurrent.py | 4 +- .../python/keras/layers/unified_lstm_test.py | 3 +- ...rflow.keras.layers.-cu-d-n-n-l-s-t-m.pbtxt | 197 ------------------ .../v2/tensorflow.keras.layers.-l-s-t-m.pbtxt | 3 +- .../golden/v2/tensorflow.keras.layers.pbtxt | 4 - tensorflow/tools/compatibility/renames_v2.py | 1 + 7 files changed, 7 insertions(+), 207 deletions(-) delete mode 100644 tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-cu-d-n-n-l-s-t-m.pbtxt diff --git a/tensorflow/python/keras/layers/cudnn_recurrent.py b/tensorflow/python/keras/layers/cudnn_recurrent.py index 16692753af..e695a68b60 100644 --- a/tensorflow/python/keras/layers/cudnn_recurrent.py +++ b/tensorflow/python/keras/layers/cudnn_recurrent.py @@ -335,7 +335,7 @@ class CuDNNGRU(_CuDNNRNN): return dict(list(base_config.items()) + list(config.items())) -@tf_export('keras.layers.CuDNNLSTM') +@tf_export(v1=['keras.layers.CuDNNLSTM']) class CuDNNLSTM(_CuDNNRNN): """Fast LSTM implementation backed by cuDNN. diff --git a/tensorflow/python/keras/layers/recurrent.py b/tensorflow/python/keras/layers/recurrent.py index fb4c1736b1..a39db7e8b1 100644 --- a/tensorflow/python/keras/layers/recurrent.py +++ b/tensorflow/python/keras/layers/recurrent.py @@ -2274,7 +2274,7 @@ class PeepholeLSTMCell(LSTMCell): return c, o -@tf_export('keras.layers.LSTM') +@tf_export(v1=['keras.layers.LSTM']) class LSTM(RNN): """Long Short-Term Memory layer - Hochreiter 1997. @@ -2532,7 +2532,7 @@ class LSTM(RNN): config['implementation'] = 1 return cls(**config) - +@tf_export('keras.layers.LSTM', v1=[]) class UnifiedLSTM(LSTM): """Long Short-Term Memory layer - Hochreiter 1997. diff --git a/tensorflow/python/keras/layers/unified_lstm_test.py b/tensorflow/python/keras/layers/unified_lstm_test.py index a2b523b00e..0219e5e426 100644 --- a/tensorflow/python/keras/layers/unified_lstm_test.py +++ b/tensorflow/python/keras/layers/unified_lstm_test.py @@ -209,8 +209,7 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): y_2 = lstm_model.predict(x_train) with test_util.device(use_gpu=True): - cudnn_layer = keras.layers.UnifiedLSTM(rnn_state_size, - recurrent_activation='sigmoid') + cudnn_layer = keras.layers.UnifiedLSTM(rnn_state_size) cudnn_model = keras.models.Model(inputs, cudnn_layer(inputs)) cudnn_model.set_weights(weights) y_3 = cudnn_model.predict(x_train) diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-cu-d-n-n-l-s-t-m.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-cu-d-n-n-l-s-t-m.pbtxt deleted file mode 100644 index 7c463ff125..0000000000 --- a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-cu-d-n-n-l-s-t-m.pbtxt +++ /dev/null @@ -1,197 +0,0 @@ -path: "tensorflow.keras.layers.CuDNNLSTM" -tf_class { - is_instance: "" - is_instance: "" - is_instance: "" - is_instance: "" - is_instance: "" - is_instance: "" - member { - name: "activity_regularizer" - mtype: "" - } - member { - name: "cell" - 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: "states" - 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\', \'units\', \'kernel_initializer\', \'recurrent_initializer\', \'bias_initializer\', \'unit_forget_bias\', \'kernel_regularizer\', \'recurrent_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'recurrent_constraint\', \'bias_constraint\', \'return_sequences\', \'return_state\', \'go_backwards\', \'stateful\'], varargs=None, keywords=kwargs, defaults=[\'glorot_uniform\', \'orthogonal\', \'zeros\', \'True\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'False\', \'False\', \'False\', \'False\'], " - } - member_method { - name: "add_loss" - argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "add_metric" - argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'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\', \'synchronization\', \'aggregation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.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\', \'mask\', \'training\', \'initial_state\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'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=[\'self\', \'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_initial_state" - argspec: "args=[\'self\', \'inputs\'], 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: "reset_states" - argspec: "args=[\'self\', \'states\'], 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/v2/tensorflow.keras.layers.-l-s-t-m.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-l-s-t-m.pbtxt index 529c750f98..9144a5b103 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-l-s-t-m.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-l-s-t-m.pbtxt @@ -1,5 +1,6 @@ path: "tensorflow.keras.layers.LSTM" tf_class { + is_instance: "" is_instance: "" is_instance: "" is_instance: "" @@ -155,7 +156,7 @@ tf_class { } member_method { name: "__init__" - argspec: "args=[\'self\', \'units\', \'activation\', \'recurrent_activation\', \'use_bias\', \'kernel_initializer\', \'recurrent_initializer\', \'bias_initializer\', \'unit_forget_bias\', \'kernel_regularizer\', \'recurrent_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'recurrent_constraint\', \'bias_constraint\', \'dropout\', \'recurrent_dropout\', \'implementation\', \'return_sequences\', \'return_state\', \'go_backwards\', \'stateful\', \'unroll\'], varargs=None, keywords=kwargs, defaults=[\'tanh\', \'hard_sigmoid\', \'True\', \'glorot_uniform\', \'orthogonal\', \'zeros\', \'True\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'0.0\', \'0.0\', \'1\', \'False\', \'False\', \'False\', \'False\', \'False\'], " + argspec: "args=[\'self\', \'units\', \'activation\', \'recurrent_activation\', \'use_bias\', \'kernel_initializer\', \'recurrent_initializer\', \'bias_initializer\', \'unit_forget_bias\', \'kernel_regularizer\', \'recurrent_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'recurrent_constraint\', \'bias_constraint\', \'dropout\', \'recurrent_dropout\', \'implementation\', \'return_sequences\', \'return_state\', \'go_backwards\', \'stateful\', \'time_major\', \'unroll\'], varargs=None, keywords=kwargs, defaults=[\'tanh\', \'sigmoid\', \'True\', \'glorot_uniform\', \'orthogonal\', \'zeros\', \'True\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'0.0\', \'0.0\', \'1\', \'False\', \'False\', \'False\', \'False\', \'False\', \'False\'], " } member_method { name: "add_loss" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.pbtxt index 3b4724ef10..10ac3a7520 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.pbtxt @@ -116,10 +116,6 @@ tf_module { name: "CuDNNGRU" mtype: "" } - member { - name: "CuDNNLSTM" - mtype: "" - } member { name: "Dense" mtype: "" diff --git a/tensorflow/tools/compatibility/renames_v2.py b/tensorflow/tools/compatibility/renames_v2.py index b757ad4647..3ab5a0d0d6 100644 --- a/tensorflow/tools/compatibility/renames_v2.py +++ b/tensorflow/tools/compatibility/renames_v2.py @@ -238,6 +238,7 @@ renames = { 'tf.is_strictly_increasing': 'tf.math.is_strictly_increasing', 'tf.is_variable_initialized': 'tf.compat.v1.is_variable_initialized', 'tf.keras.backend.get_session': 'tf.compat.v1.keras.backend.get_session', + 'tf.keras.layers.CuDNNLSTM': 'tf.compat.v1.keras.layers.CuDNNLSTM', 'tf.layers.AveragePooling1D': 'tf.compat.v1.layers.AveragePooling1D', 'tf.layers.AveragePooling2D': 'tf.compat.v1.layers.AveragePooling2D', 'tf.layers.AveragePooling3D': 'tf.compat.v1.layers.AveragePooling3D', -- GitLab From ce3a9e8eeca81a76e2f0ebb98418885fa5d75325 Mon Sep 17 00:00:00 2001 From: Kay Zhu Date: Mon, 10 Dec 2018 16:28:47 -0800 Subject: [PATCH 323/873] [XLA] Enable compare for float16 in HloEvaluator. PiperOrigin-RevId: 224905468 --- tensorflow/compiler/xla/service/hlo_evaluator.cc | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc index 3a7652a8dc..e98fc0a5de 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator.cc +++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc @@ -629,8 +629,11 @@ Status HloEvaluator::HandleCompare(HloInstruction* compare) { evaluated_[compare], Compare(compare->shape(), opcode, lhs_literal, rhs_literal)); } break; - case F16: - return Unimplemented("unhandled primitive type: F16."); + case F16: { + TF_ASSIGN_OR_RETURN( + evaluated_[compare], + Compare(compare->shape(), opcode, lhs_literal, rhs_literal)); + } break; case BF16: { TF_ASSIGN_OR_RETURN(evaluated_[compare], Compare(compare->shape(), opcode, -- GitLab From 08feaa53d2f4c3cae623eb3ea9f8cce60c9eeca7 Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Mon, 10 Dec 2018 17:07:10 -0800 Subject: [PATCH 324/873] Do not run examples_test on windows. it is a bash test, and language filters do not work properly on windows. PiperOrigin-RevId: 224912071 --- tensorflow/python/debug/BUILD | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tensorflow/python/debug/BUILD b/tensorflow/python/debug/BUILD index c6abd476d9..1dcdb880f5 100644 --- a/tensorflow/python/debug/BUILD +++ b/tensorflow/python/debug/BUILD @@ -1132,4 +1132,7 @@ sh_test( ":debug_tflearn_iris", ":offline_analyzer", ], + tags = [ + "no_windows", + ], ) -- GitLab From c136aa8255c2abfc068db18fff7c043e9da324db Mon Sep 17 00:00:00 2001 From: Russell Power Date: Mon, 10 Dec 2018 17:14:16 -0800 Subject: [PATCH 325/873] Tune keepalive timeouts for Tensorflow/GRPC This disables the keepalive watchdog for TF/GRPC channels. The watchdog ping timer is intended to monitor channels in case they have gone "stale". If this occurs, any pending RPCs are marked failed. This interacts poorly with large TF models, where we can saturate the network exchanging tensors, causing the watchdog ping to be delayed. The timer is not essential (normal deadline processing and socket termination is still respected), so we can disable it with minimal risk here. PiperOrigin-RevId: 224913045 --- tensorflow/core/distributed_runtime/rpc/grpc_channel.cc | 1 + tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc | 5 +++++ tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h | 2 +- 3 files changed, 7 insertions(+), 1 deletion(-) diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_channel.cc b/tensorflow/core/distributed_runtime/rpc/grpc_channel.cc index 781b7d65cd..1420589f82 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_channel.cc +++ b/tensorflow/core/distributed_runtime/rpc/grpc_channel.cc @@ -60,6 +60,7 @@ Status ValidateHostPortPair(const string& host_port) { // TODO(mrry): Implement secure channels. ::grpc::ChannelArguments args; args.SetInt(GRPC_ARG_MAX_MESSAGE_LENGTH, std::numeric_limits::max()); + args.SetInt(GRPC_ARG_KEEPALIVE_TIME_MS, std::numeric_limits::max()); // NOTE(mrry): Some versions of gRPC use a 20-second minimum backoff // on connection failure, which makes our tests time out. args.SetInt("grpc.testing.fixed_reconnect_backoff_ms", 1000); diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc b/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc index cbd5cd927e..33ff8e1ac4 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc +++ b/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc @@ -110,6 +110,11 @@ GrpcServer::~GrpcServer() { // - worker_env_.compute_pool } +void GrpcServer::MaybeMutateBuilder(::grpc::ServerBuilder* builder) { + builder->AddChannelArgument(GRPC_ARG_KEEPALIVE_TIME_MS, + std::numeric_limits::max()); +} + Status GrpcServer::Init( ServiceInitFunction service_func, const RendezvousMgrCreationFunction& rendezvous_mgr_func, diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h b/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h index c1395abdde..c7f543e5bf 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h +++ b/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h @@ -62,7 +62,7 @@ class GrpcServer : public ServerInterface { GrpcServer(const ServerDef& server_def, Env* env); // Allow children classes to override this and provide custom args to the // server before it is constructed. Default behavior is to do nothing. - virtual void MaybeMutateBuilder(::grpc::ServerBuilder* builder) {} + virtual void MaybeMutateBuilder(::grpc::ServerBuilder* builder); public: static Status Create(const ServerDef& server_def, Env* env, -- GitLab From 662053a4d35942d1c1b6800df98829e1046b1679 Mon Sep 17 00:00:00 2001 From: Katherine Wu Date: Mon, 10 Dec 2018 17:16:26 -0800 Subject: [PATCH 326/873] Fix kokoro tests by removing dependency on save_test. PiperOrigin-RevId: 224913339 --- tensorflow/python/keras/BUILD | 1 - .../keras/engine/training_utils_test.py | 29 +++++++++++++++++-- 2 files changed, 26 insertions(+), 4 deletions(-) diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD index faf58e0d93..36fea36389 100755 --- a/tensorflow/python/keras/BUILD +++ b/tensorflow/python/keras/BUILD @@ -848,7 +848,6 @@ py_test( deps = [ ":keras", "//tensorflow/python:client_testlib", - "//tensorflow/python/saved_model:save_test", "//third_party/py/numpy", "@absl_py//absl/testing:parameterized", ], diff --git a/tensorflow/python/keras/engine/training_utils_test.py b/tensorflow/python/keras/engine/training_utils_test.py index 0250e60426..d8acec32cb 100644 --- a/tensorflow/python/keras/engine/training_utils_test.py +++ b/tensorflow/python/keras/engine/training_utils_test.py @@ -22,10 +22,13 @@ import os import numpy as np + +from tensorflow.python.client import session as session_lib from tensorflow.python import keras from tensorflow.python.eager import context from tensorflow.python.eager import def_function from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_spec from tensorflow.python.framework import tensor_util from tensorflow.python.keras import backend as K @@ -35,8 +38,10 @@ from tensorflow.python.keras.engine import training_utils from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import array_ops from tensorflow.python.platform import test +from tensorflow.python.saved_model import loader from tensorflow.python.saved_model import save as save_lib -from tensorflow.python.saved_model import save_test +from tensorflow.python.saved_model import signature_constants +from tensorflow.python.saved_model import tag_constants class ModelInputsTest(test.TestCase): @@ -222,6 +227,25 @@ class TraceModelCallTest(keras_parameterized.TestCase): self._assert_all_close(expected_outputs, signature_outputs) +def _import_and_infer(save_dir, inputs): + """Import a SavedModel into a TF 1.x-style graph and run `signature_key`.""" + graph = ops.Graph() + with graph.as_default(), session_lib.Session() as session: + model = loader.load(session, [tag_constants.SERVING], save_dir) + signature = model.signature_def[ + signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY] + assert set(inputs.keys()) == set(signature.inputs.keys()) + feed_dict = {} + for arg_name in inputs.keys(): + feed_dict[graph.get_tensor_by_name(signature.inputs[arg_name].name)] = ( + inputs[arg_name]) + output_dict = {} + for output_name, output_tensor_info in signature.outputs.items(): + output_dict[output_name] = graph.get_tensor_by_name( + output_tensor_info.name) + return session.run(output_dict, feed_dict=feed_dict) + + class ModelSaveTest(keras_parameterized.TestCase): @keras_parameterized.run_with_all_model_types @@ -239,8 +263,7 @@ class ModelSaveTest(keras_parameterized.TestCase): self.assertAllClose( {model.output_names[0]: model.predict_on_batch(inputs)}, - save_test._import_and_infer(save_dir, - {model.input_names[0]: np.ones((8, 5))})) + _import_and_infer(save_dir, {model.input_names[0]: np.ones((8, 5))})) if __name__ == '__main__': test.main() -- GitLab From 137638999f20055f1da45067f7191117ba640449 Mon Sep 17 00:00:00 2001 From: Eugene Zhulenev Date: Mon, 10 Dec 2018 17:22:41 -0800 Subject: [PATCH 327/873] Check if it's allowed to prune side effects in model_pruner PiperOrigin-RevId: 224914276 --- tensorflow/core/grappler/grappler_item.cc | 9 +++++++++ tensorflow/core/grappler/utils/functions.cc | 16 ++++++++-------- tensorflow/core/grappler/utils/functions_test.cc | 3 +-- 3 files changed, 18 insertions(+), 10 deletions(-) diff --git a/tensorflow/core/grappler/grappler_item.cc b/tensorflow/core/grappler/grappler_item.cc index 74bde67f19..2d71ac54cc 100644 --- a/tensorflow/core/grappler/grappler_item.cc +++ b/tensorflow/core/grappler/grappler_item.cc @@ -114,6 +114,15 @@ std::unordered_set GrapplerItem::NodesToPreserve() const { result.insert(NodeName(queue_runner.cancel_op_name())); } } + + if (!allowed_optimizations_.prune_ops_with_side_effects) { + for (const NodeDef& node : graph.node()) { + if (!IsFreeOfSideEffect(node)) { + result.insert(node.name()); + } + } + } + return result; } diff --git a/tensorflow/core/grappler/utils/functions.cc b/tensorflow/core/grappler/utils/functions.cc index 57863a71f3..f2894a942b 100644 --- a/tensorflow/core/grappler/utils/functions.cc +++ b/tensorflow/core/grappler/utils/functions.cc @@ -347,6 +347,10 @@ GrapplerFunctionItem::GrapplerFunctionItem( fetch.push_back(output_tensor); } } + + // It's unsafe to prune side-effectful ops from the graph instantiated from a + // function definition (see inlining in function_optimizer.cc). + allowed_optimizations().prune_ops_with_side_effects = false; } const string& GrapplerFunctionItem::description() const { return description_; } @@ -561,7 +565,6 @@ Status MakeGrapplerFunctionItem(const FunctionDef& func, inputs.push_back(std::move(input_expansion)); } - std::vector keep_nodes; // Add all function nodes to the function body for (const NodeDef& func_def_node : func.node_def()) { NodeDef* new_node = function_body.add_node(); @@ -577,11 +580,6 @@ Status MakeGrapplerFunctionItem(const FunctionDef& func, // Register node output range in a function connectivity. TF_RETURN_IF_ERROR(RegisterFunctionBodyOutputs(*registration, func_def_node, &connectivity)); - - // Ops with side effects must be preserved in a function body. - if (!IsFreeOfSideEffect(func_def_node)) { - keep_nodes.push_back(func_def_node.name()); - } } // Rewrite inputs to use GraphDef format @@ -612,12 +610,14 @@ Status MakeGrapplerFunctionItem(const FunctionDef& func, outputs.push_back(std::move(output)); } + std::vector keep_ops; bool is_stateful = signature.is_stateful(); *item = GrapplerFunctionItem( - /*func_name=*/signature.name(), /*description=*/signature.description(), + /*func_name=*/signature.name(), + /*description=*/signature.description(), /*func_attr=*/AttrSlice(&func.attr()), std::move(inputs), - std::move(outputs), std::move(keep_nodes), graph_def_version, is_stateful, + std::move(outputs), std::move(keep_ops), graph_def_version, is_stateful, std::move(function_body)); return Status::OK(); } diff --git a/tensorflow/core/grappler/utils/functions_test.cc b/tensorflow/core/grappler/utils/functions_test.cc index 8639dec05a..5923850eca 100644 --- a/tensorflow/core/grappler/utils/functions_test.cc +++ b/tensorflow/core/grappler/utils/functions_test.cc @@ -599,8 +599,7 @@ TEST_F(FunctionsTest, FromFunctionDefWithSideEffectfulOps) { EXPECT_EQ(3, item.function_body().node_size()); EXPECT_EQ(1, item.input_size()); EXPECT_EQ(0, item.output_size()); - ASSERT_EQ(1, item.keep_ops.size()); - EXPECT_EQ("update", item.keep_ops[0]); + EXPECT_EQ(false, item.allowed_optimizations().prune_ops_with_side_effects); } TEST_F(FunctionsTest, MakeFunctionDef) { -- GitLab From 3ef97d1ed381117282f72acd19582a729bf4b821 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 10 Dec 2018 17:28:57 -0800 Subject: [PATCH 328/873] Generate informative error when attempting to execute SummaryImageOp with trivial dimensions. This bypasses a nullptr error that appears downstream due to referencing a length-zero array in a temporary buffer. PiperOrigin-RevId: 224915050 --- tensorflow/core/kernels/summary_image_op.cc | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/tensorflow/core/kernels/summary_image_op.cc b/tensorflow/core/kernels/summary_image_op.cc index 29b21ee735..68f17c2e78 100644 --- a/tensorflow/core/kernels/summary_image_op.cc +++ b/tensorflow/core/kernels/summary_image_op.cc @@ -78,6 +78,11 @@ class SummaryImageOp : public OpKernel { const int hw = h * w; // Compact these two dims for simplicity const int depth = static_cast(tensor.dim_size(3)); + OP_REQUIRES(c, hw > 0 && depth > 0, + errors::InvalidArgument( + "input tensor must have non-zero dims. Found: [", + batch_size, ", ", h, ", ", w, ", ", depth, "].")); + Summary s; if (tensor.dtype() == DT_UINT8) { // For uint8 input, no normalization is necessary -- GitLab From 9aa32a6eacd0e8f507d1c57f0658d6c3ecaecaba Mon Sep 17 00:00:00 2001 From: Francois Chollet Date: Mon, 10 Dec 2018 17:32:56 -0800 Subject: [PATCH 329/873] Enable mixing value tensors (eager tensors or numpy arrays) and Keras symbolic tensors when building Keras graphs-of-layers in an eager scope. In these cases, the value tensors are treated as symbolic constants. This enables the following pattern to work in the same way in both V1 and V2: ``` lstm = LSTM(2) inputs = keras.Input((None, 3)) outputs = lstm(inputs, initial_state=tf.ones(shape)) ``` (without this change, the above code works in V1 but fails in V2 with an artificial exception). Known issue: in case a random tensor is used, there is a (usually harmless) behavior discrepancy remaining between V1 and V2, which is that in V2 we'd be using the same random value every time, whereas in V1 we'd be drawing new random values (since the tensor would be treated as a random op and not as a constant). We think this is not a problem because in V2 users should have the mental model "tensors are values" and thus would be expecting a random tensor to behave like a constant value and not like a random generator. PiperOrigin-RevId: 224915621 --- tensorflow/python/eager/execute.py | 6 ----- .../python/keras/engine/base_layer_test.py | 25 ++++++++++++------- 2 files changed, 16 insertions(+), 15 deletions(-) diff --git a/tensorflow/python/eager/execute.py b/tensorflow/python/eager/execute.py index 6f8c780170..7415a0ae22 100644 --- a/tensorflow/python/eager/execute.py +++ b/tensorflow/python/eager/execute.py @@ -66,12 +66,6 @@ def quick_execute(op_name, num_outputs, inputs, attrs, ctx, name=None): six.raise_from(core._status_to_exception(e.code, message), None) except TypeError as e: if any(ops._is_keras_symbolic_tensor(x) for x in inputs): - if any(isinstance(x, ops.EagerTensor) for x in inputs): - raise TypeError("You are attempting to mix computation of symbolic " - "Tensors (computation rooted at tf.keras.Input()) " - "and concrete values. This is not supported. " - "If you need this support, file an issue on the " - "TensorFlow GitHub repository.") raise core._SymbolicException raise e # pylint: enable=protected-access diff --git a/tensorflow/python/keras/engine/base_layer_test.py b/tensorflow/python/keras/engine/base_layer_test.py index 798775b6a5..fa4eb48d56 100644 --- a/tensorflow/python/keras/engine/base_layer_test.py +++ b/tensorflow/python/keras/engine/base_layer_test.py @@ -167,19 +167,26 @@ class BaseLayerTest(test.TestCase): def test_mixing_keras_symbolic_tensors_and_eager_tensors(self): x1 = keras.Input((3,)) x2 = array_ops.ones((3, 3)) - with self.assertRaisesRegexp( - TypeError, - 'mix computation of symbolic Tensors'): - math_ops.matmul(x1, x2) + y = math_ops.matmul(x1, x2) + self.assertEqual(y.graph, keras.backend.get_graph()) + fn = keras.backend.function(inputs=[x1], outputs=[y]) + x_val = np.random.random((3, 3)) + y_val = np.ones((3, 3)) + self.assertAllClose(fn([x_val])[0], + np.matmul(x_val, y_val), + atol=1e-5) def test_mixing_keras_symbolic_tensors_and_numpy_arrays(self): - # For the time being we treat Numpy arrays as EagerTensors when mixing both. x1 = keras.Input((3,)) x2 = np.ones((3, 3), dtype='float32') - with self.assertRaisesRegexp( - TypeError, - 'mix computation of symbolic Tensors'): - math_ops.matmul(x1, x2) + y = math_ops.matmul(x1, x2) + self.assertEqual(y.graph, keras.backend.get_graph()) + fn = keras.backend.function(inputs=[x1], outputs=[y]) + x_val = np.random.random((3, 3)) + y_val = np.ones((3, 3)) + self.assertAllClose(fn([x_val])[0], + np.matmul(x_val, y_val), + atol=1e-5) if __name__ == '__main__': -- GitLab From e5165302eb1593b8f52eb15c8668e1c81cc771ae Mon Sep 17 00:00:00 2001 From: Dan Moldovan Date: Mon, 10 Dec 2018 18:15:31 -0800 Subject: [PATCH 330/873] Expose a `tensorflow.autograph` namespace, with a minimal core API under experimental. Clean the documentation for public symbols. PiperOrigin-RevId: 224921147 --- tensorflow/python/autograph/__init__.py | 15 +- tensorflow/python/autograph/core/converter.py | 57 ++++--- tensorflow/python/autograph/impl/api.py | 148 ++++++++++++------ tensorflow/python/ops/standard_ops.py | 2 + .../tools/api/generator/api_init_files.bzl | 2 + .../tools/api/generator/api_init_files_v1.bzl | 2 + ...flow.autograph.experimental.-feature.pbtxt | 28 ++++ ...ow.autograph.experimental.-verbosity.pbtxt | 12 ++ .../tensorflow.autograph.experimental.pbtxt | 11 ++ .../api/golden/v1/tensorflow.autograph.pbtxt | 15 ++ .../tools/api/golden/v1/tensorflow.pbtxt | 4 + ...flow.autograph.experimental.-feature.pbtxt | 28 ++++ ...ow.autograph.experimental.-verbosity.pbtxt | 12 ++ .../tensorflow.autograph.experimental.pbtxt | 11 ++ .../api/golden/v2/tensorflow.autograph.pbtxt | 15 ++ .../tools/api/golden/v2/tensorflow.pbtxt | 4 + 16 files changed, 290 insertions(+), 76 deletions(-) create mode 100644 tensorflow/tools/api/golden/v1/tensorflow.autograph.experimental.-feature.pbtxt create mode 100644 tensorflow/tools/api/golden/v1/tensorflow.autograph.experimental.-verbosity.pbtxt create mode 100644 tensorflow/tools/api/golden/v1/tensorflow.autograph.experimental.pbtxt create mode 100644 tensorflow/tools/api/golden/v1/tensorflow.autograph.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.autograph.experimental.-feature.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.autograph.experimental.-verbosity.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.autograph.experimental.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.autograph.pbtxt diff --git a/tensorflow/python/autograph/__init__.py b/tensorflow/python/autograph/__init__.py index 7252e0d9bf..6faeb01607 100644 --- a/tensorflow/python/autograph/__init__.py +++ b/tensorflow/python/autograph/__init__.py @@ -12,10 +12,20 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Autograph compiles Python code into equivalent TensorFlow code. +"""Conversion of plain Python into TensorFlow graph code. -Equivalent here means that they have the same effect when executed. +NOTE: In TensorFlow 2.0, AutoGraph is automatically applied when using +`tf.function`. This module contains lower-level APIs for advanced use. + +For more information, see the +[AutoGraph guide](https://www.tensorflow.org/guide/autograph). + +By equivalent graph code we mean code that generates a TensorFlow graph when +run. The generated graph has the same effects as the original code when executed +(for example with `tf.function` or `tf.compat.v1.Session.run`). In other words, +using AutoGraph can be thought of as running Python in TensorFlow. """ +# TODO(b/119833526): Link to the new tf.function + autograph tutorial. from __future__ import absolute_import from __future__ import division @@ -43,6 +53,7 @@ from tensorflow.python.autograph.lang.special_functions import tensor_list from tensorflow.python.autograph.pyct.transformer import AutographParseError from tensorflow.python.util.all_util import remove_undocumented +# TODO(mdan): Revisit this list once we finalize the generated code mechanism. _allowed_symbols = [ # Main API 'ConversionOptions', diff --git a/tensorflow/python/autograph/core/converter.py b/tensorflow/python/autograph/core/converter.py index e88c4674ee..eea2621056 100644 --- a/tensorflow/python/autograph/core/converter.py +++ b/tensorflow/python/autograph/core/converter.py @@ -63,8 +63,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from enum import Enum -from enum import IntEnum +import enum from tensorflow.python.autograph.core import config from tensorflow.python.autograph.core import naming @@ -83,6 +82,7 @@ from tensorflow.python.autograph.pyct.static_analysis import liveness from tensorflow.python.autograph.pyct.static_analysis import reaching_definitions from tensorflow.python.autograph.pyct.static_analysis import type_info from tensorflow.python.eager import function +from tensorflow.python.util.tf_export import tf_export # TODO(mdan): These contexts can be refactored into first class objects. # For example, we could define Program and Entity abstractions that hold on @@ -91,37 +91,42 @@ from tensorflow.python.eager import function # TODO(mdan): Add a test specific to this converter. -class Verbosity(IntEnum): - """Different levels of verbosity for printing errors. +@tf_export('autograph.experimental.Verbosity') +class Verbosity(enum.IntEnum): + """Represents conversion verbosity levels. Attributes: - * BRIEF: No logging, minimal error messages. - * VERBOSE: Detailed logging of generated code, detailed error messages. + BRIEF: No logging, minimal error messages. + VERBOSE: Detailed logging of generated code, detailed error messages. """ + BRIEF = 0 VERBOSE = 1 -class Feature(Enum): - """Constants to use when selecting AutoGraph features.""" +@tf_export('autograph.experimental.Feature') +class Feature(enum.Enum): + """Represents conversion options that can be toggled on or off. - ALL = 'Enable all features.' + Attributes: + ALL: Enable all features. + AUTO_CONTROL_DEPS: Insert of control dependencies in the generated code. + DECORATORS: Allow decorators in local functions. Note that special + decorators, like `tf.function`, are allowed regardless of this toggle. + ERROR_REWRITING: Rewrite errors that occur in the generated code to + indicate the source code to which the failing code corresponds. + LISTS: Convert list idioms, like initializers, slices, append, etc. + NAME_SCOPES: Insert name scopes that name ops according to context, like the + function they were defined in. + """ - AUTO_CONTROL_DEPS = ( - 'Insert of control dependencies in the generated code.') - DECORATORS = ( - 'Allow decorators in local functions. Note that special decorators,' - ' like ag.convert or tf.function are allowed regardless of this toggle.') - ERROR_REWRITING = ( - 'Rewrite errors that occur in the generated code to indicate the source' - ' code to which the failing code corresponds.') - LISTS = 'Convert list idioms, like initializers, slices, append, etc.' - NAME_SCOPES = ( - 'Insert name scopes that name ops according to context, like the' - ' function they were defined in.') + ALL = 'ALL' - def __repr__(self): - return self.name + AUTO_CONTROL_DEPS = 'AUTO_CONTROL_DEPS' + DECORATORS = 'DECORATORS' + ERROR_REWRITING = 'ERROR_REWRITING' + LISTS = 'LISTS' + NAME_SCOPES = 'NAME_SCOPES' class ConversionOptions(object): @@ -157,7 +162,9 @@ class ConversionOptions(object): # TODO(mdan): Rename to conversion_recursion_depth? self.internal_convert_user_code = internal_convert_user_code - if isinstance(optional_features, Feature): + if optional_features is None: + optional_features = () + elif isinstance(optional_features, Feature): optional_features = (optional_features,) optional_features = frozenset(optional_features) self.optional_features = optional_features @@ -419,7 +426,7 @@ class AnnotatedDef(reaching_definitions.Definition): self.directives = {} -class AgAnno(Enum): +class AgAnno(enum.Enum): """Annotation labels specific to AutoGraph. See anno.py.""" DIRECTIVES = 'User directives associated with the annotated statement.' diff --git a/tensorflow/python/autograph/impl/api.py b/tensorflow/python/autograph/impl/api.py index f7774888c8..54b46b1efd 100644 --- a/tensorflow/python/autograph/impl/api.py +++ b/tensorflow/python/autograph/impl/api.py @@ -40,6 +40,7 @@ from tensorflow.python.framework import tensor_util from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util import tf_decorator from tensorflow.python.util import tf_inspect +from tensorflow.python.util.tf_export import tf_export # TODO(mdan): Properly document the type hints. # TODO(mdan): Reduce the type hint information to (module, type). @@ -157,7 +158,6 @@ def do_not_convert(run_as=RunMode.GRAPH, return_dtypes=None): return decorator -# TODO(mdan): Move to a private, undocumented module. def converted_call(f, owner, options, *args, **kwargs): """Compiles a function call inline. For internal use only.""" if options.verbose >= converter.Verbosity.VERBOSE: @@ -202,7 +202,7 @@ def converted_call(f, owner, options, *args, **kwargs): return f(*args, **kwargs) # Unwrap functools.partial objects - # TODO(allenl, mdan): Consider sharing unwrapping logic with tf_inspect. + # TODO(mdan): Consider sharing unwrapping logic with tf_inspect. while isinstance(f, functools.partial): args = f.args + args new_kwargs = {} @@ -283,9 +283,9 @@ def converted_call(f, owner, options, *args, **kwargs): verbose=options.verbose, arg_values=arg_values, arg_types=arg_types, - partial_types=partial_types, strip_decorators=options.strip_decorators, - optional_features=options.optional_features) + optional_features=options.optional_features, + experimental_partial_types=partial_types) result = converted_f(*effective_args, **kwargs) @@ -314,44 +314,81 @@ def _is_not_callable(obj): return False -# TODO(mdan): Rename: to_ops? -# TODO(mdan): Look into overloading as function and decorator, like tfe.defun? -# TODO(mdan): Remove partial_types. -def to_graph(e, +@tf_export('autograph.to_graph') +def to_graph(entity, recursive=True, verbose=converter.Verbosity.VERBOSE, arg_values=None, arg_types=None, - partial_types=None, strip_decorators=None, - optional_features=converter.Feature.ALL): - """Converts a Python entity into equivalent code that uses TensorFlow ops. + optional_features=converter.Feature.ALL, + experimental_partial_types=None): + """Converts a Python entity into a TensorFlow graph. + + Also see: `tf.autograph.to_code`, `tf.function`. + + Unlike `tf.function`, `to_graph` is a low-level transpiler that converts + Python code to TensorFlow graph code. It does not implement any caching, + variable management or create any actual ops, and is best used where greater + control over the generated TensorFlow graph is desired. Another difference + from `tf.function` is that `to_graph` will not wrap the graph into a + TensorFlow function or a Python callable. Internally, `tf.function` uses + `to_graph`. + + _Example Usage_ + + ```python + def foo(x): + if x > 0: + y = x * x + else: + y = -x + return y + + converted_foo = to_graph(foo) + + x = tf.constant(1) + y = converted_foo(x) # converted_foo is a TensorFlow Op-like. + assert is_tensor(y) + ``` Supported Python entities include: * functions * classes + * object methods + + Functions are converted into new functions with converted code. - Classes are converted by converting all their methods into a new class. + Classes are converted by generating a new class whose methods use converted + code. + + Methods are converted into unbound function that have an additional first + argument called `self`. Args: - e: Union[Callable, Type], the Python entity to convert. - recursive: bool, whether to recursively convert any functions that the + entity: Python callable or class to convert. + recursive: Whether to recursively convert any functions that the converted function may call. - verbose: converter.Verbosity, the level of printing verbosity to use. - arg_values: Optional[Dict[Text, Any]], value hints for symbols including - function arguments. - arg_types: Optional[Dict[Text, Type]], type hints for symbols including - function arguments. - partial_types: Set[Type], reserved for internal use. - strip_decorators: Tuple[Callable], same as - ConversionOptions.strip_decorators. - optional_features: Union[Feature, Set[Feature]], same as - ConversionOptions.optional_features. + verbose: The level of printing verbosity to use, as a + `tf.autograph.experimental.Verbosity` value. + arg_values: Optional dict of value hints for symbols including + function arguments mapping string names to actual values. For example, + `arg_values={'a': 1}` will map the variable `a` to the value `1`. + arg_types: Optional dict of type hints for symbols including function + arguments. Type hints allow specifying just the type of a variable, rather + than a specific value. + strip_decorators: A tuple specifying decorators that should be + excluded from the compiled output. By default, when converting a function + before the decorators are applied, the compiled output will include those + decorators. + optional_features: `None`, a tuple of, or a single + `tf.autograph.experimental.Feature` value. Controls the use of + optional features in the conversion process. + experimental_partial_types: A `set` of `type` values, reserved for internal + use. Returns: - Union[Callable, Type], the converted entity, which is the same kind as e - (that is, a function is e is a function, a class if e is a class, etc.) but - its code has been converted to use TF ops. + Same as `entity`, the converted Python function or class. Raises: ValueError: If the entity could not be converted. @@ -366,11 +403,11 @@ def to_graph(e, verbose=verbose, strip_decorators=strip_decorators, optional_features=optional_features), - partial_types=partial_types, + partial_types=experimental_partial_types, 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) + _, name, namespace = conversion.entity_to_graph(entity, program_ctx, + arg_values, arg_types) nodes = [] for dep in reversed(program_ctx.conversion_order): @@ -389,8 +426,8 @@ def to_graph(e, compiled_module.__dict__[key] = val compiled = getattr(compiled_module, name) - if tf_inspect.isfunction(e): - compiled.__defaults__ = e.__defaults__ + if tf_inspect.isfunction(entity): + compiled.__defaults__ = entity.__defaults__ if hasattr(compiled, '__globals__'): # Remove self to avoid circular references. This will probably only work @@ -415,38 +452,51 @@ def to_graph(e, return compiled -def to_code(e, +@tf_export('autograph.to_code') +def to_code(entity, recursive=True, arg_values=None, arg_types=None, - partial_types=None, - indentation=' '): - """Returns the equivalent code that uses TensorFlow ops. + indentation=' ', + optional_features=converter.Feature.ALL, + experimental_partial_types=None): + """Similar to `to_graph`, but returns Python source code as a string. + + Also see: `tf.autograph.to_graph`. - Also see: `to_graph`, `convert` + `to_graph` returns the Python source code that can be used to generate a + TensorFlow graph that is functionally identical to the input Python code. Args: - e: Union[Callable, Type], the Python entity to convert. - recursive: bool, whether to recursively convert any functions that the + entity: Python callable or class to convert. + recursive: Whether to recursively convert any functions that the converted function may call. - arg_values: Optional[Dict[Text, Any]], value hints for symbols including - function arguments. - arg_types: Optional[Dict[Text, Type]], type hints for symbols including - function arguments. - partial_types: Set[Type], reserved for internal use. - indentation: Text, when to use for each level of indentation. + arg_values: Optional dict of value hints for symbols including + function arguments mapping string names to actual values. For example, + `arg_values={'a': 1}` will map the variable `a` to the value `1`. + arg_types: Optional dict of type hints for symbols including function + arguments. Type hints allow specifying just the type of a variable, rather + than a specific value. + indentation: The string to use for indenting. Typically two or four spaces, + or just the tab character. + optional_features: `None`, a tuple of, or a single + `tf.autograph.experimental.Feature` value. Controls the use of + optional features in the conversion process. + experimental_partial_types: A `set` of `type` values, reserved for internal + use. Returns: - Text, the converted code. + The converted code as string. """ program_ctx = converter.ProgramContext( options=converter.ConversionOptions( recursive=recursive, - strip_decorators=(convert, do_not_convert, converted_call)), - partial_types=partial_types, + strip_decorators=(convert, do_not_convert, converted_call), + optional_features=optional_features), + partial_types=experimental_partial_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) + conversion.entity_to_graph(entity, program_ctx, arg_values, arg_types) code = '\n'.join( compiler.ast_to_source(program_ctx.dependency_cache[dep], indentation) diff --git a/tensorflow/python/ops/standard_ops.py b/tensorflow/python/ops/standard_ops.py index c614d072ba..8ef0fe8070 100644 --- a/tensorflow/python/ops/standard_ops.py +++ b/tensorflow/python/ops/standard_ops.py @@ -22,6 +22,8 @@ from __future__ import print_function import sys as _sys +from tensorflow.python import autograph + # pylint: disable=g-bad-import-order # Imports the following modules so that @RegisterGradient get executed. from tensorflow.python.ops import array_grad diff --git a/tensorflow/python/tools/api/generator/api_init_files.bzl b/tensorflow/python/tools/api/generator/api_init_files.bzl index 0245ac50a6..58913b3208 100644 --- a/tensorflow/python/tools/api/generator/api_init_files.bzl +++ b/tensorflow/python/tools/api/generator/api_init_files.bzl @@ -4,6 +4,8 @@ TENSORFLOW_API_INIT_FILES = [ # BEGIN GENERATED FILES "__init__.py", + "autograph/__init__.py", + "autograph/experimental/__init__.py", "bitwise/__init__.py", "compat/__init__.py", "data/__init__.py", diff --git a/tensorflow/python/tools/api/generator/api_init_files_v1.bzl b/tensorflow/python/tools/api/generator/api_init_files_v1.bzl index e35b9c4374..0937f98e75 100644 --- a/tensorflow/python/tools/api/generator/api_init_files_v1.bzl +++ b/tensorflow/python/tools/api/generator/api_init_files_v1.bzl @@ -5,6 +5,8 @@ TENSORFLOW_API_INIT_FILES_V1 = [ # BEGIN GENERATED FILES "__init__.py", "app/__init__.py", + "autograph/__init__.py", + "autograph/experimental/__init__.py", "bitwise/__init__.py", "compat/__init__.py", "data/__init__.py", diff --git a/tensorflow/tools/api/golden/v1/tensorflow.autograph.experimental.-feature.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.autograph.experimental.-feature.pbtxt new file mode 100644 index 0000000000..a71da113b4 --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.autograph.experimental.-feature.pbtxt @@ -0,0 +1,28 @@ +path: "tensorflow.autograph.experimental.Feature" +tf_class { + is_instance: "" + member { + name: "ALL" + mtype: "" + } + member { + name: "AUTO_CONTROL_DEPS" + mtype: "" + } + member { + name: "DECORATORS" + mtype: "" + } + member { + name: "ERROR_REWRITING" + mtype: "" + } + member { + name: "LISTS" + mtype: "" + } + member { + name: "NAME_SCOPES" + mtype: "" + } +} diff --git a/tensorflow/tools/api/golden/v1/tensorflow.autograph.experimental.-verbosity.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.autograph.experimental.-verbosity.pbtxt new file mode 100644 index 0000000000..c4d5b77c07 --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.autograph.experimental.-verbosity.pbtxt @@ -0,0 +1,12 @@ +path: "tensorflow.autograph.experimental.Verbosity" +tf_class { + is_instance: "" + member { + name: "BRIEF" + mtype: "" + } + member { + name: "VERBOSE" + mtype: "" + } +} diff --git a/tensorflow/tools/api/golden/v1/tensorflow.autograph.experimental.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.autograph.experimental.pbtxt new file mode 100644 index 0000000000..5747dac7ab --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.autograph.experimental.pbtxt @@ -0,0 +1,11 @@ +path: "tensorflow.autograph.experimental" +tf_module { + member { + name: "Feature" + mtype: "" + } + member { + name: "Verbosity" + mtype: "" + } +} diff --git a/tensorflow/tools/api/golden/v1/tensorflow.autograph.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.autograph.pbtxt new file mode 100644 index 0000000000..34bdab95ff --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.autograph.pbtxt @@ -0,0 +1,15 @@ +path: "tensorflow.autograph" +tf_module { + member { + name: "experimental" + mtype: "" + } + member_method { + name: "to_code" + argspec: "args=[\'entity\', \'recursive\', \'arg_values\', \'arg_types\', \'indentation\', \'optional_features\', \'experimental_partial_types\'], varargs=None, keywords=None, defaults=[\'True\', \'None\', \'None\', \' \', \'Feature.ALL\', \'None\'], " + } + member_method { + name: "to_graph" + argspec: "args=[\'entity\', \'recursive\', \'verbose\', \'arg_values\', \'arg_types\', \'strip_decorators\', \'optional_features\', \'experimental_partial_types\'], varargs=None, keywords=None, defaults=[\'True\', \'Verbosity.VERBOSE\', \'None\', \'None\', \'None\', \'Feature.ALL\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v1/tensorflow.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.pbtxt index 584c74f99d..60ff59196b 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.pbtxt @@ -288,6 +288,10 @@ tf_module { name: "app" mtype: "" } + member { + name: "autograph" + mtype: "" + } member { name: "bfloat16" mtype: "" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.autograph.experimental.-feature.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.autograph.experimental.-feature.pbtxt new file mode 100644 index 0000000000..a71da113b4 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.autograph.experimental.-feature.pbtxt @@ -0,0 +1,28 @@ +path: "tensorflow.autograph.experimental.Feature" +tf_class { + is_instance: "" + member { + name: "ALL" + mtype: "" + } + member { + name: "AUTO_CONTROL_DEPS" + mtype: "" + } + member { + name: "DECORATORS" + mtype: "" + } + member { + name: "ERROR_REWRITING" + mtype: "" + } + member { + name: "LISTS" + mtype: "" + } + member { + name: "NAME_SCOPES" + mtype: "" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.autograph.experimental.-verbosity.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.autograph.experimental.-verbosity.pbtxt new file mode 100644 index 0000000000..c4d5b77c07 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.autograph.experimental.-verbosity.pbtxt @@ -0,0 +1,12 @@ +path: "tensorflow.autograph.experimental.Verbosity" +tf_class { + is_instance: "" + member { + name: "BRIEF" + mtype: "" + } + member { + name: "VERBOSE" + mtype: "" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.autograph.experimental.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.autograph.experimental.pbtxt new file mode 100644 index 0000000000..5747dac7ab --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.autograph.experimental.pbtxt @@ -0,0 +1,11 @@ +path: "tensorflow.autograph.experimental" +tf_module { + member { + name: "Feature" + mtype: "" + } + member { + name: "Verbosity" + mtype: "" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.autograph.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.autograph.pbtxt new file mode 100644 index 0000000000..34bdab95ff --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.autograph.pbtxt @@ -0,0 +1,15 @@ +path: "tensorflow.autograph" +tf_module { + member { + name: "experimental" + mtype: "" + } + member_method { + name: "to_code" + argspec: "args=[\'entity\', \'recursive\', \'arg_values\', \'arg_types\', \'indentation\', \'optional_features\', \'experimental_partial_types\'], varargs=None, keywords=None, defaults=[\'True\', \'None\', \'None\', \' \', \'Feature.ALL\', \'None\'], " + } + member_method { + name: "to_graph" + argspec: "args=[\'entity\', \'recursive\', \'verbose\', \'arg_values\', \'arg_types\', \'strip_decorators\', \'optional_features\', \'experimental_partial_types\'], varargs=None, keywords=None, defaults=[\'True\', \'Verbosity.VERBOSE\', \'None\', \'None\', \'None\', \'Feature.ALL\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.pbtxt index 4432cae53b..0f11107dc3 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.pbtxt @@ -80,6 +80,10 @@ tf_module { name: "VariableSynchronization" mtype: "" } + member { + name: "autograph" + mtype: "" + } member { name: "bfloat16" mtype: "" -- GitLab From 60f89ee911649a94b2483f71363e5dad6dda5901 Mon Sep 17 00:00:00 2001 From: "Meng, Peng" Date: Tue, 11 Dec 2018 10:46:03 +0800 Subject: [PATCH 331/873] add comments about layout Change-Id: Ie1e9f61046501d9e02586f96d232b748c77e0dd4 --- tensorflow/core/kernels/mkl_softmax_op.cc | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/tensorflow/core/kernels/mkl_softmax_op.cc b/tensorflow/core/kernels/mkl_softmax_op.cc index c35bdd5487..b84fd79d75 100644 --- a/tensorflow/core/kernels/mkl_softmax_op.cc +++ b/tensorflow/core/kernels/mkl_softmax_op.cc @@ -76,8 +76,10 @@ class MklSoftmaxOp : public OpKernel { // "nc" for 2 dim tensor, "tnc" for 3 dim tensor, "nchw" for 4 dim tensor, // and "ncdhw" for 5 dim tensor. Each of the symbols has the following // meaning: n = batch, c = channels, t = sequence length, h = height, w = - // width, d = depth. When src tensor is MKL, layout_type here is only used - // for setting TF layout type of output tensor. + // width, d = depth. When src tensor is MKL, layout_type here is only used + // for setting TF layout type of output tensor. When input is TF Tensor, + // layout here is no special sense. We use axis to define on which + // dimension to do softmax. switch (input_dims) { case 1: layout_type = memory::format::x; -- GitLab From c2ade32503f4109e4b8fcbd689f39a6e8cd96273 Mon Sep 17 00:00:00 2001 From: Justin Lebar Date: Mon, 10 Dec 2018 18:54:49 -0800 Subject: [PATCH 332/873] [XLA] Add DefaultDebugOptionsIgnoringFlags() function. This gets a DebugOptions struct with all the defaults filled in as though XLA_FLAGS were empty. This is useful when you want to run an XLA computation and explicitly ignore any XLA_FLAGS passed to the binary. PiperOrigin-RevId: 224925335 --- .../compiler/xla/debug_options_flags.cc | 51 +++++++++---------- tensorflow/compiler/xla/debug_options_flags.h | 5 +- 2 files changed, 28 insertions(+), 28 deletions(-) diff --git a/tensorflow/compiler/xla/debug_options_flags.cc b/tensorflow/compiler/xla/debug_options_flags.cc index 20609cad58..e77d0ba63b 100644 --- a/tensorflow/compiler/xla/debug_options_flags.cc +++ b/tensorflow/compiler/xla/debug_options_flags.cc @@ -22,49 +22,48 @@ limitations under the License. #include "tensorflow/compiler/xla/parse_flags_from_env.h" namespace xla { -namespace { -DebugOptions* flag_values; -std::vector* flag_objects; -std::once_flag flags_init; - -void SetDebugOptionsDefaults(DebugOptions* flags) { - flags->set_xla_llvm_enable_alias_scope_metadata(true); - flags->set_xla_llvm_enable_noalias_metadata(true); - flags->set_xla_llvm_enable_invariant_load_metadata(true); - flags->set_xla_llvm_disable_expensive_passes(false); - flags->set_xla_backend_optimization_level(3); - flags->set_xla_cpu_multi_thread_eigen(true); - flags->set_xla_gpu_cuda_data_dir("./cuda_sdk_lib"); - flags->set_xla_eliminate_hlo_implicit_broadcast(true); +DebugOptions DefaultDebugOptionsIgnoringFlags() { + DebugOptions opts; + opts.set_xla_llvm_enable_alias_scope_metadata(true); + opts.set_xla_llvm_enable_noalias_metadata(true); + opts.set_xla_llvm_enable_invariant_load_metadata(true); + opts.set_xla_llvm_disable_expensive_passes(false); + opts.set_xla_backend_optimization_level(3); + opts.set_xla_cpu_multi_thread_eigen(true); + opts.set_xla_gpu_cuda_data_dir("./cuda_sdk_lib"); + opts.set_xla_eliminate_hlo_implicit_broadcast(true); #ifdef INTEL_MKL - flags->set_xla_cpu_use_mkl_dnn(true); + opts.set_xla_cpu_use_mkl_dnn(true); #endif // INTEL_MKL - flags->set_xla_gpu_max_kernel_unroll_factor(4); + opts.set_xla_gpu_max_kernel_unroll_factor(4); // Set cudnn batchnorm off by default; it does not provide a performance win // on average. - flags->set_xla_gpu_use_cudnn_batchnorm(false); + opts.set_xla_gpu_use_cudnn_batchnorm(false); // Run all GPU work on one stream by default. Using multiple streams // increases memory usage and we lack strong motivating benchmarks for tuning // the heuristics needed to decide when to run on multiple streams. See // b/77879207. - flags->set_xla_gpu_disable_multi_streaming(true); + opts.set_xla_gpu_disable_multi_streaming(true); // TODO(jlebar): Disable fastmath once doing so is not a performance // regression. - flags->set_xla_cpu_enable_fast_math(true); - flags->set_xla_gpu_enable_fast_min_max(true); + opts.set_xla_cpu_enable_fast_math(true); + opts.set_xla_gpu_enable_fast_min_max(true); - flags->set_xla_force_host_platform_device_count(1); + opts.set_xla_force_host_platform_device_count(1); + return opts; } +static DebugOptions* flag_values; +static std::vector* flag_objects; +static std::once_flag flags_init; + // Allocates flag_values and flag_objects; this function must not be called more // than once - its call done via call_once. -void AllocateFlags() { - flag_values = new DebugOptions; - - SetDebugOptionsDefaults(flag_values); +static void AllocateFlags() { + flag_values = new DebugOptions(DefaultDebugOptionsIgnoringFlags()); // Returns a lambda that calls "member_setter" on "flag_values" with the // argument passed in to the lambda. @@ -344,8 +343,6 @@ void AllocateFlags() { ParseFlagsFromEnvAndDieIfUnknown("XLA_FLAGS", *flag_objects); } -} // namespace - void AppendDebugOptionsFlags(std::vector* flag_list) { std::call_once(flags_init, &AllocateFlags); flag_list->insert(flag_list->end(), flag_objects->begin(), diff --git a/tensorflow/compiler/xla/debug_options_flags.h b/tensorflow/compiler/xla/debug_options_flags.h index 60e59abc2a..dbf86a40f0 100644 --- a/tensorflow/compiler/xla/debug_options_flags.h +++ b/tensorflow/compiler/xla/debug_options_flags.h @@ -29,7 +29,10 @@ void AppendDebugOptionsFlags(std::vector* flag_list); // Fetches a DebugOptions proto message from flags provided to the program. // Flags must be registered with the flags parser using AppendDebugOptionsFlags // first. -xla::DebugOptions GetDebugOptionsFromFlags(); +DebugOptions GetDebugOptionsFromFlags(); + +// Gets a DebugOptions proto that reflects the defaults as if no flags were set. +DebugOptions DefaultDebugOptionsIgnoringFlags(); } // namespace xla -- GitLab From 5478c41e32d7ee455741fdee9473e60fa8e40a21 Mon Sep 17 00:00:00 2001 From: Justin Lebar Date: Mon, 10 Dec 2018 19:13:04 -0800 Subject: [PATCH 333/873] [XLA] Don't pass XLA_FLAGS down to fake computations created by replay_computation. When you pass XLA_FLAGS to replay_computation, you very likely want that only to apply to the actual computation(s) being run, not to the XLA computations that replay_computation synthesizes to generate fake data for the "real" ones' arguments. PiperOrigin-RevId: 224927003 --- tensorflow/compiler/xla/client/lib/testing.cc | 20 +++++++++++-------- tensorflow/compiler/xla/client/lib/testing.h | 11 +++++++--- .../compiler/xla/tools/replay_computation.cc | 7 ++++++- 3 files changed, 26 insertions(+), 12 deletions(-) diff --git a/tensorflow/compiler/xla/client/lib/testing.cc b/tensorflow/compiler/xla/client/lib/testing.cc index a95bbf2c8c..5db9d10dff 100644 --- a/tensorflow/compiler/xla/client/lib/testing.cc +++ b/tensorflow/compiler/xla/client/lib/testing.cc @@ -59,22 +59,25 @@ XlaOp BuildFakeDataOpOnDevice(const Shape& shape, XlaBuilder* builder) { return Tuple(builder, parts); } -std::unique_ptr MakeFakeDataViaDeviceOrDie(const Shape& shape, - Client* client) { +std::unique_ptr MakeFakeDataViaDeviceOrDie( + const Shape& shape, Client* client, DebugOptions* debug_opts) { XlaBuilder b(absl::StrCat("make_fake_", ShapeUtil::HumanString(shape))); BuildFakeDataOpOnDevice(shape, &b); XlaComputation computation = b.Build().ConsumeValueOrDie(); auto execution_options = CreateDefaultExecutionOptions(); *execution_options.mutable_shape_with_output_layout() = shape.ToProto(); + if (debug_opts) { + *execution_options.mutable_debug_options() = *debug_opts; + } return client->Execute(computation, /*arguments=*/{}, &execution_options) .ConsumeValueOrDie(); } } // namespace -std::unique_ptr MakeFakeDataOrDie(const Shape& shape, - Client* client) { +std::unique_ptr MakeFakeDataOrDie( + const Shape& shape, Client* client, DebugOptions* debug_opts /*=nullptr*/) { if (DataSizeOfShape(shape) < (1LL << 20)) { StatusOr literal_status = MakeFakeLiteral(shape); if (!literal_status.ok()) { @@ -82,24 +85,25 @@ std::unique_ptr MakeFakeDataOrDie(const Shape& shape, // an on-device computation. CHECK_EQ(literal_status.status().code(), tensorflow::error::UNIMPLEMENTED); - return MakeFakeDataViaDeviceOrDie(shape, client); + return MakeFakeDataViaDeviceOrDie(shape, client, debug_opts); } return client->TransferToServer(literal_status.ValueOrDie()).ValueOrDie(); } // If the data is large, generate it on-device. - return MakeFakeDataViaDeviceOrDie(shape, client); + return MakeFakeDataViaDeviceOrDie(shape, client, debug_opts); } std::vector> MakeFakeArgumentsOrDie( - const XlaComputation& computation, Client* client) { + const XlaComputation& computation, Client* client, + DebugOptions* debug_opts /*=nullptr*/) { CHECK(computation.proto().has_host_program_shape()) << "Computation should have progran shape."; auto program_shape = computation.proto().host_program_shape(); std::vector> results; for (const ShapeProto& shape : program_shape.parameters()) { - results.push_back(MakeFakeDataOrDie(Shape(shape), client)); + results.push_back(MakeFakeDataOrDie(Shape(shape), client, debug_opts)); } return results; } diff --git a/tensorflow/compiler/xla/client/lib/testing.h b/tensorflow/compiler/xla/client/lib/testing.h index 03695ce2a3..428fa3e93d 100644 --- a/tensorflow/compiler/xla/client/lib/testing.h +++ b/tensorflow/compiler/xla/client/lib/testing.h @@ -29,14 +29,19 @@ namespace xla { // Generates fake data of the given shape on the device or dies. The fake data // is created by performing a computation on the device rather than transferring // data from the host to the device. -std::unique_ptr MakeFakeDataOrDie(const Shape& shape, - Client* client); +// +// The optional DebugOptions are used when generating fake data on the device. +std::unique_ptr MakeFakeDataOrDie( + const Shape& shape, Client* client, DebugOptions* debug_opts = nullptr); // Returns vector of GlobalData handles of fake data (created using // MakeFakeDataOrDie) that are correctly shaped arguments for the given // xla computation. +// +// The optional DebugOptions are used when generating fake data on the device. std::vector> MakeFakeArgumentsOrDie( - const XlaComputation& computation, Client* client); + const XlaComputation& computation, Client* client, + DebugOptions* debug_opts = nullptr); } // namespace xla diff --git a/tensorflow/compiler/xla/tools/replay_computation.cc b/tensorflow/compiler/xla/tools/replay_computation.cc index ff2c339992..1a51303148 100644 --- a/tensorflow/compiler/xla/tools/replay_computation.cc +++ b/tensorflow/compiler/xla/tools/replay_computation.cc @@ -118,7 +118,12 @@ StatusOr ReplayComputation(const HloSnapshot& module, std::vector> global_data_arguments; std::vector argument_ptrs; if (opts.use_fake_data) { - global_data_arguments = MakeFakeArgumentsOrDie(computation, client); + // Run fake computations with debug options ignoring XLA_FLAGS. Users very + // likely want XLA_FLAGS only to apply to the "real" computation being run, + // not to the fake computations we use for generating arguments. + auto debug_opts = DefaultDebugOptionsIgnoringFlags(); + global_data_arguments = + MakeFakeArgumentsOrDie(computation, client, &debug_opts); for (const auto& data : global_data_arguments) { argument_ptrs.push_back( client->GlobalDataToShapedBuffer(data->handle(), /*device_ordinal=*/0) -- GitLab From ce6087616869670e0331cd4c873a0eb3d2296e0e Mon Sep 17 00:00:00 2001 From: Justin Lebar Date: Mon, 10 Dec 2018 19:27:48 -0800 Subject: [PATCH 334/873] [XLA] Add --xla_disable_all_hlo_passes flag. Previously we only had a flag for disabling specific passes. But being able to disable all passes is helpful if you have some already-optimized HLO that you just want to run. PiperOrigin-RevId: 224928095 --- tensorflow/compiler/xla/debug_options_flags.cc | 10 ++++++++++ tensorflow/compiler/xla/service/hlo_pass_pipeline.cc | 5 +++++ tensorflow/compiler/xla/xla.proto | 10 ++++++++++ 3 files changed, 25 insertions(+) diff --git a/tensorflow/compiler/xla/debug_options_flags.cc b/tensorflow/compiler/xla/debug_options_flags.cc index e77d0ba63b..c55ebcd066 100644 --- a/tensorflow/compiler/xla/debug_options_flags.cc +++ b/tensorflow/compiler/xla/debug_options_flags.cc @@ -201,6 +201,16 @@ static void AllocateFlags() { "Comma-separated list of hlo passes to be disabled. These names " "must exactly match the passes' names; no whitespace around " "commas."), + tensorflow::Flag( + "xla_disable_all_hlo_passes", + bool_setter_for(&DebugOptions::set_xla_disable_all_hlo_passes), false, + "Disables all HLO passes. Notes that some passes are necessary for " + "correctness and the invariants that must be satisfied by 'fully " + "optimized' HLO are different for different devices and may change " + "over time. The only 'guarantee', such as it is, is that if you " + "compile XLA and dump the optimized HLO for some graph, you should " + "be able to run it again on the same device with the same build of " + "XLA."), tensorflow::Flag( "xla_embed_ir_in_executable", bool_setter_for(&DebugOptions::set_xla_embed_ir_in_executable), diff --git a/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc b/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc index 51177f24f5..33ce7e23a8 100644 --- a/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc +++ b/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc @@ -77,6 +77,11 @@ std::vector HloPassPipeline::GetEnabledPasses( auto repeated_field = debug_options.xla_disable_hlo_passes(); absl::flat_hash_set disabled_pass_names(repeated_field.begin(), repeated_field.end()); + if (debug_options.xla_disable_all_hlo_passes()) { + VLOG(1) << "*All* passes disabled by --xla_disable_all_hlo_passes."; + return {}; + } + if (!disabled_pass_names.empty()) { VLOG(1) << "Passes disabled by --xla_disable_hlo_passes: " << absl::StrJoin(disabled_pass_names, ", "); diff --git a/tensorflow/compiler/xla/xla.proto b/tensorflow/compiler/xla/xla.proto index a37eac7fe4..32b51c104c 100644 --- a/tensorflow/compiler/xla/xla.proto +++ b/tensorflow/compiler/xla/xla.proto @@ -100,6 +100,14 @@ message DebugOptions { // names as specified by the HloPassInterface::name() method. repeated string xla_disable_hlo_passes = 30; + // Disables all HLO passes. Notes that some passes are necessary for + // correctness and the invariants that must be satisfied by "fully optimized" + // HLO are different for different devices and may change over time. The only + // "guarantee", such as it is, is that if you compile XLA and dump the + // optimized HLO for some graph, you should be able to run it again on the + // same device with the same build of XLA. + bool xla_disable_all_hlo_passes = 104; + // Numerical optimization level for the XLA compiler backend; the specific // interpretation of this value is left to the backends. int32 xla_backend_optimization_level = 31; @@ -216,6 +224,8 @@ message DebugOptions { // If set to true XLA:GPU invokes `ptxas` with -O0 (default is -O3). bool xla_gpu_disable_ptxas_optimizations = 103; + // Next id: 105 + // Extra options to pass to the compilation backend (e.g. LLVM); specific // interpretation of these values is left to the backend. map xla_backend_extra_options = 500; -- GitLab From 0d822c01e54126dd7e38e9c5bb186039b736121b Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 10 Dec 2018 19:52:31 -0800 Subject: [PATCH 335/873] Fix so we preserve the value of `executing_eagerly_outside_functions()` in the specific case of: * Eager execution enabled * Inside a FuncGraph, inside a graph * In a replica context (such as in a call to `tf.distribute.Strategy.call_for_each_replica()`). PiperOrigin-RevId: 224930182 --- .../python/mirrored_strategy_multigpu_test.py | 28 +++++++++++++++++++ .../python/distribute/mirrored_strategy.py | 21 ++++++++------ 2 files changed, 41 insertions(+), 8 deletions(-) diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py b/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py index 36be5c83f8..337a86b342 100644 --- a/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py +++ b/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py @@ -183,6 +183,34 @@ class MirroredStrategyVariableCreatorStackTest( expected = ("main_thread:thread_0", "main_thread:thread_1") self.assertEqual(expected, result) +@combinations.generate(combinations.combine( + distribution=[ + combinations.mirrored_strategy_with_gpu_and_cpu, + combinations.core_mirrored_strategy_with_gpu_and_cpu], + mode=["graph", "eager"])) +class MirroredStrategyCallForEachReplicaTest(test.TestCase): + + def testExecutingEagerlyOutsideFunction(self, distribution): + """Verify we preserve the value of executing_eagerly_outside_functions().""" + def model_fn(): + return ops.executing_eagerly_outside_functions() + + originally = ops.executing_eagerly_outside_functions() + with distribution.scope(): + in_scope = ops.executing_eagerly_outside_functions() + in_model_fn = distribution.extended.call_for_each_replica(model_fn) + unwrapped = distribution.unwrap(in_model_fn) + self.assertEqual(in_scope, unwrapped[0]) + self.assertEqual(in_scope, originally) + + # Verify this all again, but this time in a FuncGraph. + with func_graph.FuncGraph("fg").as_default(), distribution.scope(): + in_scope = ops.executing_eagerly_outside_functions() + in_model_fn = distribution.extended.call_for_each_replica(model_fn) + unwrapped = distribution.unwrap(in_model_fn) + self.assertEqual(in_scope, unwrapped[0]) + self.assertEqual(in_scope, originally) + @combinations.generate(combinations.combine( distribution=[ diff --git a/tensorflow/python/distribute/mirrored_strategy.py b/tensorflow/python/distribute/mirrored_strategy.py index cb94dfcfbd..9692c88dfc 100644 --- a/tensorflow/python/distribute/mirrored_strategy.py +++ b/tensorflow/python/distribute/mirrored_strategy.py @@ -50,8 +50,8 @@ from tensorflow.python.util.tf_export import tf_export @contextlib.contextmanager -def _enter_graph(g): - if context.executing_eagerly(): +def _enter_graph(g, eager): + if eager: with g.as_default(), context.eager_mode(): yield else: @@ -839,14 +839,19 @@ class MirroredExtended(distribute_lib.DistributionStrategyExtended): self.has_paused = threading.Event() # These fields have to do with inheriting various contexts from the # parent thread: + ctx = context.context() + self.in_eager = ctx.executing_eagerly() # pylint: disable=protected-access - self.context_mode = context.context()._eager_context.mode - if not context.context()._context_handle: - context.context()._initialize_handle_and_devices() + if not ctx._context_handle: + ctx._initialize_handle_and_devices() self.context_device_policy = ( pywrap_tensorflow.TFE_ContextGetDevicePlacementPolicy( - context.context()._context_handle)) + ctx._context_handle)) self.graph = ops.get_default_graph() + with ops.init_scope(): + self._init_in_eager = context.executing_eagerly() + self._init_graph = ops.get_default_graph() + 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. @@ -867,9 +872,9 @@ class MirroredExtended(distribute_lib.DistributionStrategyExtended): if self.coord.should_stop(): return with self.coord.stop_on_exception(), \ - context.context()._mode(self.context_mode), \ + _enter_graph(self._init_graph, self._init_in_eager), \ + _enter_graph(self.graph, self.in_eager), \ context.context().device_policy(self.context_device_policy), \ - _enter_graph(self.graph), \ MirroredReplicaContext(self.distribution, constant_op.constant( self.replica_id, dtypes.int32)), \ ops.device(self.device), \ -- GitLab From 10cab63fa54ee4c66c249b2c5427e080a625a8c7 Mon Sep 17 00:00:00 2001 From: Tong Shen Date: Mon, 10 Dec 2018 20:35:35 -0800 Subject: [PATCH 336/873] Outside compilation in "If" and "While". PiperOrigin-RevId: 224933587 --- tensorflow/compiler/jit/BUILD | 3 + .../jit/encapsulate_subgraphs_pass_test.cc | 201 +++-- .../jit/extract_outside_compilation_pass.cc | 760 ++++++++++++++++-- .../jit/extract_outside_compilation_pass.h | 5 +- .../extract_outside_compilation_pass_test.cc | 409 +++++++++- .../compiler/tf2xla/kernels/while_op.cc | 22 +- .../compiler/tf2xla/side_effect_util.cc | 2 + tensorflow/compiler/tf2xla/side_effect_util.h | 3 + tensorflow/compiler/tf2xla/tf2xla_util.cc | 9 + 9 files changed, 1239 insertions(+), 175 deletions(-) diff --git a/tensorflow/compiler/jit/BUILD b/tensorflow/compiler/jit/BUILD index 15dcbb2641..d8c88a9fca 100644 --- a/tensorflow/compiler/jit/BUILD +++ b/tensorflow/compiler/jit/BUILD @@ -515,6 +515,7 @@ cc_library( "//tensorflow/compiler/jit/ops:xla_ops", "//tensorflow/compiler/tf2xla:dump_graph", "//tensorflow/compiler/tf2xla:resource_operation_table", + "//tensorflow/compiler/tf2xla:side_effect_util", "//tensorflow/compiler/tf2xla:tf2xla_util", "//tensorflow/compiler/tf2xla:xla_compiler", "//tensorflow/compiler/tf2xla/cc:xla_jit_ops", @@ -613,6 +614,7 @@ tf_cc_test( "//tensorflow/cc:cc_ops", "//tensorflow/cc:cc_ops_internal", "//tensorflow/cc:function_ops", + "//tensorflow/cc:functional_ops", "//tensorflow/cc:ops", "//tensorflow/cc:resource_variable_ops", "//tensorflow/cc:scope", @@ -625,6 +627,7 @@ tf_cc_test( "//tensorflow/compiler/tf2xla/cc:xla_ops", "//tensorflow/compiler/tf2xla/kernels:xla_dummy_ops", "//tensorflow/compiler/tf2xla/kernels:xla_ops", + "//tensorflow/compiler/xla:test", "//tensorflow/core:core_cpu", "//tensorflow/core:framework", "//tensorflow/core:framework_internal", diff --git a/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc b/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc index de89be9a35..7476d1dc51 100644 --- a/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc +++ b/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc @@ -299,7 +299,7 @@ REGISTER_OP("XlaHostCompute") .Attr("Toutputs: list(type) >= 0") .Attr("ancestors: list(string) >= 0") .Attr("key: string") - .Attr("shape_inference_graph: string = ''") + .Attr("shape_inference_graph: func") .Attr("shapes: list(shape) >= 0") .SetShapeFn(::tensorflow::shape_inference::UnknownShape); @@ -901,18 +901,22 @@ TEST(EncapsulateSubgraphsTest, OneFunctionOneOutside) { { GraphDefBuilder shape(GraphDefBuilder::kFailImmediately); Node* key_constant = KeyPlaceholder("F1", shape.opts()); - Node* recv = RecvAtHost(ops::NodeOut(key_constant, 0), "F1", "O1", - {DT_FLOAT, DT_FLOAT}, shape.opts()); + Node* recv = RecvAtHost( + ops::NodeOut(key_constant, 0), "F1", "O1", {DT_FLOAT, DT_FLOAT}, + shape.opts().WithAttr(kXlaHasHostTransferAttrName, true)); Node* e = Binary(ops::NodeOut(recv, 0), ops::NodeOut(recv, 1), shape.opts() .WithName("E") .WithAttr("_encapsulate", "F1") .WithAttr("_outside", "O1")); - SendFromHost(ops::NodeOut(key_constant, 0), "F1", "O1", {e}, shape.opts()); + SendFromHost(ops::NodeOut(key_constant, 0), "F1", "O1", {e}, + shape.opts().WithAttr(kXlaHasHostTransferAttrName, true)); TF_EXPECT_OK( AddGraphDefToFunctionLibrary(shape, "F1_O1", &library_expected)); } + NameAttrList shape_inference_graph; + shape_inference_graph.set_name("_outside_compilation_shape_inference_F1_O1"); *library_expected.add_function() = test::function::XTimesTwo(); *library_expected.add_function() = FunctionDefHelper::Create( "F1", {"a_0_arg:float", "b_0_arg:float"}, {"f_0_retval_retval:float"}, {}, @@ -931,8 +935,7 @@ TEST(EncapsulateSubgraphsTest, OneFunctionOneOutside) { {"Toutputs", absl::Span({DT_FLOAT})}, {"ancestors", absl::Span({})}, {"key", "host_compute_channel_F1_O1"}, - {"shape_inference_graph", - "_outside_compilation_shape_inference_F1_O1"}, + {"shape_inference_graph", shape_inference_graph}, {"shapes", absl::Span({})}, {"_outside_compilation_subgraph", "O1"}}, {"c"}}, @@ -948,8 +951,9 @@ TEST(EncapsulateSubgraphsTest, OneFunctionOneOutside) { Node* key_constant = KeyPlaceholder("F1", b2.opts().WithName("F1_key_placeholder")); - Node* recv = RecvAtHost(ops::NodeOut(key_constant, 0), "F1", "O1", - {DT_FLOAT, DT_FLOAT}, b2.opts()); + Node* recv = RecvAtHost( + ops::NodeOut(key_constant, 0), "F1", "O1", {DT_FLOAT, DT_FLOAT}, + b2.opts().WithAttr(kXlaHasHostTransferAttrName, true)); Node* e = Binary(ops::NodeOut(recv, 0), ops::NodeOut(recv, 1), b2.opts() .WithName("E") @@ -957,7 +961,8 @@ TEST(EncapsulateSubgraphsTest, OneFunctionOneOutside) { .WithAttr("_encapsulate", "F1") .WithAttr("_outside", "O1")); Node* send = SendFromHost(ops::NodeOut(key_constant, 0), "F1", "O1", {e}, - b2.opts().WithControlInput(e)); + b2.opts().WithControlInput(e).WithAttr( + kXlaHasHostTransferAttrName, true)); Node* s = Sequencer( b2.opts().WithName("F1_sequencer").WithControlInputs({recv, send}), @@ -1022,14 +1027,16 @@ TEST(EncapsulateSubgraphsTest, OneFunctionTwoOutside) { { GraphDefBuilder shape1(GraphDefBuilder::kFailImmediately); Node* key_constant = KeyPlaceholder("F1", shape1.opts()); - Node* recv = RecvAtHost(ops::NodeOut(key_constant, 0), "F1", "O1", - {DT_FLOAT, DT_FLOAT}, shape1.opts()); + Node* recv = RecvAtHost( + ops::NodeOut(key_constant, 0), "F1", "O1", {DT_FLOAT, DT_FLOAT}, + shape1.opts().WithAttr(kXlaHasHostTransferAttrName, true)); Node* e = Binary(ops::NodeOut(recv, 0), ops::NodeOut(recv, 1), shape1.opts() .WithName("E") .WithAttr("_encapsulate", "F1") .WithAttr("_outside", "O1")); - SendFromHost(ops::NodeOut(key_constant, 0), "F1", "O1", {e}, shape1.opts()); + SendFromHost(ops::NodeOut(key_constant, 0), "F1", "O1", {e}, + shape1.opts().WithAttr(kXlaHasHostTransferAttrName, true)); TF_EXPECT_OK( AddGraphDefToFunctionLibrary(shape1, "F1_O1", &library_expected)); } @@ -1037,25 +1044,31 @@ TEST(EncapsulateSubgraphsTest, OneFunctionTwoOutside) { { GraphDefBuilder shape2(GraphDefBuilder::kFailImmediately); Node* key_constant = KeyPlaceholder("F1", shape2.opts()); - Node* recv1 = RecvAtHost(ops::NodeOut(key_constant, 0), "F1", "O1", - {DT_FLOAT, DT_FLOAT}, shape2.opts()); + Node* recv1 = RecvAtHost( + ops::NodeOut(key_constant, 0), "F1", "O1", {DT_FLOAT, DT_FLOAT}, + shape2.opts().WithAttr(kXlaHasHostTransferAttrName, true)); Node* e = Binary(ops::NodeOut(recv1, 0), ops::NodeOut(recv1, 1), shape2.opts() .WithName("E") .WithAttr("_encapsulate", "F1") .WithAttr("_outside", "O1")); - Node* recv2 = RecvAtHost(ops::NodeOut(key_constant, 0), "F1", "O2", - {DT_FLOAT, DT_FLOAT}, shape2.opts()); + Node* recv2 = RecvAtHost( + ops::NodeOut(key_constant, 0), "F1", "O2", {DT_FLOAT, DT_FLOAT}, + shape2.opts().WithAttr(kXlaHasHostTransferAttrName, true)); Node* h = Binary(ops::NodeOut(recv2, 1), e, shape2.opts() .WithName("H") .WithAttr("_encapsulate", "F1") .WithAttr("_outside", "O2")); - SendFromHost(ops::NodeOut(key_constant, 0), "F1", "O2", {h}, shape2.opts()); + SendFromHost(ops::NodeOut(key_constant, 0), "F1", "O2", {h}, + shape2.opts().WithAttr(kXlaHasHostTransferAttrName, true)); TF_EXPECT_OK( AddGraphDefToFunctionLibrary(shape2, "F1_O2", &library_expected)); } + NameAttrList shape_inference_graph1, shape_inference_graph2; + shape_inference_graph1.set_name("_outside_compilation_shape_inference_F1_O1"); + shape_inference_graph2.set_name("_outside_compilation_shape_inference_F1_O2"); *library_expected.add_function() = FunctionDefHelper::Create( "F1", {"a_0_arg:float", "b_0_arg:float"}, {"i_0_retval_retval:float"}, {}, { @@ -1076,8 +1089,7 @@ TEST(EncapsulateSubgraphsTest, OneFunctionTwoOutside) { {"Toutputs", absl::Span({DT_FLOAT})}, {"ancestors", absl::Span({})}, {"key", "host_compute_channel_F1_O2"}, - {"shape_inference_graph", - "_outside_compilation_shape_inference_F1_O2"}, + {"shape_inference_graph", shape_inference_graph2}, {"shapes", absl::Span({})}, {"_outside_compilation_subgraph", "O2"}}, {"F"}}, @@ -1088,8 +1100,7 @@ TEST(EncapsulateSubgraphsTest, OneFunctionTwoOutside) { {"Toutputs", absl::Span({DT_FLOAT})}, {"ancestors", absl::Span({})}, {"key", "host_compute_channel_F1_O1"}, - {"shape_inference_graph", - "_outside_compilation_shape_inference_F1_O1"}, + {"shape_inference_graph", shape_inference_graph1}, {"shapes", absl::Span({})}, {"_outside_compilation_subgraph", "O1"}}, {"D"}}, @@ -1105,8 +1116,9 @@ TEST(EncapsulateSubgraphsTest, OneFunctionTwoOutside) { Node* key_constant = KeyPlaceholder("F1", b2.opts().WithName("F1_key_placeholder")); - Node* recv1 = RecvAtHost(ops::NodeOut(key_constant, 0), "F1", "O1", - {DT_FLOAT, DT_FLOAT}, b2.opts()); + Node* recv1 = RecvAtHost( + ops::NodeOut(key_constant, 0), "F1", "O1", {DT_FLOAT, DT_FLOAT}, + b2.opts().WithAttr(kXlaHasHostTransferAttrName, true)); Node* e = Binary(ops::NodeOut(recv1, 0), ops::NodeOut(recv1, 1), b2.opts() .WithName("E") @@ -1114,10 +1126,12 @@ TEST(EncapsulateSubgraphsTest, OneFunctionTwoOutside) { .WithAttr("_encapsulate", "F1") .WithAttr("_outside", "O1")); Node* send1 = SendFromHost(ops::NodeOut(key_constant, 0), "F1", "O1", {e}, - b2.opts().WithControlInput(e)); + b2.opts().WithControlInput(e).WithAttr( + kXlaHasHostTransferAttrName, true)); - Node* recv2 = RecvAtHost(ops::NodeOut(key_constant, 0), "F1", "O2", - {DT_FLOAT, DT_FLOAT}, b2.opts()); + Node* recv2 = RecvAtHost( + ops::NodeOut(key_constant, 0), "F1", "O2", {DT_FLOAT, DT_FLOAT}, + b2.opts().WithAttr(kXlaHasHostTransferAttrName, true)); Node* g = Binary(e, ops::NodeOut(recv2, 0), b2.opts() .WithName("G") @@ -1130,7 +1144,8 @@ TEST(EncapsulateSubgraphsTest, OneFunctionTwoOutside) { .WithAttr("_encapsulate", "F1") .WithAttr("_outside", "O2")); Node* send2 = - SendFromHost(ops::NodeOut(key_constant, 0), "F1", "O2", {h}, b2.opts()); + SendFromHost(ops::NodeOut(key_constant, 0), "F1", "O2", {h}, + b2.opts().WithAttr(kXlaHasHostTransferAttrName, true)); Node* s = Sequencer(b2.opts() .WithName("F1_sequencer") @@ -1212,7 +1227,7 @@ TEST(EncapsulateSubgraphsTest, TwoFunctionsTwoOutside) { {"Toutputs", absl::Span({DT_FLOAT})}, {"ancestors", absl::Span({})}, {"key", "host_compute_channel_F1_O1"}, - {"shape_inference_graph", ""}, + {"shape_inference_graph", NameAttrList()}, {"shapes", absl::Span({shape_proto_expected})}, {"_outside_compilation_subgraph", "O1"}}, @@ -1235,7 +1250,7 @@ TEST(EncapsulateSubgraphsTest, TwoFunctionsTwoOutside) { {"Toutputs", absl::Span({DT_FLOAT})}, {"ancestors", absl::Span({})}, {"key", "host_compute_channel_F2_O1"}, - {"shape_inference_graph", ""}, + {"shape_inference_graph", NameAttrList()}, {"shapes", absl::Span({shape_proto_expected})}, {"_outside_compilation_subgraph", "O1"}}}, @@ -1251,8 +1266,9 @@ TEST(EncapsulateSubgraphsTest, TwoFunctionsTwoOutside) { Node* key_constant1 = KeyPlaceholder("F1", b2.opts().WithName("F1_key_placeholder")); - Node* recv1 = RecvAtHost(ops::NodeOut(key_constant1, 0), "F1", "O1", - {DT_FLOAT, DT_FLOAT}, b2.opts()); + Node* recv1 = RecvAtHost( + ops::NodeOut(key_constant1, 0), "F1", "O1", {DT_FLOAT, DT_FLOAT}, + b2.opts().WithAttr(kXlaHasHostTransferAttrName, true)); Node* e = Binary(ops::NodeOut(recv1, 0), ops::NodeOut(recv1, 1), b2.opts() .WithName("E") @@ -1260,7 +1276,8 @@ TEST(EncapsulateSubgraphsTest, TwoFunctionsTwoOutside) { .WithAttr("_encapsulate", "F1") .WithAttr("_outside", "O1")); Node* send1 = SendFromHost(ops::NodeOut(key_constant1, 0), "F1", "O1", {e}, - b2.opts().WithControlInput(e)); + b2.opts().WithControlInput(e).WithAttr( + kXlaHasHostTransferAttrName, true)); Node* s1 = Sequencer( b2.opts().WithName("F1_sequencer").WithControlInputs({recv1, send1}), "F1"); @@ -1272,15 +1289,17 @@ TEST(EncapsulateSubgraphsTest, TwoFunctionsTwoOutside) { Node* key_constant2 = KeyPlaceholder("F2", b2.opts().WithName("F2_key_placeholder")); - Node* recv2 = RecvAtHost(ops::NodeOut(key_constant2, 0), "F2", "O1", - {DT_FLOAT}, b2.opts()); + Node* recv2 = + RecvAtHost(ops::NodeOut(key_constant2, 0), "F2", "O1", {DT_FLOAT}, + b2.opts().WithAttr(kXlaHasHostTransferAttrName, true)); Node* h = Binary(ops::NodeOut(call1, 1), recv2, b2.opts() .WithName("H") .WithAttr("_encapsulate", "F2") .WithAttr("_outside", "O1")); - Node* send2 = SendFromHost(ops::NodeOut(key_constant2, 0), "F2", "O1", {h}, - b2.opts()); + Node* send2 = + SendFromHost(ops::NodeOut(key_constant2, 0), "F2", "O1", {h}, + b2.opts().WithAttr(kXlaHasHostTransferAttrName, true)); Node* s2 = Sequencer( b2.opts().WithName("F2_sequencer").WithControlInputs({recv2, send2}), @@ -1358,7 +1377,7 @@ TEST(EncapsulateSubgraphsTest, TwoFunctionsTwoOutsideDependencyFromOutside) { {"Toutputs", absl::Span({DT_FLOAT})}, {"ancestors", absl::Span({})}, {"key", "host_compute_channel_F1_O1"}, - {"shape_inference_graph", ""}, + {"shape_inference_graph", NameAttrList()}, {"shapes", absl::Span({shape_proto_expected})}, {"_outside_compilation_subgraph", "O1"}}, @@ -1380,7 +1399,7 @@ TEST(EncapsulateSubgraphsTest, TwoFunctionsTwoOutsideDependencyFromOutside) { {"Toutputs", absl::Span({DT_FLOAT})}, {"ancestors", absl::Span({})}, {"key", "host_compute_channel_F2_O1"}, - {"shape_inference_graph", ""}, + {"shape_inference_graph", NameAttrList()}, {"shapes", absl::Span({shape_proto_expected})}, {"_outside_compilation_subgraph", "O1"}}}, @@ -1489,7 +1508,7 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationNoInputs) { {"Toutputs", absl::Span({DT_FLOAT})}, {"ancestors", absl::Span({})}, {"key", "host_compute_channel_F1_O1"}, - {"shape_inference_graph", ""}, + {"shape_inference_graph", NameAttrList()}, {"shapes", absl::Span({shape_proto_expected})}, {"_outside_compilation_subgraph", "O1"}}}, @@ -1574,7 +1593,7 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationControlInput) { {"Toutputs", absl::Span({DT_FLOAT})}, {"ancestors", absl::Span({})}, {"key", "host_compute_channel_F1_O1"}, - {"shape_inference_graph", ""}, + {"shape_inference_graph", NameAttrList()}, {"shapes", absl::Span({shape_proto_expected})}, {"_outside_compilation_subgraph", "O1"}}, @@ -1657,7 +1676,7 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationNoOutputs) { {"Toutputs", absl::Span({})}, {"ancestors", absl::Span({})}, {"key", "host_compute_channel_F1_O1"}, - {"shape_inference_graph", ""}, + {"shape_inference_graph", NameAttrList()}, {"shapes", absl::Span({})}, {"_outside_compilation_subgraph", "O1"}}}, }, @@ -1739,7 +1758,7 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationControlOutput) { {"Toutputs", absl::Span({})}, {"ancestors", absl::Span({})}, {"key", "host_compute_channel_F1_O1"}, - {"shape_inference_graph", ""}, + {"shape_inference_graph", NameAttrList()}, {"shapes", absl::Span({})}, {"_outside_compilation_subgraph", "O1"}}}, }, @@ -1816,17 +1835,21 @@ TEST(EncapsulateSubgraphsTest, { GraphDefBuilder shape2(GraphDefBuilder::kFailImmediately); Node* key_constant = KeyPlaceholder("F1", shape2.opts()); - Node* recv2 = RecvAtHost(ops::NodeOut(key_constant, 0), "F1", "O2", - {DT_FLOAT}, shape2.opts()); + Node* recv2 = + RecvAtHost(ops::NodeOut(key_constant, 0), "F1", "O2", {DT_FLOAT}, + shape2.opts().WithAttr(kXlaHasHostTransferAttrName, true)); Node* g = Unary(ops::NodeOut(recv2, 0), shape2.opts() .WithName("G") .WithAttr("_encapsulate", "F1") .WithAttr("_outside", "O2")); - SendFromHost(ops::NodeOut(key_constant, 0), "F1", "O2", {g}, shape2.opts()); + SendFromHost(ops::NodeOut(key_constant, 0), "F1", "O2", {g}, + shape2.opts().WithAttr(kXlaHasHostTransferAttrName, true)); TF_EXPECT_OK( AddGraphDefToFunctionLibrary(shape2, "F1_O2", &library_expected)); } + NameAttrList shape_inference_graph; + shape_inference_graph.set_name("_outside_compilation_shape_inference_F1_O2"); *library_expected.add_function() = FunctionDefHelper::Create( "F1", {"a_0_arg:float", "b_0_arg:float"}, {"h_0_retval_retval:float"}, {}, { @@ -1843,8 +1866,7 @@ TEST(EncapsulateSubgraphsTest, {"Toutputs", absl::Span({DT_FLOAT})}, {"ancestors", absl::Span({})}, {"key", "host_compute_channel_F1_O2"}, - {"shape_inference_graph", - "_outside_compilation_shape_inference_F1_O2"}, + {"shape_inference_graph", shape_inference_graph}, {"shapes", absl::Span({})}, {"_outside_compilation_subgraph", "O2"}}}, }, @@ -1863,15 +1885,17 @@ TEST(EncapsulateSubgraphsTest, .WithAttr("_outside", "O1")); Node* key_constant = KeyPlaceholder("F1", b2.opts().WithName("F1_key_placeholder")); - Node* recv = RecvAtHost(ops::NodeOut(key_constant, 0), "F1", "O2", - {DT_FLOAT}, b2.opts()); + Node* recv = + RecvAtHost(ops::NodeOut(key_constant, 0), "F1", "O2", {DT_FLOAT}, + b2.opts().WithAttr(kXlaHasHostTransferAttrName, true)); Node* g = Unary(recv, b2.opts() .WithName("G") .WithAttr("_encapsulate", "F1") .WithAttr("_outside", "O2") .WithControlInput(e)); Node* send = - SendFromHost(ops::NodeOut(key_constant, 0), "F1", "O2", {g}, b2.opts()); + SendFromHost(ops::NodeOut(key_constant, 0), "F1", "O2", {g}, + b2.opts().WithAttr(kXlaHasHostTransferAttrName, true)); Node* s1 = Sequencer( b2.opts().WithName("F1_sequencer").WithControlInputs({recv, send}), "F1"); @@ -1925,17 +1949,21 @@ TEST(EncapsulateSubgraphsTest, { GraphDefBuilder shape1(GraphDefBuilder::kFailImmediately); Node* key_constant = KeyPlaceholder("F1", shape1.opts()); - Node* recv2 = RecvAtHost(ops::NodeOut(key_constant, 0), "F1", "O1", - {DT_FLOAT}, shape1.opts()); + Node* recv2 = + RecvAtHost(ops::NodeOut(key_constant, 0), "F1", "O1", {DT_FLOAT}, + shape1.opts().WithAttr(kXlaHasHostTransferAttrName, true)); Node* e = Unary(ops::NodeOut(recv2, 0), shape1.opts() .WithName("E") .WithAttr("_encapsulate", "F1") .WithAttr("_outside", "O1")); - SendFromHost(ops::NodeOut(key_constant, 0), "F1", "O1", {e}, shape1.opts()); + SendFromHost(ops::NodeOut(key_constant, 0), "F1", "O1", {e}, + shape1.opts().WithAttr(kXlaHasHostTransferAttrName, true)); TF_EXPECT_OK( AddGraphDefToFunctionLibrary(shape1, "F1_O1", &library_expected)); } + NameAttrList shape_inference_graph; + shape_inference_graph.set_name("_outside_compilation_shape_inference_F1_O1"); *library_expected.add_function() = FunctionDefHelper::Create( "F1", {"a_0_arg:float", "b_0_arg:float"}, {"h_0_retval_retval:float"}, {}, { @@ -1952,8 +1980,7 @@ TEST(EncapsulateSubgraphsTest, {"Toutputs", absl::Span({DT_FLOAT})}, {"ancestors", absl::Span({})}, {"key", "host_compute_channel_F1_O1"}, - {"shape_inference_graph", - "_outside_compilation_shape_inference_F1_O1"}, + {"shape_inference_graph", shape_inference_graph}, {"shapes", absl::Span({})}, {"_outside_compilation_subgraph", "O1"}}}, }, @@ -1968,14 +1995,16 @@ TEST(EncapsulateSubgraphsTest, Node* key_constant = KeyPlaceholder("F1", b2.opts().WithName("F1_key_placeholder")); - Node* recv = RecvAtHost(ops::NodeOut(key_constant, 0), "F1", "O1", - {DT_FLOAT}, b2.opts()); + Node* recv = + RecvAtHost(ops::NodeOut(key_constant, 0), "F1", "O1", {DT_FLOAT}, + b2.opts().WithAttr(kXlaHasHostTransferAttrName, true)); Node* e = Unary(recv, b2.opts() .WithName("E") .WithAttr("_encapsulate", "F1") .WithAttr("_outside", "O1")); Node* send = - SendFromHost(ops::NodeOut(key_constant, 0), "F1", "O1", {e}, b2.opts()); + SendFromHost(ops::NodeOut(key_constant, 0), "F1", "O1", {e}, + b2.opts().WithAttr(kXlaHasHostTransferAttrName, true)); /*Node* g =*/Unary(a, b2.opts() .WithName("G") .WithAttr("_encapsulate", "F1") @@ -2039,17 +2068,21 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationClusterDependency) { { GraphDefBuilder shape1(GraphDefBuilder::kFailImmediately); Node* key_constant = KeyPlaceholder("F1", shape1.opts()); - Node* recv2 = RecvAtHost(ops::NodeOut(key_constant, 0), "F1", "O1", - {DT_FLOAT}, shape1.opts()); + Node* recv2 = + RecvAtHost(ops::NodeOut(key_constant, 0), "F1", "O1", {DT_FLOAT}, + shape1.opts().WithAttr(kXlaHasHostTransferAttrName, true)); Node* e = Unary(ops::NodeOut(recv2, 0), shape1.opts() .WithName("E") .WithAttr("_encapsulate", "F1") .WithAttr("_outside", "O1")); - SendFromHost(ops::NodeOut(key_constant, 0), "F1", "O1", {e}, shape1.opts()); + SendFromHost(ops::NodeOut(key_constant, 0), "F1", "O1", {e}, + shape1.opts().WithAttr(kXlaHasHostTransferAttrName, true)); TF_EXPECT_OK( AddGraphDefToFunctionLibrary(shape1, "F1_O1", &library_expected)); } + NameAttrList shape_inference_graph; + shape_inference_graph.set_name("_outside_compilation_shape_inference_F1_O1"); *library_expected.add_function() = FunctionDefHelper::Create( "F1", {"a_0_arg:float", "b_0_arg:float"}, {"h_0_retval_retval:float"}, {}, {{{"C"}, "UnaryTest", {"a_0_arg"}}, @@ -2063,8 +2096,7 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationClusterDependency) { {"Toutputs", absl::Span({DT_FLOAT})}, {"ancestors", absl::Span({})}, {"key", "host_compute_channel_F1_O1"}, - {"shape_inference_graph", - "_outside_compilation_shape_inference_F1_O1"}, + {"shape_inference_graph", shape_inference_graph}, {"shapes", absl::Span({})}, {"_outside_compilation_subgraph", "O1"}}}, {{"outside_compilation_O2_host_compute"}, @@ -2074,7 +2106,7 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationClusterDependency) { {"Toutputs", absl::Span({})}, {"ancestors", absl::Span({})}, {"key", "host_compute_channel_F1_O2"}, - {"shape_inference_graph", ""}, + {"shape_inference_graph", NameAttrList()}, {"shapes", absl::Span({})}, {"_outside_compilation_subgraph", "O2"}}, {}}, @@ -2085,7 +2117,7 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationClusterDependency) { {"Toutputs", absl::Span({})}, {"ancestors", absl::Span({})}, {"key", "host_compute_channel_F1_O3"}, - {"shape_inference_graph", ""}, + {"shape_inference_graph", NameAttrList()}, {"shapes", absl::Span({})}, {"_outside_compilation_subgraph", "O3"}}, {}}}, @@ -2100,23 +2132,27 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationClusterDependency) { Node* key_constant = KeyPlaceholder("F1", b2.opts().WithName("F1_key_placeholder")); - Node* recv1 = RecvAtHost(ops::NodeOut(key_constant, 0), "F1", "O1", - {DT_FLOAT}, b2.opts()); + Node* recv1 = + RecvAtHost(ops::NodeOut(key_constant, 0), "F1", "O1", {DT_FLOAT}, + b2.opts().WithAttr(kXlaHasHostTransferAttrName, true)); Node* e = Unary(recv1, b2.opts() .WithName("E") .WithAttr("_encapsulate", "F1") .WithAttr("_outside", "O1")); Node* send = - SendFromHost(ops::NodeOut(key_constant, 0), "F1", "O1", {e}, b2.opts()); - Node* recv2 = RecvAtHost(ops::NodeOut(key_constant, 0), "F1", "O2", - {DT_FLOAT}, b2.opts()); + SendFromHost(ops::NodeOut(key_constant, 0), "F1", "O1", {e}, + b2.opts().WithAttr(kXlaHasHostTransferAttrName, true)); + Node* recv2 = + RecvAtHost(ops::NodeOut(key_constant, 0), "F1", "O2", {DT_FLOAT}, + b2.opts().WithAttr(kXlaHasHostTransferAttrName, true)); Node* g = Unary(recv2, b2.opts() .WithName("G") .WithAttr("_encapsulate", "F1") .WithAttr("_outside", "O2") .WithControlInput(e)); - Node* recv3 = RecvAtHost(ops::NodeOut(key_constant, 0), "F1", "O3", - {DT_FLOAT}, b2.opts()); + Node* recv3 = + RecvAtHost(ops::NodeOut(key_constant, 0), "F1", "O3", {DT_FLOAT}, + b2.opts().WithAttr(kXlaHasHostTransferAttrName, true)); /*Node* i =*/Binary(recv3, e, b2.opts() .WithName("I") @@ -2236,8 +2272,9 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationShapeInference) { { GraphDefBuilder shape(GraphDefBuilder::kFailImmediately); Node* key_constant = KeyPlaceholder("F1", shape.opts()); - Node* recv = RecvAtHost(ops::NodeOut(key_constant, 0), "F1", "O1", - {DT_FLOAT}, shape.opts()); + Node* recv = + RecvAtHost(ops::NodeOut(key_constant, 0), "F1", "O1", {DT_FLOAT}, + shape.opts().WithAttr(kXlaHasHostTransferAttrName, true)); Node* a = InputShaped(shape.opts().WithName("A")); Node* c = Unary(a, shape.opts().WithName("C")); Node* e = BinaryUnknownShape(c, recv, @@ -2245,11 +2282,14 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationShapeInference) { .WithName("E") .WithAttr("_encapsulate", "F1") .WithAttr("_outside", "O1")); - SendFromHost(ops::NodeOut(key_constant, 0), "F1", "O1", {e}, shape.opts()); + SendFromHost(ops::NodeOut(key_constant, 0), "F1", "O1", {e}, + shape.opts().WithAttr(kXlaHasHostTransferAttrName, true)); TF_EXPECT_OK( AddGraphDefToFunctionLibrary(shape, "F1_O1", &library_expected)); } + NameAttrList shape_inference_graph; + shape_inference_graph.set_name("_outside_compilation_shape_inference_F1_O1"); *library_expected.add_function() = test::function::XTimesTwo(); *library_expected.add_function() = FunctionDefHelper::Create( "F1", {"b_0_arg:float", "c_0_arg:float"}, {"f_0_retval_retval:float"}, {}, @@ -2267,8 +2307,7 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationShapeInference) { {"Toutputs", absl::Span({DT_FLOAT})}, {"ancestors", absl::Span({})}, {"key", "host_compute_channel_F1_O1"}, - {"shape_inference_graph", - "_outside_compilation_shape_inference_F1_O1"}, + {"shape_inference_graph", shape_inference_graph}, {"shapes", absl::Span({})}, {"_outside_compilation_subgraph", "O1"}}, {"c"}}, @@ -2285,8 +2324,9 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationShapeInference) { Node* key_constant = KeyPlaceholder("F1", b2.opts().WithName("F1_key_placeholder")); - Node* recv = RecvAtHost(ops::NodeOut(key_constant, 0), "F1", "O1", - {DT_FLOAT}, b2.opts()); + Node* recv = + RecvAtHost(ops::NodeOut(key_constant, 0), "F1", "O1", {DT_FLOAT}, + b2.opts().WithAttr(kXlaHasHostTransferAttrName, true)); Node* e = BinaryUnknownShape(c, ops::NodeOut(recv, 0), b2.opts() .WithName("E") @@ -2294,7 +2334,8 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationShapeInference) { .WithAttr("_encapsulate", "F1") .WithAttr("_outside", "O1")); Node* send = SendFromHost(ops::NodeOut(key_constant, 0), "F1", "O1", {e}, - b2.opts().WithControlInput(e)); + b2.opts().WithControlInput(e).WithAttr( + kXlaHasHostTransferAttrName, true)); Node* s = Sequencer( b2.opts().WithName("F1_sequencer").WithControlInputs({recv, send}), diff --git a/tensorflow/compiler/jit/extract_outside_compilation_pass.cc b/tensorflow/compiler/jit/extract_outside_compilation_pass.cc index e3c7e2f89b..feac983884 100644 --- a/tensorflow/compiler/jit/extract_outside_compilation_pass.cc +++ b/tensorflow/compiler/jit/extract_outside_compilation_pass.cc @@ -20,8 +20,10 @@ limitations under the License. #include "tensorflow/compiler/jit/encapsulate_subgraphs_pass.h" #include "tensorflow/compiler/jit/encapsulate_util.h" #include "tensorflow/compiler/tf2xla/dump_graph.h" +#include "tensorflow/compiler/tf2xla/side_effect_util.h" #include "tensorflow/compiler/tf2xla/tf2xla_util.h" #include "tensorflow/core/common_runtime/function.h" +#include "tensorflow/core/framework/function.h" #include "tensorflow/core/framework/graph_to_functiondef.h" #include "tensorflow/core/framework/node_def_builder.h" #include "tensorflow/core/framework/node_def_util.h" @@ -98,9 +100,12 @@ xla::StatusOr BuildRecvAtHostNode( recv_at_host_builder.Attr("Toutputs", recv_at_host_dtypes); // The correct device_ordinal will be inserted during replication in a // subsequent rewrite. - recv_at_host_builder.Attr("device_ordinal", 0); + AttrValue device_ordinal_value; + device_ordinal_value.set_placeholder("device_ordinal"); + recv_at_host_builder.Attr("device_ordinal", device_ordinal_value); recv_at_host_builder.Attr( "key", absl::StrCat("host_compute_channel_", oc_cluster_name)); + recv_at_host_builder.Attr(kXlaHasHostTransferAttrName, true); recv_at_host_builder.Input(key_placeholder->name(), 0, DT_STRING); TF_RETURN_IF_ERROR(recv_at_host_builder.Finalize(&recv_at_host_def)); Status s; @@ -197,9 +202,12 @@ xla::StatusOr BuildSendFromHostNode( send_from_host_builder.Attr("Tinputs", send_from_host_dtypes); // The correct device_ordinal will be inserted during replication in a // subsequent rewrite. - send_from_host_builder.Attr("device_ordinal", 0); + AttrValue device_ordinal_value; + device_ordinal_value.set_placeholder("device_ordinal"); + send_from_host_builder.Attr("device_ordinal", device_ordinal_value); send_from_host_builder.Attr( "key", absl::StrCat("host_compute_channel_", oc_cluster_name)); + send_from_host_builder.Attr(kXlaHasHostTransferAttrName, true); std::vector inputs(send_from_host_dtypes.size()); for (auto* n : ret_nodes) { int index; @@ -357,6 +365,47 @@ Status ReplaceOrRemoveOutsideCompilationCallNode( return Status::OK(); } +// Resets "device_ordinal" attr to placeholder value for related nodes +// (XlaRecvAtHost nodes; XlaSendFromHost nodes; If nodes containing +// XlaRecvAtHost/XlaSendFromHost). +Status ResetDeviceOrdinalToPlaceholderValue(Graph* g) { + AttrValue device_ordinal_value; + device_ordinal_value.set_placeholder("device_ordinal"); + for (Node* n : g->nodes()) { + if (!HasNodeAttr(n->def(), kXlaHasHostTransferAttrName)) { + continue; + } + + if (n->type_string() == "_XlaRecvAtHost" || + n->type_string() == "_XlaSendFromHost") { + n->ClearAttr("device_ordinal"); + n->AddAttr("device_ordinal", device_ordinal_value); + } else if (n->type_string() == "If") { + for (const string& attr_name : + std::vector{"then_branch", "else_branch"}) { + NameAttrList branch_func; + TF_RETURN_IF_ERROR(GetNodeAttr(n->attrs(), attr_name, &branch_func)); + (*branch_func.mutable_attr())["device_ordinal"] = device_ordinal_value; + n->ClearAttr(attr_name); + n->AddAttr(attr_name, branch_func); + } + } else if (n->type_string() == "While") { + for (const string& attr_name : std::vector{"cond", "body"}) { + NameAttrList branch_func; + TF_RETURN_IF_ERROR(GetNodeAttr(n->attrs(), attr_name, &branch_func)); + (*branch_func.mutable_attr())["device_ordinal"] = device_ordinal_value; + n->ClearAttr(attr_name); + n->AddAttr(attr_name, branch_func); + } + } else { + return errors::Internal("Unknown node marked with ", + kXlaHasHostTransferAttrName, ": ", + n->DebugString()); + } + } + return Status::OK(); +} + // For an XLA computation, builds host side graph given all outside compilation // graphs inside it. The host side graph contains: // 1) a "sequencer" node (we will add control edge between XlaRecvAtHost and @@ -368,8 +417,8 @@ Status ReplaceOrRemoveOutsideCompilationCallNode( Status ConstructHostGraph( const string& xla_cluster_name, const string& outside_compilation_attr_name, const std::vector& outside_compilation_host_graphs, - FunctionLibraryDefinition* fld, std::unique_ptr* host_graph) { - host_graph->reset(new Graph(fld)); + FunctionLibraryDefinition* fld, const string& host_graph_func_name) { + Graph host_graph(fld); // Create sequencer node in host graph. NodeDefBuilder sequencer_builder(absl::StrCat(xla_cluster_name, "_sequencer"), @@ -378,24 +427,34 @@ Status ConstructHostGraph( NodeDef sequencer_def; TF_RETURN_IF_ERROR(sequencer_builder.Finalize(&sequencer_def)); Status s; - Node* sequencer = (*host_graph)->AddNode(sequencer_def, &s); + Node* sequencer = host_graph.AddNode(sequencer_def, &s); TF_RETURN_IF_ERROR(s); // Create key placeholder in host graph. TF_ASSIGN_OR_RETURN( Node * key_placeholder, - AddHostComputeKeyPlaceholder(xla_cluster_name, host_graph->get())); + AddHostComputeKeyPlaceholder(xla_cluster_name, &host_graph)); // For each outside compilation graph, copy them to host graph with the // following changes: // a) Use key_placeholder in host graph instead of its own. - // b) Add control edge from RecvAtHost/SendFromHost to sequencer. + // b) Add control edge from host transfer nodes (XlaRecvAtHost, + // XlaSendFromHost, If/While nodes containing + // XlaRecvAtHost/XlaSendFromHost) to sequencer node. // c) Clear node_def.device(), so device placer won't get confused. for (const string& host_func : outside_compilation_host_graphs) { VLOG(4) << "Expanding host graph " << host_func; + // Temporarily use "0" as "device_ordinal". It will be reset to placeholder + // value after we expanded all host graphs. We cannot just use placeholder + // value here because FunctionDef instantiation does not allow placeholder + // value for attributes. + AttrValue device_ordinal_attr; + device_ordinal_attr.set_i(0); + protobuf::Map attrs; + attrs["device_ordinal"] = device_ordinal_attr; FunctionBody* host_fbody = nullptr; TF_RETURN_IF_ERROR(FunctionDefToBodyHelper( - *fld->Find(host_func), AttrSlice(), fld, + *fld->Find(host_func), AttrSlice(&attrs), fld, [&](const string& op, const OpDef** sig) { return fld->LookUpOpDef(op, sig); }, @@ -408,8 +467,8 @@ Status ConstructHostGraph( FixupSourceAndSinkEdges(host_fbody->graph); std::map node_map; - node_map[host_fbody->graph->source_node()] = (*host_graph)->source_node(); - node_map[host_fbody->graph->sink_node()] = (*host_graph)->sink_node(); + node_map[host_fbody->graph->source_node()] = host_graph.source_node(); + node_map[host_fbody->graph->sink_node()] = host_graph.sink_node(); Status s; ReverseDFS( *host_fbody->graph, /*enter=*/nullptr, @@ -431,7 +490,7 @@ Status ConstructHostGraph( NodeDef copy_def = n->def(); // Change c). copy_def.clear_device(); - copy = (*host_graph)->AddNode(copy_def, &s); + copy = host_graph.AddNode(copy_def, &s); if (!s.ok()) { return; } @@ -446,22 +505,23 @@ Status ConstructHostGraph( e->src()->DebugString()); return; } - (*host_graph) - ->AddEdge(node_map[e->src()], e->src_output(), copy, - e->dst_input()); + host_graph.AddEdge(node_map[e->src()], e->src_output(), copy, + e->dst_input()); } // Change b). - if (copy->type_string() == "_XlaRecvAtHost" || - copy->type_string() == "_XlaSendFromHost") { - (*host_graph)->AddControlEdge(copy, sequencer); + if (HasNodeAttr(copy->def(), kXlaHasHostTransferAttrName)) { + host_graph.AddControlEdge(copy, sequencer); } }, NodeComparatorID()); + if (!s.ok()) { return s; } } + // Reset "device_ordinal" to placeholder value. + TF_RETURN_IF_ERROR(ResetDeviceOrdinalToPlaceholderValue(&host_graph)); // sequencer and key_placeholder might be dead nodes. Prune them if necessary. // - sequencer should be pruned iff it has no input control edges from @@ -470,21 +530,30 @@ Status ConstructHostGraph( // - key_placeholder should be pruned iff there's no RecvAtHost/SendFromHost. // We don't need to do anything special. if (!sequencer->in_edges().empty()) { - (*host_graph)->AddControlEdge(sequencer, (*host_graph)->sink_node()); + host_graph.AddControlEdge(sequencer, host_graph.sink_node()); } PruneForReverseReachability( - host_graph->get(), - std::unordered_set{(*host_graph)->sink_node()}); + &host_graph, std::unordered_set{host_graph.sink_node()}); // Postprocess edges between different outside compilations. TF_RETURN_IF_ERROR(PostprocessEdgesBetweenOutsideCompilations( - host_graph->get(), outside_compilation_attr_name)); + &host_graph, outside_compilation_attr_name)); if (VLOG_IS_ON(4)) { dump_graph::DumpGraphToFile( absl::StrCat("extract_outside_compilation_host_graph_for_", xla_cluster_name), - **host_graph, fld); + host_graph, fld); + } + + FunctionDef host_graph_fdef; + TF_RETURN_IF_ERROR( + GraphToFunctionDef(host_graph, host_graph_func_name, &host_graph_fdef)); + if (fld->Find(host_graph_func_name)) { + TF_RETURN_IF_ERROR( + fld->ReplaceFunction(host_graph_func_name, host_graph_fdef)); + } else { + TF_RETURN_IF_ERROR(fld->AddFunctionDef(host_graph_fdef)); } return Status::OK(); @@ -492,8 +561,28 @@ Status ConstructHostGraph( // Expand XLA computation's outside compilation host side graph into main graph. // Add a control edge between sequencer node and the XLA computation node. -Status ExpandHostGraphIntoMainGraph(Graph* main_graph, Graph* host_graph, +Status ExpandHostGraphIntoMainGraph(Graph* main_graph, + FunctionLibraryDefinition* fld, + const string& host_graph_func_name, Node* xla_computation_node) { + // Temporarily use "0" as "device_ordinal". It will be rewritten with the + // correct value in a later pass. We cannot just use placeholder value here + // because FunctionDef instantiation does not allow placeholder value for + // attributes. + AttrValue device_ordinal_attr; + device_ordinal_attr.set_i(0); + protobuf::Map attrs; + attrs["device_ordinal"] = device_ordinal_attr; + FunctionBody* fbody = nullptr; + TF_RETURN_IF_ERROR(FunctionDefToBodyHelper( + *fld->Find(host_graph_func_name), AttrSlice(&attrs), fld, + [&](const string& op, const OpDef** sig) { + return fld->LookUpOpDef(op, sig); + }, + &fbody)); + std::unique_ptr fbody_deleter(fbody); + Graph* host_graph = fbody->graph; + // We use ReverseDFS() to copy nodes. Make sure all nodes are reverse // reachable from sink node so all nodes will be copied. // TODO(b/77601805): consolidate copy graph functions. @@ -559,9 +648,14 @@ Status ExpandHostGraphIntoMainGraph(Graph* main_graph, Graph* host_graph, Status RewriteShapeInferenceGraph(const string& shape_inference_graph_name, Graph* host_graph, FunctionLibraryDefinition* fld) { + // Use "0" as "device_ordinal". It does not matter for shape inference. + AttrValue device_ordinal_attr; + device_ordinal_attr.set_i(0); + protobuf::Map attrs; + attrs["device_ordinal"] = device_ordinal_attr; FunctionBody* fbody = nullptr; TF_RETURN_IF_ERROR(FunctionDefToBodyHelper( - *fld->Find(shape_inference_graph_name), AttrSlice(), fld, + *fld->Find(shape_inference_graph_name), AttrSlice(&attrs), fld, [&](const string& op, const OpDef** sig) { return fld->LookUpOpDef(op, sig); }, @@ -669,6 +763,567 @@ Status RewriteShapeInferenceGraph(const string& shape_inference_graph_name, return Status::OK(); } +// Builds XlaSendToHost node which sends cond predicate to host. +xla::StatusOr BuildSendIfPredNode(const string& name, + const string& host_transfer_key, + Node* pred_node, Graph* g) { + NodeDefBuilder send_pred_builder(name, "XlaSendToHost"); + send_pred_builder.Attr("Tinput", DT_BOOL); + send_pred_builder.Attr("key", absl::StrCat(host_transfer_key, "_dtoh_0")); + send_pred_builder.Attr(kXlaTokenInputNodesAttrName, + std::vector{kXlaTokenArgNodeName}); + send_pred_builder.Input(pred_node->name(), 0, DT_BOOL); + NodeDef send_pred_def; + TF_RETURN_IF_ERROR(send_pred_builder.Finalize(&send_pred_def)); + Status s; + Node* send_pred_node = g->AddNode(send_pred_def, &s); + TF_RETURN_IF_ERROR(s); + g->AddEdge(pred_node, 0, send_pred_node, 0); + return send_pred_node; +} + +// Replaces key placeholder node with an _Arg node. +Status ReplaceKeyPlaceholderWithArgNode(const string& xla_cluster_name, + const string& func_name, + FunctionLibraryDefinition* fld) { + // Temporarily use "0" as "device_ordinal". It will be reset to placeholder + // value after rewriting. + AttrValue device_ordinal_attr; + device_ordinal_attr.set_i(0); + protobuf::Map attrs; + attrs["device_ordinal"] = device_ordinal_attr; + FunctionBody* fbody = nullptr; + TF_RETURN_IF_ERROR(FunctionDefToBodyHelper( + *fld->Find(func_name), AttrSlice(&attrs), fld, + [&](const string& op, const OpDef** sig) { + return fld->LookUpOpDef(op, sig); + }, + &fbody)); + std::unique_ptr fbody_deleter(fbody); + Graph* g = fbody->graph; + + // Find or create the key placeholder node. + Node* key_placeholder = nullptr; + for (Node* n : g->nodes()) { + if (IsKeyPlaceholderNode(*n)) { + key_placeholder = n; + break; + } + } + if (!key_placeholder) { + TF_ASSIGN_OR_RETURN(key_placeholder, + AddHostComputeKeyPlaceholder(xla_cluster_name, g)); + } + + // Build the _Arg node, and replace key placeholder node with it. + NodeDefBuilder arg_builder("key_arg", FunctionLibraryDefinition::kArgOp); + arg_builder.Attr("T", DT_STRING); + arg_builder.Attr("index", 0); + NodeDef arg_def; + TF_RETURN_IF_ERROR(arg_builder.Finalize(&arg_def)); + TF_RETURN_IF_ERROR(ReplaceNode(g, key_placeholder, arg_def).status()); + + // Reset "device_ordinal" to placeholder value. + TF_RETURN_IF_ERROR(ResetDeviceOrdinalToPlaceholderValue(g)); + + FunctionDef replace_fdef; + TF_RETURN_IF_ERROR(GraphToFunctionDef(*g, func_name, &replace_fdef)); + TF_RETURN_IF_ERROR(fld->ReplaceFunction(func_name, replace_fdef)); + return Status::OK(); +} + +// Builds host side graph for If node. +Status BuildHostGraphForIfNode(const string& xla_cluster_attr_name, + const string& outside_compilation_attr_name, + const string& xla_cluster_name, + const string& if_node_name, + const string& host_transfer_key, + const string& host_graph_func_name, + FunctionLibraryDefinition* fld, + const string& then_branch_host_func_name, + const string& else_branch_host_func_name) { + Graph host_graph(fld); + string outside_compilation_name = absl::StrCat("oc_if_", if_node_name); + AttrValue device_ordinal_value; + device_ordinal_value.set_placeholder("device_ordinal"); + + // Step 1: add key placeholder node. + TF_ASSIGN_OR_RETURN( + Node * key_placeholder, + AddHostComputeKeyPlaceholder(xla_cluster_name, &host_graph)); + + // Step 2: build XlaRecvAtHost node to recv predicate. + NodeDefBuilder recv_pred_builder( + absl::StrCat("recv_oc_if_pred_", if_node_name), "_XlaRecvAtHost"); + recv_pred_builder.Attr("Toutputs", std::vector{DT_BOOL}); + recv_pred_builder.Attr("key", host_transfer_key); + recv_pred_builder.Attr("device_ordinal", device_ordinal_value); + recv_pred_builder.Attr(xla_cluster_attr_name, xla_cluster_name); + recv_pred_builder.Attr(outside_compilation_attr_name, + outside_compilation_name); + recv_pred_builder.Attr(kXlaHasHostTransferAttrName, true); + recv_pred_builder.Input(key_placeholder->name(), 0, DT_STRING); + NodeDef recv_pred_def; + TF_RETURN_IF_ERROR(recv_pred_builder.Finalize(&recv_pred_def)); + Status s; + Node* recv_pred_node = host_graph.AddNode(recv_pred_def, &s); + TF_RETURN_IF_ERROR(s); + host_graph.AddEdge(key_placeholder, 0, recv_pred_node, 0); + + // Step 3: rewrite `{then, else}_branch_host_func_name`, replace key + // placeholder with an _Arg node. + TF_RETURN_IF_ERROR(ReplaceKeyPlaceholderWithArgNode( + xla_cluster_name, then_branch_host_func_name, fld)); + TF_RETURN_IF_ERROR(ReplaceKeyPlaceholderWithArgNode( + xla_cluster_name, else_branch_host_func_name, fld)); + + // Step 4: build If node to choose between `{then, else}_branch_host_graph`. + NodeDefBuilder if_builder(absl::StrCat("oc_if_", if_node_name), "If"); + if_builder.Attr("Tcond", DT_BOOL); + if_builder.Attr("Tin", std::vector{DT_STRING}); + if_builder.Attr("Tout", std::vector{}); + NameAttrList host_then_branch, host_else_branch; + host_then_branch.set_name(then_branch_host_func_name); + (*host_then_branch.mutable_attr())["device_ordinal"] = device_ordinal_value; + host_else_branch.set_name(else_branch_host_func_name); + (*host_else_branch.mutable_attr())["device_ordinal"] = device_ordinal_value; + if_builder.Attr("then_branch", host_then_branch); + if_builder.Attr("else_branch", host_else_branch); + if_builder.Attr(kXlaHasHostTransferAttrName, true); + if_builder.Attr(xla_cluster_attr_name, xla_cluster_name); + if_builder.Attr(outside_compilation_attr_name, outside_compilation_name); + if_builder.Input(recv_pred_node->name(), 0, DT_BOOL); + std::vector if_inputs{ + {key_placeholder->name(), 0, DT_STRING}}; + if_builder.Input(if_inputs); + NodeDef if_def; + TF_RETURN_IF_ERROR(if_builder.Finalize(&if_def)); + Node* if_node = host_graph.AddNode(if_def, &s); + TF_RETURN_IF_ERROR(s); + host_graph.AddEdge(recv_pred_node, 0, if_node, 0); + host_graph.AddEdge(key_placeholder, 0, if_node, 1); + + // Convert `host_graph` to function, and add a "device_ordinal" attr. + FunctionDef oc_host_graph_fdef; + TF_RETURN_IF_ERROR(GraphToFunctionDef(host_graph, host_graph_func_name, + &oc_host_graph_fdef)); + if (fld->Find(host_graph_func_name)) { + TF_RETURN_IF_ERROR( + fld->ReplaceFunction(host_graph_func_name, oc_host_graph_fdef)); + } else { + TF_RETURN_IF_ERROR(fld->AddFunctionDef(oc_host_graph_fdef)); + } + + return Status::OK(); +} + +// Rewrites loop cond to add a node which sends loop cond to host. +Status AddSendLoopPredToLoopCond(FunctionLibraryDefinition* fld, + const NameAttrList& loop_cond_func, + const string& while_node_name, + const string& host_transfer_key) { + // Instantiate the loop cond function. + FunctionBody* fbody = nullptr; + TF_RETURN_IF_ERROR(FunctionDefToBodyHelper( + *fld->Find(loop_cond_func.name()), AttrSlice(&loop_cond_func.attr()), fld, + [&](const string& op, const OpDef** sig) { + return fld->LookUpOpDef(op, sig); + }, + &fbody)); + std::unique_ptr fbody_deleter(fbody); + Graph* g = fbody->graph; + + // Find the _Retval node and the loop cond node. + Node* ret_node = nullptr; + for (Node* n : g->nodes()) { + if (n->type_string() == "_Retval") { + if (ret_node) { + return errors::Internal("Multiple return node for loop cond function ", + loop_cond_func.name(), ": ", + ret_node->DebugString(), " and ", + n->DebugString()); + } else { + ret_node = n; + } + } + } + if (!ret_node) { + return errors::Internal("No _Retval node for loop cond function ", + loop_cond_func.name()); + } + Node* loop_cond; + TF_RETURN_IF_ERROR(ret_node->input_node(0, &loop_cond)); + + // Build the XlaSendToHost node. + NodeDefBuilder send_loop_cond_builder( + absl::StrCat("send_oc_while_cond_", while_node_name), "XlaSendToHost"); + send_loop_cond_builder.Attr("Tinput", DT_BOOL); + send_loop_cond_builder.Attr("key", + absl::StrCat(host_transfer_key, "_dtoh_0")); + send_loop_cond_builder.Attr(kXlaTokenInputNodesAttrName, + std::vector{kXlaTokenArgNodeName}); + send_loop_cond_builder.Input(loop_cond->name(), 0, DT_BOOL); + NodeDef send_loop_cond_def; + TF_RETURN_IF_ERROR(send_loop_cond_builder.Finalize(&send_loop_cond_def)); + Status s; + Node* send_loop_cond_node = g->AddNode(send_loop_cond_def, &s); + TF_RETURN_IF_ERROR(s); + g->AddEdge(loop_cond, 0, send_loop_cond_node, 0); + + // Replace original function. + FunctionDef replace_fdef; + TF_RETURN_IF_ERROR( + GraphToFunctionDef(*g, loop_cond_func.name(), &replace_fdef)); + TF_RETURN_IF_ERROR(fld->ReplaceFunction(loop_cond_func.name(), replace_fdef)); + + return Status::OK(); +} + +// Rewrites while loop cond function for host. +Status RewriteHostWhileLoopCond( + const string& cond_host_func_name, const string& while_node_name, + const string& host_transfer_key, const string& xla_cluster_attr_name, + const string& xla_cluster_name, const string& outside_compilation_attr_name, + const string& outside_compilation_name, FunctionLibraryDefinition* fld) { + // Replace key placeholder node with _Arg node. + TF_RETURN_IF_ERROR(ReplaceKeyPlaceholderWithArgNode( + xla_cluster_name, cond_host_func_name, fld)); + + // Instantiate cond function. + AttrValue device_ordinal_temp_value; + device_ordinal_temp_value.set_i(0); + protobuf::Map attrs; + attrs["device_ordinal"] = device_ordinal_temp_value; + FunctionBody* cond_fbody = nullptr; + TF_RETURN_IF_ERROR(FunctionDefToBodyHelper( + *fld->Find(cond_host_func_name), AttrSlice(&attrs), fld, + [&](const string& op, const OpDef** sig) { + return fld->LookUpOpDef(op, sig); + }, + &cond_fbody)); + std::unique_ptr cond_fbody_deleter(cond_fbody); + Graph* cond_graph = cond_fbody->graph; + Node* key_arg = nullptr; + for (Node* n : cond_graph->nodes()) { + if (n->type_string() == "_Arg") { + key_arg = n; + } + } + if (!key_arg) { + return errors::Internal( + "No _Arg node found for host compute key in function ", + cond_host_func_name); + } + + // Add an XlaRecvAtHost node to use as cond function return value. + // We don't need to set kXlaHasHostTransferAttrName for this node, because + // it's already added for the "While" node on the host. + NodeDefBuilder recv_pred_builder( + absl::StrCat("recv_oc_while_cond_", while_node_name), "_XlaRecvAtHost"); + recv_pred_builder.Attr("Toutputs", std::vector{DT_BOOL}); + recv_pred_builder.Attr("key", host_transfer_key); + AttrValue device_ordinal_value; + device_ordinal_value.set_placeholder("device_ordinal"); + recv_pred_builder.Attr("device_ordinal", device_ordinal_value); + recv_pred_builder.Attr(xla_cluster_attr_name, xla_cluster_name); + recv_pred_builder.Attr(outside_compilation_attr_name, + outside_compilation_name); + recv_pred_builder.Input(key_arg->name(), 0, DT_STRING); + NodeDef recv_pred_def; + TF_RETURN_IF_ERROR(recv_pred_builder.Finalize(&recv_pred_def)); + Status s; + Node* recv_pred_node = cond_graph->AddNode(recv_pred_def, &s); + TF_RETURN_IF_ERROR(s); + cond_graph->AddEdge(key_arg, 0, recv_pred_node, 0); + NodeDefBuilder ret_builder( + absl::StrCat("recv_oc_while_cond_ret_", while_node_name), "_Retval"); + ret_builder.Attr("T", DT_BOOL); + ret_builder.Attr("index", 0); + ret_builder.Input(recv_pred_node->name(), 0, DT_BOOL); + NodeDef ret_def; + TF_RETURN_IF_ERROR(ret_builder.Finalize(&ret_def)); + Node* ret_node = cond_graph->AddNode(ret_def, &s); + TF_RETURN_IF_ERROR(s); + cond_graph->AddEdge(recv_pred_node, 0, ret_node, 0); + + // Reset device_ordinal to placeholder value. + TF_RETURN_IF_ERROR(ResetDeviceOrdinalToPlaceholderValue(cond_graph)); + + // Replace original function. + FunctionDef cond_replace_fdef; + TF_RETURN_IF_ERROR( + GraphToFunctionDef(*cond_graph, cond_host_func_name, &cond_replace_fdef)); + TF_RETURN_IF_ERROR( + fld->ReplaceFunction(cond_host_func_name, cond_replace_fdef)); + + return Status::OK(); +} + +// Rewrites while loop body function for host. +Status RewriteHostWhileLoopBody( + const string& body_host_func_name, const string& while_node_name, + const string& host_transfer_key, const string& xla_cluster_attr_name, + const string& xla_cluster_name, const string& outside_compilation_attr_name, + const string& outside_compilation_name, FunctionLibraryDefinition* fld) { + // Replace key placeholder node with _Arg node. + TF_RETURN_IF_ERROR(ReplaceKeyPlaceholderWithArgNode( + xla_cluster_name, body_host_func_name, fld)); + + // Instantiate body function. + AttrValue device_ordinal_temp_value; + device_ordinal_temp_value.set_i(0); + protobuf::Map attrs; + attrs["device_ordinal"] = device_ordinal_temp_value; + FunctionBody* body_fbody = nullptr; + TF_RETURN_IF_ERROR(FunctionDefToBodyHelper( + *fld->Find(body_host_func_name), AttrSlice(&attrs), fld, + [&](const string& op, const OpDef** sig) { + return fld->LookUpOpDef(op, sig); + }, + &body_fbody)); + std::unique_ptr body_fbody_deleter(body_fbody); + Graph* body_graph = body_fbody->graph; + Node* key_arg = nullptr; + for (Node* n : body_graph->nodes()) { + if (n->type_string() == "_Arg") { + key_arg = n; + } + } + if (!key_arg) { + return errors::Internal( + "No _Arg node found for host compute key in function ", + body_host_func_name); + } + + // Add a _Retval node to loop body. + NodeDefBuilder ret_builder( + absl::StrCat("recv_oc_while_body_ret_", while_node_name), "_Retval"); + ret_builder.Attr("T", DT_STRING); + ret_builder.Attr("index", 0); + ret_builder.Input(key_arg->name(), 0, DT_STRING); + NodeDef ret_def; + TF_RETURN_IF_ERROR(ret_builder.Finalize(&ret_def)); + Status s; + Node* ret_node = body_graph->AddNode(ret_def, &s); + TF_RETURN_IF_ERROR(s); + body_graph->AddEdge(key_arg, 0, ret_node, 0); + + // Reset device_ordinal to placeholder value. + TF_RETURN_IF_ERROR(ResetDeviceOrdinalToPlaceholderValue(body_graph)); + + // Replace original function. + FunctionDef body_replace_fdef; + TF_RETURN_IF_ERROR( + GraphToFunctionDef(*body_graph, body_host_func_name, &body_replace_fdef)); + TF_RETURN_IF_ERROR( + fld->ReplaceFunction(body_host_func_name, body_replace_fdef)); + + return Status::OK(); +} + +// Builds host side graph for while node. +Status BuildHostGraphForWhileNode( + const string& xla_cluster_attr_name, + const string& outside_compilation_attr_name, const string& xla_cluster_name, + const string& while_node_name, const string& host_transfer_key, + const string& host_graph_func_name, FunctionLibraryDefinition* fld, + const string& cond_host_func_name, const string& body_host_func_name) { + Graph host_graph(fld); + string outside_compilation_name = absl::StrCat("oc_while_", while_node_name); + + // Step 1: add key placeholder node. + TF_ASSIGN_OR_RETURN( + Node * key_placeholder, + AddHostComputeKeyPlaceholder(xla_cluster_name, &host_graph)); + + // Step 2: rewrite cond function. + TF_RETURN_IF_ERROR(RewriteHostWhileLoopCond( + cond_host_func_name, while_node_name, host_transfer_key, + xla_cluster_attr_name, xla_cluster_name, outside_compilation_attr_name, + outside_compilation_name, fld)); + + // Step 3: rewrite body function. + TF_RETURN_IF_ERROR(RewriteHostWhileLoopBody( + body_host_func_name, while_node_name, host_transfer_key, + xla_cluster_attr_name, xla_cluster_name, outside_compilation_attr_name, + outside_compilation_name, fld)); + + // Step 4: build While node. + NodeDefBuilder while_builder(absl::StrCat("oc_while_", while_node_name), + "While"); + while_builder.Attr("T", std::vector{DT_STRING}); + NameAttrList func; + AttrValue device_ordinal_value; + device_ordinal_value.set_placeholder("device_ordinal"); + (*func.mutable_attr())["device_ordinal"] = device_ordinal_value; + func.set_name(cond_host_func_name); + while_builder.Attr("cond", func); + func.set_name(body_host_func_name); + while_builder.Attr("body", func); + while_builder.Attr(kXlaHasHostTransferAttrName, true); + while_builder.Attr(xla_cluster_attr_name, xla_cluster_name); + while_builder.Attr(outside_compilation_attr_name, outside_compilation_name); + std::vector while_inputs{ + {key_placeholder->name(), 0, DT_STRING}}; + while_builder.Input(while_inputs); + NodeDef while_def; + TF_RETURN_IF_ERROR(while_builder.Finalize(&while_def)); + Status s; + Node* while_node = host_graph.AddNode(while_def, &s); + TF_RETURN_IF_ERROR(s); + host_graph.AddEdge(key_placeholder, 0, while_node, 0); + + // Convert `host_graph` to function. + FunctionDef oc_host_graph_fdef; + TF_RETURN_IF_ERROR(GraphToFunctionDef(host_graph, host_graph_func_name, + &oc_host_graph_fdef)); + if (fld->Find(host_graph_func_name)) { + TF_RETURN_IF_ERROR( + fld->ReplaceFunction(host_graph_func_name, oc_host_graph_fdef)); + } else { + TF_RETURN_IF_ERROR(fld->AddFunctionDef(oc_host_graph_fdef)); + } + + return Status::OK(); +} + +Status ExtractOutsideCompilationForNodesWithAssociatedFunctions( + Graph* g, const string& xla_cluster_attr_name, + const string& outside_compilation_attr_name, const string& xla_cluster_name, + const std::map& host_compute_core, + FunctionLibraryDefinition* fld, std::vector* host_graphs, + std::vector* shape_inference_graphs, + bool* has_outside_compilation) { + std::vector if_nodes, while_nodes; + for (Node* n : g->nodes()) { + if (n->type_string() == "If") { + if_nodes.push_back(n); + } else if (n->type_string() == "While") { + while_nodes.push_back(n); + } + } + + for (Node* n : if_nodes) { + // Instantiate "then_branch" and "else_branch". + NameAttrList then_branch, else_branch; + TF_RETURN_IF_ERROR(GetNodeAttr(n->attrs(), "then_branch", &then_branch)); + TF_RETURN_IF_ERROR(GetNodeAttr(n->attrs(), "else_branch", &else_branch)); + + // Extract outside compilation for then_branch and else_branch. + bool then_branch_has_outside_compilation = false; + bool else_branch_has_outside_compilation = false; + string then_branch_host_func_name = + absl::StrCat("oc_then_branch_host_if_", n->name()), + else_branch_host_func_name = + absl::StrCat("oc_else_branch_host_if_", n->name()); + string then_branch_xla_func_name = absl::StrCat(then_branch.name(), "_oc"), + else_branch_xla_func_name = absl::StrCat(else_branch.name(), "_oc"); + TF_RETURN_IF_ERROR(ExtractOutsideCompilationForFunction( + xla_cluster_attr_name, outside_compilation_attr_name, xla_cluster_name, + then_branch, then_branch_xla_func_name, then_branch_host_func_name, + host_compute_core, fld, shape_inference_graphs, + &then_branch_has_outside_compilation)); + TF_RETURN_IF_ERROR(ExtractOutsideCompilationForFunction( + xla_cluster_attr_name, outside_compilation_attr_name, xla_cluster_name, + else_branch, else_branch_xla_func_name, else_branch_host_func_name, + host_compute_core, fld, shape_inference_graphs, + &else_branch_has_outside_compilation)); + + // If then/else branch do not have outside compilation, nothing to do. + if (!then_branch_has_outside_compilation && + !else_branch_has_outside_compilation) { + continue; + } + + *has_outside_compilation = true; + + // Change If node to call the new functions. + then_branch.set_name(then_branch_xla_func_name); + n->ClearAttr("then_branch"); + n->AddAttr("then_branch", then_branch); + else_branch.set_name(else_branch_xla_func_name); + n->ClearAttr("else_branch"); + n->AddAttr("else_branch", else_branch); + + string host_transfer_key = absl::StrCat("oc_if_pred_", n->name()); + + // XLA computation: add a SendToHost node to send cond predicate. + Node* pred_node; + TF_RETURN_IF_ERROR(n->input_node(0, &pred_node)); + TF_ASSIGN_OR_RETURN( + Node * send_pred_node, + BuildSendIfPredNode(absl::StrCat("send_oc_if_pred_", n->name()), + host_transfer_key, pred_node, g)); + n->AddAttr(kXlaTokenInputNodesAttrName, + std::vector{send_pred_node->name()}); + + // Build host side graph for the "If" node. + string oc_host_graph_name = absl::StrCat("oc_if_host_graph_", n->name()); + TF_RETURN_IF_ERROR(BuildHostGraphForIfNode( + xla_cluster_attr_name, outside_compilation_attr_name, xla_cluster_name, + n->name(), host_transfer_key, oc_host_graph_name, fld, + then_branch_host_func_name, else_branch_host_func_name)); + host_graphs->push_back(oc_host_graph_name); + } + + for (Node* n : while_nodes) { + // Instantiate "cond" and "body". + NameAttrList cond, body; + TF_RETURN_IF_ERROR(GetNodeAttr(n->attrs(), "cond", &cond)); + TF_RETURN_IF_ERROR(GetNodeAttr(n->attrs(), "body", &body)); + + // Extract outside compilation for cond and body. + bool cond_has_outside_compilation = false; + bool body_has_outside_compilation = false; + string cond_host_func_name = absl::StrCat("oc_cond_host_while_", n->name()), + body_host_func_name = absl::StrCat("oc_body_host_while_", n->name()); + string cond_xla_func_name = absl::StrCat(cond.name(), "_oc"), + body_xla_func_name = absl::StrCat(body.name(), "_oc"); + TF_RETURN_IF_ERROR(ExtractOutsideCompilationForFunction( + xla_cluster_attr_name, outside_compilation_attr_name, xla_cluster_name, + cond, cond_xla_func_name, cond_host_func_name, host_compute_core, fld, + shape_inference_graphs, &cond_has_outside_compilation)); + TF_RETURN_IF_ERROR(ExtractOutsideCompilationForFunction( + xla_cluster_attr_name, outside_compilation_attr_name, xla_cluster_name, + body, body_xla_func_name, body_host_func_name, host_compute_core, fld, + shape_inference_graphs, &body_has_outside_compilation)); + + // If cond/body do not have outside compilation, nothing to do. + if (!cond_has_outside_compilation && !body_has_outside_compilation) { + continue; + } + + *has_outside_compilation = true; + + // Change While node to call the new functions. + cond.set_name(cond_xla_func_name); + n->ClearAttr("cond"); + n->AddAttr("cond", cond); + body.set_name(body_xla_func_name); + n->ClearAttr("body"); + n->AddAttr("body", body); + + string host_transfer_key = absl::StrCat("oc_while_pred_", n->name()); + + // XLA computation: rewrite cond function to add a SendToHost node to send + // loop predicate. + TF_RETURN_IF_ERROR( + AddSendLoopPredToLoopCond(fld, cond, n->name(), host_transfer_key)); + n->AddAttr(kXlaTokenInputNodesAttrName, + std::vector{kXlaTokenArgNodeName}); + + // Build host side graph for the "While" node. + string oc_host_graph_name = absl::StrCat("oc_while_host_graph_", n->name()); + TF_RETURN_IF_ERROR(BuildHostGraphForWhileNode( + xla_cluster_attr_name, outside_compilation_attr_name, xla_cluster_name, + n->name(), host_transfer_key, oc_host_graph_name, fld, + cond_host_func_name, body_host_func_name)); + host_graphs->push_back(oc_host_graph_name); + } + + return Status::OK(); +} + } // namespace Status RewriteOutsideCompilationSubgraphFn::operator()( @@ -755,12 +1410,15 @@ Status RewriteOutsideCompilationSubgraphFn::operator()( // it with HostCompute node later. AddNodeAttr("_outside_compilation_subgraph", old_name, node_def); if (shapes) { - AddNodeAttr("shape_inference_graph", "", node_def); + NameAttrList shape_inference_graph; + AddNodeAttr("shape_inference_graph", shape_inference_graph, node_def); AddNodeAttr("shapes", *shapes, node_def); } else { string shape_inference_func_name = absl::StrCat("_outside_compilation_shape_inference_", new_name); - AddNodeAttr("shape_inference_graph", shape_inference_func_name, node_def); + NameAttrList shape_inference_graph; + shape_inference_graph.set_name(shape_inference_func_name); + AddNodeAttr("shape_inference_graph", shape_inference_graph, node_def); AddNodeAttr("shapes", std::vector{}, node_def); } AddNodeAttr("ancestors", std::vector{}, node_def); @@ -775,11 +1433,10 @@ Status ExtractOutsideCompilationForFunction( const string& xla_cluster_attr_name, const string& outside_compilation_attr_name, const string& xla_cluster_name, const NameAttrList& func_name_attrs, const string& new_func_name, + const string& host_graph_func_name, const std::map& host_compute_core, - FunctionLibraryDefinition* fld, std::unique_ptr* host_graph, - std::vector* shape_inference_graphs, + FunctionLibraryDefinition* fld, std::vector* shape_inference_graphs, bool* has_outside_compilation) { - // Early return if function does not have any outside compilation nodes. const string& func_name = func_name_attrs.name(); const FunctionDef* fdef = fld->Find(func_name); if (!fdef) { @@ -792,9 +1449,8 @@ Status ExtractOutsideCompilationForFunction( break; } } - if (!has_outside_compilation) { - return Status::OK(); - } + // We cannot early return here, because we might have outside compilation in + // If/While function body. // Convert the function to graph. FunctionBody* fbody = nullptr; @@ -835,11 +1491,11 @@ Status ExtractOutsideCompilationForFunction( // If we could not infer shapes for XlaSendFromHost inputs statically, we // will set the "shape_inference_graph" attribute. In that case, copy // outside compilation subgraph as shape inference graph in `fld`. - string shape_inference_graph; + NameAttrList shape_inference_graph; TF_RETURN_IF_ERROR(GetNodeAttr(n->attrs(), "shape_inference_graph", &shape_inference_graph)); - if (!shape_inference_graph.empty()) { - shape_inference_graphs->push_back(shape_inference_graph); + if (!shape_inference_graph.name().empty()) { + shape_inference_graphs->push_back(shape_inference_graph.name()); const FunctionDef* xla_fdef = fld->Find(n->name()); if (!xla_fdef) { @@ -847,9 +1503,9 @@ Status ExtractOutsideCompilationForFunction( } FunctionDef shape_inference_fdef = *xla_fdef; shape_inference_fdef.mutable_signature()->set_name( - shape_inference_graph); - if (fld->Find(shape_inference_graph)) { - TF_RETURN_IF_ERROR(fld->ReplaceFunction(shape_inference_graph, + shape_inference_graph.name()); + if (fld->Find(shape_inference_graph.name())) { + TF_RETURN_IF_ERROR(fld->ReplaceFunction(shape_inference_graph.name(), shape_inference_fdef)); } else { TF_RETURN_IF_ERROR(fld->AddFunctionDef(shape_inference_fdef)); @@ -867,12 +1523,17 @@ Status ExtractOutsideCompilationForFunction( *graph_out, fld); } + // Handle nodes with associated functions. + TF_RETURN_IF_ERROR(ExtractOutsideCompilationForNodesWithAssociatedFunctions( + graph_out.get(), xla_cluster_attr_name, outside_compilation_attr_name, + xla_cluster_name, host_compute_core, fld, + &outside_compilation_host_graphs, shape_inference_graphs, + has_outside_compilation)); + // Construct host graph. - if (!outside_compilation_host_graphs.empty()) { - TF_RETURN_IF_ERROR( - ConstructHostGraph(xla_cluster_name, outside_compilation_attr_name, - outside_compilation_host_graphs, fld, host_graph)); - } + TF_RETURN_IF_ERROR(ConstructHostGraph( + xla_cluster_name, outside_compilation_attr_name, + outside_compilation_host_graphs, fld, host_graph_func_name)); // Remove the outside compilation graphs from function library. for (const string& func : outside_compilation_host_graphs) { @@ -909,14 +1570,15 @@ Status ExtractOutsideCompilation( auto const& host_compute_core = iter.second.host_compute_core; bool has_outside_compilation; - std::unique_ptr host_graph; + string host_graph_func_name = absl::StrCat("oc_host_graph_", n->name()); TF_RETURN_IF_ERROR(ExtractOutsideCompilationForFunction( xla_cluster_attr_name, outside_compilation_attr_name, xla_cluster_name, - func_name_attrs, func_name_attrs.name(), host_compute_core, fld, - &host_graph, &shape_inference_graphs, &has_outside_compilation)); - if (host_graph) { - TF_RETURN_IF_ERROR(ExpandHostGraphIntoMainGraph(g, host_graph.get(), n)); - } + func_name_attrs, func_name_attrs.name(), host_graph_func_name, + host_compute_core, fld, &shape_inference_graphs, + &has_outside_compilation)); + TF_RETURN_IF_ERROR( + ExpandHostGraphIntoMainGraph(g, fld, host_graph_func_name, n)); + TF_RETURN_IF_ERROR(fld->RemoveFunction(host_graph_func_name)); } if (VLOG_IS_ON(4)) { diff --git a/tensorflow/compiler/jit/extract_outside_compilation_pass.h b/tensorflow/compiler/jit/extract_outside_compilation_pass.h index 2a4f07cca2..e07e7c5dd0 100644 --- a/tensorflow/compiler/jit/extract_outside_compilation_pass.h +++ b/tensorflow/compiler/jit/extract_outside_compilation_pass.h @@ -88,9 +88,10 @@ Status ExtractOutsideCompilationForFunction( const string& xla_cluster_attr_name, const string& outside_compilation_attr_name, const string& xla_cluster_name, const NameAttrList& func_name_attrs, const string& new_func_name, + const string& host_graph_func_name, const std::map& host_compute_core, - FunctionLibraryDefinition* fld, std::unique_ptr* host_graph, - std::vector* shape_inference_graphs, bool* has_outside_compilation); + FunctionLibraryDefinition* fld, std::vector* shape_inference_graphs, + bool* has_outside_compilation); // Rewrites XLA computation in `clusters` to replace outside compilation nodes // with XlaHostCompute, and moves those outside compilations into `g`. If shapes diff --git a/tensorflow/compiler/jit/extract_outside_compilation_pass_test.cc b/tensorflow/compiler/jit/extract_outside_compilation_pass_test.cc index bff956100d..0887fbcde9 100644 --- a/tensorflow/compiler/jit/extract_outside_compilation_pass_test.cc +++ b/tensorflow/compiler/jit/extract_outside_compilation_pass_test.cc @@ -19,8 +19,10 @@ limitations under the License. #include "tensorflow/cc/framework/scope.h" #include "tensorflow/cc/ops/array_ops.h" #include "tensorflow/cc/ops/function_ops.h" +#include "tensorflow/cc/ops/functional_ops.h" #include "tensorflow/cc/ops/standard_ops.h" #include "tensorflow/compiler/jit/encapsulate_util.h" +#include "tensorflow/compiler/xla/test.h" #include "tensorflow/core/common_runtime/function.h" #include "tensorflow/core/framework/common_shape_fns.h" #include "tensorflow/core/framework/function.h" @@ -109,10 +111,10 @@ TEST(RewriteOutsideCompilationSubgraphFnTest, Basic) { } EXPECT_TRUE(has_control_edge_to_send_from_host); // Verify step 7: necessary attrs added to call_node_def. - string shape_inference_graph; + NameAttrList shape_inference_graph; TF_CHECK_OK(GetNodeAttr(AttrSlice(&call_node_def.attr()), "shape_inference_graph", &shape_inference_graph)); - EXPECT_EQ(shape_inference_graph, + EXPECT_EQ(shape_inference_graph.name(), "_outside_compilation_shape_inference_cluster_0"); } @@ -249,27 +251,26 @@ TEST(ExtractOutsideCompilationForFunctionTest, Basic) { protobuf::Map attrs; std::map host_compute_core = {{"0", 1}, {"1", 0}}; - std::unique_ptr host_graph; std::vector shape_inference_graphs; bool has_outside_compilation; NameAttrList name_attrs; name_attrs.set_name("cluster"); *name_attrs.mutable_attr() = attrs; TF_CHECK_OK(ExtractOutsideCompilationForFunction( - "_xla", "_oc", "cluster", name_attrs, "cluster_rewritten", - host_compute_core, &fld, &host_graph, &shape_inference_graphs, + "_xla", "_oc", "cluster", name_attrs, "cluster_rewritten", "host_graph", + host_compute_core, &fld, &shape_inference_graphs, &has_outside_compilation)); // Get rewritten XLA computation function. - FunctionBody *fbody = nullptr; - TF_CHECK_OK(FunctionDefToBodyHelper(*fld.Find("cluster_rewritten"), - AttrSlice(), &fld, - [&](const string &op, const OpDef **sig) { - return fld.LookUpOpDef(op, sig); - }, - &fbody)); - std::unique_ptr fbody_deleter(fbody); - auto node_name_index = fbody->graph->BuildNodeNameIndex(); + FunctionBody *xla_fbody = nullptr; + TF_CHECK_OK(FunctionDefToBodyHelper( + *fld.Find("cluster_rewritten"), AttrSlice(), &fld, + [&](const string &op, const OpDef **sig) { + return fld.LookUpOpDef(op, sig); + }, + &xla_fbody)); + std::unique_ptr xla_fbody_deleter(xla_fbody); + auto node_name_index = xla_fbody->graph->BuildNodeNameIndex(); // Check XlaHostCompute nodes. Node *host_compute_0 = node_name_index["outside_compilation_0_host_compute"]; @@ -292,18 +293,31 @@ TEST(ExtractOutsideCompilationForFunctionTest, Basic) { EXPECT_EQ(shapes[0].dim_size(), 1); // Check XlaHostCompute nodes' "shape_inference_graph" attr. Both should have // empty values. - string shape_inference_graph; + NameAttrList shape_inference_graph; TF_CHECK_OK(GetNodeAttr(host_compute_0->attrs(), "shape_inference_graph", &shape_inference_graph)); - EXPECT_EQ(shape_inference_graph, ""); + EXPECT_EQ(shape_inference_graph.name(), ""); TF_CHECK_OK(GetNodeAttr(host_compute_1->attrs(), "shape_inference_graph", &shape_inference_graph)); - EXPECT_EQ(shape_inference_graph, ""); + EXPECT_EQ(shape_inference_graph.name(), ""); // Check `shape_inference_graphs`. EXPECT_EQ(shape_inference_graphs.size(), 0); - // Check `host_graph`: verify we have key placeholder and sequencer. + // Check host graph: verify we have key placeholder and sequencer. + FunctionBody *host_fbody = nullptr; + AttrValue device_ordinal_temp_value; + device_ordinal_temp_value.set_i(0); + protobuf::Map host_func_attrs; + host_func_attrs["device_ordinal"] = device_ordinal_temp_value; + TF_CHECK_OK(FunctionDefToBodyHelper( + *fld.Find("host_graph"), AttrSlice(&host_func_attrs), &fld, + [&](const string &op, const OpDef **sig) { + return fld.LookUpOpDef(op, sig); + }, + &host_fbody)); + std::unique_ptr host_fbody_deleter(host_fbody); + Graph *host_graph = host_fbody->graph; Node *key_placeholder = nullptr, *sequencer = nullptr; for (Node *n : host_graph->nodes()) { if (n->type_string() == "Placeholder" && @@ -365,25 +379,37 @@ TEST(ExtractOutsideCompilationForFunctionTest, NoHostGraph) { protobuf::Map attrs; std::map host_compute_core = {{"0", 1}, {"1", 0}}; - std::unique_ptr host_graph; std::vector shape_inference_graphs; bool has_outside_compilation; NameAttrList name_attrs; name_attrs.set_name("cluster"); *name_attrs.mutable_attr() = attrs; TF_CHECK_OK(ExtractOutsideCompilationForFunction( - "_xla", "_oc", "cluster", name_attrs, "cluster_rewritten", - host_compute_core, &fld, &host_graph, &shape_inference_graphs, + "_xla", "_oc", "cluster", name_attrs, "cluster_rewritten", "host_graph", + host_compute_core, &fld, &shape_inference_graphs, &has_outside_compilation)); - // Check `host_graph` is empty. - EXPECT_FALSE(host_graph); + // Check host graph is empty. + FunctionBody *host_fbody = nullptr; + AttrValue device_ordinal_temp_value; + device_ordinal_temp_value.set_i(0); + protobuf::Map host_func_attrs; + host_func_attrs["device_ordinal"] = device_ordinal_temp_value; + TF_CHECK_OK(FunctionDefToBodyHelper( + *fld.Find("host_graph"), AttrSlice(&host_func_attrs), &fld, + [&](const string &op, const OpDef **sig) { + return fld.LookUpOpDef(op, sig); + }, + &host_fbody)); + std::unique_ptr host_fbody_deleter(host_fbody); + Graph *host_graph = host_fbody->graph; + EXPECT_EQ(host_graph->num_nodes(), 2); } TEST(ExtractOutsideCompilationForFunctionTest, XlaHostComputeRemoved) { // Build the XLA computation func. // "const0" - // "const1" (outside compilation clsuter "0") + // "const1" (outside compilation cluster "0") FunctionDefLibrary fdl; { tensorflow::Scope s = tensorflow::Scope::NewRootScope(); @@ -401,31 +427,43 @@ TEST(ExtractOutsideCompilationForFunctionTest, XlaHostComputeRemoved) { protobuf::Map attrs; std::map host_compute_core = {{"0", 1}, {"1", 0}}; - std::unique_ptr host_graph; std::vector shape_inference_graphs; bool has_outside_compilation; NameAttrList name_attrs; name_attrs.set_name("cluster"); *name_attrs.mutable_attr() = attrs; TF_CHECK_OK(ExtractOutsideCompilationForFunction( - "_xla", "_oc", "cluster", name_attrs, "cluster_rewritten", - host_compute_core, &fld, &host_graph, &shape_inference_graphs, + "_xla", "_oc", "cluster", name_attrs, "cluster_rewritten", "host_graph", + host_compute_core, &fld, &shape_inference_graphs, &has_outside_compilation)); // Check rewritten XLA graph: verify that we have no XlaHostCompute. - FunctionBody *fbody = nullptr; - TF_CHECK_OK(FunctionDefToBodyHelper(*fld.Find("cluster_rewritten"), - AttrSlice(), &fld, - [&](const string &op, const OpDef **sig) { - return fld.LookUpOpDef(op, sig); - }, - &fbody)); - std::unique_ptr fbody_deleter(fbody); - for (Node *n : fbody->graph->nodes()) { + FunctionBody *xla_fbody = nullptr; + TF_CHECK_OK(FunctionDefToBodyHelper( + *fld.Find("cluster_rewritten"), AttrSlice(), &fld, + [&](const string &op, const OpDef **sig) { + return fld.LookUpOpDef(op, sig); + }, + &xla_fbody)); + std::unique_ptr xla_fbody_deleter(xla_fbody); + for (Node *n : xla_fbody->graph->nodes()) { EXPECT_NE(n->type_string(), "XlaHostCompute"); } - // Check `host_graph`: verify we have no placeholder, but we have "const1". + // Check host graph: verify we have no placeholder, but we have "const1". + FunctionBody *host_fbody = nullptr; + AttrValue device_ordinal_temp_value; + device_ordinal_temp_value.set_i(0); + protobuf::Map host_func_attrs; + host_func_attrs["device_ordinal"] = device_ordinal_temp_value; + TF_CHECK_OK(FunctionDefToBodyHelper( + *fld.Find("host_graph"), AttrSlice(&host_func_attrs), &fld, + [&](const string &op, const OpDef **sig) { + return fld.LookUpOpDef(op, sig); + }, + &host_fbody)); + std::unique_ptr host_fbody_deleter(host_fbody); + Graph *host_graph = host_fbody->graph; int num_key_placeholders = 0; for (Node *n : host_graph->nodes()) { if (n->type_string() == "Placeholder" && @@ -438,4 +476,301 @@ TEST(ExtractOutsideCompilationForFunctionTest, XlaHostComputeRemoved) { EXPECT_NE(node_name_index.find("const1"), node_name_index.end()); } +REGISTER_OP("XlaSendToHost") + .Input("input: Tinput") + .Attr("Tinput: type") + .Attr("key: string") + .SetIsStateful(); + +REGISTER_OP("XlaRecvFromHost") + .Output("output: Toutput") + .Attr("Toutput: type") + .Attr("shape: shape") + .Attr("key: string") + .SetIsStateful(); + +TEST(ExtractOutsideCompilationForFunctionTest, OutsideCompilationInIf) { + // Build the XLA computation func. + // "const0" (bool) + // "const1" (int32) + // "if0" (pred = "const0", input = "const1", then_branch = "true_fn", + // else_branch = "false_fn") + FunctionDefLibrary fdl; + { + tensorflow::Scope s = tensorflow::Scope::NewRootScope(); + Output arg = ops::_Arg(s.WithOpName("arg"), DT_INT32, 0); + Output identity = ops::Identity(s.WithOpName("identity_true_fn"), arg); + ops::_Retval retval(s.WithOpName("retval"), identity, 0); + std::unique_ptr g(new Graph(OpRegistry::Global())); + TF_CHECK_OK(s.ToGraph(g.get())); + auto node_name_image = g->BuildNodeNameIndex(); + node_name_image["identity_true_fn"]->AddAttr("_oc", "0"); + PartialTensorShape shape({2}); + node_name_image["identity_true_fn"]->AddAttr( + kXlaInferredShapesAttrName, std::vector{shape}); + + FunctionDef *true_fn_fdef = fdl.add_function(); + TF_CHECK_OK(GraphToFunctionDef(*g, "true_fn", true_fn_fdef)); + } + { + tensorflow::Scope s = tensorflow::Scope::NewRootScope(); + Output arg = ops::_Arg(s.WithOpName("arg"), DT_INT32, 0); + Output identity = ops::Identity(s.WithOpName("identity_false_fn"), arg); + ops::_Retval retval(s.WithOpName("retval"), identity, 0); + std::unique_ptr g(new Graph(OpRegistry::Global())); + TF_CHECK_OK(s.ToGraph(g.get())); + auto node_name_image = g->BuildNodeNameIndex(); + node_name_image["identity_false_fn"]->AddAttr("_oc", "0"); + PartialTensorShape shape({2}); + node_name_image["identity_false_fn"]->AddAttr( + kXlaInferredShapesAttrName, std::vector{shape}); + + FunctionDef *false_fn_fdef = fdl.add_function(); + TF_CHECK_OK(GraphToFunctionDef(*g, "false_fn", false_fn_fdef)); + } + { + tensorflow::Scope s = tensorflow::Scope::NewRootScope(); + Output cond = ops::Const(s.WithOpName("const0"), true, {2}); + Output input = ops::Const(s.WithOpName("const1"), 1, {2}); + NameAttrList true_fn; + true_fn.set_name("true_fn"); + NameAttrList false_fn; + false_fn.set_name("false_fn"); + auto if_op = ops::If(s.WithOpName("if"), cond, + std::initializer_list{cond, input}, {DT_INT32}, + true_fn, false_fn); + ops::_Retval retval(s.WithOpName("retval"), if_op.output[0], 0); + std::unique_ptr g(new Graph(OpRegistry::Global())); + TF_CHECK_OK(s.ToGraph(g.get())); + + FunctionDef *xla_fdef = fdl.add_function(); + TF_CHECK_OK(GraphToFunctionDef(*g, "cluster", xla_fdef)); + } + FunctionLibraryDefinition fld(OpRegistry::Global(), fdl); + + protobuf::Map attrs; + std::map host_compute_core; + std::vector shape_inference_graphs; + bool has_outside_compilation; + NameAttrList name_attrs; + name_attrs.set_name("cluster"); + *name_attrs.mutable_attr() = attrs; + TF_CHECK_OK(ExtractOutsideCompilationForFunction( + "_xla", "_oc", "cluster", name_attrs, "cluster_rewritten", "host_graph", + host_compute_core, &fld, &shape_inference_graphs, + &has_outside_compilation)); + + // Check host graph. + { + FunctionBody *host_fbody = nullptr; + AttrValue device_ordinal_temp_value; + device_ordinal_temp_value.set_i(0); + protobuf::Map host_func_attrs; + host_func_attrs["device_ordinal"] = device_ordinal_temp_value; + TF_CHECK_OK(FunctionDefToBodyHelper( + *fld.Find("host_graph"), AttrSlice(&host_func_attrs), &fld, + [&](const string &op, const OpDef **sig) { + return fld.LookUpOpDef(op, sig); + }, + &host_fbody)); + std::unique_ptr host_fbody_deleter(host_fbody); + Graph *host_graph = host_fbody->graph; + auto node_name_index = host_graph->BuildNodeNameIndex(); + + // Verify we have XlaRecvAtHost to receive "If" predicate. + Node *recv_if_pred_node = node_name_index["recv_oc_if_pred_if"]; + EXPECT_NE(recv_if_pred_node, nullptr); + + // Verify we have an "If" to choose outside compilation between then_branch + // and else_branch, and it has `recv_if_pred_node` as cond input. + Node *if_oc_node = node_name_index["oc_if_if"]; + EXPECT_NE(if_oc_node, nullptr); + Node *if_oc_node_cond_input; + TF_CHECK_OK(if_oc_node->input_node(0, &if_oc_node_cond_input)); + EXPECT_EQ(if_oc_node_cond_input, recv_if_pred_node); + + // Check that then_branch outside compilation has node "identity_true_fn". + const FunctionDef *true_def = fld.Find("oc_then_branch_host_if_if"); + EXPECT_NE(true_def, nullptr); + bool has_identity_true_fn_node = false; + for (const auto &node_def : true_def->node_def()) { + if (node_def.name() == "identity_true_fn") { + has_identity_true_fn_node = true; + break; + } + } + EXPECT_TRUE(has_identity_true_fn_node); + + // Check that else_branch outside compilation has node "identity_false_fn". + const FunctionDef *false_def = fld.Find("oc_else_branch_host_if_if"); + EXPECT_NE(false_def, nullptr); + bool has_identity_false_fn_node = false; + for (const auto &node_def : false_def->node_def()) { + if (node_def.name() == "identity_false_fn") { + has_identity_false_fn_node = true; + break; + } + } + EXPECT_TRUE(has_identity_false_fn_node); + } + + // Check XLA graph. + { + FunctionBody *xla_fbody = nullptr; + TF_CHECK_OK(FunctionDefToBodyHelper( + *fld.Find("cluster_rewritten"), AttrSlice(), &fld, + [&](const string &op, const OpDef **sig) { + return fld.LookUpOpDef(op, sig); + }, + &xla_fbody)); + std::unique_ptr xla_fbody_deleter(xla_fbody); + Graph *xla_graph = xla_fbody->graph; + auto node_name_index = xla_graph->BuildNodeNameIndex(); + + // Check that we have XlaSendToHost to send cond predicate to host. + Node *send_if_pred_node = node_name_index["send_oc_if_pred_if"]; + EXPECT_NE(send_if_pred_node, nullptr); + + // Check that the "If" node now has `send_if_pred_node` as attribute + // _xla_token_input_nodes. + Node *if_node = node_name_index["if"]; + EXPECT_NE(if_node, nullptr); + std::vector token_inputs; + TF_CHECK_OK( + GetNodeAttr(if_node->def(), "_xla_token_input_nodes", &token_inputs)); + EXPECT_THAT(token_inputs, ::testing::ElementsAre("send_oc_if_pred_if")); + } +} + +TEST(ExtractOutsideCompilationForFunctionTest, OutsideCompilationInWhile) { + // Build the XLA computation func. + // "const0" (bool) + // "while0" (input = "const0", cond = "cond_fn", body = "body_fn") + FunctionDefLibrary fdl; + { + tensorflow::Scope s = tensorflow::Scope::NewRootScope(); + Output arg = ops::_Arg(s.WithOpName("arg"), DT_BOOL, 0); + Output identity = ops::Identity(s.WithOpName("identity_cond_fn"), arg); + ops::_Retval retval(s.WithOpName("retval"), identity, 0); + std::unique_ptr g(new Graph(OpRegistry::Global())); + TF_CHECK_OK(s.ToGraph(g.get())); + auto node_name_image = g->BuildNodeNameIndex(); + node_name_image["identity_cond_fn"]->AddAttr("_oc", "0"); + PartialTensorShape shape({2}); + node_name_image["identity_cond_fn"]->AddAttr( + kXlaInferredShapesAttrName, std::vector{shape}); + + FunctionDef *cond_fn_fdef = fdl.add_function(); + TF_CHECK_OK(GraphToFunctionDef(*g, "cond_fn", cond_fn_fdef)); + } + { + tensorflow::Scope s = tensorflow::Scope::NewRootScope(); + Output arg = ops::_Arg(s.WithOpName("arg"), DT_BOOL, 0); + Output identity = ops::Identity(s.WithOpName("identity_body_fn"), arg); + ops::_Retval retval(s.WithOpName("retval"), identity, 0); + std::unique_ptr g(new Graph(OpRegistry::Global())); + TF_CHECK_OK(s.ToGraph(g.get())); + auto node_name_image = g->BuildNodeNameIndex(); + node_name_image["identity_body_fn"]->AddAttr("_oc", "0"); + PartialTensorShape shape({2}); + node_name_image["identity_body_fn"]->AddAttr( + kXlaInferredShapesAttrName, std::vector{shape}); + + FunctionDef *body_fn_fdef = fdl.add_function(); + TF_CHECK_OK(GraphToFunctionDef(*g, "body_fn", body_fn_fdef)); + } + { + tensorflow::Scope s = tensorflow::Scope::NewRootScope(); + Output input = ops::Const(s.WithOpName("const0"), true, {2}); + NameAttrList cond_fn; + cond_fn.set_name("cond_fn"); + NameAttrList body_fn; + body_fn.set_name("body_fn"); + auto while_op = + ops::While(s.WithOpName("while"), std::initializer_list{input}, + cond_fn, body_fn); + ops::_Retval retval(s.WithOpName("retval"), while_op.output[0], 0); + std::unique_ptr g(new Graph(OpRegistry::Global())); + TF_CHECK_OK(s.ToGraph(g.get())); + + FunctionDef *xla_fdef = fdl.add_function(); + TF_CHECK_OK(GraphToFunctionDef(*g, "cluster", xla_fdef)); + } + FunctionLibraryDefinition fld(OpRegistry::Global(), fdl); + + protobuf::Map attrs; + std::map host_compute_core; + std::vector shape_inference_graphs; + bool has_outside_compilation; + NameAttrList name_attrs; + name_attrs.set_name("cluster"); + *name_attrs.mutable_attr() = attrs; + TF_CHECK_OK(ExtractOutsideCompilationForFunction( + "_xla", "_oc", "cluster", name_attrs, "cluster_rewritten", "host_graph", + host_compute_core, &fld, &shape_inference_graphs, + &has_outside_compilation)); + + // Check host graph. + { + FunctionBody *host_fbody = nullptr; + AttrValue device_ordinal_temp_value; + device_ordinal_temp_value.set_i(0); + protobuf::Map host_func_attrs; + host_func_attrs["device_ordinal"] = device_ordinal_temp_value; + TF_CHECK_OK(FunctionDefToBodyHelper( + *fld.Find("host_graph"), AttrSlice(&host_func_attrs), &fld, + [&](const string &op, const OpDef **sig) { + return fld.LookUpOpDef(op, sig); + }, + &host_fbody)); + std::unique_ptr host_fbody_deleter(host_fbody); + Graph *host_graph = host_fbody->graph; + auto node_name_index = host_graph->BuildNodeNameIndex(); + + // Verify we have an "While" to execute outside compilation. + Node *while_oc_node = node_name_index["oc_while_while"]; + EXPECT_NE(while_oc_node, nullptr); + + // Check that cond outside compilation has node "identity_cond_fn". + const FunctionDef *cond_def = fld.Find("oc_cond_host_while_while"); + EXPECT_NE(cond_def, nullptr); + bool has_identity_cond_fn_node = false; + for (const auto &node_def : cond_def->node_def()) { + if (node_def.name() == "identity_cond_fn") { + has_identity_cond_fn_node = true; + break; + } + } + EXPECT_TRUE(has_identity_cond_fn_node); + + // Check that body outside compilation has node "identity_body_fn". + const FunctionDef *body_def = fld.Find("oc_body_host_while_while"); + EXPECT_NE(body_def, nullptr); + bool has_identity_body_fn_node = false; + for (const auto &node_def : body_def->node_def()) { + if (node_def.name() == "identity_body_fn") { + has_identity_body_fn_node = true; + break; + } + } + EXPECT_TRUE(has_identity_body_fn_node); + } + + // Check XLA graph. + { + // Verify that rewritten cond fn has XlaSendToHost to send loop predicate to + // host. + const FunctionDef *cond_def = fld.Find("cond_fn_oc"); + EXPECT_NE(cond_def, nullptr); + bool has_send_oc_while_cond_node = false; + for (const auto &node_def : cond_def->node_def()) { + if (node_def.name() == "send_oc_while_cond_while") { + has_send_oc_while_cond_node = true; + break; + } + } + EXPECT_TRUE(has_send_oc_while_cond_node); + } +} + } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/while_op.cc b/tensorflow/compiler/tf2xla/kernels/while_op.cc index ce007fc04a..89b577bfc0 100644 --- a/tensorflow/compiler/tf2xla/kernels/while_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/while_op.cc @@ -41,8 +41,7 @@ Status MakeXlaCompilerArgumentsFromInputs( *has_uninitialized_vars = false; *has_tensor_arrays = false; for (int i = 0; i < ctx->num_inputs(); ++i) { - VLOG(2) << " Input " << i - << " type: " << DataTypeString(ctx->input_type(i)) + VLOG(2) << " Input " << i << " type: " << DataTypeString(ctx->input_type(i)) << " shape: " << ctx->InputShape(i).DebugString(); XlaCompiler::Argument& arg = (*args)[i]; DataType type = ctx->input_type(i); @@ -233,13 +232,22 @@ void XlaWhileOp::Compile(XlaOpKernelContext* ctx) { xla::ShapeUtil::HumanString(body_input_shape), " vs. ", xla::ShapeUtil::HumanString(body.xla_output_shape))); - xla::Shape expected_cond_output_shape = xla::ShapeUtil::MakeTupleShape( - {xla::ShapeUtil::MakeShape(xla::PRED, {})}); + xla::Shape expected_cond_output_shape_without_side_effect = + xla::ShapeUtil::MakeTupleShape( + {xla::ShapeUtil::MakeShape(xla::PRED, {})}); + xla::Shape expected_cond_output_shape_with_side_effect = + xla::ShapeUtil::MakeTupleShape({xla::ShapeUtil::MakeShape(xla::PRED, {}), + xla::ShapeUtil::MakeTokenShape()}); OP_REQUIRES(ctx, - xla::ShapeUtil::Compatible(cond.xla_output_shape, - expected_cond_output_shape), + xla::ShapeUtil::Compatible( + cond.xla_output_shape, + expected_cond_output_shape_without_side_effect) || + xla::ShapeUtil::Compatible( + cond.xla_output_shape, + expected_cond_output_shape_with_side_effect), errors::InvalidArgument( - "Output shape of loop condition should be (pred[]), got: ", + "Output shape of loop condition should be (pred[]) or " + "(pred[], token[]), got: ", xla::ShapeUtil::HumanString(cond.xla_output_shape))); int num_inputs = body.input_mapping.size(); diff --git a/tensorflow/compiler/tf2xla/side_effect_util.cc b/tensorflow/compiler/tf2xla/side_effect_util.cc index b233e6b2c2..b62f8e9115 100644 --- a/tensorflow/compiler/tf2xla/side_effect_util.cc +++ b/tensorflow/compiler/tf2xla/side_effect_util.cc @@ -24,6 +24,8 @@ const char kXlaTokenInputNodesAttrName[] = "_xla_token_input_nodes"; const char kXlaTokenArgNodeName[] = "_xla_token_arg_node"; +const char kXlaHasHostTransferAttrName[] = "_xla_has_host_transfer"; + std::set CalculateTokenInputsForOutputToken(const Graph& g) { std::set results; Node* first_side_effecting_node_on_path = nullptr; diff --git a/tensorflow/compiler/tf2xla/side_effect_util.h b/tensorflow/compiler/tf2xla/side_effect_util.h index f22ddb2f58..7081b362c3 100644 --- a/tensorflow/compiler/tf2xla/side_effect_util.h +++ b/tensorflow/compiler/tf2xla/side_effect_util.h @@ -35,6 +35,9 @@ extern const char kXlaTokenInputNodesAttrName[]; // node has side-effect dependency on current graph's token input. extern const char kXlaTokenArgNodeName[]; +// This node have XlaRecvAtHost/XlaSendFromHost in its associated functions. +extern const char kXlaHasHostTransferAttrName[]; + // Calculates side-effect dependencies for the graph's token output. // Returns a set of node names representing these dependencies. std::set CalculateTokenInputsForOutputToken(const Graph& g); diff --git a/tensorflow/compiler/tf2xla/tf2xla_util.cc b/tensorflow/compiler/tf2xla/tf2xla_util.cc index cc81772e8c..6cc8ae3afd 100644 --- a/tensorflow/compiler/tf2xla/tf2xla_util.cc +++ b/tensorflow/compiler/tf2xla/tf2xla_util.cc @@ -557,6 +557,12 @@ bool HasAssociatedFunction(const NodeDef& node_def, return true; } + if (node_def.op() == "XlaHostCompute") { + // XlaHostCompute has "shape_inference_graph" func attr, but that's not + // related to graph execution. + return false; + } + for (const auto& iter : node_def.attr()) { if (iter.second.has_func()) { return true; @@ -578,6 +584,9 @@ std::vector GetAssociatedFunctions( // This is a SymbolicGradient op. AttrValueMap attrs(node.attrs().begin(), node.attrs().end()); results.emplace_back(AssociatedFunctionInfo::SymbolicGradient(op, attrs)); + } else if (node.type_string() == "XlaHostCompute") { + // XlaHostCompute has "shape_inference_graph" func attr, but that's not + // related to graph execution. } else { // Collect all function attrs for the node. for (auto& iter : node.attrs()) { -- GitLab From c07721a4aca474c09d2f07a667e0edeb4e826957 Mon Sep 17 00:00:00 2001 From: Taylor Robie Date: Mon, 10 Dec 2018 21:17:21 -0800 Subject: [PATCH 337/873] Apply string compat function to address when creating a coordinator. PiperOrigin-RevId: 224936924 --- .../python/distribute/cluster_resolver/tpu_cluster_resolver.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver.py b/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver.py index 72a27b915c..52ac07d7ea 100644 --- a/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver.py +++ b/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver.py @@ -481,7 +481,8 @@ class TPUClusterResolver(ClusterResolver): return self._environment def _start_local_server(self): - address = self._requestComputeMetadata('instance/network-interfaces/0/ip') + address = compat.as_text(self._requestComputeMetadata( + 'instance/network-interfaces/0/ip')) self._server = server_lib.Server( { 'local': ['0.0.0.0:0'] -- GitLab From 68834966daf6bd27add401f6d9402b5a0e3da5ec Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 10 Dec 2018 21:20:16 -0800 Subject: [PATCH 338/873] Export ragged ops. PiperOrigin-RevId: 224937131 --- tensorflow/python/ops/ragged/BUILD | 12 +- .../python/ops/ragged/ragged_factory_ops.py | 3 + .../ops/ragged/ragged_functional_ops.py | 2 + .../python/ops/ragged/ragged_math_ops.py | 2 + tensorflow/python/ops/ragged/ragged_tensor.py | 2 + .../python/ops/ragged/ragged_tensor_value.py | 3 + .../python/ops/ragged/segment_id_ops.py | 3 + .../tools/api/generator/api_init_files.bzl | 1 + .../tools/api/generator/api_init_files_v1.bzl | 1 + .../python/tools/api/generator/doc_srcs.py | 1 + .../golden/v1/tensorflow.-ragged-tensor.pbtxt | 125 ++++++++++++++++++ .../tools/api/golden/v1/tensorflow.pbtxt | 8 ++ ...nsorflow.ragged.-ragged-tensor-value.pbtxt | 41 ++++++ .../api/golden/v1/tensorflow.ragged.pbtxt | 31 +++++ .../golden/v2/tensorflow.-ragged-tensor.pbtxt | 125 ++++++++++++++++++ .../tools/api/golden/v2/tensorflow.pbtxt | 8 ++ .../api/golden/v2/tensorflow.ragged.pbtxt | 23 ++++ tensorflow/tools/compatibility/renames_v2.py | 3 + 18 files changed, 393 insertions(+), 1 deletion(-) create mode 100644 tensorflow/tools/api/golden/v1/tensorflow.-ragged-tensor.pbtxt create mode 100644 tensorflow/tools/api/golden/v1/tensorflow.ragged.-ragged-tensor-value.pbtxt create mode 100644 tensorflow/tools/api/golden/v1/tensorflow.ragged.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.-ragged-tensor.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.ragged.pbtxt diff --git a/tensorflow/python/ops/ragged/BUILD b/tensorflow/python/ops/ragged/BUILD index d88543c400..c0db8bfbb5 100644 --- a/tensorflow/python/ops/ragged/BUILD +++ b/tensorflow/python/ops/ragged/BUILD @@ -62,6 +62,7 @@ py_library( "//tensorflow/python:ragged_array_ops_gen", "//tensorflow/python:tensor_shape", "//tensorflow/python:tensor_util", + "//tensorflow/python:util", ], ) @@ -82,6 +83,7 @@ py_library( "//tensorflow/python:ragged_conversion_ops_gen", "//tensorflow/python:sparse_tensor", "//tensorflow/python:tensor_shape", + "//tensorflow/python:util", ], ) @@ -95,6 +97,7 @@ py_library( "//tensorflow/python:framework_ops", "//tensorflow/python:math_ops", "//tensorflow/python:tensor_util", + "//tensorflow/python:util", "//tensorflow/python/ops/ragged:ragged_tensor", "//tensorflow/python/ops/ragged:ragged_tensor_value", "//third_party/py/numpy", @@ -110,6 +113,7 @@ py_library( ":ragged_tensor", ":ragged_util", "//tensorflow/python:framework_ops", + "//tensorflow/python:util", ], ) @@ -147,6 +151,7 @@ py_library( "//tensorflow/python:math_ops", "//tensorflow/python:ragged_math_ops_gen", "//tensorflow/python:tensor_util", + "//tensorflow/python:util", ], ) @@ -189,6 +194,7 @@ py_library( "//tensorflow/python:framework_ops", "//tensorflow/python:session", "//tensorflow/python:tensor_shape", + "//tensorflow/python:util", ], ) @@ -216,7 +222,10 @@ py_library( name = "ragged_tensor_value", srcs = ["ragged_tensor_value.py"], srcs_version = "PY2AND3", - deps = ["//third_party/py/numpy"], + deps = [ + "//tensorflow/python:util", + "//third_party/py/numpy", + ], ) py_library( @@ -245,6 +254,7 @@ py_library( "//tensorflow/python:math_ops", "//tensorflow/python:tensor_shape", "//tensorflow/python:tensor_util", + "//tensorflow/python:util", ], ) diff --git a/tensorflow/python/ops/ragged/ragged_factory_ops.py b/tensorflow/python/ops/ragged/ragged_factory_ops.py index 2c63e1c799..695accc652 100644 --- a/tensorflow/python/ops/ragged/ragged_factory_ops.py +++ b/tensorflow/python/ops/ragged/ragged_factory_ops.py @@ -24,11 +24,13 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import ops from tensorflow.python.ops.ragged import ragged_tensor from tensorflow.python.ops.ragged import ragged_tensor_value +from tensorflow.python.util.tf_export import tf_export #=============================================================================== # Op to construct a constant RaggedTensor from a nested Python list. #=============================================================================== +@tf_export("ragged.constant") def constant(pylist, dtype=None, ragged_rank=None, inner_shape=None, name=None): """Constructs a constant RaggedTensor from a nested Python list. @@ -74,6 +76,7 @@ def constant(pylist, dtype=None, ragged_rank=None, inner_shape=None, name=None): inner_shape) +@tf_export(v1=["ragged.constant_value"]) def constant_value(pylist, dtype=None, ragged_rank=None, inner_shape=None): """Constructs a RaggedTensorValue from a nested Python list. diff --git a/tensorflow/python/ops/ragged/ragged_functional_ops.py b/tensorflow/python/ops/ragged/ragged_functional_ops.py index 751f2c7359..7344c96465 100644 --- a/tensorflow/python/ops/ragged/ragged_functional_ops.py +++ b/tensorflow/python/ops/ragged/ragged_functional_ops.py @@ -21,8 +21,10 @@ from __future__ import print_function from tensorflow.python.framework import ops from tensorflow.python.ops.ragged import ragged_tensor from tensorflow.python.ops.ragged import ragged_util +from tensorflow.python.util.tf_export import tf_export +@tf_export("ragged.map_flat_values") def map_flat_values(op, *args, **kwargs): """Applies `op` to the inner values of one or more RaggedTensors. diff --git a/tensorflow/python/ops/ragged/ragged_math_ops.py b/tensorflow/python/ops/ragged/ragged_math_ops.py index f774c1eb58..02e927b699 100644 --- a/tensorflow/python/ops/ragged/ragged_math_ops.py +++ b/tensorflow/python/ops/ragged/ragged_math_ops.py @@ -31,12 +31,14 @@ from tensorflow.python.ops.ragged import ragged_functional_ops from tensorflow.python.ops.ragged import ragged_tensor from tensorflow.python.ops.ragged import ragged_util from tensorflow.python.ops.ragged import segment_id_ops +from tensorflow.python.util.tf_export import tf_export #=============================================================================== # ragged.range #=============================================================================== # pylint: disable=redefined-builtin +@tf_export('ragged.range') def range(starts, limits=None, deltas=1, dtype=None, name=None): """Returns a `RaggedTensor` containing the specified sequences of numbers. diff --git a/tensorflow/python/ops/ragged/ragged_tensor.py b/tensorflow/python/ops/ragged/ragged_tensor.py index 567c50203a..acf3a3841d 100644 --- a/tensorflow/python/ops/ragged/ragged_tensor.py +++ b/tensorflow/python/ops/ragged/ragged_tensor.py @@ -32,6 +32,7 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops.ragged import ragged_tensor_value from tensorflow.python.ops.ragged import ragged_util from tensorflow.python.ops.ragged import segment_id_ops +from tensorflow.python.util.tf_export import tf_export # pylint: disable=protected-access _eval_using_default_session = ops._eval_using_default_session @@ -43,6 +44,7 @@ _eval_using_default_session = ops._eval_using_default_session #=============================================================================== +@tf_export("RaggedTensor") class RaggedTensor(object): """Represents a ragged tensor (go/ragged). diff --git a/tensorflow/python/ops/ragged/ragged_tensor_value.py b/tensorflow/python/ops/ragged/ragged_tensor_value.py index bf0ac4482a..1162487f0f 100644 --- a/tensorflow/python/ops/ragged/ragged_tensor_value.py +++ b/tensorflow/python/ops/ragged/ragged_tensor_value.py @@ -20,7 +20,10 @@ from __future__ import print_function import numpy as np +from tensorflow.python.util.tf_export import tf_export + +@tf_export(v1=["ragged.RaggedTensorValue"]) class RaggedTensorValue(object): """Represents the value of a `RaggedTensor`. diff --git a/tensorflow/python/ops/ragged/segment_id_ops.py b/tensorflow/python/ops/ragged/segment_id_ops.py index fa2970c3e7..ee17e4d636 100644 --- a/tensorflow/python/ops/ragged/segment_id_ops.py +++ b/tensorflow/python/ops/ragged/segment_id_ops.py @@ -25,10 +25,12 @@ from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.ragged import ragged_util +from tensorflow.python.util.tf_export import tf_export # For background on "segments" and "segment ids", see: # https://www.tensorflow.org/api_guides/python/math_ops#Segmentation +@tf_export("ragged.row_splits_to_segment_ids") def row_splits_to_segment_ids(splits, name=None): """Generates the segmentation corresponding to a RaggedTensor `splits` vector. @@ -63,6 +65,7 @@ def row_splits_to_segment_ids(splits, name=None): # For background on "segments" and "segment ids", see: # https://www.tensorflow.org/api_guides/python/math_ops#Segmentation +@tf_export("ragged.segment_ids_to_row_splits") def segment_ids_to_row_splits(segment_ids, num_segments=None, name=None): """Generates the RaggedTensor `splits` vector corresponding to a segmentation. diff --git a/tensorflow/python/tools/api/generator/api_init_files.bzl b/tensorflow/python/tools/api/generator/api_init_files.bzl index 58913b3208..25d0c0f75c 100644 --- a/tensorflow/python/tools/api/generator/api_init_files.bzl +++ b/tensorflow/python/tools/api/generator/api_init_files.bzl @@ -69,6 +69,7 @@ TENSORFLOW_API_INIT_FILES = [ "nn/__init__.py", "nn/rnn_cell/__init__.py", "quantization/__init__.py", + "ragged/__init__.py", "random/__init__.py", "saved_model/__init__.py", "sets/__init__.py", diff --git a/tensorflow/python/tools/api/generator/api_init_files_v1.bzl b/tensorflow/python/tools/api/generator/api_init_files_v1.bzl index 0937f98e75..99c8495ce5 100644 --- a/tensorflow/python/tools/api/generator/api_init_files_v1.bzl +++ b/tensorflow/python/tools/api/generator/api_init_files_v1.bzl @@ -79,6 +79,7 @@ TENSORFLOW_API_INIT_FILES_V1 = [ "profiler/__init__.py", "python_io/__init__.py", "quantization/__init__.py", + "ragged/__init__.py", "random/__init__.py", "resource_loader/__init__.py", "strings/__init__.py", diff --git a/tensorflow/python/tools/api/generator/doc_srcs.py b/tensorflow/python/tools/api/generator/doc_srcs.py index abb5886deb..b567eead3d 100644 --- a/tensorflow/python/tools/api/generator/doc_srcs.py +++ b/tensorflow/python/tools/api/generator/doc_srcs.py @@ -54,6 +54,7 @@ _TENSORFLOW_DOC_SOURCES = { 'nn': DocSource(docstring_module_name='ops.nn_ops'), 'nn.rnn_cell': DocSource(docstring_module_name='ops.rnn_cell'), 'python_io': DocSource(docstring_module_name='lib.io.python_io'), + 'ragged': DocSource(docstring_module_name='ops.ragged'), 'resource_loader': DocSource( docstring_module_name='platform.resource_loader'), 'sets': DocSource(docstring_module_name='ops.sets'), diff --git a/tensorflow/tools/api/golden/v1/tensorflow.-ragged-tensor.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.-ragged-tensor.pbtxt new file mode 100644 index 0000000000..c0ed956535 --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.-ragged-tensor.pbtxt @@ -0,0 +1,125 @@ +path: "tensorflow.RaggedTensor" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "dtype" + mtype: "" + } + member { + name: "flat_values" + mtype: "" + } + member { + name: "nested_row_splits" + mtype: "" + } + member { + name: "ragged_rank" + mtype: "" + } + member { + name: "row_splits" + mtype: "" + } + member { + name: "shape" + mtype: "" + } + member { + name: "values" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'values\', \'row_splits\', \'cached_row_lengths\', \'cached_value_rowids\', \'cached_nrows\', \'internal\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'False\'], " + } + member_method { + name: "bounding_shape" + argspec: "args=[\'self\', \'axis\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "from_nested_row_lengths" + argspec: "args=[\'cls\', \'flat_values\', \'nested_row_lengths\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "from_nested_row_splits" + argspec: "args=[\'cls\', \'flat_values\', \'nested_row_splits\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "from_nested_value_rowids" + argspec: "args=[\'cls\', \'flat_values\', \'nested_value_rowids\', \'nested_nrows\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "from_row_lengths" + argspec: "args=[\'cls\', \'values\', \'row_lengths\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "from_row_limits" + argspec: "args=[\'cls\', \'values\', \'row_limits\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "from_row_splits" + argspec: "args=[\'cls\', \'values\', \'row_splits\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "from_row_starts" + argspec: "args=[\'cls\', \'values\', \'row_starts\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "from_sparse" + argspec: "args=[\'cls\', \'st_input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "from_tensor" + argspec: "args=[\'cls\', \'tensor\', \'lengths\', \'padding\', \'ragged_rank\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'1\', \'None\'], " + } + member_method { + name: "from_value_rowids" + argspec: "args=[\'cls\', \'values\', \'value_rowids\', \'nrows\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "nested_row_lengths" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "nrows" + argspec: "args=[\'self\', \'out_type\', \'name\'], varargs=None, keywords=None, defaults=[\"\", \'None\'], " + } + member_method { + name: "row_lengths" + argspec: "args=[\'self\', \'axis\', \'name\'], varargs=None, keywords=None, defaults=[\'1\', \'None\'], " + } + member_method { + name: "row_limits" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "row_starts" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "to_list" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "to_sparse" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "to_tensor" + argspec: "args=[\'self\', \'default_value\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "value_rowids" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "with_flat_values" + argspec: "args=[\'self\', \'new_values\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "with_values" + argspec: "args=[\'self\', \'new_values\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v1/tensorflow.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.pbtxt index 60ff59196b..5592a4c59d 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.pbtxt @@ -172,6 +172,10 @@ tf_module { name: "QueueBase" mtype: "" } + member { + name: "RaggedTensor" + mtype: "" + } member { name: "RandomShuffleQueue" mtype: "" @@ -516,6 +520,10 @@ tf_module { name: "quint8" mtype: "" } + member { + name: "ragged" + mtype: "" + } member { name: "random" mtype: "" diff --git a/tensorflow/tools/api/golden/v1/tensorflow.ragged.-ragged-tensor-value.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.ragged.-ragged-tensor-value.pbtxt new file mode 100644 index 0000000000..96c895e0a4 --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.ragged.-ragged-tensor-value.pbtxt @@ -0,0 +1,41 @@ +path: "tensorflow.ragged.RaggedTensorValue" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "dtype" + mtype: "" + } + member { + name: "flat_values" + mtype: "" + } + member { + name: "nested_row_splits" + mtype: "" + } + member { + name: "ragged_rank" + mtype: "" + } + member { + name: "row_splits" + mtype: "" + } + member { + name: "shape" + mtype: "" + } + member { + name: "values" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'values\', \'row_splits\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "to_list" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v1/tensorflow.ragged.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.ragged.pbtxt new file mode 100644 index 0000000000..22ca7e931f --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.ragged.pbtxt @@ -0,0 +1,31 @@ +path: "tensorflow.ragged" +tf_module { + member { + name: "RaggedTensorValue" + mtype: "" + } + member_method { + name: "constant" + argspec: "args=[\'pylist\', \'dtype\', \'ragged_rank\', \'inner_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "constant_value" + argspec: "args=[\'pylist\', \'dtype\', \'ragged_rank\', \'inner_shape\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + } + member_method { + name: "map_flat_values" + argspec: "args=[\'op\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "range" + argspec: "args=[\'starts\', \'limits\', \'deltas\', \'dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'None\', \'None\'], " + } + member_method { + name: "row_splits_to_segment_ids" + argspec: "args=[\'splits\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "segment_ids_to_row_splits" + argspec: "args=[\'segment_ids\', \'num_segments\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-ragged-tensor.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-ragged-tensor.pbtxt new file mode 100644 index 0000000000..c0ed956535 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-ragged-tensor.pbtxt @@ -0,0 +1,125 @@ +path: "tensorflow.RaggedTensor" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "dtype" + mtype: "" + } + member { + name: "flat_values" + mtype: "" + } + member { + name: "nested_row_splits" + mtype: "" + } + member { + name: "ragged_rank" + mtype: "" + } + member { + name: "row_splits" + mtype: "" + } + member { + name: "shape" + mtype: "" + } + member { + name: "values" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'values\', \'row_splits\', \'cached_row_lengths\', \'cached_value_rowids\', \'cached_nrows\', \'internal\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'False\'], " + } + member_method { + name: "bounding_shape" + argspec: "args=[\'self\', \'axis\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "from_nested_row_lengths" + argspec: "args=[\'cls\', \'flat_values\', \'nested_row_lengths\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "from_nested_row_splits" + argspec: "args=[\'cls\', \'flat_values\', \'nested_row_splits\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "from_nested_value_rowids" + argspec: "args=[\'cls\', \'flat_values\', \'nested_value_rowids\', \'nested_nrows\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "from_row_lengths" + argspec: "args=[\'cls\', \'values\', \'row_lengths\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "from_row_limits" + argspec: "args=[\'cls\', \'values\', \'row_limits\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "from_row_splits" + argspec: "args=[\'cls\', \'values\', \'row_splits\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "from_row_starts" + argspec: "args=[\'cls\', \'values\', \'row_starts\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "from_sparse" + argspec: "args=[\'cls\', \'st_input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "from_tensor" + argspec: "args=[\'cls\', \'tensor\', \'lengths\', \'padding\', \'ragged_rank\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'1\', \'None\'], " + } + member_method { + name: "from_value_rowids" + argspec: "args=[\'cls\', \'values\', \'value_rowids\', \'nrows\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "nested_row_lengths" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "nrows" + argspec: "args=[\'self\', \'out_type\', \'name\'], varargs=None, keywords=None, defaults=[\"\", \'None\'], " + } + member_method { + name: "row_lengths" + argspec: "args=[\'self\', \'axis\', \'name\'], varargs=None, keywords=None, defaults=[\'1\', \'None\'], " + } + member_method { + name: "row_limits" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "row_starts" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "to_list" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "to_sparse" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "to_tensor" + argspec: "args=[\'self\', \'default_value\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "value_rowids" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "with_flat_values" + argspec: "args=[\'self\', \'new_values\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "with_values" + argspec: "args=[\'self\', \'new_values\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.pbtxt index 0f11107dc3..5f31d27480 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.pbtxt @@ -32,6 +32,10 @@ tf_module { name: "Operation" mtype: "" } + member { + name: "RaggedTensor" + mtype: "" + } member { name: "RegisterGradient" mtype: "" @@ -260,6 +264,10 @@ tf_module { name: "quint8" mtype: "" } + member { + name: "ragged" + mtype: "" + } member { name: "random" mtype: "" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.ragged.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.ragged.pbtxt new file mode 100644 index 0000000000..5fde488ffd --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.ragged.pbtxt @@ -0,0 +1,23 @@ +path: "tensorflow.ragged" +tf_module { + member_method { + name: "constant" + argspec: "args=[\'pylist\', \'dtype\', \'ragged_rank\', \'inner_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "map_flat_values" + argspec: "args=[\'op\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "range" + argspec: "args=[\'starts\', \'limits\', \'deltas\', \'dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'None\', \'None\'], " + } + member_method { + name: "row_splits_to_segment_ids" + argspec: "args=[\'splits\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "segment_ids_to_row_splits" + argspec: "args=[\'segment_ids\', \'num_segments\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } +} diff --git a/tensorflow/tools/compatibility/renames_v2.py b/tensorflow/tools/compatibility/renames_v2.py index 3ab5a0d0d6..9a3f4460f7 100644 --- a/tensorflow/tools/compatibility/renames_v2.py +++ b/tensorflow/tools/compatibility/renames_v2.py @@ -421,6 +421,9 @@ renames = { 'tf.qr': 'tf.linalg.qr', 'tf.quantize': 'tf.quantization.quantize', 'tf.quantized_concat': 'tf.quantization.quantized_concat', + 'tf.ragged.constant_value': 'tf.compat.v1.ragged.constant_value', + 'tf.ragged.convert_to_tensor_or_ragged_tensor': 'tf.compat.v1.ragged.convert_to_tensor_or_ragged_tensor', + 'tf.ragged.RaggedTensorValue': 'tf.compat.v1.ragged.RaggedTensorValue', 'tf.random.get_seed': 'tf.compat.v1.random.get_seed', 'tf.random.set_random_seed': 'tf.compat.v1.random.set_random_seed', 'tf.random_crop': 'tf.image.random_crop', -- GitLab From c2255b0f32991813a4bfbcc3e1ee178a5b5eeecd Mon Sep 17 00:00:00 2001 From: Jing Li Date: Mon, 10 Dec 2018 23:35:19 -0800 Subject: [PATCH 339/873] Rewrite Adam and LazyAdam optimizer to take global step for computing beta1 and beta2 accumulators, instead of having the optimizer instance to keep its own independent beta1 and beta2 accumulators as non-slot variables. PiperOrigin-RevId: 224948020 --- tensorflow/contrib/opt/BUILD | 36 ++ tensorflow/contrib/opt/__init__.py | 4 + .../opt/python/training/adam_gs_optimizer.py | 217 ++++++++++ .../python/training/adam_gs_optimizer_test.py | 382 +++++++++++++++++ .../python/training/lazy_adam_gs_optimizer.py | 114 +++++ .../training/lazy_adam_gs_optimizer_test.py | 402 ++++++++++++++++++ 6 files changed, 1155 insertions(+) create mode 100644 tensorflow/contrib/opt/python/training/adam_gs_optimizer.py create mode 100644 tensorflow/contrib/opt/python/training/adam_gs_optimizer_test.py create mode 100644 tensorflow/contrib/opt/python/training/lazy_adam_gs_optimizer.py create mode 100644 tensorflow/contrib/opt/python/training/lazy_adam_gs_optimizer_test.py diff --git a/tensorflow/contrib/opt/BUILD b/tensorflow/contrib/opt/BUILD index f4ac70eb1a..0446e823d9 100644 --- a/tensorflow/contrib/opt/BUILD +++ b/tensorflow/contrib/opt/BUILD @@ -14,6 +14,7 @@ py_library( name = "opt_py", srcs = [ "__init__.py", + "python/training/adam_gs_optimizer.py", "python/training/adamax.py", "python/training/addsign.py", "python/training/agn_optimizer.py", @@ -22,6 +23,7 @@ py_library( "python/training/external_optimizer.py", "python/training/ggt.py", "python/training/lars_optimizer.py", + "python/training/lazy_adam_gs_optimizer.py", "python/training/lazy_adam_optimizer.py", "python/training/matrix_functions.py", "python/training/model_average_optimizer.py", @@ -60,6 +62,21 @@ py_library( ], ) +py_test( + name = "adam_gs_optimizer_test", + srcs = ["python/training/adam_gs_optimizer_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":opt_py", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:math_ops", + "//tensorflow/python:training", + "//third_party/py/numpy", + ], +) + py_test( name = "adamax_test", srcs = ["python/training/adamax_test.py"], @@ -148,6 +165,25 @@ py_test( ], ) +py_test( + name = "lazy_adam_gs_optimizer_test", + srcs = ["python/training/lazy_adam_gs_optimizer_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":opt_py", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:constant_op", + "//tensorflow/python:dtypes", + "//tensorflow/python:framework_ops", + "//tensorflow/python:math_ops", + "//tensorflow/python:resource_variable_ops", + "//tensorflow/python:variables", + "//third_party/py/numpy", + "@absl_py//absl/testing:parameterized", + ], +) + py_test( name = "lazy_adam_optimizer_test", srcs = ["python/training/lazy_adam_optimizer_test.py"], diff --git a/tensorflow/contrib/opt/__init__.py b/tensorflow/contrib/opt/__init__.py index c7ea68efa9..e8fc52342c 100644 --- a/tensorflow/contrib/opt/__init__.py +++ b/tensorflow/contrib/opt/__init__.py @@ -19,6 +19,7 @@ from __future__ import division from __future__ import print_function # pylint: disable=wildcard-import +from tensorflow.contrib.opt.python.training.adam_gs_optimizer import * from tensorflow.contrib.opt.python.training.adamax import * from tensorflow.contrib.opt.python.training.addsign import * from tensorflow.contrib.opt.python.training.agn_optimizer import * @@ -28,6 +29,7 @@ from tensorflow.contrib.opt.python.training.external_optimizer import * from tensorflow.contrib.opt.python.training.lars_optimizer import * from tensorflow.contrib.opt.python.training.ggt import * from tensorflow.contrib.opt.python.training.lazy_adam_optimizer import * +from tensorflow.contrib.opt.python.training.lazy_adam_gs_optimizer import * from tensorflow.contrib.opt.python.training.model_average_optimizer import * from tensorflow.contrib.opt.python.training.moving_average_optimizer import * from tensorflow.contrib.opt.python.training.multitask_optimizer_wrapper import * @@ -44,12 +46,14 @@ from tensorflow.python.util.all_util import remove_undocumented _allowed_symbols = [ 'AdaMaxOptimizer', + 'AdamGSOptimizer', 'PowerSignOptimizer', 'AddSignOptimizer', 'DelayCompensatedGradientDescentOptimizer', 'DropStaleGradientOptimizer', 'ExternalOptimizerInterface', 'LARSOptimizer', + 'LazyAdamGSOptimizer', 'LazyAdamOptimizer', 'NadamOptimizer', 'MovingAverageOptimizer', diff --git a/tensorflow/contrib/opt/python/training/adam_gs_optimizer.py b/tensorflow/contrib/opt/python/training/adam_gs_optimizer.py new file mode 100644 index 0000000000..3fb649ea82 --- /dev/null +++ b/tensorflow/contrib/opt/python/training/adam_gs_optimizer.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. +# ============================================================================== + +"""Adam rewrite to use global step for computing beta1 & beta2 accumulation.""" +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 control_flow_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import resource_variable_ops +from tensorflow.python.ops import state_ops +from tensorflow.python.training import optimizer +from tensorflow.python.training import training_ops +from tensorflow.python.util.tf_export import tf_export + + +@tf_export("train.AdamOptimizer") +class AdamGSOptimizer(optimizer.Optimizer): + """Optimizer that implements the Adam algorithm. + + See [Kingma et al., 2014](http://arxiv.org/abs/1412.6980) + ([pdf](http://arxiv.org/pdf/1412.6980.pdf)). + """ + + def __init__(self, global_step=0, learning_rate=0.001, + beta1=0.9, beta2=0.999, epsilon=1e-8, + use_locking=False, name="Adam"): + """Construct a new Adam optimizer. + + Branched from tf.train.AdamOptimizer. The only difference is to pass + global step for computing beta1 and beta2 accumulators, instead of having + optimizer keep its own independent beta1 and beta2 accumulators as non-slot + variables. + + Initialization: + + $$m_0 := 0 \text{(Initialize initial 1st moment vector)}$$ + $$v_0 := 0 \text{(Initialize initial 2nd moment vector)}$$ + $$t := 0 \text{(Initialize timestep)}$$ + + The update rule for `variable` with gradient `g` uses an optimization + described at the end of section2 of the paper: + + $$t := t + 1$$ + $$lr_t := \text{learning\_rate} * \sqrt{1 - beta_2^t} / (1 - beta_1^t)$$ + + $$m_t := beta_1 * m_{t-1} + (1 - beta_1) * g$$ + $$v_t := beta_2 * v_{t-1} + (1 - beta_2) * g * g$$ + $$variable := variable - lr_t * m_t / (\sqrt{v_t} + \epsilon)$$ + + The default value of 1e-8 for epsilon might not be a good default in + general. For example, when training an Inception network on ImageNet a + current good choice is 1.0 or 0.1. Note that since AdamOptimizer uses the + formulation just before Section 2.1 of the Kingma and Ba paper rather than + the formulation in Algorithm 1, the "epsilon" referred to here is "epsilon + hat" in the paper. + + The sparse implementation of this algorithm (used when the gradient is an + IndexedSlices object, typically because of `tf.gather` or an embedding + lookup in the forward pass) does apply momentum to variable slices even if + they were not used in the forward pass (meaning they have a gradient equal + to zero). Momentum decay (beta1) is also applied to the entire momentum + accumulator. This means that the sparse behavior is equivalent to the dense + behavior (in contrast to some momentum implementations which ignore momentum + unless a variable slice was actually used). + + Args: + global_step: tensorflow variable indicating the step. + learning_rate: A Tensor or a floating point value. The learning rate. + beta1: A float value or a constant float tensor. + The exponential decay rate for the 1st moment estimates. + beta2: A float value or a constant float tensor. + The exponential decay rate for the 2nd moment estimates. + epsilon: A small constant for numerical stability. This epsilon is + "epsilon hat" in the Kingma and Ba paper (in the formula just before + Section 2.1), not the epsilon in Algorithm 1 of the paper. + use_locking: If True use locks for update operations. + name: Optional name for the operations created when applying gradients. + Defaults to "Adam". + + @compatibility(eager) + When eager execution is enabled, `learning_rate`, `beta1`, `beta2`, and + `epsilon` can each be a callable that takes no arguments and returns the + actual value to use. This can be useful for changing these values across + different invocations of optimizer functions. + @end_compatibility + """ + super(AdamGSOptimizer, self).__init__(use_locking, name) + self._lr = learning_rate + self._beta1 = beta1 + self._beta2 = beta2 + self._epsilon = epsilon + self._global_step = global_step + self._global_step_on_worker = None + + # Tensor versions of the constructor arguments, created in _prepare(). + self._lr_t = None + self._beta1_t = None + self._beta2_t = None + self._epsilon_t = None + + # Created in SparseApply if needed. + self._updated_lr = None + + def _get_beta_accumulators(self): + return (math_ops.pow(self._beta1_t, self._global_step_on_worker), + math_ops.pow(self._beta2_t, self._global_step_on_worker)) + + def _create_slots(self, var_list): + # Create slots for the first and second moments. + for v in var_list: + self._zeros_slot(v, "m", self._name) + self._zeros_slot(v, "v", self._name) + + def _prepare(self): + lr = self._call_if_callable(self._lr) + beta1 = self._call_if_callable(self._beta1) + beta2 = self._call_if_callable(self._beta2) + epsilon = self._call_if_callable(self._epsilon) + + self._lr_t = ops.convert_to_tensor(lr, name="learning_rate") + self._beta1_t = ops.convert_to_tensor(beta1, name="beta1") + self._beta2_t = ops.convert_to_tensor(beta2, name="beta2") + self._epsilon_t = ops.convert_to_tensor(epsilon, name="epsilon") + + # Performance optimization so that worker creates a copy of the global step + # to avoid overloading the parameter server holding the global step. + self._global_step_on_worker = math_ops.cast( + array_ops.identity(self._global_step) + 1, dtypes.float32) + + def _apply_dense(self, grad, var): + m = self.get_slot(var, "m") + v = self.get_slot(var, "v") + beta1_power, beta2_power = self._get_beta_accumulators() + return training_ops.apply_adam( + var, m, v, + math_ops.cast(beta1_power, var.dtype.base_dtype), + math_ops.cast(beta2_power, var.dtype.base_dtype), + math_ops.cast(self._lr_t, var.dtype.base_dtype), + math_ops.cast(self._beta1_t, var.dtype.base_dtype), + math_ops.cast(self._beta2_t, var.dtype.base_dtype), + math_ops.cast(self._epsilon_t, var.dtype.base_dtype), + grad, use_locking=self._use_locking).op + + def _resource_apply_dense(self, grad, var): + m = self.get_slot(var, "m") + v = self.get_slot(var, "v") + beta1_power, beta2_power = self._get_beta_accumulators() + return training_ops.resource_apply_adam( + var.handle, m.handle, v.handle, + math_ops.cast(beta1_power, grad.dtype.base_dtype), + math_ops.cast(beta2_power, grad.dtype.base_dtype), + math_ops.cast(self._lr_t, grad.dtype.base_dtype), + math_ops.cast(self._beta1_t, grad.dtype.base_dtype), + math_ops.cast(self._beta2_t, grad.dtype.base_dtype), + math_ops.cast(self._epsilon_t, grad.dtype.base_dtype), + grad, use_locking=self._use_locking) + + def _apply_sparse_shared(self, grad, var, indices, scatter_add): + beta1_power, beta2_power = self._get_beta_accumulators() + beta1_power = math_ops.cast(beta1_power, var.dtype.base_dtype) + beta2_power = math_ops.cast(beta2_power, var.dtype.base_dtype) + lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype) + beta1_t = math_ops.cast(self._beta1_t, var.dtype.base_dtype) + beta2_t = math_ops.cast(self._beta2_t, var.dtype.base_dtype) + epsilon_t = math_ops.cast(self._epsilon_t, var.dtype.base_dtype) + lr = (lr_t * math_ops.sqrt(1 - beta2_power) / (1 - beta1_power)) + # m_t = beta1 * m + (1 - beta1) * g_t + m = self.get_slot(var, "m") + m_scaled_g_values = grad * (1 - beta1_t) + m_t = state_ops.assign(m, m * beta1_t, + use_locking=self._use_locking) + with ops.control_dependencies([m_t]): + m_t = scatter_add(m, indices, m_scaled_g_values) + # v_t = beta2 * v + (1 - beta2) * (g_t * g_t) + v = self.get_slot(var, "v") + v_scaled_g_values = (grad * grad) * (1 - beta2_t) + v_t = state_ops.assign(v, v * beta2_t, use_locking=self._use_locking) + with ops.control_dependencies([v_t]): + v_t = scatter_add(v, indices, v_scaled_g_values) + v_sqrt = math_ops.sqrt(v_t) + var_update = state_ops.assign_sub(var, + lr * m_t / (v_sqrt + epsilon_t), + use_locking=self._use_locking) + return control_flow_ops.group(*[var_update, m_t, v_t]) + + def _apply_sparse(self, grad, var): + return self._apply_sparse_shared( + grad.values, var, grad.indices, + lambda x, i, v: state_ops.scatter_add( # pylint: disable=g-long-lambda + x, i, v, use_locking=self._use_locking)) + + def _resource_scatter_add(self, x, i, v): + with ops.control_dependencies( + [resource_variable_ops.resource_scatter_add( + x.handle, i, v)]): + return x.value() + + def _resource_apply_sparse(self, grad, var, indices): + return self._apply_sparse_shared( + grad, var, indices, self._resource_scatter_add) diff --git a/tensorflow/contrib/opt/python/training/adam_gs_optimizer_test.py b/tensorflow/contrib/opt/python/training/adam_gs_optimizer_test.py new file mode 100644 index 0000000000..c68c965aef --- /dev/null +++ b/tensorflow/contrib/opt/python/training/adam_gs_optimizer_test.py @@ -0,0 +1,382 @@ +# 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 AdamGS.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.opt.python.training import adam_gs_optimizer +from tensorflow.python.client import session +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 array_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import resource_variable_ops +from tensorflow.python.ops import variables +from tensorflow.python.platform import test + + +def adam_update_numpy(param, + g_t, + t, + m, + v, + alpha=0.001, + beta1=0.9, + beta2=0.999, + epsilon=1e-8): + alpha_t = alpha * np.sqrt(1 - beta2**t) / (1 - beta1**t) + + m_t = beta1 * m + (1 - beta1) * g_t + v_t = beta2 * v + (1 - beta2) * g_t * g_t + + param_t = param - alpha_t * m_t / (np.sqrt(v_t) + epsilon) + return param_t, m_t, v_t + + +class AdamGSOptimizerTest(test.TestCase): + + def doTestSparse(self, use_resource=False): + for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: + with self.cached_session(): + # Initialize variables for numpy implementation. + m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0 + 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: + global_step = resource_variable_ops.ResourceVariable( + array_ops.zeros([], dtypes.int64)) + var0 = resource_variable_ops.ResourceVariable(var0_np) + var1 = resource_variable_ops.ResourceVariable(var1_np) + else: + global_step = variables.Variable(array_ops.zeros([], dtypes.int64)) + var0 = variables.Variable(var0_np) + var1 = variables.Variable(var1_np) + grads0_np_indices = np.array([0, 1], dtype=np.int32) + grads0 = ops.IndexedSlices( + constant_op.constant(grads0_np), + constant_op.constant(grads0_np_indices), constant_op.constant([2])) + grads1_np_indices = np.array([0, 1], dtype=np.int32) + grads1 = ops.IndexedSlices( + constant_op.constant(grads1_np), + constant_op.constant(grads1_np_indices), constant_op.constant([2])) + opt = adam_gs_optimizer.AdamGSOptimizer(global_step=global_step) + update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]), + global_step=global_step) + variables.global_variables_initializer().run() + + # Fetch params to validate initial values + self.assertAllClose([1.0, 2.0], self.evaluate(var0)) + self.assertAllClose([3.0, 4.0], self.evaluate(var1)) + + beta1_power, beta2_power = opt._get_beta_accumulators() + + # Run 3 steps of Adam + for t in range(1, 4): + self.assertAllCloseAccordingToType(0.9**t, self.evaluate(beta1_power)) + self.assertAllCloseAccordingToType(0.999**t, + self.evaluate(beta2_power)) + update.run() + + var0_np, m0, v0 = adam_update_numpy(var0_np, grads0_np, t, m0, v0) + var1_np, m1, v1 = adam_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)) + + def testSparse(self): + self.doTestSparse(use_resource=False) + + def testResourceSparse(self): + self.doTestSparse(use_resource=True) + + def testSparseDevicePlacement(self): + for index_dtype in [dtypes.int32, dtypes.int64]: + with self.cached_session(force_gpu=test.is_gpu_available()): + # If a GPU is available, tests that all optimizer ops can be placed on + # it (i.e. they have GPU kernels). + var = variables.Variable([[1.0], [2.0]]) + indices = constant_op.constant([0, 1], dtype=index_dtype) + gathered_sum = math_ops.reduce_sum(array_ops.gather(var, indices)) + optimizer = adam_gs_optimizer.AdamGSOptimizer(3.0) + minimize_op = optimizer.minimize(gathered_sum) + variables.global_variables_initializer().run() + minimize_op.run() + + def testSparseRepeatedIndices(self): + for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: + with self.cached_session(): + repeated_index_global_step = variables.Variable( + array_ops.zeros([], dtypes.int64)) + aggregated_global_step = variables.Variable( + array_ops.zeros([], dtypes.int64)) + repeated_index_update_var = variables.Variable( + [[1.0], [2.0]], dtype=dtype) + aggregated_update_var = variables.Variable( + [[1.0], [2.0]], dtype=dtype) + grad_repeated_index = ops.IndexedSlices( + constant_op.constant( + [0.1, 0.1], shape=[2, 1], dtype=dtype), + constant_op.constant([1, 1]), + constant_op.constant([2, 1])) + grad_aggregated = ops.IndexedSlices( + constant_op.constant( + [0.2], shape=[1, 1], dtype=dtype), + constant_op.constant([1]), + constant_op.constant([2, 1])) + repeated_update = adam_gs_optimizer.AdamGSOptimizer( + global_step=repeated_index_global_step).apply_gradients( + [(grad_repeated_index, repeated_index_update_var)], + global_step=repeated_index_global_step) + aggregated_update = adam_gs_optimizer.AdamGSOptimizer( + global_step=aggregated_global_step).apply_gradients( + [(grad_aggregated, aggregated_update_var)], + global_step=aggregated_global_step) + variables.global_variables_initializer().run() + self.assertAllClose(aggregated_update_var.eval(), + self.evaluate(repeated_index_update_var)) + for _ in range(3): + repeated_update.run() + aggregated_update.run() + self.assertAllClose(aggregated_update_var.eval(), + self.evaluate(repeated_index_update_var)) + + def doTestBasic(self, use_resource=False, use_callable_params=False): + for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): + with self.session(graph=ops.Graph()): + # Initialize variables for numpy implementation. + m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0 + 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: + global_step = resource_variable_ops.ResourceVariable( + array_ops.zeros([], dtypes.int64), name="global_step_%d" % i) + var0 = resource_variable_ops.ResourceVariable( + var0_np, name="var0_%d" % i) + var1 = resource_variable_ops.ResourceVariable( + var1_np, name="var1_%d" % i) + else: + global_step = variables.Variable(array_ops.zeros([], dtypes.int64)) + var0 = variables.Variable(var0_np) + var1 = variables.Variable(var1_np) + grads0 = constant_op.constant(grads0_np) + grads1 = constant_op.constant(grads1_np) + + learning_rate = lambda: 0.001 + beta1 = lambda: 0.9 + beta2 = lambda: 0.999 + epsilon = lambda: 1e-8 + if not use_callable_params: + learning_rate = learning_rate() + beta1 = beta1() + beta2 = beta2() + epsilon = epsilon() + + opt = adam_gs_optimizer.AdamGSOptimizer(global_step=global_step, + learning_rate=learning_rate) + update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]), + global_step=global_step) + opt_variables = opt.variables() + beta1_power, beta2_power = opt._get_beta_accumulators() + self.assertTrue(beta1_power is not None) + self.assertTrue(beta2_power is not None) + self.assertNotIn(beta1_power, opt_variables) + self.assertNotIn(beta2_power, opt_variables) + + if not context.executing_eagerly(): + with ops.Graph().as_default(): + # Shouldn't return non-slot variables from other graphs. + self.assertEqual(0, len(opt.variables())) + 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)) + + # Run 3 steps of Adam + for t in range(1, 4): + if not context.executing_eagerly(): + self.evaluate(update) + self.assertAllCloseAccordingToType( + 0.9**(t + 1), self.evaluate(beta1_power)) + self.assertAllCloseAccordingToType( + 0.999**(t + 1), self.evaluate(beta2_power)) + else: + if t > 1: + opt.apply_gradients(zip([grads0, grads1], [var0, var1]), + global_step=global_step) + beta1_power, beta2_power = opt._get_beta_accumulators() + self.assertAllCloseAccordingToType( + 0.9**t, self.evaluate(beta1_power)) + self.assertAllCloseAccordingToType( + 0.999**t, self.evaluate(beta2_power)) + + var0_np, m0, v0 = adam_update_numpy(var0_np, grads0_np, t, m0, v0) + var1_np, m1, v1 = adam_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)) + if use_resource: + self.assertEqual("var0_%d/Adam:0" % (i,), + opt.get_slot(var=var0, name="m").name) + + def testBasic(self): + with self.cached_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) + + def testBasicCallableParams(self): + with context.eager_mode(): + self.doTestBasic(use_resource=True, use_callable_params=True) + + def testTensorLearningRate(self): + for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: + with self.cached_session(): + global_step = variables.Variable(array_ops.zeros([], dtypes.int64)) + # Initialize variables for numpy implementation. + m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0 + 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) + + var0 = variables.Variable(var0_np) + var1 = variables.Variable(var1_np) + grads0 = constant_op.constant(grads0_np) + grads1 = constant_op.constant(grads1_np) + opt = adam_gs_optimizer.AdamGSOptimizer( + global_step=global_step, learning_rate=constant_op.constant(0.001)) + update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]), + global_step=global_step) + variables.global_variables_initializer().run() + + # Fetch params to validate initial values + self.assertAllClose([1.0, 2.0], self.evaluate(var0)) + self.assertAllClose([3.0, 4.0], self.evaluate(var1)) + + beta1_power, beta2_power = opt._get_beta_accumulators() + + # Run 3 steps of Adam + for t in range(1, 4): + self.assertAllCloseAccordingToType(0.9**t, self.evaluate(beta1_power)) + self.assertAllCloseAccordingToType(0.999**t, + self.evaluate(beta2_power)) + update.run() + + var0_np, m0, v0 = adam_update_numpy(var0_np, grads0_np, t, m0, v0) + var1_np, m1, v1 = adam_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)) + + def testSharing(self): + for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: + with self.cached_session(): + global_step = variables.Variable(array_ops.zeros([], dtypes.int64)) + # Initialize variables for numpy implementation. + m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0 + 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) + + var0 = variables.Variable(var0_np) + var1 = variables.Variable(var1_np) + grads0 = constant_op.constant(grads0_np) + grads1 = constant_op.constant(grads1_np) + opt = adam_gs_optimizer.AdamGSOptimizer(global_step=global_step) + update1 = opt.apply_gradients(zip([grads0, grads1], [var0, var1]), + global_step=global_step) + update2 = opt.apply_gradients(zip([grads0, grads1], [var0, var1]), + global_step=global_step) + variables.global_variables_initializer().run() + + beta1_power, beta2_power = opt._get_beta_accumulators() + + # Fetch params to validate initial values + self.assertAllClose([1.0, 2.0], self.evaluate(var0)) + self.assertAllClose([3.0, 4.0], self.evaluate(var1)) + + # Run 3 steps of intertwined Adam1 and Adam2. + for t in range(1, 4): + self.assertAllCloseAccordingToType(0.9**t, self.evaluate(beta1_power)) + self.assertAllCloseAccordingToType(0.999**t, + self.evaluate(beta2_power)) + if t % 2 == 0: + update1.run() + else: + update2.run() + + var0_np, m0, v0 = adam_update_numpy(var0_np, grads0_np, t, m0, v0) + var1_np, m1, v1 = adam_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)) + + def testTwoSessions(self): + optimizer = adam_gs_optimizer.AdamGSOptimizer() + + with context.eager_mode(): + var0 = variables.Variable(np.array([1.0, 2.0]), name="v0") + grads0 = constant_op.constant(np.array([0.1, 0.1])) + optimizer.apply_gradients([(grads0, var0)]) + + g = ops.Graph() + with g.as_default(): + with session.Session(): + var0 = variables.Variable(np.array([1.0, 2.0]), name="v0") + grads0 = constant_op.constant(np.array([0.1, 0.1])) + optimizer.apply_gradients([(grads0, var0)]) + + gg = ops.Graph() + with gg.as_default(): + with session.Session(): + var0 = variables.Variable(np.array([1.0, 2.0]), name="v0") + grads0 = constant_op.constant(np.array([0.1, 0.1])) + + # If the optimizer saves any state not keyed by graph the following line + # fails. + optimizer.apply_gradients([(grads0, var0)]) + + def testSlotsUniqueEager(self): + with context.eager_mode(): + v1 = resource_variable_ops.ResourceVariable(1.) + v2 = resource_variable_ops.ResourceVariable(1.) + opt = adam_gs_optimizer.AdamGSOptimizer(1.) + opt.minimize(lambda: v1 + v2) + # There should be two unique slot variables for v1 and v2 respectively. + self.assertEqual(4, len(set(opt.variables()))) + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/opt/python/training/lazy_adam_gs_optimizer.py b/tensorflow/contrib/opt/python/training/lazy_adam_gs_optimizer.py new file mode 100644 index 0000000000..8827007e4d --- /dev/null +++ b/tensorflow/contrib/opt/python/training/lazy_adam_gs_optimizer.py @@ -0,0 +1,114 @@ +# 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. +# ============================================================================== + +"""LazyAdam rewrite to use global step for computing beta1 & beta2 accumulation. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.opt.python.training import adam_gs_optimizer +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 resource_variable_ops +from tensorflow.python.ops import state_ops + + +class LazyAdamGSOptimizer(adam_gs_optimizer.AdamGSOptimizer): + """Variant of the Adam optimizer that handles sparse updates more efficiently. + + Branched from tf.contrib.opt.LazyAdamGSOptimizer. The only difference is to + pass global step for computing beta1 and beta2 accumulators, instead of having + optimizer keep its own independent beta1 and beta2 accumulators as non-slot + variables. + + The original Adam algorithm maintains two moving-average accumulators for + each trainable variable; the accumulators are updated at every step. + This class provides lazier handling of gradient updates for sparse variables. + It only updates moving-average accumulators for sparse variable indices that + appear in the current batch, rather than updating the accumulators for all + indices. Compared with the original Adam optimizer, it can provide large + improvements in model training throughput for some applications. However, it + provides slightly different semantics than the original Adam algorithm, and + may lead to different empirical results. + """ + + def _apply_sparse(self, grad, var): + beta1_power, beta2_power = self._get_beta_accumulators() + beta1_power = math_ops.cast(beta1_power, var.dtype.base_dtype) + beta2_power = math_ops.cast(beta2_power, var.dtype.base_dtype) + lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype) + beta1_t = math_ops.cast(self._beta1_t, var.dtype.base_dtype) + beta2_t = math_ops.cast(self._beta2_t, var.dtype.base_dtype) + epsilon_t = math_ops.cast(self._epsilon_t, var.dtype.base_dtype) + lr = (lr_t * math_ops.sqrt(1 - beta2_power) / (1 - beta1_power)) + + # \\(m := beta1 * m + (1 - beta1) * g_t\\) + m = self.get_slot(var, "m") + m_t = state_ops.scatter_update(m, grad.indices, + beta1_t * array_ops.gather(m, grad.indices) + + (1 - beta1_t) * grad.values, + use_locking=self._use_locking) + + # \\(v := beta2 * v + (1 - beta2) * (g_t * g_t)\\) + v = self.get_slot(var, "v") + v_t = state_ops.scatter_update(v, grad.indices, + beta2_t * array_ops.gather(v, grad.indices) + + (1 - beta2_t) * math_ops.square(grad.values), + use_locking=self._use_locking) + + # \\(variable -= learning_rate * m_t / (epsilon_t + sqrt(v_t))\\) + m_t_slice = array_ops.gather(m_t, grad.indices) + v_t_slice = array_ops.gather(v_t, grad.indices) + denominator_slice = math_ops.sqrt(v_t_slice) + epsilon_t + var_update = state_ops.scatter_sub(var, grad.indices, + lr * m_t_slice / denominator_slice, + use_locking=self._use_locking) + return control_flow_ops.group(var_update, m_t, v_t) + + def _resource_apply_sparse(self, grad, var, indices): + beta1_power, beta2_power = self._get_beta_accumulators() + beta1_power = math_ops.cast(beta1_power, var.dtype.base_dtype) + beta2_power = math_ops.cast(beta2_power, var.dtype.base_dtype) + lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype) + beta1_t = math_ops.cast(self._beta1_t, var.dtype.base_dtype) + beta2_t = math_ops.cast(self._beta2_t, var.dtype.base_dtype) + epsilon_t = math_ops.cast(self._epsilon_t, var.dtype.base_dtype) + lr = (lr_t * math_ops.sqrt(1 - beta2_power) / (1 - beta1_power)) + + # \\(m := beta1 * m + (1 - beta1) * g_t\\) + m = self.get_slot(var, "m") + m_t_slice = beta1_t * array_ops.gather(m, indices) + (1 - beta1_t) * grad + m_update_op = resource_variable_ops.resource_scatter_update(m.handle, + indices, + m_t_slice) + + # \\(v := beta2 * v + (1 - beta2) * (g_t * g_t)\\) + v = self.get_slot(var, "v") + v_t_slice = (beta2_t * array_ops.gather(v, indices) + + (1 - beta2_t) * math_ops.square(grad)) + v_update_op = resource_variable_ops.resource_scatter_update(v.handle, + indices, + v_t_slice) + + # \\(variable -= learning_rate * m_t / (epsilon_t + sqrt(v_t))\\) + var_slice = lr * m_t_slice / (math_ops.sqrt(v_t_slice) + epsilon_t) + var_update_op = resource_variable_ops.resource_scatter_sub(var.handle, + indices, + var_slice) + + return control_flow_ops.group(var_update_op, m_update_op, v_update_op) diff --git a/tensorflow/contrib/opt/python/training/lazy_adam_gs_optimizer_test.py b/tensorflow/contrib/opt/python/training/lazy_adam_gs_optimizer_test.py new file mode 100644 index 0000000000..bdc9a02a54 --- /dev/null +++ b/tensorflow/contrib/opt/python/training/lazy_adam_gs_optimizer_test.py @@ -0,0 +1,402 @@ +# 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 LazyAdamGSOptimizer.""" + +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.contrib.opt.python.training import lazy_adam_gs_optimizer +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 array_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import resource_variable_ops +from tensorflow.python.ops import variables +from tensorflow.python.platform import test + + +def adam_update_numpy(param, + g_t, + t, + m, + v, + alpha=0.001, + beta1=0.9, + beta2=0.999, + epsilon=1e-8): + alpha_t = alpha * np.sqrt(1 - beta2**t) / (1 - beta1**t) + + m_t = beta1 * m + (1 - beta1) * g_t + v_t = beta2 * v + (1 - beta2) * g_t * g_t + + param_t = param - alpha_t * m_t / (np.sqrt(v_t) + epsilon) + return param_t, m_t, v_t + + +class LazyAdamGSOptimizerTest(test.TestCase, parameterized.TestCase): + + @parameterized.parameters([False, True]) + def testSparse(self, use_resource): + for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: + with self.cached_session(): + # Initialize variables for numpy implementation. + m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0 + 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: + global_step = resource_variable_ops.ResourceVariable( + array_ops.zeros([], dtypes.int64)) + var0 = resource_variable_ops.ResourceVariable(var0_np) + var1 = resource_variable_ops.ResourceVariable(var1_np) + else: + global_step = variables.Variable(array_ops.zeros([], dtypes.int64)) + var0 = variables.Variable(var0_np) + var1 = variables.Variable(var1_np) + + grads0_np_indices = np.array([0, 1], dtype=np.int32) + grads0 = ops.IndexedSlices( + constant_op.constant(grads0_np), + constant_op.constant(grads0_np_indices), constant_op.constant([2])) + grads1_np_indices = np.array([0, 1], dtype=np.int32) + grads1 = ops.IndexedSlices( + constant_op.constant(grads1_np), + constant_op.constant(grads1_np_indices), constant_op.constant([2])) + opt = lazy_adam_gs_optimizer.LazyAdamGSOptimizer( + global_step=global_step) + update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]), + global_step=global_step) + variables.global_variables_initializer().run() + + # Fetch params to validate initial values + self.assertAllClose([1.0, 2.0], var0.eval()) + self.assertAllClose([3.0, 4.0], var1.eval()) + + beta1_power, beta2_power = opt._get_beta_accumulators() + + # Run 3 steps of Adam + for t in range(1, 4): + self.assertAllCloseAccordingToType(0.9**t, beta1_power.eval()) + self.assertAllCloseAccordingToType(0.999**t, beta2_power.eval()) + update.run() + + var0_np, m0, v0 = adam_update_numpy(var0_np, grads0_np, t, m0, v0) + var1_np, m1, v1 = adam_update_numpy(var1_np, grads1_np, t, m1, v1) + + # Validate updated params + self.assertAllCloseAccordingToType(var0_np, var0.eval()) + self.assertAllCloseAccordingToType(var1_np, var1.eval()) + + @parameterized.parameters([False, True]) + def testSparseDevicePlacement(self, use_resource): + for index_dtype in [dtypes.int32, dtypes.int64]: + with self.cached_session(force_gpu=test.is_gpu_available()): + # If a GPU is available, tests that all optimizer ops can be placed on + # it (i.e. they have GPU kernels). + if use_resource: + global_step = resource_variable_ops.ResourceVariable( + array_ops.zeros([], dtypes.int64)) + var = resource_variable_ops.ResourceVariable([[1.0], [2.0]]) + else: + global_step = variables.Variable(array_ops.zeros([], dtypes.int64)) + var = variables.Variable([[1.0], [2.0]]) + + indices = constant_op.constant([0, 1], dtype=index_dtype) + gathered_sum = math_ops.reduce_sum(array_ops.gather(var, indices)) + optimizer = lazy_adam_gs_optimizer.LazyAdamGSOptimizer( + global_step=global_step, learning_rate=3.0) + minimize_op = optimizer.minimize(gathered_sum, global_step=global_step) + variables.global_variables_initializer().run() + minimize_op.run() + + @parameterized.parameters([False, True]) + def testSparseRepeatedIndices(self, use_resource): + for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: + with self.cached_session(): + if use_resource: + repeated_index_global_step = resource_variable_ops.ResourceVariable( + array_ops.zeros([], dtypes.int64)) + aggregated_global_step = resource_variable_ops.ResourceVariable( + array_ops.zeros([], dtypes.int64)) + repeated_index_update_var = resource_variable_ops.ResourceVariable( + [[1.0], [2.0]], dtype=dtype) + aggregated_update_var = resource_variable_ops.ResourceVariable( + [[1.0], [2.0]], dtype=dtype) + else: + repeated_index_global_step = variables.Variable( + array_ops.zeros([], dtypes.int64)) + aggregated_global_step = variables.Variable( + array_ops.zeros([], dtypes.int64)) + repeated_index_update_var = variables.Variable( + [[1.0], [2.0]], dtype=dtype) + aggregated_update_var = variables.Variable( + [[1.0], [2.0]], dtype=dtype) + + grad_repeated_index = ops.IndexedSlices( + constant_op.constant( + [0.1, 0.1], shape=[2, 1], dtype=dtype), + constant_op.constant([1, 1]), + constant_op.constant([2, 1])) + grad_aggregated = ops.IndexedSlices( + constant_op.constant( + [0.2], shape=[1, 1], dtype=dtype), + constant_op.constant([1]), + constant_op.constant([2, 1])) + repeated_update_opt = lazy_adam_gs_optimizer.LazyAdamGSOptimizer( + global_step=repeated_index_global_step) + repeated_update = repeated_update_opt.apply_gradients( + [(grad_repeated_index, repeated_index_update_var)], + global_step=repeated_index_global_step) + aggregated_update_opt = lazy_adam_gs_optimizer.LazyAdamGSOptimizer( + global_step=aggregated_global_step) + aggregated_update = aggregated_update_opt.apply_gradients( + [(grad_aggregated, aggregated_update_var)], + global_step=aggregated_global_step) + variables.global_variables_initializer().run() + self.assertAllClose(aggregated_update_var.eval(), + repeated_index_update_var.eval()) + for _ in range(3): + repeated_update.run() + aggregated_update.run() + self.assertAllClose(aggregated_update_var.eval(), + repeated_index_update_var.eval()) + + def doTestBasic(self, use_resource=False, use_callable_params=False): + for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): + with self.session(graph=ops.Graph()): + # Initialize variables for numpy implementation. + m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0 + 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: + global_step = resource_variable_ops.ResourceVariable( + array_ops.zeros([], dtypes.int64), name="global_step_%d" % i) + var0 = resource_variable_ops.ResourceVariable( + var0_np, name="var0_%d" % i) + var1 = resource_variable_ops.ResourceVariable( + var1_np, name="var1_%d" % i) + else: + global_step = variables.Variable(array_ops.zeros([], dtypes.int64)) + var0 = variables.Variable(var0_np) + var1 = variables.Variable(var1_np) + grads0 = constant_op.constant(grads0_np) + grads1 = constant_op.constant(grads1_np) + + learning_rate = lambda: 0.001 + beta1 = lambda: 0.9 + beta2 = lambda: 0.999 + epsilon = lambda: 1e-8 + if not use_callable_params: + learning_rate = learning_rate() + beta1 = beta1() + beta2 = beta2() + epsilon = epsilon() + + opt = lazy_adam_gs_optimizer.LazyAdamGSOptimizer( + global_step=global_step, learning_rate=learning_rate) + update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]), + global_step=global_step) + opt_variables = opt.variables() + beta1_power, beta2_power = opt._get_beta_accumulators() + self.assertIsNotNone(beta1_power) + self.assertIsNotNone(beta2_power is not None) + self.assertNotIn(beta1_power, opt_variables) + self.assertNotIn(beta2_power, opt_variables) + + if not context.executing_eagerly(): + with ops.Graph().as_default(): + # Shouldn't return non-slot variables from other graphs. + self.assertEqual(0, len(opt.variables())) + 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)) + + # Run 3 steps of Adam + for t in range(1, 4): + if not context.executing_eagerly(): + self.evaluate(update) + self.assertAllCloseAccordingToType( + 0.9**(t + 1), self.evaluate(beta1_power)) + self.assertAllCloseAccordingToType( + 0.999**(t + 1), self.evaluate(beta2_power)) + else: + if t > 1: + opt.apply_gradients(zip([grads0, grads1], [var0, var1]), + global_step=global_step) + beta1_power, beta2_power = opt._get_beta_accumulators() + self.assertAllCloseAccordingToType( + 0.9**t, self.evaluate(beta1_power)) + self.assertAllCloseAccordingToType( + 0.999**t, self.evaluate(beta2_power)) + + var0_np, m0, v0 = adam_update_numpy(var0_np, grads0_np, t, m0, v0) + var1_np, m1, v1 = adam_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)) + if use_resource: + self.assertEqual("var0_%d/Adam:0" % (i,), + opt.get_slot(var=var0, name="m").name) + + def testBasic(self): + with self.cached_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) + + def testBasicCallableParams(self): + with context.eager_mode(): + self.doTestBasic(use_resource=True, use_callable_params=True) + + def testTensorLearningRate(self): + for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: + with self.cached_session(): + global_step = variables.Variable(array_ops.zeros([], dtypes.int64)) + # Initialize variables for numpy implementation. + m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0 + 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) + + var0 = variables.Variable(var0_np) + var1 = variables.Variable(var1_np) + grads0 = constant_op.constant(grads0_np) + grads1 = constant_op.constant(grads1_np) + opt = lazy_adam_gs_optimizer.LazyAdamGSOptimizer( + global_step=global_step, learning_rate=constant_op.constant(0.001)) + update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]), + global_step=global_step) + variables.global_variables_initializer().run() + + # Fetch params to validate initial values + self.assertAllClose([1.0, 2.0], var0.eval()) + self.assertAllClose([3.0, 4.0], var1.eval()) + + beta1_power, beta2_power = opt._get_beta_accumulators() + + # Run 3 steps of Adam + for t in range(1, 4): + self.assertAllCloseAccordingToType(0.9**t, beta1_power.eval()) + self.assertAllCloseAccordingToType(0.999**t, beta2_power.eval()) + update.run() + + var0_np, m0, v0 = adam_update_numpy(var0_np, grads0_np, t, m0, v0) + var1_np, m1, v1 = adam_update_numpy(var1_np, grads1_np, t, m1, v1) + + # Validate updated params + self.assertAllCloseAccordingToType(var0_np, var0.eval()) + self.assertAllCloseAccordingToType(var1_np, var1.eval()) + + def testSharing(self): + for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: + with self.cached_session(): + global_step = variables.Variable(array_ops.zeros([], dtypes.int64)) + # Initialize variables for numpy implementation. + m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0 + 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) + + var0 = variables.Variable(var0_np) + var1 = variables.Variable(var1_np) + grads0 = constant_op.constant(grads0_np) + grads1 = constant_op.constant(grads1_np) + opt = lazy_adam_gs_optimizer.LazyAdamGSOptimizer( + global_step=global_step) + update1 = opt.apply_gradients(zip([grads0, grads1], [var0, var1]), + global_step=global_step) + update2 = opt.apply_gradients(zip([grads0, grads1], [var0, var1]), + global_step=global_step) + variables.global_variables_initializer().run() + + beta1_power, beta2_power = opt._get_beta_accumulators() + + # Fetch params to validate initial values + self.assertAllClose([1.0, 2.0], var0.eval()) + self.assertAllClose([3.0, 4.0], var1.eval()) + + # Run 3 steps of intertwined Adam1 and Adam2. + for t in range(1, 4): + self.assertAllCloseAccordingToType(0.9**t, beta1_power.eval()) + self.assertAllCloseAccordingToType(0.999**t, beta2_power.eval()) + if t % 2 == 0: + update1.run() + else: + update2.run() + + var0_np, m0, v0 = adam_update_numpy(var0_np, grads0_np, t, m0, v0) + var1_np, m1, v1 = adam_update_numpy(var1_np, grads1_np, t, m1, v1) + + # Validate updated params + self.assertAllCloseAccordingToType(var0_np, var0.eval()) + self.assertAllCloseAccordingToType(var1_np, var1.eval()) + + def testTwoSessions(self): + optimizer = lazy_adam_gs_optimizer.LazyAdamGSOptimizer() + + with context.eager_mode(): + var0 = variables.Variable(np.array([1.0, 2.0]), name="v0") + grads0 = constant_op.constant(np.array([0.1, 0.1])) + optimizer.apply_gradients([(grads0, var0)]) + + g = ops.Graph() + with g.as_default(): + with self.session(graph=g): + var0 = variables.Variable(np.array([1.0, 2.0]), name="v0") + grads0 = constant_op.constant(np.array([0.1, 0.1])) + optimizer.apply_gradients([(grads0, var0)]) + + gg = ops.Graph() + with gg.as_default(): + with self.session(graph=gg): + var0 = variables.Variable(np.array([1.0, 2.0]), name="v0") + grads0 = constant_op.constant(np.array([0.1, 0.1])) + + # If the optimizer saves any state not keyed by graph the following line + # fails. + optimizer.apply_gradients([(grads0, var0)]) + + def testSlotsUniqueEager(self): + with context.eager_mode(): + v1 = resource_variable_ops.ResourceVariable(1.) + v2 = resource_variable_ops.ResourceVariable(1.) + opt = lazy_adam_gs_optimizer.LazyAdamGSOptimizer(1.) + opt.minimize(lambda: v1 + v2) + # There should be two non-slot variables, and two unique slot variables + # for v1 and v2 respectively. + self.assertLen(set(opt.variables()), 4) + + +if __name__ == "__main__": + test.main() -- GitLab From f5aed4f8f10fdd3c3910bdb544c882a0dc96ba14 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 11 Dec 2018 01:02:44 -0800 Subject: [PATCH 340/873] compat: Update forward compatibility horizon to 2018-12-11 PiperOrigin-RevId: 224956744 --- tensorflow/python/compat/compat.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/compat/compat.py b/tensorflow/python/compat/compat.py index f11e97b211..679dcf9696 100644 --- a/tensorflow/python/compat/compat.py +++ b/tensorflow/python/compat/compat.py @@ -32,7 +32,7 @@ from tensorflow.python.ops import variable_scope from tensorflow.python.util import tf_contextlib from tensorflow.python.util.tf_export import tf_export -_FORWARD_COMPATIBILITY_HORIZON = datetime.date(2018, 12, 10) +_FORWARD_COMPATIBILITY_HORIZON = datetime.date(2018, 12, 11) @tf_export("compat.forward_compatible") -- GitLab From 221f4d23c6cffa2ad5fb492a300fafda2a640cd8 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 11 Dec 2018 02:10:55 -0800 Subject: [PATCH 341/873] Switch to mounting the current source in the remote config docker. Add workaround for the latest toolchain repository not supporting older bazel versions; only load it conditionally. PiperOrigin-RevId: 224965872 --- WORKSPACE | 35 +++++----- tensorflow/opensource_only.files | 1 + tensorflow/version_check.bzl | 66 ++++++++++--------- .../preconfig/generate/archives.bzl | 25 +++++++ .../preconfig/generate/generate.bzl | 4 +- .../toolchains/preconfig/generate/generate.sh | 2 +- 6 files changed, 79 insertions(+), 54 deletions(-) create mode 100644 third_party/toolchains/preconfig/generate/archives.bzl diff --git a/WORKSPACE b/WORKSPACE index 7cc08e0164..99d368ff91 100644 --- a/WORKSPACE +++ b/WORKSPACE @@ -16,30 +16,27 @@ load("@io_bazel_rules_closure//closure:defs.bzl", "closure_repositories") closure_repositories() -http_archive( - name = "base_images_docker", - sha256 = "e2b1b7254270bb7605e814a9dbf6d1e4ae04a11136ff1714fbfdabe3f87f7cf9", - strip_prefix = "base-images-docker-12801524f867e657fbb5d1a74f31618aff181ac6", - urls = ["https://github.com/GoogleCloudPlatform/base-images-docker/archive/12801524f867e657fbb5d1a74f31618aff181ac6.tar.gz"], -) +load("//third_party/toolchains/preconfig/generate:archives.bzl", + "bazel_toolchains_archive") -http_archive( - name = "bazel_toolchains", - sha256 = "15b5858b1b5541ec44df31b94c3b8672815b31d71215a98398761ea9f4c4eedb", - strip_prefix = "bazel-toolchains-6200b238c9c2d137c0d9a7262c80cc71d98e692b", - urls = [ - "https://github.com/bazelbuild/bazel-toolchains/archive/6200b238c9c2d137c0d9a7262c80cc71d98e692b.tar.gz", - ], +bazel_toolchains_archive() + +load( + "@bazel_toolchains//repositories:repositories.bzl", + bazel_toolchains_repositories = "repositories", ) -http_archive( - name = "io_bazel_rules_docker", - sha256 = "29d109605e0d6f9c892584f07275b8c9260803bf0c6fcb7de2623b2bedc910bd", - strip_prefix = "rules_docker-0.5.1", - urls = ["https://github.com/bazelbuild/rules_docker/archive/v0.5.1.tar.gz"], +bazel_toolchains_repositories() + +load( + "@io_bazel_rules_docker//container:container.bzl", + container_repositories = "repositories", ) -load("//third_party/toolchains/preconfig/generate:workspace.bzl", "remote_config_workspace") +container_repositories() + +load("//third_party/toolchains/preconfig/generate:workspace.bzl", + "remote_config_workspace") remote_config_workspace() diff --git a/tensorflow/opensource_only.files b/tensorflow/opensource_only.files index 347dc9fc6b..418ef1a369 100644 --- a/tensorflow/opensource_only.files +++ b/tensorflow/opensource_only.files @@ -49,6 +49,7 @@ tensorflow/third_party/toolchains/preconfig/ubuntu14.04/nccl2/BUILD tensorflow/third_party/toolchains/preconfig/generate/workspace.bzl tensorflow/third_party/toolchains/preconfig/generate/containers.bzl tensorflow/third_party/toolchains/preconfig/generate/generate.bzl +tensorflow/third_party/toolchains/preconfig/generate/archives.bzl tensorflow/third_party/toolchains/preconfig/generate/BUILD tensorflow/third_party/toolchains/preconfig/win_1803/bazel_018/BUILD tensorflow/third_party/toolchains/preconfig/win_1803/bazel_018/dummy_toolchain.bzl diff --git a/tensorflow/version_check.bzl b/tensorflow/version_check.bzl index 79e721dab4..74feaa19ff 100644 --- a/tensorflow/version_check.bzl +++ b/tensorflow/version_check.bzl @@ -1,48 +1,52 @@ """ Helpers to check minimum version of bazel.""" def _extract_version_number(bazel_version): - """Extracts the semantic version number from a version string + """Extracts the semantic version number from a version string - Args: - bazel_version: the version string that begins with the semantic version - e.g. "1.2.3rc1 abc1234" where "abc1234" is a commit hash. + Args: + bazel_version: the version string that begins with the semantic version + e.g. "1.2.3rc1 abc1234" where "abc1234" is a commit hash. - Returns: - The semantic version string, like "1.2.3". - """ - for i in range(len(bazel_version)): - c = bazel_version[i] - if not (c.isdigit() or c == "."): - return bazel_version[:i] - return bazel_version + Returns: + The semantic version string, like "1.2.3". + """ + for i in range(len(bazel_version)): + c = bazel_version[i] + if not (c.isdigit() or c == "."): + return bazel_version[:i] + return bazel_version # Parse the bazel version string from `native.bazel_version`. # e.g. # "0.10.0rc1 abc123d" => (0, 10, 0) # "0.3.0" => (0, 3, 0) def _parse_bazel_version(bazel_version): - """Parses a version string into a 3-tuple of ints + """Parses a version string into a 3-tuple of ints - int tuples can be compared directly using binary operators (<, >). + int tuples can be compared directly using binary operators (<, >). - Args: - bazel_version: the Bazel version string + Args: + bazel_version: the Bazel version string - Returns: - An int 3-tuple of a (major, minor, patch) version. - """ + Returns: + An int 3-tuple of a (major, minor, patch) version. + """ - version = _extract_version_number(bazel_version) - return tuple([int(n) for n in version.split(".")]) + version = _extract_version_number(bazel_version) + return tuple([int(n) for n in version.split(".")]) def check_bazel_version_at_least(minimum_bazel_version): - if "bazel_version" not in dir(native): - fail("\nCurrent Bazel version is lower than 0.2.1, expected at least %s\n" % minimum_bazel_version) - elif not native.bazel_version: - print("\nCurrent Bazel is not a release version, cannot check for compatibility.") - print("Make sure that you are running at least Bazel %s.\n" % minimum_bazel_version) - return - - if _parse_bazel_version(native.bazel_version) < _parse_bazel_version(minimum_bazel_version): - fail("\nCurrent Bazel version is {}, expected at least {}\n".format( - native.bazel_version, minimum_bazel_version)) + if "bazel_version" not in dir(native): + fail("\nCurrent Bazel version is lower than 0.2.1, expected at least %s\n" % minimum_bazel_version) + elif not native.bazel_version: + print("\nCurrent Bazel is not a release version, cannot check for compatibility.") + print("Make sure that you are running at least Bazel %s.\n" % minimum_bazel_version) + return + + if _parse_bazel_version(native.bazel_version) < _parse_bazel_version(minimum_bazel_version): + fail("\nCurrent Bazel version is {}, expected at least {}\n".format( + native.bazel_version, + minimum_bazel_version, + )) + +parse_bazel_version = _parse_bazel_version diff --git a/third_party/toolchains/preconfig/generate/archives.bzl b/third_party/toolchains/preconfig/generate/archives.bzl new file mode 100644 index 0000000000..086b75b62e --- /dev/null +++ b/third_party/toolchains/preconfig/generate/archives.bzl @@ -0,0 +1,25 @@ +load("//tensorflow:version_check.bzl", "parse_bazel_version") +load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive") + +def bazel_toolchains_archive(): + if parse_bazel_version(native.bazel_version) >= parse_bazel_version("0.19"): + # This version of the toolchains repo is incompatible with older bazel + # versions - we can remove this once TensorFlow drops support for bazel + # before 0.19. + http_archive( + name = "bazel_toolchains", + sha256 = "41c48a189be489e2d15dec40e0057ea15b95ee5b39cc2a7e6cf663e31432c75e", + strip_prefix = "bazel-toolchains-3f8c58fe530fedc446de04673bc1e32985887dea", + urls = [ + "https://github.com/nlopezgi/bazel-toolchains/archive/3f8c58fe530fedc446de04673bc1e32985887dea.tar.gz", + ], + ) + else: + http_archive( + name = "bazel_toolchains", + sha256 = "15b5858b1b5541ec44df31b94c3b8672815b31d71215a98398761ea9f4c4eedb", + strip_prefix = "bazel-toolchains-6200b238c9c2d137c0d9a7262c80cc71d98e692b", + urls = [ + "https://github.com/bazelbuild/bazel-toolchains/archive/6200b238c9c2d137c0d9a7262c80cc71d98e692b.tar.gz", + ], + ) diff --git a/third_party/toolchains/preconfig/generate/generate.bzl b/third_party/toolchains/preconfig/generate/generate.bzl index 2fb3a94cdc..fb2af02a53 100644 --- a/third_party/toolchains/preconfig/generate/generate.bzl +++ b/third_party/toolchains/preconfig/generate/generate.bzl @@ -36,9 +36,7 @@ def _tensorflow_rbe_config(name, cuda_version, cudnn_version, python_version, co "TF_NCCL_VERSION": "2", "CUDNN_INSTALL_PATH": "/usr/lib/x86_64-linux-gnu", }, - # TODO(klimek): We should use the sources that we currently work on, not - # just the latest snapshot of tensorflow that is checked in. - git_repo = "https://github.com/tensorflow/tensorflow", + mount_project = "$(mount_project)", tags = ["manual"], incompatible_changes_off = True, ) diff --git a/third_party/toolchains/preconfig/generate/generate.sh b/third_party/toolchains/preconfig/generate/generate.sh index 37c5211278..1f39fcdf6d 100755 --- a/third_party/toolchains/preconfig/generate/generate.sh +++ b/third_party/toolchains/preconfig/generate/generate.sh @@ -46,7 +46,7 @@ echo "CUDA: ${CUDA_VERSION}" echo "CUDNN: ${CUDNN_VERSION}" echo "NCCL: ${NCCL_VERSION}" -bazel build "${PKG}/generate:${TARGET}" +bazel build --define=mount_project="${PWD}" "${PKG}/generate:${TARGET}" cd "${TEMPDIR}" tar xvf "${ROOT}/bazel-bin/${PKG}/generate/${TARGET}_outputs.tar" -- GitLab From 62e8e1fa7ed38b76870ed851121d56df524c7287 Mon Sep 17 00:00:00 2001 From: hyunyoung Date: Tue, 11 Dec 2018 21:37:25 +0900 Subject: [PATCH 342/873] fix typo in _InsertQuantOp docstring --- tensorflow/contrib/quantize/python/quantize.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/quantize/python/quantize.py b/tensorflow/contrib/quantize/python/quantize.py index 21d1b12130..7c973fe597 100644 --- a/tensorflow/contrib/quantize/python/quantize.py +++ b/tensorflow/contrib/quantize/python/quantize.py @@ -685,7 +685,7 @@ def _InsertQuantOp(context, [1; 2^bits - 1] or wide range [0; 2^bits - 1]. producer_scope: The restriction of producer scope. If not None, the new op will be inserted only when the producer is in this scope. - consumer_scope: The restriction of producer scope. If not None, the new op + consumer_scope: The restriction of consumer scope. If not None, the new op will be inserted only when all the consumers are in this scope. Raises: ValueError: When producer operation is not directly connected to the -- GitLab From 0f2e0d1037be7f8423700e1d8dd455ef969cfbec Mon Sep 17 00:00:00 2001 From: Sergei Lebedev Date: Tue, 11 Dec 2018 05:20:26 -0800 Subject: [PATCH 343/873] Deprecated `Variable.count_up_to` and `tf.count_up_to`. `count_up_to` is currently implemented as a variable-specific op with independent implementations for `RefVariable` and `ResourceVariable`. While it can be implemented in a more generic way in terms of `Variable.assign_add`, a better solution is to use `Dataset.range` for counting. PiperOrigin-RevId: 224984695 --- tensorflow/python/ops/resource_variable_ops.py | 2 ++ tensorflow/python/ops/state_ops.py | 2 ++ tensorflow/python/ops/variables.py | 2 ++ 3 files changed, 6 insertions(+) diff --git a/tensorflow/python/ops/resource_variable_ops.py b/tensorflow/python/ops/resource_variable_ops.py index 1066b357b4..dc53fb8e92 100644 --- a/tensorflow/python/ops/resource_variable_ops.py +++ b/tensorflow/python/ops/resource_variable_ops.py @@ -43,6 +43,7 @@ from tensorflow.python.ops.gen_resource_variable_ops import * # pylint: enable=wildcard-import from tensorflow.python.training.checkpointable import base as checkpointable from tensorflow.python.util import compat +from tensorflow.python.util.deprecation import deprecated def get_resource_handle_data(graph_op): @@ -685,6 +686,7 @@ class ResourceVariable(variables.RefVariable): raise NotImplementedError( "numpy() is only available when eager execution is enabled.") + @deprecated(None, "Prefer Dataset.range instead.") def count_up_to(self, limit): """Increments this variable until it reaches `limit`. diff --git a/tensorflow/python/ops/state_ops.py b/tensorflow/python/ops/state_ops.py index 3ac69c1c20..71aaceee27 100644 --- a/tensorflow/python/ops/state_ops.py +++ b/tensorflow/python/ops/state_ops.py @@ -33,6 +33,7 @@ from tensorflow.python.ops import gen_state_ops from tensorflow.python.ops.gen_state_ops import * # pylint: enable=wildcard-import from tensorflow.python.util import deprecation +from tensorflow.python.util.deprecation import deprecated from tensorflow.python.util.tf_export import tf_export @@ -224,6 +225,7 @@ def assign(ref, value, validate_shape=None, use_locking=None, name=None): @tf_export(v1=["count_up_to"]) +@deprecated(None, "Prefer Dataset.range instead.") def count_up_to(ref, limit, name=None): r"""Increments 'ref' until it reaches 'limit'. diff --git a/tensorflow/python/ops/variables.py b/tensorflow/python/ops/variables.py index a31ce65518..e231343825 100644 --- a/tensorflow/python/ops/variables.py +++ b/tensorflow/python/ops/variables.py @@ -837,6 +837,7 @@ class Variable(six.with_metaclass(VariableMetaclass, """ raise NotImplementedError + @deprecated(None, "Prefer Dataset.range instead.") def count_up_to(self, limit): """Increments this variable until it reaches `limit`. @@ -2117,6 +2118,7 @@ class RefVariable(VariableV1): new_axis_mask=new_axis_mask, shrink_axis_mask=shrink_axis_mask) + @deprecated(None, "Prefer Dataset.range instead.") def count_up_to(self, limit): """Increments this variable until it reaches `limit`. -- GitLab From dba64a3f5a7998166b36e4b9287504ed506e9379 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 11 Dec 2018 08:18:58 -0800 Subject: [PATCH 344/873] Reset XRT memory allocations at ConfigureDistributedTPU time. Using XRTAllocate to register device memory, a user gets back int64 handles which needs to be explicitly deleted in order to avoid memory leaks. If a client crashes (or has bugs in its handle release logic), a remote TF server will be leaking memory with no possibility of recover. Since clients always run a ConfigureDistributedTPU at boot time, we clear the XRT allocated resource manager container at that time. Also add a new XRTReleaseAllAllocations operation, to clear all the XRT memory on the target host. PiperOrigin-RevId: 225006277 --- .../compiler/xrt/kernels/xrt_state_ops.cc | 5 +++ .../compiler/xrt/kernels/xrt_state_ops.h | 20 ++++++++++++ tensorflow/compiler/xrt/ops/xrt_state_ops.cc | 7 +++++ tensorflow/compiler/xrt/tests/raw_api_test.cc | 31 +++++++++++++++++++ tensorflow/compiler/xrt/xrt_state.cc | 5 +++ tensorflow/compiler/xrt/xrt_state.h | 4 +++ 6 files changed, 72 insertions(+) diff --git a/tensorflow/compiler/xrt/kernels/xrt_state_ops.cc b/tensorflow/compiler/xrt/kernels/xrt_state_ops.cc index 3258286c10..1a5bfac337 100644 --- a/tensorflow/compiler/xrt/kernels/xrt_state_ops.cc +++ b/tensorflow/compiler/xrt/kernels/xrt_state_ops.cc @@ -120,4 +120,9 @@ REGISTER_KERNEL_BUILDER(Name("XRTReleaseAllocationHandle") .HostMemory("handle"), XRTReleaseAllocationOp); +REGISTER_KERNEL_BUILDER(Name("XRTReleaseAllAllocations").Device(DEVICE_XLA_GPU), + XRTReleaseAllAllocationsOp); +REGISTER_KERNEL_BUILDER(Name("XRTReleaseAllAllocations").Device(DEVICE_XLA_CPU), + XRTReleaseAllAllocationsOp); + } // namespace tensorflow diff --git a/tensorflow/compiler/xrt/kernels/xrt_state_ops.h b/tensorflow/compiler/xrt/kernels/xrt_state_ops.h index 26a58fa42d..e3b292e790 100644 --- a/tensorflow/compiler/xrt/kernels/xrt_state_ops.h +++ b/tensorflow/compiler/xrt/kernels/xrt_state_ops.h @@ -469,6 +469,26 @@ class XRTReleaseAllocationOp : public OpKernel { } }; +// Op that discards a handle to device memory. +template +class XRTReleaseAllAllocationsOp : public OpKernel { + public: + explicit XRTReleaseAllAllocationsOp(OpKernelConstruction* ctx) + : OpKernel(ctx) {} + ~XRTReleaseAllAllocationsOp() override = default; + XRTReleaseAllAllocationsOp(const XRTReleaseAllAllocationsOp&) = delete; + XRTReleaseAllAllocationsOp& operator=(const XRTReleaseAllAllocationsOp&) = + delete; + + void Compute(OpKernelContext* ctx) override { + VLOG(1) << "XRTReleaseAllAllocationsOp::Compute"; + + ResourceMgr* rm; + OP_REQUIRES_OK(ctx, DeviceAccessor::GetResourceManager(ctx, &rm)); + OP_REQUIRES_OK(ctx, XRTTupleAllocation::ReleaseAllAllocations(rm)); + } +}; + } // namespace tensorflow #endif // TENSORFLOW_COMPILER_XRT_KERNELS_XRT_STATE_OPS_H_ diff --git a/tensorflow/compiler/xrt/ops/xrt_state_ops.cc b/tensorflow/compiler/xrt/ops/xrt_state_ops.cc index a3d63106fa..fe6bee0dac 100644 --- a/tensorflow/compiler/xrt/ops/xrt_state_ops.cc +++ b/tensorflow/compiler/xrt/ops/xrt_state_ops.cc @@ -133,4 +133,11 @@ used. 'handle' is the id returned from the Op that produced the on-device allocation. )"); +REGISTER_OP("XRTReleaseAllAllocations") + .SetShapeFn(tensorflow::shape_inference::NoOutputs) + .Doc( + R"( +Discards all the XRT allocations. All the client held handles will be invalid. +)"); + } // namespace tensorflow diff --git a/tensorflow/compiler/xrt/tests/raw_api_test.cc b/tensorflow/compiler/xrt/tests/raw_api_test.cc index abaa17e50e..730a227167 100644 --- a/tensorflow/compiler/xrt/tests/raw_api_test.cc +++ b/tensorflow/compiler/xrt/tests/raw_api_test.cc @@ -265,6 +265,37 @@ TEST(RawApiTest, AllocAndRewrite) { &outputs)); } +TEST(RawApiTest, AllocAndClearAll) { + xrt::XLAAllocation alloc; + alloc.set_device_ordinal(0); + *alloc.mutable_value() = + xla::LiteralUtil::CreateR2({{4, 5}, {6, 7}}).ToProto(); + + Scope root = Scope::NewRootScope().WithDevice(DeviceFromFlag()); + auto value = + ops::Const(root.WithDevice("/device:CPU:0"), alloc.SerializeAsString()); + auto handle = ops::XRTAllocate(root, value); + TF_ASSERT_OK(root.status()); + + tensorflow::ClientSession session(root); + std::vector outputs; + TF_EXPECT_OK(session.Run({handle}, &outputs)); + EXPECT_EQ(outputs.size(), 1); + + int64 allocation_handle = outputs[0].scalar()(); + + auto clear_all = ops::XRTReleaseAllAllocations(root); + + outputs.clear(); + TF_EXPECT_OK(session.Run(tensorflow::ClientSession::FeedType(), {}, + {clear_all}, &outputs)); + EXPECT_EQ(outputs.size(), 0); + + auto read_after_clear = ops::XRTReadLiteral(root, Input(allocation_handle)); + EXPECT_EQ(session.Run({read_after_clear}, &outputs).code(), + tensorflow::error::Code::NOT_FOUND); +} + TEST(RawApiTest, ReadAndWriteState) { xrt::XLAAllocation alloc; alloc.set_device_ordinal(0); diff --git a/tensorflow/compiler/xrt/xrt_state.cc b/tensorflow/compiler/xrt/xrt_state.cc index 31603e044d..343460ff10 100644 --- a/tensorflow/compiler/xrt/xrt_state.cc +++ b/tensorflow/compiler/xrt/xrt_state.cc @@ -272,6 +272,11 @@ const se::DeviceMemoryBase& XRTTupleAllocation::root_allocation() { return rm->Delete(kTupleContainer, key_string); } +/* static */ Status XRTTupleAllocation::ReleaseAllAllocations(ResourceMgr* rm) { + VLOG(1) << "Releasing all XRT held device memory"; + return rm->Cleanup(kTupleContainer); +} + // Helper typedef to make ShapeTree ForEach helper lambda signatures more // readable. They need a type of const T& where in this case T is the // following pointer. diff --git a/tensorflow/compiler/xrt/xrt_state.h b/tensorflow/compiler/xrt/xrt_state.h index 3664c0cd4e..3e3d502412 100644 --- a/tensorflow/compiler/xrt/xrt_state.h +++ b/tensorflow/compiler/xrt/xrt_state.h @@ -129,6 +129,10 @@ class XRTTupleAllocation : public ResourceBase { // Deletes the reference in the rm to an allocation interned under key. static Status DeleteFromResourceManager(ResourceMgr* rm, int64 key); + // Releases all the device memory allocated by XRT within the resource + // manager. + static Status ReleaseAllAllocations(ResourceMgr* rm); + // Adds the allocation to a ResourceMgr and returns the key that will be used // to retrieve it. Transfers a reference on *this to rm. Status Intern(ResourceMgr* rm, int64* key); -- GitLab From b7e2c36719dd290308ecb5ff604276fd8c059aae Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 11 Dec 2018 08:54:36 -0800 Subject: [PATCH 345/873] Fix erroneous dimension .value call PiperOrigin-RevId: 225011350 --- tensorflow/python/keras/layers/core.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/keras/layers/core.py b/tensorflow/python/keras/layers/core.py index 854774c569..1b406677d9 100644 --- a/tensorflow/python/keras/layers/core.py +++ b/tensorflow/python/keras/layers/core.py @@ -549,7 +549,8 @@ class Flatten(Layer): inputs = array_ops.transpose(inputs, perm=permutation) outputs = array_ops.reshape( - inputs, (inputs.shape[0].value or array_ops.shape(inputs)[0], -1)) + inputs, (tensor_shape.dimension_value(inputs.shape[0]) + or array_ops.shape(inputs)[0], -1)) if not context.executing_eagerly(): outputs.set_shape(self.compute_output_shape(inputs.get_shape())) return outputs -- GitLab From f7a9503c9ce346ae1a442fe6aa6551d9475a931f Mon Sep 17 00:00:00 2001 From: Bixia Zheng Date: Tue, 11 Dec 2018 09:05:05 -0800 Subject: [PATCH 346/873] [XLA:GPU] Convert the reduction implementation to the kernel mapping scheme. Convert the implementation of scalar reduction, row reduction and column reduction to use EmitTiledKernel, which is a more general kernel tiling implementation that is based on the information defined by an object of KernelMappingScheme. For scalar reduction and row reduction, the new implementation should generate the similar optimized code as the old implementation. For column reduction, the new implementation is not exactly the same as the old implementation for a few reasons. First, in the old implementation, routine IrEmitterUnnested::EmitColumnReduction uses kTileWidth to control the number of output elements for which each thread computes a partial result and set the value of kTileWidth to 2. The new implementation is equivalent to the old implementation with kTileWidth=1. Supporting kTileWidth=1 in the new implementation will complicate the implementation and our experiment didn't show much benefit of kTileWidth=2. Second, the old implementation tries to maximize the hardware thread blocks. The new implementation currently only uses one hardware thread block to precess one block of tiles because it uses the hardware block ID as the index for the block of tiles and uses the hardware thread ID as the index for the elements within a tile. PiperOrigin-RevId: 225013188 --- .../xla/service/gpu/ir_emitter_unnested.cc | 1834 +++++++---------- .../xla/service/gpu/ir_emitter_unnested.h | 109 +- .../xla/service/gpu/partition_assignment.cc | 35 +- .../xla/service/gpu/partition_assignment.h | 3 + .../xla/service/llvm_ir/kernel_tiling.cc | 18 +- .../xla/service/llvm_ir/kernel_tiling.h | 19 +- 6 files changed, 795 insertions(+), 1223 deletions(-) diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index c8b5343e61..87d16c0afc 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -22,7 +22,6 @@ limitations under the License. #include "tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h" #include "absl/algorithm/container.h" -#include "absl/container/inlined_vector.h" #include "absl/memory/memory.h" #include "absl/strings/str_cat.h" #include "absl/types/optional.h" @@ -548,91 +547,7 @@ Status IrEmitterUnnested::HandleFusion(HloInstruction* fusion) { // TODO(b/112040122): Support variadic reduce. return Unimplemented("Variadic reduce is not supported on GPU"); } - VLOG(3) << "Emitting fused reduction to vector: " << fusion->ToString(); - std::vector> thunks; - absl::Span output_instructions = - root->opcode() == HloOpcode::kTuple - ? root->operands() - : absl::Span(&root, 1); - - // For multi-output fusion emit an initializer for each tuple element. - // Otherwise it's sufficient to just initialize the single output. - HloInstruction* first_reduce = nullptr; - for (int i = 0, e = output_instructions.size(); i != e; ++i) { - if (output_instructions[i]->opcode() == HloOpcode::kReduce) { - TF_ASSIGN_OR_RETURN( - std::unique_ptr initializer_thunk, - BuildInitializerThunk(fusion, output_instructions[i] == root - ? ShapeIndex() - : ShapeIndex({i}))); - thunks.push_back(std::move(initializer_thunk)); - first_reduce = - first_reduce == nullptr ? output_instructions[i] : first_reduce; - } - } - CHECK(first_reduce != nullptr); - std::unique_ptr kernel_thunk = - BuildKernelThunk(fusion, /*implements_whole_instruction=*/false); - GpuElementalIrEmitter elemental_emitter( - hlo_module_config_, ir_emitter_context_->llvm_module(), &b_, - GetNestedComputer()); - FusedIrEmitter fused_emitter(GetGeneratorForOperandIrArrays(fusion), - &elemental_emitter); - TF_RETURN_IF_ERROR(root->Accept(&fused_emitter)); - - // For multi-output fusion CHECK the constraints and feed all the - // reduces into a single loop code generator. Single-output reduce - // fusion is a special case of that. - InlinedVector input_gens; - InlinedVector init_value_gens; - std::vector> - extra_output_gens; - InlinedVector reducers; - InlinedVector reduce_output_shapes; - for (int i = 0, e = output_instructions.size(); i != e; ++i) { - const HloInstruction* inst = output_instructions[i]; - ShapeIndex output_shape_index; - if (root->opcode() == HloOpcode::kTuple) { - output_shape_index = {i}; - } - if (inst->opcode() == HloOpcode::kReduce) { - CHECK(IsReductionToVector(*inst)) - << "Only reductions to vector are supported"; - // 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( - fused_emitter.GetGenerator(inst->operand(1))); - 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)); - } - } - const Shape& input_shape = first_reduce->operand(0)->shape(); - TF_CHECK_OK(EmitReductionToVector( - kernel_thunk.get(), first_reduce, input_shape, input_gens, - init_value_gens, first_reduce->dimensions(), reducers, - reduce_output_shapes, extra_output_gens)); - thunks.push_back(std::move(kernel_thunk)); - std::unique_ptr sequential_thunk = - absl::make_unique(std::move(thunks), fusion); - AddThunkToThunkSequence(std::move(sequential_thunk)); - return Status::OK(); + return EmitReductionToVector(fusion); } default: LOG(FATAL) << "Bad opcode for input fusion: " @@ -702,13 +617,12 @@ Status IrEmitterUnnested::HandleCopy(HloInstruction* copy) { } Status IrEmitterUnnested::EmitExtraOutputsForReduce( - const HloInstruction* reduce, const IrArray::Index& index, + const HloInstruction* unnested_hlo, const IrArray::Index& index, absl::Span> extra_output_gens) { for (int i = 0; i != extra_output_gens.size(); ++i) { - const HloInstruction* output = reduce->parent()->FusionInstruction(); llvm::Value* extra_output_address = - GetIrArray(*output, *output, extra_output_gens[i].second) + GetIrArray(*unnested_hlo, *unnested_hlo, extra_output_gens[i].second) .EmitArrayElementAddress(index, &b_, "extra_output_element_address"); TF_ASSIGN_OR_RETURN(llvm::Value* const extra_output_ir_value, @@ -718,984 +632,13 @@ Status IrEmitterUnnested::EmitExtraOutputsForReduce( return Status::OK(); } -Status IrEmitterUnnested::EmitReductionToScalar( - KernelThunk* kernel_thunk, HloInstruction* reduce, const Shape& input_shape, - absl::Span input_gens, - absl::Span init_value_gens, - absl::Span reducers, - absl::Span reduce_output_shapes, - absl::Span> - extra_output_gens) { - // Number of elements processed by a single thread. - constexpr int64 kTileSize = 16; - int64 num_elems = ShapeUtil::ElementsIn(input_shape); - - // Round up the number of tiles to a multiple of the warp size. This is - // necessary for correctness. We launch one thread per tile, and if the - // number of threads isn't a multiple of the number of the warp size, our - // shuffles will read from inactive threads, producing undefined values. - 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.launch_bound(), &b_); - - auto index_typed_constant = [&](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. - bool all_threads_in_bounds = num_tiles * kTileSize == num_elems; - - // __global__ void full_reduce_kernel() { - // x_in_tiles = threadIdx.x + blockIdx.x * blockDim.x; - // x = x_in_tiles * kTileSize; - // - // partial_result = init_value; - // if (all_threads_in_bounds || x + kTileSize <= num_elems) { - // for (i = 0; i < kTileSize; ++i) { - // partial_result = Reducer(partial_result, input[x + i]); - // } - // } else { - // for (i = 0; i < kTileSize; ++i) { - // if (x + i < num_elems) { - // partial_result = Reducer(partial_result, input[x + i]); - // } - // } - // } - // for (i = warpSize / 2; i > 0; i /= 2) { - // partial_result = Reducer(partial_result, - // __shfl_down(partial_result, i)); - // } - // if (lane_id == 0) { - // AtomicReducer(&output[y], partial_result); - // } - // } - // - // // Choose num_blocks and threads_per_block such that: - // // - // // num_blocks * threads_per_block = - // // RoundUpToNextMultipleOf(Ceil(num_elems / kTileSize), warpSize), - // // - // // and threads_per_block is a multiple of warpSize. - // reduce_kernel // - auto loop_body_emitter = [=](const IrArray::Index& tile_index) -> Status { - const int num_reduces = reducers.size(); - llvm::Type* element_ir_type = - llvm_ir::PrimitiveTypeToIrType(input_shape.element_type(), module_); - std::vector partial_reduction_result_addresses; - for (int i = 0; i != num_reduces; ++i) { - llvm::Value* partial_reduction_result_address = - Alloca(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](IrArray::Index(index_ty))); - Store(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 = ZExtOrTrunc(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", index_typed_constant(0), - index_typed_constant(kTileSize), index_typed_constant(1), &b_); - - // Emit the body of the partial reduction loop. - llvm_ir::SetToFirstInsertPoint(tile_element_loop->GetBodyBasicBlock(), - &b_); - llvm::Value* x = - NSWAdd(NSWMul(x_in_tiles, index_typed_constant(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( - ICmpULT(x, index_typed_constant(num_elems)), "x_in_bounds", &b_); - - // Emit code that reads the input element and accumulates it to - // the partial reduction result. - llvm_ir::SetToFirstInsertPoint(if_data.true_block, &b_); - } - - IrArray::Index input_index( - /*linear=*/x, input_shape, &b_); - llvm::Value* input_address = Alloca(element_ir_type); - for (int i = 0; i != num_reduces; ++i) { - TF_ASSIGN_OR_RETURN(llvm::Value* const input_ir_value, - input_gens[i](input_index)); - Store(input_ir_value, input_address); - TF_RETURN_IF_ERROR(EmitCallToNestedComputation( - *reducers[i], - {partial_reduction_result_addresses[i], input_address}, - partial_reduction_result_addresses[i])); - } - return EmitExtraOutputsForReduce(reduce, input_index, extra_output_gens); - }; - - // x_end = kTileSize + x_in_tiles * kTileSize, i.e., the location that's - // immediately beyond the tile. - llvm::Value* x_end = - NSWAdd(index_typed_constant(kTileSize), - NSWMul(x_in_tiles, index_typed_constant(kTileSize))); - // The tile is entirely in bound if all_threads_in_bounds or - // x_end <= num_elems. - llvm::Value* tile_in_bounds = - Or(ICmpULE(x_end, index_typed_constant(num_elems)), - b_.getInt1(all_threads_in_bounds)); - llvm_ir::LlvmIfData if_tile_in_bounds_data = - llvm_ir::EmitIfThenElse(tile_in_bounds, "tile_in_bounds", &b_); - llvm_ir::SetToFirstInsertPoint(if_tile_in_bounds_data.true_block, &b_); - TF_RETURN_IF_ERROR(emit_tile_element_loop(/*tile_in_bounds=*/true)); - llvm_ir::SetToFirstInsertPoint(if_tile_in_bounds_data.false_block, &b_); - TF_RETURN_IF_ERROR(emit_tile_element_loop(/*tile_in_bounds=*/false)); - - // After the if-then-else statement on tile_in_bounds, emit calls to - // shfl_down that accumulate the partial reduction results of all threads - // from the warp. - llvm_ir::SetToFirstInsertPoint(if_tile_in_bounds_data.after_block, &b_); - int bit_width = llvm_ir::GetSizeInBits(element_ir_type); - // bitcast cannot be applied to aggregate types (even packed ones), so we - // instead bitcast addresses of load/store to intN* of the same bit-width. - llvm::Type* shuffle_ir_type = element_ir_type->isStructTy() - ? b_.getIntNTy(bit_width) - : element_ir_type; - for (int shuffle_distance = kWarpSize / 2; shuffle_distance >= 1; - shuffle_distance /= 2) { - llvm::Value* result_from_other_lane = - Alloca(element_ir_type, nullptr, "result_from_other_lane"); - for (int i = 0; i != num_reduces; ++i) { - llvm::Value* partial_reduction_result = - Load(BitCast(partial_reduction_result_addresses[i], - shuffle_ir_type->getPointerTo()), - "partial_reduction_result"); - CHECK_EQ(launch_dimensions.threads_per_block() % kWarpSize, 0) - << "Requires block size a multiple of the warp size, otherwise we " - "will read undefined elements."; - Store(EmitFullWarpShuffleDown(partial_reduction_result, - b_.getInt32(shuffle_distance), &b_), - BitCast(result_from_other_lane, shuffle_ir_type->getPointerTo())); - TF_RETURN_IF_ERROR(EmitCallToNestedComputation( - *reducers[i], - {partial_reduction_result_addresses[i], result_from_other_lane}, - partial_reduction_result_addresses[i])); - } - } - - const HloInstruction* output = - reduce->IsFused() ? reduce->parent()->FusionInstruction() : reduce; - - // Emit an atomic operation that accumulates the partial reduction result of - // lane 0 (which holds the partially accumulated result for its warp) to the - // output element. - llvm::Value* lane_id = - URem(x_in_tiles, index_typed_constant(kWarpSize), "lane_id"); - llvm_ir::LlvmIfData if_lane_id_is_zero_data = llvm_ir::EmitIfThenElse( - ICmpEQ(lane_id, index_typed_constant(0)), "lane_id_is_zero", &b_); - llvm_ir::SetToFirstInsertPoint(if_lane_id_is_zero_data.true_block, &b_); - - for (int i = 0; i != num_reduces; ++i) { - llvm::Value* output_address = - GetIrArray(*output, *output, reduce_output_shapes[i]) - .EmitArrayElementAddress( - IrArray::Index( - /*linear=*/b_.getInt64(0), - ShapeUtil::GetSubshape(output->shape(), - reduce_output_shapes[i]), - &b_), - &b_, "output_element_address"); - TF_RETURN_IF_ERROR(EmitAtomicOperationForNestedComputation( - *reducers[i], output_address, partial_reduction_result_addresses[i])); - } - return Status::OK(); - }; - - // Emit a parallel loop that iterates through all input tiles, one per thread. - UpdateLaunchDimensions(launch_dimensions, kernel_thunk, - ir_emitter_context_->llvm_module()); - return ParallelLoopEmitter(loop_body_emitter, tiled_input_shape, - launch_dimensions, &b_) - .EmitLoop(IrName(reduce), index_ty); -} - -Status IrEmitterUnnested::EmitColumnReduction( - KernelThunk* kernel_thunk, int64 height, int64 width, - HloInstruction* reduce, const Shape& input_shape, - absl::Span input_gens, - absl::Span init_value_gens, - absl::Span reducers, - absl::Span reduce_output_shapes, - absl::Span> - extra_output_gens) { - // Divide the input matrix into tiles of size KxL. For example, when the - // input matrix is 4x4, K=2, and L=1 the tiled matrix looks like - // - // 0123 - // 0123 - // 4567 - // 4567 // Numbers indicate tile IDs. - // - // Each tile is first partially reduced to a scalar by a thread, and then the - // scalar is accumulated to the output vector using atomic operations. - // - // We choose 128 as the tile size based on empirical evidence. It's big enough - // to reduce the amount of atomic adds in the end, maximizing the memory - // bandwidth. A tile width of 2 allows for high memory bandwidth utilization - // on 16b input data. - constexpr int64 kTileHeight = 128; - constexpr int64 kTileWidth = 2; - - // If the height is not a multiple of kTileHeight, we pad the bottom of the - // input matrix. - const int64 height_in_tiles = CeilOfRatio(height, kTileHeight); - // If width is not a multiple of kTileWidth the rightmost thread will process - // fewer input elements. - const int64 width_in_tiles = CeilOfRatio(width, kTileWidth); - Shape tiled_input_shape = - ShapeUtil::MakeShapeWithLayout(reduce->shape().element_type(), - {height_in_tiles, width_in_tiles}, {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 = b_.getInt64Ty(); - - auto index_typed_constant = [&](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_in_tiles; - // linear_index += blockDim.x * gridDim.x) { - // y_in_tiles = linear_index / width_in_tiles; - // x_in_tiles = linear_index % width_in_tiles; - // - // partial_results[kTileWidth] = init_values; - // tile_in_y_bounds = height % kTileHeight == 0 || - // y_in_tiles * kTileHeight + kTileHeight <= height; - // tile_in_x_bounds = width % kTileWidth == 0 || - // x_in_tiles * kTileWidth + kTileWidth <= width; - // // The implementation handles y and x bound checks separately. - // if (tile_in_y_bounds && tile_in_x_bounds) { - // for (y_offset : range(kTileHeight)) { - // y = y_in_tiles * kTileHeight + y_offset; - // for (x_offset : range(kTileWidth)) { - // x = x_in_tiles * kTileWidth + x_offset; - // partial_result = Reducer(partial_result[x_offset], input[y][x]); - // } - // } - // } else { - // for (y_offset : range(kTileHeight)) { - // y = y_in_tiles * kTileHeight + y_offset; - // for (y_offset : range(kTileHeight)) { - // x = x_in_tiles * kTileWidth + x_offset; - // if (y < height && x < width) { - // partial_result = Reducer(partial_result, input[y][x]); - // } - // } - // } - // } - // for (x_offset : range(kTileWidth)) { - // AtomicReducer(&output[x + x_offset], partial_result[x_offset]); - // } - // } - auto loop_body_emitter = [=](const IrArray::Index& tile_index) -> Status { - const int num_reduces = reducers.size(); - // Emit the loop body that reduces one tile. - llvm::Type* element_ir_type = - llvm_ir::PrimitiveTypeToIrType(input_shape.element_type(), module_); - std::vector partial_reduction_result_addresses; - for (int i = 0; i != num_reduces; ++i) { - for (int x_offset = 0; x_offset < kTileWidth; ++x_offset) { - llvm::Value* partial_reduction_result_address = - Alloca(element_ir_type, /*ArraySize=*/nullptr, - "partial_reduction_result." + - llvm::Twine(i * kTileWidth + x_offset)); - TF_ASSIGN_OR_RETURN(llvm::Value* const init_ir_value, - init_value_gens[i](IrArray::Index(index_ty))); - Store(init_ir_value, partial_reduction_result_address); - partial_reduction_result_addresses.push_back( - partial_reduction_result_address); - } - } - - // Emit an inner for-loop that partially reduces the elements in the given - // tile. - llvm::Value* y_in_tiles = tile_index[0]; - llvm::Value* x_in_tiles = tile_index[1]; - - y_in_tiles = ZExtOrTrunc(y_in_tiles, index_ty); - x_in_tiles = ZExtOrTrunc(x_in_tiles, index_ty); - - auto emit_tile_element_loop = [=](bool tile_in_y_bounds, - bool tile_in_x_bounds) -> Status { - std::unique_ptr tile_element_loop = - llvm_ir::ForLoop::EmitForLoop( - "element_id_in_tile", index_typed_constant(0), - index_typed_constant(kTileHeight), index_typed_constant(1), &b_); - - // Emit the body of the partial reduction loop. - llvm_ir::SetToFirstInsertPoint(tile_element_loop->GetBodyBasicBlock(), - &b_); - llvm::Value* y = - NSWAdd(NSWMul(y_in_tiles, index_typed_constant(kTileHeight)), - tile_element_loop->GetIndVarValue()); - - // Unless we know that y is in bounds, we have to emit a check before - // reading from the input. - if (!tile_in_y_bounds) { - llvm_ir::LlvmIfData if_data = llvm_ir::EmitIfThenElse( - ICmpULT(y, index_typed_constant(height)), "y_in_bounds", &b_); - - // Emit code that reads the input element and accumulates it to - // the partial reduction result. - llvm_ir::SetToFirstInsertPoint(if_data.true_block, &b_); - } - for (int x_offset = 0; x_offset < kTileWidth; ++x_offset) { - llvm::Value* x = - NSWAdd(NSWMul(x_in_tiles, index_typed_constant(kTileWidth)), - index_typed_constant(x_offset)); - // Unless we know that x is in bounds, we have to emit a check before - // reading from the input. - if (!tile_in_x_bounds) { - llvm_ir::LlvmIfData if_data = llvm_ir::EmitIfThenElse( - ICmpULT(x, index_typed_constant(width)), "x_in_bounds", &b_); - llvm_ir::SetToFirstInsertPoint(if_data.true_block, &b_); - } - llvm::Value* input_address = Alloca(element_ir_type); - // {y,x} is an index to input_matrix_shape [height,width]. We need to - // convert that to an index to input_shape (the shape of the operand of - // "reduce"). This conversion is composed of a transposition from - // input_shape to normalized_input_shape and a reshape from - // normalized_input_shape to input_matrix_shape. - const Shape normalized_input_shape = - ShapeUtil::MakeShapeWithDescendingLayoutAndSamePhysicalLayout( - input_shape); - auto input_shape_min2maj = LayoutUtil::MinorToMajor(input_shape); - const std::vector transpose_dimension_mapping( - input_shape_min2maj.rbegin(), input_shape_min2maj.rend()); - - const Shape input_matrix_shape = - ShapeUtil::MakeShapeWithDescendingLayout(input_shape.element_type(), - {height, width}); - const IrArray::Index input_matrix_index({y, x}, input_matrix_shape, - &b_); - const IrArray::Index input_index = - input_matrix_index - .SourceIndexOfReshape(input_matrix_shape, - normalized_input_shape, &b_) - .SourceIndexOfTranspose(normalized_input_shape, input_shape, - transpose_dimension_mapping, &b_); - for (int i = 0; i != num_reduces; ++i) { - TF_ASSIGN_OR_RETURN(llvm::Value* const input_ir_value, - input_gens[i](input_index)); - Store(input_ir_value, input_address); - TF_RETURN_IF_ERROR(EmitCallToNestedComputation( - *reducers[i], - {partial_reduction_result_addresses[i * kTileWidth + x_offset], - input_address}, - partial_reduction_result_addresses[i * kTileWidth + x_offset])); - TF_RETURN_IF_ERROR(EmitExtraOutputsForReduce(reduce, input_index, - extra_output_gens)); - } - } - return Status::OK(); - }; - - // y_end = kTileHeight + y_in_tiles * kTileHeight, i.e., the y location - // that's immediately beyond the tile. - llvm::Value* y_end = - NSWAdd(index_typed_constant(kTileHeight), - NSWMul(y_in_tiles, index_typed_constant(kTileHeight))); - // x_end = kTileWidth + x_in_tiles * kTileWidth, i.e., the x location - // that's immediately beyond the tile. - llvm::Value* x_end = - NSWAdd(index_typed_constant(kTileWidth), - NSWMul(x_in_tiles, index_typed_constant(kTileWidth))); - llvm::Value* tile_in_y_bounds = - Or(ICmpULE(y_end, index_typed_constant(height)), - b_.getInt1(height % kTileHeight == 0)); - llvm::Value* tile_in_x_bounds = - Or(ICmpULE(x_end, index_typed_constant(width)), - b_.getInt1(width % kTileWidth == 0)); - // The tile is in y bounds if "height" is a multiple of kTileHeight or - // y_end <= height. - llvm_ir::LlvmIfData if_tile_in_y_bounds_data = - llvm_ir::EmitIfThenElse(tile_in_y_bounds, "tile_in_y_bounds", &b_); - llvm_ir::SetToFirstInsertPoint(if_tile_in_y_bounds_data.true_block, &b_); - // The tile is in x bounds if "width" is a multiple of kTileWidth or - // x_end <= width. - llvm_ir::LlvmIfData if_tile_in_x_bounds_data = - llvm_ir::EmitIfThenElse(tile_in_x_bounds, "tile_in_x_bounds", &b_); - llvm_ir::SetToFirstInsertPoint(if_tile_in_x_bounds_data.true_block, &b_); - TF_RETURN_IF_ERROR(emit_tile_element_loop(/*tile_in_y_bounds=*/true, - /*tile_in_x_bounds=*/true)); - llvm_ir::SetToFirstInsertPoint(if_tile_in_x_bounds_data.false_block, &b_); - TF_RETURN_IF_ERROR(emit_tile_element_loop(/*tile_in_y_bounds=*/true, - /*tile_in_x_bounds=*/false)); - llvm_ir::SetToFirstInsertPoint(if_tile_in_y_bounds_data.false_block, &b_); - if_tile_in_x_bounds_data = - llvm_ir::EmitIfThenElse(tile_in_x_bounds, "tile_in_x_bounds", &b_); - llvm_ir::SetToFirstInsertPoint(if_tile_in_x_bounds_data.true_block, &b_); - TF_RETURN_IF_ERROR(emit_tile_element_loop(/*tile_in_y_bounds=*/false, - /*tile_in_x_bounds=*/true)); - llvm_ir::SetToFirstInsertPoint(if_tile_in_x_bounds_data.false_block, &b_); - TF_RETURN_IF_ERROR(emit_tile_element_loop(/*tile_in_y_bounds=*/false, - /*tile_in_x_bounds=*/false)); - - // After the nested if-then-else statement on tile_in_y_bounds and - // tile_in_x_bounds, emit atomic operations to accumulate the partial - // reduction result to the output element. - llvm_ir::SetToFirstInsertPoint(if_tile_in_y_bounds_data.after_block, &b_); - const HloInstruction* output = - reduce->IsFused() ? reduce->parent()->FusionInstruction() : reduce; - for (int i = 0; i != num_reduces; ++i) { - for (int x_offset = 0; x_offset < kTileWidth; ++x_offset) { - llvm::Value* x = - NSWAdd(NSWMul(x_in_tiles, index_typed_constant(kTileWidth)), - index_typed_constant(x_offset)); - llvm::Value* output_address = - GetIrArray(*output, *output, reduce_output_shapes[i]) - .EmitArrayElementAddress( - IrArray::Index( - x, - ShapeUtil::GetSubshape(output->shape(), - reduce_output_shapes[i]), - &b_), - &b_, "output_element_address"); - TF_RETURN_IF_ERROR(EmitAtomicOperationForNestedComputation( - *reducers[i], output_address, - partial_reduction_result_addresses[i * kTileWidth + x_offset])); - } - } - return Status::OK(); - }; - - // Emit a parallel loop that iterate through all input tiles. - UpdateLaunchDimensions(launch_dimensions, kernel_thunk, - ir_emitter_context_->llvm_module()); - return ParallelLoopEmitter(loop_body_emitter, tiled_input_shape, - launch_dimensions, &b_) - .EmitLoop(IrName(reduce), index_ty); -} - -static std::pair ComputeKernelMappingSchemeForReduction( - int64 depth, int64 width, int64 kWarpSize) { - constexpr int64 kTargetNumElementsPerThread = 64; - int64 x_tile_size = kTargetNumElementsPerThread; - int64 z_tile_size = 1; - - // Only tile along the x dimension with tile size kTargetNumElementsPerThread - // if doing so doesn't require a slow version of loop with bound check on each - // dimension. A more sophisticated heuristics is to enable tile along the - // x dimension with tile size kTargetNumElementsPerThread when either width is - // a factor of (kWarpSize * kTargetNumElementsPerThread) or width is big - // enough so that only a small fraction of the threads execute the slow - // version of loop with bound check. - if (width % (kWarpSize * kTargetNumElementsPerThread) != 0) { - x_tile_size = 8; - z_tile_size = 8; - while (depth % z_tile_size != 0) { - z_tile_size -= 1; - } - } - - return std::pair(x_tile_size, z_tile_size); -} - -Status IrEmitterUnnested::EmitRowReduction( - KernelThunk* kernel_thunk, int64 depth, int64 height, int64 width, - HloInstruction* reduce, const Shape& input_shape, - absl::Span input_gens, - absl::Span init_value_gens, - absl::Span reducers, - absl::Span reduce_output_shapes, - absl::Span> - extra_output_gens) { - // A naive algorithm is: - // 1. Divide the x dimension of the input tensor into tiles of size 1x1xX. - // 2. Partially reduces each tile to a scalar using one thread. - // 3. Accumulates that scalar to the output vector using atomic operations. - // - // for (linear_index = threadIdx.x + blockIdx.x * blockDim.x; - // linear_index < depth * height * width_in_tiles; - // linear_index += blockDim.x * gridDim.x) { - // int x_in_tiles = linear_index % width_in_tiles; - // int y = linear_index / width_in_tiles % height; - // int z = linear_index / (height * width_in_tiles); - // float partial_result = 0; - // for (element_id_in_tile : range(x_tile_size)) { - // int x = x_in_tiles * x_tile_size + element_id_in_tile; - // if (x < width) - // partial_result = reducer(partial_result, input[z][y][x]); - // } - // AtomicReducer(&output[y], partial_result); - // } - // - // Four optimizations are performed. - // - // 1. To coalesce global memory accesses, dilate the tile with a factor of 32 - // (i.e. the warp size). For example, suppose the width is 8x32=256. Instead - // of making each tile consecutive, we let make tile 0 column - // [0,32,64,...,224], tile 1 column [1,33,65,...,225], and so on. This ensures - // that threads in a warp access consecutive memory in one iteration (i.e. - // coalesced). In the above example, the warp that contains thread 0-31 - // accesses column 0-31 in the first iteration, and 32-63 in the second - // iteration, and so on. - // - // 2. Partially accumulate partial reduced results computed by threads in the - // same warp using shfl_down. Using shfl_down is faster than directly using - // atomic operations because shfl_down transfers the data between threads - // using shared memory and threads in the same warp run in lock step (thus no - // extra synchronization needed). See - // https://devblogs.nvidia.com/parallelforall/faster-parallel-reductions-kepler/ - // for details. The downside is, to produce correct results when using - // shfl_down, we need to guarantee threads in the same warp work on input - // elements with the same y, so the number of tiles in each row must be a - // multiple of 32. - // - // 3. Specialize the case that the entire tile is in bounds. When that is - // true, we don't need to emit "if(x 0; shuffle_distance /= 2) - // partial_result = Reducer( - // partial_result, - // __shfl_down_sync(CUDA_WARP_ALL, partial_result, shuffle_distance)); - // if (lane_id == 0) - // AtomicReducer(&output[y], partial_result); - // } - // - - int64 x_tile_size; - int64 z_tile_size; - std::tie(x_tile_size, z_tile_size) = - ComputeKernelMappingSchemeForReduction(depth, width, kWarpSize); - - // Round the width in tiles up to the nearest multiple of kWarpSize, so that - // 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.launch_bound(), &b_); - - auto index_typed_constant = [&](uint64 c) -> llvm::Constant* { - return llvm::ConstantInt::get(index_ty, c); - }; - - auto loop_body_emitter = [=](const IrArray::Index& tile_index) { - const int num_reduces = reducers.size(); - llvm::Type* element_ir_type = llvm_ir::PrimitiveTypeToIrType( - input_shape.element_type(), ir_emitter_context_->llvm_module()); - std::vector partial_reduction_result_addresses; - for (int i = 0; i != num_reduces; ++i) { - llvm::Value* partial_reduction_result_address = - Alloca(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](IrArray::Index(index_ty))); - Store(init_ir_value, partial_reduction_result_address); - partial_reduction_result_addresses.push_back( - partial_reduction_result_address); - } - - llvm::Value* z_tile = tile_index[0]; - llvm::Value* y = tile_index[1]; - llvm::Value* x_tile = tile_index[2]; - - x_tile = ZExtOrTrunc(x_tile, index_ty); - - llvm::Value* warp_id = - UDiv(x_tile, index_typed_constant(kWarpSize), "warp_id"); - llvm::Value* lane_id = - URem(x_tile, index_typed_constant(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 = NSWAdd( - lane_id, - NSWMul(index_typed_constant(kWarpSize), - NSWAdd(index_typed_constant(x_tile_size - 1), - NSWMul(warp_id, index_typed_constant(x_tile_size))))); - - KernelSupportLibrary ksl( - &b_, - /*unroll_mode=*/xla::llvm_ir::UnrollMode::kFullyUnroll, - /*prevent_vectorization=*/false); - - // Emit a for-loop that partially reduces the elements in the given - // z-x-tile. - auto emit_z_x_tile_element_loop = [&](bool x_tile_in_bounds, - int64 x_tile_loop_bound) -> Status { - auto emit_z_tile_element_loop = [&](llvm::Value* z_indvar) -> Status { - llvm::Value* z = - NSWAdd(z_indvar, NSWMul(index_typed_constant(z_tile_size), z_tile)); - TF_RETURN_IF_ERROR(ksl.ForWithStatus( - "x_tile", - /*start=*/index_typed_constant(0), - /*end=*/index_typed_constant(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 = NSWAdd( - lane_id, - NSWMul(index_typed_constant(kWarpSize), - NSWAdd(x_indvar, - NSWMul(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( - ICmpULT(x, index_typed_constant(width)), "x_in_bounds", - &b_); - // Points b_ to the then-block. - llvm_ir::SetToFirstInsertPoint(if_x_in_bounds_data.true_block, - &b_); - } - - // Emit code that reads the input element and accumulates it - // to the partial reduction result. - llvm::Value* input_address = Alloca(element_ir_type); - { - // {z,y,x} is an index to input_3d_tensor_shape - // [depth,height,width]. We need to convert that to an index - // to input_shape (the shape of the operand of "reduce"). - // This conversion is composed of a transposition from - // input_shape to normalized_input_shape and a reshape from - // normalized_input_shape to input_3d_tensor_shape. - const Shape normalized_input_shape = ShapeUtil:: - MakeShapeWithDescendingLayoutAndSamePhysicalLayout( - input_shape); - auto input_shape_min2maj = - LayoutUtil::MinorToMajor(input_shape); - const std::vector transpose_dimension_mapping( - input_shape_min2maj.rbegin(), input_shape_min2maj.rend()); - const Shape input_3d_tensor_shape = - ShapeUtil::MakeShapeWithDescendingLayout( - input_shape.element_type(), {depth, height, width}); - const IrArray::Index input_3d_tensor_index( - {z, y, x}, input_3d_tensor_shape, &b_); - const IrArray::Index input_index = - input_3d_tensor_index - .SourceIndexOfReshape(input_3d_tensor_shape, - normalized_input_shape, &b_) - .SourceIndexOfTranspose( - normalized_input_shape, input_shape, - transpose_dimension_mapping, &b_); - - for (int i = 0; i != num_reduces; ++i) { - TF_ASSIGN_OR_RETURN(llvm::Value* const input_ir_value, - input_gens[i](input_index)); - Store(input_ir_value, input_address); - TF_RETURN_IF_ERROR(EmitCallToNestedComputation( - *reducers[i], - {partial_reduction_result_addresses[i], input_address}, - partial_reduction_result_addresses[i])); - } - return EmitExtraOutputsForReduce(reduce, input_index, - extra_output_gens); - } - })); - return Status::OK(); - }; - - return ksl.ForWithStatus("z_tile", - /*start=*/index_typed_constant(0), - /*end=*/index_typed_constant(z_tile_size), - /*step=*/1, emit_z_tile_element_loop); - }; - - llvm::Value* tile_in_bounds = - Or(b_.getInt1(width % (x_tile_size * kWarpSize) == 0), - ICmpULT(last_x, index_typed_constant(width))); - - TF_RETURN_IF_ERROR(ksl.IfWithStatus( - tile_in_bounds, - /*true_block_generator=*/ - [&]() -> Status { - return emit_z_x_tile_element_loop(/*x_tile_in_bounds=*/true, - x_tile_size); - }, - /*false_block_generator=*/ - [&]() -> Status { - return emit_z_x_tile_element_loop( - /*x_tile_in_bounds=*/false, - CeilOfRatio(width % (x_tile_size * kWarpSize), kWarpSize)); - })); - - // After accumulating the elements of the z_x_tile, emit calls to - // shfl_down that accumulate the partial reduction results of all - // threads in a warp. - int bit_width = llvm_ir::GetSizeInBits(element_ir_type); - // bitcast cannot be applied to aggregate types (even packed ones), so we - // instead bitcast addresses of load/store to intN* of the same bit-width. - llvm::Type* shuffle_ir_type = element_ir_type->isStructTy() - ? b_.getIntNTy(bit_width) - : element_ir_type; - for (int shuffle_distance = 16; shuffle_distance >= 1; - shuffle_distance /= 2) { - llvm::Value* result_from_other_lane = - Alloca(element_ir_type, nullptr, "result_from_other_lane"); - for (int i = 0; i != num_reduces; ++i) { - llvm::Value* partial_reduction_result = - Load(BitCast(partial_reduction_result_addresses[i], - shuffle_ir_type->getPointerTo()), - "partial_reduction_result"); - CHECK_EQ(launch_dimensions.threads_per_block() % kWarpSize, 0) - << "Requires block size a multiple of the warp size, otherwise we " - "will read undefined elements."; - Store(EmitFullWarpShuffleDown(partial_reduction_result, - b_.getInt32(shuffle_distance), &b_), - BitCast(result_from_other_lane, shuffle_ir_type->getPointerTo())); - TF_RETURN_IF_ERROR(EmitCallToNestedComputation( - *reducers[i], - {partial_reduction_result_addresses[i], result_from_other_lane}, - partial_reduction_result_addresses[i])); - } - } - - const HloInstruction* output = - reduce->IsFused() ? reduce->parent()->FusionInstruction() : reduce; - - // Emit an atomic operation that accumulates the partial reduction result of - // 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( - ICmpEQ(lane_id, index_typed_constant(0)), "lane_id_is_zero", &b_); - llvm_ir::SetToFirstInsertPoint(if_lane_id_is_zero_data.true_block, &b_); - for (int i = 0; i != num_reduces; ++i) { - llvm::Value* output_address = - GetIrArray(*output, *output, reduce_output_shapes[i]) - .EmitArrayElementAddress( - IrArray::Index(y, - ShapeUtil::GetSubshape( - output->shape(), reduce_output_shapes[i]), - &b_), - &b_, "output_element_address"); - // We don't need to emit atomic operations if there is only one tile of - // results. 'depth' is the z dimension, 'width' is the x dimension. - if (z_tile_size >= depth && x_tile_size >= width) { - TF_RETURN_IF_ERROR(EmitCallToNestedComputation( - *reducers[i], - {output_address, partial_reduction_result_addresses[i]}, - output_address)); - } else { - TF_RETURN_IF_ERROR(EmitAtomicOperationForNestedComputation( - *reducers[i], output_address, - partial_reduction_result_addresses[i])); - } - } - return Status::OK(); - }; - - // Emit a parallel loop that iterates through every input tiles. - UpdateLaunchDimensions(launch_dimensions, kernel_thunk, - ir_emitter_context_->llvm_module()); - return ParallelLoopEmitter(loop_body_emitter, tiled_input_shape, - launch_dimensions, &b_) - .EmitLoop(IrName(reduce), index_ty); -} - -// Figures out whether `reduce` is a row or column reduction, and which -// dimensions to reduce, and calls either `EmitRowReduction` or -// `EmitColumnReduction` as appropriate. -// Prerequisite: all the dimensions to keep are contiguous in the input layout -// and, if `reduce` is fused, the fused subgraph is pure -// elementwise. -Status IrEmitterUnnested::EmitReductionToVector( - KernelThunk* kernel_thunk, HloInstruction* reduce, const Shape& input_shape, - absl::Span input_gens, - absl::Span init_value_gens, - absl::Span dimensions_to_reduce, - absl::Span reducers, - absl::Span reduce_output_shapes, - absl::Span> - extra_output_gens) { - // This emission requires "reduce" to have an input layout. It is either set - // by LayoutAssignment (for a top-level kReduce) or by InstructionFusion (for - // a fused kReduce). - CHECK(input_shape.has_layout()) << "LayoutAssignment or InstructionFusion " - "doesn't set the input layout of " - << reduce->ToString(); - - // Specialize multi-dimensional-array-to-vector reduction. - std::vector input_dims_to_keep; - for (int64 input_dim = 0; input_dim < ShapeUtil::Rank(input_shape); - ++input_dim) { - if (std::find(dimensions_to_reduce.begin(), dimensions_to_reduce.end(), - input_dim) == dimensions_to_reduce.end()) { - input_dims_to_keep.push_back(input_dim); - } - } - - // Sort the dimensions to keep from minor to major, to facilitate checking - // whether another dimension is major or minor of them. - std::sort(input_dims_to_keep.begin(), input_dims_to_keep.end(), - [&input_shape](int64 dim_a, int64 dim_b) { - return PositionInContainer(LayoutUtil::MinorToMajor(input_shape), - dim_a) < - PositionInContainer(LayoutUtil::MinorToMajor(input_shape), - dim_b); - }); - // Now, if output rank is at least 1, `input_dims_to_keep.front()` is - // minormost and `input_dims_to_keep.back()` is majormost. - - // If the dimensions to keep are minormost, emit a column reduction. As all - // the dimensions to keep are contiguous, by prerequisite of - // `EmitReductionToVector`, we only need to check whether the minormost - // dimension of the input is to keep. - if (ShapeUtil::IsEffectiveScalar(reduce->shape())) { - return EmitReductionToScalar(kernel_thunk, reduce, input_shape, input_gens, - init_value_gens, reducers, - reduce_output_shapes, extra_output_gens); - } else if (input_dims_to_keep.front() == - LayoutUtil::Minor(input_shape.layout(), 0)) { - // Column reduction. Treat the result of "input" as a matrix whose width - // is the most minor dimension and height the product of other dimensions, - // and treat "reduce" as a column reduction of the input matrix. - const int64 width = ShapeUtil::ElementsIn(reduce->shape()); - // "width" can be zero, so don't do - // height = ShapeUtil::ElementsIn(input_shape) / width; - int64 height = 1; - for (int64 input_dim = 0; input_dim < ShapeUtil::Rank(input_shape); - ++input_dim) { - if (!std::count(input_dims_to_keep.begin(), input_dims_to_keep.end(), - input_dim)) { - height *= input_shape.dimensions(input_dim); - } - } - return EmitColumnReduction(kernel_thunk, height, width, reduce, input_shape, - input_gens, init_value_gens, reducers, - reduce_output_shapes, extra_output_gens); - } else { - // Reduce the row dimension of a matrix or reduce dimension 0 and 2 in a - // 3D tensor. The size of dimension 1 (the height) is the size of the - // dimension to keep, the size of dimension 0 (the depth) is the product - // of dimensions that are more major than the dimension to keep, and the - // size of dimension 2 (the width) is the product of more minor - // dimensions. - int64 depth = 1; - int64 width = 1; - for (int64 input_dim = 0; input_dim < ShapeUtil::Rank(input_shape); - ++input_dim) { - if (PositionInContainer(LayoutUtil::MinorToMajor(input_shape), - input_dim) > - PositionInContainer(LayoutUtil::MinorToMajor(input_shape), - input_dims_to_keep.back())) { - depth *= input_shape.dimensions(input_dim); - } else if (PositionInContainer(LayoutUtil::MinorToMajor(input_shape), - input_dim) < - PositionInContainer(LayoutUtil::MinorToMajor(input_shape), - input_dims_to_keep.front())) { - width *= input_shape.dimensions(input_dim); - } - } - const int64 height = ShapeUtil::ElementsIn(reduce->shape()); - return EmitRowReduction(kernel_thunk, depth, height, width, reduce, - input_shape, input_gens, init_value_gens, reducers, - reduce_output_shapes, extra_output_gens); - } -} - Status IrEmitterUnnested::HandleReduce(HloInstruction* reduce) { // TODO(b/112040122): Support multi-output reduce. if (!ShapeUtil::IsArray(reduce->shape())) { return Unimplemented("Multi-output reduce is not supported on GPU"); } - auto input = reduce->operand(0); - auto init_value = reduce->operand(1); - absl::Span dimensions_to_reduce(reduce->dimensions()); - 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. if (IsReductionToVector(*reduce)) { - TF_ASSIGN_OR_RETURN(std::unique_ptr initializer_thunk, - BuildInitializerThunk(reduce)); - std::vector> thunks; - thunks.push_back(std::move(initializer_thunk)); - std::unique_ptr kernel_thunk = - BuildKernelThunk(reduce, /*implements_whole_instruction=*/false); - - TF_CHECK_OK(EmitReductionToVector( - kernel_thunk.get(), reduce, input->shape(), - {[&](const IrArray::Index& index) { - return GetIrArray(*input, *reduce).EmitReadArrayElement(index, &b_); - }}, - {[&](const IrArray::Index& index) { - return GetIrArray(*init_value, *reduce) - .EmitReadArrayElement(index, &b_); - }}, - dimensions_to_reduce, {reducer}, {{}}, {})); - - thunks.push_back(std::move(kernel_thunk)); - - std::unique_ptr sequential_thunk = - absl::make_unique(std::move(thunks), reduce); - AddThunkToThunkSequence(std::move(sequential_thunk)); - return Status::OK(); + return EmitReductionToVector(reduce); } return IrEmitter::HandleReduce(reduce); @@ -1820,7 +763,7 @@ Status IrEmitterUnnested::HandleSelectAndScatter( // Create the inner loop to iterate over the window. llvm_ir::ForLoopNest window_loops(IrName(select_and_scatter, "inner"), &b_, index_type); - std::vector window_size; + DimensionVector window_size; for (const auto& dim : window.dimensions()) { window_size.push_back(dim.size()); CHECK_GT(dim.size(), 0); @@ -3193,34 +2136,36 @@ int IrEmitterUnnested::ConstructInputReducedShapeAndCastInputIrArrayToShape( namespace { -void EmitFullTile(const KernelMappingScheme* mapping_scheme, - const IrArray::Index& tile_origin_index, - llvm::IRBuilder<>* builder, llvm::Value* y, llvm::Value* x, - llvm::Type* index_ty, - const std::function& emit_elem_function) { +void EmitFullElementalTile( + const KernelMappingScheme* mapping_scheme, + const IrArray::Index& tile_origin_index, const string& loop_name, + KernelSupportLibrary* ksl, llvm::IRBuilder<>* builder, llvm::Value* y, + llvm::Value* x, llvm::Type* index_ty, + const std::function& emit_elem_function) { int64 num_threads_x = mapping_scheme->GetNumberOfThreadsForDimensionX(); int64 num_threads_y = mapping_scheme->GetNumberOfThreadsForDimensionY(); int64 tile_size_x = mapping_scheme->GetTileSizeForDimensionX(); int64 tile_size_y = mapping_scheme->GetTileSizeForDimensionY(); - for (int64 i = 0; i < tile_size_y; i += num_threads_y) { - IrArray::Index source_idx_y = - tile_origin_index.AddOffsetToDim(llvm::ConstantInt::get(index_ty, i), - KernelMappingScheme::DimY, builder); - llvm::Value* y_loc = - builder->CreateAdd(llvm::ConstantInt::get(index_ty, i), y); - for (int64 j = 0; j < tile_size_x; j += num_threads_x) { - IrArray::Index source_idx = - source_idx_y.AddOffsetToDim(llvm::ConstantInt::get(index_ty, j), - KernelMappingScheme::DimX, builder); - llvm::Value* x_loc = - builder->CreateAdd(llvm::ConstantInt::get(index_ty, j), x); - emit_elem_function(source_idx, y_loc, x_loc); - } - } -} - -void EmitPartialTile( + ksl->For(loop_name + "_y", /*start=*/llvm::ConstantInt::get(index_ty, 0), + /*end=*/llvm::ConstantInt::get(index_ty, tile_size_y), + /*step=*/llvm::ConstantInt::get(index_ty, num_threads_y), + [&](llvm::Value* y_indvar) { + IrArray::Index source_idx_y = tile_origin_index.AddOffsetToDim( + y_indvar, KernelMappingScheme::DimY, builder); + llvm::Value* y_loc = builder->CreateAdd(y_indvar, y); + for (int64 j = 0; j < tile_size_x; j += num_threads_x) { + IrArray::Index source_idx = source_idx_y.AddOffsetToDim( + llvm::ConstantInt::get(index_ty, j), + KernelMappingScheme::DimX, builder); + llvm::Value* x_loc = + builder->CreateAdd(llvm::ConstantInt::get(index_ty, j), x); + emit_elem_function(source_idx, y_loc, x_loc); + } + }); +} + +void EmitPartialElementalTile( const KernelMappingScheme* mapping_scheme, const IrArray::Index& tile_origin_index, const string& loop_name, KernelSupportLibrary* ksl, llvm::IRBuilder<>* builder, llvm::Value* y, @@ -3240,7 +2185,8 @@ void EmitPartialTile( builder->CreateAdd(llvm::ConstantInt::get(index_ty, j), x); ksl->If( - "x_in_tile", builder->CreateICmpULT(x_loc, tile_width), [&] { + loop_name + "_x_in_tile", builder->CreateICmpULT(x_loc, tile_width), + [&] { // tile_height_bound = // ceil(tile_height / num_threads_y) * num_threads_y llvm::Value* ceiling_of_ratio = builder->CreateUDiv( @@ -3257,8 +2203,8 @@ void EmitPartialTile( [&](llvm::Value* y_indvar) { llvm::Value* y_loc = builder->CreateAdd(y_indvar, y); ksl->If( - "y_in_tile", builder->CreateICmpULT(y_loc, tile_height), - [&] { + loop_name + "_y_in_tile", + builder->CreateICmpULT(y_loc, tile_height), [&] { emit_elem_function( source_idx.AddOffsetToDim( y_indvar, KernelMappingScheme::DimY, builder), @@ -3289,20 +2235,20 @@ void EmitTiledElementalCodeWithBoundsCheck( llvm::Type* index_ty = tile_width->getType(); ksl->If( - "full_tile", + loop_name + "_full_tile", builder->CreateAnd( builder->CreateICmpEQ(llvm::ConstantInt::get(index_ty, tile_size_x), tile_width), builder->CreateICmpEQ(llvm::ConstantInt::get(index_ty, tile_size_y), tile_height)), [&] { - EmitFullTile(mapping_scheme, tile_origin_index, builder, y, x, index_ty, - emit_elem_function); + EmitFullElementalTile(mapping_scheme, tile_origin_index, loop_name, ksl, + builder, y, x, index_ty, emit_elem_function); }, [&] { - EmitPartialTile(mapping_scheme, tile_origin_index, loop_name, ksl, - builder, y, x, tile_height, tile_width, index_ty, - emit_elem_function); + EmitPartialElementalTile(mapping_scheme, tile_origin_index, loop_name, + ksl, builder, y, x, tile_height, tile_width, + index_ty, emit_elem_function); }); } } // namespace @@ -3380,7 +2326,395 @@ void IrEmitterUnnested::EmitTileElementForFusion( } } -// Emits a block of tiles, given a function object to emit one tile. +// Information to support the code generation for a tiled reduction kernel. +using AddressVector = InlinedVector; +class ReductionCodegenInfo : public IrEmitterUnnested::KernelCodegenInfo { + public: + explicit ReductionCodegenInfo(llvm_ir::KernelMappingScheme* mapping_scheme, + bool is_row_reduction) + : KernelCodegenInfo(mapping_scheme), + current_output_linear_index_address_(nullptr), + current_output_inbound_address_(nullptr), + is_row_reduction_(is_row_reduction) {} + + void SetCurrentOutputLinearIndexAddress(llvm::AllocaInst* a) { + current_output_linear_index_address_ = a; + } + // Returns the address of the memory that stores the linear index of the + // current output. Since we are processing reduction to contiguous physical + // dimensions, this linear index is the linear index of the 1D output array. + llvm::AllocaInst* GetCurrentOutputLinearIndexAddress() const { + return current_output_linear_index_address_; + } + + void SetCurrentOutputInboundAddress(llvm::AllocaInst* a) { + current_output_inbound_address_ = a; + } + + llvm::AllocaInst* GetCurrentOutputInboundAddress() const { + return current_output_inbound_address_; + } + + AddressVector* GetMutablePartialResultAddresses() { + return &partial_result_addresses_; + } + absl::Span GetPartialResultAddresses() const { + return partial_result_addresses_; + } + + AddressVector* GetMutableReductionInputAddresses() { + return &reduction_input_addresses_; + } + absl::Span GetReductionInputAddresses() const { + return reduction_input_addresses_; + } + + InlinedVector* GetMutableReducers() { return &reducers_; } + const InlinedVector& GetReducers() const { + return reducers_; + } + int GetNumberOfReduces() const { return reducers_.size(); } + + InlinedVector* GetMutableReductionOutputShapeIndices() { + return &reduction_output_shape_indices_; + } + absl::Span GetReductionOutputShapeIndices() const { + return reduction_output_shape_indices_; + } + + bool IsRowReduction() const { return is_row_reduction_; } + + // Return the dimension that is being reduced between DimX and DimY. + int GetReducedDimensionEnum() const { + return IsRowReduction() ? llvm_ir::KernelMappingScheme::DimX + : llvm_ir::KernelMappingScheme::DimY; + } + + // Return the dimension that is being ketp between DimX and DimY. + int GetKeptDimensionEnum() const { + return IsRowReduction() ? llvm_ir::KernelMappingScheme::DimY + : llvm_ir::KernelMappingScheme::DimX; + } + + private: + AddressVector partial_result_addresses_; + AddressVector reduction_input_addresses_; + InlinedVector reducers_; + InlinedVector reduction_output_shape_indices_; + llvm::AllocaInst* current_output_linear_index_address_; + llvm::AllocaInst* current_output_inbound_address_; + bool is_row_reduction_; +}; + +namespace { +// Returns a group of instructions that generate the output for the kernel +// containing the given HLO instruction. The result may be an unnested kReduce +// HLO, a nested kReduce HLO of a kInput fusion, or the operands of the tuple +// for a multiple output fusion. +absl::Span GetOutputInstructions( + HloInstruction* const* reduce_or_tuple_pointer) { + HloOpcode opcode = (*reduce_or_tuple_pointer)->opcode(); + CHECK(opcode == HloOpcode::kReduce || opcode == HloOpcode::kTuple); + return opcode == HloOpcode::kTuple + ? (*reduce_or_tuple_pointer)->operands() + : absl::Span(reduce_or_tuple_pointer, 1); +} + +const HloInstruction* GetFirstReduceInstruction( + absl::Span instructions) { + auto first_reduce_iter = + absl::c_find_if(instructions, [](const HloInstruction* inst) { + return inst->opcode() == HloOpcode::kReduce; + }); + CHECK_NE(first_reduce_iter, instructions.end()); + return *first_reduce_iter; +} + +}; // namespace + +void IrEmitterUnnested::EmitPrologueForOneReduction( + HloInstruction* unnested_hlo, HloInstruction* reduce_inst, int reduce_idx, + KernelCodegenInfo* kernel_info, GpuElementalIrEmitter* elemental_emitter, + ShapeIndex output_shape_index) { + ReductionCodegenInfo* reduction_info = + static_cast(kernel_info); + + InlinedVector* reducers = + reduction_info->GetMutableReducers(); + CHECK(IsReductionToVector(*reduce_inst)); + reducers->push_back(reduce_inst->to_apply()); + + InlinedVector* reduction_output_shape_indices = + reduction_info->GetMutableReductionOutputShapeIndices(); + reduction_output_shape_indices->push_back(std::move(output_shape_index)); + + AddressVector* reduction_input_addresses = + reduction_info->GetMutableReductionInputAddresses(); + llvm::Type* element_type = llvm_ir::PrimitiveTypeToIrType( + reduce_inst->shape().element_type(), ir_emitter_context_->llvm_module()); + llvm::AllocaInst* reduction_input_address = Alloca(element_type); + reduction_input_addresses->push_back(reduction_input_address); + + AddressVector* partial_result_addresses = + reduction_info->GetMutablePartialResultAddresses(); + llvm::AllocaInst* partial_result_address = + Alloca(element_type, /*ArraySize=*/nullptr, + "partial_reduction_result." + llvm::Twine(reduce_idx)); + partial_result_addresses->push_back(partial_result_address); + + // Initialize the partial result with the initial value of the reduction. + llvm::Value* init_ir_value; + if (unnested_hlo->opcode() == HloOpcode::kFusion) { + HloInstruction* init_value_operand = reduce_inst->mutable_operand(1); + FusedIrEmitter fused_emitter(GetGeneratorForOperandIrArrays(unnested_hlo), + elemental_emitter); + + TF_CHECK_OK(init_value_operand->Accept(&fused_emitter)); + init_ir_value = + fused_emitter + .GetGenerator(init_value_operand)(IrArray::Index(b_.getInt32Ty())) + .ValueOrDie(); + } else { + const HloInstruction* init_value = unnested_hlo->operand(1); + init_ir_value = + GetIrArray(*init_value, *unnested_hlo) + .EmitReadArrayElement(IrArray::Index(b_.getInt32Ty()), &b_); + } + + Store(init_ir_value, partial_result_address); +} + +void IrEmitterUnnested::EmitPrologueForReduction( + HloInstruction* unnested_hlo, KernelCodegenInfo* kernel_info) { + VLOG(10) << "Emit prologue for reduction " << unnested_hlo->ToString(); + // Find the unnested kReduce or the tuple that contains a list of kReduce. + HloInstruction* reduce_or_tuple = unnested_hlo->opcode() == HloOpcode::kFusion + ? unnested_hlo->fused_expression_root() + : unnested_hlo; + absl::Span output_instructions = + GetOutputInstructions(&reduce_or_tuple); + ReductionCodegenInfo* reduction_info = + static_cast(kernel_info); + GpuElementalIrEmitter elemental_emitter(hlo_module_config_, + ir_emitter_context_->llvm_module(), + &b_, GetNestedComputer()); + const HloInstruction* first_reduce = nullptr; + for (int i = 0, e = output_instructions.size(); i != e; ++i) { + if (output_instructions[i]->opcode() != HloOpcode::kReduce) { + continue; + } + HloInstruction* reduce_inst = output_instructions[i]; + if (first_reduce == nullptr) { + first_reduce = reduce_inst; + } else { + CHECK(first_reduce->dimensions() == reduce_inst->dimensions()); + } + ShapeIndex output_shape_index; + if (reduce_or_tuple->opcode() == HloOpcode::kTuple) { + output_shape_index = {i}; + } + + EmitPrologueForOneReduction(unnested_hlo, reduce_inst, i, kernel_info, + &elemental_emitter, + std::move(output_shape_index)); + } + + // Allocate stack storage to store the current output linear index and record + // the address of the storage. + reduction_info->SetCurrentOutputLinearIndexAddress( + Alloca(reduction_info->GetIndexType())); + + if (!reduction_info->IsRowReduction()) { + llvm::Type* bool_ty = b_.getInt1Ty(); + llvm::AllocaInst* output_inbound_addr = Alloca(bool_ty); + Store(llvm::ConstantInt::get(bool_ty, 0), output_inbound_addr); + reduction_info->SetCurrentOutputInboundAddress(output_inbound_addr); + } +} + +void IrEmitterUnnested::EmitFullWarpShuffleDownLoopForAllReduces( + absl::Span reducers, + absl::Span partial_result_addresses) { + for (int distance = 16; distance >= 1; distance /= 2) { + for (int i = 0; i != reducers.size(); ++i) { + llvm::Type* element_type = + partial_result_addresses[i]->getType()->getElementType(); + int bit_width = llvm_ir::GetSizeInBits(element_type); + llvm::Value* result_from_other_lane = Alloca( + element_type, nullptr, "result_from_other_lane" + llvm::Twine(i)); + // Bitcast cannot be applied to aggregate types (even packed ones), so + // we bitcast addresses of load/store to intN* of the same bit-width. + llvm::Type* shuffled_value_type = + element_type->isStructTy() ? b_.getIntNTy(bit_width) : element_type; + auto convert_pointer_for_shuffle = [&](llvm::Value* ptr) { + return BitCast(ptr, shuffled_value_type->getPointerTo()); + }; + llvm::Value* partial_result = + Load(convert_pointer_for_shuffle(partial_result_addresses[i]), + "partial_reduction_result"); + Store(EmitFullWarpShuffleDown(partial_result, b_.getInt32(distance), &b_), + convert_pointer_for_shuffle(result_from_other_lane)); + TF_CHECK_OK(EmitCallToNestedComputation( + *reducers[i], {partial_result_addresses[i], result_from_other_lane}, + partial_result_addresses[i])); + } + } +} + +void IrEmitterUnnested::EmitEpilogueForReduction( + HloInstruction* unnested_hlo, KernelCodegenInfo* kernel_info) { + ReductionCodegenInfo* reduction_info = + static_cast(kernel_info); + int num_reduces = reduction_info->GetNumberOfReduces(); + absl::Span partial_result_addresses = + reduction_info->GetPartialResultAddresses(); + const InlinedVector& reducers = + reduction_info->GetReducers(); + absl::Span reduction_output_shape_indices = + reduction_info->GetReductionOutputShapeIndices(); + + if (reduction_info->IsRowReduction()) { + EmitFullWarpShuffleDownLoopForAllReduces(reducers, + partial_result_addresses); + llvm::Value* lane_id = reduction_info->GetLaneId(); + llvm_ir::LlvmIfData if_lane_id_is_zero_data = llvm_ir::EmitIfThenElse( + ICmpEQ(lane_id, llvm::ConstantInt::get(lane_id->getType(), 0)), + "lane_id_is_zero", &b_); + llvm_ir::SetToFirstInsertPoint(if_lane_id_is_zero_data.true_block, &b_); + } else { + llvm::Value* output_inbound_addr = + reduction_info->GetCurrentOutputInboundAddress(); + llvm::Value* output_inbound = Load(output_inbound_addr); + llvm_ir::LlvmIfData if_output_inbound_data = llvm_ir::EmitIfThenElse( + ICmpEQ(output_inbound, + llvm::ConstantInt::get(output_inbound->getType(), 1)), + "output_inbound", &b_); + llvm_ir::SetToFirstInsertPoint(if_output_inbound_data.true_block, &b_); + } + + // Emit an atomic operation that accumulates the partial reduction to the + // output element. For row reduction, this is only for lane 0 due to the + // if-statement emitted above. + for (int i = 0; i != num_reduces; ++i) { + IrArray::Index element_index( + /*linear=*/Load(reduction_info->GetCurrentOutputLinearIndexAddress(), + "output_linear_addr"), + ShapeUtil::GetSubshape(unnested_hlo->shape(), + reduction_output_shape_indices[i]), + &b_); + llvm::Value* output_address = + GetIrArray(*unnested_hlo, *unnested_hlo, + reduction_output_shape_indices[i]) + .EmitArrayElementAddress(element_index, &b_, + "output_element_address"); + // Do not emit atomic operations if each element in the reduction result is + // computed by one block, that is the dimension being reduced has only one + // block. + const llvm_ir::KernelMappingScheme* mapping_scheme = + reduction_info->GetKernelMappingScheme(); + if (mapping_scheme->GetTileBlockSizeForDimension( + llvm_ir::KernelMappingScheme::DimZ) == 1 && + mapping_scheme->GetTileBlockSizeForDimension( + reduction_info->GetReducedDimensionEnum()) == 1) { + TF_CHECK_OK(EmitCallToNestedComputation( + *reducers[i], {output_address, partial_result_addresses[i]}, + output_address)); + } else { + TF_CHECK_OK(EmitAtomicOperationForNestedComputation( + *reducers[i], output_address, partial_result_addresses[i])); + } + } +} + +void IrEmitterUnnested::EmitTileElementForReduction( + HloInstruction* unnested_hlo, const llvm_ir::IrArray::Index& index, + const KernelCodegenInfo* kernel_info, llvm::Value* y_loc, + llvm::Value* x_loc) { + VLOG(10) << "Emit tile element for reduce " << unnested_hlo->ToString(); + HloInstruction* reduce_or_tuple = unnested_hlo->opcode() == HloOpcode::kFusion + ? unnested_hlo->fused_expression_root() + : unnested_hlo; + llvm_ir::TiledParameterInfo* tiled_param_info = + kernel_info->GetTiledParameterInfo(); + tiled_param_info->set_y(y_loc); + tiled_param_info->set_x(x_loc); + + // Record the linear address for the current reduction. + const ReductionCodegenInfo* reduction_info = + dynamic_cast(kernel_info); + Store(index[reduction_info->GetKeptDimensionEnum()], + reduction_info->GetCurrentOutputLinearIndexAddress()); + if (!reduction_info->IsRowReduction()) { + llvm::Type* bool_ty = b_.getInt1Ty(); + llvm::AllocaInst* output_inbound_addr = + reduction_info->GetCurrentOutputInboundAddress(); + Store(llvm::ConstantInt::get(bool_ty, 1), output_inbound_addr); + } + + InlinedVector input_gens; + std::vector> + extra_output_gens; + GpuElementalIrEmitter elem_emitter(hlo_module_config_, module_, &b_, + GetNestedComputer()); + FusedIrEmitter fused_emitter(GetGeneratorForOperandIrArrays(unnested_hlo), + &elem_emitter); + absl::Span output_instructions = + GetOutputInstructions(&reduce_or_tuple); + // Construct the ElementGenerator for each reduction and extra output in the + // the group of output instructions. + if (unnested_hlo->opcode() == HloOpcode::kFusion) { + fused_emitter.SetTiledParameterInfo(tiled_param_info); + TF_CHECK_OK(unnested_hlo->fused_expression_root()->Accept(&fused_emitter)); + + for (int i = 0, e = output_instructions.size(); i != e; ++i) { + const HloInstruction* inst = output_instructions[i]; + ShapeIndex output_shape_index; + if (reduce_or_tuple->opcode() == HloOpcode::kTuple) { + output_shape_index = {i}; + } + if (inst->opcode() == HloOpcode::kReduce) { + input_gens.push_back(fused_emitter.GetGenerator(inst->operand(0))); + } else { + extra_output_gens.emplace_back(fused_emitter.GetGenerator(inst), + std::move(output_shape_index)); + } + } + } else { + input_gens.push_back([&](const IrArray::Index& index) { + return GetIrArray(*unnested_hlo->operand(0), *unnested_hlo) + .EmitReadArrayElement(index, &b_); + }); + } + + IrArray::Index input_index = + reduction_info->GetKernelMappingScheme()->GetUnnormalizedIndex( + index, + GetFirstReduceInstruction(output_instructions)->operand(0)->shape()); + absl::Span partial_reduction_result_addresses = + reduction_info->GetPartialResultAddresses(); + absl::Span reduction_input_addresses = + reduction_info->GetReductionInputAddresses(); + const InlinedVector& reducers = + reduction_info->GetReducers(); + + // Emit code to generate the input and perform the reduction computation for + // each reduction instruction. + for (int i = 0; i != reducers.size(); ++i) { + llvm::Value* const input_ir_value = input_gens[i](input_index).ValueOrDie(); + Store(input_ir_value, reduction_input_addresses[i]); + TF_CHECK_OK(EmitCallToNestedComputation( + *reducers[i], + {partial_reduction_result_addresses[i], reduction_input_addresses[i]}, + partial_reduction_result_addresses[i])); + } + + // Emit code to generate the output for the non-reduction instructions in the + // fusion, if any. + TF_CHECK_OK( + EmitExtraOutputsForReduce(unnested_hlo, input_index, extra_output_gens)); +} + +// Emits a kernel for the hlo instruction using the given tiling scheme. void IrEmitterUnnested::EmitBlock(const TileGenerator& emit_one_tile, const KernelCodegenInfo* kernel_info, KernelSupportLibrary& ksl, @@ -3506,11 +2840,22 @@ LaunchDimensions IrEmitterUnnested::EmitKernel( << llvm_ir::DumpToString(*param_shmem_buffers[id]); } - CHECK_EQ(mapping_scheme->GetThreadsPerTile() % kWarpSize, 0); - LaunchDimensions launch_dimensions = LaunchDimensions( - mapping_scheme->GetNumberOfBlocks(), mapping_scheme->GetThreadsPerTile()); - llvm::Type* index_ty = GetIndexTypeForKernel( - unnested_hlo, launch_dimensions.launch_bound(), &b_); + const ReductionCodegenInfo* reduction_info = + dynamic_cast(kernel_info); + bool is_column_reduction = + (reduction_info && !reduction_info->IsRowReduction()); + + LaunchDimensions launch_dimensions = + LaunchDimensions(mapping_scheme->GetNumberOfBlocks(), + mapping_scheme->GetThreadsPerBlock()); + + // TODO(b/110211620): Enable int32 index type for column reduction. + llvm::Type* index_ty = + is_column_reduction + ? b_.getInt64Ty() + : GetIndexTypeForKernel(unnested_hlo, + launch_dimensions.launch_bound(), &b_); + auto index_typed_constant = [&](uint64 c) -> llvm::Constant* { return llvm::ConstantInt::get(index_ty, c); }; @@ -3520,7 +2865,7 @@ LaunchDimensions IrEmitterUnnested::EmitKernel( // but we do it at the beginning in the hopes of reducing register pressure, // since we touch threadIdx.x and blockIdx.x at the beginning of the kernel // *anyway*. - if (unnested_hlo->IsMultiOutputFusion()) { + if (!reduction_info && unnested_hlo->IsMultiOutputFusion()) { KernelSupportLibrary{&b_}.If( "emit_mof_tuple", IsBlock0Thread0(&b_), [&] { llvm_ir::EmitTuple(GetIrArray(*unnested_hlo, *unnested_hlo), @@ -3549,6 +2894,7 @@ LaunchDimensions IrEmitterUnnested::EmitKernel( kernel_info->SetLaneId( mapping_scheme->GetNumberOfThreadsForDimensionX() == kWarpSize ? x : nullptr); + kernel_info->SetIndexType(index_ty); KernelSupportLibrary ksl(&b_, llvm_ir::UnrollMode::kDefaultUnroll); // Curry a few parameters to EmitTiledElementalCodeWithBoundsCheck. @@ -3573,29 +2919,31 @@ LaunchDimensions IrEmitterUnnested::EmitKernel( input_tile_origin.AddOffsetToDim(x, KernelMappingScheme::DimX, &b_) .AddOffsetToDim(y, KernelMappingScheme::DimY, &b_); - // Copy input parameter values to shared memory buffers: - // tile[y, x] = input[index] - // Note that tile_width and tile_height are flipped here because we are - // reading a transposed tile. - emit_tiled_elemental_code_with_bounds_check( - input_index, "input", output_tile_bounds[2], output_tile_bounds[1], - [&](const IrArray::Index& index, llvm::Value* y_loc, - llvm::Value* x_loc) { - for (int64 id : tiled_param_ids) { - IrArray& input_in_logical_shape = param_in_reduced_shape_arrays[id]; - llvm::Value* shmem_buffer = param_shmem_buffers[id]; - // TODO(jlebar): Add AA metadata to this store. Tile buffers are - // global variables, so LLVM can't infer much about it. - Store(input_in_logical_shape.EmitReadArrayElement(index, &b_, - "input_element"), - GEP(shmem_buffer, {index_typed_constant(0), y_loc, x_loc})); - } - }); - // If shared memory transpose is needed, wait for all threads to reach this // point, lest we copy a value from tile to output before the other thread // copies it from input to tile. This is `__syncthreads` in CUDA. if (!tiled_param_ids.empty()) { + // Copy input parameter values to shared memory buffers: + // tile[y, x] = input[index] + // Note that tile_width and tile_height are flipped here because we are + // reading a transposed tile. + emit_tiled_elemental_code_with_bounds_check( + input_index, "input", output_tile_bounds[2], output_tile_bounds[1], + [&](const IrArray::Index& index, llvm::Value* y_loc, + llvm::Value* x_loc) { + for (int64 id : tiled_param_ids) { + IrArray& input_in_logical_shape = + param_in_reduced_shape_arrays[id]; + llvm::Value* shmem_buffer = param_shmem_buffers[id]; + // TODO(jlebar): Add AA metadata to this store. Tile buffers are + // global variables, so LLVM can't infer much about it. + Store(input_in_logical_shape.EmitReadArrayElement( + index, &b_, "input_element"), + GEP(shmem_buffer, {index_typed_constant(0), y_loc, x_loc})); + } + }); + + // Wait for all threads to reach this point using `__syncthreads` in CUDA. llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::nvvm_barrier0, {}, {}, &b_); } @@ -3615,6 +2963,7 @@ LaunchDimensions IrEmitterUnnested::EmitKernel( kernel_generator.GetTileElementGenerator()(unnested_hlo, index, kernel_info, y_loc, x_loc); }); + // If a tile block contains multiple tiles and shared memory buffers are // used, we need to wait for all threads to finish using the shared memory // buffer for the current tile before we move on to process the next tile @@ -3810,6 +3159,249 @@ bool IrEmitterUnnested::CheckAndEmitHloWithTile021(HloInstruction* hlo) { return true; } +namespace { +// Checks that the outputs of a fusion with reduction are consistent. +Status AreFusedReductionOutputsConsistent( + absl::Span output_instructions, + const HloInstruction* first_reduce) { + for (const HloInstruction* inst : output_instructions) { + if (inst->opcode() == HloOpcode::kReduce) { + // Shapes, layouts and dimensions must be the same for all reduces + // inside of this fusion. + TF_RET_CHECK(ShapeUtil::Equal(first_reduce->shape(), inst->shape())); + TF_RET_CHECK(ShapeUtil::Equal(first_reduce->operand(0)->shape(), + inst->operand(0)->shape())); + TF_RET_CHECK(ShapeUtil::Equal(first_reduce->operand(1)->shape(), + inst->operand(1)->shape())); + TF_RET_CHECK(first_reduce->dimensions() == inst->dimensions()); + } else { + // For extra outputs we can relax shape equality to allow different + // types (with the same number of elements). Layouts still have to + // match. + TF_RET_CHECK(ShapeUtil::CompatibleIgnoringElementType( + first_reduce->operand(0)->shape(), inst->shape())); + TF_RET_CHECK(LayoutUtil::Equal(first_reduce->operand(0)->shape().layout(), + inst->shape().layout())); + } + } + return Status::OK(); +} + +// Finds the dimensions to keep for the reduction, sorts and returns the +// dimensions from minor to major. +DimensionVector GetDimensionsToKeepMinorToMajor( + const Shape& input_shape, absl::Span dims_to_reduce) { + DimensionVector input_dims(ShapeUtil::Rank(input_shape), 0); + absl::c_iota(input_dims, 0); + DimensionVector input_dims_to_keep; + for (int input_dim : input_dims) { + auto it = absl::c_find_if(dims_to_reduce, [&](int64 dim_to_reduce) { + return dim_to_reduce == input_dim; + }); + if (it == dims_to_reduce.end()) { + input_dims_to_keep.push_back(input_dim); + } + } + + // Sort the dimensions to keep from minor to major. + absl::c_sort(input_dims_to_keep, [&input_shape](int64 dim_a, int64 dim_b) { + return PositionInContainer(LayoutUtil::MinorToMajor(input_shape), dim_a) < + PositionInContainer(LayoutUtil::MinorToMajor(input_shape), dim_b); + }); + + VLOG(10) << "dims to keep minor to major" + << absl::StrJoin(input_dims_to_keep, ","); + return input_dims_to_keep; +} + +// Given the input shape and dimensions to reduce for the reduction to vector, +// returns : +// num_kept: the number of elements in the contiguous dimensions to keep. +// num_reduced_major: the number of elements in the dimensions to reduce that +// are more major than the dimensions to keep. +// num_reduced_minor: the number of elements in the dimensions to reduce that +// are more minor than the dimensions to kept. +std::tuple GetReductionToVectorDimensions( + const Shape& input_shape, absl::Span dims_to_reduce) { + DimensionVector input_dims_to_keep_minor_to_major = + GetDimensionsToKeepMinorToMajor(input_shape, dims_to_reduce); + CHECK(LayoutUtil::AreDimensionsConsecutive( + input_shape.layout(), input_dims_to_keep_minor_to_major)); + int num_reduced_major = 1, num_kept = 1, num_reduced_minor = 1; + if (input_dims_to_keep_minor_to_major.empty()) { + return std::make_tuple(num_reduced_major, num_kept, num_reduced_minor); + } + DimensionVector input_dims(ShapeUtil::Rank(input_shape), 0); + absl::c_iota(input_dims, 0); + absl::Span minor_to_major = + LayoutUtil::MinorToMajor(input_shape); + for (int input_dim : input_dims) { + int64 curr_dim_size = input_shape.dimensions(input_dim); + if (PositionInContainer(minor_to_major, input_dim) > + PositionInContainer(minor_to_major, + input_dims_to_keep_minor_to_major.back())) { + num_reduced_major *= curr_dim_size; + } else if (PositionInContainer(minor_to_major, input_dim) < + PositionInContainer(minor_to_major, + input_dims_to_keep_minor_to_major.front())) { + num_reduced_minor *= curr_dim_size; + } else { + num_kept *= curr_dim_size; + } + } + + return std::make_tuple(num_reduced_major, num_kept, num_reduced_minor); +} + +} // namespace + +std::tuple +IrEmitterUnnested::ComputeMappingSchemeAndReductionKind( + const HloInstruction* first_reduce) { + int64 depth = 1; + int64 height = 1; + int64 width = 1; + bool is_row_reduction = true; + int64 tile_size_x = 1; + int64 tile_size_y = 1; + int64 block_size_z = 1; + int64 num_threads_x = 1; + int64 num_threads_y = 1; + const Shape& input_shape = first_reduce->operand(0)->shape(); + int64 num_input_elems = ShapeUtil::ElementsIn(input_shape); + int64 num_output_elems = ShapeUtil::ElementsIn(first_reduce->shape()); + int64 num_reduced_major, num_kept, num_reduced_minor; + std::tie(num_reduced_major, num_kept, num_reduced_minor) = + GetReductionToVectorDimensions(input_shape, first_reduce->dimensions()); + CHECK_EQ(num_output_elems, num_kept); + + if (num_kept == 1) { + // Scalar reduction is a special row reduction with depth = height = 1. + width = num_input_elems; + tile_size_x = kWarpSize * 16; + num_threads_x = kWarpSize; + } else if (num_reduced_minor == 1) { + // Column reduction reduces inputs with dimension [height, width], where + // width is the minor dimension, to dimension [width]. + height = num_reduced_major; + width = num_kept; + is_row_reduction = false; + // Column reduction without transpose doesn't require communication among + // threads processing elements in the same tile. The current implementation + // only support the use of on hardware thread block to process one block of + // tiles in the KernelMappingScheme. We try to maximize the values of + // num_threads_x and tile_size_x to allow a bigger hardware thread block. + int64 hw_threads_per_block_limit = + ThreadsPerBlockLimit(ir_emitter_context_->device_description()); + tile_size_x = std::min(hw_threads_per_block_limit, num_kept); + num_threads_x = tile_size_x; + int64 kNumElementsPerPartialSum = 128; + tile_size_y = kNumElementsPerPartialSum; + } else { + // Row reduction reduces inputs with dimension [depth, height, width], + // where width is the most minor dimension, to dimension [height] . + depth = num_reduced_major; + height = num_kept; + width = num_reduced_minor; + num_threads_x = kWarpSize; + if (width % (kWarpSize * 64) == 0) { + tile_size_x = kWarpSize * 64; + } else { + tile_size_x = kWarpSize * 8; + block_size_z = 8; + while (depth % block_size_z != 0) { + block_size_z -= 1; + } + } + } + DCHECK_EQ(depth * height * width, num_input_elems); + VLOG(10) << "is_row_reduction " << is_row_reduction << depth << " " << height + << " " << width; + + DimensionVector dims_in_elem{depth, height, width}; + DimensionVector req_block_sizes{block_size_z, 1, 1}; + llvm_ir::KernelMappingScheme mapping_scheme( + dims_in_elem, tile_size_y, tile_size_x, req_block_sizes, num_threads_y, + num_threads_x, &b_); + return std::make_tuple(mapping_scheme, is_row_reduction); +} + +Status IrEmitterUnnested::EmitReductionToVector(HloInstruction* unnested_hlo) { + VLOG(10) << "Emitting reduction to vector " << unnested_hlo->ToString(); + + HloInstruction* reduce_or_tuple = unnested_hlo->opcode() == HloOpcode::kFusion + ? unnested_hlo->fused_expression_root() + : unnested_hlo; + absl::Span output_instructions = + GetOutputInstructions(&reduce_or_tuple); + const HloInstruction* first_reduce = + GetFirstReduceInstruction(output_instructions); + + if (output_instructions.size() > 1) { + TF_RETURN_IF_ERROR( + AreFusedReductionOutputsConsistent(output_instructions, first_reduce)); + } + + // Build an initializer thunk to initialize each reduction output. + std::vector> thunks; + for (int i = 0, e = output_instructions.size(); i != e; ++i) { + if (output_instructions[i]->opcode() != HloOpcode::kReduce) { + continue; + } + TF_ASSIGN_OR_RETURN( + std::unique_ptr initializer_thunk, + BuildInitializerThunk(unnested_hlo, + (output_instructions[i] == reduce_or_tuple) + ? ShapeIndex() + : ShapeIndex({i}))); + thunks.push_back(std::move(initializer_thunk)); + } + + // Build a kernel thunk to compute all the outputs. + std::unique_ptr kernel_thunk = + BuildKernelThunk(unnested_hlo, /*implements_whole_instruction=*/false); + + const Shape& input_shape = first_reduce->operand(0)->shape(); + // The layout of a reduction input is either set by LayoutAssignment for + // unnested kReduce or by InstructionFusion for fused kReduce. + CHECK(input_shape.has_layout()) << "LayoutAssignment or InstructionFusion " + "doesn't set the input layout of " + << first_reduce->ToString(); + + bool is_row_reduction; + llvm_ir::KernelMappingScheme mapping_scheme; + std::tie(mapping_scheme, is_row_reduction) = + ComputeMappingSchemeAndReductionKind(first_reduce); + ReductionCodegenInfo reduction_info(&mapping_scheme, is_row_reduction); + KernelCodeGenerator kernel_generator( + /*tile_element_generator=*/ + [&](HloInstruction* hlo, const llvm_ir::IrArray::Index& index, + const KernelCodegenInfo* kernel_info, llvm::Value* y_loc, + llvm::Value* x_loc) { + EmitTileElementForReduction(hlo, index, kernel_info, y_loc, x_loc); + }, + /*block_prologue_generator=*/ + [&](HloInstruction* hlo, KernelCodegenInfo* kernel_info) { + EmitPrologueForReduction(hlo, kernel_info); + }, + /*block_epilogue_generator*/ + [&](HloInstruction* hlo, KernelCodegenInfo* kernel_info) { + EmitEpilogueForReduction(hlo, kernel_info); + }); + + LaunchDimensions launch_dimensions = + EmitKernel(unnested_hlo, {}, kernel_generator, &reduction_info); + UpdateLaunchDimensions(launch_dimensions, kernel_thunk.get(), + ir_emitter_context_->llvm_module()); + + thunks.push_back(std::move(kernel_thunk)); + std::unique_ptr sequential_thunk = + absl::make_unique(std::move(thunks), unnested_hlo); + AddThunkToThunkSequence(std::move(sequential_thunk)); + + return Status::OK(); +} + Status IrEmitterUnnested::EmitConstantGlobals() { for (const BufferAllocation& allocation : ir_emitter_context_->buffer_assignment().Allocations()) { diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h index e09ed657a8..1ebea7ab48 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h @@ -16,6 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_GPU_IR_EMITTER_UNNESTED_H_ #define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_IR_EMITTER_UNNESTED_H_ +#include "absl/container/inlined_vector.h" #include "tensorflow/compiler/xla/service/gpu/ir_emitter.h" #include "tensorflow/compiler/xla/service/gpu/sequential_thunk.h" #include "tensorflow/compiler/xla/service/gpu/thunk.h" @@ -68,9 +69,12 @@ class IrEmitterUnnested : public IrEmitter { explicit KernelCodegenInfo(llvm_ir::KernelMappingScheme* mapping_scheme) : mapping_scheme_(mapping_scheme), tiled_param_info_(nullptr), - lane_id_(nullptr) {} + lane_id_(nullptr), + index_ty_(nullptr) {} + virtual ~KernelCodegenInfo() {} void SetLaneId(llvm::Value* v) { lane_id_ = v; } + void SetIndexType(llvm::Type* t) { index_ty_ = t; } void SetTiledParamInfo(llvm_ir::TiledParameterInfo* tiled_param_info) { CHECK_EQ(tiled_param_info_, nullptr); tiled_param_info_ = tiled_param_info; @@ -83,11 +87,13 @@ class IrEmitterUnnested : public IrEmitter { llvm_ir::TiledParameterInfo* GetTiledParameterInfo() const { return tiled_param_info_; } + llvm::Type* GetIndexType() const { return index_ty_; } private: llvm_ir::KernelMappingScheme* mapping_scheme_; llvm_ir::TiledParameterInfo* tiled_param_info_; llvm::Value* lane_id_; + llvm::Type* index_ty_; }; // A function object to prepare for the code generation for a tile block. @@ -200,82 +206,19 @@ class IrEmitterUnnested : public IrEmitter { // Helper for writing extra outputs from inside a reduce kernel. Status EmitExtraOutputsForReduce( - const HloInstruction* reduce, const llvm_ir::IrArray::Index& index, + const HloInstruction* unnested_hlo, const llvm_ir::IrArray::Index& index, absl::Span> extra_output_gens); - // EmitColumnReduction and EmitRowReduction emit code for column and row - // reduction of a matrix and/or 3D tensor. Row and column reduction have - // different memory access pattern, so for performance their implementations - // are significantly different. + // Generates code for reduction to contiguous dimensions. // - // Emits code that reduces a matrix of shape [height x width] to a vector of - // [width]. Other parameters have the same meaning as those of - // `EmitReductionToVector`. Note that input shape might not be - // [height x width], but can be bitcast to [height x width] with "height" - // being the major dimension. - Status EmitColumnReduction( - KernelThunk* kernel_thunk, int64 height, int64 width, - HloInstruction* reduce, const Shape& input_shape, - absl::Span input_gens, - absl::Span init_value_gens, - absl::Span reducers, - absl::Span reduce_output_shapes, - absl::Span> - extra_output_gens); - - // Emits code that reduces a 3D tensor of shape [depth x height x width] to a - // vector of shape [height]. Other parameters have the same meaning as those - // of `EmitReductionToVector`. Note that input shape might not be - // [depth x height x width], but can be bitcast to [depth x height x width] - // with "depth" being the most major dimension. - Status EmitRowReduction( - KernelThunk* kernel_thunk, int64 depth, int64 height, int64 width, - HloInstruction* reduce, const Shape& input_shape, - absl::Span input_gens, - absl::Span init_value_gens, - absl::Span reducers, - absl::Span reduce_output_shapes, - absl::Span> - extra_output_gens); - - // Emits code that reduces a tensor of arbitrary rank to a scalar. - Status EmitReductionToScalar( - KernelThunk* kernel_thunk, HloInstruction* reduce, - const Shape& input_shape, - absl::Span input_gens, - absl::Span init_value_gens, - absl::Span reducers, - absl::Span reduce_output_shapes, - absl::Span> - extra_output_gens); + // Prerequisite: `IsReductionToVector(*unnested_hlo)` + Status EmitReductionToVector(HloInstruction* unnested_hlo); - // Figures out whether `reduce` is a row or column reduction, and which - // dimensions to reduce, and calls either `EmitRowReduction` or - // `EmitColumnReduction` as appropriate. `input_shape` is the shape of the - // input array, which is the operand of the Reduce instruction if unfused or - // of the Fusion instruction if fused. `input_gen` and `init_value_gen` - // generate elements of the input and the initial value. Other parameters mean - // the same as for `HandleReduce`. - // - // Multiple reduces can be emitted in the same loop, assuming they have the - // same input and output shapes, and the same reduce dimensions. - // - // extra_output_gens can contain extra generators for intermediate outputs. - // These must have the same shape as the reduce input as they are computed - // when the reduce inputs are being read. - // - // Prerequisite: `IsReductionToVector(*reduce)` - Status EmitReductionToVector( - KernelThunk* kernel_thunk, HloInstruction* reduce, - const Shape& input_shape, - absl::Span input_gens, - absl::Span init_value_gens, - absl::Span dimensions_to_reduce, - absl::Span reducers, - absl::Span reduce_output_shapes, - absl::Span> - extra_output_gens); + // Computes the KernelMappingScheme for the reduce HLO and indicates whether + // the reduction is a row reduction. + std::tuple + ComputeMappingSchemeAndReductionKind(const HloInstruction* first_reduce); // Emits code for an in-place scatter, modifying `thunk`s launch dimensions in // the process. `scatter` may be fused, scatter indices are taken from @@ -314,6 +257,28 @@ class IrEmitterUnnested : public IrEmitter { const llvm_ir::IrArray::Index& index, const KernelCodegenInfo* kernel_info, llvm::Value* y_loc, llvm::Value* x_loc); + // Emits code to process a tensor element in a tile for the given input hlo + // that is either a unnested kReduce or a kInput fusion. + void EmitTileElementForReduction(HloInstruction* unnested_hlo, + const llvm_ir::IrArray::Index& index, + const KernelCodegenInfo* kernel_info, + llvm::Value* y_loc, llvm::Value* x_loc); + // Prepares for the code generation for a tile block of a reduction kernel. + void EmitPrologueForReduction(HloInstruction* unnested_hlo, + KernelCodegenInfo* kernel_info); + void EmitPrologueForOneReduction(HloInstruction* unnested_hlo, + HloInstruction* reduce_inst, int reduce_idx, + KernelCodegenInfo* kernel_info, + GpuElementalIrEmitter* elemental_emitter, + ShapeIndex output_shape_index); + // Wraps up the code generation for a tile block of a reduction kernel. + void EmitEpilogueForReduction(HloInstruction* unnested_hlo, + KernelCodegenInfo* kernel_info); + // For each reducer, emits the shuffle-down loop to accumulate the partial + // result to the global result. + void EmitFullWarpShuffleDownLoopForAllReduces( + absl::Span reducers, + absl::Span partial_result_addresses); // Generates the IrArray for each input of an hlo and returns a vector that // constains such IrArrays. diff --git a/tensorflow/compiler/xla/service/gpu/partition_assignment.cc b/tensorflow/compiler/xla/service/gpu/partition_assignment.cc index 375f68a159..bfed4f5230 100644 --- a/tensorflow/compiler/xla/service/gpu/partition_assignment.cc +++ b/tensorflow/compiler/xla/service/gpu/partition_assignment.cc @@ -39,6 +39,25 @@ std::ostream& operator<<(std::ostream& out, return out; } +int64 ThreadsPerBlockLimit(const se::DeviceDescription& device_desc) { + int64 threads_per_block = device_desc.threads_per_block_limit(); + if (threads_per_block == 0) { + static std::atomic log_count{0}; + if (log_count.fetch_add(1) < 8) { + LOG(WARNING) << "Attempting to calculate launch dimensions for GPU " + "without full information about its capabilities. " + "StreamExecutor's PopulateDeviceDescription should be " + "updated for this device."; + } + threads_per_block = device_desc.threads_per_warp(); + if (threads_per_block == 0) { + // Fall back to *something* if we can't even get num threads per warp. + threads_per_block = 32; + } + } + return threads_per_block; +} + // Calculates the launch dimensions used to invoke `hlo`. LaunchDimensions CalculateLaunchDimensions( const Shape& shape, const se::DeviceDescription& device_desc, @@ -62,21 +81,7 @@ LaunchDimensions CalculateLaunchDimensions( // // * = - int64 threads_per_block = device_desc.threads_per_block_limit(); - if (threads_per_block == 0) { - static std::atomic log_count{0}; - if (log_count.fetch_add(1) < 8) { - LOG(WARNING) << "Attempting to calculate launch dimensions for GPU " - "without full information about its capabilities. " - "StreamExecutor's PopulateDeviceDescription should be " - "updated for this device."; - } - threads_per_block = device_desc.threads_per_warp(); - if (threads_per_block == 0) { - // Fall back to *something* if we can't even get num threads per warp. - threads_per_block = 32; - } - } + int64 threads_per_block = ThreadsPerBlockLimit(device_desc); if (num_elements < threads_per_block) { threads_per_block = num_elements; diff --git a/tensorflow/compiler/xla/service/gpu/partition_assignment.h b/tensorflow/compiler/xla/service/gpu/partition_assignment.h index 02471129e0..eb41dcccb9 100644 --- a/tensorflow/compiler/xla/service/gpu/partition_assignment.h +++ b/tensorflow/compiler/xla/service/gpu/partition_assignment.h @@ -57,6 +57,9 @@ class LaunchDimensions { std::ostream& operator<<(std::ostream& out, const LaunchDimensions& launch_dims); +// Returns the maximum number of threads per block allowed by the device. +int64 ThreadsPerBlockLimit(const se::DeviceDescription& device_desc); + LaunchDimensions CalculateLaunchDimensions( const Shape& shape, const se::DeviceDescription& device_desc, int unroll_factor = 1); diff --git a/tensorflow/compiler/xla/service/llvm_ir/kernel_tiling.cc b/tensorflow/compiler/xla/service/llvm_ir/kernel_tiling.cc index c26711e526..cebbc42901 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/kernel_tiling.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/kernel_tiling.cc @@ -120,7 +120,7 @@ KernelMappingScheme::KernelMappingScheme( absl::Span req_block_sizes, int64 num_threads_y, int64 num_threads_x, llvm::IRBuilder<>* b) : b_(b), - dims_in_elems_(dims_in_elems), + dims_in_elems_(dims_in_elems.begin(), dims_in_elems.end()), tile_sizes_{1, tile_size_y, tile_size_x}, num_threads_x_(num_threads_x), num_threads_y_(num_threads_y) { @@ -170,14 +170,16 @@ IrArray::Index KernelMappingScheme::EmitBlockIndex(llvm::Type* index_ty) { IrArray::Index KernelMappingScheme::GetTileIndexForBlockOrigin( const IrArray::Index& block_index) { - IrArray::Index tile_index = block_index; + DCHECK_EQ(block_index.size(), block_sizes_.size()); + std::vector multidim; + multidim.reserve(block_sizes_.size()); for (int i = 0; i < block_sizes_.size(); ++i) { - tile_index[i] = b_->CreateMul( + multidim.push_back(b_->CreateMul( block_index[i], llvm::ConstantInt::get(block_index[i]->getType(), block_sizes_[i]), - "block_origin." + std::to_string(i)); + "block_origin." + std::to_string(i))); } - return tile_index; + return IrArray::Index(multidim, block_index[0]->getType()); } IrArray::Index KernelMappingScheme::GetElementIndexForTileOrigin( @@ -217,14 +219,14 @@ KernelMappingScheme::EmitThreadYXCoordinate(llvm::Type* index_ty) { // defined by (num_thread_y, num_thread_x) from thread_id. llvm::CallInst* thread_id_raw = llvm_ir::EmitCallToIntrinsic( llvm::Intrinsic::nvvm_read_ptx_sreg_tid_x, {}, {}, b_); - llvm_ir::AddRangeMetadata(0, GetThreadsPerTile(), thread_id_raw); + llvm_ir::AddRangeMetadata(0, GetThreadsPerBlock(), thread_id_raw); llvm::Value* thread_id_int = b_->CreateIntCast(thread_id_raw, index_ty, /*isSigned=*/true, "thread.id.x"); llvm::Value* num_thread_x = llvm::ConstantInt::get(index_ty, GetNumberOfThreadsForDimensionX()); - llvm::Value* x = b_->CreateURem(thread_id_int, num_thread_x); - llvm::Value* y = b_->CreateUDiv(thread_id_int, num_thread_x); + llvm::Value* x = b_->CreateURem(thread_id_int, num_thread_x, "thread.x"); + llvm::Value* y = b_->CreateUDiv(thread_id_int, num_thread_x, "thread.y"); return std::make_tuple(y, x); } diff --git a/tensorflow/compiler/xla/service/llvm_ir/kernel_tiling.h b/tensorflow/compiler/xla/service/llvm_ir/kernel_tiling.h index 06002d57b0..fb633b12e6 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/kernel_tiling.h +++ b/tensorflow/compiler/xla/service/llvm_ir/kernel_tiling.h @@ -90,15 +90,16 @@ class KernelMappingScheme { enum { DimZ = 0, DimY, DimX, DimTot }; public: + KernelMappingScheme() {} // dims_in_elems: the normalized tensor dimensions. // req_block_sizes: the requested block size in number of tiles for each // dimension. The actual block size is set to min(req_block_size, // dims_in_number_of_blocks). - explicit KernelMappingScheme(absl::Span dims_in_elems, - int64 tile_size_y, int64 tile_size_x, - absl::Span req_block_sizes, - int64 num_threads_y, int64 num_threads_x, - llvm::IRBuilder<>* b); + KernelMappingScheme(absl::Span dims_in_elems, int64 tile_size_y, + int64 tile_size_x, + absl::Span req_block_sizes, + int64 num_threads_y, int64 num_threads_x, + llvm::IRBuilder<>* b); absl::Span GetDimensionsInElements() const { return dims_in_elems_; @@ -133,11 +134,15 @@ class KernelMappingScheme { } absl::Span GetBlockSizes() const { return block_sizes_; } + int64 GetTileBlockSizeForDimension(int d) const { + DCHECK(d >= DimZ && d <= DimX); + return dims_in_blocks_[d]; + } int64 GetNumberOfThreadsForDimensionX() const { return num_threads_x_; } int64 GetNumberOfThreadsForDimensionY() const { return num_threads_y_; } - int64 GetThreadsPerTile() const { + int64 GetThreadsPerBlock() const { return GetNumberOfThreadsForDimensionX() * GetNumberOfThreadsForDimensionY(); } @@ -163,7 +168,7 @@ class KernelMappingScheme { private: llvm::IRBuilder<>* b_; // The number of elements in each dimension. - absl::Span dims_in_elems_; + std::vector dims_in_elems_; // The number of elements for each dimension of a tile. std::vector tile_sizes_; -- GitLab From 15c5a3bc95931a5540669b09ab9fe56d139de420 Mon Sep 17 00:00:00 2001 From: Gaurav Jain Date: Tue, 11 Dec 2018 09:36:27 -0800 Subject: [PATCH 347/873] Reduce flakiness of testScanCapturesVariables PiperOrigin-RevId: 225017976 --- tensorflow/python/kernel_tests/ctc_loss_op_test.py | 2 +- tensorflow/python/ops/ctc_ops.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/python/kernel_tests/ctc_loss_op_test.py b/tensorflow/python/kernel_tests/ctc_loss_op_test.py index e24f304c1b..39a637d831 100644 --- a/tensorflow/python/kernel_tests/ctc_loss_op_test.py +++ b/tensorflow/python/kernel_tests/ctc_loss_op_test.py @@ -811,7 +811,7 @@ class CTCLossTestV2(test.TestCase): x = random_ops.random_uniform([]) fn = lambda accum, elem: accum + x * elem out = ctc_ops._scan(fn, constant_op.constant([0.0, 1.0, 2.0]), 23.0) - self.assertAllEqual(*sess.run([ + self.assertAllClose(*sess.run([ [23.0 + x * 0.0, 23.0 + x * 1.0, 23.0 + x * 3.0], out ])) diff --git a/tensorflow/python/ops/ctc_ops.py b/tensorflow/python/ops/ctc_ops.py index 3a7eb9355a..db7f9d2378 100644 --- a/tensorflow/python/ops/ctc_ops.py +++ b/tensorflow/python/ops/ctc_ops.py @@ -1029,7 +1029,7 @@ def _scan(fn, elems, initial, reverse=False, inclusive=False, final_only=False): for the forward backward use case. Examples: - scan(lambda a, e: a + e, [1.0, 2.0, 3.0], 1.0) => [2.0, 3.0, 4.0] + scan(lambda a, e: a + e, [1.0, 2.0, 3.0], 1.0) => [2.0, 4.0, 7.0] Multiple accumulators: scan(lambda a, e: (a[0] + e, a[1] * e), [1.0, 2.0, 3.0], (0.0, 1.0)) -- GitLab From 73b5a64e38c0fb03eb1b860464ea48f5eb03e288 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 11 Dec 2018 09:41:03 -0800 Subject: [PATCH 348/873] Update Google Cloud Bigtable C++ Client to the v0.4.0 release. PiperOrigin-RevId: 225018765 --- .../kernels/test_kernels/bigtable_test_client.cc | 11 +++++++++++ .../kernels/test_kernels/bigtable_test_client.h | 7 +++++++ tensorflow/workspace.bzl | 8 ++++---- 3 files changed, 22 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client.cc b/tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client.cc index e95dc57718..3fe71a2ea7 100644 --- a/tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client.cc +++ b/tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client.cc @@ -399,6 +399,17 @@ BigtableTestClient::AsyncMutateRows( return nullptr; } +std::unique_ptr> +BigtableTestClient::AsyncCheckAndMutateRow( + grpc::ClientContext* context, + const google::bigtable::v2::CheckAndMutateRowRequest& request, + grpc::CompletionQueue* cq) { + LOG(WARNING) << "Call to InMemoryDataClient::" << __func__ + << "(); this will likely cause a crash!"; + return nullptr; +} + std::shared_ptr BigtableTestClient::Channel() { LOG(WARNING) << "Call to InMemoryDataClient::Channel(); this will likely " "cause a crash!"; diff --git a/tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client.h b/tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client.h index c4a1f06bc5..8570590457 100644 --- a/tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client.h +++ b/tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client.h @@ -80,6 +80,13 @@ class BigtableTestClient : public ::google::cloud::bigtable::DataClient { const ::google::bigtable::v2::MutateRowsRequest& request, ::grpc::CompletionQueue* cq, void* tag) override; + std::unique_ptr> + AsyncCheckAndMutateRow( + grpc::ClientContext* context, + const google::bigtable::v2::CheckAndMutateRowRequest& request, + grpc::CompletionQueue* cq) override; + std::shared_ptr Channel() override; private: diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 60dcca3207..5210df240d 100755 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -179,15 +179,15 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""): tf_http_archive( name = "com_github_googlecloudplatform_google_cloud_cpp", - sha256 = "3ade2072e6588ff56c0434abe6c63aa5f3f2d56be15a299bafc7e9cdf0a12c17", - strip_prefix = "google-cloud-cpp-0.3.0", + sha256 = "44eee8bd47cbd5ff192e895b45f9f913e2e117f10fdb9af0fd3b1a87a7b53bc3", + strip_prefix = "google-cloud-cpp-0.4.0", system_build_file = clean_dep("//third_party/systemlibs:google_cloud_cpp.BUILD"), system_link_files = { "//third_party/systemlibs:google_cloud_cpp.google.cloud.bigtable.BUILD": "google/cloud/bigtable/BUILD", }, urls = [ - "https://mirror.bazel.build/github.com/GoogleCloudPlatform/google-cloud-cpp/archive/v0.3.0.tar.gz", - "https://github.com/GoogleCloudPlatform/google-cloud-cpp/archive/v0.3.0.tar.gz", + "https://mirror.bazel.build/github.com/GoogleCloudPlatform/google-cloud-cpp/archive/v0.4.0.tar.gz", + "https://github.com/GoogleCloudPlatform/google-cloud-cpp/archive/v0.4.0.tar.gz", ], ) -- GitLab From dd80d3f78710c6ebb4bfd8cad9c5cc01a1acf51e Mon Sep 17 00:00:00 2001 From: "Wen-Heng (Jack) Chung" Date: Tue, 11 Dec 2018 18:01:34 +0000 Subject: [PATCH 349/873] Rename CudnnScratchAllocator to DnnScratchAllocator Rename CudnnScratchAllocator as the logic is applicable for not only Cudnn, but also other DNN algorithm libraries such as MIOpen. --- .../kernels/fused_conv2d_bias_activation_op.cc | 6 +++--- tensorflow/core/kernels/conv_grad_filter_ops.cc | 6 +++--- tensorflow/core/kernels/conv_grad_input_ops.cc | 6 +++--- tensorflow/core/kernels/conv_grad_ops_3d.cc | 12 ++++++------ tensorflow/core/kernels/conv_ops.cc | 8 ++++---- tensorflow/core/kernels/conv_ops_3d.cc | 6 +++--- tensorflow/core/kernels/conv_ops_gpu.h | 10 +++++----- 7 files changed, 27 insertions(+), 27 deletions(-) diff --git a/tensorflow/contrib/fused_conv/kernels/fused_conv2d_bias_activation_op.cc b/tensorflow/contrib/fused_conv/kernels/fused_conv2d_bias_activation_op.cc index 93b1aaa85e..c541c71f99 100644 --- a/tensorflow/contrib/fused_conv/kernels/fused_conv2d_bias_activation_op.cc +++ b/tensorflow/contrib/fused_conv/kernels/fused_conv2d_bias_activation_op.cc @@ -522,7 +522,7 @@ void LaunchFusedConv2DBiasActivationOp:: auto bias_ptr = AsDeviceMemory(bias.template flat().data(), bias.template flat().size()); - static int64 ConvolveScratchSize = GetCudnnWorkspaceLimit( + static int64 ConvolveScratchSize = GetDnnWorkspaceLimit( // default value is in bytes despite the name of the environment variable "TF_CUDNN_WORKSPACE_LIMIT_IN_MB", 1LL << 32 // 4GB ); @@ -570,7 +570,7 @@ void LaunchFusedConv2DBiasActivationOp:: for (auto profile_algorithm : algorithms) { // TODO(zhengxq): profile each algorithm multiple times to better // accuracy. - CudnnScratchAllocator scratch_allocator(ConvolveScratchSize, ctx); + DnnScratchAllocator scratch_allocator(ConvolveScratchSize, ctx); dnn::ProfileResult profile_result; bool cudnn_launch_status = stream @@ -609,7 +609,7 @@ void LaunchFusedConv2DBiasActivationOp:: algorithm_config); } - CudnnScratchAllocator scratch_allocator(ConvolveScratchSize, ctx); + DnnScratchAllocator scratch_allocator(ConvolveScratchSize, ctx); bool cudnn_launch_status = stream ->ThenFusedConvolveWithAlgorithm( diff --git a/tensorflow/core/kernels/conv_grad_filter_ops.cc b/tensorflow/core/kernels/conv_grad_filter_ops.cc index bc30da4099..efd8772226 100644 --- a/tensorflow/core/kernels/conv_grad_filter_ops.cc +++ b/tensorflow/core/kernels/conv_grad_filter_ops.cc @@ -903,7 +903,7 @@ void LaunchConv2DBackpropFilterOp::operator()( auto input_ptr = AsDeviceMemory(transformed_input.template flat().data(), transformed_input.template flat().size()); - static int64 ConvolveBackwardFilterScratchSize = GetCudnnWorkspaceLimit( + static int64 ConvolveBackwardFilterScratchSize = GetDnnWorkspaceLimit( "TF_CUDNN_WORKSPACE_LIMIT_IN_MB", 1LL << 32 // 4GB by default ); int device_id = stream->parent()->device_ordinal(); @@ -939,7 +939,7 @@ void LaunchConv2DBackpropFilterOp::operator()( for (auto profile_algorithm : algorithms) { // TODO(zhengxq): profile each algorithm multiple times to better // accuracy. - CudnnScratchAllocator scratch_allocator(ConvolveBackwardFilterScratchSize, + DnnScratchAllocator scratch_allocator(ConvolveBackwardFilterScratchSize, ctx); ProfileResult profile_result; bool cudnn_launch_status = @@ -977,7 +977,7 @@ void LaunchConv2DBackpropFilterOp::operator()( AutoTuneConvBwdFilter::GetInstance()->Insert(conv_parameters, algorithm_config); } - CudnnScratchAllocator scratch_allocator(ConvolveBackwardFilterScratchSize, + DnnScratchAllocator scratch_allocator(ConvolveBackwardFilterScratchSize, ctx); bool cudnn_launch_status = stream diff --git a/tensorflow/core/kernels/conv_grad_input_ops.cc b/tensorflow/core/kernels/conv_grad_input_ops.cc index e06af15f2f..7339fb736f 100644 --- a/tensorflow/core/kernels/conv_grad_input_ops.cc +++ b/tensorflow/core/kernels/conv_grad_input_ops.cc @@ -951,10 +951,10 @@ void LaunchConv2DBackpropInputOp::operator()( AsDeviceMemory(pre_transformed_in_backprop.template flat().data(), pre_transformed_in_backprop.template flat().size()); - static int64 ConvolveBackwardDataScratchSize = GetCudnnWorkspaceLimit( + static int64 ConvolveBackwardDataScratchSize = GetDnnWorkspaceLimit( "TF_CUDNN_WORKSPACE_LIMIT_IN_MB", 1LL << 32 // 4GB by default ); - CudnnScratchAllocator scratch_allocator(ConvolveBackwardDataScratchSize, ctx); + DnnScratchAllocator scratch_allocator(ConvolveBackwardDataScratchSize, ctx); int device_id = stream->parent()->device_ordinal(); DataType dtype = out_backprop.dtype(); ConvParameters conv_parameters = { @@ -988,7 +988,7 @@ void LaunchConv2DBackpropInputOp::operator()( for (auto profile_algorithm : algorithms) { // TODO(zhengxq): profile each algorithm multiple times to better // accuracy. - CudnnScratchAllocator scratch_allocator(ConvolveBackwardDataScratchSize, + DnnScratchAllocator scratch_allocator(ConvolveBackwardDataScratchSize, ctx); ProfileResult profile_result; bool cudnn_launch_status = diff --git a/tensorflow/core/kernels/conv_grad_ops_3d.cc b/tensorflow/core/kernels/conv_grad_ops_3d.cc index e4c49efea0..a518fcc874 100644 --- a/tensorflow/core/kernels/conv_grad_ops_3d.cc +++ b/tensorflow/core/kernels/conv_grad_ops_3d.cc @@ -1333,7 +1333,7 @@ class Conv3DBackpropInputOp : public OpKernel { AsDeviceMemory(pre_transformed_in_backprop.template flat().data(), pre_transformed_in_backprop.template flat().size()); - static int64 ConvolveBackwardDataScratchSize = GetCudnnWorkspaceLimit( + static int64 ConvolveBackwardDataScratchSize = GetDnnWorkspaceLimit( "TF_CUDNN_WORKSPACE_LIMIT_IN_MB", 1LL << 32); // 4GB by default const int device_id = stream->parent()->device_ordinal(); @@ -1368,7 +1368,7 @@ class Conv3DBackpropInputOp : public OpKernel { for (auto profile_algorithm : algorithms) { // TODO(zhengxq): profile each algorithm multiple times to better // accuracy. - CudnnScratchAllocator scratch_allocator(ConvolveBackwardDataScratchSize, + DnnScratchAllocator scratch_allocator(ConvolveBackwardDataScratchSize, context); ProfileResult profile_result; bool cudnn_launch_status = @@ -1405,7 +1405,7 @@ class Conv3DBackpropInputOp : public OpKernel { AutoTuneConv3dBwdData::GetInstance()->Insert(conv_parameters, algorithm_config); } - CudnnScratchAllocator scratch_allocator(ConvolveBackwardDataScratchSize, + DnnScratchAllocator scratch_allocator(ConvolveBackwardDataScratchSize, context); bool cudnn_launch_status = stream @@ -1739,7 +1739,7 @@ class Conv3DBackpropFilterOp : public OpKernel { AsDeviceMemory(transformed_input.template flat().data(), transformed_input.template flat().size()); - static int64 ConvolveBackwardFilterScratchSize = GetCudnnWorkspaceLimit( + static int64 ConvolveBackwardFilterScratchSize = GetDnnWorkspaceLimit( "TF_CUDNN_WORKSPACE_LIMIT_IN_MB", 1LL << 32); // 4GB by default const int device_id = stream->parent()->device_ordinal(); @@ -1774,7 +1774,7 @@ class Conv3DBackpropFilterOp : public OpKernel { for (auto profile_algorithm : algorithms) { // TODO(zhengxq): profile each algorithm multiple times to better // accuracy. - CudnnScratchAllocator scratch_allocator( + DnnScratchAllocator scratch_allocator( ConvolveBackwardFilterScratchSize, context); ProfileResult profile_result; bool cudnn_launch_status = @@ -1812,7 +1812,7 @@ class Conv3DBackpropFilterOp : public OpKernel { AutoTuneConv3dBwdFilter::GetInstance()->Insert(conv_parameters, algorithm_config); } - CudnnScratchAllocator scratch_allocator(ConvolveBackwardFilterScratchSize, + DnnScratchAllocator scratch_allocator(ConvolveBackwardFilterScratchSize, context); bool cudnn_launch_status = stream diff --git a/tensorflow/core/kernels/conv_ops.cc b/tensorflow/core/kernels/conv_ops.cc index 74857fc207..8c2deeed0e 100644 --- a/tensorflow/core/kernels/conv_ops.cc +++ b/tensorflow/core/kernels/conv_ops.cc @@ -521,7 +521,7 @@ template struct LaunchConv2DOp; template struct LaunchConv2DOp; #if GOOGLE_CUDA -int64 GetCudnnWorkspaceLimit(const string& envvar_in_mb, +int64 GetDnnWorkspaceLimit(const string& envvar_in_mb, int64 default_value_in_bytes) { const char* workspace_limit_in_mb_str = getenv(envvar_in_mb.c_str()); if (workspace_limit_in_mb_str != nullptr && @@ -759,7 +759,7 @@ void LaunchConv2DOp::operator()( AsDeviceMemory(transformed_output.template flat().data(), transformed_output.template flat().size()); - static int64 ConvolveScratchSize = GetCudnnWorkspaceLimit( + static int64 ConvolveScratchSize = GetDnnWorkspaceLimit( // default value is in bytes despite the name of the environment variable "TF_CUDNN_WORKSPACE_LIMIT_IN_MB", 1LL << 32 // 4GB ); @@ -803,7 +803,7 @@ void LaunchConv2DOp::operator()( for (auto profile_algorithm : algorithms) { // TODO(zhengxq): profile each algorithm multiple times to better // accuracy. - CudnnScratchAllocator scratch_allocator(ConvolveScratchSize, ctx); + DnnScratchAllocator scratch_allocator(ConvolveScratchSize, ctx); ProfileResult profile_result; bool cudnn_launch_status = stream @@ -841,7 +841,7 @@ void LaunchConv2DOp::operator()( AutoTuneConv::GetInstance()->Insert(conv_parameters, algorithm_config); } - CudnnScratchAllocator scratch_allocator(ConvolveScratchSize, ctx); + DnnScratchAllocator scratch_allocator(ConvolveScratchSize, ctx); bool cudnn_launch_status = stream ->ThenConvolveWithAlgorithm(input_desc, input_ptr, filter_desc, diff --git a/tensorflow/core/kernels/conv_ops_3d.cc b/tensorflow/core/kernels/conv_ops_3d.cc index f20ac93b5a..5a59e20cc2 100644 --- a/tensorflow/core/kernels/conv_ops_3d.cc +++ b/tensorflow/core/kernels/conv_ops_3d.cc @@ -407,7 +407,7 @@ struct LaunchConvOp { AsDeviceMemory(transformed_output.template flat().data(), transformed_output.template flat().size()); - static int64 ConvolveScratchSize = GetCudnnWorkspaceLimit( + static int64 ConvolveScratchSize = GetDnnWorkspaceLimit( "TF_CUDNN_WORKSPACE_LIMIT_IN_MB", 1LL << 32); // 4GB by default int device_id = stream->parent()->device_ordinal(); @@ -450,7 +450,7 @@ struct LaunchConvOp { for (auto profile_algorithm : algorithms) { // TODO(zhengxq): profile each algorithm multiple times to better // accuracy. - CudnnScratchAllocator scratch_allocator(ConvolveScratchSize, ctx); + DnnScratchAllocator scratch_allocator(ConvolveScratchSize, ctx); ProfileResult profile_result; bool cudnn_launch_status = stream @@ -486,7 +486,7 @@ struct LaunchConvOp { AutoTuneConv3d::GetInstance()->Insert(conv_parameters, algorithm_config); } - CudnnScratchAllocator scratch_allocator(ConvolveScratchSize, ctx); + DnnScratchAllocator scratch_allocator(ConvolveScratchSize, ctx); bool cudnn_launch_status = stream ->ThenConvolveWithAlgorithm(input_desc, input_ptr, filter_desc, diff --git a/tensorflow/core/kernels/conv_ops_gpu.h b/tensorflow/core/kernels/conv_ops_gpu.h index 21d135decd..19fc45b756 100644 --- a/tensorflow/core/kernels/conv_ops_gpu.h +++ b/tensorflow/core/kernels/conv_ops_gpu.h @@ -27,19 +27,19 @@ limitations under the License. namespace tensorflow { -// Get the Cudnn workspace limit from the environment variable, which is in MB. +// Get the Dnn workspace limit from the environment variable, which is in MB. // Return the workspace memory limit in bytes. If no value is set, return the // default value. -int64 GetCudnnWorkspaceLimit(const string& envvar_in_mb, +int64 GetDnnWorkspaceLimit(const string& envvar_in_mb, int64 default_value_in_bytes); // A class to provide scratch-space allocator for Stream-Executor Cudnn // callback. TensorFlow is responsible for releasing the temporary buffers after // the kernel finishes. -class CudnnScratchAllocator : public se::ScratchAllocator { +class DnnScratchAllocator : public se::ScratchAllocator { public: - virtual ~CudnnScratchAllocator() {} - CudnnScratchAllocator(int64 memory_limit, OpKernelContext* context) + virtual ~DnnScratchAllocator() {} + DnnScratchAllocator(int64 memory_limit, OpKernelContext* context) : memory_limit_(memory_limit), total_byte_size_(0), context_(context) {} int64 GetMemoryLimitInBytes(se::Stream* stream) override { return memory_limit_; -- GitLab From c6129ba7abc2245f8b05ce16aea95aed954985a1 Mon Sep 17 00:00:00 2001 From: James Keeling Date: Tue, 11 Dec 2018 10:04:00 -0800 Subject: [PATCH 350/873] Fix comment in Eager C API. Somehow this comment seems to have been repeated. I delete the second copy and re-flow the existing one. PiperOrigin-RevId: 225022682 --- tensorflow/c/eager/c_api.h | 22 +++++----------------- 1 file changed, 5 insertions(+), 17 deletions(-) diff --git a/tensorflow/c/eager/c_api.h b/tensorflow/c/eager/c_api.h index f80ae5a6d0..120748ab76 100755 --- a/tensorflow/c/eager/c_api.h +++ b/tensorflow/c/eager/c_api.h @@ -170,23 +170,11 @@ TF_CAPI_EXPORT extern int64_t TFE_TensorHandleDim(TFE_TensorHandle* h, int dim_index, TF_Status* status); -// Returns the device of the operation that produced `h`. -// If `h` was produced by a copy, returns the destination device of -// the copy. Note that returned device name is not always the device -// holding the tensor handle's memory. If you want the latter, use -// TFE_TensorHandleBackingDeviceName. -// This function will block till the operation that produces `h` has completed. -// -// Device on which the kernel of the operation that produced `h` ran. -// -// If `h` was produced by a copy, returns the destination device of -// the copy. -// -// Note that returned device name is not always the device that owns the memory -// that backs the tensor handle. For the latter see -// TFE_TensorHandleBackingDeviceName. -// -// This function will block till the operation that produces `h` has completed. +// Returns the device of the operation that produced `h`. If `h` was produced by +// a copy, returns the destination device of the copy. Note that the returned +// device name is not always the device holding the tensor handle's memory. If +// you want the latter, use TFE_TensorHandleBackingDeviceName. This function +// will block till the operation that produces `h` has completed. TF_CAPI_EXPORT extern const char* TFE_TensorHandleDeviceName( TFE_TensorHandle* h, TF_Status* status); -- GitLab From 7d1c9e739453d3a4d082a6b63ce05ee9048538aa Mon Sep 17 00:00:00 2001 From: Mihai Maruseac Date: Tue, 11 Dec 2018 10:22:05 -0800 Subject: [PATCH 351/873] Move some class symbols from tf 2.0. - io.PaddingFIFOQueue -> queue.PaddingFIFOQueue - io.PriorityQueue -> queue.PriorityQueue - io.QueueBase -> queue.QueueBase - io.RandomShuffleQueue -> queue.RandomShuffleQueue - FIFOQueue -> queue.FIFOQueue - train.Server -> distribute.Server PiperOrigin-RevId: 225025906 --- tensorflow/python/ops/data_flow_ops.py | 25 ++++--- .../tools/api/generator/api_init_files.bzl | 1 + .../tools/api/generator/api_init_files_v1.bzl | 1 + tensorflow/python/training/server_lib.py | 4 +- .../tensorflow.distribute.-server.pbtxt} | 2 +- .../api/golden/v1/tensorflow.distribute.pbtxt | 4 ++ .../tools/api/golden/v1/tensorflow.pbtxt | 4 ++ .../tensorflow.queue.-f-i-f-o-queue.pbtxt} | 2 +- ...orflow.queue.-padding-f-i-f-o-queue.pbtxt} | 2 +- .../tensorflow.queue.-priority-queue.pbtxt} | 2 +- .../tensorflow.queue.-queue-base.pbtxt} | 2 +- ...sorflow.queue.-random-shuffle-queue.pbtxt} | 2 +- .../api/golden/v1/tensorflow.queue.pbtxt | 23 +++++++ .../v2/tensorflow.distribute.-server.pbtxt | 29 ++++++++ .../api/golden/v2/tensorflow.distribute.pbtxt | 4 ++ .../tools/api/golden/v2/tensorflow.io.pbtxt | 16 ----- .../tools/api/golden/v2/tensorflow.pbtxt | 8 +-- .../v2/tensorflow.queue.-f-i-f-o-queue.pbtxt | 66 +++++++++++++++++++ ...sorflow.queue.-padding-f-i-f-o-queue.pbtxt | 66 +++++++++++++++++++ .../v2/tensorflow.queue.-priority-queue.pbtxt | 66 +++++++++++++++++++ .../v2/tensorflow.queue.-queue-base.pbtxt | 65 ++++++++++++++++++ ...nsorflow.queue.-random-shuffle-queue.pbtxt | 66 +++++++++++++++++++ .../api/golden/v2/tensorflow.queue.pbtxt | 23 +++++++ .../api/golden/v2/tensorflow.train.pbtxt | 4 -- tensorflow/tools/compatibility/renames_v2.py | 16 +++-- 25 files changed, 457 insertions(+), 46 deletions(-) rename tensorflow/tools/api/golden/{v2/tensorflow.train.-server.pbtxt => v1/tensorflow.distribute.-server.pbtxt} (96%) rename tensorflow/tools/api/golden/{v2/tensorflow.-f-i-f-o-queue.pbtxt => v1/tensorflow.queue.-f-i-f-o-queue.pbtxt} (98%) rename tensorflow/tools/api/golden/{v2/tensorflow.io.-padding-f-i-f-o-queue.pbtxt => v1/tensorflow.queue.-padding-f-i-f-o-queue.pbtxt} (98%) rename tensorflow/tools/api/golden/{v2/tensorflow.io.-priority-queue.pbtxt => v1/tensorflow.queue.-priority-queue.pbtxt} (98%) rename tensorflow/tools/api/golden/{v2/tensorflow.io.-queue-base.pbtxt => v1/tensorflow.queue.-queue-base.pbtxt} (98%) rename tensorflow/tools/api/golden/{v2/tensorflow.io.-random-shuffle-queue.pbtxt => v1/tensorflow.queue.-random-shuffle-queue.pbtxt} (97%) create mode 100644 tensorflow/tools/api/golden/v1/tensorflow.queue.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.distribute.-server.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.queue.-f-i-f-o-queue.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.queue.-padding-f-i-f-o-queue.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.queue.-priority-queue.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.queue.-queue-base.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.queue.-random-shuffle-queue.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.queue.pbtxt diff --git a/tensorflow/python/ops/data_flow_ops.py b/tensorflow/python/ops/data_flow_ops.py index 2030332e4e..1557bdf0ed 100644 --- a/tensorflow/python/ops/data_flow_ops.py +++ b/tensorflow/python/ops/data_flow_ops.py @@ -113,8 +113,9 @@ def _shape_common(s1, s2): # pylint: disable=protected-access -@tf_export("io.QueueBase", v1=["io.QueueBase", "QueueBase"]) -@deprecation.deprecated_endpoints("QueueBase") +@tf_export("queue.QueueBase", + v1=["queue.QueueBase", "io.QueueBase", "QueueBase"]) +@deprecation.deprecated_endpoints(["io.QueueBase", "QueueBase"]) class QueueBase(object): """Base class for queue implementations. @@ -616,8 +617,11 @@ def _shared_name(shared_name): @tf_export( - "io.RandomShuffleQueue", v1=["io.RandomShuffleQueue", "RandomShuffleQueue"]) -@deprecation.deprecated_endpoints("RandomShuffleQueue") + "queue.RandomShuffleQueue", + v1=["queue.RandomShuffleQueue", + "io.RandomShuffleQueue", "RandomShuffleQueue"]) +@deprecation.deprecated_endpoints( + ["io.RandomShuffleQueue", "RandomShuffleQueue"]) class RandomShuffleQueue(QueueBase): """A queue implementation that dequeues elements in a random order. @@ -702,7 +706,8 @@ class RandomShuffleQueue(QueueBase): super(RandomShuffleQueue, self).__init__(dtypes, shapes, names, queue_ref) -@tf_export("FIFOQueue") +@tf_export("queue.FIFOQueue", v1=["queue.FIFOQueue", "FIFOQueue"]) +@deprecation.deprecated_endpoints("FIFOQueue") class FIFOQueue(QueueBase): """A queue implementation that dequeues elements in first-in first-out order. @@ -760,8 +765,9 @@ class FIFOQueue(QueueBase): @tf_export( - "io.PaddingFIFOQueue", v1=["io.PaddingFIFOQueue", "PaddingFIFOQueue"]) -@deprecation.deprecated_endpoints("PaddingFIFOQueue") + "queue.PaddingFIFOQueue", + v1=["queue.PaddingFIFOQueue", "io.PaddingFIFOQueue", "PaddingFIFOQueue"]) +@deprecation.deprecated_endpoints(["io.PaddingFIFOQueue", "PaddingFIFOQueue"]) class PaddingFIFOQueue(QueueBase): """A FIFOQueue that supports batching variable-sized tensors by padding. @@ -835,8 +841,9 @@ class PaddingFIFOQueue(QueueBase): super(PaddingFIFOQueue, self).__init__(dtypes, shapes, names, queue_ref) -@tf_export("io.PriorityQueue", v1=["io.PriorityQueue", "PriorityQueue"]) -@deprecation.deprecated_endpoints("PriorityQueue") +@tf_export("queue.PriorityQueue", + v1=["queue.PriorityQueue", "io.PriorityQueue", "PriorityQueue"]) +@deprecation.deprecated_endpoints(["io.PriorityQueue", "PriorityQueue"]) class PriorityQueue(QueueBase): """A queue implementation that dequeues elements in prioritized order. diff --git a/tensorflow/python/tools/api/generator/api_init_files.bzl b/tensorflow/python/tools/api/generator/api_init_files.bzl index 25d0c0f75c..5fee9c5eaf 100644 --- a/tensorflow/python/tools/api/generator/api_init_files.bzl +++ b/tensorflow/python/tools/api/generator/api_init_files.bzl @@ -20,6 +20,7 @@ TENSORFLOW_API_INIT_FILES = [ "graph_util/__init__.py", "image/__init__.py", "io/__init__.py", + "queue/__init__.py", "initializers/__init__.py", "keras/__init__.py", "keras/activations/__init__.py", diff --git a/tensorflow/python/tools/api/generator/api_init_files_v1.bzl b/tensorflow/python/tools/api/generator/api_init_files_v1.bzl index 99c8495ce5..8d3b86bf26 100644 --- a/tensorflow/python/tools/api/generator/api_init_files_v1.bzl +++ b/tensorflow/python/tools/api/generator/api_init_files_v1.bzl @@ -23,6 +23,7 @@ TENSORFLOW_API_INIT_FILES_V1 = [ "graph_util/__init__.py", "image/__init__.py", "io/__init__.py", + "queue/__init__.py", "initializers/__init__.py", "keras/__init__.py", "keras/activations/__init__.py", diff --git a/tensorflow/python/training/server_lib.py b/tensorflow/python/training/server_lib.py index 302ca2dd44..b3c21d5337 100644 --- a/tensorflow/python/training/server_lib.py +++ b/tensorflow/python/training/server_lib.py @@ -23,6 +23,7 @@ from tensorflow.core.protobuf import tensorflow_server_pb2 from tensorflow.python import pywrap_tensorflow as c_api from tensorflow.python.framework import errors from tensorflow.python.util import compat +from tensorflow.python.util import deprecation from tensorflow.python.util.tf_export import tf_export @@ -93,7 +94,8 @@ def _make_server_def(server_or_cluster_def, job_name, task_index, protocol, return server_def -@tf_export("train.Server") +@tf_export("distribute.Server", v1=["distribute.Server", "train.Server"]) +@deprecation.deprecated_endpoints("train.Server") class Server(object): """An in-process TensorFlow server, for use in distributed training. diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-server.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.distribute.-server.pbtxt similarity index 96% rename from tensorflow/tools/api/golden/v2/tensorflow.train.-server.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.distribute.-server.pbtxt index 9b8f185f5b..6c39bf4fc4 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.train.-server.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.distribute.-server.pbtxt @@ -1,4 +1,4 @@ -path: "tensorflow.train.Server" +path: "tensorflow.distribute.Server" tf_class { is_instance: "" is_instance: "" diff --git a/tensorflow/tools/api/golden/v1/tensorflow.distribute.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.distribute.pbtxt index b0dd73ca1d..31dc6e0716 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.distribute.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.distribute.pbtxt @@ -20,6 +20,10 @@ tf_module { name: "ReplicaContext" mtype: "" } + member { + name: "Server" + mtype: "" + } member { name: "Strategy" mtype: "" diff --git a/tensorflow/tools/api/golden/v1/tensorflow.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.pbtxt index 5592a4c59d..4ed4deea13 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.pbtxt @@ -512,6 +512,10 @@ tf_module { name: "quantization" mtype: "" } + member { + name: "queue" + mtype: "" + } member { name: "quint16" mtype: "" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-f-i-f-o-queue.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.queue.-f-i-f-o-queue.pbtxt similarity index 98% rename from tensorflow/tools/api/golden/v2/tensorflow.-f-i-f-o-queue.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.queue.-f-i-f-o-queue.pbtxt index a095616c00..724ab5fe82 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.-f-i-f-o-queue.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.queue.-f-i-f-o-queue.pbtxt @@ -1,4 +1,4 @@ -path: "tensorflow.FIFOQueue" +path: "tensorflow.queue.FIFOQueue" tf_class { is_instance: "" is_instance: "" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.io.-padding-f-i-f-o-queue.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.queue.-padding-f-i-f-o-queue.pbtxt similarity index 98% rename from tensorflow/tools/api/golden/v2/tensorflow.io.-padding-f-i-f-o-queue.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.queue.-padding-f-i-f-o-queue.pbtxt index 85306fdcac..9ef0a4d9eb 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.io.-padding-f-i-f-o-queue.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.queue.-padding-f-i-f-o-queue.pbtxt @@ -1,4 +1,4 @@ -path: "tensorflow.io.PaddingFIFOQueue" +path: "tensorflow.queue.PaddingFIFOQueue" tf_class { is_instance: "" is_instance: "" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.io.-priority-queue.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.queue.-priority-queue.pbtxt similarity index 98% rename from tensorflow/tools/api/golden/v2/tensorflow.io.-priority-queue.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.queue.-priority-queue.pbtxt index 02d8037b34..bb66beb13a 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.io.-priority-queue.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.queue.-priority-queue.pbtxt @@ -1,4 +1,4 @@ -path: "tensorflow.io.PriorityQueue" +path: "tensorflow.queue.PriorityQueue" tf_class { is_instance: "" is_instance: "" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.io.-queue-base.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.queue.-queue-base.pbtxt similarity index 98% rename from tensorflow/tools/api/golden/v2/tensorflow.io.-queue-base.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.queue.-queue-base.pbtxt index a30481a0ea..8faaad22af 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.io.-queue-base.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.queue.-queue-base.pbtxt @@ -1,4 +1,4 @@ -path: "tensorflow.io.QueueBase" +path: "tensorflow.queue.QueueBase" tf_class { is_instance: "" is_instance: "" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.io.-random-shuffle-queue.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.queue.-random-shuffle-queue.pbtxt similarity index 97% rename from tensorflow/tools/api/golden/v2/tensorflow.io.-random-shuffle-queue.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.queue.-random-shuffle-queue.pbtxt index 82cbf9884f..31cd503b13 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.io.-random-shuffle-queue.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.queue.-random-shuffle-queue.pbtxt @@ -1,4 +1,4 @@ -path: "tensorflow.io.RandomShuffleQueue" +path: "tensorflow.queue.RandomShuffleQueue" tf_class { is_instance: "" is_instance: "" diff --git a/tensorflow/tools/api/golden/v1/tensorflow.queue.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.queue.pbtxt new file mode 100644 index 0000000000..c16e95e211 --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.queue.pbtxt @@ -0,0 +1,23 @@ +path: "tensorflow.queue" +tf_module { + member { + name: "FIFOQueue" + mtype: "" + } + member { + name: "PaddingFIFOQueue" + mtype: "" + } + member { + name: "PriorityQueue" + mtype: "" + } + member { + name: "QueueBase" + mtype: "" + } + member { + name: "RandomShuffleQueue" + mtype: "" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.distribute.-server.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.distribute.-server.pbtxt new file mode 100644 index 0000000000..6c39bf4fc4 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.distribute.-server.pbtxt @@ -0,0 +1,29 @@ +path: "tensorflow.distribute.Server" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "server_def" + mtype: "" + } + member { + name: "target" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'server_or_cluster_def\', \'job_name\', \'task_index\', \'protocol\', \'config\', \'start\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'True\'], " + } + member_method { + name: "create_local_server" + argspec: "args=[\'config\', \'start\'], varargs=None, keywords=None, defaults=[\'None\', \'True\'], " + } + member_method { + name: "join" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "start" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.distribute.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.distribute.pbtxt index b0dd73ca1d..31dc6e0716 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.distribute.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.distribute.pbtxt @@ -20,6 +20,10 @@ tf_module { name: "ReplicaContext" mtype: "" } + member { + name: "Server" + mtype: "" + } member { name: "Strategy" mtype: "" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.io.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.io.pbtxt index 8906329742..2d9c759e3c 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.io.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.io.pbtxt @@ -8,22 +8,6 @@ tf_module { name: "FixedLenSequenceFeature" mtype: "" } - member { - name: "PaddingFIFOQueue" - mtype: "" - } - member { - name: "PriorityQueue" - mtype: "" - } - member { - name: "QueueBase" - mtype: "" - } - member { - name: "RandomShuffleQueue" - mtype: "" - } member { name: "SparseFeature" mtype: "" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.pbtxt index 5f31d27480..ee81e86fd5 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.pbtxt @@ -12,10 +12,6 @@ tf_module { name: "Event" mtype: "" } - member { - name: "FIFOQueue" - mtype: "" - } member { name: "GradientTape" mtype: "" @@ -256,6 +252,10 @@ tf_module { name: "quantization" mtype: "" } + member { + name: "queue" + mtype: "" + } member { name: "quint16" mtype: "" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.queue.-f-i-f-o-queue.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.queue.-f-i-f-o-queue.pbtxt new file mode 100644 index 0000000000..724ab5fe82 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.queue.-f-i-f-o-queue.pbtxt @@ -0,0 +1,66 @@ +path: "tensorflow.queue.FIFOQueue" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "dtypes" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "names" + mtype: "" + } + member { + name: "queue_ref" + mtype: "" + } + member { + name: "shapes" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'capacity\', \'dtypes\', \'shapes\', \'names\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'fifo_queue\'], " + } + member_method { + name: "close" + argspec: "args=[\'self\', \'cancel_pending_enqueues\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " + } + member_method { + name: "dequeue" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "dequeue_many" + argspec: "args=[\'self\', \'n\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "dequeue_up_to" + argspec: "args=[\'self\', \'n\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "enqueue" + argspec: "args=[\'self\', \'vals\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "enqueue_many" + argspec: "args=[\'self\', \'vals\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "from_list" + argspec: "args=[\'index\', \'queues\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_closed" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "size" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.queue.-padding-f-i-f-o-queue.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.queue.-padding-f-i-f-o-queue.pbtxt new file mode 100644 index 0000000000..9ef0a4d9eb --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.queue.-padding-f-i-f-o-queue.pbtxt @@ -0,0 +1,66 @@ +path: "tensorflow.queue.PaddingFIFOQueue" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "dtypes" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "names" + mtype: "" + } + member { + name: "queue_ref" + mtype: "" + } + member { + name: "shapes" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'capacity\', \'dtypes\', \'shapes\', \'names\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'padding_fifo_queue\'], " + } + member_method { + name: "close" + argspec: "args=[\'self\', \'cancel_pending_enqueues\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " + } + member_method { + name: "dequeue" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "dequeue_many" + argspec: "args=[\'self\', \'n\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "dequeue_up_to" + argspec: "args=[\'self\', \'n\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "enqueue" + argspec: "args=[\'self\', \'vals\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "enqueue_many" + argspec: "args=[\'self\', \'vals\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "from_list" + argspec: "args=[\'index\', \'queues\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_closed" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "size" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.queue.-priority-queue.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.queue.-priority-queue.pbtxt new file mode 100644 index 0000000000..bb66beb13a --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.queue.-priority-queue.pbtxt @@ -0,0 +1,66 @@ +path: "tensorflow.queue.PriorityQueue" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "dtypes" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "names" + mtype: "" + } + member { + name: "queue_ref" + mtype: "" + } + member { + name: "shapes" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'capacity\', \'types\', \'shapes\', \'names\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'priority_queue\'], " + } + member_method { + name: "close" + argspec: "args=[\'self\', \'cancel_pending_enqueues\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " + } + member_method { + name: "dequeue" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "dequeue_many" + argspec: "args=[\'self\', \'n\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "dequeue_up_to" + argspec: "args=[\'self\', \'n\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "enqueue" + argspec: "args=[\'self\', \'vals\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "enqueue_many" + argspec: "args=[\'self\', \'vals\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "from_list" + argspec: "args=[\'index\', \'queues\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_closed" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "size" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.queue.-queue-base.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.queue.-queue-base.pbtxt new file mode 100644 index 0000000000..8faaad22af --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.queue.-queue-base.pbtxt @@ -0,0 +1,65 @@ +path: "tensorflow.queue.QueueBase" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "dtypes" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "names" + mtype: "" + } + member { + name: "queue_ref" + mtype: "" + } + member { + name: "shapes" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'dtypes\', \'shapes\', \'names\', \'queue_ref\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "close" + argspec: "args=[\'self\', \'cancel_pending_enqueues\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " + } + member_method { + name: "dequeue" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "dequeue_many" + argspec: "args=[\'self\', \'n\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "dequeue_up_to" + argspec: "args=[\'self\', \'n\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "enqueue" + argspec: "args=[\'self\', \'vals\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "enqueue_many" + argspec: "args=[\'self\', \'vals\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "from_list" + argspec: "args=[\'index\', \'queues\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_closed" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "size" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.queue.-random-shuffle-queue.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.queue.-random-shuffle-queue.pbtxt new file mode 100644 index 0000000000..31cd503b13 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.queue.-random-shuffle-queue.pbtxt @@ -0,0 +1,66 @@ +path: "tensorflow.queue.RandomShuffleQueue" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "dtypes" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "names" + mtype: "" + } + member { + name: "queue_ref" + mtype: "" + } + member { + name: "shapes" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'capacity\', \'min_after_dequeue\', \'dtypes\', \'shapes\', \'names\', \'seed\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'random_shuffle_queue\'], " + } + member_method { + name: "close" + argspec: "args=[\'self\', \'cancel_pending_enqueues\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " + } + member_method { + name: "dequeue" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "dequeue_many" + argspec: "args=[\'self\', \'n\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "dequeue_up_to" + argspec: "args=[\'self\', \'n\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "enqueue" + argspec: "args=[\'self\', \'vals\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "enqueue_many" + argspec: "args=[\'self\', \'vals\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "from_list" + argspec: "args=[\'index\', \'queues\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_closed" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "size" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.queue.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.queue.pbtxt new file mode 100644 index 0000000000..c16e95e211 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.queue.pbtxt @@ -0,0 +1,23 @@ +path: "tensorflow.queue" +tf_module { + member { + name: "FIFOQueue" + mtype: "" + } + member { + name: "PaddingFIFOQueue" + mtype: "" + } + member { + name: "PriorityQueue" + mtype: "" + } + member { + name: "QueueBase" + mtype: "" + } + member { + name: "RandomShuffleQueue" + mtype: "" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.pbtxt index 8c327f88f3..cc63a7fd82 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.train.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.pbtxt @@ -68,10 +68,6 @@ tf_module { name: "SequenceExample" mtype: "" } - member { - name: "Server" - mtype: "" - } member { name: "ServerDef" mtype: "" diff --git a/tensorflow/tools/compatibility/renames_v2.py b/tensorflow/tools/compatibility/renames_v2.py index 9a3f4460f7..ad4c3d2750 100644 --- a/tensorflow/tools/compatibility/renames_v2.py +++ b/tensorflow/tools/compatibility/renames_v2.py @@ -34,6 +34,7 @@ renames = { 'tf.ConfigProto': 'tf.compat.v1.ConfigProto', 'tf.DeviceSpec': 'tf.compat.v1.DeviceSpec', 'tf.Dimension': 'tf.compat.v1.Dimension', + 'tf.FIFOQueue': 'tf.queue.FIFOQueue', 'tf.FixedLenFeature': 'tf.io.FixedLenFeature', 'tf.FixedLenSequenceFeature': 'tf.io.FixedLenSequenceFeature', 'tf.FixedLengthRecordReader': 'tf.compat.v1.FixedLengthRecordReader', @@ -58,12 +59,12 @@ renames = { 'tf.NotDifferentiable': 'tf.no_gradient', 'tf.OpError': 'tf.errors.OpError', 'tf.OptimizerOptions': 'tf.compat.v1.OptimizerOptions', - 'tf.PaddingFIFOQueue': 'tf.io.PaddingFIFOQueue', + 'tf.PaddingFIFOQueue': 'tf.queue.PaddingFIFOQueue', 'tf.Print': 'tf.compat.v1.Print', - 'tf.PriorityQueue': 'tf.io.PriorityQueue', + 'tf.PriorityQueue': 'tf.queue.PriorityQueue', 'tf.QUANTIZED_DTYPES': 'tf.dtypes.QUANTIZED_DTYPES', - 'tf.QueueBase': 'tf.io.QueueBase', - 'tf.RandomShuffleQueue': 'tf.io.RandomShuffleQueue', + 'tf.QueueBase': 'tf.queue.QueueBase', + 'tf.RandomShuffleQueue': 'tf.queue.RandomShuffleQueue', 'tf.ReaderBase': 'tf.compat.v1.ReaderBase', 'tf.RunMetadata': 'tf.compat.v1.RunMetadata', 'tf.RunOptions': 'tf.compat.v1.RunOptions', @@ -229,6 +230,10 @@ renames = { 'tf.initializers.tables_initializer': 'tf.compat.v1.initializers.tables_initializer', 'tf.initializers.variables': 'tf.compat.v1.initializers.variables', 'tf.invert_permutation': 'tf.math.invert_permutation', + 'tf.io.PaddingFIFOQueue': 'tf.queue.PaddingFIFOQueue', + 'tf.io.PriorityQueue': 'tf.queue.PriorityQueue', + 'tf.io.QueueBase': 'tf.queue.QueueBase', + 'tf.io.RandomShuffleQueue': 'tf.queue.RandomShuffleQueue', 'tf.io.tf_record_iterator': 'tf.compat.v1.io.tf_record_iterator', 'tf.is_finite': 'tf.math.is_finite', 'tf.is_inf': 'tf.math.is_inf', @@ -527,9 +532,7 @@ renames = { 'tf.sparse_merge': 'tf.compat.v1.sparse_merge', 'tf.sparse_minimum': 'tf.sparse.minimum', 'tf.sparse_placeholder': 'tf.compat.v1.sparse_placeholder', - 'tf.sparse_reduce_max': 'tf.compat.v1.sparse_reduce_max', 'tf.sparse_reduce_max_sparse': 'tf.compat.v1.sparse_reduce_max_sparse', - 'tf.sparse_reduce_sum': 'tf.compat.v1.sparse_reduce_sum', 'tf.sparse_reduce_sum_sparse': 'tf.compat.v1.sparse_reduce_sum_sparse', 'tf.sparse_reorder': 'tf.sparse.reorder', 'tf.sparse_reset_shape': 'tf.sparse.reset_shape', @@ -619,6 +622,7 @@ renames = { 'tf.train.SaverDef': 'tf.compat.v1.train.SaverDef', 'tf.train.Scaffold': 'tf.compat.v1.train.Scaffold', 'tf.train.SecondOrStepTimer': 'tf.estimator.SecondOrStepTimer', + 'tf.train.Server': 'tf.distribute.Server', 'tf.train.SessionCreator': 'tf.compat.v1.train.SessionCreator', 'tf.train.SessionManager': 'tf.compat.v1.train.SessionManager', 'tf.train.SessionRunArgs': 'tf.compat.v1.train.SessionRunArgs', -- GitLab From 932f281c6467865654e55f2e7f139d9fed2a349a Mon Sep 17 00:00:00 2001 From: "Wen-Heng (Jack) Chung" Date: Tue, 11 Dec 2018 18:14:05 +0000 Subject: [PATCH 352/873] Address clang-format checks --- .../kernels/fused_conv2d_bias_activation_op.cc | 2 +- tensorflow/core/kernels/conv_grad_filter_ops.cc | 7 +++---- tensorflow/core/kernels/conv_grad_input_ops.cc | 4 ++-- tensorflow/core/kernels/conv_grad_ops_3d.cc | 10 +++++----- tensorflow/core/kernels/conv_ops.cc | 4 ++-- tensorflow/core/kernels/conv_ops_gpu.h | 2 +- 6 files changed, 14 insertions(+), 15 deletions(-) diff --git a/tensorflow/contrib/fused_conv/kernels/fused_conv2d_bias_activation_op.cc b/tensorflow/contrib/fused_conv/kernels/fused_conv2d_bias_activation_op.cc index c541c71f99..1c40b6a414 100644 --- a/tensorflow/contrib/fused_conv/kernels/fused_conv2d_bias_activation_op.cc +++ b/tensorflow/contrib/fused_conv/kernels/fused_conv2d_bias_activation_op.cc @@ -525,7 +525,7 @@ void LaunchFusedConv2DBiasActivationOp:: static int64 ConvolveScratchSize = GetDnnWorkspaceLimit( // default value is in bytes despite the name of the environment variable "TF_CUDNN_WORKSPACE_LIMIT_IN_MB", 1LL << 32 // 4GB - ); + ); int device_id = stream->parent()->device_ordinal(); FusedConvParameters fused_conv_parameters = { diff --git a/tensorflow/core/kernels/conv_grad_filter_ops.cc b/tensorflow/core/kernels/conv_grad_filter_ops.cc index efd8772226..58a4f6ba86 100644 --- a/tensorflow/core/kernels/conv_grad_filter_ops.cc +++ b/tensorflow/core/kernels/conv_grad_filter_ops.cc @@ -905,7 +905,7 @@ void LaunchConv2DBackpropFilterOp::operator()( static int64 ConvolveBackwardFilterScratchSize = GetDnnWorkspaceLimit( "TF_CUDNN_WORKSPACE_LIMIT_IN_MB", 1LL << 32 // 4GB by default - ); + ); int device_id = stream->parent()->device_ordinal(); DataType dtype = input.dtype(); ConvParameters conv_parameters = { @@ -940,7 +940,7 @@ void LaunchConv2DBackpropFilterOp::operator()( // TODO(zhengxq): profile each algorithm multiple times to better // accuracy. DnnScratchAllocator scratch_allocator(ConvolveBackwardFilterScratchSize, - ctx); + ctx); ProfileResult profile_result; bool cudnn_launch_status = stream @@ -977,8 +977,7 @@ void LaunchConv2DBackpropFilterOp::operator()( AutoTuneConvBwdFilter::GetInstance()->Insert(conv_parameters, algorithm_config); } - DnnScratchAllocator scratch_allocator(ConvolveBackwardFilterScratchSize, - ctx); + DnnScratchAllocator scratch_allocator(ConvolveBackwardFilterScratchSize, ctx); bool cudnn_launch_status = stream ->ThenConvolveBackwardFilterWithAlgorithm( diff --git a/tensorflow/core/kernels/conv_grad_input_ops.cc b/tensorflow/core/kernels/conv_grad_input_ops.cc index 7339fb736f..e799016852 100644 --- a/tensorflow/core/kernels/conv_grad_input_ops.cc +++ b/tensorflow/core/kernels/conv_grad_input_ops.cc @@ -953,7 +953,7 @@ void LaunchConv2DBackpropInputOp::operator()( static int64 ConvolveBackwardDataScratchSize = GetDnnWorkspaceLimit( "TF_CUDNN_WORKSPACE_LIMIT_IN_MB", 1LL << 32 // 4GB by default - ); + ); DnnScratchAllocator scratch_allocator(ConvolveBackwardDataScratchSize, ctx); int device_id = stream->parent()->device_ordinal(); DataType dtype = out_backprop.dtype(); @@ -989,7 +989,7 @@ void LaunchConv2DBackpropInputOp::operator()( // TODO(zhengxq): profile each algorithm multiple times to better // accuracy. DnnScratchAllocator scratch_allocator(ConvolveBackwardDataScratchSize, - ctx); + ctx); ProfileResult profile_result; bool cudnn_launch_status = stream diff --git a/tensorflow/core/kernels/conv_grad_ops_3d.cc b/tensorflow/core/kernels/conv_grad_ops_3d.cc index a518fcc874..562a9c8aed 100644 --- a/tensorflow/core/kernels/conv_grad_ops_3d.cc +++ b/tensorflow/core/kernels/conv_grad_ops_3d.cc @@ -1369,7 +1369,7 @@ class Conv3DBackpropInputOp : public OpKernel { // TODO(zhengxq): profile each algorithm multiple times to better // accuracy. DnnScratchAllocator scratch_allocator(ConvolveBackwardDataScratchSize, - context); + context); ProfileResult profile_result; bool cudnn_launch_status = stream @@ -1406,7 +1406,7 @@ class Conv3DBackpropInputOp : public OpKernel { algorithm_config); } DnnScratchAllocator scratch_allocator(ConvolveBackwardDataScratchSize, - context); + context); bool cudnn_launch_status = stream ->ThenConvolveBackwardDataWithAlgorithm( @@ -1774,8 +1774,8 @@ class Conv3DBackpropFilterOp : public OpKernel { for (auto profile_algorithm : algorithms) { // TODO(zhengxq): profile each algorithm multiple times to better // accuracy. - DnnScratchAllocator scratch_allocator( - ConvolveBackwardFilterScratchSize, context); + DnnScratchAllocator scratch_allocator(ConvolveBackwardFilterScratchSize, + context); ProfileResult profile_result; bool cudnn_launch_status = stream @@ -1813,7 +1813,7 @@ class Conv3DBackpropFilterOp : public OpKernel { algorithm_config); } DnnScratchAllocator scratch_allocator(ConvolveBackwardFilterScratchSize, - context); + context); bool cudnn_launch_status = stream ->ThenConvolveBackwardFilterWithAlgorithm( diff --git a/tensorflow/core/kernels/conv_ops.cc b/tensorflow/core/kernels/conv_ops.cc index 8c2deeed0e..a1917862e7 100644 --- a/tensorflow/core/kernels/conv_ops.cc +++ b/tensorflow/core/kernels/conv_ops.cc @@ -522,7 +522,7 @@ template struct LaunchConv2DOp; #if GOOGLE_CUDA int64 GetDnnWorkspaceLimit(const string& envvar_in_mb, - int64 default_value_in_bytes) { + int64 default_value_in_bytes) { const char* workspace_limit_in_mb_str = getenv(envvar_in_mb.c_str()); if (workspace_limit_in_mb_str != nullptr && strcmp(workspace_limit_in_mb_str, "") != 0) { @@ -762,7 +762,7 @@ void LaunchConv2DOp::operator()( static int64 ConvolveScratchSize = GetDnnWorkspaceLimit( // default value is in bytes despite the name of the environment variable "TF_CUDNN_WORKSPACE_LIMIT_IN_MB", 1LL << 32 // 4GB - ); + ); int device_id = stream->parent()->device_ordinal(); DataType dtype = input.dtype(); diff --git a/tensorflow/core/kernels/conv_ops_gpu.h b/tensorflow/core/kernels/conv_ops_gpu.h index 19fc45b756..7a67658c4d 100644 --- a/tensorflow/core/kernels/conv_ops_gpu.h +++ b/tensorflow/core/kernels/conv_ops_gpu.h @@ -31,7 +31,7 @@ namespace tensorflow { // Return the workspace memory limit in bytes. If no value is set, return the // default value. int64 GetDnnWorkspaceLimit(const string& envvar_in_mb, - int64 default_value_in_bytes); + int64 default_value_in_bytes); // A class to provide scratch-space allocator for Stream-Executor Cudnn // callback. TensorFlow is responsible for releasing the temporary buffers after -- GitLab From 4143d8d30b1f7d2737426c8c181c88bcd8dba5d5 Mon Sep 17 00:00:00 2001 From: Mark Heffernan Date: Tue, 11 Dec 2018 10:32:54 -0800 Subject: [PATCH 353/873] Support F16 and BF16 for iota HLO in evaluator. Also clean up type error reporting in evaluator. PiperOrigin-RevId: 225028144 --- .../compiler/xla/g3doc/operation_semantics.md | 320 +++++++++--------- .../xla/service/hlo_evaluator_typed_visitor.h | 94 +++-- tensorflow/compiler/xla/tests/iota_test.cc | 2 +- 3 files changed, 219 insertions(+), 197 deletions(-) diff --git a/tensorflow/compiler/xla/g3doc/operation_semantics.md b/tensorflow/compiler/xla/g3doc/operation_semantics.md index d888b1f23f..002ebc31b9 100644 --- a/tensorflow/compiler/xla/g3doc/operation_semantics.md +++ b/tensorflow/compiler/xla/g3doc/operation_semantics.md @@ -38,25 +38,25 @@ Alltoall is a collective operation that sends data from all cores to all cores. It has two phases: 1. the scatter phase. On each core, the operand is split into `split_count` - number of blocks along the `split_dimensions`, and the blocks are scattered - to all cores, e.g., the ith block is send to the ith core. +number of blocks along the `split_dimensions`, and the blocks are scattered +to all cores, e.g., the ith block is send to the ith core. 2. the gather phase. Each core concatenates the received blocks along the - `concat_dimension`. +`concat_dimension`. The participating cores can be configured by: - `replica_groups`: each ReplicaGroup contains a list of replica id. If empty, - all replicas belong to one group in the order of 0 - (n-1). Alltoall will be - applied within subgroups in the specified order. For example, replica - groups = {{1,2,3},{4,5,0}} means, an Alltoall will be applied within replica - 1, 2, 3, and in the gather phase, the received blocks will be concatenated - in the order of 1, 2, 3; another Alltoall will be applied within replica 4, - 5, 0, and the concatenation order is 4, 5, 0. +all replicas belong to one group in the order of 0 - (n-1). Alltoall will be +applied within subgroups in the specified order. For example, replica +groups = {{1,2,3},{4,5,0}} means, an Alltoall will be applied within replica +1, 2, 3, and in the gather phase, the received blocks will be concatenated +in the order of 1, 2, 3; another Alltoall will be applied within replica 4, +5, 0, and the concatenation order is 4, 5, 0. Prerequisites: - The dimension size of the operand on the split_dimension is divisible by - split_count. +split_count. - The operand's shape is not tuple. `AllToAll(operand, split_dimension, concat_dimension, split_count, @@ -93,7 +93,7 @@ AllToAll(x, /*split_dimension=*/1, /*concat_dimension=*/0, /*split_count=*/4); ```

- +
In this example, there are 4 cores participating the Alltoall. On each core, the @@ -387,34 +387,34 @@ For example, let v be an array of 24 elements: ``` let v = f32[4x2x3] {{{10, 11, 12}, {15, 16, 17}}, - {{20, 21, 22}, {25, 26, 27}}, - {{30, 31, 32}, {35, 36, 37}}, - {{40, 41, 42}, {45, 46, 47}}}; +{{20, 21, 22}, {25, 26, 27}}, +{{30, 31, 32}, {35, 36, 37}}, +{{40, 41, 42}, {45, 46, 47}}}; // Collapse to a single dimension, leaving one dimension. let v012 = Collapse(v, {0,1,2}); then v012 == f32[24] {10, 11, 12, 15, 16, 17, - 20, 21, 22, 25, 26, 27, - 30, 31, 32, 35, 36, 37, - 40, 41, 42, 45, 46, 47}; +20, 21, 22, 25, 26, 27, +30, 31, 32, 35, 36, 37, +40, 41, 42, 45, 46, 47}; // Collapse the two lower dimensions, leaving two dimensions. let v01 = Collapse(v, {0,1}); then v01 == f32[4x6] {{10, 11, 12, 15, 16, 17}, - {20, 21, 22, 25, 26, 27}, - {30, 31, 32, 35, 36, 37}, - {40, 41, 42, 45, 46, 47}}; +{20, 21, 22, 25, 26, 27}, +{30, 31, 32, 35, 36, 37}, +{40, 41, 42, 45, 46, 47}}; // Collapse the two higher dimensions, leaving two dimensions. let v12 = Collapse(v, {1,2}); then v12 == f32[8x3] {{10, 11, 12}, - {15, 16, 17}, - {20, 21, 22}, - {25, 26, 27}, - {30, 31, 32}, - {35, 36, 37}, - {40, 41, 42}, - {45, 46, 47}}; +{15, 16, 17}, +{20, 21, 22}, +{25, 26, 27}, +{30, 31, 32}, +{35, 36, 37}, +{40, 41, 42}, +{45, 46, 47}}; ``` @@ -441,9 +441,9 @@ replicas. Note that there are the following restrictions on the `source_target_pair`: - Any two pairs should not have the same target replica id, and they should - not have the same source replica id. +not have the same source replica id. - If a replica id is not a target in any pair, then the output on that replica - is a tensor consists of 0(s) with the same shape as the input. +is a tensor consists of 0(s) with the same shape as the input. ## Concatenate @@ -480,25 +480,25 @@ Concat({{2, 3}, {4, 5}, {6, 7}}, 0) ``` let a = { - {1, 2}, - {3, 4}, - {5, 6}, +{1, 2}, +{3, 4}, +{5, 6}, }; let b = { - {7, 8}, +{7, 8}, }; Concat({a, b}, 0) >>> { - {1, 2}, - {3, 4}, - {5, 6}, - {7, 8}, +{1, 2}, +{3, 4}, +{5, 6}, +{7, 8}, } ``` Diagram:
- +
## Conditional @@ -566,20 +566,20 @@ the rhs is also an input. In a neural network, these are the input activations. The n+2 dimensions are, in this order: * `batch`: Each coordinate in this dimension represents an independent input - for which convolution is carried out. +for which convolution is carried out. * `z/depth/features`: Each (y,x) position in the base area has a vector - associated to it, which goes into this dimension. +associated to it, which goes into this dimension. * `spatial_dims`: Describes the `n` spatial dimensions that define the base - area that the window moves across. +area that the window moves across. The `rhs` argument is a rank n+2 array describing the convolutional filter/kernel/window. The dimensions are, in this order: * `output-z`: The `z` dimension of the output. * `input-z`: The size of this dimension times `feature_group_count` should - equal the size of the `z` dimension in lhs. +equal the size of the `z` dimension in lhs. * `spatial_dims`: Describes the `n` spatial dimensions that define the n-d - window that moves across the base area. +window that moves across the base area. The `window_strides` argument specifies the stride of the convolutional window in the spatial dimensions. For example, if the stride in the first spatial @@ -633,7 +633,7 @@ The output shape has these dimensions, in this order: * `batch`: Same size as `batch` on the input (`lhs`). * `z`: Same size as `output-z` on the kernel (`rhs`). * `spatial_dims`: One value for each valid placement of the convolutional - window. +window. The valid placements of the convolutional window are determined by the strides and the size of the base area after padding. @@ -658,15 +658,15 @@ Here is pseudo-code for a 2d convolution with padding and striding: ``` for (b, oz, oy, ox) { // output coordinates - value = 0; - for (iz, ky, kx) { // kernel coordinates and input z - iy = oy*stride_y + ky - pad_low_y; - ix = ox*stride_x + kx - pad_low_x; - if ((iy, ix) inside the base area considered without padding) { - value += input(b, iz, iy, ix) * kernel(oz, iz, ky, kx); - } - } - output(b, oz, oy, ox) = value; +value = 0; +for (iz, ky, kx) { // kernel coordinates and input z +iy = oy*stride_y + ky - pad_low_y; +ix = ox*stride_x + kx - pad_low_x; +if ((iy, ix) inside the base area considered without padding) { +value += input(b, iz, iy, ix) * kernel(oz, iz, ky, kx); +} +} +output(b, oz, oy, ox) = value; } ``` @@ -777,19 +777,19 @@ Here is an example of an implementation of `myfunc`: ``` extern "C" void myfunc(void* out, void** in) { - float (&x)[2] = *static_cast(in[0]); - float (&y)[2][3] = *static_cast(in[1]); - EXPECT_EQ(1, x[0]); - EXPECT_EQ(2, x[1]); - EXPECT_EQ(10, y[0][0]); - EXPECT_EQ(20, y[0][1]); - EXPECT_EQ(30, y[0][2]); - EXPECT_EQ(40, y[1][0]); - EXPECT_EQ(50, y[1][1]); - EXPECT_EQ(60, y[1][2]); - float (&z)[3][3] = *static_cast(out); - z[0][0] = x[1] + y[1][0]; - // ... +float (&x)[2] = *static_cast(in[0]); +float (&y)[2][3] = *static_cast(in[1]); +EXPECT_EQ(1, x[0]); +EXPECT_EQ(2, x[1]); +EXPECT_EQ(10, y[0][0]); +EXPECT_EQ(20, y[0][1]); +EXPECT_EQ(30, y[0][2]); +EXPECT_EQ(40, y[1][0]); +EXPECT_EQ(50, y[1][1]); +EXPECT_EQ(60, y[1][2]); +float (&z)[3][3] = *static_cast(out); +z[0][0] = x[1] + y[1][0]; +// ... } ``` @@ -864,17 +864,17 @@ Example with contracting dimension numbers: ``` lhs = { {1.0, 2.0, 3.0}, - {4.0, 5.0, 6.0} } +{4.0, 5.0, 6.0} } rhs = { {1.0, 1.0, 1.0}, - {2.0, 2.0, 2.0} } +{2.0, 2.0, 2.0} } DotDimensionNumbers dnums; dnums.add_lhs_contracting_dimensions(1); dnums.add_rhs_contracting_dimensions(1); DotGeneral(lhs, rhs, dnums) -> { {6.0, 12.0}, - {15.0, 30.0} } +{15.0, 30.0} } ``` Associated batch dimension numbers from the 'lhs' and 'rhs' must have the same @@ -886,14 +886,14 @@ Example with batch dimension numbers (batch size 2, 2x2 matrices): ``` lhs = { { {1.0, 2.0}, - {3.0, 4.0} }, - { {5.0, 6.0}, - {7.0, 8.0} } } +{3.0, 4.0} }, +{ {5.0, 6.0}, +{7.0, 8.0} } } rhs = { { {1.0, 0.0}, - {0.0, 1.0} }, - { {1.0, 0.0}, - {0.0, 1.0} } } +{0.0, 1.0} }, +{ {1.0, 0.0}, +{0.0, 1.0} } } DotDimensionNumbers dnums; dnums.add_lhs_contracting_dimensions(2); @@ -902,9 +902,9 @@ dnums.add_lhs_batch_dimensions(0); dnums.add_rhs_batch_dimensions(0); DotGeneral(lhs, rhs, dnums) -> { { {1.0, 2.0}, - {3.0, 4.0} }, - { {5.0, 6.0}, - {7.0, 8.0} } } +{3.0, 4.0} }, +{ {5.0, 6.0}, +{7.0, 8.0} } } ``` | Input | Output | Semantics | @@ -963,22 +963,22 @@ let a = {0.0, 1.0, 2.0, 3.0, 4.0} let s = {2} DynamicSlice(a, s, {2}) produces: - {2.0, 3.0} +{2.0, 3.0} ``` 2-dimensional example: ``` let b = - { {0.0, 1.0, 2.0}, - {3.0, 4.0, 5.0}, - {6.0, 7.0, 8.0}, - {9.0, 10.0, 11.0} } +{ {0.0, 1.0, 2.0}, +{3.0, 4.0, 5.0}, +{6.0, 7.0, 8.0}, +{9.0, 10.0, 11.0} } let s = {2, 1} DynamicSlice(b, s, {2, 2}) produces: - { { 7.0, 8.0}, - {10.0, 11.0} } +{ { 7.0, 8.0}, +{10.0, 11.0} } ``` ## DynamicUpdateSlice @@ -1027,29 +1027,29 @@ let u = {5.0, 6.0} let s = {2} DynamicUpdateSlice(a, u, s) produces: - {0.0, 1.0, 5.0, 6.0, 4.0} +{0.0, 1.0, 5.0, 6.0, 4.0} ``` 2-dimensional example: ``` let b = - { {0.0, 1.0, 2.0}, - {3.0, 4.0, 5.0}, - {6.0, 7.0, 8.0}, - {9.0, 10.0, 11.0} } +{ {0.0, 1.0, 2.0}, +{3.0, 4.0, 5.0}, +{6.0, 7.0, 8.0}, +{9.0, 10.0, 11.0} } let u = - { {12.0, 13.0}, - {14.0, 15.0}, - {16.0, 17.0} } +{ {12.0, 13.0}, +{14.0, 15.0}, +{16.0, 17.0} } let s = {1, 1} DynamicUpdateSlice(b, u, s) produces: - { {0.0, 1.0, 2.0}, - {3.0, 12.0, 13.0}, - {6.0, 14.0, 15.0}, - {9.0, 16.0, 17.0} } +{ {0.0, 1.0, 2.0}, +{3.0, 12.0, 13.0}, +{6.0, 14.0, 15.0}, +{9.0, 16.0, 17.0} } ``` ## Element-wise binary arithmetic operations @@ -1235,42 +1235,42 @@ shape of `start_indices` to be `[6,7,1]`). The bounds for the output array along dimension `i` is computed as follows: - 1. If `i` is present in `batch_dims` (i.e. is equal to `batch_dims[k]` for - some `k`) then we pick the corresponding dimension bounds out of - `start_indices.shape`, skipping `index_vector_dim` (i.e. pick - `start_indices.shape.dims`[`k`] if `k` < `index_vector_dim` and - `start_indices.shape.dims`[`k`+`1`] otherwise). +1. If `i` is present in `batch_dims` (i.e. is equal to `batch_dims[k]` for +some `k`) then we pick the corresponding dimension bounds out of +`start_indices.shape`, skipping `index_vector_dim` (i.e. pick +`start_indices.shape.dims`[`k`] if `k` < `index_vector_dim` and +`start_indices.shape.dims`[`k`+`1`] otherwise). - 2. If `i` is present in `offset_dims` (i.e. equal to `offset_dims`[`k`] for - some `k`) then we pick the corresponding bound out of `slice_sizes` after - accounting for `collapsed_slice_dims` (i.e. we pick - `adjusted_slice_sizes`[`k`] where `adjusted_slice_sizes` is `slice_sizes` - with the bounds at indices `collapsed_slice_dims` removed). +2. If `i` is present in `offset_dims` (i.e. equal to `offset_dims`[`k`] for +some `k`) then we pick the corresponding bound out of `slice_sizes` after +accounting for `collapsed_slice_dims` (i.e. we pick +`adjusted_slice_sizes`[`k`] where `adjusted_slice_sizes` is `slice_sizes` +with the bounds at indices `collapsed_slice_dims` removed). Formally, the operand index `In` corresponding to an output index `Out` is computed as follows: - 1. Let `G` = { `Out`[`k`] for `k` in `batch_dims` }. Use `G` to slice out - vector `S` such that `S`[`i`] = `start_indices`[Combine(`G`, `i`)] where - Combine(A, b) inserts b at position `index_vector_dim` into A. Note that - this is well defined even if `G` is empty -- if `G` is empty then `S` = - `start_indices`. - - 2. Create a starting index, `S``in`, into `operand` using `S` by - scattering `S` using `start_index_map`. More precisely: - 1. `S``in`[`start_index_map`[`k`]] = `S`[`k`] if `k` < - `start_index_map.size`. - 2. `S``in`[`_`] = `0` otherwise. - - 3. Create an index `O``in` into `operand` by scattering the indices - at the offset dimensions in `Out` according to the `collapsed_slice_dims` - set. More precisely: - 1. `O``in`[`expand_offset_dims`(`k`)] = - `Out`[`offset_dims`[`k`]] if `k` < `offset_dims.size` - (`expand_offset_dims` is defined below). - 2. `O``in`[`_`] = `0` otherwise. - 4. `In` is `O``in` + `S``in` where + is element-wise - addition. +1. Let `G` = { `Out`[`k`] for `k` in `batch_dims` }. Use `G` to slice out +vector `S` such that `S`[`i`] = `start_indices`[Combine(`G`, `i`)] where +Combine(A, b) inserts b at position `index_vector_dim` into A. Note that +this is well defined even if `G` is empty -- if `G` is empty then `S` = +`start_indices`. + +2. Create a starting index, `S``in`, into `operand` using `S` by +scattering `S` using `start_index_map`. More precisely: +1. `S``in`[`start_index_map`[`k`]] = `S`[`k`] if `k` < +`start_index_map.size`. +2. `S``in`[`_`] = `0` otherwise. + +3. Create an index `O``in` into `operand` by scattering the indices +at the offset dimensions in `Out` according to the `collapsed_slice_dims` +set. More precisely: +1. `O``in`[`expand_offset_dims`(`k`)] = +`Out`[`offset_dims`[`k`]] if `k` < `offset_dims.size` +(`expand_offset_dims` is defined below). +2. `O``in`[`_`] = `0` otherwise. +4. `In` is `O``in` + `S``in` where + is element-wise +addition. `expand_offset_dims` is the monotonic function with domain [`0`, `offset.size`) and range [`0`, `operand.rank`) \ `collapsed_slice_dims`. So if, e.g., @@ -1282,21 +1282,21 @@ and range [`0`, `operand.rank`) \ `collapsed_slice_dims`. So if, e.g., Informally, every index `Out` in the output array corresponds to an element `E` in the operand array, computed as follows: - - We use the batch dimensions in `Out` to look up a starting index from - `start_indices`. +- We use the batch dimensions in `Out` to look up a starting index from +`start_indices`. - - We use `start_index_map` to map the starting index (which may have size less - than operand.rank) to a "full" starting index into operand. +- We use `start_index_map` to map the starting index (which may have size less +than operand.rank) to a "full" starting index into operand. - - We dynamic-slice out a slice with size `slice_sizes` using the full starting - index. +- We dynamic-slice out a slice with size `slice_sizes` using the full starting +index. - - We reshape the slice by collapsing the `collapsed_slice_dims` dimensions. - Since all collapsed slice dimensions have to have bound 1 this reshape is - always legal. +- We reshape the slice by collapsing the `collapsed_slice_dims` dimensions. +Since all collapsed slice dimensions have to have bound 1 this reshape is +always legal. - - We use the offset dimensions in `Out` to index into this slice to get the - input element, `E`, corresponding to output index `Out`. +- We use the offset dimensions in `Out` to index into this slice to get the +input element, `E`, corresponding to output index `Out`. `index_vector_dim` is set to `start_indices.rank` - `1` in all of the examples that follow. More interesting values for `index_vector_dim` does not @@ -1315,7 +1315,7 @@ the output shape, and maps it to an element in the input array in the following way:
- +
We first select an (`X`,`Y`) vector from the gather indices array using `G`. @@ -1334,7 +1334,7 @@ version of the example above using a "gather indices" array of shape `[4,5,2]` would translate indices like this:
- +
Again, this acts as a batch dynamic slice `G``0` and @@ -1343,27 +1343,27 @@ Again, this acts as a batch dynamic slice `G``0` and The gather operation in XLA generalizes the informal semantics outlined above in the following ways: - 1. We can configure which dimensions in the output shape are the offset - dimensions (dimensions containing `O``0`, `O``1` in - the last example). The output batch dimensions (dimensions containing - `G``0`, `G``1` in the last example) are defined to be - the output dimensions that are not offset dimensions. +1. We can configure which dimensions in the output shape are the offset +dimensions (dimensions containing `O``0`, `O``1` in +the last example). The output batch dimensions (dimensions containing +`G``0`, `G``1` in the last example) are defined to be +the output dimensions that are not offset dimensions. - 2. The number of output offset dimensions explicitly present in the output - shape may be smaller than the input rank. These "missing" dimensions, which - are listed explicitly as `collapsed_slice_dims`, must have a slice size of - `1`. Since they have a slice size of `1` the only valid index for them is - `0` and eliding them does not introduce ambiguity. +2. The number of output offset dimensions explicitly present in the output +shape may be smaller than the input rank. These "missing" dimensions, which +are listed explicitly as `collapsed_slice_dims`, must have a slice size of +`1`. Since they have a slice size of `1` the only valid index for them is +`0` and eliding them does not introduce ambiguity. - 3. The slice extracted from the "Gather Indices" array ((`X`, `Y`) in the last - example) may have fewer elements than the input array rank, and an explicit - mapping dictates how the index should be expanded to have the same rank as - the input. +3. The slice extracted from the "Gather Indices" array ((`X`, `Y`) in the last +example) may have fewer elements than the input array rank, and an explicit +mapping dictates how the index should be expanded to have the same rank as +the input. As a final example, we use (2) and (3) to implement `tf.gather_nd`:
- +
`G``0` and `G``1` are used to slice out a starting index @@ -1442,11 +1442,11 @@ dependency between the while loops. ``` result1 = while (condition, init = init_value) { - Infeed(shape) +Infeed(shape) } result2 = while (condition, init = result1) { - Infeed(shape) +Infeed(shape) } ``` @@ -1464,7 +1464,9 @@ Infeed of the device. Builds a constant literal on device rather than a potentially large host transfer. Creates a rank 1 array of values starting at zero and incrementing by -one. +one. For floating-point types, the produced array is equivalent to +`ConvertElementType(Iota(...))` where the `Iota` is of integral type and the +conversion is to the floating-point type. Arguments | Type | Semantics ---------------- | --------------- | ------------------------------------ diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h index b87fc3e340..cd79117cbe 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h +++ b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h @@ -105,6 +105,12 @@ bool SafeLess(const NativeT& a, const NativeT& b) { template class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { private: + Status UnsupportedTypeError(HloInstruction* instruction) { + return InvalidArgument( + "Unsupported type for %s: %s", HloOpcodeString(instruction->opcode()), + PrimitiveType_Name(instruction->shape().element_type())); + } + // Get the value in the given literal static_cast as a double. template < typename NativeT, @@ -224,7 +230,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { typename NativeT, typename std::enable_if::value>::type* = nullptr> Status HandleRound(HloInstruction* round) { - return InvalidArgument("Unsupported type for Round"); + return UnsupportedTypeError(round); } Status HandleRound(HloInstruction* round) override { @@ -246,7 +252,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { typename NativeT, typename std::enable_if::value>::type* = nullptr> Status HandleCeil(HloInstruction* ceil) { - return InvalidArgument("Unsupported type for Ceil"); + return UnsupportedTypeError(ceil); } Status HandleCeil(HloInstruction* ceil) override { @@ -297,8 +303,8 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { template < typename NativeT, typename std::enable_if::value>::type* = nullptr> - Status HandleExpm1(HloInstruction* floor) { - return InvalidArgument("Unsupported type for Expm1"); + Status HandleExpm1(HloInstruction* expm1) { + return UnsupportedTypeError(expm1); } Status HandleExpm1(HloInstruction* floor) override { @@ -321,7 +327,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { typename NativeT, typename std::enable_if::value>::type* = nullptr> Status HandleFloor(HloInstruction* floor) { - return InvalidArgument("Unsupported type for Floor"); + return UnsupportedTypeError(floor); } Status HandleFloor(HloInstruction* floor) override { @@ -351,12 +357,12 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { template < typename NativeT, typename std::enable_if::value>::type* = nullptr> - Status HandleLog1p(HloInstruction* floor) { - return InvalidArgument("Unsupported type for Log1p"); + Status HandleLog1p(HloInstruction* log1p) { + return UnsupportedTypeError(log1p); } - Status HandleLog1p(HloInstruction* floor) override { - return HandleLog1p(floor); + Status HandleLog1p(HloInstruction* log1p) override { + return HandleLog1p(log1p); } template ::value>::type* = nullptr> Status HandleNot(HloInstruction* not_) { - return InvalidArgument("Unsupported type for Not"); + return UnsupportedTypeError(not_); } Status HandleNot(HloInstruction* not_) override { @@ -476,7 +482,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { template ::value>::type* = nullptr> Status HandleAtan2(HloInstruction* atan2) { - return InvalidArgument("Unsupported type for Atan2"); + return UnsupportedTypeError(atan2); } Status HandleAtan2(HloInstruction* atan2) override { @@ -624,7 +630,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { typename NativeT, typename std::enable_if::value>::type* = nullptr> Status HandleMaximum(HloInstruction* maximum) { - return InvalidArgument("Unsupported type for Maximum"); + return UnsupportedTypeError(maximum); } Status HandleMaximum(HloInstruction* maximum) override { @@ -659,7 +665,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { typename NativeT, typename std::enable_if::value>::type* = nullptr> Status HandleMinimum(HloInstruction* minimum) { - return InvalidArgument("Unsupported type for Minimum"); + return UnsupportedTypeError(minimum); } Status HandleMinimum(HloInstruction* minimum) override { @@ -724,7 +730,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { typename NativeT, typename std::enable_if::value>::type* = nullptr> Status HandleRemainder(HloInstruction* remainder) { - return InvalidArgument("Unsupported type for Remainder"); + return UnsupportedTypeError(remainder); } Status HandleRemainder(HloInstruction* remainder) override { @@ -746,14 +752,14 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { template ::value>::type* = nullptr> Status HandleAnd(HloInstruction* and_) { - return InvalidArgument("Unsupported type for And"); + return UnsupportedTypeError(and_); } template < typename NativeT, typename std::enable_if::value>::type* = nullptr> Status HandleAnd(HloInstruction* and_) { - return InvalidArgument("Unsupported type for And"); + return UnsupportedTypeError(and_); } Status HandleAnd(HloInstruction* and_) override { @@ -775,7 +781,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { template ::value>::type* = nullptr> Status HandleOr(HloInstruction* or_) { - return InvalidArgument("Unsupported type for Or"); + return UnsupportedTypeError(or_); } template < @@ -804,14 +810,14 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { template ::value>::type* = nullptr> Status HandleXor(HloInstruction* xor_) { - return InvalidArgument("Unsupported type for Xor"); + return UnsupportedTypeError(xor_); } template < typename NativeT, typename std::enable_if::value>::type* = nullptr> Status HandleXor(HloInstruction* xor_) { - return InvalidArgument("Unsupported type for Xor"); + return UnsupportedTypeError(xor_); } Status HandleXor(HloInstruction* xor_) override { @@ -836,8 +842,8 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { typename std::enable_if::value || std::is_same::value>::type* = nullptr> - Status HandleShiftLeft(HloInstruction*) { - return InvalidArgument("Unsupported type for ShiftLeft"); + Status HandleShiftLeft(HloInstruction* shift) { + return UnsupportedTypeError(shift); } Status HandleShiftLeft(HloInstruction* shl) override { @@ -866,8 +872,8 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { typename std::enable_if::value || std::is_same::value>::type* = nullptr> - Status HandleShiftRightArithmetic(HloInstruction*) { - return InvalidArgument("Unsupported type for ShiftRightArithmetic"); + Status HandleShiftRightArithmetic(HloInstruction* shift) { + return UnsupportedTypeError(shift); } Status HandleShiftRightArithmetic(HloInstruction* shra) override { @@ -897,8 +903,8 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { typename std::enable_if::value || std::is_same::value>::type* = nullptr> - Status HandleShiftRightLogical(HloInstruction*) { - return InvalidArgument("Unsupported type for ShiftRightLogical"); + Status HandleShiftRightLogical(HloInstruction* shift) { + return UnsupportedTypeError(shift); } Status HandleShiftRightLogical(HloInstruction* shrl) override { @@ -923,8 +929,8 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { template < typename NativeT, typename std::enable_if::value>::type* = nullptr> - Status HandleClamp(HloInstruction*) { - return InvalidArgument("Unsupported type for Clamp"); + Status HandleClamp(HloInstruction* clamp) { + return UnsupportedTypeError(clamp); } Status HandleClamp(HloInstruction* clamp) override { @@ -1578,7 +1584,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { std::is_same::value>::type* = nullptr> Status HandleSort(HloInstruction* sort) { - return InvalidArgument("Unsupported type for Sort"); + return UnsupportedTypeError(sort); } Status HandleSort(HloInstruction* sort) override { @@ -2357,7 +2363,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { std::is_same::value || std::is_same::value)>::type* = nullptr> Status HandleClz(HloInstruction* clz) { - return InvalidArgument("Unsupported type for Clz"); + return UnsupportedTypeError(clz); } template ::value || is_complex_t::value>::type* = nullptr> Status HandleSin(HloInstruction* sin) { - return InvalidArgument("Unsupported type for Sin"); + return UnsupportedTypeError(sin); } Status HandleSin(HloInstruction* sin) override { @@ -2425,7 +2431,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { typename std::enable_if::value || is_complex_t::value>::type* = nullptr> Status HandleCos(HloInstruction* cos) { - return InvalidArgument("Unsupported type for Cos"); + return UnsupportedTypeError(cos); } Status HandleCos(HloInstruction* cos) override { @@ -2534,7 +2540,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { typename std::enable_if::value || is_complex_t::value>::type* = nullptr> Status HandleReducePrecision(HloInstruction* reduce_precision) { - return InvalidArgument("Unsupported type for reduce precision"); + return UnsupportedTypeError(reduce_precision); } Status HandleReducePrecision(HloInstruction* reduce_precision) override { @@ -2543,15 +2549,27 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { template ::value || + std::is_same::value || std::is_integral::value || std::is_floating_point::value>::type* = nullptr> Status HandleIota(HloInstruction* instruction) { auto* iota = Cast(instruction); + const int64 iota_size = iota->shape().dimensions(iota->iota_dimension()); // Avoid using std::vector since std::vector does not convert to // absl::Span. - absl::InlinedVector data( - iota->shape().dimensions(iota->iota_dimension())); - std::iota(data.begin(), data.end(), 0); + absl::InlinedVector data(iota_size); + // We don't use std::iota for two reasons: + // + // (1) std:iota does not support bfloat16 and float16. + // + // (2) std::iota saturates for floating point types when the value is not + // representable, but the definition of HLO iota is the value as a + // 64-bit integer cast to the native type. + for (int64 i = 0; i < iota_size; ++i) { + // static_cast is required for Eigen::half (F16). + data[i] = static_cast(i); + } auto result = LiteralUtil::CreateR1(data); if (ShapeUtil::Rank(iota->shape()) > 1) { @@ -2567,10 +2585,12 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { } template ::value || + !(std::is_same::value || + std::is_same::value || + std::is_integral::value || std::is_floating_point::value)>::type* = nullptr> Status HandleIota(HloInstruction* iota) { - return InvalidArgument("Unsupported type for iota"); + return UnsupportedTypeError(iota); } Status HandleIota(HloInstruction* iota) override { return HandleIota(iota); diff --git a/tensorflow/compiler/xla/tests/iota_test.cc b/tensorflow/compiler/xla/tests/iota_test.cc index 65205f53dd..37b2c635ee 100644 --- a/tensorflow/compiler/xla/tests/iota_test.cc +++ b/tensorflow/compiler/xla/tests/iota_test.cc @@ -80,7 +80,7 @@ TEST_P(IotaR2Test, DoIt) { } INSTANTIATE_TEST_CASE_P(IotaR2TestInstantiation, IotaR2Test, - ::testing::Combine(::testing::Values(F32, S32), + ::testing::Combine(::testing::Values(F32, S32, BF16), ::testing::Range(/*start=*/10, /*end=*/1001, /*step=*/10), -- GitLab From 5741f4b94090a33b01875c2ae42c42644fe4b46d Mon Sep 17 00:00:00 2001 From: Scott Zhu Date: Tue, 11 Dec 2018 10:36:04 -0800 Subject: [PATCH 354/873] Fix GRU cell breakage when reset_after=True in eager mode. Also added unit test to cover that. PiperOrigin-RevId: 225028823 --- tensorflow/python/keras/layers/gru_test.py | 23 ++++++++++++++++ tensorflow/python/keras/layers/recurrent.py | 30 ++++++++++----------- 2 files changed, 37 insertions(+), 16 deletions(-) diff --git a/tensorflow/python/keras/layers/gru_test.py b/tensorflow/python/keras/layers/gru_test.py index 9988c9fae5..1b2881a26b 100644 --- a/tensorflow/python/keras/layers/gru_test.py +++ b/tensorflow/python/keras/layers/gru_test.py @@ -81,6 +81,29 @@ class GRULayerTest(test.TestCase): 'implementation': mode}, input_shape=(num_samples, timesteps, embedding_dim)) + @tf_test_util.run_in_graph_and_eager_modes + def test_reset_after_GRU(self): + num_samples = 2 + timesteps = 3 + embedding_dim = 4 + units = 2 + + (x_train, y_train), _ = testing_utils.get_test_data( + train_samples=num_samples, + test_samples=0, + input_shape=(timesteps, embedding_dim), + num_classes=units) + y_train = keras.utils.to_categorical(y_train, units) + + inputs = keras.layers.Input(shape=[timesteps, embedding_dim]) + gru_layer = keras.layers.GRU(units, + reset_after=True) + output = gru_layer(inputs) + gru_model = keras.models.Model(inputs, output) + gru_model.compile('rmsprop', 'mse') + gru_model.fit(x_train, y_train) + gru_model.predict(x_train) + def test_statefulness_GRU(self): num_samples = 2 timesteps = 3 diff --git a/tensorflow/python/keras/layers/recurrent.py b/tensorflow/python/keras/layers/recurrent.py index a39db7e8b1..1c6f2bd3f8 100644 --- a/tensorflow/python/keras/layers/recurrent.py +++ b/tensorflow/python/keras/layers/recurrent.py @@ -1497,12 +1497,6 @@ class GRUCell(Layer): initializer=self.bias_initializer, regularizer=self.bias_regularizer, constraint=self.bias_constraint) - if not self.reset_after: - self.input_bias, self.recurrent_bias = self.bias, None - else: - self.input_bias = K.flatten(self.bias[0]) - self.recurrent_bias = K.flatten(self.bias[1]) - else: self.bias = None self.built = True @@ -1529,6 +1523,12 @@ class GRUCell(Layer): # dropout matrices for recurrent units rec_dp_mask = self._recurrent_dropout_mask + if self.use_bias: + if not self.reset_after: + input_bias, recurrent_bias = self.bias, None + else: + input_bias, recurrent_bias = array_ops.unstack(self.bias) + if self.implementation == 1: if 0. < self.dropout < 1.: inputs_z = inputs * dp_mask[0] @@ -1544,9 +1544,9 @@ class GRUCell(Layer): x_h = K.dot(inputs_h, self.kernel[:, self.units * 2:]) if self.use_bias: - x_z = K.bias_add(x_z, self.input_bias[:self.units]) - x_r = K.bias_add(x_r, self.input_bias[self.units: self.units * 2]) - x_h = K.bias_add(x_h, self.input_bias[self.units * 2:]) + x_z = K.bias_add(x_z, input_bias[:self.units]) + x_r = K.bias_add(x_r, input_bias[self.units: self.units * 2]) + x_h = K.bias_add(x_h, input_bias[self.units * 2:]) if 0. < self.recurrent_dropout < 1.: h_tm1_z = h_tm1 * rec_dp_mask[0] @@ -1561,10 +1561,9 @@ class GRUCell(Layer): recurrent_r = K.dot(h_tm1_r, self.recurrent_kernel[:, self.units:self.units * 2]) if self.reset_after and self.use_bias: - recurrent_z = K.bias_add(recurrent_z, self.recurrent_bias[:self.units]) + recurrent_z = K.bias_add(recurrent_z, recurrent_bias[:self.units]) recurrent_r = K.bias_add(recurrent_r, - self.recurrent_bias[self.units: - self.units * 2]) + recurrent_bias[self.units:self.units * 2]) z = self.recurrent_activation(x_z + recurrent_z) r = self.recurrent_activation(x_r + recurrent_r) @@ -1573,8 +1572,7 @@ class GRUCell(Layer): if self.reset_after: recurrent_h = K.dot(h_tm1_h, self.recurrent_kernel[:, self.units * 2:]) if self.use_bias: - recurrent_h = K.bias_add(recurrent_h, - self.recurrent_bias[self.units * 2:]) + recurrent_h = K.bias_add(recurrent_h, recurrent_bias[self.units * 2:]) recurrent_h = r * recurrent_h else: recurrent_h = K.dot(r * h_tm1_h, @@ -1589,7 +1587,7 @@ class GRUCell(Layer): matrix_x = K.dot(inputs, self.kernel) if self.use_bias: # biases: bias_z_i, bias_r_i, bias_h_i - matrix_x = K.bias_add(matrix_x, self.input_bias) + matrix_x = K.bias_add(matrix_x, input_bias) x_z = matrix_x[:, :self.units] x_r = matrix_x[:, self.units: 2 * self.units] @@ -1602,7 +1600,7 @@ class GRUCell(Layer): # hidden state projected by all gate matrices at once matrix_inner = K.dot(h_tm1, self.recurrent_kernel) if self.use_bias: - matrix_inner = K.bias_add(matrix_inner, self.recurrent_bias) + matrix_inner = K.bias_add(matrix_inner, recurrent_bias) else: # hidden state projected separately for update/reset and new matrix_inner = K.dot(h_tm1, self.recurrent_kernel[:, :2 * self.units]) -- GitLab From 90a840fbcb0d5db6049de261061c48061d345678 Mon Sep 17 00:00:00 2001 From: Peter Buchlovsky Date: Tue, 11 Dec 2018 10:37:05 -0800 Subject: [PATCH 355/873] Add the run function from the revised Distribution Strategy proposal. PiperOrigin-RevId: 225028975 --- .../python/distribute/distribute_lib.py | 36 +++++++++++++++++++ ...orflow.distribute.-mirrored-strategy.pbtxt | 4 +++ .../v1/tensorflow.distribute.-strategy.pbtxt | 4 +++ ...orflow.distribute.-mirrored-strategy.pbtxt | 4 +++ .../v2/tensorflow.distribute.-strategy.pbtxt | 4 +++ 5 files changed, 52 insertions(+) diff --git a/tensorflow/python/distribute/distribute_lib.py b/tensorflow/python/distribute/distribute_lib.py index 87bf510ec5..60bb75ded0 100644 --- a/tensorflow/python/distribute/distribute_lib.py +++ b/tensorflow/python/distribute/distribute_lib.py @@ -422,6 +422,42 @@ class DistributionStrategy(object): return self.extended._make_input_fn_iterator( # pylint: disable=protected-access input_fn, replication_mode=replication_mode) + def experimental_run(self, fn, input_iterator=None): + """Runs ops in `fn` on each replica, with inputs from `input_iterator`. + + When eager execution is enabled, executes ops specified by `fn` on each + replica. Otherwise, builds a graph to execute the ops on each replica. + + Each replica will take a single, different input from the inputs provided by + one `get_next` call on the input iterator. + + `fn` may call `tf.distribute.get_replica_context()` to access members such + as `replica_id_in_sync_group`. + + IMPORTANT: Depending on the `DistributionStrategy` being used, and whether + eager execution is enabled, `fn` may be called one or more times (once for + each replica). + + Args: + fn: function to run. The inputs to the function must match the outputs of + `input_iterator.get_next()`. The output must be a `tf.nest` of + `Tensor`s. + input_iterator: (Optional) input iterator from which the inputs are taken. + + Returns: + Merged return value of `fn` across replicas. The structure of the return + value is the same as the return value from `fn`. Each element in the + structure can either be `PerReplica` (if the values are unsynchronized), + `Mirrored` (if the values are kept in sync), or `Tensor` (if running on a + single replica). + """ + with self.scope(): + if input_iterator is None: + return self._extended.call_for_each_replica(fn) + else: + inputs = input_iterator.get_next() + return self._extended.call_for_each_replica(fn, args=(inputs,)) + @doc_controls.do_not_generate_docs # DEPRECATED, moving to `extended` def broadcast(self, tensor, destinations=None): """DEPRECATED: use extended.broadcast_to() instead.""" diff --git a/tensorflow/tools/api/golden/v1/tensorflow.distribute.-mirrored-strategy.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.distribute.-mirrored-strategy.pbtxt index a613e2d3d1..81224f00a4 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.distribute.-mirrored-strategy.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.distribute.-mirrored-strategy.pbtxt @@ -75,6 +75,10 @@ tf_class { name: "experimental_initialize" argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "experimental_run" + argspec: "args=[\'self\', \'fn\', \'input_iterator\'], varargs=None, keywords=None, defaults=[\'None\'], " + } member_method { name: "finalize" argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" diff --git a/tensorflow/tools/api/golden/v1/tensorflow.distribute.-strategy.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.distribute.-strategy.pbtxt index 9eb73d2c0d..63b6584caf 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.distribute.-strategy.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.distribute.-strategy.pbtxt @@ -74,6 +74,10 @@ tf_class { name: "experimental_initialize" argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "experimental_run" + argspec: "args=[\'self\', \'fn\', \'input_iterator\'], varargs=None, keywords=None, defaults=[\'None\'], " + } member_method { name: "finalize" argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.distribute.-mirrored-strategy.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.distribute.-mirrored-strategy.pbtxt index a613e2d3d1..81224f00a4 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.distribute.-mirrored-strategy.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.distribute.-mirrored-strategy.pbtxt @@ -75,6 +75,10 @@ tf_class { name: "experimental_initialize" argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "experimental_run" + argspec: "args=[\'self\', \'fn\', \'input_iterator\'], varargs=None, keywords=None, defaults=[\'None\'], " + } member_method { name: "finalize" argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.distribute.-strategy.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.distribute.-strategy.pbtxt index 9eb73d2c0d..63b6584caf 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.distribute.-strategy.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.distribute.-strategy.pbtxt @@ -74,6 +74,10 @@ tf_class { name: "experimental_initialize" argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "experimental_run" + argspec: "args=[\'self\', \'fn\', \'input_iterator\'], varargs=None, keywords=None, defaults=[\'None\'], " + } member_method { name: "finalize" argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" -- GitLab From bd312687ad05ad36b5ed0589b0303df848bea266 Mon Sep 17 00:00:00 2001 From: Trevor Morris Date: Tue, 11 Dec 2018 10:52:07 -0800 Subject: [PATCH 356/873] Remove unneeded import --- tensorflow/contrib/tensorrt/test/quantization_mnist_test.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tensorflow/contrib/tensorrt/test/quantization_mnist_test.py b/tensorflow/contrib/tensorrt/test/quantization_mnist_test.py index b96d965bad..e7d6ec4ad3 100644 --- a/tensorflow/contrib/tensorrt/test/quantization_mnist_test.py +++ b/tensorflow/contrib/tensorrt/test/quantization_mnist_test.py @@ -24,7 +24,6 @@ from tensorflow.contrib.tensorrt.python.ops import trt_engine_op # pylint: enable=unused-import from tensorflow.core.protobuf import config_pb2 from tensorflow.python import data -#from tensorflow.python.data.ops import dataset_ops from tensorflow.python import keras from tensorflow.python.estimator.estimator import Estimator from tensorflow.python.estimator.model_fn import EstimatorSpec -- GitLab From 10aba412d7db31ac7bce1e46c967c979b5d85ca1 Mon Sep 17 00:00:00 2001 From: Trevor Morris Date: Tue, 11 Dec 2018 10:58:18 -0800 Subject: [PATCH 357/873] Fix clang-format --- .../contrib/tensorrt/convert/convert_nodes.cc | 32 ++++++++++--------- 1 file changed, 17 insertions(+), 15 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 18e8599a01..5fe284c042 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -1539,9 +1539,9 @@ tensorflow::Status ConvertConv2DHelper(OpConverterParams* params, int group) { node_def.name()); } if (inputs.at(1).is_tensor()) { - return tensorflow::errors::Unimplemented( - "Kernel for ", node_def.op(), " must be constant weights, at ", - node_def.name()); + return tensorflow::errors::Unimplemented("Kernel for ", node_def.op(), + " must be constant weights, at ", + node_def.name()); } TRT_ShapedWeights weights_rsck = inputs.at(1).weights(); VLOG(2) << "weight shape: " << weights_rsck.DebugString(); @@ -1658,7 +1658,7 @@ tensorflow::Status ConvertConv2DHelper(OpConverterParams* params, case ConvolutionType::DEPTHWISE_CONV: return ConvertConv2DHelper(params, 0); } - return tensorflow::errors::Unimplemented("unsupported convolution type at, " + + return tensorflow::errors::Unimplemented("Unsupported convolution type, at ", params->node_def.name()); } @@ -2050,16 +2050,14 @@ tensorflow::Status ConvertPool(OpConverterParams* params) { } else if (node_def.op() == "AvgPool") { type = nvinfer1::PoolingType::kAVERAGE; } else { - return tensorflow::errors::Unimplemented("Unsupported pooling type: ", - node_def.op(), ", at ", - node_def.name()); + return tensorflow::errors::Unimplemented( + "Unsupported pooling type: ", node_def.op(), ", at ", node_def.name()); } TFAttrs attrs(node_def); const string padding_type = attrs.get("padding"); if ((padding_type != "SAME") && (padding_type != "VALID")) { - return tensorflow::errors::Unimplemented("Unsupported padding type: ", - padding_type, ", at ", - node_def.name()); + return tensorflow::errors::Unimplemented( + "Unsupported padding type: ", padding_type, ", at ", node_def.name()); } if (params->validation_only) return Status::OK(); @@ -2988,20 +2986,24 @@ tensorflow::Status ConvertFusedBatchNorm(OpConverterParams* params) { bool is_training = attrs.get("is_training"); if (is_training) { return tensorflow::errors::Unimplemented( - node_def.op(), " only supports is_training=false. If you are using " + node_def.op(), + " only supports is_training=false. If you are using " "Keras, please use keras.backend.set_learning_phase(0). At ", node_def.name()); } if (inputs.at(0).is_weights()) { return tensorflow::errors::Unimplemented( - node_def.op(), " is only implemented for tensor inputs, not weights, " - "at ", node_def.name()); + node_def.op(), + " is only implemented for tensor inputs, not weights, at ", + node_def.name()); } for (int i = 1; i < 5; i++) { if (inputs.at(i).is_tensor()) { return tensorflow::errors::Unimplemented( - node_def.op(), " must have constant inputs for scale, offset, mean " - "and variance, at ", node_def.name()); + node_def.op(), + " must have constant inputs for scale, offset, mean and variance, " + "at ", + node_def.name()); } } nvinfer1::ITensor const* tensor = inputs.at(0).tensor(); -- GitLab From c99ecfa992eaa09a799e841fcdcfadd60b98f0c2 Mon Sep 17 00:00:00 2001 From: Michael Kuperstein Date: Tue, 11 Dec 2018 11:01:27 -0800 Subject: [PATCH 358/873] [XLA] Split out HloDynamicUpdateSliceInstruction This doesn't have any benefit in terms of sizeof(HloInstruction), but it's awkward to have a sublcass for DS and not DUS. Also adds an intermediate class in the hierarchy that avoids having to hard-code the index operand's number. PiperOrigin-RevId: 225033893 --- .../compiler/xla/service/hlo_instruction.cc | 8 ++----- .../compiler/xla/service/hlo_instructions.cc | 11 +++++++++- .../compiler/xla/service/hlo_instructions.h | 21 ++++++++++++++++++- 3 files changed, 32 insertions(+), 8 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc index 21b1dbc167..5c1f1a61cc 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction.cc @@ -914,12 +914,8 @@ HloInstruction::CreateDynamicUpdateSlice(const Shape& shape, HloInstruction* operand, HloInstruction* update, HloInstruction* start_indices) { - auto instruction = absl::WrapUnique( - new HloInstruction(HloOpcode::kDynamicUpdateSlice, shape)); - instruction->AppendOperand(operand); - instruction->AppendOperand(update); - instruction->AppendOperand(start_indices); - return instruction; + return absl::make_unique( + shape, operand, update, start_indices); } /* static */ std::unique_ptr HloInstruction::CreateConcatenate( diff --git a/tensorflow/compiler/xla/service/hlo_instructions.cc b/tensorflow/compiler/xla/service/hlo_instructions.cc index 1ea02cf9c0..2fe6395efe 100644 --- a/tensorflow/compiler/xla/service/hlo_instructions.cc +++ b/tensorflow/compiler/xla/service/hlo_instructions.cc @@ -1994,12 +1994,21 @@ std::unique_ptr HloPadInstruction::CloneWithNewOperandsImpl( HloDynamicSliceInstruction::HloDynamicSliceInstruction( const Shape& shape, HloInstruction* operand, HloInstruction* start_indices, absl::Span slice_sizes) - : HloInstruction(HloOpcode::kDynamicSlice, shape), + : HloDynamicIndexInstruction(HloOpcode::kDynamicSlice, shape), dynamic_slice_sizes_(slice_sizes.begin(), slice_sizes.end()) { AppendOperand(operand); AppendOperand(start_indices); } +HloDynamicUpdateSliceInstruction::HloDynamicUpdateSliceInstruction( + const Shape& shape, HloInstruction* operand, HloInstruction* update, + HloInstruction* start_indices) + : HloDynamicIndexInstruction(HloOpcode::kDynamicUpdateSlice, shape) { + AppendOperand(operand); + AppendOperand(update); + AppendOperand(start_indices); +} + HloInstructionProto HloDynamicSliceInstruction::ToProto() const { HloInstructionProto proto = HloInstruction::ToProto(); for (int64 slice_size : dynamic_slice_sizes_) { diff --git a/tensorflow/compiler/xla/service/hlo_instructions.h b/tensorflow/compiler/xla/service/hlo_instructions.h index b5c28137a1..5420d4ce11 100644 --- a/tensorflow/compiler/xla/service/hlo_instructions.h +++ b/tensorflow/compiler/xla/service/hlo_instructions.h @@ -1171,7 +1171,14 @@ class HloPadInstruction : public HloInstruction { PaddingConfig padding_config_; }; -class HloDynamicSliceInstruction : public HloInstruction { +class HloDynamicIndexInstruction : public HloInstruction { + public: + explicit HloDynamicIndexInstruction(HloOpcode opcode, const Shape& shape) + : HloInstruction(opcode, shape) {} + virtual int64 index_operand_number() const = 0; +}; + +class HloDynamicSliceInstruction : public HloDynamicIndexInstruction { public: explicit HloDynamicSliceInstruction(const Shape& shape, HloInstruction* operand, @@ -1189,6 +1196,8 @@ class HloDynamicSliceInstruction : public HloInstruction { // Returns a serialized representation of this instruction. HloInstructionProto ToProto() const override; + int64 index_operand_number() const override { return 1; } + private: std::vector ExtraAttributesToStringImpl( const HloPrintOptions& options) const override; @@ -1206,6 +1215,16 @@ class HloDynamicSliceInstruction : public HloInstruction { std::vector dynamic_slice_sizes_; }; +class HloDynamicUpdateSliceInstruction : public HloDynamicIndexInstruction { + public: + explicit HloDynamicUpdateSliceInstruction(const Shape& shape, + HloInstruction* operand, + HloInstruction* update, + HloInstruction* start_indices); + + int64 index_operand_number() const override { return 2; } +}; + class HloGatherInstruction : public HloInstruction { public: explicit HloGatherInstruction( -- GitLab From 9b964193d9e9cc2b082f634010102b320daf70e2 Mon Sep 17 00:00:00 2001 From: Peter Hawkins Date: Tue, 11 Dec 2018 11:15:58 -0800 Subject: [PATCH 359/873] [XLA] [TF:XLA] Move Cholesky decomposition into xla/client/lib/cholesky.* Move loop helpers used by Cholesky decomposition into xla/client/lib/loops.*. PiperOrigin-RevId: 225037112 --- tensorflow/compiler/tf2xla/kernels/BUILD | 13 +- .../compiler/tf2xla/kernels/cholesky_op.cc | 4 +- .../compiler/tf2xla/kernels/gather_op.cc | 1 - .../compiler/tf2xla/kernels/image_ops.cc | 8 +- .../compiler/tf2xla/kernels/random_ops.cc | 6 +- tensorflow/compiler/tf2xla/lib/BUILD | 42 +---- tensorflow/compiler/tf2xla/lib/qr.cc | 10 +- tensorflow/compiler/tf2xla/lib/scatter.cc | 1 - tensorflow/compiler/xla/client/lib/BUILD | 63 ++++++- .../{tf2xla => xla/client}/lib/cholesky.cc | 98 +++++------ .../{tf2xla => xla/client}/lib/cholesky.h | 10 +- .../compiler/xla/client/lib/cholesky_test.cc | 166 ++++++++++++++++++ .../while_loop.cc => xla/client/lib/loops.cc} | 90 +++++----- .../while_loop.h => xla/client/lib/loops.h} | 43 +++-- 14 files changed, 361 insertions(+), 194 deletions(-) rename tensorflow/compiler/{tf2xla => xla/client}/lib/cholesky.cc (68%) rename tensorflow/compiler/{tf2xla => xla/client}/lib/cholesky.h (87%) create mode 100644 tensorflow/compiler/xla/client/lib/cholesky_test.cc rename tensorflow/compiler/{tf2xla/lib/while_loop.cc => xla/client/lib/loops.cc} (50%) rename tensorflow/compiler/{tf2xla/lib/while_loop.h => xla/client/lib/loops.h} (62%) diff --git a/tensorflow/compiler/tf2xla/kernels/BUILD b/tensorflow/compiler/tf2xla/kernels/BUILD index 8bc3292296..901b97736b 100644 --- a/tensorflow/compiler/tf2xla/kernels/BUILD +++ b/tensorflow/compiler/tf2xla/kernels/BUILD @@ -1,16 +1,11 @@ +load("//tensorflow:tensorflow.bzl", "tf_copts", "tf_kernel_library") + licenses(["notice"]) # Apache 2.0 package( default_visibility = ["//tensorflow/compiler/tf2xla:internal"], ) -load("//tensorflow:tensorflow.bzl", "tf_copts") -load("//tensorflow:tensorflow.bzl", "tf_kernel_library") -load( - "//third_party/mkl:build_defs.bzl", - "if_mkl", -) - tf_kernel_library( name = "xla_ops", srcs = [ @@ -122,12 +117,10 @@ tf_kernel_library( "//tensorflow/compiler/tf2xla:common", "//tensorflow/compiler/tf2xla:xla_compiler", "//tensorflow/compiler/tf2xla/lib:broadcast", - "//tensorflow/compiler/tf2xla/lib:cholesky", "//tensorflow/compiler/tf2xla/lib:qr", "//tensorflow/compiler/tf2xla/lib:random", "//tensorflow/compiler/tf2xla/lib:scatter", "//tensorflow/compiler/tf2xla/lib:util", - "//tensorflow/compiler/tf2xla/lib:while_loop", "//tensorflow/compiler/tf2xla/ops:xla_ops", "//tensorflow/compiler/xla:array4d", "//tensorflow/compiler/xla:literal", @@ -140,7 +133,9 @@ tf_kernel_library( "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", + "//tensorflow/compiler/xla/client/lib:cholesky", "//tensorflow/compiler/xla/client/lib:constants", + "//tensorflow/compiler/xla/client/lib:loops", "//tensorflow/compiler/xla/client/lib:math", "//tensorflow/compiler/xla/client/lib:matrix", "//tensorflow/compiler/xla/client/lib:pooling", diff --git a/tensorflow/compiler/tf2xla/kernels/cholesky_op.cc b/tensorflow/compiler/tf2xla/kernels/cholesky_op.cc index 9fcbc86adc..0ed3044efa 100644 --- a/tensorflow/compiler/tf2xla/kernels/cholesky_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/cholesky_op.cc @@ -13,9 +13,9 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/tf2xla/lib/cholesky.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/lib/cholesky.h" namespace tensorflow { namespace { @@ -24,7 +24,7 @@ class CholeskyOp : public XlaOpKernel { public: explicit CholeskyOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} void Compile(XlaOpKernelContext* ctx) override { - ctx->SetOutput(0, Cholesky(ctx->Input(0))); + ctx->SetOutput(0, xla::Cholesky(ctx->Input(0))); } }; diff --git a/tensorflow/compiler/tf2xla/kernels/gather_op.cc b/tensorflow/compiler/tf2xla/kernels/gather_op.cc index 20b0de193d..41c31d0ed5 100644 --- a/tensorflow/compiler/tf2xla/kernels/gather_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/gather_op.cc @@ -14,7 +14,6 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/tf2xla/kernels/gather_op_helpers.h" -#include "tensorflow/compiler/tf2xla/lib/while_loop.h" #include "tensorflow/compiler/tf2xla/shape_util.h" #include "tensorflow/compiler/tf2xla/type_util.h" #include "tensorflow/compiler/tf2xla/xla_context.h" diff --git a/tensorflow/compiler/tf2xla/kernels/image_ops.cc b/tensorflow/compiler/tf2xla/kernels/image_ops.cc index e9bb0a77e9..96ddd42e2a 100644 --- a/tensorflow/compiler/tf2xla/kernels/image_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/image_ops.cc @@ -15,12 +15,12 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/kernels/gather_op_helpers.h" #include "tensorflow/compiler/tf2xla/lib/util.h" -#include "tensorflow/compiler/tf2xla/lib/while_loop.h" #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/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/lib/constants.h" +#include "tensorflow/compiler/xla/client/lib/loops.h" #include "tensorflow/compiler/xla/client/lib/sorting.h" #include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/shape_util.h" @@ -505,9 +505,9 @@ class NonMaxSuppressionOp : public XlaOpKernel { init_values.push_back(included_iou); auto suppress_loop_result = - XlaWhileLoop(WhileCondFn(num_boxes, output_size), - SuppressBodyFn(num_boxes), init_values, "suppress_loop", - builder) + xla::WhileLoopHelper(WhileCondFn(num_boxes, output_size), + SuppressBodyFn(num_boxes), init_values, + "suppress_loop", builder) .ValueOrDie(); xla::XlaOp included_score = diff --git a/tensorflow/compiler/tf2xla/kernels/random_ops.cc b/tensorflow/compiler/tf2xla/kernels/random_ops.cc index 8822e29f7e..2d92056e4f 100644 --- a/tensorflow/compiler/tf2xla/kernels/random_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/random_ops.cc @@ -20,12 +20,12 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/kernels/gather_op_helpers.h" #include "tensorflow/compiler/tf2xla/lib/random.h" #include "tensorflow/compiler/tf2xla/lib/util.h" -#include "tensorflow/compiler/tf2xla/lib/while_loop.h" #include "tensorflow/compiler/tf2xla/shape_util.h" #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/lib/arithmetic.h" +#include "tensorflow/compiler/xla/client/lib/loops.h" #include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/tensor.h" @@ -175,8 +175,8 @@ class RandomShuffleOp : public XlaOpKernel { }; // for i in range(n): auto swap_loop_result = - XlaForEachIndex(n, xla::S32, swap_body_fn, {swaps, indices}, - "indices_swap_loop", builder) + xla::ForEachIndex(n, xla::S32, swap_body_fn, {swaps, indices}, + "indices_swap_loop", builder) .ValueOrDie(); auto swapped_indices = swap_loop_result[1]; diff --git a/tensorflow/compiler/tf2xla/lib/BUILD b/tensorflow/compiler/tf2xla/lib/BUILD index 3e7a761120..9ec9e9bdc0 100644 --- a/tensorflow/compiler/tf2xla/lib/BUILD +++ b/tensorflow/compiler/tf2xla/lib/BUILD @@ -15,8 +15,6 @@ filegroup( ]), ) -load("//tensorflow/compiler/xla/tests:build_defs.bzl", "xla_test") - cc_library( name = "broadcast", srcs = ["broadcast.cc"], @@ -33,27 +31,6 @@ cc_library( ], ) -cc_library( - name = "cholesky", - srcs = ["cholesky.cc"], - hdrs = ["cholesky.h"], - deps = [ - ":util", - ":while_loop", - "//tensorflow/compiler/xla:literal", - "//tensorflow/compiler/xla:shape_util", - "//tensorflow/compiler/xla:status_macros", - "//tensorflow/compiler/xla:statusor", - "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client:xla_builder", - "//tensorflow/compiler/xla/client/lib:constants", - "//tensorflow/compiler/xla/client/lib:matrix", - "//tensorflow/compiler/xla/client/lib:slicing", - "//tensorflow/compiler/xla/client/lib:triangular_solve", - "//tensorflow/core:lib", - ], -) - cc_library( name = "random", srcs = ["random.cc"], @@ -75,7 +52,6 @@ cc_library( hdrs = ["qr.h"], deps = [ ":util", - ":while_loop", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", @@ -84,6 +60,7 @@ cc_library( "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client/lib:arithmetic", "//tensorflow/compiler/xla/client/lib:constants", + "//tensorflow/compiler/xla/client/lib:loops", "//tensorflow/compiler/xla/client/lib:math", "//tensorflow/compiler/xla/client/lib:matrix", "//tensorflow/compiler/xla/client/lib:slicing", @@ -97,7 +74,6 @@ cc_library( hdrs = ["scatter.h"], deps = [ ":util", - ":while_loop", "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", @@ -128,19 +104,3 @@ cc_library( "@com_google_absl//absl/types:span", ], ) - -cc_library( - name = "while_loop", - srcs = ["while_loop.cc"], - hdrs = ["while_loop.h"], - deps = [ - ":util", - "//tensorflow/compiler/xla:shape_util", - "//tensorflow/compiler/xla:status_macros", - "//tensorflow/compiler/xla:statusor", - "//tensorflow/compiler/xla/client:xla_builder", - "//tensorflow/compiler/xla/client:xla_computation", - "@com_google_absl//absl/strings", - "@com_google_absl//absl/types:span", - ], -) diff --git a/tensorflow/compiler/tf2xla/lib/qr.cc b/tensorflow/compiler/tf2xla/lib/qr.cc index d600774860..057045fc0c 100644 --- a/tensorflow/compiler/tf2xla/lib/qr.cc +++ b/tensorflow/compiler/tf2xla/lib/qr.cc @@ -19,9 +19,9 @@ limitations under the License. #include #include "tensorflow/compiler/tf2xla/lib/util.h" -#include "tensorflow/compiler/tf2xla/lib/while_loop.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/lib/constants.h" +#include "tensorflow/compiler/xla/client/lib/loops.h" #include "tensorflow/compiler/xla/client/lib/math.h" #include "tensorflow/compiler/xla/client/lib/matrix.h" #include "tensorflow/compiler/xla/client/lib/slicing.h" @@ -225,8 +225,8 @@ xla::StatusOr QRBlock( builder, xla::ShapeUtil::MakeShape(type, ConcatVectors(batch_dims, {n}))); TF_ASSIGN_OR_RETURN(auto values, - XlaForEachIndex(std::min(m, n), xla::S32, qr_body_fn, - {a, vs, taus}, "qr", builder)); + xla::ForEachIndex(std::min(m, n), xla::S32, qr_body_fn, + {a, vs, taus}, "qr", builder)); QRBlockResult result; result.r = values[0]; @@ -301,8 +301,8 @@ xla::StatusOr ComputeWYRepresentation( w = UpdateSliceInMinorDims(w, bv, {0}); TF_ASSIGN_OR_RETURN( - auto values, XlaForEachIndex(n - 1, xla::S32, body_fn, {w, y, vs, taus}, - "wy", builder)); + auto values, xla::ForEachIndex(n - 1, xla::S32, body_fn, {w, y, vs, taus}, + "wy", builder)); return values[0]; } diff --git a/tensorflow/compiler/tf2xla/lib/scatter.cc b/tensorflow/compiler/tf2xla/lib/scatter.cc index 2b1c2ced92..688056791f 100644 --- a/tensorflow/compiler/tf2xla/lib/scatter.cc +++ b/tensorflow/compiler/tf2xla/lib/scatter.cc @@ -20,7 +20,6 @@ limitations under the License. #include "absl/types/span.h" #include "tensorflow/compiler/tf2xla/lib/util.h" -#include "tensorflow/compiler/tf2xla/lib/while_loop.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal.h" diff --git a/tensorflow/compiler/xla/client/lib/BUILD b/tensorflow/compiler/xla/client/lib/BUILD index 41db8de29f..bf21b267c5 100644 --- a/tensorflow/compiler/xla/client/lib/BUILD +++ b/tensorflow/compiler/xla/client/lib/BUILD @@ -1,5 +1,7 @@ # Common computation builders for XLA. +load("//tensorflow/compiler/xla/tests:build_defs.bzl", "generate_backend_suites", "xla_test") + licenses(["notice"]) # Apache 2.0 package(default_visibility = ["//tensorflow/compiler/xla/client:friends"]) @@ -13,9 +15,6 @@ filegroup( ]), ) -load("//tensorflow/compiler/xla/tests:build_defs.bzl", "xla_test") -load("//tensorflow/compiler/xla/tests:build_defs.bzl", "generate_backend_suites") - # Generate test_suites for all backends, named "${backend}_tests". generate_backend_suites() @@ -35,6 +34,48 @@ cc_library( ], ) +cc_library( + name = "cholesky", + srcs = ["cholesky.cc"], + hdrs = ["cholesky.h"], + deps = [ + ":math", + "//tensorflow/compiler/xla:literal", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client/lib:constants", + "//tensorflow/compiler/xla/client/lib:loops", + "//tensorflow/compiler/xla/client/lib:matrix", + "//tensorflow/compiler/xla/client/lib:slicing", + "//tensorflow/compiler/xla/client/lib:triangular_solve", + "//tensorflow/core:lib", + ], +) + +xla_test( + name = "cholesky_test", + srcs = ["cholesky_test.cc"], + tags = ["optonly"], + deps = [ + ":arithmetic", + ":cholesky", + ":matrix", + "//tensorflow/compiler/xla:array2d", + "//tensorflow/compiler/xla:literal", + "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla:test", + "//tensorflow/compiler/xla:types", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/tests:client_library_test_base", + "//tensorflow/compiler/xla/tests:literal_test_util", + "//tensorflow/compiler/xla/tests:xla_internal_test_main", + "//tensorflow/core:test", + ], +) + cc_library( name = "constants", srcs = ["constants.cc"], @@ -75,6 +116,22 @@ cc_library( ], ) +cc_library( + name = "loops", + srcs = ["loops.cc"], + hdrs = ["loops.h"], + deps = [ + ":constants", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", + "@com_google_absl//absl/strings", + "@com_google_absl//absl/types:span", + ], +) + cc_library( name = "math", srcs = ["math.cc"], diff --git a/tensorflow/compiler/tf2xla/lib/cholesky.cc b/tensorflow/compiler/xla/client/lib/cholesky.cc similarity index 68% rename from tensorflow/compiler/tf2xla/lib/cholesky.cc rename to tensorflow/compiler/xla/client/lib/cholesky.cc index 550ab5b056..fd98049968 100644 --- a/tensorflow/compiler/tf2xla/lib/cholesky.cc +++ b/tensorflow/compiler/xla/client/lib/cholesky.cc @@ -13,14 +13,14 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/tf2xla/lib/cholesky.h" +#include "tensorflow/compiler/xla/client/lib/cholesky.h" #include #include -#include "tensorflow/compiler/tf2xla/lib/util.h" -#include "tensorflow/compiler/tf2xla/lib/while_loop.h" #include "tensorflow/compiler/xla/client/lib/constants.h" +#include "tensorflow/compiler/xla/client/lib/loops.h" +#include "tensorflow/compiler/xla/client/lib/math.h" #include "tensorflow/compiler/xla/client/lib/matrix.h" #include "tensorflow/compiler/xla/client/lib/slicing.h" #include "tensorflow/compiler/xla/client/lib/triangular_solve.h" @@ -31,7 +31,7 @@ limitations under the License. #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/core/lib/core/errors.h" -namespace tensorflow { +namespace xla { namespace { @@ -50,26 +50,25 @@ namespace { // l[..., j+1:, j] = (a[..., j+1:, j] - np.dot(l[..., j+1:, :j], row_t)) / // l[..., j, j] // return l -xla::XlaOp CholeskyUnblocked(xla::XlaOp a, - xla::PrecisionConfig::Precision precision) { - xla::XlaBuilder* builder = a.builder(); - return builder->ReportErrorOrReturn([&]() -> xla::StatusOr { - TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); - const int n_dims = xla::ShapeUtil::Rank(a_shape); - const int64 n = xla::ShapeUtil::GetDimension(a_shape, -1); - auto major_dims = xla::AsInt64Slice(a_shape.dimensions()) +XlaOp CholeskyUnblocked(XlaOp a, PrecisionConfig::Precision precision) { + XlaBuilder* builder = a.builder(); + return builder->ReportErrorOrReturn([&]() -> StatusOr { + TF_ASSIGN_OR_RETURN(Shape a_shape, builder->GetShape(a)); + const int n_dims = ShapeUtil::Rank(a_shape); + const int64 n = ShapeUtil::GetDimension(a_shape, -1); + auto major_dims = AsInt64Slice(a_shape.dimensions()) .subspan( /*pos=*/0, /*len=*/n_dims - 2); - xla::XlaOp l = xla::ZerosLike(a); + XlaOp l = ZerosLike(a); // Construct the for loop body to iterate over rows. - auto body_fn = [&](xla::XlaOp i, absl::Span loop_vars, - xla::XlaBuilder* body_builder) - -> xla::StatusOr> { - xla::Shape col_shape; - xla::Shape row_shape; + auto body_fn = + [&](XlaOp i, absl::Span loop_vars, + XlaBuilder* body_builder) -> StatusOr> { + Shape col_shape; + Shape row_shape; for (int64 d : major_dims) { row_shape.add_dimensions(d); col_shape.add_dimensions(d); @@ -77,43 +76,40 @@ xla::XlaOp CholeskyUnblocked(xla::XlaOp a, row_shape.add_dimensions(1); row_shape.add_dimensions(n); row_shape.set_element_type(a_shape.element_type()); - auto mask_zeros_row = xla::Zeros(body_builder, row_shape); + auto mask_zeros_row = Zeros(body_builder, row_shape); col_shape.add_dimensions(n); col_shape.add_dimensions(1); col_shape.set_element_type(a_shape.element_type()); - auto mask_zeros_col = xla::Zeros(body_builder, col_shape); + auto mask_zeros_col = Zeros(body_builder, col_shape); std::vector mask_vector(n); std::iota(mask_vector.begin(), mask_vector.end(), 0); - auto mask_range = xla::ConstantR1(body_builder, mask_vector); + auto mask_range = ConstantR1(body_builder, mask_vector); auto mask_range_row = - xla::Broadcast(xla::Reshape(mask_range, {0}, {1, n}), major_dims); + Broadcast(Reshape(mask_range, {0}, {1, n}), major_dims); auto mask_range_col = - xla::Broadcast(xla::Reshape(mask_range, {0}, {n, 1}), major_dims); + Broadcast(Reshape(mask_range, {0}, {n, 1}), major_dims); auto body_a = loop_vars[0]; auto body_l = loop_vars[1]; // row = l[..., i, :i] // select the whole i-th row, then mask out all columns past i-1 - auto zero = xla::ConstantR0(body_builder, 0); + auto zero = ConstantR0(body_builder, 0); auto l_i = DynamicSliceInMinorDims(body_l, {i, zero}, {1, n}); - auto row = xla::Select(xla::Ge(mask_range_row, i), mask_zeros_row, l_i); + auto row = Select(Ge(mask_range_row, i), mask_zeros_row, l_i); // a[..., i, i] auto a_ii = DynamicSliceInMinorDims(body_a, {i, i}, {1, 1}); // np.dot(row, np.swapaxes(row, -1, -2)) auto diag_dot = BatchDot(row, TransposeInMinorDims(row), precision); // l[..., i, i] = np.sqrt(a[..., i, i] - np.dot(row, // np.swapaxes(row, -1, -2))) - auto l_ii = - xla::Pow(a_ii - diag_dot, - FloatLiteral(body_builder, a_shape.element_type(), 0.5)); + auto l_ii = Sqrt(a_ii - diag_dot); // a[..., i+1:, i] // select the whole i-th column, then mask out all rows above i+1 auto a_0i = DynamicSliceInMinorDims(body_a, {i}, {1}); - auto a_ip1i = - xla::Select(xla::Le(mask_range_col, i), mask_zeros_col, a_0i); + auto a_ip1i = Select(Le(mask_range_col, i), mask_zeros_col, a_0i); // l[..., i+1:, i] = (a[..., i+1:, i] - np.dot(l[..., i+1:, :i], r.T)) / // l[..., i, i] @@ -122,8 +118,7 @@ xla::XlaOp CholeskyUnblocked(xla::XlaOp a, // r.T) auto dot = BatchDot(body_l, TransposeInMinorDims(row), precision); // np.dot(l[..., i+1:, :i], r.T) - auto dot_ip1 = - xla::Select(xla::Le(mask_range_col, i), mask_zeros_col, dot); + auto dot_ip1 = Select(Le(mask_range_col, i), mask_zeros_col, dot); body_l = DynamicUpdateSliceInMinorDims(body_l, (a_ip1i - dot_ip1) / l_ii, {i}); @@ -131,12 +126,12 @@ xla::XlaOp CholeskyUnblocked(xla::XlaOp a, // column assign will wrap around and overwrite the diagonal assign. body_l = DynamicUpdateSliceInMinorDims(body_l, l_ii, {i, i}); - return std::vector{body_a, body_l}; + return std::vector{body_a, body_l}; }; TF_ASSIGN_OR_RETURN( auto cholesky_while, - XlaForEachIndex(n, xla::S32, body_fn, {a, l}, "unblocked", builder)); + ForEachIndex(n, S32, body_fn, {a, l}, "unblocked", builder)); return cholesky_while[1]; }); @@ -144,34 +139,35 @@ xla::XlaOp CholeskyUnblocked(xla::XlaOp a, } // namespace -xla::XlaOp Cholesky(xla::XlaOp a, int64 block_size, - xla::PrecisionConfig::Precision precision) { - xla::XlaBuilder* builder = a.builder(); - return builder->ReportErrorOrReturn([&]() -> xla::StatusOr { - TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); - const int ndims = xla::ShapeUtil::Rank(a_shape); +XlaOp Cholesky(XlaOp a, int64 block_size, + PrecisionConfig::Precision precision) { + XlaBuilder* builder = a.builder(); + return builder->ReportErrorOrReturn([&]() -> StatusOr { + TF_ASSIGN_OR_RETURN(Shape a_shape, builder->GetShape(a)); + const int ndims = ShapeUtil::Rank(a_shape); if (ndims < 2) { - return errors::InvalidArgument( - "Arguments to Cholesky must have rank >= 2: ", ndims); + return InvalidArgument( + "Argument to Cholesky must have rank >= 2; shape was %s", + a_shape.ToString()); } - const int64 n = xla::ShapeUtil::GetDimension(a_shape, -1); - if (n != xla::ShapeUtil::GetDimension(a_shape, -2)) { - return errors::InvalidArgument( - "Arguments to Cholesky must be square matrices: ", - xla::ShapeUtil::HumanString(a_shape)); + const int64 n = ShapeUtil::GetDimension(a_shape, -1); + if (n != ShapeUtil::GetDimension(a_shape, -2)) { + return InvalidArgument( + "Argument to Cholesky must be batched square matrices; got shape %s", + ShapeUtil::HumanString(a_shape)); } if (block_size < 1) { - return errors::InvalidArgument( - "block_size argument to Cholesky must be >= 1; got ", block_size); + return InvalidArgument( + "block_size argument to Cholesky must be >= 1; got %d", block_size); } // Blocked left-looking Cholesky factorization. // Algorithm 1 from // Haidar, Azzam, et al. "High-performance Cholesky factorization for // GPU-only execution." Proceedings of General Purpose GPUs. ACM, 2017. - xla::XlaOp l = xla::ZerosLike(a); + XlaOp l = ZerosLike(a); for (int64 i = 0; i < n; i += block_size) { int64 k = std::min(block_size, n - i); if (i > 0) { @@ -207,4 +203,4 @@ xla::XlaOp Cholesky(xla::XlaOp a, int64 block_size, }); } -} // namespace tensorflow +} // namespace xla diff --git a/tensorflow/compiler/tf2xla/lib/cholesky.h b/tensorflow/compiler/xla/client/lib/cholesky.h similarity index 87% rename from tensorflow/compiler/tf2xla/lib/cholesky.h rename to tensorflow/compiler/xla/client/lib/cholesky.h index 9a561c34b9..0bae26837c 100644 --- a/tensorflow/compiler/tf2xla/lib/cholesky.h +++ b/tensorflow/compiler/xla/client/lib/cholesky.h @@ -13,13 +13,13 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_COMPILER_TF2XLA_LIB_CHOLESKY_H_ -#define TENSORFLOW_COMPILER_TF2XLA_LIB_CHOLESKY_H_ +#ifndef TENSORFLOW_COMPILER_XLA_CLIENT_LIB_CHOLESKY_H_ +#define TENSORFLOW_COMPILER_XLA_CLIENT_LIB_CHOLESKY_H_ #include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/xla_data.pb.h" -namespace tensorflow { +namespace xla { // Computes the Cholesky decompositions of a batch of symmetric positive // definite matrices. @@ -34,6 +34,6 @@ xla::XlaOp Cholesky( xla::XlaOp a, int64 block_size = 256, xla::PrecisionConfig::Precision precision = xla::PrecisionConfig::HIGHEST); -} // namespace tensorflow +} // namespace xla -#endif // TENSORFLOW_COMPILER_TF2XLA_LIB_CHOLESKY_H_ +#endif // TENSORFLOW_COMPILER_XLA_CLIENT_LIB_CHOLESKY_H_ diff --git a/tensorflow/compiler/xla/client/lib/cholesky_test.cc b/tensorflow/compiler/xla/client/lib/cholesky_test.cc new file mode 100644 index 0000000000..ba9580a3d3 --- /dev/null +++ b/tensorflow/compiler/xla/client/lib/cholesky_test.cc @@ -0,0 +1,166 @@ +/* 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/client/lib/cholesky.h" + +#include +#include +#include + +#include "tensorflow/compiler/xla/array2d.h" +#include "tensorflow/compiler/xla/client/lib/arithmetic.h" +#include "tensorflow/compiler/xla/client/lib/matrix.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" +#include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/compiler/xla/test.h" +#include "tensorflow/compiler/xla/tests/client_library_test_base.h" +#include "tensorflow/compiler/xla/tests/literal_test_util.h" +#include "tensorflow/compiler/xla/tests/test_macros.h" +#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/core/lib/core/status_test_util.h" + +namespace { + +using xla::int64; + +using CholeskyTest = xla::ClientLibraryTestBase; + +XLA_TEST_F(CholeskyTest, Simple) { + xla::XlaBuilder builder(TestName()); + + xla::Array2D a_vals({ + {4, 6, 8, 10}, + {6, 45, 54, 63}, + {8, 54, 146, 166}, + {10, 63, 166, 310}, + }); + + xla::XlaOp a; + auto a_data = CreateR2Parameter(a_vals, 0, "a", &builder, &a); + xla::Cholesky(a, /*block_size=*/2); + + xla::Array2D expected({ + {2, 0, 0, 0}, + {3, 6, 0, 0}, + {4, 7, 9, 0}, + {5, 8, 10, 11}, + }); + + ComputeAndCompareR2(&builder, expected, {a_data.get()}, + xla::ErrorSpec(1e-4, 1e-4)); +} + +XLA_TEST_F(CholeskyTest, Simple2) { + xla::XlaBuilder builder(TestName()); + + xla::Array2D a_vals({ + {16, 24, 8, 12}, + {24, 61, 82, 48}, + {8, 82, 456, 106}, + {12, 48, 106, 62}, + }); + + xla::XlaOp a; + auto a_data = CreateR2Parameter(a_vals, 0, "a", &builder, &a); + xla::Cholesky(a); + + xla::Array2D expected( + {{4, 0, 0, 0}, {6, 5, 0, 0}, {2, 14, 16, 0}, {3, 6, 1, 4}}); + + ComputeAndCompareR2(&builder, expected, {a_data.get()}, + xla::ErrorSpec(1e-4, 1e-4)); +} + +XLA_TEST_F(CholeskyTest, SimpleBatched) { + xla::XlaBuilder builder(TestName()); + + xla::Array3D a_vals({ + { + {4, 6, 8, 10}, + {6, 45, 54, 63}, + {8, 54, 146, 166}, + {10, 63, 166, 310}, + }, + { + {16, 24, 8, 12}, + {24, 61, 82, 48}, + {8, 82, 456, 106}, + {12, 48, 106, 62}, + }, + }); + + xla::XlaOp a; + auto a_data = CreateR3Parameter(a_vals, 0, "a", &builder, &a); + xla::Cholesky(a); + + xla::Array3D expected({ + { + {2, 0, 0, 0}, + {3, 6, 0, 0}, + {4, 7, 9, 0}, + {5, 8, 10, 11}, + }, + {{4, 0, 0, 0}, {6, 5, 0, 0}, {2, 14, 16, 0}, {3, 6, 1, 4}}, + }); + + ComputeAndCompareR3(&builder, expected, {a_data.get()}, + xla::ErrorSpec(1e-4, 1e-4)); +} + +using CholeskyTestCase = std::tuple; + +class RandomCholeskyTest + : public xla::ClientLibraryTestBase, + public ::testing::WithParamInterface {}; + +XLA_TEST_P(RandomCholeskyTest, Random) { + xla::XlaBuilder builder(TestName()); + + auto test_params = GetParam(); + std::vector dimensions = {std::get<0>(test_params), + std::get<1>(test_params), + std::get<1>(test_params)}; + xla::Shape shape = xla::ShapeUtil::MakeShape(xla::F32, dimensions); + TF_ASSERT_OK_AND_ASSIGN( + auto literal, + xla::LiteralUtil::CreateRandomLiteral(shape, 0.0, 1.0)); + + auto input = xla::Parameter(&builder, 0, shape, "input"); + // Form a random positive definite matrix. + auto matrix = xla::BatchDot(input, TransposeInMinorDims(input), + xla::PrecisionConfig::HIGHEST); + + auto cholesky = xla::Cholesky(matrix, /*block_size=*/4); + + // Verify that ||matrix - cholesky * cholesky_t||_2 ~= 0 + auto verification = xla::BatchDot(cholesky, TransposeInMinorDims(cholesky), + xla::PrecisionConfig::HIGHEST); + auto delta = matrix - verification; + xla::Reduce(delta * delta, xla::ConstantR0(&builder, 0.0), + CreateScalarAddComputation(xla::F32, &builder), {0, 1, 2}); + + TF_ASSERT_OK_AND_ASSIGN(auto input_data, client_->TransferToServer(literal)); + ComputeAndCompareR0(&builder, 0.0, {input_data.get()}, + xla::ErrorSpec(1e-4, 1e-4)); +} + +INSTANTIATE_TEST_CASE_P(RandomCholeskyTestInstance, RandomCholeskyTest, + ::testing::Values(CholeskyTestCase{1, 1}, + CholeskyTestCase{1, 2}, + CholeskyTestCase{10, 5}, + CholeskyTestCase{2, 20})); + +} // namespace diff --git a/tensorflow/compiler/tf2xla/lib/while_loop.cc b/tensorflow/compiler/xla/client/lib/loops.cc similarity index 50% rename from tensorflow/compiler/tf2xla/lib/while_loop.cc rename to tensorflow/compiler/xla/client/lib/loops.cc index 594ab1dfd0..721f987628 100644 --- a/tensorflow/compiler/tf2xla/lib/while_loop.cc +++ b/tensorflow/compiler/xla/client/lib/loops.cc @@ -13,44 +13,43 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/tf2xla/lib/while_loop.h" -#include "tensorflow/compiler/tf2xla/lib/util.h" +#include "tensorflow/compiler/xla/client/lib/loops.h" + +#include "tensorflow/compiler/xla/client/lib/constants.h" #include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" -namespace tensorflow { +namespace xla { -xla::StatusOr> XlaWhileLoop( - const LoopConditionFunction& condition_function, - const LoopBodyFunction& body_function, - absl::Span initial_values, absl::string_view name, - xla::XlaBuilder* builder) { +StatusOr> WhileLoopHelper( + const WhileLoopHelperConditionFunction& condition_function, + const WhileLoopHelperBodyFunction& body_function, + absl::Span initial_values, absl::string_view name, + XlaBuilder* builder) { int arity = initial_values.size(); - std::vector var_shapes; + std::vector var_shapes; var_shapes.reserve(arity); - for (const xla::XlaOp& input : initial_values) { + for (const XlaOp& input : initial_values) { TF_ASSIGN_OR_RETURN(auto shape, builder->GetShape(input)); var_shapes.push_back(std::move(shape)); } - xla::Shape tuple_shape = xla::ShapeUtil::MakeTupleShape(var_shapes); + Shape tuple_shape = ShapeUtil::MakeTupleShape(var_shapes); // Unpacks a tuple into its component parts. - auto unpack_tuple = [](xla::XlaOp tuple, int arity, - xla::XlaBuilder* builder) { - std::vector elements(arity); + auto unpack_tuple = [](XlaOp tuple, int arity, XlaBuilder* builder) { + std::vector elements(arity); for (int i = 0; i < arity; ++i) { - elements[i] = xla::GetTupleElement(tuple, i); + elements[i] = GetTupleElement(tuple, i); } return elements; }; // Build the condition. - std::unique_ptr cond_builder = + std::unique_ptr cond_builder = builder->CreateSubBuilder(absl::StrCat(name, "_condition")); { - auto parameter = - xla::Parameter(cond_builder.get(), 0, tuple_shape, "parameter"); + auto parameter = Parameter(cond_builder.get(), 0, tuple_shape, "parameter"); TF_RETURN_IF_ERROR( condition_function(unpack_tuple(parameter, arity, cond_builder.get()), @@ -60,11 +59,10 @@ xla::StatusOr> XlaWhileLoop( TF_ASSIGN_OR_RETURN(auto cond, cond_builder->Build()); // Build the body. - std::unique_ptr body_builder = + std::unique_ptr body_builder = builder->CreateSubBuilder(absl::StrCat(name, "_body")); { - auto parameter = - xla::Parameter(body_builder.get(), 0, tuple_shape, "parameter"); + auto parameter = Parameter(body_builder.get(), 0, tuple_shape, "parameter"); TF_ASSIGN_OR_RETURN( auto result, @@ -72,56 +70,54 @@ xla::StatusOr> XlaWhileLoop( body_builder.get())); TF_RET_CHECK(result.size() == initial_values.size()); - xla::Tuple(body_builder.get(), result); + Tuple(body_builder.get(), result); } TF_ASSIGN_OR_RETURN(auto body, body_builder->Build()); - auto outputs = xla::While(cond, body, xla::Tuple(builder, initial_values)); + auto outputs = While(cond, body, Tuple(builder, initial_values)); return unpack_tuple(outputs, arity, builder); } -xla::StatusOr> XlaForEachIndex( - int64 num_iterations, xla::PrimitiveType num_iterations_type, +StatusOr> ForEachIndex( + int64 num_iterations, PrimitiveType num_iterations_type, const ForEachIndexBodyFunction& body_function, - absl::Span initial_values, absl::string_view name, - xla::XlaBuilder* builder) { - auto while_cond_fn = - [&](absl::Span values, - xla::XlaBuilder* cond_builder) -> xla::StatusOr { - return xla::Lt(values[0], IntegerLiteral(cond_builder, num_iterations_type, - num_iterations)); + absl::Span initial_values, absl::string_view name, + XlaBuilder* builder) { + auto while_cond_fn = [&](absl::Span values, + XlaBuilder* cond_builder) -> StatusOr { + return Lt(values[0], ConstantR0WithType(cond_builder, num_iterations_type, + num_iterations)); }; - auto while_body_fn = [&](absl::Span values, - xla::XlaBuilder* body_builder) - -> xla::StatusOr> { - xla::XlaOp iteration = values[0]; + auto while_body_fn = + [&](absl::Span values, + XlaBuilder* body_builder) -> StatusOr> { + XlaOp iteration = values[0]; - std::vector updated_values; + std::vector updated_values; updated_values.reserve(values.size()); - updated_values.push_back(xla::Add( + updated_values.push_back(Add( iteration, - xla::ConstantLiteral(body_builder, - xla::LiteralUtil::One(num_iterations_type)))); + ConstantLiteral(body_builder, LiteralUtil::One(num_iterations_type)))); values.remove_prefix(1); - TF_ASSIGN_OR_RETURN(std::vector body_outputs, + TF_ASSIGN_OR_RETURN(std::vector body_outputs, body_function(iteration, values, body_builder)); updated_values.insert(updated_values.end(), body_outputs.begin(), body_outputs.end()); return updated_values; }; - std::vector values; + std::vector values; values.reserve(initial_values.size() + 1); - values.push_back(xla::ConstantLiteral( - builder, xla::LiteralUtil::Zero(num_iterations_type))); + values.push_back( + ConstantLiteral(builder, LiteralUtil::Zero(num_iterations_type))); values.insert(values.end(), initial_values.begin(), initial_values.end()); - TF_ASSIGN_OR_RETURN(values, XlaWhileLoop(while_cond_fn, while_body_fn, values, - name, builder)); + TF_ASSIGN_OR_RETURN(values, WhileLoopHelper(while_cond_fn, while_body_fn, + values, name, builder)); values.erase(values.begin(), values.begin() + 1); return values; } -} // namespace tensorflow +} // namespace xla diff --git a/tensorflow/compiler/tf2xla/lib/while_loop.h b/tensorflow/compiler/xla/client/lib/loops.h similarity index 62% rename from tensorflow/compiler/tf2xla/lib/while_loop.h rename to tensorflow/compiler/xla/client/lib/loops.h index f2134bb449..e11de59493 100644 --- a/tensorflow/compiler/tf2xla/lib/while_loop.h +++ b/tensorflow/compiler/xla/client/lib/loops.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_COMPILER_TF2XLA_LIB_WHILE_LOOP_H_ -#define TENSORFLOW_COMPILER_TF2XLA_LIB_WHILE_LOOP_H_ +#ifndef TENSORFLOW_COMPILER_XLA_CLIENT_LIB_LOOPS_H_ +#define TENSORFLOW_COMPILER_XLA_CLIENT_LIB_LOOPS_H_ #include #include @@ -25,19 +25,18 @@ limitations under the License. #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/statusor.h" -namespace tensorflow { +namespace xla { // Function that builds a loop condition. Takes as input a sequence of input // values, and returns a boolean value representing if the condition succeeds. -typedef std::function(absl::Span, - xla::XlaBuilder*)> - LoopConditionFunction; +typedef std::function(absl::Span, XlaBuilder*)> + WhileLoopHelperConditionFunction; // Function that builds a loop body. Takes as input a sequence of input values // and returns a sequence of output values. -typedef std::function>( - absl::Span, xla::XlaBuilder*)> - LoopBodyFunction; +typedef std::function>(absl::Span, + XlaBuilder*)> + WhileLoopHelperBodyFunction; // Helper function for building an XLA while loop, where the values carried by // the loop are a tuple of values, e.g., (a, b, c): @@ -47,27 +46,27 @@ typedef std::function>( // init: (a, b, c) // ) // 'name' is a descriptive name for the loop. -xla::StatusOr> XlaWhileLoop( - const LoopConditionFunction& condition_function, - const LoopBodyFunction& body_function, - absl::Span initial_values, absl::string_view name, - xla::XlaBuilder* builder); +StatusOr> WhileLoopHelper( + const WhileLoopHelperConditionFunction& condition_function, + const WhileLoopHelperBodyFunction& body_function, + absl::Span initial_values, absl::string_view name, + XlaBuilder* builder); // Builds an XLA loop that repeats a computation `num_iterations` times. // // The body function (ForEachIndexBodyFunction) takes as input a pair of // (current iteration number, loop-carried values), and returns an updated // vector of the loop-carried values. -typedef std::function>( - xla::XlaOp, absl::Span, xla::XlaBuilder*)> +typedef std::function>( + XlaOp, absl::Span, XlaBuilder*)> ForEachIndexBodyFunction; -xla::StatusOr> XlaForEachIndex( - int64 num_iterations, xla::PrimitiveType num_iterations_type, +StatusOr> ForEachIndex( + int64 num_iterations, PrimitiveType num_iterations_type, const ForEachIndexBodyFunction& body_function, - absl::Span initial_values, absl::string_view name, - xla::XlaBuilder* builder); + absl::Span initial_values, absl::string_view name, + XlaBuilder* builder); -} // namespace tensorflow +} // namespace xla -#endif // TENSORFLOW_COMPILER_TF2XLA_LIB_WHILE_LOOP_H_ +#endif // TENSORFLOW_COMPILER_XLA_CLIENT_LIB_LOOPS_H_ -- GitLab From 74ba3593214d5efd173ac91ed2c2f2bc3d58232e Mon Sep 17 00:00:00 2001 From: Michael Case Date: Tue, 11 Dec 2018 11:38:37 -0800 Subject: [PATCH 360/873] Move importing Estimator to after API_PLACEHOLDER text in template. PiperOrigin-RevId: 225041387 --- tensorflow/api_template.__init__.py | 4 ++-- tensorflow/api_template_v1.__init__.py | 4 ++-- tensorflow/compat_template_v1.__init__.py | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/tensorflow/api_template.__init__.py b/tensorflow/api_template.__init__.py index d81cf067eb..4eba763129 100644 --- a/tensorflow/api_template.__init__.py +++ b/tensorflow/api_template.__init__.py @@ -20,14 +20,14 @@ from __future__ import print_function as _print_function import os as _os +# API IMPORTS PLACEHOLDER + # pylint: disable=g-bad-import-order from tensorflow.python.tools import component_api_helper as _component_api_helper _component_api_helper.package_hook( parent_package_str=__name__, child_package_str=('tensorflow_estimator.python.estimator.api.estimator')) -# API IMPORTS PLACEHOLDER - # Make sure directory containing top level submodules is in # the __path__ so that "from tensorflow.foo import bar" works. # We're using bitwise, but there's nothing special about that. diff --git a/tensorflow/api_template_v1.__init__.py b/tensorflow/api_template_v1.__init__.py index 65bdb6cb1b..21b5277614 100644 --- a/tensorflow/api_template_v1.__init__.py +++ b/tensorflow/api_template_v1.__init__.py @@ -23,13 +23,13 @@ import os as _os # pylint: disable=g-bad-import-order from tensorflow.python import pywrap_tensorflow # pylint: disable=unused-import +# API IMPORTS PLACEHOLDER + from tensorflow.python.tools import component_api_helper as _component_api_helper _component_api_helper.package_hook( parent_package_str=__name__, child_package_str=('tensorflow_estimator.python.estimator.api.estimator')) -# 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/compat_template_v1.__init__.py b/tensorflow/compat_template_v1.__init__.py index 7df80ec012..d58acde09f 100644 --- a/tensorflow/compat_template_v1.__init__.py +++ b/tensorflow/compat_template_v1.__init__.py @@ -23,12 +23,12 @@ import os as _os # pylint: disable=g-bad-import-order from tensorflow.python import pywrap_tensorflow # pylint: disable=unused-import +# API IMPORTS PLACEHOLDER + from tensorflow.python.tools import component_api_helper as _component_api_helper _component_api_helper.package_hook( parent_package_str=__name__, child_package_str=('tensorflow_estimator.python.estimator.api.estimator')) -# API IMPORTS PLACEHOLDER - from tensorflow.python.platform import flags # pylint: disable=g-import-not-at-top app.flags = flags # pylint: disable=undefined-variable -- GitLab From a3ad14bbd2fdb941b8dcf076b27389000e1ee17e Mon Sep 17 00:00:00 2001 From: Michael Kuperstein Date: Tue, 11 Dec 2018 11:55:47 -0800 Subject: [PATCH 361/873] [XLA] Verify instruction IDs don't over/under-flow int. The proto field is int64, but the class field is int. PiperOrigin-RevId: 225044350 --- tensorflow/compiler/xla/service/hlo_instruction.cc | 5 +++++ tensorflow/compiler/xla/service/hlo_proto_util.cc | 1 + 2 files changed, 6 insertions(+) diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc index 5c1f1a61cc..152a451c18 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction.cc @@ -569,6 +569,11 @@ StatusOr> HloInstruction::CreateFromProto( instruction->SetAndSanitizeName(proto.name()); instruction->metadata_ = proto.metadata(); instruction->backend_config_ = proto.backend_config(); + + TF_RET_CHECK(proto.id() >= 0) + << "Instruction with negative id: " << proto.id(); + TF_RET_CHECK(proto.id() <= INT_MAX) + << "Instruction with id > INT_MAX: " << proto.id(); instruction->unique_id_ = proto.id(); if (proto.has_sharding()) { diff --git a/tensorflow/compiler/xla/service/hlo_proto_util.cc b/tensorflow/compiler/xla/service/hlo_proto_util.cc index 981d06ce10..3a9ee57e55 100644 --- a/tensorflow/compiler/xla/service/hlo_proto_util.cc +++ b/tensorflow/compiler/xla/service/hlo_proto_util.cc @@ -39,6 +39,7 @@ HloProto MakeHloProto(const HloModule& module) { StatusOr> CreateModuleFromProto( const HloModuleProto& proto, const HloModuleConfig& module_config) { + VLOG(4) << proto.ShortDebugString(); TF_ASSIGN_OR_RETURN(std::unique_ptr module, HloModule::CreateFromProto(proto, module_config)); TF_RETURN_IF_ERROR( -- GitLab From 1390ba8f7877af2d673413ac7ef7cb2500e96c27 Mon Sep 17 00:00:00 2001 From: Peter Hawkins Date: Tue, 11 Dec 2018 11:57:09 -0800 Subject: [PATCH 362/873] [XLA] Move QR decomposition out of TF2XLA and into xla/client/lib. Add a couple of simple C++ tests. PiperOrigin-RevId: 225044584 --- tensorflow/compiler/tf2xla/kernels/BUILD | 2 +- tensorflow/compiler/tf2xla/kernels/qr_op.cc | 4 +- tensorflow/compiler/tf2xla/lib/BUILD | 22 -- tensorflow/compiler/xla/client/lib/BUILD | 42 ++++ .../compiler/{tf2xla => xla/client}/lib/qr.cc | 207 +++++++++--------- .../compiler/{tf2xla => xla/client}/lib/qr.h | 20 +- tensorflow/compiler/xla/client/lib/qr_test.cc | 93 ++++++++ 7 files changed, 250 insertions(+), 140 deletions(-) rename tensorflow/compiler/{tf2xla => xla/client}/lib/qr.cc (62%) rename tensorflow/compiler/{tf2xla => xla/client}/lib/qr.h (74%) create mode 100644 tensorflow/compiler/xla/client/lib/qr_test.cc diff --git a/tensorflow/compiler/tf2xla/kernels/BUILD b/tensorflow/compiler/tf2xla/kernels/BUILD index 901b97736b..a18a4e92d6 100644 --- a/tensorflow/compiler/tf2xla/kernels/BUILD +++ b/tensorflow/compiler/tf2xla/kernels/BUILD @@ -117,7 +117,6 @@ tf_kernel_library( "//tensorflow/compiler/tf2xla:common", "//tensorflow/compiler/tf2xla:xla_compiler", "//tensorflow/compiler/tf2xla/lib:broadcast", - "//tensorflow/compiler/tf2xla/lib:qr", "//tensorflow/compiler/tf2xla/lib:random", "//tensorflow/compiler/tf2xla/lib:scatter", "//tensorflow/compiler/tf2xla/lib:util", @@ -140,6 +139,7 @@ tf_kernel_library( "//tensorflow/compiler/xla/client/lib:matrix", "//tensorflow/compiler/xla/client/lib:pooling", "//tensorflow/compiler/xla/client/lib:prng", + "//tensorflow/compiler/xla/client/lib:qr", "//tensorflow/compiler/xla/client/lib:sorting", "//tensorflow/compiler/xla/client/lib:triangular_solve", "//tensorflow/core:framework", diff --git a/tensorflow/compiler/tf2xla/kernels/qr_op.cc b/tensorflow/compiler/tf2xla/kernels/qr_op.cc index 7ea0afc1f5..66ec40a946 100644 --- a/tensorflow/compiler/tf2xla/kernels/qr_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/qr_op.cc @@ -13,9 +13,9 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/tf2xla/lib/qr.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/lib/qr.h" namespace tensorflow { namespace { @@ -26,7 +26,7 @@ class QROp : public XlaOpKernel { OP_REQUIRES_OK(ctx, ctx->GetAttr("full_matrices", &full_matrices_)); } void Compile(XlaOpKernelContext* ctx) override { - auto result = QRDecomposition(ctx->Input(0), full_matrices_); + auto result = xla::QRDecomposition(ctx->Input(0), full_matrices_); if (!result.ok()) { ctx->SetStatus(result.status()); return; diff --git a/tensorflow/compiler/tf2xla/lib/BUILD b/tensorflow/compiler/tf2xla/lib/BUILD index 9ec9e9bdc0..3d7b0bc959 100644 --- a/tensorflow/compiler/tf2xla/lib/BUILD +++ b/tensorflow/compiler/tf2xla/lib/BUILD @@ -46,28 +46,6 @@ cc_library( ], ) -cc_library( - name = "qr", - srcs = ["qr.cc"], - hdrs = ["qr.h"], - deps = [ - ":util", - "//tensorflow/compiler/xla:literal_util", - "//tensorflow/compiler/xla:shape_util", - "//tensorflow/compiler/xla:status_macros", - "//tensorflow/compiler/xla:statusor", - "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client:xla_builder", - "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/lib:constants", - "//tensorflow/compiler/xla/client/lib:loops", - "//tensorflow/compiler/xla/client/lib:math", - "//tensorflow/compiler/xla/client/lib:matrix", - "//tensorflow/compiler/xla/client/lib:slicing", - "//tensorflow/core:lib", - ], -) - cc_library( name = "scatter", srcs = ["scatter.cc"], diff --git a/tensorflow/compiler/xla/client/lib/BUILD b/tensorflow/compiler/xla/client/lib/BUILD index bf21b267c5..8fc221ee2b 100644 --- a/tensorflow/compiler/xla/client/lib/BUILD +++ b/tensorflow/compiler/xla/client/lib/BUILD @@ -234,6 +234,48 @@ cc_library( ], ) +cc_library( + name = "qr", + srcs = ["qr.cc"], + hdrs = ["qr.h"], + deps = [ + ":arithmetic", + ":constants", + ":loops", + ":math", + ":matrix", + ":slicing", + "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/core:lib", + ], +) + +xla_test( + name = "qr_test", + srcs = ["qr_test.cc"], + tags = ["optonly"], + deps = [ + ":matrix", + ":qr", + "//tensorflow/compiler/xla:array2d", + "//tensorflow/compiler/xla:array3d", + "//tensorflow/compiler/xla:literal", + "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla:test", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/tests:client_library_test_base", + "//tensorflow/compiler/xla/tests:literal_test_util", + "//tensorflow/compiler/xla/tests:xla_internal_test_main", + "//tensorflow/core:test", + ], +) + cc_library( name = "slicing", srcs = ["slicing.cc"], diff --git a/tensorflow/compiler/tf2xla/lib/qr.cc b/tensorflow/compiler/xla/client/lib/qr.cc similarity index 62% rename from tensorflow/compiler/tf2xla/lib/qr.cc rename to tensorflow/compiler/xla/client/lib/qr.cc index 057045fc0c..72ca653173 100644 --- a/tensorflow/compiler/tf2xla/lib/qr.cc +++ b/tensorflow/compiler/xla/client/lib/qr.cc @@ -13,12 +13,11 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/tf2xla/lib/qr.h" +#include "tensorflow/compiler/xla/client/lib/qr.h" #include #include -#include "tensorflow/compiler/tf2xla/lib/util.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/lib/constants.h" #include "tensorflow/compiler/xla/client/lib/loops.h" @@ -32,10 +31,18 @@ limitations under the License. #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/core/lib/core/errors.h" -namespace tensorflow { +namespace xla { namespace { +std::vector ConcatVectors(absl::Span xs, + absl::Span ys) { + std::vector output(xs.size() + ys.size()); + std::copy(xs.begin(), xs.end(), output.begin()); + std::copy(ys.begin(), ys.end(), output.begin() + xs.size()); + return output; +} + // Computes a Householder reflection of the form: // H = I - tau v v.T. // such that @@ -65,52 +72,47 @@ namespace { // return (v, tau, beta) // TODO(phawkins): LAPACK's xLARFG implementation has code for handling // overflows in the norm/beta calculations. Perhaps do the same here. -xla::Status House(xla::XlaOp x, xla::XlaOp k, - absl::Span batch_dims, const int64 m, - xla::XlaOp* v, xla::XlaOp* tau, xla::XlaOp* beta) { - xla::XlaBuilder* const builder = x.builder(); - TF_ASSIGN_OR_RETURN(xla::Shape x_shape, builder->GetShape(x)); - const xla::PrimitiveType type = x_shape.element_type(); +Status House(XlaOp x, XlaOp k, absl::Span batch_dims, + const int64 m, XlaOp* v, XlaOp* tau, XlaOp* beta) { + XlaBuilder* const builder = x.builder(); + TF_ASSIGN_OR_RETURN(Shape x_shape, builder->GetShape(x)); + const PrimitiveType type = x_shape.element_type(); std::vector batch_dim_ids(batch_dims.size()); std::iota(batch_dim_ids.begin(), batch_dim_ids.end(), 0); const int64 minor_dim = batch_dims.size(); - xla::XlaOp zero = xla::ScalarLike(x, 0.0); - xla::XlaOp one = xla::ScalarLike(x, 1.0); + XlaOp zero = ScalarLike(x, 0.0); + XlaOp one = ScalarLike(x, 1.0); // alpha = x[k] - xla::XlaOp alpha = - xla::Reshape(DynamicSliceInMinorDims(x, {k}, {1}), batch_dims); + XlaOp alpha = Reshape(DynamicSliceInMinorDims(x, {k}, {1}), batch_dims); // Compute x[k+1:] (padded with zeros in elements 0..k) - xla::XlaOp iota = xla::Iota(builder, xla::S32, m); - xla::XlaOp x_after_k = - xla::Mul(x, xla::ConvertElementType(xla::Gt(iota, k), type), - /*broadcast_dimensions=*/{minor_dim}); + XlaOp iota = Iota(builder, S32, m); + XlaOp x_after_k = Mul(x, ConvertElementType(Gt(iota, k), type), + /*broadcast_dimensions=*/{minor_dim}); // sigma = np.dot(x[k+1:], x[k+1:]) - auto sigma = - xla::Reduce(x_after_k * x_after_k, zero, - xla::CreateScalarAddComputation(type, builder), {minor_dim}); + auto sigma = Reduce(x_after_k * x_after_k, zero, + CreateScalarAddComputation(type, builder), {minor_dim}); // mu = np.sqrt(x[k]*x[k] + sigma) - auto mu = xla::Sqrt(xla::Square(alpha) + sigma); + auto mu = Sqrt(Square(alpha) + sigma); - auto sigma_is_zero = xla::Eq(sigma, zero); + auto sigma_is_zero = Eq(sigma, zero); - *beta = xla::Select(sigma_is_zero, alpha, -xla::Sign(alpha) * mu); - *tau = xla::Select(sigma_is_zero, xla::Broadcast(zero, batch_dims), - (*beta - alpha) / *beta); - auto divisor = xla::Select(sigma_is_zero, xla::Broadcast(one, batch_dims), - alpha - *beta); + *beta = Select(sigma_is_zero, alpha, -Sign(alpha) * mu); + *tau = Select(sigma_is_zero, Broadcast(zero, batch_dims), + (*beta - alpha) / *beta); + auto divisor = + Select(sigma_is_zero, Broadcast(one, batch_dims), alpha - *beta); - auto e_k = xla::Broadcast(xla::ConvertElementType(xla::Eq(iota, k), type), - std::vector(batch_dims.size(), 1)); + auto e_k = Broadcast(ConvertElementType(Eq(iota, k), type), + std::vector(batch_dims.size(), 1)); // Form v as [0, 0, ..., 1] ++ x[k+1:] / divisor // If sigma is zero, x[k+1:] is zero, so use any non-zero divisor. - *v = e_k + - xla::Div(x_after_k, divisor, /*broadcast_dimensions=*/batch_dim_ids); + *v = e_k + Div(x_after_k, divisor, /*broadcast_dimensions=*/batch_dim_ids); return Status::OK(); } @@ -143,90 +145,86 @@ xla::Status House(xla::XlaOp x, xla::XlaOp k, // return (q, vs, taus) struct QRBlockResult { // The factored R value - xla::XlaOp r; + XlaOp r; // Representation of the Householder matrices I - beta v v.T - xla::XlaOp taus; // Shape: [..., n] - xla::XlaOp vs; // Shape: [..., m, n] + XlaOp taus; // Shape: [..., n] + XlaOp vs; // Shape: [..., m, n] }; -xla::StatusOr QRBlock( - xla::XlaOp a, xla::PrecisionConfig::Precision precision) { - xla::XlaBuilder* builder = a.builder(); - TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); - const int num_dims = xla::ShapeUtil::Rank(a_shape); +StatusOr QRBlock(XlaOp a, PrecisionConfig::Precision precision) { + XlaBuilder* builder = a.builder(); + TF_ASSIGN_OR_RETURN(Shape a_shape, builder->GetShape(a)); + const int num_dims = ShapeUtil::Rank(a_shape); if (num_dims < 2) { - return errors::InvalidArgument("Arguments to QR must have rank >= 2: ", - num_dims); + return InvalidArgument("Argument to QR must have rank >= 2; got shape %s", + a_shape.ToString()); } - xla::PrimitiveType type = a_shape.element_type(); + PrimitiveType type = a_shape.element_type(); - const int64 m = xla::ShapeUtil::GetDimension(a_shape, -2); - const int64 n = xla::ShapeUtil::GetDimension(a_shape, -1); + const int64 m = ShapeUtil::GetDimension(a_shape, -2); + const int64 n = ShapeUtil::GetDimension(a_shape, -1); const int64 num_batch_dims = num_dims - 2; std::vector batch_dims(num_batch_dims); for (int i = 0; i < num_batch_dims; ++i) { - batch_dims[i] = xla::ShapeUtil::GetDimension(a_shape, i); + batch_dims[i] = ShapeUtil::GetDimension(a_shape, i); } std::vector batch_dim_indices(num_batch_dims); std::iota(batch_dim_indices.begin(), batch_dim_indices.end(), 0); - auto qr_body_fn = - [&](xla::XlaOp j, absl::Span values, - xla::XlaBuilder* builder) -> xla::StatusOr> { + auto qr_body_fn = [&](XlaOp j, absl::Span values, + XlaBuilder* builder) -> StatusOr> { auto a = values[0]; auto vs = values[1]; auto taus = values[2]; // v, beta = house(a[:, j], j) auto x = DynamicSliceInMinorDims(a, {j}, {1}); - xla::XlaOp v, tau, beta; - TF_RETURN_IF_ERROR(House(xla::Collapse(x, {num_dims - 2, num_dims - 1}), j, + XlaOp v, tau, beta; + TF_RETURN_IF_ERROR(House(Collapse(x, {num_dims - 2, num_dims - 1}), j, batch_dims, m, &v, &tau, &beta)); std::vector shape = batch_dims; shape.push_back(1); shape.push_back(m); - auto v_broadcast = xla::Reshape(v, shape); + auto v_broadcast = Reshape(v, shape); // a[:, :] -= tau * np.dot(v[:, np.newaxis], // np.dot(v[np.newaxis, :], a[:, :])) auto vva = BatchDot(v_broadcast, a, precision); vva = BatchDot(TransposeInMinorDims(v_broadcast), vva, precision); - a = a - xla::Mul(tau, vva, - /*broadcast_dimensions=*/batch_dim_indices); + a = a - Mul(tau, vva, + /*broadcast_dimensions=*/batch_dim_indices); // It is more precise to populate column 'k' explicitly, rather than // computing it implicitly by applying the Householder transformation. // a[k,k] = beta // a[k+1:,k] = np.zeros([m-k-1], dtype=a.dtype) - auto iota = xla::Reshape(xla::Iota(a.builder(), xla::S32, m), {m, 1}); - auto predecessor_mask = xla::ConvertElementType(xla::Lt(iota, j), type); - auto mask = xla::Broadcast(xla::ConvertElementType(xla::Eq(iota, j), type), - std::vector(batch_dims.size(), 1)); - auto new_x = - xla::Mul(x, predecessor_mask, - /*broadcast_dimensions=*/{num_dims - 2, num_dims - 1}) + - xla::Mul(beta, mask, /*broadcast_dimensions=*/batch_dim_indices); + auto iota = Reshape(Iota(a.builder(), S32, m), {m, 1}); + auto predecessor_mask = ConvertElementType(Lt(iota, j), type); + auto mask = Broadcast(ConvertElementType(Eq(iota, j), type), + std::vector(batch_dims.size(), 1)); + auto new_x = Mul(x, predecessor_mask, + /*broadcast_dimensions=*/{num_dims - 2, num_dims - 1}) + + Mul(beta, mask, /*broadcast_dimensions=*/batch_dim_indices); a = DynamicUpdateSliceInMinorDims(a, new_x, {j}); // vs[:, j] = v vs = DynamicUpdateSliceInMinorDims( - vs, xla::Reshape(v, ConcatVectors(batch_dims, {m, 1})), {j}); + vs, Reshape(v, ConcatVectors(batch_dims, {m, 1})), {j}); // taus[j] = tau taus = DynamicUpdateSliceInMinorDims( - taus, xla::Reshape(tau, ConcatVectors(batch_dims, {1})), {j}); - return std::vector{a, vs, taus}; + taus, Reshape(tau, ConcatVectors(batch_dims, {1})), {j}); + return std::vector{a, vs, taus}; }; - auto vs = xla::Zeros(builder, xla::ShapeUtil::MakeShape( - type, ConcatVectors(batch_dims, {m, n}))); - auto taus = xla::Zeros( - builder, xla::ShapeUtil::MakeShape(type, ConcatVectors(batch_dims, {n}))); + auto vs = Zeros( + builder, ShapeUtil::MakeShape(type, ConcatVectors(batch_dims, {m, n}))); + auto taus = Zeros(builder, + ShapeUtil::MakeShape(type, ConcatVectors(batch_dims, {n}))); - TF_ASSIGN_OR_RETURN(auto values, - xla::ForEachIndex(std::min(m, n), xla::S32, qr_body_fn, - {a, vs, taus}, "qr", builder)); + TF_ASSIGN_OR_RETURN(auto values, ForEachIndex(std::min(m, n), S32, qr_body_fn, + {a, vs, taus}, "qr", builder)); QRBlockResult result; result.r = values[0]; @@ -250,24 +248,23 @@ xla::StatusOr QRBlock( // return W // There is no need to return Y since at termination of the loop it is equal to // vs. -xla::StatusOr ComputeWYRepresentation( - xla::PrimitiveType type, absl::Span batch_dims, xla::XlaOp vs, - xla::XlaOp taus, int64 m, int64 n, - xla::PrecisionConfig::Precision precision) { +StatusOr ComputeWYRepresentation(PrimitiveType type, + absl::Span batch_dims, + XlaOp vs, XlaOp taus, int64 m, int64 n, + PrecisionConfig::Precision precision) { std::vector batch_dim_indices(batch_dims.size()); std::iota(batch_dim_indices.begin(), batch_dim_indices.end(), 0); int64 n_index = batch_dims.size() + 1; - auto body_fn = - [&](xla::XlaOp j, absl::Span values, - xla::XlaBuilder* builder) -> xla::StatusOr> { + auto body_fn = [&](XlaOp j, absl::Span values, + XlaBuilder* builder) -> StatusOr> { auto w = values[0]; auto y = values[1]; const auto vs = values[2]; const auto taus = values[3]; // Want j values in range [1, ... n). - j = j + xla::ConstantR0(builder, 1); + j = j + ConstantR0(builder, 1); // vs has shape [..., m, 1] auto v = DynamicSliceInMinorDims(vs, {j}, {1}); // beta has shape [..., 1] @@ -278,31 +275,31 @@ xla::StatusOr ComputeWYRepresentation( // wyv has shape [..., m, 1] auto wyv = BatchDot(w, yv, precision); - auto z = xla::Mul( + auto z = Mul( -beta, v + wyv, /*broadcast_dimensions=*/ConcatVectors(batch_dim_indices, {n_index})); w = DynamicUpdateSliceInMinorDims(w, z, {j}); y = DynamicUpdateSliceInMinorDims(y, v, {j}); - return std::vector{w, y, vs, taus}; + return std::vector{w, y, vs, taus}; }; - xla::XlaBuilder* builder = vs.builder(); - auto w = xla::Zeros(builder, xla::ShapeUtil::MakeShape( - type, ConcatVectors(batch_dims, {m, n}))); + XlaBuilder* builder = vs.builder(); + auto w = Zeros(builder, + ShapeUtil::MakeShape(type, ConcatVectors(batch_dims, {m, n}))); auto y = w; auto v = SliceInMinorDims(vs, {0}, {1}); auto beta = SliceInMinorDims(taus, {0}, {1}); y = UpdateSliceInMinorDims(y, v, {0}); - auto bv = xla::Mul( - -beta, v, - /*broadcast_dimensions=*/ConcatVectors(batch_dim_indices, {n_index})); + auto bv = + Mul(-beta, v, + /*broadcast_dimensions=*/ConcatVectors(batch_dim_indices, {n_index})); w = UpdateSliceInMinorDims(w, bv, {0}); TF_ASSIGN_OR_RETURN( - auto values, xla::ForEachIndex(n - 1, xla::S32, body_fn, {w, y, vs, taus}, - "wy", builder)); + auto values, + ForEachIndex(n - 1, S32, body_fn, {w, y, vs, taus}, "wy", builder)); return values[0]; } @@ -323,34 +320,34 @@ xla::StatusOr ComputeWYRepresentation( // return (q, a) // TODO(phawkins): consider using UT transformations (in the form I - V U V') // rather than WY transformations. -xla::StatusOr QRDecomposition( - xla::XlaOp a, bool full_matrices, int64 block_size, - xla::PrecisionConfig::Precision precision) { - xla::XlaBuilder* builder = a.builder(); - TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); - const int num_dims = xla::ShapeUtil::Rank(a_shape); +StatusOr QRDecomposition( + XlaOp a, bool full_matrices, int64 block_size, + PrecisionConfig::Precision precision) { + XlaBuilder* builder = a.builder(); + TF_ASSIGN_OR_RETURN(Shape a_shape, builder->GetShape(a)); + const int num_dims = ShapeUtil::Rank(a_shape); if (num_dims < 2) { - return errors::InvalidArgument("Arguments to QR must have rank >= 2: ", - num_dims); + return InvalidArgument("Arguments to QR must have rank >= 2: got shape %s", + a_shape.ToString()); } - xla::PrimitiveType type = a_shape.element_type(); + PrimitiveType type = a_shape.element_type(); - const int64 m = xla::ShapeUtil::GetDimension(a_shape, -2); - const int64 n = xla::ShapeUtil::GetDimension(a_shape, -1); + const int64 m = ShapeUtil::GetDimension(a_shape, -2); + const int64 n = ShapeUtil::GetDimension(a_shape, -1); const int64 p = std::min(m, n); if (block_size < 1) { - return errors::InvalidArgument( - "block_size argument to QR must be >= 1; got ", block_size); + return InvalidArgument("block_size argument to QR must be >= 1; got %d", + block_size); } const int64 num_batch_dims = num_dims - 2; std::vector batch_dims(num_batch_dims); for (int i = 0; i < num_batch_dims; ++i) { - batch_dims[i] = xla::ShapeUtil::GetDimension(a_shape, i); + batch_dims[i] = ShapeUtil::GetDimension(a_shape, i); } - auto q = xla::Broadcast(xla::IdentityMatrix(builder, type, m, m), batch_dims); + auto q = Broadcast(IdentityMatrix(builder, type, m, m), batch_dims); for (int64 i = 0; i < p; i += block_size) { int64 k = std::min(block_size, p - i); @@ -393,4 +390,4 @@ xla::StatusOr QRDecomposition( return result; } -} // namespace tensorflow +} // namespace xla diff --git a/tensorflow/compiler/tf2xla/lib/qr.h b/tensorflow/compiler/xla/client/lib/qr.h similarity index 74% rename from tensorflow/compiler/tf2xla/lib/qr.h rename to tensorflow/compiler/xla/client/lib/qr.h index 24b537ac8b..827c8eeca0 100644 --- a/tensorflow/compiler/tf2xla/lib/qr.h +++ b/tensorflow/compiler/xla/client/lib/qr.h @@ -13,13 +13,13 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_COMPILER_TF2XLA_LIB_QR_H_ -#define TENSORFLOW_COMPILER_TF2XLA_LIB_QR_H_ +#ifndef TENSORFLOW_COMPILER_XLA_CLIENT_LIB_QR_H_ +#define TENSORFLOW_COMPILER_XLA_CLIENT_LIB_QR_H_ #include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/xla_data.pb.h" -namespace tensorflow { +namespace xla { // Computes the QR decompositions of a batch of matrices. That is, // given a (batched) matrix a, computes an orthonormal matrix Q and an @@ -29,14 +29,14 @@ namespace tensorflow { // the block size to use. // TODO(phawkins): handle the complex case. struct QRDecompositionResult { - xla::XlaOp q; - xla::XlaOp r; + XlaOp q; + XlaOp r; }; -xla::StatusOr QRDecomposition( - xla::XlaOp a, bool full_matrices, int64 block_size = 128, - xla::PrecisionConfig::Precision precision = xla::PrecisionConfig::HIGHEST); +StatusOr QRDecomposition( + XlaOp a, bool full_matrices, int64 block_size = 128, + PrecisionConfig::Precision precision = PrecisionConfig::HIGHEST); -} // namespace tensorflow +} // namespace xla -#endif // TENSORFLOW_COMPILER_TF2XLA_LIB_QR_H_ +#endif // TENSORFLOW_COMPILER_XLA_CLIENT_LIB_QR_H_ diff --git a/tensorflow/compiler/xla/client/lib/qr_test.cc b/tensorflow/compiler/xla/client/lib/qr_test.cc new file mode 100644 index 0000000000..b27d364b62 --- /dev/null +++ b/tensorflow/compiler/xla/client/lib/qr_test.cc @@ -0,0 +1,93 @@ +/* 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/client/lib/qr.h" + +#include "tensorflow/compiler/xla/array2d.h" +#include "tensorflow/compiler/xla/array3d.h" +#include "tensorflow/compiler/xla/client/lib/matrix.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" +#include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/compiler/xla/test.h" +#include "tensorflow/compiler/xla/tests/client_library_test_base.h" +#include "tensorflow/compiler/xla/tests/literal_test_util.h" +#include "tensorflow/compiler/xla/tests/test_macros.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/core/status_test_util.h" + +namespace { + +using QrTest = xla::ClientLibraryTestBase; + +XLA_TEST_F(QrTest, Simple) { + xla::XlaBuilder builder(TestName()); + + xla::Array2D a_vals({ + {4, 6, 8, 10}, + {6, 45, 54, 63}, + {8, 54, 146, 166}, + {10, 63, 166, 310}, + }); + + xla::XlaOp a; + auto a_data = CreateR2Parameter(a_vals, 0, "a", &builder, &a); + TF_ASSERT_OK_AND_ASSIGN( + auto result, + xla::QRDecomposition(a, /*full_matrices=*/true, /*block_size=*/2)); + + // Verifies that the decomposition composes back to the original matrix. + // + // This isn't a terribly demanding test, (e.g., we should verify that Q is + // orthonormal and R is upper-triangular) but it's awkward to write such tests + // without more linear algebra libraries. It's easier to test the numerics + // from Python, anyway, where we have access to numpy and scipy. + xla::BatchDot(result.q, result.r, xla::PrecisionConfig::HIGHEST); + + ComputeAndCompareR2(&builder, a_vals, {a_data.get()}, + xla::ErrorSpec(1e-4, 1e-4)); +} + +XLA_TEST_F(QrTest, SimpleBatched) { + xla::XlaBuilder builder(TestName()); + + xla::Array3D a_vals({ + { + {4, 6, 8, 10}, + {6, 45, 54, 63}, + {8, 54, 146, 166}, + {10, 63, 166, 310}, + }, + { + {16, 24, 8, 12}, + {24, 61, 82, 48}, + {8, 82, 456, 106}, + {12, 48, 106, 62}, + }, + }); + + xla::XlaOp a; + auto a_data = CreateR3Parameter(a_vals, 0, "a", &builder, &a); + TF_ASSERT_OK_AND_ASSIGN( + auto result, + xla::QRDecomposition(a, /*full_matrices=*/true, /*block_size=*/2)); + + xla::BatchDot(result.q, result.r, xla::PrecisionConfig::HIGHEST); + + ComputeAndCompareR3(&builder, a_vals, {a_data.get()}, + xla::ErrorSpec(1e-4, 1e-4)); +} + +} // namespace -- GitLab From 06c60fb179befb6011ad85cf8632315c70ddcba1 Mon Sep 17 00:00:00 2001 From: Billy Lamberta Date: Tue, 11 Dec 2018 12:01:56 -0800 Subject: [PATCH 363/873] TF Lite models page skeleton PiperOrigin-RevId: 225045442 --- tensorflow/lite/g3doc/_book.yaml | 26 ++++ tensorflow/lite/g3doc/models/_index.yaml | 125 ++++++++++++++++++ .../lite/g3doc/models/image/label/android.md | 3 + .../lite/g3doc/models/image/label/ios.md | 3 + .../lite/g3doc/models/image/label/overview.md | 8 ++ 5 files changed, 165 insertions(+) create mode 100644 tensorflow/lite/g3doc/models/_index.yaml create mode 100644 tensorflow/lite/g3doc/models/image/label/android.md create mode 100644 tensorflow/lite/g3doc/models/image/label/ios.md create mode 100644 tensorflow/lite/g3doc/models/image/label/overview.md diff --git a/tensorflow/lite/g3doc/_book.yaml b/tensorflow/lite/g3doc/_book.yaml index 36bf4f4618..0c79e79fdd 100644 --- a/tensorflow/lite/g3doc/_book.yaml +++ b/tensorflow/lite/g3doc/_book.yaml @@ -77,6 +77,32 @@ upper_tabs: - title: Optimizing for mobile path: /lite/tfmobile/optimizing + # - name: Models + # contents: + # - title: Overview + # path: /lite/models/ + # - heading: Beginner + # style: divider + # - title: Image labeling + # section: + # - title: Overview + # path: /lite/models/image/label/overview + # - title: Android + # path: /lite/models/image/label/android + # - title: iOS + # path: /lite/models/image/label/ios + # - heading: Advanced + # style: divider + # - heading: Image + # - title: Image classification + # path: /lite/models/image/classification/ + # - heading: Audio + # - title: Hot word detection + # path: /lite/models/audio/hot_word/ + # - heading: Text + # - title: Text classification + # path: /lite/models/text/classification/ + - name: API skip_translation: true contents: diff --git a/tensorflow/lite/g3doc/models/_index.yaml b/tensorflow/lite/g3doc/models/_index.yaml new file mode 100644 index 0000000000..f4d8bc40a9 --- /dev/null +++ b/tensorflow/lite/g3doc/models/_index.yaml @@ -0,0 +1,125 @@ +project_path: /lite/_project.yaml +book_path: /lite/_book.yaml +description: +landing_page: + body_class: tfo-hide-page-nav + custom_css_path: /site-assets/css/style.css + show_side_navs: true + rows: + + # Hero + - classname: > + devsite-landing-row-50 + devsite-landing-row-large-headings + devsite-landing-row-no-image-background + foreground: theme + items: + - heading: Models marketplace + description: > + The TensorFlow Lite models marketplace, your neighborhood model shoppe. + image_path: /resources/images/tflite-card-16x9.png + + # Features + - background: grey + items: + - heading: Optimized for mobile + description: > + Machine learning can make your apps more engaging, personalized, and + helpful, and provides solutions that are optimized to run on-device. + - heading: Built with Google expertise + description: > + Models offer the technologies that have long powered Google's own + experiences on mobile. + - heading: Approachable and comprehensive + description: > + Use out-of-the-box solutions (base APIs) or custom models, running + on-device or in the Cloud, depending on your specific needs. + + # Beginner models + - classname: devsite-landing-row-100 + heading: "Build machine learning into your apps" + items: + - heading: > + Image labeling + description: > + Identify objects, locations, activities, animal species, products, and + more + icon: + path: ../images/landing-page/assistant_logo.png + path: /lite/image/labeling/ + - heading: > + Text recognition (OCR) + description: > + Recognize and extract text from images + icon: + path: ../images/landing-page/assistant_logo.png + path: /lite/image/labeling/ + - heading: > + Face detection + description: > + Detect faces and facial landmarks + icon: + path: ../images/landing-page/assistant_logo.png + path: /lite/image/labeling/ + + - items: + - heading: > + Barcode scanning + description: > + Scan and process barcodes + icon: + path: ../images/landing-page/assistant_logo.png + path: /lite/image/labeling/ + - heading: > + Landmark detection + description: > + Identify popular landmarks in an image + icon: + path: ../images/landing-page/assistant_logo.png + path: /lite/image/labeling/ + - heading: > + Smart reply + description: > + Provide suggested text snippet that fits context + icon: + path: ../images/landing-page/assistant_logo.png + path: /lite/image/labeling/ + + # Custom models + - classname: > + devsite-landing-row-no-image-background + devsite-landing-row-50 + devsite-landing-row-large-headings + foreground: theme + background: grey + items: + - heading: Custom models + description: > +

If models don’t cover your use cases, you can always + bring your own existing TensorFlow Lite models. Just upload your model, + and we’ll take care of hosting and serving it to your app.

+ +

Models acts as an API layer to your custom model, making it easy to + run and use. In addition to deploying your models, we are releasing an + experimental model compression flow that aims to reduce model size (up + to orders of magnitudes) while maintaining similar accuracy. Sign up at + g.co/firebase/signup

+ +

And if you’re new to machine learning and want more information on + custom models for mobile, you can learn more about TensorFlow + Lite.

+ image_path: /resources/images/tflite-card-16x9.png + image_left: true + - classname: devsite-landing-row-large-headings + foreground: theme + items: + - heading: Just the beginning + description: > + Our ultimate goal is to reduce idea–to–implementation cycles and make AI + an essential and intuitive part of a developer's toolkit. We will do so + by continuing to add new Base APIs that leverage Google’s machine + learning expertise. Base APIs will ultimately cover significantly more + use cases in the vision, speech, and text fields. We will also continue + to simplify use of custom models, adding tools to deploy, compress, and + create them. diff --git a/tensorflow/lite/g3doc/models/image/label/android.md b/tensorflow/lite/g3doc/models/image/label/android.md new file mode 100644 index 0000000000..9cd54aad1e --- /dev/null +++ b/tensorflow/lite/g3doc/models/image/label/android.md @@ -0,0 +1,3 @@ +# Android + +lorem diff --git a/tensorflow/lite/g3doc/models/image/label/ios.md b/tensorflow/lite/g3doc/models/image/label/ios.md new file mode 100644 index 0000000000..904c6450ac --- /dev/null +++ b/tensorflow/lite/g3doc/models/image/label/ios.md @@ -0,0 +1,3 @@ +# iOS + +lorem diff --git a/tensorflow/lite/g3doc/models/image/label/overview.md b/tensorflow/lite/g3doc/models/image/label/overview.md new file mode 100644 index 0000000000..b3d9133bb2 --- /dev/null +++ b/tensorflow/lite/g3doc/models/image/label/overview.md @@ -0,0 +1,8 @@ +# Overview + +Image labeling gives you insight into the content of images. When you use the +API, you get a list of the entities that were recognized: people, things, +places, activities, and so on. Each label found comes with a score that +indicates the confidence the ML model has in its relevance. With this +information, you can perform tasks such as automatic metadata generation +and content moderation. -- GitLab From cf9878d6a691c1ee8277c83a94f86adcd5fedc65 Mon Sep 17 00:00:00 2001 From: Peter Hawkins Date: Tue, 11 Dec 2018 12:14:29 -0800 Subject: [PATCH 364/873] [XLA:Python] Add Cholesky, QR, and TriangularSolve to the XLA Python API. This allows non-TF Python clients to reuse the TensorFlow implementations of these ops (and any future improvements to be shared between users). PiperOrigin-RevId: 225047881 --- tensorflow/compiler/xla/python/BUILD | 3 ++ .../xla/python/local_computation_builder.cc | 24 +++++++++++ .../xla/python/local_computation_builder.h | 7 ++++ .../xla/python/local_computation_builder.i | 3 ++ tensorflow/compiler/xla/python/xla_client.py | 14 +++++++ .../compiler/xla/python/xla_client_test.py | 41 +++++++++++++++++-- 6 files changed, 89 insertions(+), 3 deletions(-) diff --git a/tensorflow/compiler/xla/python/BUILD b/tensorflow/compiler/xla/python/BUILD index 63ac1c6649..4a57b1051e 100644 --- a/tensorflow/compiler/xla/python/BUILD +++ b/tensorflow/compiler/xla/python/BUILD @@ -66,7 +66,10 @@ cc_library( "//tensorflow/compiler/xla/client:local_client", "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", + "//tensorflow/compiler/xla/client/lib:cholesky", "//tensorflow/compiler/xla/client/lib:math", + "//tensorflow/compiler/xla/client/lib:qr", + "//tensorflow/compiler/xla/client/lib:triangular_solve", "//tensorflow/compiler/xla/service:platform_util", "//tensorflow/compiler/xla/service:shaped_buffer", "//tensorflow/compiler/xrt:xrt_proto", diff --git a/tensorflow/compiler/xla/python/local_computation_builder.cc b/tensorflow/compiler/xla/python/local_computation_builder.cc index 6e2ee86632..d4d31fb8c0 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.cc +++ b/tensorflow/compiler/xla/python/local_computation_builder.cc @@ -24,7 +24,10 @@ limitations under the License. #include "tensorflow/cc/framework/ops.h" #include "tensorflow/cc/framework/scope.h" #include "tensorflow/cc/ops/standard_ops.h" +#include "tensorflow/compiler/xla/client/lib/cholesky.h" #include "tensorflow/compiler/xla/client/lib/math.h" +#include "tensorflow/compiler/xla/client/lib/qr.h" +#include "tensorflow/compiler/xla/client/lib/triangular_solve.h" #include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/executable_run_options.h" @@ -865,6 +868,27 @@ LocalOp LocalComputationBuilder::SortKeyVal(const LocalOp& keys, return xla::Sort(keys.op(), {values.op()}, dimension); } +LocalOp LocalComputationBuilder::Cholesky(const LocalOp& a) { + return xla::Cholesky(a.op()); +} + +LocalOp LocalComputationBuilder::QR(const LocalOp& a, bool full_matrices) { + XlaBuilder* builder = a.op().builder(); + return builder->ReportErrorOrReturn([&]() -> StatusOr { + TF_ASSIGN_OR_RETURN(auto qr, xla::QRDecomposition(a.op(), full_matrices)); + return xla::Tuple(builder, {qr.q, qr.r}); + }); +} + +LocalOp LocalComputationBuilder::TriangularSolve(const LocalOp& a, + const LocalOp& b, + bool left_side, bool lower, + bool transpose_a, + bool conjugate_a) { + return xla::TriangularSolve(a.op(), b.op(), left_side, lower, transpose_a, + conjugate_a); +} + StatusOr LocalComputationBuilder::BuildConstantSubGraph( const LocalOp& operand) { TF_ASSIGN_OR_RETURN(XlaComputation computation, diff --git a/tensorflow/compiler/xla/python/local_computation_builder.h b/tensorflow/compiler/xla/python/local_computation_builder.h index 149e44570d..7647ef44ad 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.h +++ b/tensorflow/compiler/xla/python/local_computation_builder.h @@ -394,6 +394,13 @@ class LocalComputationBuilder { LocalOp SortKeyVal(const LocalOp& keys, const LocalOp& values, int64 dimension); + LocalOp QR(const LocalOp& a, bool full_matrices); + + LocalOp Cholesky(const LocalOp& a); + + LocalOp TriangularSolve(const LocalOp& a, const LocalOp& b, bool left_side, + bool lower, bool transpose_a, bool conjugate_a); + StatusOr BuildConstantSubGraph(const LocalOp& operand); #define _FORWARD(method_name, return_sig, args_sig) \ diff --git a/tensorflow/compiler/xla/python/local_computation_builder.i b/tensorflow/compiler/xla/python/local_computation_builder.i index d23d693c1e..82d25304f0 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.i +++ b/tensorflow/compiler/xla/python/local_computation_builder.i @@ -1144,6 +1144,9 @@ tensorflow::ImportNumpy(); %unignore xla::swig::LocalComputationBuilder::Imag; %unignore xla::swig::LocalComputationBuilder::Conj; %unignore xla::swig::LocalComputationBuilder::Complex; +%unignore xla::swig::LocalComputationBuilder::Cholesky; +%unignore xla::swig::LocalComputationBuilder::QR; +%unignore xla::swig::LocalComputationBuilder::TriangularSolve; %unignore xla::swig::DeleteLocalComputation; %unignore xla::swig::DestructureLocalShapedBufferTuple; %unignore xla::swig::DestructureXrtAllocationTuple; diff --git a/tensorflow/compiler/xla/python/xla_client.py b/tensorflow/compiler/xla/python/xla_client.py index c91a2aaf56..3366a83543 100644 --- a/tensorflow/compiler/xla/python/xla_client.py +++ b/tensorflow/compiler/xla/python/xla_client.py @@ -1411,6 +1411,20 @@ class ComputationBuilder(object): """Enqueues a key-value sort operation onto the computation.""" return self._client.SortKeyVal(keys, values, dimension) + def Cholesky(self, a): + """Enqueues a Cholesky decomposition onto the computation.""" + return self._client.Cholesky(a) + + def QR(self, a, full_matrices=True): + """Enqueues a QR decomposition onto the computation.""" + return self._client.QR(a, full_matrices) + + def TriangularSolve(self, a, b, left_side=False, lower=False, + transpose_a=False, conjugate_a=False): + """Enqueues a triangular-solve operation onto the computation.""" + return self._client.TriangularSolve( + a, b, left_side, lower, transpose_a, conjugate_a) + def _forward_methods_to_local_builder(): """Forward remaining ComputationBuilder methods to the C API. diff --git a/tensorflow/compiler/xla/python/xla_client_test.py b/tensorflow/compiler/xla/python/xla_client_test.py index 21b5c93b61..a4c615846e 100644 --- a/tensorflow/compiler/xla/python/xla_client_test.py +++ b/tensorflow/compiler/xla/python/xla_client_test.py @@ -18,6 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import functools import itertools import threading @@ -51,9 +52,11 @@ class LocalComputationTest(unittest.TestCase): def _ExecuteAndCompareExact(self, c, arguments=(), expected=None): self._ExecuteAndAssertWith(np.testing.assert_equal, c, arguments, expected) - def _ExecuteAndCompareClose(self, c, arguments=(), expected=None): - self._ExecuteAndAssertWith(np.testing.assert_allclose, c, arguments, - expected) + def _ExecuteAndCompareClose(self, c, arguments=(), expected=None, rtol=1e-7, + atol=0): + self._ExecuteAndAssertWith( + functools.partial(np.testing.assert_allclose, rtol=rtol, atol=atol), + c, arguments, expected) def NumpyArrayF32(*args, **kwargs): @@ -1057,6 +1060,38 @@ class SingleOpTest(LocalComputationTest): self.assertTrue(np.all(lo <= result)) self.assertTrue(np.all(result < hi)) + def testCholesky(self): + l = np.array([[4, 0, 0, 0], [6, 5, 0, 0], [2, 14, 16, 0], [3, 6, 1, 4]], + dtype=np.float32) + c = self._NewComputation() + c.Cholesky(c.Constant(np.dot(l, l.T))) + self._ExecuteAndCompareClose(c, expected=l, rtol=1e-4) + + def testQR(self): + a = np.array( + [[4, 6, 8, 10], [6, 45, 54, 63], [8, 54, 146, 166], [10, 63, 166, 310]], + dtype=np.float32) + c = self._NewComputation() + c.QR(c.Constant(a), full_matrices=True) + q, r = self._Execute(c, ()) + np.testing.assert_allclose(np.dot(q, r), a, rtol=1e-4) + + def testTriangularSolve(self): + a_vals = np.array( + [[2, 0, 0, 0], [3, 6, 0, 0], [4, 7, 9, 0], [5, 8, 10, 11]], + dtype=np.float32) + b_vals = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]], + dtype=np.float32) + + c = self._NewComputation() + c.TriangularSolve(c.Constant(a_vals), c.Constant(b_vals), left_side=False, + lower=True, transpose_a=True) + self._ExecuteAndCompareClose(c, expected=np.array([ + [0.5, 0.08333334, 0.04629629, 0.03367003], + [2.5, -0.25, -0.1388889, -0.1010101], + [4.5, -0.58333331, -0.32407406, -0.23569024], + ], dtype=np.float32), rtol=1e-4) + def testIsConstant(self): c = self._NewComputation() a = c.ConstantS32Scalar(3) -- GitLab From 316660063aaaaeb95b63d08a54e746934de659c0 Mon Sep 17 00:00:00 2001 From: Allen Lavoie Date: Tue, 11 Dec 2018 12:15:01 -0800 Subject: [PATCH 365/873] Remove/avoid deprecation warnings in 2.x saving utilities Having the warning on little-used 1.x utilities isn't super important, but the 2.x utilities shouldn't print them during normal use for sure. PiperOrigin-RevId: 225047956 --- tensorflow/python/training/checkpoint_management.py | 4 ---- tensorflow/python/training/checkpointable/util.py | 2 +- 2 files changed, 1 insertion(+), 5 deletions(-) diff --git a/tensorflow/python/training/checkpoint_management.py b/tensorflow/python/training/checkpoint_management.py index f745ab4824..a7ad1f70e5 100644 --- a/tensorflow/python/training/checkpoint_management.py +++ b/tensorflow/python/training/checkpoint_management.py @@ -56,10 +56,6 @@ def _GetCheckpointFilename(save_dir, latest_filename): return os.path.join(save_dir, latest_filename) -@deprecation.deprecated( - date=None, - instructions=("Use tf.train.CheckpointManager to manage checkpoints rather " - "than editing the Checkpoint proto manually.")) @tf_export(v1=["train.generate_checkpoint_state_proto"]) def generate_checkpoint_state_proto(save_dir, model_checkpoint_path, diff --git a/tensorflow/python/training/checkpointable/util.py b/tensorflow/python/training/checkpointable/util.py index a54f41a54f..ce1b9c6fc5 100644 --- a/tensorflow/python/training/checkpointable/util.py +++ b/tensorflow/python/training/checkpointable/util.py @@ -1863,7 +1863,7 @@ class Checkpoint(tracking.Checkpointable): checkpoint_number = assign_op.numpy() file_path = self.write("%s-%d" % (file_prefix, checkpoint_number), session=session) - checkpoint_management.update_checkpoint_state( + checkpoint_management.update_checkpoint_state_internal( save_dir=os.path.dirname(file_prefix), model_checkpoint_path=file_path, all_model_checkpoint_paths=[file_path]) -- GitLab From 4fe05f35cfab9324caedc4fc8da3c16b0f412d27 Mon Sep 17 00:00:00 2001 From: Peter Hawkins Date: Tue, 11 Dec 2018 12:15:39 -0800 Subject: [PATCH 366/873] [XLA:CPU] Add support for CustomCall targets that return tuples. Populate the tuple index table of the return value; the callee cannot do this since it does not know the buffer assignments. Explicitly enable custom_call_test only for cpu in the BUILD file, rather than disabling it on non-CPU backends. These tests would not work on any non-CPU backend. PiperOrigin-RevId: 225048065 --- .../compiler/xla/service/cpu/ir_emitter.cc | 16 ++++++++ tensorflow/compiler/xla/tests/BUILD | 19 ++++----- .../compiler/xla/tests/custom_call_test.cc | 39 ++++++++++++++++--- 3 files changed, 58 insertions(+), 16 deletions(-) diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc index 4032c2da2f..38ab5b78d2 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc @@ -2271,6 +2271,22 @@ Status IrEmitter::HandleCustomCall(HloInstruction* custom_call) { /*isVarArg=*/false))); TF_RETURN_IF_ERROR(EmitTargetAddressForOp(custom_call)); + // Write the tuple table if the output is a tuple. + if (ShapeUtil::IsTuple(custom_call->shape())) { + std::vector base_ptrs; + for (int i = 0; i < ShapeUtil::TupleElementCount(custom_call->shape()); + ++i) { + const Shape& elem_shape = + ShapeUtil::GetTupleElementShape(custom_call->shape(), i); + TF_RET_CHECK(!ShapeUtil::IsTuple(elem_shape)) + << "Nested tuples not implemented"; + TF_ASSIGN_OR_RETURN(const BufferAllocation::Slice slice, + assignment_.GetUniqueSlice(custom_call, {i})); + llvm::Value* addr = EmitBufferPointer(slice, elem_shape); + base_ptrs.push_back(addr); + } + llvm_ir::EmitTuple(GetIrArrayFor(custom_call), base_ptrs, &b_, module_); + } auto* output_address_arg = PointerCast(GetEmittedValueFor(custom_call), i8_ptr_type); diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD index 5a7a4faa7e..0300b64ed5 100644 --- a/tensorflow/compiler/xla/tests/BUILD +++ b/tensorflow/compiler/xla/tests/BUILD @@ -1,6 +1,13 @@ # Description: # Base testing infrastructure for XLA. +load("//tensorflow/compiler/xla/tests:build_defs.bzl", "generate_backend_suites", "generate_backend_test_macros", "xla_test", "xla_test_library") +load( + "//tensorflow/core:platform/default/build_config_root.bzl", + "tf_cuda_tests_tags", +) +load("//tensorflow:tensorflow.bzl", "tf_cc_binary", "tf_cc_test") + licenses(["notice"]) # Apache 2.0 package( @@ -23,17 +30,6 @@ filegroup( ]), ) -load("//tensorflow/compiler/xla/tests:build_defs.bzl", "xla_test") -load("//tensorflow/compiler/xla/tests:build_defs.bzl", "xla_test_library") -load("//tensorflow/compiler/xla/tests:build_defs.bzl", "generate_backend_suites") -load("//tensorflow/compiler/xla/tests:build_defs.bzl", "generate_backend_test_macros") -load("//tensorflow:tensorflow.bzl", "tf_cc_binary") -load("//tensorflow:tensorflow.bzl", "tf_cc_test") -load( - "//tensorflow/core:platform/default/build_config_root.bzl", - "tf_cuda_tests_tags", -) - # Generate test_suites for all backends, named "${backend}_tests". generate_backend_suites() @@ -1348,6 +1344,7 @@ xla_test( xla_test( name = "custom_call_test", srcs = ["custom_call_test.cc"], + backends = ["cpu"], deps = [ "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", diff --git a/tensorflow/compiler/xla/tests/custom_call_test.cc b/tensorflow/compiler/xla/tests/custom_call_test.cc index 738b644235..cad43d1b55 100644 --- a/tensorflow/compiler/xla/tests/custom_call_test.cc +++ b/tensorflow/compiler/xla/tests/custom_call_test.cc @@ -54,11 +54,20 @@ void Add1ToValues(float* out, float** in) { out[2] = array[2] + 1; out[3] = array[3] + 1; } + +void F32TupleSwap(float** out, float** in) { + TF_ANNOTATE_MEMORY_IS_INITIALIZED(in[0], sizeof(float)); + TF_ANNOTATE_MEMORY_IS_INITIALIZED(in[1], sizeof(float)); + *out[0] = *in[1]; + *out[1] = *in[0]; +} + } // namespace REGISTER_CUSTOM_CALL_TARGET(R0F32Add2); REGISTER_CUSTOM_CALL_TARGET(R2F32ReduceSum); REGISTER_CUSTOM_CALL_TARGET(Add1ToValues); +REGISTER_CUSTOM_CALL_TARGET(F32TupleSwap); namespace xla { namespace { @@ -69,7 +78,7 @@ class CustomCallTest : public HloTestBase { Shape r2f32_ = ShapeUtil::MakeShape(F32, {2, 2}); }; -XLA_TEST_F(CustomCallTest, DISABLED_ON_GPU(CustomCallR0F32Add2)) { +XLA_TEST_F(CustomCallTest, CustomCallR0F32Add2) { auto module = CreateNewUnverifiedModule(); auto builder = HloComputation::Builder(TestName()); @@ -84,7 +93,7 @@ XLA_TEST_F(CustomCallTest, DISABLED_ON_GPU(CustomCallR0F32Add2)) { LiteralTestUtil::ExpectR0Near(44.0f, result, error_spec_); } -XLA_TEST_F(CustomCallTest, DISABLED_ON_GPU(CustomCallR2F32Reduce)) { +XLA_TEST_F(CustomCallTest, CustomCallR2F32Reduce) { auto module = CreateNewUnverifiedModule(); auto builder = HloComputation::Builder(TestName()); @@ -105,7 +114,7 @@ XLA_TEST_F(CustomCallTest, DISABLED_ON_GPU(CustomCallR2F32Reduce)) { LiteralTestUtil::ExpectR0Near(10.0f, result, error_spec_); } -XLA_TEST_F(CustomCallTest, DISABLED_ON_GPU(UsedInOtherComputations)) { +XLA_TEST_F(CustomCallTest, UsedInOtherComputations) { auto module = CreateNewUnverifiedModule(); auto b = HloComputation::Builder(TestName()); @@ -129,7 +138,7 @@ XLA_TEST_F(CustomCallTest, DISABLED_ON_GPU(UsedInOtherComputations)) { Array3D{{{2, 3}, {4, 5}}, {{3, 4}, {5, 6}}}, result); } -XLA_TEST_F(CustomCallTest, DISABLED_ON_GPU(InputAndOutputLayoutDiffer)) { +XLA_TEST_F(CustomCallTest, InputAndOutputLayoutDiffer) { auto module = CreateNewUnverifiedModule(); auto b = HloComputation::Builder(TestName()); @@ -151,7 +160,7 @@ XLA_TEST_F(CustomCallTest, DISABLED_ON_GPU(InputAndOutputLayoutDiffer)) { LiteralTestUtil::ExpectR2Equal({{2.f, 4.f}, {3.f, 5.f}}, result); } -XLA_TEST_F(CustomCallTest, DISABLED_ON_GPU(LayoutConstrained)) { +XLA_TEST_F(CustomCallTest, LayoutConstrained) { // The argument and result of the computation are set to different layouts, // but the custom call is layout constrained to a fixed operand and result // layout, so the correct result should be produced. @@ -176,6 +185,26 @@ XLA_TEST_F(CustomCallTest, DISABLED_ON_GPU(LayoutConstrained)) { LiteralTestUtil::ExpectR2Equal({{2.f, 3.f}, {4.f, 5.f}}, result); } +XLA_TEST_F(CustomCallTest, TupleOutput) { + const char* kModuleStr = R"( + HloModule m + test { + p0 = f32[] parameter(0) + p1 = f32[] parameter(1) + ROOT %custom-call = (f32[], f32[]) custom-call(f32[] %p0, f32[] %p1), custom_call_target="F32TupleSwap", operand_layout_constraints={f32[], f32[]} + } + )"; + TF_ASSERT_OK_AND_ASSIGN(auto module, + ParseAndReturnVerifiedModule(kModuleStr)); + + Literal arg0 = LiteralUtil::CreateR0(7.f); + Literal arg1 = LiteralUtil::CreateR0(42.f); + + Literal expected = LiteralUtil::MakeTuple({&arg1, &arg0}); + Literal result = ExecuteAndTransfer(std::move(module), {&arg0, &arg1}); + EXPECT_EQ(result, expected); +} + class CustomCallClientAPITest : public ClientLibraryTestBase {}; // When using the client API, CustomCall targets can't begin with '$' -- these -- GitLab From 39b6e1924ebfbc439e8cbb9b66c70d68bad37077 Mon Sep 17 00:00:00 2001 From: Mihai Maruseac Date: Tue, 11 Dec 2018 12:19:24 -0800 Subject: [PATCH 367/873] Fix documentation formatting for OneHot op. PiperOrigin-RevId: 225048682 --- .../api_def/base_api/api_def_OneHot.pbtxt | 57 +++++++++---------- 1 file changed, 28 insertions(+), 29 deletions(-) diff --git a/tensorflow/core/api_def/base_api/api_def_OneHot.pbtxt b/tensorflow/core/api_def/base_api/api_def_OneHot.pbtxt index 807b8ae310..b325df1c8c 100644 --- a/tensorflow/core/api_def/base_api/api_def_OneHot.pbtxt +++ b/tensorflow/core/api_def/base_api/api_def_OneHot.pbtxt @@ -66,7 +66,6 @@ Examples ========= Suppose that - ``` indices = [0, 2, -1, 1] depth = 3 @@ -76,16 +75,15 @@ Suppose that ``` Then output is `[4 x 3]`: - - ```output = - [5.0 0.0 0.0] // one_hot(0) - [0.0 0.0 5.0] // one_hot(2) - [0.0 0.0 0.0] // one_hot(-1) - [0.0 5.0 0.0] // one_hot(1) - ``` +``` +output = + [5.0 0.0 0.0] // one_hot(0) + [0.0 0.0 5.0] // one_hot(2) + [0.0 0.0 0.0] // one_hot(-1) + [0.0 5.0 0.0] // one_hot(1) +``` Suppose that - ``` indices = [0, 2, -1, 1] depth = 3 @@ -95,19 +93,19 @@ Suppose that ``` Then output is `[3 x 4]`: +``` +output = + [0.0 3.0 3.0 3.0] + [3.0 3.0 3.0 0.0] + [3.0 3.0 3.0 3.0] + [3.0 0.0 3.0 3.0] +// ^ one_hot(0) +// ^ one_hot(2) +// ^ one_hot(-1) +// ^ one_hot(1) +``` - ```output = - [0.0 3.0 3.0 3.0] - [3.0 3.0 3.0 0.0] - [3.0 3.0 3.0 3.0] - [3.0 0.0 3.0 3.0] - // ^ one_hot(0) - // ^ one_hot(2) - // ^ one_hot(-1) - // ^ one_hot(1) - ``` Suppose that - ``` indices = [[0, 2], [1, -1]] depth = 3 @@ -117,14 +115,15 @@ Suppose that ``` Then output is `[2 x 2 x 3]`: - - ```output = - [ - [1.0, 0.0, 0.0] // one_hot(0) - [0.0, 0.0, 1.0] // one_hot(2) - ][ - [0.0, 1.0, 0.0] // one_hot(1) - [0.0, 0.0, 0.0] // one_hot(-1) - ]``` +``` +output = + [ + [1.0, 0.0, 0.0] // one_hot(0) + [0.0, 0.0, 1.0] // one_hot(2) + ][ + [0.0, 1.0, 0.0] // one_hot(1) + [0.0, 0.0, 0.0] // one_hot(-1) + ] +``` END } -- GitLab From 6756eee557e6a6b14ebb6c3dcb738951c44ff295 Mon Sep 17 00:00:00 2001 From: Gaurav Jain Date: Tue, 11 Dec 2018 12:23:07 -0800 Subject: [PATCH 368/873] Fix up tests to work with TensorShapeV2 PiperOrigin-RevId: 225049315 --- tensorflow/lite/python/convert_saved_model_test.py | 7 +++---- tensorflow/python/eager/backprop.py | 9 +++++++-- tensorflow/python/eager/backprop_test.py | 9 --------- tensorflow/python/eager/ops_test.py | 1 - tensorflow/python/keras/integration_test.py | 1 - tensorflow/python/keras/layers/core.py | 4 ++-- tensorflow/python/keras/layers/core_test.py | 2 -- .../python/kernel_tests/control_flow_ops_py_test.py | 13 ++++++------- tensorflow/python/kernel_tests/ctc_loss_op_test.py | 10 ---------- .../kernel_tests/linalg/linear_operator_test.py | 6 +++--- tensorflow/python/layers/core_test.py | 7 ++++++- tensorflow/python/ops/ctc_ops.py | 4 +++- tensorflow/python/ops/linalg/linear_operator.py | 5 ++++- 13 files changed, 34 insertions(+), 44 deletions(-) diff --git a/tensorflow/lite/python/convert_saved_model_test.py b/tensorflow/lite/python/convert_saved_model_test.py index 11bfcdc795..fdcbc79ee9 100644 --- a/tensorflow/lite/python/convert_saved_model_test.py +++ b/tensorflow/lite/python/convert_saved_model_test.py @@ -93,7 +93,7 @@ class TensorFunctionsTest(test_util.TensorFlowTestCase): str(error.exception)) self.assertEqual([None, 3, 5], tensor.shape.as_list()) - @test_util.run_v1_only("b/120545219") + @test_util.run_deprecated_v1 def testSetTensorShapeDimensionInvalid(self): # Tests set_tensor_shape where the shape passed in is incompatiable. tensor = array_ops.placeholder(shape=[None, 3, 5], dtype=dtypes.float32) @@ -102,9 +102,8 @@ class TensorFunctionsTest(test_util.TensorFlowTestCase): with self.assertRaises(ValueError) as error: convert_saved_model.set_tensor_shapes([tensor], {"Placeholder": [1, 5, 5]}) - self.assertIn( - "The shape of tensor 'Placeholder' cannot be changed from " - "(?, 3, 5) to [1, 5, 5].", str(error.exception)) + self.assertIn("The shape of tensor 'Placeholder' cannot be changed", + str(error.exception)) self.assertEqual([None, 3, 5], tensor.shape.as_list()) @test_util.run_v1_only("b/120545219") diff --git a/tensorflow/python/eager/backprop.py b/tensorflow/python/eager/backprop.py index 29f9b2cda3..481f680f56 100644 --- a/tensorflow/python/eager/backprop.py +++ b/tensorflow/python/eager/backprop.py @@ -1104,8 +1104,13 @@ class GradientTape(object): dimension of `target` and `source` do not match. """ target_shape = target.shape - if not target_shape.with_rank_at_least(2)[0].is_compatible_with( - source.shape.with_rank_at_least(2)[0]): + if target_shape.rank is None: + dim = Dimension(None) + else: + dim = target_shape.dims[0] + if not (target_shape.with_rank_at_least(2) and + source.shape.with_rank_at_least(2) and + dim.is_compatible_with(source.shape[0])): raise ValueError( "Need first dimension of target shape (%s) and " "source shape (%s) to match." % (target.shape, source.shape)) diff --git a/tensorflow/python/eager/backprop_test.py b/tensorflow/python/eager/backprop_test.py index 61c47a29fd..477d18e214 100644 --- a/tensorflow/python/eager/backprop_test.py +++ b/tensorflow/python/eager/backprop_test.py @@ -1338,17 +1338,14 @@ class BatchJacobianTest(test.TestCase): array_ops.diag(2 * x[1] * y[1])]) return batch_jacobian, answer - @test_util.run_v1_only('b/120545219') def testPfor(self): batch_jacobian, answer = self._batch_jacobian(experimental_use_pfor=True) self.assertAllEqual(answer, batch_jacobian) - @test_util.run_v1_only('b/120545219') def testWhileLoop(self): batch_jacobian, answer = self._batch_jacobian(experimental_use_pfor=False) self.assertAllEqual(answer, batch_jacobian) - @test_util.run_v1_only('b/120545219') def testPforDefun(self): @function.defun @@ -1358,7 +1355,6 @@ class BatchJacobianTest(test.TestCase): batch_jacobian, answer = _f() self.assertAllEqual(answer, batch_jacobian) - @test_util.run_v1_only('b/120545219') def testWhileLoopDefun(self): @function.defun @@ -1368,7 +1364,6 @@ class BatchJacobianTest(test.TestCase): batch_jacobian, answer = _f() self.assertAllEqual(answer, batch_jacobian) - @test_util.run_v1_only('b/120545219') def testPersistentTape(self): if not context.executing_eagerly(): return @@ -1379,7 +1374,6 @@ class BatchJacobianTest(test.TestCase): with self.assertRaisesRegexp(RuntimeError, 'persistent'): g.batch_jacobian(y, x, experimental_use_pfor=False) - @test_util.run_v1_only('b/120545219') def testBadShape(self): x = random_ops.random_uniform([2, 3]) with backprop.GradientTape() as g: @@ -1387,7 +1381,6 @@ class BatchJacobianTest(test.TestCase): with self.assertRaisesRegexp(ValueError, 'Need first dimension'): g.batch_jacobian(y, x) - @test_util.run_v1_only('b/120545219') def testBadInputRank(self): x = random_ops.random_uniform([2]) with backprop.GradientTape() as g: @@ -1402,7 +1395,6 @@ class BatchJacobianTest(test.TestCase): with self.assertRaisesRegexp(ValueError, 'must have rank at least 2'): g.batch_jacobian(y, x) - @test_util.run_v1_only('b/120545219') def testPforException(self): var = variables.Variable([1.]) @@ -1423,7 +1415,6 @@ class BatchJacobianTest(test.TestCase): with self.assertRaisesRegexp(ValueError, 'No converter'): g.batch_jacobian(y, x, experimental_use_pfor=True) - @test_util.run_v1_only('b/120545219') def test_parallel_iterations(self): with backprop.GradientTape(persistent=True) as g: x = constant_op.constant([[1., 2], [3, 4]]) diff --git a/tensorflow/python/eager/ops_test.py b/tensorflow/python/eager/ops_test.py index 91d0d5c6f0..17a090d526 100644 --- a/tensorflow/python/eager/ops_test.py +++ b/tensorflow/python/eager/ops_test.py @@ -330,7 +330,6 @@ class OpsTest(test_util.TensorFlowTestCase): self.assertEquals(t, dtypes.string) self.assertEquals(r[0].dtype, dtypes.string) - @test_util.run_v1_only('b/120545219') def testFlattenLayer(self): flatten_layer = core.Flatten() x = constant_op.constant([[[-10, -20], [-30, -40]], [[10, 20], [30, 40]]]) diff --git a/tensorflow/python/keras/integration_test.py b/tensorflow/python/keras/integration_test.py index c516514f63..8d65f63aba 100644 --- a/tensorflow/python/keras/integration_test.py +++ b/tensorflow/python/keras/integration_test.py @@ -134,7 +134,6 @@ class KerasIntegrationTest(test.TestCase): verbose=2) self.assertGreater(history.history['val_acc'][-1], 0.7) - @test_util.run_v1_only('b/120545219') def test_image_classification_sequential(self): with self.cached_session(): np.random.seed(1337) diff --git a/tensorflow/python/keras/layers/core.py b/tensorflow/python/keras/layers/core.py index 1b406677d9..39bcb82c72 100644 --- a/tensorflow/python/keras/layers/core.py +++ b/tensorflow/python/keras/layers/core.py @@ -549,8 +549,8 @@ class Flatten(Layer): inputs = array_ops.transpose(inputs, perm=permutation) outputs = array_ops.reshape( - inputs, (tensor_shape.dimension_value(inputs.shape[0]) - or array_ops.shape(inputs)[0], -1)) + inputs, (tensor_shape.dimension_value(inputs.shape[0]) or + array_ops.shape(inputs)[0], -1)) if not context.executing_eagerly(): outputs.set_shape(self.compute_output_shape(inputs.get_shape())) return outputs diff --git a/tensorflow/python/keras/layers/core_test.py b/tensorflow/python/keras/layers/core_test.py index b8def07190..f138adf760 100644 --- a/tensorflow/python/keras/layers/core_test.py +++ b/tensorflow/python/keras/layers/core_test.py @@ -135,7 +135,6 @@ class CoreLayersTest(test.TestCase): kwargs={'dims': (1, 4, 2)}, input_shape=(3, 2, 4)) @tf_test_util.run_in_graph_and_eager_modes - @tf_test_util.run_v1_only('b/120545219') def test_flatten(self): testing_utils.layer_test( keras.layers.Flatten, kwargs={}, input_shape=(3, 2, 4)) @@ -151,7 +150,6 @@ class CoreLayersTest(test.TestCase): self.assertAllClose(outputs, target_outputs) @tf_test_util.run_in_graph_and_eager_modes - @tf_test_util.run_v1_only('b/120545219') def test_flatten_scalar_channels(self): testing_utils.layer_test( keras.layers.Flatten, kwargs={}, input_shape=(3,)) diff --git a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py index 21ded25a11..f4a7d5bec9 100644 --- a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py +++ b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py @@ -1516,12 +1516,12 @@ class ControlFlowTest(test.TestCase): ] _, r = control_flow_ops.while_loop(c, b, [i, x]) - self.assertEqual(r.dense_shape.get_shape()[0].value, 1) + self.assertEqual(r.dense_shape.get_shape()[0], 1) _, r = control_flow_ops.while_loop( c, b, [i, x], [i.get_shape(), tensor_shape.TensorShape([None])]) - self.assertTrue(r.dense_shape.get_shape()[0].value is None) + self.assertEqual(r.dense_shape.get_shape().as_list(), [None]) with self.assertRaisesRegexp(ValueError, "is not compatible with"): _, r = control_flow_ops.while_loop( @@ -1548,15 +1548,14 @@ class ControlFlowTest(test.TestCase): ] _, r = control_flow_ops.while_loop(c, b, [i, x]) - self.assertEqual(r.dense_shape.get_shape()[0].value, 2) + self.assertEqual(r.dense_shape.get_shape()[0], 2) self.assertEqual(r.values.get_shape(), tensor_shape.TensorShape([2, 2])) _, r = control_flow_ops.while_loop( c, b, [i, x], [i.get_shape(), tensor_shape.TensorShape([None, 2])]) - self.assertEqual(r.dense_shape.get_shape()[0].value, 2) - self.assertTrue(r.values.get_shape()[0].value is None) - self.assertEqual(r.values.get_shape()[1].value, 2) + self.assertEqual(r.dense_shape.get_shape()[0], 2) + self.assertEqual(r.values.get_shape().as_list(), [None, 2]) with self.assertRaisesRegexp(ValueError, "is not compatible with"): _, r = control_flow_ops.while_loop( @@ -1925,7 +1924,7 @@ class ControlFlowTest(test.TestCase): self.assertAllClose(np.array([10.0, 10.0, 10.0]), result2) @test_util.disable_control_flow_v2("b/113324949 (RefVariable)") - @test_util.run_deprecated_v1 + @test_util.run_v1_only("b/120545219") def testWhileUpdateVariable_3(self): with self.cached_session(): select = variables.Variable([3.0, 4.0, 5.0]) diff --git a/tensorflow/python/kernel_tests/ctc_loss_op_test.py b/tensorflow/python/kernel_tests/ctc_loss_op_test.py index 39a637d831..352dedea4a 100644 --- a/tensorflow/python/kernel_tests/ctc_loss_op_test.py +++ b/tensorflow/python/kernel_tests/ctc_loss_op_test.py @@ -242,7 +242,6 @@ class CTCLossTest(test.TestCase): self._testCTCLoss(inputs, seq_lens, labels, loss_truth, grad_truth) - @test_util.run_v1_only("b/120545219") def test_time_major(self): """Testing time_major param. @@ -565,7 +564,6 @@ class CTCLossTestV2(test.TestCase): rtol=2e-06, atol=2e-06) - @test_util.run_v1_only("b/120545219") def testCollapseRepeated(self): collapsed, new_seq_lengths = ctc_ops.collapse_repeated( labels=[[1, 3, 3, 3, 0], @@ -579,7 +577,6 @@ class CTCLossTestV2(test.TestCase): [1, 4, 0, 0], [4, 2, 9, 4]]) - @test_util.run_v1_only("b/120545219") def testCollapseRepeatedPreservesDtypes(self): collapsed, new_seq_lengths = ctc_ops.collapse_repeated( labels=constant_op.constant( @@ -597,7 +594,6 @@ class CTCLossTestV2(test.TestCase): [1, 4, 0, 0], [4, 2, 9, 4]]) - @test_util.run_v1_only("b/120545219") def testCollapseRepeatedExtraPadding(self): collapsed, new_seq_lengths = ctc_ops.collapse_repeated( labels=[[1, 3, 3, 3, 0, 0, 0], @@ -611,7 +607,6 @@ class CTCLossTestV2(test.TestCase): [1, 4, 0, 0], [4, 2, 9, 4]]) - @test_util.run_v1_only("b/120545219") def testCollapseRepeatedFrontRepeats(self): collapsed, new_seq_lengths = ctc_ops.collapse_repeated( labels=[[1, 1, 1, 2, 2], @@ -625,7 +620,6 @@ class CTCLossTestV2(test.TestCase): [1, 2], [1, 0]]) - @test_util.run_v1_only("b/120545219") def testCollapseRepeatedAllLabelsTheSame(self): collapsed, new_seq_lengths = ctc_ops.collapse_repeated( labels=[[1, 1, 1, 1, 1], @@ -658,7 +652,6 @@ class CTCLossTestV2(test.TestCase): self.assertAllEqual(padded_dense, new_dense) - @test_util.run_v1_only("b/120545219") def testUnique(self): labels = [ [3, 4, 4, 3], @@ -674,7 +667,6 @@ class CTCLossTestV2(test.TestCase): [0, 0, 0, 1], ], idx) - @test_util.run_v1_only("b/120545219") def testSumStates(self): idx = [ [0, 1, 0, 1], @@ -694,7 +686,6 @@ class CTCLossTestV2(test.TestCase): [1.8, 0.8, 0.0, 0.0]] ], sum_of_states) - @test_util.run_v1_only("b/120545219") def testStateToOlabel(self): labels = [ [3, 4, 3, 4], @@ -733,7 +724,6 @@ class CTCLossTestV2(test.TestCase): [22.0 + 23.0 + 24.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]], ]) - @test_util.run_v1_only("b/120545219") def testStateToOlabelUnique(self): labels = [ [3, 4, 3, 4], diff --git a/tensorflow/python/kernel_tests/linalg/linear_operator_test.py b/tensorflow/python/kernel_tests/linalg/linear_operator_test.py index 18e13a76a0..8f8b15e8ed 100644 --- a/tensorflow/python/kernel_tests/linalg/linear_operator_test.py +++ b/tensorflow/python/kernel_tests/linalg/linear_operator_test.py @@ -214,7 +214,7 @@ class LinearOperatorTest(test.TestCase): operator = LinearOperatorMatmulSolve(matrix, is_square=True) self.assertTrue(operator.is_square) - @test_util.run_v1_only("b/120545219") + @test_util.run_deprecated_v1 def test_linear_operator_matmul_hints_closed(self): matrix = array_ops.placeholder(dtypes.float32) operator1 = LinearOperatorMatmulSolve(matrix) @@ -241,7 +241,7 @@ class LinearOperatorTest(test.TestCase): self.assertTrue(operator_matmul.is_self_adjoint) self.assertEqual(None, operator_matmul.is_positive_definite) - @test_util.run_v1_only("b/120545219") + @test_util.run_deprecated_v1 def test_linear_operator_matmul_hints_false(self): matrix = array_ops.placeholder(dtypes.float32) operator1 = LinearOperatorMatmulSolve( @@ -274,7 +274,7 @@ class LinearOperatorTest(test.TestCase): self.assertEqual(None, operator_matmul.is_self_adjoint) self.assertEqual(None, operator_matmul.is_positive_definite) - @test_util.run_v1_only("b/120545219") + @test_util.run_deprecated_v1 def test_linear_operator_matmul_hint_infer_square(self): matrix1 = array_ops.placeholder(shape=[2, 3], dtype=dtypes.float32) matrix2 = array_ops.placeholder(shape=[3, 2], dtype=dtypes.float32) diff --git a/tensorflow/python/layers/core_test.py b/tensorflow/python/layers/core_test.py index 3338e55f82..b40a268238 100644 --- a/tensorflow/python/layers/core_test.py +++ b/tensorflow/python/layers/core_test.py @@ -463,9 +463,9 @@ class DropoutTest(test.TestCase): self.assertAllClose(np.ones((5, 5)), np_output) -@test_util.run_v1_only('b/120545219') class FlattenTest(test.TestCase): + @test_util.run_deprecated_v1 def testCreateFlatten(self): with self.cached_session() as sess: x = array_ops.placeholder(shape=(None, 2, 3), dtype='float32') @@ -490,6 +490,7 @@ class FlattenTest(test.TestCase): shape = core_layers.Flatten().compute_output_shape((None, 3, None)) self.assertEqual(shape.as_list(), [None, None]) + @test_util.run_deprecated_v1 def testDataFormat5d(self): np_input_channels_last = np.arange( 120, dtype='float32').reshape([1, 5, 4, 3, 2]) @@ -507,6 +508,7 @@ class FlattenTest(test.TestCase): self.assertAllEqual(np_output_cl, np_output_cf) + @test_util.run_deprecated_v1 def testDataFormat4d(self): np_input_channels_last = np.arange( 24, dtype='float32').reshape([1, 4, 3, 2]) @@ -524,11 +526,13 @@ class FlattenTest(test.TestCase): self.assertAllEqual(np_output_cl, np_output_cf) + @test_util.run_deprecated_v1 def testFunctionalFlatten(self): x = array_ops.placeholder(shape=(None, 2, 3), dtype='float32') y = core_layers.flatten(x, name='flatten') self.assertEqual(y.get_shape().as_list(), [None, 6]) + @test_util.run_deprecated_v1 def testFlatten0D(self): x = array_ops.placeholder(shape=(None,), dtype='float32') y = core_layers.Flatten()(x) @@ -537,6 +541,7 @@ class FlattenTest(test.TestCase): self.assertEqual(list(np_output.shape), [5, 1]) self.assertEqual(y.shape.as_list(), [None, 1]) + @test_util.run_deprecated_v1 def testFlattenUnknownAxes(self): with self.cached_session() as sess: x = array_ops.placeholder(shape=(5, None, None), dtype='float32') diff --git a/tensorflow/python/ops/ctc_ops.py b/tensorflow/python/ops/ctc_ops.py index db7f9d2378..45286f7c18 100644 --- a/tensorflow/python/ops/ctc_ops.py +++ b/tensorflow/python/ops/ctc_ops.py @@ -24,6 +24,7 @@ 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 functional_ops @@ -1127,4 +1128,5 @@ def _scan(fn, elems, initial, reverse=False, inclusive=False, final_only=False): def _get_dim(tensor, i): """Get value of tensor shape[i] preferring static value if available.""" - return tensor.shape[i].value or array_ops.shape(tensor)[i] + return tensor_shape.dimension_value( + tensor.shape[i]) or array_ops.shape(tensor)[i] diff --git a/tensorflow/python/ops/linalg/linear_operator.py b/tensorflow/python/ops/linalg/linear_operator.py index 8efafda3a1..6be81f4b34 100644 --- a/tensorflow/python/ops/linalg/linear_operator.py +++ b/tensorflow/python/ops/linalg/linear_operator.py @@ -381,7 +381,10 @@ class LinearOperator(object): `Dimension` object. """ # Derived classes get this "for free" once .shape is implemented. - return self.shape[-1] + if self.shape.rank is None: + return tensor_shape.Dimension(None) + else: + return self.shape.dims[-1] def domain_dimension_tensor(self, name="domain_dimension_tensor"): """Dimension (in the sense of vector spaces) of the domain of this operator. -- GitLab From 806ccc2cf778407edacfc78bb864a3be01033f06 Mon Sep 17 00:00:00 2001 From: Alexandre Passos Date: Tue, 11 Dec 2018 12:28:45 -0800 Subject: [PATCH 369/873] Fixes race condition. PiperOrigin-RevId: 225050185 --- tensorflow/core/kernels/training_op_helpers.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/core/kernels/training_op_helpers.h b/tensorflow/core/kernels/training_op_helpers.h index 98e2b3c0f2..715dd8af7d 100644 --- a/tensorflow/core/kernels/training_op_helpers.h +++ b/tensorflow/core/kernels/training_op_helpers.h @@ -178,7 +178,7 @@ VariableInputLockHolder MaybeLockVariableInputMutexesInOrder( mutex* mu = GetTrainingVariableMutex(ctx, input, sparse, &var); core::ScopedUnref scoped_unref(var); if (mu != nullptr) { - if (do_lock) { + if (!sparse || do_lock) { locks->emplace_back(*mu); } else { shared_locks->emplace_back(*mu); -- GitLab From e3d751c2a85a74b74c5eacf038721f2c67eb2da5 Mon Sep 17 00:00:00 2001 From: Sergei Lebedev Date: Tue, 11 Dec 2018 12:30:48 -0800 Subject: [PATCH 370/873] IS_IN_GRAPH_MODE should not force-init the eager context. This caused hard to diagnose failures in enable_eager_execution calls. PiperOrigin-RevId: 225050519 --- tensorflow/python/eager/context.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/tensorflow/python/eager/context.py b/tensorflow/python/eager/context.py index cbbe5cf49e..848b300eba 100644 --- a/tensorflow/python/eager/context.py +++ b/tensorflow/python/eager/context.py @@ -923,6 +923,10 @@ def add_function(fdef): # but they do all import this file. Note that IS_IN_GRAPH_MODE and # in_graph_mode are both parameterless functions. def _tmp_in_graph_mode(): + if context_safe() is None: + # Context not yet initialized. Assume graph mode following the + # default implementation in `is_in_graph_mode`. + return True return not executing_eagerly() -- GitLab From d6a46850353acfe26625c5ab1ffe7bd5c5a4aaf0 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 11 Dec 2018 12:39:12 -0800 Subject: [PATCH 371/873] Improve build rules to compile NCCL from source, in particular for clang. PiperOrigin-RevId: 225051897 --- third_party/nccl/archive.BUILD | 154 ++++----- third_party/nccl/build_defs.bzl.tpl | 467 ++++++++++++++++++---------- 2 files changed, 351 insertions(+), 270 deletions(-) diff --git a/third_party/nccl/archive.BUILD b/third_party/nccl/archive.BUILD index 7a08f97ef3..22b9728017 100644 --- a/third_party/nccl/archive.BUILD +++ b/third_party/nccl/archive.BUILD @@ -1,157 +1,110 @@ # NVIDIA NCCL 2 # A package of optimized primitives for collective multi-GPU communication. -licenses(["restricted"]) +licenses(["notice"]) exports_files(["LICENSE.txt"]) load( "@local_config_nccl//:build_defs.bzl", - "gen_nccl_h", - "nccl_library", - "rdc_copts", - "rdc_library", -) -load( - "@local_config_cuda//cuda:build_defs.bzl", - "cuda_default_copts", + "cuda_rdc_library", + "gen_device_srcs", + "process_srcs", ) +load("@org_tensorflow//tensorflow:tensorflow.bzl", "tf_cuda_library") -# Generate the nccl.h header file. -gen_nccl_h( - name = "nccl_h", - output = "src/nccl.h", - template = "src/nccl.h.in", +process_srcs( + name = "process_srcs", + srcs = glob([ + "**/*.cc", + "**/*.h", + ]), ) -nccl_library( +cc_library( name = "src_hdrs", hdrs = [ - "src/nccl.h", - # src/include/common_coll.h #includes "collectives/collectives.h". - # All other #includes of collectives.h are patched in process_srcs. "src/collectives/collectives.h", + "src/nccl.h", ], + data = [":process_srcs"], strip_include_prefix = "src", ) -nccl_library( +cc_library( name = "include_hdrs", hdrs = glob(["src/include/*.h"]), + data = [":process_srcs"], strip_include_prefix = "src/include", ) -filegroup( +cc_library( name = "device_hdrs", - srcs = glob(["src/collectives/device/*.h"]), + hdrs = glob(["src/collectives/device/*.h"]), + strip_include_prefix = "src/collectives/device", ) filegroup( name = "device_srcs", srcs = [ - "src/collectives/device/all_gather.cu", - "src/collectives/device/all_reduce.cu", - "src/collectives/device/broadcast.cu", - "src/collectives/device/reduce.cu", - "src/collectives/device/reduce_scatter.cu", + "src/collectives/device/all_gather.cu.cc", + "src/collectives/device/all_reduce.cu.cc", + "src/collectives/device/broadcast.cu.cc", + "src/collectives/device/reduce.cu.cc", + "src/collectives/device/reduce_scatter.cu.cc", ], ) -nccl_library( +# NCCL compiles the same source files with different NCCL_OP defines. RDC +# compilation requires that each compiled module has a unique ID. Clang derives +# the module ID from the path only so we need to rename the files to get +# different IDs for different parts of compilation. NVCC does not have that +# problem because it generates IDs based on preprocessed content. +gen_device_srcs( name = "sum", - srcs = [ - ":device_hdrs", - ":device_srcs", - ], - copts = ["-DNCCL_OP=0"] + rdc_copts(), - linkstatic = True, - prefix = "sum_", - deps = [ - ":include_hdrs", - ":src_hdrs", - "@local_config_cuda//cuda:cuda_headers", - ], + srcs = [":device_srcs"], + NCCL_OP = 0, ) -nccl_library( +gen_device_srcs( name = "prod", - srcs = [ - ":device_hdrs", - ":device_srcs", - ], - copts = ["-DNCCL_OP=1"] + rdc_copts(), - linkstatic = True, - prefix = "_prod", - deps = [ - ":include_hdrs", - ":src_hdrs", - "@local_config_cuda//cuda:cuda_headers", - ], + srcs = [":device_srcs"], + NCCL_OP = 1, ) -nccl_library( +gen_device_srcs( name = "min", - srcs = [ - ":device_hdrs", - ":device_srcs", - ], - copts = ["-DNCCL_OP=2"] + rdc_copts(), - linkstatic = True, - prefix = "min_", - deps = [ - ":include_hdrs", - ":src_hdrs", - "@local_config_cuda//cuda:cuda_headers", - ], + srcs = [":device_srcs"], + NCCL_OP = 2, ) -nccl_library( +gen_device_srcs( name = "max", - srcs = [ - ":device_hdrs", - ":device_srcs", - ], - copts = ["-DNCCL_OP=3"] + rdc_copts(), - linkstatic = True, - prefix = "max_", - deps = [ - ":include_hdrs", - ":src_hdrs", - "@local_config_cuda//cuda:cuda_headers", - ], + srcs = [":device_srcs"], + NCCL_OP = 3, ) -nccl_library( - name = "functions", +cuda_rdc_library( + name = "device", srcs = [ - "src/collectives/device/functions.cu", - ":device_hdrs", - ], - copts = rdc_copts(), - linkstatic = True, - deps = [ - ":include_hdrs", - ":src_hdrs", - "@local_config_cuda//cuda:cuda_headers", - ], -) - -rdc_library( - name = "device_code", - deps = [ - ":functions", + "src/collectives/device/functions.cu.cc", ":max", ":min", ":prod", ":sum", ], + deps = [ + ":device_hdrs", + ":include_hdrs", + ":src_hdrs", + ], ) # Primary NCCL target. -nccl_library( +tf_cuda_library( name = "nccl", srcs = glob( - include = ["src/**/*.cu"], + include = ["src/**/*.cu.cc"], # Exclude device-library code. exclude = ["src/collectives/device/**"], ) + [ @@ -162,13 +115,14 @@ nccl_library( "src/nccl.h", ], hdrs = ["src/nccl.h"], - copts = cuda_default_copts(), + copts = ["-Wno-vla"], include_prefix = "third_party/nccl", strip_include_prefix = "src", visibility = ["//visibility:public"], deps = [ - ":device_code", + ":device", ":include_hdrs", ":src_hdrs", + "@local_config_cuda//cuda:cudart_static", ], ) diff --git a/third_party/nccl/build_defs.bzl.tpl b/third_party/nccl/build_defs.bzl.tpl index 42de79c411..fe16f10432 100644 --- a/third_party/nccl/build_defs.bzl.tpl +++ b/third_party/nccl/build_defs.bzl.tpl @@ -1,87 +1,86 @@ """Repository rule for NCCL.""" -load("@local_config_cuda//cuda:build_defs.bzl", "cuda_default_copts") - -def _gen_nccl_h_impl(ctx): - """Creates nccl.h from a template.""" - ctx.actions.expand_template( - output = ctx.outputs.output, - template = ctx.file.template, - substitutions = { - "${nccl:Major}": "2", - "${nccl:Minor}": "3", - "${nccl:Patch}": "5", - "${nccl:Suffix}": "", - "${nccl:Version}": "2305", - }, - ) - -gen_nccl_h = rule( - implementation = _gen_nccl_h_impl, - attrs = { - "template": attr.label(allow_single_file = True), - "output": attr.output(), - }, -) -"""Creates the NCCL header file.""" +load("@org_tensorflow//tensorflow:tensorflow.bzl", "tf_cuda_library") +load("@bazel_tools//tools/cpp:toolchain_utils.bzl", "find_cpp_toolchain") def _process_srcs_impl(ctx): """Appends .cc to .cu files, patches include directives.""" files = [] for src in ctx.files.srcs: - if not src.is_source: - # Process only once, specifically "src/nccl.h". - files.append(src) - continue + substitutions = { + "\"collectives.h": "\"collectives/collectives.h", + "\"../collectives.h": "\"collectives/collectives.h", + # Clang does not define __CUDACC_VER_*__, use CUDA_VERSION instead. + # TODO(csigg): Apply substitutions upstream and remove here. + "#if __CUDACC_VER_MAJOR__ >= 10 || (__CUDACC_VER_MAJOR__ >= 9 && __CUDACC_VER_MINOR__ >= 2)": "#if CUDA_VERSION >= 9200", + "#if __CUDACC_VER_MAJOR__ >= 10": "#if CUDA_VERSION >= 10000", + "#if __CUDACC_VER_MAJOR__ >= 9": "#if CUDA_VERSION >= 9000", + "#if __CUDACC_VER_MAJOR__ < 9": "#if CUDA_VERSION < 9000", + "nullptr_t": "std::nullptr_t", + } name = src.basename + if name == "nccl.in.h": + name = "nccl.h" + substitutions.update({ + "${nccl:Major}": "2", + "${nccl:Minor}": "3", + "${nccl:Patch}": "5", + "${nccl:Suffix}": "", + "${nccl:Version}": "2305", + }) + if name == "functions.cu": + # Don't try to initialize the host shadow copy of this device-side + # global variable. There is no host pointer to a device-side + # function, which confuses clang. + # TODO(csigg): remove when fixed in clang. + substitutions.update({ + "NCCL_FUNCS2B(ncclBroadcast),": "#if __CUDA_ARCH__\nNCCL_FUNCS2B(ncclBroadcast),", + "NCCL_FUNCS2A(ncclAllReduce)": "NCCL_FUNCS2A(ncclAllReduce)\n#endif", + }) if src.extension == "cu": - name = ctx.attr.prefix + name + ".cc" + name += ".cc" file = ctx.actions.declare_file(name, sibling = src) ctx.actions.expand_template( output = file, template = src, - substitutions = { - "\"collectives.h": "\"collectives/collectives.h", - "\"../collectives.h": "\"collectives/collectives.h", - "#if __CUDACC_VER_MAJOR__": "#if defined __CUDACC_VER_MAJOR__ && __CUDACC_VER_MAJOR__", - # Substitutions are applied in order. - "std::nullptr_t": "nullptr_t", - "nullptr_t": "std::nullptr_t", - }, + substitutions = substitutions, ) files.append(file) return [DefaultInfo(files = depset(files))] -_process_srcs = rule( +process_srcs = rule( implementation = _process_srcs_impl, attrs = { "srcs": attr.label_list(allow_files = True), - "prefix": attr.string(default = ""), }, ) """Processes the NCCL srcs so they can be compiled with bazel and clang.""" -def nccl_library(name, srcs = None, hdrs = None, prefix = None, **kwargs): - """Processes the srcs and hdrs and creates a cc_library.""" - - _process_srcs( - name = name + "_srcs", - srcs = srcs, - prefix = prefix, - ) - _process_srcs( - name = name + "_hdrs", - srcs = hdrs, - ) +def _gen_device_srcs_impl(ctx): + files = [] + for src in ctx.files.srcs: + name = "%s_%s" % (ctx.attr.name, src.basename) + file = ctx.actions.declare_file(name, sibling = src) + ctx.actions.expand_template( + output = file, + template = src, + substitutions = { + "#define UNROLL 4": "#define UNROLL 4\n#define NCCL_OP %d" % ctx.attr.NCCL_OP, + }, + ) + files.append(file) + return [DefaultInfo(files = depset(files))] - native.cc_library( - name = name, - srcs = [name + "_srcs"] if srcs else [], - hdrs = [name + "_hdrs"] if hdrs else [], - **kwargs - ) +gen_device_srcs = rule( + implementation = _gen_device_srcs_impl, + attrs = { + "srcs": attr.label_list(allow_files = True), + "NCCL_OP": attr.int(), + }, +) +"""Adds prefix to each file name in srcs and adds #define NCCL_OP.""" -def rdc_copts(): +def _rdc_copts(): """Returns copts for compiling relocatable device code.""" # The global functions can not have a lower register count than the @@ -89,7 +88,7 @@ def rdc_copts(): # https://github.com/NVIDIA/nccl/blob/f93fe9bfd94884cec2ba711897222e0df5569a53/makefiles/common.mk#L48 maxrregcount = "-maxrregcount=96" - return cuda_default_copts() + select({ + return select({ "@local_config_cuda//cuda:using_nvcc": [ "-nvcc_options", "relocatable-device-code=true", @@ -100,118 +99,255 @@ def rdc_copts(): "-fcuda-rdc", "-Xcuda-ptxas", maxrregcount, + # Work around for clang bug (fixed in r348662), declaring + # '__device__ operator delete(void*, std::size_t)' non-inline. + # TODO(csigg): Only add this option for older clang versions. + "-std=gnu++11", ], "//conditions:default": [], - }) + ["-fvisibility=hidden"] + }) -def _filter_impl(ctx): - suffix = ctx.attr.suffix - files = [src for src in ctx.files.srcs if src.path.endswith(suffix)] - return [DefaultInfo(files = depset(files))] +def _lookup_file(filegroup, path): + """Extracts file at (relative) path in filegroup.""" + for file in filegroup.files: + if file.path.endswith(path): + return file + return None -_filter = rule( - implementation = _filter_impl, - attrs = { - "srcs": attr.label_list(allow_files = True), - "suffix": attr.string(), - }, -) -"""Filters the srcs to the ones ending with suffix.""" +def _pic_only(files): + """Returns the PIC files if there are any in 'files', otherwise 'files'.""" + pic_only = [f for f in files if f.basename.find(".pic.") >= 0] + return pic_only if pic_only else files + +def _device_link_impl(ctx): + if not ctx.attr.gpu_archs: + fail("No GPU architecture specified. NCCL requires --config=cuda or similar.") + + inputs = [] + for dep in ctx.attr.deps: + inputs += dep.files.to_list() + inputs = _pic_only(inputs) -def _gen_link_src_impl(ctx): + # Device-link to cubins for each architecture. + name = ctx.attr.name + register_h = None + cubins = [] + images = [] + for arch in ctx.attr.gpu_archs: + cubin = ctx.actions.declare_file("%s_%s.cubin" % (name, arch)) + register_h = ctx.actions.declare_file("%s_register_%s.h" % (name, arch)) + ctx.actions.run( + outputs = [register_h, cubin], + inputs = inputs, + executable = ctx.file._nvlink, + arguments = ctx.attr.nvlink_args + [ + "--arch=%s" % arch, + "--register-link-binaries=%s" % register_h.path, + "--output-file=%s" % cubin.path, + ] + [file.path for file in inputs], + mnemonic = "nvlink", + ) + cubins.append(cubin) + images.append("--image=profile=%s,file=%s" % (arch, cubin.path)) + + # Generate fatbin header from all cubins. + tmp_fatbin = ctx.actions.declare_file("%s.fatbin" % name) + fatbin_h = ctx.actions.declare_file("%s_fatbin.h" % name) + bin2c = ctx.file._bin2c + ctx.actions.run( + outputs = [tmp_fatbin, fatbin_h], + inputs = cubins, + executable = ctx.file._fatbinary, + arguments = [ + "-64", + "--cmdline=--compile-only", + "--link", + "--compress-all", + "--bin2c-path=%s" % bin2c.dirname, + "--create=%s" % tmp_fatbin.path, + "--embedded-fatbin=%s" % fatbin_h.path, + ] + images, + tools = [bin2c], + mnemonic = "fatbinary", + ) + + # Generate the source file #including the headers generated above. ctx.actions.expand_template( - output = ctx.outputs.output, - template = ctx.file.template, + output = ctx.outputs.out, + template = ctx.file._link_stub, substitutions = { - "REGISTERLINKBINARYFILE": '"%s"' % ctx.file.register_hdr.short_path, - "FATBINFILE": '"%s"' % ctx.file.fatbin_hdr.short_path, + "REGISTERLINKBINARYFILE": '"%s"' % register_h.short_path, + "FATBINFILE": '"%s"' % fatbin_h.short_path, }, ) -_gen_link_src = rule( - implementation = _gen_link_src_impl, + return [DefaultInfo(files = depset([register_h, fatbin_h]))] + +_device_link = rule( + implementation = _device_link_impl, attrs = { - "register_hdr": attr.label(allow_single_file = True), - "fatbin_hdr": attr.label(allow_single_file = True), - "template": attr.label(allow_single_file = True), - "output": attr.output(), + "deps": attr.label_list(), + "out": attr.output(mandatory = True), + "gpu_archs": attr.string_list(), + "nvlink_args": attr.string_list(), + "_nvlink": attr.label( + default = Label("@local_config_nccl//:nvlink"), + allow_single_file = True, + executable = True, + cfg = "host", + ), + "_fatbinary": attr.label( + default = Label("@local_config_nccl//:cuda/bin/fatbinary"), + allow_single_file = True, + executable = True, + cfg = "host", + ), + "_bin2c": attr.label( + default = Label("@local_config_nccl//:cuda/bin/bin2c"), + allow_single_file = True, + executable = True, + cfg = "host", + ), + "_link_stub": attr.label( + default = Label("@local_config_nccl//:cuda/bin/crt/link.stub"), + allow_single_file = True, + ), }, ) -"""Patches the include directives for the link.stub file.""" - -def rdc_library(name, deps): - """Produces a cc_library from deps containing relocatable device code.""" - - # From .a and .pic.a archives, just use the latter. Otherwise we get - # multiply defined symbols. - # TODO(csigg): C++ Sandwich once available should allow passing this target - # to a cc_library dependency, which would avoid the linking order issue. - _filter( - name = name + "_deps_a", - srcs = deps, - suffix = ".pic.a", +"""Links device code and generates source code for kernel registration.""" + +def _merge_archive_impl(ctx): + # Generate an mri script to the merge archives in srcs and pass it to 'ar'. + # See https://stackoverflow.com/a/23621751. + files = _pic_only(ctx.files.srcs) + mri_script = "create " + ctx.outputs.out.path + for f in files: + mri_script += "\\naddlib " + f.path + mri_script += "\\nsave\\nend" + + cc_toolchain = find_cpp_toolchain(ctx) + ctx.actions.run_shell( + inputs = ctx.files.srcs, # + ctx.files._crosstool, + outputs = [ctx.outputs.out], + command = ("printf \"%s\" " % mri_script + + "| %s -M" % cc_toolchain.ar_executable), ) - # Device-link to cubins for each architecture. - images = [] - cubins = [] - for arch in %{gpu_architectures}: - cubin = "%s_%s.cubin" % (name, arch) - register_hdr = "%s_%s.h" % (name, arch) - nvlink = "@local_config_nccl//:nvlink" - cmd = ("$(location %s) " % nvlink + - select({ - # NCCL is only supported on Linux. - "@org_tensorflow//tensorflow:linux_x86_64": "--cpu-arch=X86_64 ", - "@org_tensorflow//tensorflow:linux_ppc64le": "--cpu-arch=PPC64LE ", - "//conditions:default": "", - }) + - "--arch=%s $(SRCS) " % arch + - "--register-link-binaries=$(location %s) " % register_hdr + - "--output-file=$(location %s)" % cubin) - native.genrule( - name = "%s_%s" % (name, arch), - outs = [register_hdr, cubin], - srcs = [name + "_deps_a"], - cmd = cmd, - tools = [nvlink], - ) - images.append("--image=profile=%s,file=$(location %s)" % (arch, cubin)) - cubins.append(cubin) +_merge_archive = rule( + implementation = _merge_archive_impl, + attrs = { + "srcs": attr.label_list(mandatory = True, allow_files = True), + "_cc_toolchain": attr.label(default = "@bazel_tools//tools/cpp:current_cc_toolchain"), + # "_crosstool": attr.label_list(cfg = "host", default = ["@bazel_tools//tools/cpp:crosstool"]), + }, + outputs = {"out": "lib%{name}.a"}, +) +"""Merges srcs into a single archive.""" - # Generate fatbin header from all cubins. - fatbin_hdr = name + ".fatbin.h" - fatbinary = "@local_config_nccl//:cuda/bin/fatbinary" - bin2c = "@local_config_nccl//:cuda/bin/bin2c" - cmd = ("$(location %s) -64 --cmdline=--compile-only " % fatbinary + - "--link --bin2c-path $$(dirname $(location %s)) " % bin2c + - "--compress-all %s --create=%%{name}.fatbin " % " ".join(images) + - "--embedded-fatbin=$@") - native.genrule( - name = name + "_fatbin_h", - outs = [fatbin_hdr], - srcs = cubins, - cmd = cmd, - tools = [fatbinary, bin2c], +def cuda_rdc_library(name, hdrs = None, copts = None, linkstatic = True, **kwargs): + """Produces a cuda_library using separate compilation and linking. + + CUDA separate compilation and linking allows device function calls across + translation units. This is different from the normal whole program + compilation where each translation unit contains all device code. For more + background, see + https://devblogs.nvidia.com/separate-compilation-linking-cuda-device-code/, + https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html#nvcc-options-for-separate-compilation + + During separate compilation, the different CUDA source files are compiled + to 'relocatable device code' (RDC) and embedded in the host object files. + When using nvcc, linking the device code for each supported GPU + architecture and generating kernel registration code for the CUDA runtime + is handled automatically. Clang supports generating relocatable device + code, but it can't link it. We therefore rely on tools provided by the CUDA + SDK to link the device code and generate the host code to register the + kernels. + + The nvlink tool extracts the RDC code from the object files and links it + into cubin files, one per GPU architecture. It also produces a header file + with a list of kernel names to register. The cubins are merged into a + binary blob using the fatbinary tool, and converted to a C header file with + the help of the bin2c tool. The registration header file, the fatbinary + header file, and the link.stub file (shipped with the CUDA SDK) are + compiled as ordinary host code. + + Here is a diagram of the CUDA separate compilation trajectory: + + x.cu.cc y.cu.cc + \ / cc_library (compile RDC and archive) + xy.a + / \ * nvlink + register.h xy.cubin + : | * fatbinary and bin2c + : xy.fatbin.h + : : * #include + dlink.cc * Expanded from crt/dlink.stub template + | cc_library (host compile and archive) + dlink.a + + The steps marked with '*' are implemented in the _device_link rule. + + The object files in both xy.a and dlink.a reference symbols defined in the + other archive. The separate archives are a side effect of using two + cc_library targets to implement a single compilation trajectory. We could + fix this once bazel supports C++ sandwich. For now, we just merge the two + archives to avoid unresolved symbols: + + xy.a dlink.a + \ / merge archive + xy_dlink.a + | cc_library (or alternatively, cc_import) + final target + + Another complication is that cc_library produces (depending on the + configuration) both PIC and non-PIC archives, but the distinction + is hidden from Starlark until C++ sandwich becomes available. We work + around this by dropping the non-PIC files if PIC files are available. + + Args: + name: Target name. + hdrs: Header files. + copts: Compiler options. + linkstatic: Must be true. + **kwargs: Any other arguments. + """ + + if not hdrs: + hdrs = [] + if not copts: + copts = [] + + # Compile host and device code into library. + lib = name + "_lib" + tf_cuda_library( + name = lib, + hdrs = hdrs, + copts = _rdc_copts() + copts, + linkstatic = linkstatic, + **kwargs ) - # Generate the source file #including the headers generated above. - _gen_link_src( - name = name + "_dlink_src", - # Include just the last one, they are equivalent. - register_hdr = register_hdr, - fatbin_hdr = fatbin_hdr, - template = "@local_config_nccl//:cuda/bin/crt/link.stub", - output = name + ".cc", + # Generate source file containing linked device code. + dlink_hdrs = name + "_dlink_hdrs" + dlink_cc = name + "_dlink.cc" + _device_link( + name = dlink_hdrs, + deps = [lib], + out = dlink_cc, + gpu_archs = %{gpu_architectures}, + nvlink_args = select({ + "@org_tensorflow//tensorflow:linux_x86_64": ["--cpu-arch=X86_64"], + "@org_tensorflow//tensorflow:linux_ppc64le": ["--cpu-arch=PPC64LE"], + "//conditions:default": [], + }), ) - # Compile the source file into the cc_library. + # Compile the source file into a library. + dlink = name + "_dlink" native.cc_library( - name = name + "_dlink_a", - srcs = [ - name + "_dlink_src", - ], - textual_hdrs = [register_hdr, fatbin_hdr], + name = dlink, + srcs = [dlink_cc], + textual_hdrs = [dlink_hdrs], deps = [ "@local_config_cuda//cuda:cuda_headers", ], @@ -222,31 +358,22 @@ def rdc_library(name, deps): "__NV_EXTRA_INITIALIZATION=", "__NV_EXTRA_FINALIZATION=", ], - linkstatic = True, + linkstatic = linkstatic, ) - # Repackage deps into a single archive. This avoid unresolved symbols when - # the archives happen to be linked in the wrong order. For more details, see + # Repackage the two libs into a single archive. This is required because + # both libs reference symbols defined in the other one. For details, see # https://eli.thegreenplace.net/2013/07/09/library-order-in-static-linking - native.genrule( - name = name + "_a", - srcs = [ - name + "_deps_a", - name + "_dlink_a", - ], - outs = [name + ".a"], - # See https://stackoverflow.com/a/23621751 - cmd = """ -addlibs=$$(echo $(SRCS) | sed "s/[^ ]* */\\naddlib &/g") -printf "create $@$${addlibs}\\nsave\\nend" | $(AR) -M -""", + archive = name + "_a" + _merge_archive( + name = archive, + srcs = [lib, dlink], ) + # Create cc target from archive. native.cc_library( name = name, - srcs = [name + "_a"], - deps = [ - "@local_config_cuda//cuda:cudart_static", - ], - linkstatic = True, + srcs = [archive], + hdrs = hdrs, + linkstatic = linkstatic, ) -- GitLab From f9dbe98610790fff9ccec148e3ec088bc779460f Mon Sep 17 00:00:00 2001 From: Tim Shen Date: Tue, 11 Dec 2018 12:53:13 -0800 Subject: [PATCH 372/873] Tweak logger dependencies. PiperOrigin-RevId: 225054204 --- tensorflow/core/BUILD | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 66714235b5..5f5ca63540 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -445,16 +445,20 @@ cc_library( ] + tf_additional_human_readable_json_deps(), ) +cc_library( + name = "logger_interface", + hdrs = ["platform/logger.h"], + copts = tf_copts(), + visibility = ["//visibility:public"], + deps = [":platform_protobuf"], +) + cc_library( name = "logger", srcs = tf_platform_srcs(["logger.cc"]), - hdrs = ["platform/logger.h"] + tf_platform_hdrs(["logger.h"]), copts = tf_copts(), visibility = ["//visibility:public"], - deps = [ - ":lib", - ":lib_internal", - ] + tf_additional_logger_deps(), + deps = [":logger_interface"] + tf_additional_logger_deps(), ) filegroup( @@ -1619,7 +1623,6 @@ filegroup( "util/reporter.*", "platform/**/cuda_libdevice_path.*", "platform/**/logger.cc", - "platform/**/logger.h", "platform/default/test_benchmark.*", "platform/cuda.h", "platform/google/**/*", -- GitLab From 93439a553937e77e8877a149d13039960da59abf Mon Sep 17 00:00:00 2001 From: Anna R Date: Tue, 11 Dec 2018 13:44:11 -0800 Subject: [PATCH 373/873] Use "in symbol.__dict__" instead of "hasattr" to check if a symbol has api names set. The former would behave correctly for subclasses. Also, moving get_v1_names|constants and get_v2_names|constants functions to tf_export.py to reduce code duplication. PiperOrigin-RevId: 225063242 --- tensorflow/python/util/tf_export.py | 88 +++++++++++++++++++ tensorflow/python/util/tf_export_test.py | 28 ++++++ .../tools/compatibility/tf_upgrade_v2_test.py | 38 ++------ .../update/generate_v2_renames_map.py | 62 ++----------- .../update/generate_v2_reorders_map.py | 36 +------- 5 files changed, 128 insertions(+), 124 deletions(-) diff --git a/tensorflow/python/util/tf_export.py b/tensorflow/python/util/tf_export.py index ec70cae7d2..74afc3746f 100644 --- a/tensorflow/python/util/tf_export.py +++ b/tensorflow/python/util/tf_export.py @@ -147,6 +147,94 @@ def get_canonical_name(api_names, deprecated_api_names): return None +def get_v1_names(symbol): + """Get a list of TF 1.* names for this symbol. + + Args: + symbol: symbol to get API names for. + + Returns: + List of all API names for this symbol including TensorFlow and + Estimator names. + """ + names_v1 = [] + tensorflow_api_attr_v1 = API_ATTRS_V1[TENSORFLOW_API_NAME].names + estimator_api_attr_v1 = API_ATTRS_V1[ESTIMATOR_API_NAME].names + + if not hasattr(symbol, tensorflow_api_attr_v1): + return names_v1 + if tensorflow_api_attr_v1 in symbol.__dict__: + names_v1.extend(getattr(symbol, tensorflow_api_attr_v1)) + if estimator_api_attr_v1 in symbol.__dict__: + names_v1.extend(getattr(symbol, estimator_api_attr_v1)) + return names_v1 + + +def get_v2_names(symbol): + """Get a list of TF 2.0 names for this symbol. + + Args: + symbol: symbol to get API names for. + + Returns: + List of all API names for this symbol including TensorFlow and + Estimator names. + """ + names_v2 = [] + tensorflow_api_attr = API_ATTRS[TENSORFLOW_API_NAME].names + estimator_api_attr = API_ATTRS[ESTIMATOR_API_NAME].names + + if not hasattr(symbol, tensorflow_api_attr): + return names_v2 + if tensorflow_api_attr in symbol.__dict__: + names_v2.extend(getattr(symbol, tensorflow_api_attr)) + if estimator_api_attr in symbol.__dict__: + names_v2.extend(getattr(symbol, estimator_api_attr)) + return names_v2 + + +def get_v1_constants(module): + """Get a list of TF 1.* constants in this module. + + Args: + module: TensorFlow module. + + Returns: + List of all API constants under the given module including TensorFlow and + Estimator constants. + """ + constants_v1 = [] + tensorflow_constants_attr_v1 = API_ATTRS_V1[TENSORFLOW_API_NAME].constants + estimator_constants_attr_v1 = API_ATTRS_V1[ESTIMATOR_API_NAME].constants + + if hasattr(module, tensorflow_constants_attr_v1): + constants_v1.extend(getattr(module, tensorflow_constants_attr_v1)) + if hasattr(module, estimator_constants_attr_v1): + constants_v1.extend(getattr(module, estimator_constants_attr_v1)) + return constants_v1 + + +def get_v2_constants(module): + """Get a list of TF 2.0 constants in this module. + + Args: + module: TensorFlow module. + + Returns: + List of all API constants under the given module including TensorFlow and + Estimator constants. + """ + constants_v2 = [] + tensorflow_constants_attr = API_ATTRS[TENSORFLOW_API_NAME].constants + estimator_constants_attr = API_ATTRS[ESTIMATOR_API_NAME].constants + + if hasattr(module, tensorflow_constants_attr): + constants_v2.extend(getattr(module, tensorflow_constants_attr)) + if hasattr(module, estimator_constants_attr): + constants_v2.extend(getattr(module, estimator_constants_attr)) + return constants_v2 + + class api_export(object): # pylint: disable=invalid-name """Provides ways to export symbols to the TensorFlow API.""" diff --git a/tensorflow/python/util/tf_export_test.py b/tensorflow/python/util/tf_export_test.py index a0fac8bf36..20625792e9 100644 --- a/tensorflow/python/util/tf_export_test.py +++ b/tensorflow/python/util/tf_export_test.py @@ -62,6 +62,10 @@ class ValidateExportTest(test.TestCase): del symbol._tf_api_names if hasattr(symbol, '_tf_api_names_v1'): del symbol._tf_api_names_v1 + if hasattr(symbol, '_estimator_api_names'): + del symbol._estimator_api_names + if hasattr(symbol, '_estimator_api_names_v1'): + del symbol._estimator_api_names_v1 def _CreateMockModule(self, name): mock_module = self.MockModule(name) @@ -74,6 +78,10 @@ class ValidateExportTest(test.TestCase): decorated_function = export_decorator(_test_function) self.assertEquals(decorated_function, _test_function) self.assertEquals(('nameA', 'nameB'), decorated_function._tf_api_names) + self.assertEquals(['nameA', 'nameB'], + tf_export.get_v1_names(decorated_function)) + self.assertEquals(['nameA', 'nameB'], + tf_export.get_v2_names(decorated_function)) def testExportMultipleFunctions(self): export_decorator1 = tf_export.tf_export('nameA', 'nameB') @@ -95,6 +103,22 @@ class ValidateExportTest(test.TestCase): export_decorator_b(TestClassB) self.assertEquals(('TestClassA1',), TestClassA._tf_api_names) self.assertEquals(('TestClassB1',), TestClassB._tf_api_names) + self.assertEquals(['TestClassA1'], tf_export.get_v1_names(TestClassA)) + self.assertEquals(['TestClassB1'], tf_export.get_v1_names(TestClassB)) + + def testExportClassInEstimator(self): + export_decorator_a = tf_export.tf_export('TestClassA1') + export_decorator_a(TestClassA) + self.assertEquals(('TestClassA1',), TestClassA._tf_api_names) + + export_decorator_b = tf_export.estimator_export( + 'estimator.TestClassB1') + export_decorator_b(TestClassB) + self.assertTrue('_tf_api_names' not in TestClassB.__dict__) + self.assertEquals(('TestClassA1',), TestClassA._tf_api_names) + self.assertEquals(['TestClassA1'], tf_export.get_v1_names(TestClassA)) + self.assertEquals(['estimator.TestClassB1'], + tf_export.get_v1_names(TestClassB)) def testExportSingleConstant(self): module1 = self._CreateMockModule('module1') @@ -103,6 +127,10 @@ class ValidateExportTest(test.TestCase): export_decorator.export_constant('module1', 'test_constant') self.assertEquals([(('NAME_A', 'NAME_B'), 'test_constant')], module1._tf_api_constants) + self.assertEquals([(('NAME_A', 'NAME_B'), 'test_constant')], + tf_export.get_v1_constants(module1)) + self.assertEquals([(('NAME_A', 'NAME_B'), 'test_constant')], + tf_export.get_v2_constants(module1)) def testExportMultipleConstants(self): module1 = self._CreateMockModule('module1') diff --git a/tensorflow/tools/compatibility/tf_upgrade_v2_test.py b/tensorflow/tools/compatibility/tf_upgrade_v2_test.py index 0fc7a18734..2cc874fe7f 100644 --- a/tensorflow/tools/compatibility/tf_upgrade_v2_test.py +++ b/tensorflow/tools/compatibility/tf_upgrade_v2_test.py @@ -37,32 +37,6 @@ from tensorflow.tools.compatibility import ast_edits from tensorflow.tools.compatibility import tf_upgrade_v2 -_TENSORFLOW_API_ATTR_V1 = ( - tf_export.API_ATTRS_V1[tf_export.TENSORFLOW_API_NAME].names) -_TENSORFLOW_API_ATTR = tf_export.API_ATTRS[tf_export.TENSORFLOW_API_NAME].names -_ESTIMATOR_API_ATTR_V1 = ( - tf_export.API_ATTRS_V1[tf_export.ESTIMATOR_API_NAME].names) -_ESTIMATOR_API_ATTR = tf_export.API_ATTRS[tf_export.ESTIMATOR_API_NAME].names - - -def get_v1_names(symbol): - names_v1 = [] - if hasattr(symbol, _TENSORFLOW_API_ATTR_V1): - names_v1.extend(getattr(symbol, _TENSORFLOW_API_ATTR_V1)) - if hasattr(symbol, _ESTIMATOR_API_ATTR_V1): - names_v1.extend(getattr(symbol, _ESTIMATOR_API_ATTR_V1)) - return names_v1 - - -def get_v2_names(symbol): - names_v2 = set() - if hasattr(symbol, _TENSORFLOW_API_ATTR): - names_v2.update(getattr(symbol, _TENSORFLOW_API_ATTR)) - if hasattr(symbol, _ESTIMATOR_API_ATTR): - names_v2.update(getattr(symbol, _ESTIMATOR_API_ATTR)) - return list(names_v2) - - def get_symbol_for_name(root, name): name_parts = name.split(".") symbol = root @@ -118,7 +92,7 @@ class TestUpgrade(test_util.TensorFlowTestCase): def symbol_collector(unused_path, unused_parent, children): for child in children: _, attr = tf_decorator.unwrap(child[1]) - api_names_v2 = get_v2_names(attr) + api_names_v2 = tf_export.get_v2_names(attr) for name in api_names_v2: cls.v2_symbols["tf." + name] = attr @@ -166,7 +140,7 @@ class TestUpgrade(test_util.TensorFlowTestCase): def conversion_visitor(unused_path, unused_parent, children): for child in children: _, attr = tf_decorator.unwrap(child[1]) - api_names = get_v1_names(attr) + api_names = tf_export.get_v1_names(attr) for name in api_names: _, _, _, text = self._upgrade("tf." + name) if (text and @@ -190,7 +164,7 @@ class TestUpgrade(test_util.TensorFlowTestCase): def conversion_visitor(unused_path, unused_parent, children): for child in children: _, attr = tf_decorator.unwrap(child[1]) - api_names = get_v1_names(attr) + api_names = tf_export.get_v1_names(attr) for name in api_names: if collect: v1_symbols.add("tf." + name) @@ -219,7 +193,7 @@ class TestUpgrade(test_util.TensorFlowTestCase): def arg_test_visitor(unused_path, unused_parent, children): for child in children: _, attr = tf_decorator.unwrap(child[1]) - names_v1 = get_v1_names(attr) + names_v1 = tf_export.get_v1_names(attr) for name in names_v1: name = "tf.%s" % name @@ -270,7 +244,7 @@ class TestUpgrade(test_util.TensorFlowTestCase): _, attr = tf_decorator.unwrap(child[1]) if not tf_inspect.isfunction(attr): continue - names_v1 = get_v1_names(attr) + names_v1 = tf_export.get_v1_names(attr) arg_names_v1 = get_args(attr) for name in names_v1: @@ -340,7 +314,7 @@ bazel-bin/tensorflow/tools/compatibility/update/generate_v2_reorders_map # get other names for this function attr = get_symbol_for_name(tf.compat.v1, name) _, attr = tf_decorator.unwrap(attr) - v1_names = get_v1_names(attr) + v1_names = tf_export.get_v1_names(attr) self.assertTrue(v1_names) v1_names = ["tf.%s" % n for n in v1_names] # check if any other name is in diff --git a/tensorflow/tools/compatibility/update/generate_v2_renames_map.py b/tensorflow/tools/compatibility/update/generate_v2_renames_map.py index 19ad6c3a2a..a2c5e7cf82 100644 --- a/tensorflow/tools/compatibility/update/generate_v2_renames_map.py +++ b/tensorflow/tools/compatibility/update/generate_v2_renames_map.py @@ -64,58 +64,6 @@ from __future__ import print_function """ -_TENSORFLOW_API_ATTR_V1 = ( - tf_export.API_ATTRS_V1[tf_export.TENSORFLOW_API_NAME].names) -_TENSORFLOW_API_ATTR = tf_export.API_ATTRS[tf_export.TENSORFLOW_API_NAME].names -_TENSORFLOW_CONSTANTS_ATTR_V1 = ( - tf_export.API_ATTRS_V1[tf_export.TENSORFLOW_API_NAME].constants) -_TENSORFLOW_CONSTANTS_ATTR = ( - tf_export.API_ATTRS[tf_export.TENSORFLOW_API_NAME].constants) - -_ESTIMATOR_API_ATTR_V1 = ( - tf_export.API_ATTRS_V1[tf_export.ESTIMATOR_API_NAME].names) -_ESTIMATOR_API_ATTR = tf_export.API_ATTRS[tf_export.ESTIMATOR_API_NAME].names -_ESTIMATOR_CONSTANTS_ATTR_V1 = ( - tf_export.API_ATTRS_V1[tf_export.ESTIMATOR_API_NAME].constants) -_ESTIMATOR_CONSTANTS_ATTR = ( - tf_export.API_ATTRS[tf_export.ESTIMATOR_API_NAME].constants) - - -def get_v1_names(symbol): - names_v1 = [] - if hasattr(symbol, _TENSORFLOW_API_ATTR_V1): - names_v1.extend(getattr(symbol, _TENSORFLOW_API_ATTR_V1)) - if hasattr(symbol, _ESTIMATOR_API_ATTR_V1): - names_v1.extend(getattr(symbol, _ESTIMATOR_API_ATTR_V1)) - return names_v1 - - -def get_v2_names(symbol): - names_v2 = [] - if hasattr(symbol, _TENSORFLOW_API_ATTR): - names_v2.extend(getattr(symbol, _TENSORFLOW_API_ATTR)) - if hasattr(symbol, _ESTIMATOR_API_ATTR): - names_v2.extend(getattr(symbol, _ESTIMATOR_API_ATTR)) - return list(names_v2) - - -def get_v1_constants(module): - constants_v1 = [] - if hasattr(module, _TENSORFLOW_CONSTANTS_ATTR_V1): - constants_v1.extend(getattr(module, _TENSORFLOW_CONSTANTS_ATTR_V1)) - if hasattr(module, _ESTIMATOR_CONSTANTS_ATTR_V1): - constants_v1.extend(getattr(module, _ESTIMATOR_CONSTANTS_ATTR_V1)) - return constants_v1 - - -def get_v2_constants(module): - constants_v2 = [] - if hasattr(module, _TENSORFLOW_CONSTANTS_ATTR): - constants_v2.extend(getattr(module, _TENSORFLOW_CONSTANTS_ATTR)) - if hasattr(module, _ESTIMATOR_CONSTANTS_ATTR): - constants_v2.extend(getattr(module, _ESTIMATOR_CONSTANTS_ATTR)) - return constants_v2 - def get_canonical_name(v2_names, v1_name): if v2_names: @@ -131,7 +79,7 @@ def get_all_v2_names(): """Visitor that collects TF 2.0 names.""" for child in children: _, attr = tf_decorator.unwrap(child[1]) - api_names_v2 = get_v2_names(attr) + api_names_v2 = tf_export.get_v2_names(attr) for name in api_names_v2: v2_names.add(name) @@ -149,8 +97,8 @@ def collect_constant_renames(): """ renames = set() for module in sys.modules.values(): - constants_v1_list = get_v1_constants(module) - constants_v2_list = get_v2_constants(module) + constants_v1_list = tf_export.get_v1_constants(module) + constants_v2_list = tf_export.get_v2_constants(module) # _tf_api_constants attribute contains a list of tuples: # (api_names_list, constant_name) @@ -186,8 +134,8 @@ def collect_function_renames(): """Visitor that collects rename strings to add to rename_line_set.""" for child in children: _, attr = tf_decorator.unwrap(child[1]) - api_names_v1 = get_v1_names(attr) - api_names_v2 = get_v2_names(attr) + api_names_v1 = tf_export.get_v1_names(attr) + api_names_v2 = tf_export.get_v2_names(attr) deprecated_api_names = set(api_names_v1) - set(api_names_v2) for name in deprecated_api_names: renames.add((name, get_canonical_name(api_names_v2, name))) diff --git a/tensorflow/tools/compatibility/update/generate_v2_reorders_map.py b/tensorflow/tools/compatibility/update/generate_v2_reorders_map.py index 63541771bf..0eb942d396 100644 --- a/tensorflow/tools/compatibility/update/generate_v2_reorders_map.py +++ b/tensorflow/tools/compatibility/update/generate_v2_reorders_map.py @@ -64,40 +64,6 @@ from __future__ import print_function """ -_TENSORFLOW_API_ATTR_V1 = ( - tf_export.API_ATTRS_V1[tf_export.TENSORFLOW_API_NAME].names) -_TENSORFLOW_API_ATTR = tf_export.API_ATTRS[tf_export.TENSORFLOW_API_NAME].names -_TENSORFLOW_CONSTANTS_ATTR_V1 = ( - tf_export.API_ATTRS_V1[tf_export.TENSORFLOW_API_NAME].constants) -_TENSORFLOW_CONSTANTS_ATTR = ( - tf_export.API_ATTRS[tf_export.TENSORFLOW_API_NAME].constants) - -_ESTIMATOR_API_ATTR_V1 = ( - tf_export.API_ATTRS_V1[tf_export.ESTIMATOR_API_NAME].names) -_ESTIMATOR_API_ATTR = tf_export.API_ATTRS[tf_export.ESTIMATOR_API_NAME].names -_ESTIMATOR_CONSTANTS_ATTR_V1 = ( - tf_export.API_ATTRS_V1[tf_export.ESTIMATOR_API_NAME].constants) -_ESTIMATOR_CONSTANTS_ATTR = ( - tf_export.API_ATTRS[tf_export.ESTIMATOR_API_NAME].constants) - - -def get_v1_names(symbol): - names_v1 = [] - if hasattr(symbol, _TENSORFLOW_API_ATTR_V1): - names_v1.extend(getattr(symbol, _TENSORFLOW_API_ATTR_V1)) - if hasattr(symbol, _ESTIMATOR_API_ATTR_V1): - names_v1.extend(getattr(symbol, _ESTIMATOR_API_ATTR_V1)) - return names_v1 - - -def get_v2_names(symbol): - names_v2 = [] - if hasattr(symbol, _TENSORFLOW_API_ATTR): - names_v2.extend(getattr(symbol, _TENSORFLOW_API_ATTR)) - if hasattr(symbol, _ESTIMATOR_API_ATTR): - names_v2.extend(getattr(symbol, _ESTIMATOR_API_ATTR)) - return list(names_v2) - def collect_function_arg_names(function_names): """Determines argument names for reordered function signatures. @@ -115,7 +81,7 @@ def collect_function_arg_names(function_names): """Visitor that collects arguments for reordered functions.""" for child in children: _, attr = tf_decorator.unwrap(child[1]) - api_names_v1 = get_v1_names(attr) + api_names_v1 = tf_export.get_v1_names(attr) api_names_v1 = ['tf.%s' % name for name in api_names_v1] matches_function_names = any( name in function_names for name in api_names_v1) -- GitLab From 795f16f1fbf7b2018475c81c43e1050a1f87ce8e Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Tue, 11 Dec 2018 13:52:38 -0800 Subject: [PATCH 374/873] remove `global data` --- tensorflow/examples/tutorials/word2vec/word2vec_basic.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py index bbcfc32098..77889effc8 100644 --- a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py +++ b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py @@ -125,7 +125,6 @@ data_index = 0 # Step 3: Function to generate a training batch for the skip-gram model. def generate_batch(batch_size, num_skips, skip_window): - global data global data_index assert batch_size % num_skips == 0 assert num_skips <= 2 * skip_window -- GitLab From ec6df8e4fb10b25737295bcb49791842eb478400 Mon Sep 17 00:00:00 2001 From: James Ring Date: Tue, 11 Dec 2018 13:47:51 -0800 Subject: [PATCH 375/873] Fix TF_TensorFromTensor not setting status on success PiperOrigin-RevId: 225063980 --- tensorflow/c/c_api.cc | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/c/c_api.cc b/tensorflow/c/c_api.cc index 94d18eb8b0..9580215a31 100644 --- a/tensorflow/c/c_api.cc +++ b/tensorflow/c/c_api.cc @@ -488,6 +488,7 @@ static TF_Tensor* EmptyTensor(TF_DataType dtype, const TensorShape& shape) { // Non-static for testing. TF_Tensor* TF_TensorFromTensor(const tensorflow::Tensor& src, TF_Status* status) { + TF_SetStatus(status, TF_OK, ""); if (!src.IsInitialized()) { status->status = FailedPrecondition( "attempt to use a tensor with an uninitialized value"); -- GitLab From 5dd912f2d734342441d9649a7b5259150d197f23 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 11 Dec 2018 13:50:53 -0800 Subject: [PATCH 376/873] Automated rollback of commit d09435e0cc8b21e5b10eb0f9750e7a24c2031e85 PiperOrigin-RevId: 225064608 --- tensorflow/contrib/tpu/profiler/dump_tpu_profile.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/tpu/profiler/dump_tpu_profile.cc b/tensorflow/contrib/tpu/profiler/dump_tpu_profile.cc index ef35e84ba5..b4b06a40a2 100644 --- a/tensorflow/contrib/tpu/profiler/dump_tpu_profile.cc +++ b/tensorflow/contrib/tpu/profiler/dump_tpu_profile.cc @@ -98,7 +98,7 @@ Status DumpOpProfileToLogDirectory(StringPiece run_dir, if (!status.ok()) { return errors::Internal( "Failed to convert op profile to json. Skipping... ", - string(status.message())); + string(status.error_message())); } TF_RETURN_IF_ERROR(WriteStringToFile(Env::Default(), path, json)); if (os) { -- GitLab From 24b6319fdf70e6b2b35fd804dccdfa3cc07b2537 Mon Sep 17 00:00:00 2001 From: Goldie Gadde Date: Tue, 11 Dec 2018 14:19:51 -0800 Subject: [PATCH 377/873] Updated --- tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h | 8 -------- tensorflow/python/ops/confusion_matrix.py | 1 + 2 files changed, 1 insertion(+), 8 deletions(-) diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h index 164be226b7..85a0e5328c 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h @@ -215,14 +215,6 @@ class IrEmitterUnnested : public IrEmitter { // Prerequisite: `IsReductionToVector(*unnested_hlo)` Status EmitReductionToVector(HloInstruction* unnested_hlo); - // Emits code for an in-place scatter, modifying `thunk`s launch dimensions in - // the process. `scatter` may be fused, scatter indices are taken from - // `scatter_indices_gen`, updates from`updates_gen`. The output buffer is - // expected to have the operand values in it already. - Status EmitScatter(Thunk* thunk, HloInstruction* scatter, - const llvm_ir::ElementGenerator& scatter_indices_gen, - const llvm_ir::ElementGenerator& updates_gen); - // Emits code for an in-place scatter, modifying `thunk`s launch dimensions in // the process. `scatter` may be fused, scatter indices are taken from // `scatter_indices_gen`, updates from`updates_gen`. The output buffer is diff --git a/tensorflow/python/ops/confusion_matrix.py b/tensorflow/python/ops/confusion_matrix.py index fb584cc6f8..ccfe3b65c2 100644 --- a/tensorflow/python/ops/confusion_matrix.py +++ b/tensorflow/python/ops/confusion_matrix.py @@ -26,6 +26,7 @@ from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import sparse_ops +from tensorflow.python.util import deprecation from tensorflow.python.util.tf_export import tf_export -- GitLab From 40345bd2c3cfdcb095f8cdd7595f4a1eb9698f8f Mon Sep 17 00:00:00 2001 From: Yuefeng Zhou Date: Tue, 11 Dec 2018 14:18:26 -0800 Subject: [PATCH 378/873] Re-submit the coordinator change. PiperOrigin-RevId: 225069740 --- .../python/estimator_training_test.py | 7 ++-- .../python/multi_worker_test_base.py | 10 ++++-- .../distribute/distribute_coordinator.py | 35 +++++++++++++------ 3 files changed, 36 insertions(+), 16 deletions(-) diff --git a/tensorflow/contrib/distribute/python/estimator_training_test.py b/tensorflow/contrib/distribute/python/estimator_training_test.py index b369a7fefe..3f55a8a1c8 100644 --- a/tensorflow/contrib/distribute/python/estimator_training_test.py +++ b/tensorflow/contrib/distribute/python/estimator_training_test.py @@ -375,11 +375,13 @@ class DistributeCoordinatorIntegrationTest( threads = self.run_multiple_tasks_in_threads(self._independent_worker_fn, cluster_spec, train_distribute, eval_distribute) + threads_to_join = [] for task_type, ts in threads.items(): if task_type == PS: continue for t in ts: - t.join() + threads_to_join.append(t) + self.join_independent_workers(threads_to_join) estimator = self._get_estimator(train_distribute, eval_distribute) self._inspect_train_and_eval_events(estimator) @@ -413,8 +415,7 @@ class DistributeCoordinatorIntegrationTest( threads = self.run_multiple_tasks_in_threads(self._independent_worker_fn, cluster_spec, train_distribute, eval_distribute) - threads[WORKER][0].join() - threads[EVALUATOR][0].join() + self.join_independent_workers([threads[WORKER][0], threads[EVALUATOR][0]]) estimator = self._get_estimator(train_distribute, eval_distribute) self._inspect_train_and_eval_events(estimator) diff --git a/tensorflow/contrib/distribute/python/multi_worker_test_base.py b/tensorflow/contrib/distribute/python/multi_worker_test_base.py index 147c9b83f8..b05aac431f 100644 --- a/tensorflow/contrib/distribute/python/multi_worker_test_base.py +++ b/tensorflow/contrib/distribute/python/multi_worker_test_base.py @@ -40,6 +40,7 @@ from tensorflow.python.client import session from tensorflow.python.estimator import run_config from tensorflow.python.platform import test from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.training import coordinator from tensorflow.python.training import server_lib ASSIGNED_PORTS = set() @@ -360,6 +361,7 @@ class IndependentWorkerTestBase(test.TestCase): self._mock_os_env = MockOsEnv() self._mock_context = test.mock.patch.object(os, 'environ', self._mock_os_env) + self._coord = coordinator.Coordinator() super(IndependentWorkerTestBase, self).setUp() self._mock_context.__enter__() @@ -368,8 +370,9 @@ class IndependentWorkerTestBase(test.TestCase): super(IndependentWorkerTestBase, self).tearDown() def _task_thread(self, task_fn, tf_config, *args, **kwargs): - os.environ['TF_CONFIG'] = json.dumps(tf_config) - task_fn(*args, **kwargs) + with self._coord.stop_on_exception(): + os.environ['TF_CONFIG'] = json.dumps(tf_config) + task_fn(*args, **kwargs) def _run_task_in_thread(self, task_fn, cluster_spec, task_type, task_id, *args, **kwargs): @@ -403,3 +406,6 @@ class IndependentWorkerTestBase(test.TestCase): *args, **kwargs) threads[task_type].append(t) return threads + + def join_independent_workers(self, worker_threads): + self._coord.join(worker_threads) diff --git a/tensorflow/python/distribute/distribute_coordinator.py b/tensorflow/python/distribute/distribute_coordinator.py index c0f9b8a1fd..78c995a578 100644 --- a/tensorflow/python/distribute/distribute_coordinator.py +++ b/tensorflow/python/distribute/distribute_coordinator.py @@ -29,6 +29,7 @@ from tensorflow.python.client import session from tensorflow.python.distribute import distribute_coordinator_context from tensorflow.python.distribute import multi_worker_util from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.training import coordinator from tensorflow.python.training import monitored_session from tensorflow.python.training import server_lib @@ -328,7 +329,8 @@ def _run_single_worker(worker_fn, task_id, session_config, rpc_layer="", - worker_barrier=None): + worker_barrier=None, + coord=None): """Runs a single worker by calling `worker_fn` under context.""" session_config = copy.deepcopy(session_config) strategy = copy.deepcopy(strategy) @@ -350,7 +352,11 @@ def _run_single_worker(worker_fn, rpc_layer=rpc_layer, worker_barrier=worker_barrier) with context: - return worker_fn(strategy) + if coord: + with coord.stop_on_exception(): + return worker_fn(strategy) + else: + return worker_fn(strategy) def _split_cluster_for_evaluator(cluster_spec, task_type): @@ -423,6 +429,7 @@ def _run_std_server(cluster_spec=None, def _run_between_graph_client(worker_fn, strategy, eval_fn, eval_strategy, cluster_spec, session_config, rpc_layer): """Runs a standalone client for between-graph replication.""" + coord = coordinator.Coordinator() eval_thread = None if _TaskType.EVALUATOR in cluster_spec.jobs: eval_thread = threading.Thread( @@ -431,6 +438,7 @@ def _run_between_graph_client(worker_fn, strategy, eval_fn, eval_strategy, session_config), kwargs={ "rpc_layer": rpc_layer, + "coord": coord, }) eval_thread.start() @@ -444,18 +452,18 @@ def _run_between_graph_client(worker_fn, strategy, eval_fn, eval_strategy, session_config), kwargs={ "rpc_layer": rpc_layer, - "worker_barrier": worker_barrier + "worker_barrier": worker_barrier, + "coord": coord, }) t.start() threads.append(t) - # TODO(yuefengz): wrap threads into thread coordinator? - for t in threads: - t.join() - - # TODO(yuefengz): is it necessary to join eval thread? if eval_thread: - eval_thread.join() + # TODO(yuefengz): is it necessary to join eval thread? + threads_to_join = threads + [eval_thread] + else: + threads_to_join = threads + coord.join(threads_to_join) # TODO(yuefengz): we probably want to return results from all workers? return None @@ -464,6 +472,7 @@ def _run_between_graph_client(worker_fn, strategy, eval_fn, eval_strategy, def _run_in_graph_client(worker_fn, strategy, eval_fn, eval_strategy, cluster_spec, session_config, rpc_layer): """Runs a standalone client for in-graph replication.""" + coord = coordinator.Coordinator() eval_thread = None if _TaskType.EVALUATOR in cluster_spec.jobs: eval_thread = threading.Thread( @@ -472,6 +481,7 @@ def _run_in_graph_client(worker_fn, strategy, eval_fn, eval_strategy, session_config), kwargs={ "rpc_layer": rpc_layer, + "coord": coord, }) eval_thread.start() @@ -482,9 +492,12 @@ def _run_in_graph_client(worker_fn, strategy, eval_fn, eval_strategy, None, None, session_config, - rpc_layer=rpc_layer) + rpc_layer=rpc_layer, + coord=coord) + if eval_thread: - eval_thread.join() + coord.join([eval_thread]) + return worker_result -- GitLab From 2a87c2df921753fb8c1cba585f78bd3ab6087be2 Mon Sep 17 00:00:00 2001 From: Allen Lavoie Date: Tue, 11 Dec 2018 14:36:14 -0800 Subject: [PATCH 379/873] Skeleton for PolymorphicFunction serialization Missing things like variables, function/argument names, support for arguments that aren't a flat list of Tensors, and many other things. But it does manage to save, restore, and call a function. Starts saving a bit of extra metadata when a new function trace is created. Since this does not have to be computed each time the function is called, I expect the performance impact to be minimal. PiperOrigin-RevId: 225072712 --- tensorflow/python/eager/def_function.py | 21 ++++++ tensorflow/python/eager/def_function_test.py | 19 +++++ tensorflow/python/eager/function.py | 21 ++++++ tensorflow/python/framework/function.py | 22 +++--- tensorflow/python/framework/function_test.py | 10 +-- tensorflow/python/framework/importer.py | 4 +- tensorflow/python/saved_model/BUILD | 35 ++++++++- .../saved_model/function_deserialization.py | 46 ++++++++++++ .../saved_model/function_serialization.py | 71 +++++++++++++++++++ tensorflow/python/saved_model/load.py | 18 ++++- tensorflow/python/saved_model/load_test.py | 2 + tensorflow/python/saved_model/save.py | 4 ++ .../saved_model/saved_object_graph.proto | 11 +++ 13 files changed, 263 insertions(+), 21 deletions(-) create mode 100644 tensorflow/python/saved_model/function_deserialization.py create mode 100644 tensorflow/python/saved_model/function_serialization.py diff --git a/tensorflow/python/eager/def_function.py b/tensorflow/python/eager/def_function.py index 3663d72999..cdbf39ddd5 100644 --- a/tensorflow/python/eager/def_function.py +++ b/tensorflow/python/eager/def_function.py @@ -242,6 +242,7 @@ class PolymorphicFunction(object): raise NotImplementedError() self._created_variables = None self._stateful_fn = None + self._stateless_fn = None self._descriptor_cache = weakref.WeakKeyDictionary() self._name = name @@ -382,6 +383,26 @@ class PolymorphicFunction(object): return initialize_variables.get_concrete_function() + @property + def _cached_input_signatures(self): + """All input signatures used to call this PolymorphicFunction.""" + seen = set() + # Preserves signature ordering rather than returning a set() so that we + # don't need to re-sort signatures later to work around Python 2's set + # nondeterminism. + # pylint: disable=protected-access + concrete_functions = [] + if self._stateful_fn: + concrete_functions.extend(self._stateful_fn._function_cache.values()) + if self._stateless_fn: + concrete_functions.extend(self._stateless_fn._function_cache.values()) + for concrete_function in concrete_functions: + signature = concrete_function._python_call_signature + if signature not in seen: + yield signature + seen.add(signature) + # pylint: enable=protected-access + def get_concrete_function(self, *args, **kwargs): """Returns a `Function` object specialized to inputs and execution context. diff --git a/tensorflow/python/eager/def_function_test.py b/tensorflow/python/eager/def_function_test.py index 4100a10044..8b4c40791a 100644 --- a/tensorflow/python/eager/def_function_test.py +++ b/tensorflow/python/eager/def_function_test.py @@ -238,6 +238,25 @@ class DefFunctionTest(test.TestCase): concrete = compute.get_concrete_function( tensor_spec.TensorSpec(None, dtypes.float32)) self.assertAllClose(4., concrete(constant_op.constant(2.))) + input_signature, = compute._cached_input_signatures + self.assertEqual( + tuple(input_signature), + (tensor_spec.TensorSpec(None, dtypes.float32),)) + + def test_serialization_signature_cache(self): + + @def_function.function + def f(x, y): + return x, y + + f(constant_op.constant([[3., 4.]]), constant_op.constant([2.])) + f(constant_op.constant([[3, 4, 5]]), constant_op.constant([2])) + self.assertEqual( + set(f._cached_input_signatures), + set(((tensor_spec.TensorSpec([1, 2], dtypes.float32), + tensor_spec.TensorSpec([1], dtypes.float32)), + (tensor_spec.TensorSpec([1, 3], dtypes.int32), + tensor_spec.TensorSpec([1], dtypes.int32))))) if __name__ == '__main__': diff --git a/tensorflow/python/eager/function.py b/tensorflow/python/eager/function.py index 520c85a2c2..0de0cd96ac 100644 --- a/tensorflow/python/eager/function.py +++ b/tensorflow/python/eager/function.py @@ -748,6 +748,19 @@ class Function(object): return ret +class UnknownArgument(object): + """Signifies an argument which is not currently handled.""" + pass + + +def _encode_arg_for_serialization(arg): + """A representation for this argument, for serializing signatures.""" + if isinstance(arg, ops.Tensor): + return tensor_spec.TensorSpec(arg.shape, arg.dtype) + else: + return UnknownArgument() + + pywrap_tensorflow.RegisterType("Tensor", ops.Tensor) pywrap_tensorflow.RegisterType("IndexedSlices", ops.IndexedSlices) @@ -1163,6 +1176,14 @@ class PolymorphicFunction(object): autograph=self._autograph, arg_names=arg_names), self._function_attributes) + if self._input_signature: + python_call_signature = self._input_signature + else: + python_call_signature = tuple( + _encode_arg_for_serialization(arg) for arg in args) + # Save information about non-Tensor arguments with the concrete + # function. Used to serialize PolymorphicFunctions. + graph_function._python_call_signature = python_call_signature # pylint: disable=protected-access self._function_cache[cache_key] = graph_function return graph_function, args, kwargs diff --git a/tensorflow/python/framework/function.py b/tensorflow/python/framework/function.py index cfdc915a1b..afc11b17bf 100644 --- a/tensorflow/python/framework/function.py +++ b/tensorflow/python/framework/function.py @@ -993,17 +993,18 @@ def _call(sig, *inputs, **kwargs): name = kwargs.pop("name", None) g = ops.get_default_graph() func_name = sig.name + if name is None: + name = func_name attrs = _parse_kwargs_as_attrs(func_name, **kwargs) output_types = [dtypes.DType(x.type) for x in sig.output_arg] - with ops.name_scope(name, func_name, inputs) as name: - op = g.create_op( - func_name, - list(inputs), - output_types, - name=name, - attrs=attrs, - op_def=sig, - compute_shapes=False) + op = g.create_op( + func_name, + list(inputs), + output_types, + name=name, + attrs=attrs, + op_def=sig, + compute_shapes=False) if op.outputs: if len(op.outputs) == 1: ret = op.outputs[0] @@ -1046,12 +1047,13 @@ def _from_definition(fdef, grad_func=None): c_func = c_api.TF_FunctionImportFunctionDef(serialized) result._c_func = c_api_util.ScopedTFFunction(c_func) result._extra_inputs = [] + result._op_def = fdef.signature # pylint: enable=protected-access return result -def _from_library(lib): +def from_library(lib): """Creates _DefinedFunctions initialized from a FunctionDefLibrary proto. This method handles assigning the correct gradient functions to each diff --git a/tensorflow/python/framework/function_test.py b/tensorflow/python/framework/function_test.py index 6ec71ba8e9..7543376bcf 100644 --- a/tensorflow/python/framework/function_test.py +++ b/tensorflow/python/framework/function_test.py @@ -1287,7 +1287,7 @@ class FunctionsFromProtos(test.TestCase): gradients_impl.gradients([f1, f2, f3, f4], c) library = g.as_graph_def().library - new_funcs = function._from_library(library) + new_funcs = function.from_library(library) def CheckNewFunc(func): new_func = [f for f in new_funcs if f.name == func.name] @@ -1303,7 +1303,7 @@ class FunctionsFromProtos(test.TestCase): def testFromLibraryEmptyLib(self): library = function_pb2.FunctionDefLibrary() - self.assertEqual(len(function._from_library(library)), 0) + self.assertEqual(len(function.from_library(library)), 0) def testFromLibraryMissingFuncDef(self): @@ -1327,7 +1327,7 @@ class FunctionsFromProtos(test.TestCase): with self.assertRaisesRegexp( ValueError, "FunctionDefLibrary missing 'G1_[0-9a-zA-Z]{8,11}' FunctionDef"): - function._from_library(library) + function.from_library(library) # Create invalid function def that is missing F1 function def library = function_pb2.FunctionDefLibrary() @@ -1337,7 +1337,7 @@ class FunctionsFromProtos(test.TestCase): with self.assertRaisesRegexp( ValueError, "FunctionDefLibrary missing 'F1_[0-9a-zA-Z]{8,11}' FunctionDef"): - function._from_library(library) + function.from_library(library) def testFromLibraryCyclicGradFuncs(self): @@ -1366,7 +1366,7 @@ class FunctionsFromProtos(test.TestCase): with self.assertRaisesRegexp( ValueError, "FunctionDefLibrary contains cyclic gradient functions!"): - function._from_library(library) + function.from_library(library) def testExperimentalAttrs(self): diff --git a/tensorflow/python/framework/importer.py b/tensorflow/python/framework/importer.py index 98c7aeccc4..c737bd4881 100644 --- a/tensorflow/python/framework/importer.py +++ b/tensorflow/python/framework/importer.py @@ -442,11 +442,9 @@ def import_graph_def(graph_def, _ProcessNewOps(graph) if graph_def.library and graph_def.library.function: - # pylint: disable=protected-access - functions = function._from_library(graph_def.library) + functions = function.from_library(graph_def.library) for f in functions: f.add_to_graph(graph) - # pylint: enable=protected-access # Treat input mappings that don't appear in the graph as an error, because # they are likely to be due to a typo. diff --git a/tensorflow/python/saved_model/BUILD b/tensorflow/python/saved_model/BUILD index 53d0640542..71d9e34592 100644 --- a/tensorflow/python/saved_model/BUILD +++ b/tensorflow/python/saved_model/BUILD @@ -287,7 +287,7 @@ py_library( deps = [ ":builder", ":constants", - ":loader", + ":function_serialization", ":saved_object_graph_py", ":signature_constants", ":signature_def_utils", @@ -295,15 +295,20 @@ py_library( ":utils", "//tensorflow/core:protos_all_py", "//tensorflow/python:array_ops", + "//tensorflow/python:constant_op", + "//tensorflow/python:control_flow_ops", + "//tensorflow/python:dtypes", "//tensorflow/python:framework", "//tensorflow/python:framework_ops", "//tensorflow/python:lib", "//tensorflow/python:resource_variable_ops", + "//tensorflow/python:tensor_spec", "//tensorflow/python:util", "//tensorflow/python/eager:context", "//tensorflow/python/eager:def_function", "//tensorflow/python/eager:function", "//tensorflow/python/training/checkpointable:base", + "//tensorflow/python/training/checkpointable:tracking", "//tensorflow/python/training/checkpointable:util", ], ) @@ -330,8 +335,12 @@ py_library( ], srcs_version = "PY2AND3", deps = [ + ":constants", + ":function_deserialization", ":loader", ":saved_object_graph_py", + ":utils", + "//tensorflow/python:function", "//tensorflow/python:lib", "//tensorflow/python:util", "//tensorflow/python/training/checkpointable:tracking", @@ -345,10 +354,34 @@ py_test( deps = [ ":load", ":save", + "//tensorflow/python:constant_op", "//tensorflow/python:dtypes", + "//tensorflow/python:lib", "//tensorflow/python:tensor_spec", "//tensorflow/python/eager:def_function", "//tensorflow/python/eager:test", "//tensorflow/python/training/checkpointable:tracking", ], ) + +py_library( + name = "function_serialization", + srcs = [ + "function_serialization.py", + ], + srcs_version = "PY2AND3", + deps = [ + ":saved_object_graph_py", + "//tensorflow/python/eager:def_function", + "//tensorflow/python/eager:function", + ], +) + +py_library( + name = "function_deserialization", + srcs = [ + "function_deserialization.py", + ], + srcs_version = "PY2AND3", + deps = ["//tensorflow/python/eager:def_function"], +) diff --git a/tensorflow/python/saved_model/function_deserialization.py b/tensorflow/python/saved_model/function_deserialization.py new file mode 100644 index 0000000000..46bd69ad03 --- /dev/null +++ b/tensorflow/python/saved_model/function_deserialization.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. +# ============================================================================== +"""Tools for deserializing PolymorphicFunctions.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.eager import def_function + + +def recreate_polymorphic_function( + saved_polymorphic_function, defined_functions): + """Creates a PolymorphicFunction which runs restored function definitions.""" + @def_function.function + def restored_function(*args): + """Calls a restored function.""" + # Try calling each function, return a value from the first one whose + # signature matches. + # TODO(allenl): Consider re-populating the function cache directly. + # TODO(allenl): Functions saved with input_signatures should revive with + # input_signatures. + for monomorphic_function in saved_polymorphic_function.monomorphic_function: + try: + # TODO(allenl): Passing an explicit name here prevents invalid name + # errors. We should replace this with something based on the actual + # Python function name. + return defined_functions[monomorphic_function.concrete_function]( + *args, name="imported_function") + except ValueError: + continue + raise AssertionError( + "Could not find matching function to call for arguments: %s" % (args,)) + return restored_function diff --git a/tensorflow/python/saved_model/function_serialization.py b/tensorflow/python/saved_model/function_serialization.py new file mode 100644 index 0000000000..7cf82776bd --- /dev/null +++ b/tensorflow/python/saved_model/function_serialization.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. +# ============================================================================== +"""Tools for serializing PolymorphicFunctions.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.eager import def_function +from tensorflow.python.eager import function as defun_lib +from tensorflow.python.saved_model import saved_object_graph_pb2 + + +def _serialize_polymorphic_function(function): + """Represents a PolymorphicFunction in a SavedModel. + + Adds `function`'s concrete functions to the current graph. + + Args: + function: A `PolymorphicFunction` to serialize. + + Returns: + An unserialized `SavedPolymorphicFunction` protocol buffer object. + """ + monomorphic_functions = [] + for signature in function._cached_input_signatures: # pylint: disable=protected-access + if any(isinstance(arg, defun_lib.UnknownArgument) for arg in signature): + continue + concrete_function = function.get_concrete_function(*signature) + concrete_function.add_to_graph() + monomorphic_functions.append( + saved_object_graph_pb2.SavedMonomorphicFunction( + concrete_function=concrete_function.name)) + return saved_object_graph_pb2.SavedPolymorphicFunction( + monomorphic_function=monomorphic_functions) + + +def add_polymorphic_functions_to_object_graph_proto( + checkpointable_objects, saved_object_graph): + """Finds PolymorphicFunctions attached to objects and saves them.""" + existing_objects = list(zip(checkpointable_objects, saved_object_graph.nodes)) + for obj, obj_proto in existing_objects: + for attribute_name in dir(obj): + try: + attribute_value = getattr(obj, attribute_name, None) + except: # pylint: disable=bare-except + # We really don't want to throw an exception just because some object's + # attribute accessor is broken. + attribute_value = None + # TODO(allenl): Consider de-duplicating functions which are referenced + # from multiple attributes. + if isinstance(attribute_value, def_function.PolymorphicFunction): + function_node_id = len(saved_object_graph.nodes) + function_node = saved_object_graph.nodes.add() + function_node.function.CopyFrom( + _serialize_polymorphic_function(attribute_value)) + reference = obj_proto.children.add() + reference.node_id = function_node_id + reference.local_name = attribute_name diff --git a/tensorflow/python/saved_model/load.py b/tensorflow/python/saved_model/load.py index e3095f4ee5..28c0af2b65 100644 --- a/tensorflow/python/saved_model/load.py +++ b/tensorflow/python/saved_model/load.py @@ -20,8 +20,10 @@ from __future__ import print_function import os +from tensorflow.python.framework import function as function_lib from tensorflow.python.lib.io import file_io from tensorflow.python.saved_model import constants +from tensorflow.python.saved_model import function_deserialization from tensorflow.python.saved_model import loader_impl from tensorflow.python.saved_model import saved_object_graph_pb2 from tensorflow.python.saved_model import utils_impl as saved_model_utils @@ -33,9 +35,17 @@ class _Loader(object): """Helper class to load an object-based SavedModel.""" def __init__(self, object_graph_proto, saved_model_proto, export_dir): - self._asset_file_def = saved_model_proto.meta_graphs[0].asset_file_def + meta_graph = saved_model_proto.meta_graphs[0] + self._asset_file_def = meta_graph.asset_file_def self._proto = object_graph_proto self._export_dir = export_dir + self._defined_functions = {} + for defined_function in function_lib.from_library( + meta_graph.graph_def.library): + # TODO(allenl): Do we need to do name mapping here? Not quite sure what + # happens when loaded names collide with existing names. + defined_function.add_to_graph(None) + self._defined_functions[defined_function.name] = defined_function self._load_all() def _load_all(self): @@ -52,6 +62,7 @@ class _Loader(object): factory = { "user_object": lambda: self._recreate_user_object(proto.user_object), "asset": lambda: self._recreate_asset(proto.asset), + "function": lambda: self._recreate_function(proto.function) } kind = proto.WhichOneof("kind") if kind not in factory: @@ -68,6 +79,10 @@ class _Loader(object): self._asset_file_def[proto.asset_file_def_index].filename) return tracking.TrackableAsset(filename) + def _recreate_function(self, proto): + return function_deserialization.recreate_polymorphic_function( + proto, self._defined_functions) + def _load_saved_object_graph_proto(filename): with file_io.FileIO(filename, "rb") as f: @@ -92,5 +107,4 @@ def load(export_dir): raise NotImplementedError( "Currently only SavedModels exported with `tf.saved_model.save` may be " "imported. Other SavedModels may eventually be supported via load().") - # TODO(allenl): load functions from the SavedModel into the eager context return root diff --git a/tensorflow/python/saved_model/load_test.py b/tensorflow/python/saved_model/load_test.py index a2971101cd..6a10ac432d 100644 --- a/tensorflow/python/saved_model/load_test.py +++ b/tensorflow/python/saved_model/load_test.py @@ -23,6 +23,7 @@ import tempfile from tensorflow.python.eager import def_function from tensorflow.python.eager import test +from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import tensor_spec from tensorflow.python.lib.io import file_io @@ -47,6 +48,7 @@ class LoadTest(test.TestCase): imported = load.load(save_dir) self.assertIs(imported.dep_three, imported.dep_two.dep) self.assertIsNot(imported.dep_one, imported.dep_two) + self.assertEqual(4., imported.f(constant_op.constant(2.)).numpy()) def _make_asset(self, contents): filename = tempfile.mktemp(prefix=self.get_temp_dir()) diff --git a/tensorflow/python/saved_model/save.py b/tensorflow/python/saved_model/save.py index e2726087a5..b065a5a265 100644 --- a/tensorflow/python/saved_model/save.py +++ b/tensorflow/python/saved_model/save.py @@ -37,6 +37,7 @@ from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.saved_model import builder_impl from tensorflow.python.saved_model import constants +from tensorflow.python.saved_model import function_serialization from tensorflow.python.saved_model import saved_object_graph_pb2 from tensorflow.python.saved_model import signature_constants from tensorflow.python.saved_model import signature_def_utils @@ -511,6 +512,9 @@ def _write_object_graph(root, export_dir, asset_file_def_index): for obj, obj_proto in zip(checkpointable_objects, proto.nodes): _write_object_proto(obj, obj_proto, asset_file_def_index) + function_serialization.add_polymorphic_functions_to_object_graph_proto( + checkpointable_objects, proto) + extra_asset_dir = os.path.join( compat.as_bytes(export_dir), compat.as_bytes(constants.EXTRA_ASSETS_DIRECTORY)) diff --git a/tensorflow/python/saved_model/saved_object_graph.proto b/tensorflow/python/saved_model/saved_object_graph.proto index 3991fbede4..ed5c63935f 100644 --- a/tensorflow/python/saved_model/saved_object_graph.proto +++ b/tensorflow/python/saved_model/saved_object_graph.proto @@ -48,6 +48,7 @@ message SavedObject { oneof kind { SavedUserObject user_object = 4; SavedAsset asset = 5; + SavedPolymorphicFunction function = 6; } } @@ -71,3 +72,13 @@ message SavedAsset { // `AssetFileDef.tensor_info`, MUST be ignored. uint32 asset_file_def_index = 1; } + +// A function with multiple signatures, possibly with non-Tensor arguments. +message SavedPolymorphicFunction { + repeated SavedMonomorphicFunction monomorphic_function = 1; +} + +message SavedMonomorphicFunction { + // A reference to a TensorFlow function in the MetaGraph's FunctionDefLibrary + string concrete_function = 1; +} -- GitLab From 6ea1bab952c7e343986b3d1f894970876faa8412 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 11 Dec 2018 14:37:01 -0800 Subject: [PATCH 380/873] Enhance the Tensor-inspector in the following ways: (1) Combine tensors from multiple replicas into a single tensor; each replica may have its own trace file. (2) Accept two tensor traces and report their difference. (3) Summarize and print the value of a tensor in terms of: (a) full tensor value, (b) partial tensor value, (c) any NaN/Inf, (d) the vector Norm, (e) Max-absolute value across all elements. (4) Various print order: topological, numerical, alphabetical. (5) Many more unit tests. PiperOrigin-RevId: 225072821 --- tensorflow/python/BUILD | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index 0a3ee65bc4..8a7c001321 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -6,6 +6,7 @@ visibility = [ "//engedu/ml/tf_from_scratch:__pkg__", + "//third_party/cloud_tpu/convergence_tools:__subpackages__", "//tensorflow:internal", "//tensorflow/lite/toco/python:__pkg__", "//tensorflow_models:__subpackages__", -- GitLab From 3b94c63e1b113b8504221c635c83a5477666605b Mon Sep 17 00:00:00 2001 From: Andrew Selle Date: Tue, 11 Dec 2018 14:48:11 -0800 Subject: [PATCH 381/873] Fix filename PiperOrigin-RevId: 225074738 --- .../src/main/java/org/tensorflow/demo/DetectorActivity.java | 4 ++-- .../org/tensorflow/demo/TFLiteObjectDetectionAPIModel.java | 3 +-- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/tensorflow/lite/examples/android/app/src/main/java/org/tensorflow/demo/DetectorActivity.java b/tensorflow/lite/examples/android/app/src/main/java/org/tensorflow/demo/DetectorActivity.java index 87160f6b3f..2feca79e88 100644 --- a/tensorflow/lite/examples/android/app/src/main/java/org/tensorflow/demo/DetectorActivity.java +++ b/tensorflow/lite/examples/android/app/src/main/java/org/tensorflow/demo/DetectorActivity.java @@ -52,8 +52,8 @@ public class DetectorActivity extends CameraActivity implements OnImageAvailable private static final int TF_OD_API_INPUT_SIZE = 300; private static final boolean TF_OD_API_IS_QUANTIZED = true; private static final String TF_OD_API_MODEL_FILE = "detect.tflite"; - private static final String TF_OD_API_LABELS_FILE = "file:///android_asset/coco_labels_list.txt"; - + private static final String TF_OD_API_LABELS_FILE = "coco_labels_list.txt"; + // Which detection model to use: by default uses Tensorflow Object Detection API frozen // checkpoints. private enum DetectorMode { diff --git a/tensorflow/lite/examples/android/app/src/main/java/org/tensorflow/demo/TFLiteObjectDetectionAPIModel.java b/tensorflow/lite/examples/android/app/src/main/java/org/tensorflow/demo/TFLiteObjectDetectionAPIModel.java index 9eb21de9d0..afbf317831 100644 --- a/tensorflow/lite/examples/android/app/src/main/java/org/tensorflow/demo/TFLiteObjectDetectionAPIModel.java +++ b/tensorflow/lite/examples/android/app/src/main/java/org/tensorflow/demo/TFLiteObjectDetectionAPIModel.java @@ -105,8 +105,7 @@ public class TFLiteObjectDetectionAPIModel implements Classifier { final TFLiteObjectDetectionAPIModel d = new TFLiteObjectDetectionAPIModel(); InputStream labelsInput = null; - String actualFilename = labelFilename.split("file:///android_asset/")[1]; - labelsInput = assetManager.open(actualFilename); + labelsInput = assetManager.open(labelFilename); BufferedReader br = null; br = new BufferedReader(new InputStreamReader(labelsInput)); String line; -- GitLab From 5440a744940b5c773f6a4e0ae84a569cb20acac6 Mon Sep 17 00:00:00 2001 From: Trevor Morris Date: Tue, 11 Dec 2018 14:58:03 -0800 Subject: [PATCH 382/873] Add warning when using batchnorm in training mode, since the error will now go to VLOG(1) by the segmenter. This is a very common problem so we want users to see the warning --- .../contrib/tensorrt/convert/convert_nodes.cc | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 5fe284c042..ba1c2e80b2 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -2985,10 +2985,16 @@ tensorflow::Status ConvertFusedBatchNorm(OpConverterParams* params) { } bool is_training = attrs.get("is_training"); if (is_training) { + // Trying to use batchnorm in training mode is a very common problem. + // Because the error message will only be printed in VLOG(1) by the + // segmenter, we issue a special warning so that users will actually see it. + LOG(WARNING) << node_def.op() << " only supports is_training=false. If you " + << "are using Keras, please call " + << "keras.backend.set_learning_phase(0) before constructing " + << "your model. At " + << node_def.name()); return tensorflow::errors::Unimplemented( - node_def.op(), - " only supports is_training=false. If you are using " - "Keras, please use keras.backend.set_learning_phase(0). At ", + node_def.op(), " only supports is_training=false, at ", node_def.name()); } if (inputs.at(0).is_weights()) { @@ -3003,7 +3009,7 @@ tensorflow::Status ConvertFusedBatchNorm(OpConverterParams* params) { node_def.op(), " must have constant inputs for scale, offset, mean and variance, " "at ", - node_def.name()); + node_def.name()); } } nvinfer1::ITensor const* tensor = inputs.at(0).tensor(); -- GitLab From ecb1d048a8b6a651c1c647038b21eae09717fb96 Mon Sep 17 00:00:00 2001 From: Pooya Davoodi Date: Tue, 11 Dec 2018 15:05:59 -0800 Subject: [PATCH 383/873] TFTRT: Change LOG(ERROR) to VLOG(1) when use_calibration=True with fp32/fp16 --- tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc index c1688d4db8..d57f2300f8 100644 --- a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc +++ b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc @@ -226,8 +226,9 @@ tensorflow::Status TRTOptimizationPass::Optimize( tensorflow::tensorrt::convert::ConversionParams cp; if (use_calibration_ && precision_mode_ != INT8MODE) { - LOG(ERROR) << "Calibration with FP32 or FP16 is not implemented. " - << "Falling back to use_calibration = False."; + VLOG(1) << "Calibration with FP32 or FP16 is not implemented. " + << "Falling back to use_calibration = False." + << "Note that the default value of use_calibration is True."; use_calibration_ = false; } -- GitLab From 3dfe44784dcfdc8cca87e59ce8eb1a47b9d95bfd Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 11 Dec 2018 15:12:10 -0800 Subject: [PATCH 384/873] Small refactor of `thresholds` default value and validation steps. The number of thresholds is used instead of the user specified type of the `thresholds` kwarg to determine the output of the result method: `thresholds` is a scalar or single element list/tuple -> return scalar `thresholds` is a multi element list/tuple -> return list This is functionally equivalent to the previous code except for cases where the user passes in a single element list for the thresholds kwarg. In the previous code, this would cause the result method to return a list whereas now it returns a scalar. PiperOrigin-RevId: 225079221 --- tensorflow/python/keras/metrics.py | 41 ++++++++++++++++-------------- 1 file changed, 22 insertions(+), 19 deletions(-) diff --git a/tensorflow/python/keras/metrics.py b/tensorflow/python/keras/metrics.py index 1d1f3b4586..c8ccb7f624 100644 --- a/tensorflow/python/keras/metrics.py +++ b/tensorflow/python/keras/metrics.py @@ -177,6 +177,12 @@ def _assert_thresholds_range(thresholds): .format(invalid_thresholds)) +def _parse_init_thresholds(thresholds, default_threshold=0.5): + thresholds = to_list(default_threshold if thresholds is None else thresholds) + _assert_thresholds_range(thresholds) + return thresholds + + def _update_confusion_matrix_variables(variables_to_update, y_true, y_pred, @@ -869,12 +875,11 @@ class _ConfusionMatrixConditionCount(Metric): """ super(_ConfusionMatrixConditionCount, self).__init__(name=name, dtype=dtype) self._confusion_matrix_cond = confusion_matrix_cond - self.thresholds = 0.5 if thresholds is None else thresholds - thresholds_list = to_list(self.thresholds) - _assert_thresholds_range(thresholds_list) + self.thresholds = _parse_init_thresholds( + thresholds, default_threshold=0.5) self.accumulator = self.add_weight( 'accumulator', - shape=(len(thresholds_list),), + shape=(len(self.thresholds),), initializer=init_ops.zeros_initializer) def update_state(self, y_true, y_pred, sample_weight=None): @@ -895,10 +900,10 @@ class _ConfusionMatrixConditionCount(Metric): }, y_true, y_pred, self.thresholds, sample_weight) def result(self): - if isinstance(self.thresholds, (list, tuple)): - result = self.accumulator - else: + if len(self.thresholds) == 1: result = self.accumulator[0] + else: + result = self.accumulator return ops.convert_to_tensor(result) def reset_states(self): @@ -1152,16 +1157,15 @@ class Precision(Metric): dtype: (Optional) data type of the metric result. """ super(Precision, self).__init__(name=name, dtype=dtype) - self.thresholds = 0.5 if thresholds is None else thresholds - thresholds_list = to_list(self.thresholds) - _assert_thresholds_range(thresholds_list) + self.thresholds = _parse_init_thresholds( + thresholds, default_threshold=0.5) self.tp = self.add_weight( 'true_positives', - shape=(len(thresholds_list),), + shape=(len(self.thresholds),), initializer=init_ops.zeros_initializer) self.fp = self.add_weight( 'false_positives', - shape=(len(thresholds_list),), + shape=(len(self.thresholds),), initializer=init_ops.zeros_initializer) def update_state(self, y_true, y_pred, sample_weight=None): @@ -1184,7 +1188,7 @@ class Precision(Metric): def result(self): result = math_ops.div_no_nan(self.tp, self.tp + self.fp) - return result if isinstance(self.thresholds, (list, tuple)) else result[0] + return result[0] if len(self.thresholds) == 1 else result def reset_states(self): num_thresholds = len(to_list(self.thresholds)) @@ -1237,16 +1241,15 @@ class Recall(Metric): dtype: (Optional) data type of the metric result. """ super(Recall, self).__init__(name=name, dtype=dtype) - self.thresholds = 0.5 if thresholds is None else thresholds - thresholds_list = to_list(self.thresholds) - _assert_thresholds_range(thresholds_list) + self.thresholds = _parse_init_thresholds( + thresholds, default_threshold=0.5) self.tp = self.add_weight( 'true_positives', - shape=(len(thresholds_list),), + shape=(len(self.thresholds),), initializer=init_ops.zeros_initializer) self.fn = self.add_weight( 'false_negatives', - shape=(len(thresholds_list),), + shape=(len(self.thresholds),), initializer=init_ops.zeros_initializer) def update_state(self, y_true, y_pred, sample_weight=None): @@ -1269,7 +1272,7 @@ class Recall(Metric): def result(self): result = math_ops.div_no_nan(self.tp, self.tp + self.fn) - return result if isinstance(self.thresholds, (list, tuple)) else result[0] + return result[0] if len(self.thresholds) == 1 else result def reset_states(self): num_thresholds = len(to_list(self.thresholds)) -- GitLab From 2087bffc231c4c0c864a6933988da286e4137a4b Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 11 Dec 2018 15:46:04 -0800 Subject: [PATCH 385/873] Automated rollback of commit 221f4d23c6cffa2ad5fb492a300fafda2a640cd8 PiperOrigin-RevId: 225085109 --- WORKSPACE | 35 ++++++++++--------- tensorflow/opensource_only.files | 1 - tensorflow/version_check.bzl | 2 -- .../preconfig/generate/archives.bzl | 25 ------------- .../preconfig/generate/generate.bzl | 4 ++- .../toolchains/preconfig/generate/generate.sh | 2 +- 6 files changed, 23 insertions(+), 46 deletions(-) delete mode 100644 third_party/toolchains/preconfig/generate/archives.bzl diff --git a/WORKSPACE b/WORKSPACE index 99d368ff91..7cc08e0164 100644 --- a/WORKSPACE +++ b/WORKSPACE @@ -16,27 +16,30 @@ load("@io_bazel_rules_closure//closure:defs.bzl", "closure_repositories") closure_repositories() -load("//third_party/toolchains/preconfig/generate:archives.bzl", - "bazel_toolchains_archive") - -bazel_toolchains_archive() - -load( - "@bazel_toolchains//repositories:repositories.bzl", - bazel_toolchains_repositories = "repositories", +http_archive( + name = "base_images_docker", + sha256 = "e2b1b7254270bb7605e814a9dbf6d1e4ae04a11136ff1714fbfdabe3f87f7cf9", + strip_prefix = "base-images-docker-12801524f867e657fbb5d1a74f31618aff181ac6", + urls = ["https://github.com/GoogleCloudPlatform/base-images-docker/archive/12801524f867e657fbb5d1a74f31618aff181ac6.tar.gz"], ) -bazel_toolchains_repositories() - -load( - "@io_bazel_rules_docker//container:container.bzl", - container_repositories = "repositories", +http_archive( + name = "bazel_toolchains", + sha256 = "15b5858b1b5541ec44df31b94c3b8672815b31d71215a98398761ea9f4c4eedb", + strip_prefix = "bazel-toolchains-6200b238c9c2d137c0d9a7262c80cc71d98e692b", + urls = [ + "https://github.com/bazelbuild/bazel-toolchains/archive/6200b238c9c2d137c0d9a7262c80cc71d98e692b.tar.gz", + ], ) -container_repositories() +http_archive( + name = "io_bazel_rules_docker", + sha256 = "29d109605e0d6f9c892584f07275b8c9260803bf0c6fcb7de2623b2bedc910bd", + strip_prefix = "rules_docker-0.5.1", + urls = ["https://github.com/bazelbuild/rules_docker/archive/v0.5.1.tar.gz"], +) -load("//third_party/toolchains/preconfig/generate:workspace.bzl", - "remote_config_workspace") +load("//third_party/toolchains/preconfig/generate:workspace.bzl", "remote_config_workspace") remote_config_workspace() diff --git a/tensorflow/opensource_only.files b/tensorflow/opensource_only.files index 418ef1a369..347dc9fc6b 100644 --- a/tensorflow/opensource_only.files +++ b/tensorflow/opensource_only.files @@ -49,7 +49,6 @@ tensorflow/third_party/toolchains/preconfig/ubuntu14.04/nccl2/BUILD tensorflow/third_party/toolchains/preconfig/generate/workspace.bzl tensorflow/third_party/toolchains/preconfig/generate/containers.bzl tensorflow/third_party/toolchains/preconfig/generate/generate.bzl -tensorflow/third_party/toolchains/preconfig/generate/archives.bzl tensorflow/third_party/toolchains/preconfig/generate/BUILD tensorflow/third_party/toolchains/preconfig/win_1803/bazel_018/BUILD tensorflow/third_party/toolchains/preconfig/win_1803/bazel_018/dummy_toolchain.bzl diff --git a/tensorflow/version_check.bzl b/tensorflow/version_check.bzl index 74feaa19ff..3b61827139 100644 --- a/tensorflow/version_check.bzl +++ b/tensorflow/version_check.bzl @@ -48,5 +48,3 @@ def check_bazel_version_at_least(minimum_bazel_version): native.bazel_version, minimum_bazel_version, )) - -parse_bazel_version = _parse_bazel_version diff --git a/third_party/toolchains/preconfig/generate/archives.bzl b/third_party/toolchains/preconfig/generate/archives.bzl deleted file mode 100644 index 086b75b62e..0000000000 --- a/third_party/toolchains/preconfig/generate/archives.bzl +++ /dev/null @@ -1,25 +0,0 @@ -load("//tensorflow:version_check.bzl", "parse_bazel_version") -load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive") - -def bazel_toolchains_archive(): - if parse_bazel_version(native.bazel_version) >= parse_bazel_version("0.19"): - # This version of the toolchains repo is incompatible with older bazel - # versions - we can remove this once TensorFlow drops support for bazel - # before 0.19. - http_archive( - name = "bazel_toolchains", - sha256 = "41c48a189be489e2d15dec40e0057ea15b95ee5b39cc2a7e6cf663e31432c75e", - strip_prefix = "bazel-toolchains-3f8c58fe530fedc446de04673bc1e32985887dea", - urls = [ - "https://github.com/nlopezgi/bazel-toolchains/archive/3f8c58fe530fedc446de04673bc1e32985887dea.tar.gz", - ], - ) - else: - http_archive( - name = "bazel_toolchains", - sha256 = "15b5858b1b5541ec44df31b94c3b8672815b31d71215a98398761ea9f4c4eedb", - strip_prefix = "bazel-toolchains-6200b238c9c2d137c0d9a7262c80cc71d98e692b", - urls = [ - "https://github.com/bazelbuild/bazel-toolchains/archive/6200b238c9c2d137c0d9a7262c80cc71d98e692b.tar.gz", - ], - ) diff --git a/third_party/toolchains/preconfig/generate/generate.bzl b/third_party/toolchains/preconfig/generate/generate.bzl index fb2af02a53..2fb3a94cdc 100644 --- a/third_party/toolchains/preconfig/generate/generate.bzl +++ b/third_party/toolchains/preconfig/generate/generate.bzl @@ -36,7 +36,9 @@ def _tensorflow_rbe_config(name, cuda_version, cudnn_version, python_version, co "TF_NCCL_VERSION": "2", "CUDNN_INSTALL_PATH": "/usr/lib/x86_64-linux-gnu", }, - mount_project = "$(mount_project)", + # TODO(klimek): We should use the sources that we currently work on, not + # just the latest snapshot of tensorflow that is checked in. + git_repo = "https://github.com/tensorflow/tensorflow", tags = ["manual"], incompatible_changes_off = True, ) diff --git a/third_party/toolchains/preconfig/generate/generate.sh b/third_party/toolchains/preconfig/generate/generate.sh index 1f39fcdf6d..37c5211278 100755 --- a/third_party/toolchains/preconfig/generate/generate.sh +++ b/third_party/toolchains/preconfig/generate/generate.sh @@ -46,7 +46,7 @@ echo "CUDA: ${CUDA_VERSION}" echo "CUDNN: ${CUDNN_VERSION}" echo "NCCL: ${NCCL_VERSION}" -bazel build --define=mount_project="${PWD}" "${PKG}/generate:${TARGET}" +bazel build "${PKG}/generate:${TARGET}" cd "${TEMPDIR}" tar xvf "${ROOT}/bazel-bin/${PKG}/generate/${TARGET}_outputs.tar" -- GitLab From 9748092a5dbc67f59983f9361c932530bbfdfe68 Mon Sep 17 00:00:00 2001 From: Eugene Brevdo Date: Tue, 11 Dec 2018 16:05:17 -0800 Subject: [PATCH 386/873] [TF port] Add port::GetCurrentCPU and port::NumTotalCPUs. GetCurrentCPU: returns the current CPU of the calling thread. NumTotalCPUs: attempts to get the total number of physical cores on the system When both return non-failing values, we expect 0 <= GetCurrentCPU < NumTotalCPUs. PiperOrigin-RevId: 225088316 --- tensorflow/core/platform/cpu_info.h | 15 ++++++++- tensorflow/core/platform/port_test.cc | 12 ++++++++ tensorflow/core/platform/posix/port.cc | 39 ++++++++++++++++++++++-- tensorflow/core/platform/windows/port.cc | 25 +++++++++++++++ 4 files changed, 87 insertions(+), 4 deletions(-) diff --git a/tensorflow/core/platform/cpu_info.h b/tensorflow/core/platform/cpu_info.h index 6eba83224a..c9208cc755 100644 --- a/tensorflow/core/platform/cpu_info.h +++ b/tensorflow/core/platform/cpu_info.h @@ -32,9 +32,22 @@ namespace port { // Returns an estimate of the number of schedulable CPUs for this // process. Usually, it's constant throughout the lifetime of a // process, but it might change if the underlying cluster management -// software can change it dynamically. +// software can change it dynamically. If the underlying call fails, a default +// value (e.g. `4`) may be returned. int NumSchedulableCPUs(); +// Returns the total number of CPUs on the system. This number should +// not change even if the underlying cluster management software may +// change the number of schedulable CPUs. Unlike `NumSchedulableCPUs`, if the +// underlying call fails, an invalid value of -1 will be returned; +// the user must check for validity. +static constexpr int kUnknownCPU = -1; +int NumTotalCPUs(); + +// Returns the id of the current CPU. Returns -1 if the current CPU cannot be +// identified. If successful, the return value will be in [0, NumTotalCPUs()). +int GetCurrentCPU(); + // Returns an estimate of the number of hyperthreads per physical core // on the CPU int NumHyperthreadsPerCore(); diff --git a/tensorflow/core/platform/port_test.cc b/tensorflow/core/platform/port_test.cc index 15c3cb24f0..9d144efbfd 100644 --- a/tensorflow/core/platform/port_test.cc +++ b/tensorflow/core/platform/port_test.cc @@ -33,6 +33,12 @@ TEST(Port, AlignedMalloc) { } } +TEST(Port, GetCurrentCPU) { + const int cpu = GetCurrentCPU(); + EXPECT_GE(cpu, 0); + EXPECT_LT(cpu, NumTotalCPUs()); +} + TEST(ConditionVariable, WaitForMilliseconds_Timeout) { mutex m; mutex_lock l(m); @@ -78,3 +84,9 @@ TEST(TestCPUFeature, TestFeature) { } // namespace port } // namespace tensorflow + +int main(int argc, char** argv) { + // On Linux, add: FLAGS_logtostderr = true; + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/core/platform/posix/port.cc b/tensorflow/core/platform/posix/port.cc index acdd7798ea..0fac8b1a88 100644 --- a/tensorflow/core/platform/posix/port.cc +++ b/tensorflow/core/platform/posix/port.cc @@ -25,7 +25,14 @@ limitations under the License. #if defined(__linux__) && !defined(__ANDROID__) #include #include +#else +#include +#endif + +#if !defined(__APPLE__) && (__x86_64__ || __i386__) +#include #endif + #include #include #include @@ -69,6 +76,34 @@ int NumSchedulableCPUs() { return kDefaultCores; } +int NumTotalCPUs() { + int count = absl::base_internal::NumCPUs(); + return (count == 0) ? kUnknownCPU : count; +} + +int GetCurrentCPU() { +#if defined(__linux__) && !defined(__ANDROID__) + return sched_getcpu(); +#elif defined(__cpuid_count) + // Attempt to use cpuid on all other platforms. If that fails, perform a + // syscall. + uint32_t eax, ebx, ecx, edx; + __cpuid_count(/*leaf=*/1, /*subleaf=*/0, eax, ebx, ecx, edx); + if ((edx & (1 << 9)) != 0) { + // EBX bits 24-31 are APIC ID + return static_cast(ebx >> 24); + } +#elif defined(__NR_getcpu) + unsigned int cpu; + if (syscall(__NR_getcpu, &cpu, NULL, NULL) < 0) { + return kUnknownCPU; + } else { + return static_cast(cpu); + } +#endif + return kUnknownCPU; +} + int NumHyperthreadsPerCore() { static const int ht_per_core = tensorflow::port::CPUIDNumSMT(); return (ht_per_core > 0) ? ht_per_core : 1; @@ -83,9 +118,7 @@ int NUMANumNodes() { return 1; } void NUMASetThreadNodeAffinity(int node) {} -int NUMAGetThreadNodeAffinity() { - return kNUMANoAffinity; -} +int NUMAGetThreadNodeAffinity() { return kNUMANoAffinity; } void* AlignedMalloc(size_t size, int minimum_alignment) { #if defined(__ANDROID__) diff --git a/tensorflow/core/platform/windows/port.cc b/tensorflow/core/platform/windows/port.cc index 911ea1902f..b902c85cdc 100644 --- a/tensorflow/core/platform/windows/port.cc +++ b/tensorflow/core/platform/windows/port.cc @@ -21,6 +21,7 @@ limitations under the License. #endif #include +#include #include #include "tensorflow/core/platform/cpu_info.h" @@ -54,6 +55,30 @@ int NumSchedulableCPUs() { return system_info.dwNumberOfProcessors; } +int NumTotalCPUs() { + // TODO(ebrevdo): Make this more accurate. + // + // This only returns the number of processors in the current + // processor group; which may be undercounting if you have more than 64 cores. + // For that case, one needs to call + // GetLogicalProcessorInformationEx(RelationProcessorCore, ...) and accumulate + // the Size fields by iterating over the written-to buffer. Since I can't + // easily test this on Windows, I'm deferring this to someone who can! + // + // If you fix this, also consider updatig GetCurrentCPU below. + return NumSchedulableCPUs(); +} + +int GetCurrentCPU() { + // NOTE(ebrevdo): This returns the processor number within the processor + // group on systems with >64 processors. Therefore it doesn't necessarily map + // naturally to an index in NumSchedulableCPUs(). + // + // On the plus side, this number is probably guaranteed to be within + // [0, NumTotalCPUs()) due to its incomplete implementation. + return GetCurrentProcessorNumber(); +} + bool NUMAEnabled() { // Not yet implemented: coming soon. return false; -- GitLab From d846d90c5a07c8a93c35622c4f6047031a412166 Mon Sep 17 00:00:00 2001 From: Sami Kama Date: Wed, 5 Dec 2018 21:57:38 -0800 Subject: [PATCH 387/873] Pass visible devices info down to the backend construction. Don't use stream executors in backend --- tensorflow/compiler/jit/xla_device.cc | 6 ++- tensorflow/compiler/jit/xla_device.h | 7 +++ tensorflow/compiler/jit/xla_gpu_device.cc | 46 +++++++++++-------- .../compiler/xla/client/client_library.cc | 21 +++++++-- .../compiler/xla/client/client_library.h | 14 +++++- tensorflow/compiler/xla/service/backend.cc | 14 +++++- tensorflow/compiler/xla/service/backend.h | 13 ++++++ .../compiler/xla/service/local_service.cc | 6 ++- .../compiler/xla/service/platform_util.cc | 8 +++- .../compiler/xla/service/platform_util.h | 3 +- tensorflow/compiler/xla/service/service.cc | 18 +++++--- tensorflow/compiler/xla/service/service.h | 7 +++ 12 files changed, 125 insertions(+), 38 deletions(-) diff --git a/tensorflow/compiler/jit/xla_device.cc b/tensorflow/compiler/jit/xla_device.cc index 4201ff91a8..77cd2f4462 100644 --- a/tensorflow/compiler/jit/xla_device.cc +++ b/tensorflow/compiler/jit/xla_device.cc @@ -201,7 +201,8 @@ XlaDevice::XlaDevice(const SessionOptions& session_options, jit_device_name_(options.compilation_device_name), platform_(options.platform), use_multiple_streams_(options.use_multiple_streams), - shape_representation_fn_(options.shape_representation_fn) { + shape_representation_fn_(options.shape_representation_fn), + allowed_devices_(options.allowed_devices) { VLOG(1) << "Created XLA device " << options.compilation_device_name << " " << this; thread_pool_.reset(new thread::ThreadPool(session_options.env, "xla_device", @@ -234,7 +235,8 @@ xla::LocalClient* XlaDevice::client() const { // TODO(b/78468222): This can fail, at least when the backend is GPU and // there is no GPU on the host. - return xla::ClientLibrary::GetOrCreateLocalClient(platform_).ValueOrDie(); + return xla::ClientLibrary::GetOrCreateLocalClient(platform_, allowed_devices_) + .ValueOrDie(); } Allocator* XlaDevice::GetAllocator(AllocatorAttributes attr) { diff --git a/tensorflow/compiler/jit/xla_device.h b/tensorflow/compiler/jit/xla_device.h index c8bb276cdb..90d544e2a1 100644 --- a/tensorflow/compiler/jit/xla_device.h +++ b/tensorflow/compiler/jit/xla_device.h @@ -24,6 +24,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_JIT_XLA_DEVICE_H_ #define TENSORFLOW_COMPILER_JIT_XLA_DEVICE_H_ +#include #include "tensorflow/compiler/jit/xla_device_context.h" #include "tensorflow/compiler/jit/xla_tensor.h" @@ -123,6 +124,8 @@ class XlaDevice : public LocalDevice { // If padded_shape_fn is empty, a default implementation that returns // the logical on-device shape without padding is used. PaddedShapeFn padded_shape_fn; + // Set of allowed devices. -1 is all devices + std::set allowed_devices = {-1}; }; // Creates a new XLA Device. @@ -165,6 +168,7 @@ class XlaDevice : public LocalDevice { bool RequiresSyncOnCompletion() const override LOCKS_EXCLUDED(mu_); + // A simple RAII handle. On construction the device's // outstanding_asynchronous_operations_ field is incremented; on destruction // it is decremented. @@ -256,6 +260,9 @@ class XlaDevice : public LocalDevice { // completion. int64 outstanding_asynchronous_operations_ GUARDED_BY(mu_) = 0; condition_variable outstanding_asynchronous_operations_cv_; + + // Set of allowed gpu devices at the time of construction. + std::set allowed_devices_ = {-1}; }; // Builds OpKernel registrations on 'device' for the JIT operators diff --git a/tensorflow/compiler/jit/xla_gpu_device.cc b/tensorflow/compiler/jit/xla_gpu_device.cc index 0191315a66..877af52dde 100644 --- a/tensorflow/compiler/jit/xla_gpu_device.cc +++ b/tensorflow/compiler/jit/xla_gpu_device.cc @@ -33,8 +33,34 @@ class XlaGpuDeviceFactory : public DeviceFactory { public: Status CreateDevices(const SessionOptions& options, const string& name_prefix, std::vector>* devices) override; + // Returns a set containing the device ids contained in visible_device_list or + // -1 if the string is empty. + static xla::StatusOr> ParseVisibleDeviceList( + const string& visible_device_list); }; +xla::StatusOr> XlaGpuDeviceFactory::ParseVisibleDeviceList( + const string& visible_device_list) { + std::set gpu_ids; + if (visible_device_list.length() == 0) { + gpu_ids.insert(-1); + return gpu_ids; + } + const std::vector visible_devices = + absl::StrSplit(visible_device_list, ','); + for (const string& platform_gpu_id_str : visible_devices) { + int32 platform_gpu_id; + if (!absl::SimpleAtoi(platform_gpu_id_str, &platform_gpu_id)) { + return errors::InvalidArgument( + "Could not parse entry in 'visible_device_list': '", + platform_gpu_id_str, + "'. visible_device_list = ", visible_device_list); + } + gpu_ids.insert(platform_gpu_id); + } + return gpu_ids; +} + Status XlaGpuDeviceFactory::CreateDevices( const SessionOptions& session_options, const string& name_prefix, std::vector>* devices) { @@ -64,24 +90,7 @@ Status XlaGpuDeviceFactory::CreateDevices( gpu_ids.insert(i); } } else { - // For loop below is copied from gpu/gpu_device.cc. It validates - // the visible_device_list and populates gpu_ids set. - const std::vector visible_devices = - absl::StrSplit(allowed_gpus, ','); - for (const string& platform_gpu_id_str : visible_devices) { - int32 platform_gpu_id; - if (!absl::SimpleAtoi(platform_gpu_id_str, &platform_gpu_id)) { - return errors::InvalidArgument( - "Could not parse entry in 'visible_device_list': '", - platform_gpu_id_str, "'. visible_device_list = ", allowed_gpus); - } - if (platform_gpu_id < 0 || platform_gpu_id >= num_visible_devices) { - return errors::InvalidArgument( - "'visible_device_list' listed an invalid GPU id '", platform_gpu_id, - "' but visible device count is ", num_visible_devices); - } - gpu_ids.insert(platform_gpu_id); - } + gpu_ids = ParseVisibleDeviceList(allowed_gpus).ValueOrDie(); } for (int i : gpu_ids) { XlaDevice::Options options; @@ -91,6 +100,7 @@ Status XlaGpuDeviceFactory::CreateDevices( options.device_ordinal = i; options.compilation_device_name = DEVICE_GPU_XLA_JIT; options.use_multiple_streams = true; + options.allowed_devices=gpu_ids; auto device = absl::make_unique(session_options, options); Status status = device->UseGpuDeviceInfo(); diff --git a/tensorflow/compiler/xla/client/client_library.cc b/tensorflow/compiler/xla/client/client_library.cc index 27b7fa7b29..33d8fa9841 100644 --- a/tensorflow/compiler/xla/client/client_library.cc +++ b/tensorflow/compiler/xla/client/client_library.cc @@ -26,10 +26,12 @@ namespace xla { LocalClientOptions::LocalClientOptions(se::Platform* platform, int number_of_replicas, - int intra_op_parallelism_threads) + int intra_op_parallelism_threads, + std::set device_set) : platform_(platform), number_of_replicas_(number_of_replicas), - intra_op_parallelism_threads_(intra_op_parallelism_threads) {} + intra_op_parallelism_threads_(intra_op_parallelism_threads), + allowed_devices_(device_set) {} LocalClientOptions& LocalClientOptions::set_platform(se::Platform* platform) { platform_ = platform; @@ -58,6 +60,16 @@ int LocalClientOptions::intra_op_parallelism_threads() const { return intra_op_parallelism_threads_; } +LocalClientOptions& LocalClientOptions::set_allowed_devices( + std::set device_set) { + allowed_devices_ = device_set; + return *this; +} + +std::set LocalClientOptions::get_allowed_devices() const { + return allowed_devices_; +} + /* static */ ClientLibrary& ClientLibrary::Singleton() { static ClientLibrary* c = new ClientLibrary; return *c; @@ -67,9 +79,10 @@ ClientLibrary::ClientLibrary() = default; ClientLibrary::~ClientLibrary() = default; /* static */ StatusOr ClientLibrary::GetOrCreateLocalClient( - se::Platform* platform) { + se::Platform* platform, const std::set device_set) { LocalClientOptions default_options; default_options.set_platform(platform); + default_options.set_allowed_devices(device_set); return GetOrCreateLocalClient(default_options); } @@ -94,7 +107,7 @@ ClientLibrary::~ClientLibrary() = default; service_options.set_number_of_replicas(replica_count); service_options.set_intra_op_parallelism_threads( options.intra_op_parallelism_threads()); - + service_options.set_allowed_devices(options.get_allowed_devices()); auto instance = absl::make_unique(); TF_ASSIGN_OR_RETURN(instance->service, LocalService::NewService(service_options)); diff --git a/tensorflow/compiler/xla/client/client_library.h b/tensorflow/compiler/xla/client/client_library.h index 3ad558fa53..d180c8f7ca 100644 --- a/tensorflow/compiler/xla/client/client_library.h +++ b/tensorflow/compiler/xla/client/client_library.h @@ -25,6 +25,7 @@ limitations under the License. #include #include #include +#include #include "tensorflow/compiler/xla/client/compile_only_client.h" #include "tensorflow/compiler/xla/client/local_client.h" @@ -45,7 +46,8 @@ class LocalClientOptions { public: LocalClientOptions(se::Platform* platform = nullptr, int number_of_replicas = 1, - int intra_op_parallelism_threads = -1); + int intra_op_parallelism_threads = -1, + std::set device_set = {-1}); // Set the platform backing the service, or nullptr for the default platform. LocalClientOptions& set_platform(se::Platform* platform); @@ -60,10 +62,16 @@ class LocalClientOptions { LocalClientOptions& set_intra_op_parallelism_threads(int num_threads); int intra_op_parallelism_threads() const; + // Sets the allowed_devices set for creation of stream executors. + LocalClientOptions& set_allowed_devices(const std::set device_set); + + std::set get_allowed_devices() const; + private: se::Platform* platform_; int number_of_replicas_; int intra_op_parallelism_threads_; + std::set allowed_devices_; }; class ClientLibrary { @@ -73,8 +81,10 @@ class ClientLibrary { // // platform : The platform the underlying XLA service should target. If // null then default platform is used. + // device_set: Set of device IDs for which the stream executor will be created + // for, for the given platform. static StatusOr GetOrCreateLocalClient( - se::Platform* platform = nullptr); + se::Platform* platform = nullptr, const std::set device_set = {-1}); static StatusOr GetOrCreateLocalClient( const LocalClientOptions& options); diff --git a/tensorflow/compiler/xla/service/backend.cc b/tensorflow/compiler/xla/service/backend.cc index 5c180cbdd4..385c5eefa0 100644 --- a/tensorflow/compiler/xla/service/backend.cc +++ b/tensorflow/compiler/xla/service/backend.cc @@ -57,6 +57,15 @@ int BackendOptions::intra_op_parallelism_threads() const { return intra_op_parallelism_threads_; } +BackendOptions& BackendOptions::set_allowed_devices(std::set device_set) { + allowed_devices_ = device_set; + return *this; +} + +std::set BackendOptions::get_allowed_devices() const { + return allowed_devices_; +} + // Define this in .cc file to avoid having to include eigen or forward declare // these types in the header. struct Backend::EigenThreadPoolWrapper { @@ -77,7 +86,8 @@ struct Backend::EigenThreadPoolWrapper { se::Platform* platform = options.platform(); TF_ASSIGN_OR_RETURN(auto compiler, Compiler::GetForPlatform(platform)); TF_ASSIGN_OR_RETURN(auto stream_executors, - PlatformUtil::GetStreamExecutors(platform)); + PlatformUtil::GetStreamExecutors( + platform, options.get_allowed_devices())); TF_ASSIGN_OR_RETURN(auto transfer_manager, TransferManager::GetForPlatform(platform)); TF_ASSIGN_OR_RETURN(auto computation_placer, @@ -172,7 +182,7 @@ StatusOr Backend::stream_executor( device_ordinal, stream_executors_.back()->device_ordinal()); } for (auto* executor : stream_executors_) { - if (executor->device_ordinal() == device_ordinal) { + if (executor && executor->device_ordinal() == device_ordinal) { return executor; } } diff --git a/tensorflow/compiler/xla/service/backend.h b/tensorflow/compiler/xla/service/backend.h index a2dafbe803..26bae43c1a 100644 --- a/tensorflow/compiler/xla/service/backend.h +++ b/tensorflow/compiler/xla/service/backend.h @@ -20,6 +20,7 @@ limitations under the License. #include #include #include +#include #include "absl/strings/str_cat.h" #include "absl/types/span.h" @@ -53,9 +54,15 @@ class BackendOptions { BackendOptions& set_intra_op_parallelism_threads(int num_threads); int intra_op_parallelism_threads() const; + // Sets the allowed_devices set for creation of stream executors. + BackendOptions& set_allowed_devices(const std::set device_set); + + std::set get_allowed_devices() const; + private: se::Platform* platform_ = nullptr; int intra_op_parallelism_threads_ = -1; + std::set allowed_devices_ = {-1}; }; // Class which encapsulates an XLA backend. It includes everything necessary @@ -106,6 +113,12 @@ class Backend { // can be > 1). se::StreamExecutor* default_stream_executor() const { CHECK(!stream_executors_.empty()); + + for(se::StreamExecutor* e :stream_executors_){ + if(e){ + return e; + } + } return stream_executors_[0]; } diff --git a/tensorflow/compiler/xla/service/local_service.cc b/tensorflow/compiler/xla/service/local_service.cc index 6c89700983..4b0b8fc347 100644 --- a/tensorflow/compiler/xla/service/local_service.cc +++ b/tensorflow/compiler/xla/service/local_service.cc @@ -52,8 +52,10 @@ namespace xla { } BackendOptions backend_options; - backend_options.set_platform(platform).set_intra_op_parallelism_threads( - options.intra_op_parallelism_threads()); + backend_options.set_platform(platform) + .set_intra_op_parallelism_threads(options.intra_op_parallelism_threads()) + .set_allowed_devices(options.get_allowed_devices()); + TF_ASSIGN_OR_RETURN(std::unique_ptr backend, Backend::CreateBackend(backend_options)); diff --git a/tensorflow/compiler/xla/service/platform_util.cc b/tensorflow/compiler/xla/service/platform_util.cc index c227106511..b8fb2047e6 100644 --- a/tensorflow/compiler/xla/service/platform_util.cc +++ b/tensorflow/compiler/xla/service/platform_util.cc @@ -205,7 +205,8 @@ static bool IsDeviceSupported(se::StreamExecutor* executor) { } /* static */ StatusOr> -PlatformUtil::GetStreamExecutors(se::Platform* platform) { +PlatformUtil::GetStreamExecutors(se::Platform* platform, + std::set allowed_devices) { int device_count = platform->VisibleDeviceCount(); if (device_count <= 0) { return NotFound("no %s devices found", platform->Name()); @@ -226,6 +227,11 @@ PlatformUtil::GetStreamExecutors(se::Platform* platform) { tensorflow::thread::ThreadPool thread_pool( tensorflow::Env::Default(), "device_initialization", device_count); for (int i = 0; i < device_count; ++i) { + if (allowed_devices.count(-1) == 0 && allowed_devices.count(i) == 0) { + VLOG(1) << "Skipping stream executor for device " << i + << " since it is not in the visible device list"; + continue; + } thread_pool.Schedule([platform, i, &stream_executors]() { VLOG(1) << "Started device init " << i; se::StreamExecutorConfig config; diff --git a/tensorflow/compiler/xla/service/platform_util.h b/tensorflow/compiler/xla/service/platform_util.h index 571451ba43..04af762fb6 100644 --- a/tensorflow/compiler/xla/service/platform_util.h +++ b/tensorflow/compiler/xla/service/platform_util.h @@ -18,6 +18,7 @@ limitations under the License. #include #include +#include #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/types.h" @@ -63,7 +64,7 @@ class PlatformUtil { // // If the platform has no visible devices, a not-found error is returned. static StatusOr> GetStreamExecutors( - se::Platform* platform); + se::Platform* platform, std::set allowed_devices = {-1}); private: TF_DISALLOW_COPY_AND_ASSIGN(PlatformUtil); diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc index 5ec7fe2ade..b9e5be486a 100644 --- a/tensorflow/compiler/xla/service/service.cc +++ b/tensorflow/compiler/xla/service/service.cc @@ -113,6 +113,15 @@ int ServiceOptions::intra_op_parallelism_threads() const { return intra_op_parallelism_threads_; } +ServiceOptions& ServiceOptions::set_allowed_devices(std::set device_set) { + allowed_devices_ = device_set; + return *this; +} + +std::set ServiceOptions::get_allowed_devices() const { + return allowed_devices_; +} + /* static */ StatusOr> Service::NewService( se::Platform* platform) { ServiceOptions default_options; @@ -129,6 +138,7 @@ int ServiceOptions::intra_op_parallelism_threads() const { } BackendOptions backend_options; backend_options.set_platform(platform); + backend_options.set_allowed_devices(options.get_allowed_devices()); TF_ASSIGN_OR_RETURN(execute_backend, Backend::CreateBackend(backend_options)); std::unique_ptr service( @@ -150,17 +160,13 @@ Service::Service(const ServiceOptions& options, LOG(INFO) << StrFormat( "XLA service %p executing computations on platform %s. Devices:", this, execute_backend_->platform()->Name()); + auto stream_executors=execute_backend_->stream_executors(); for (int i = 0; i < execute_backend_->device_count(); ++i) { - if (execute_backend_->device_ordinal_supported(i)) { - se::StreamExecutor* executor = - execute_backend_->stream_executor(i).ValueOrDie(); + se::StreamExecutor* executor =stream_executors.at(i); const auto& description = executor->GetDeviceDescription(); LOG(INFO) << StrFormat(" StreamExecutor device (%d): %s, %s", i, description.name(), description.platform_version()); - } else { - LOG(INFO) << StrFormat(" StreamExecutor device (%d) not supported", i); - } } } else { VLOG(1) << "XLA compile-only service constructed"; diff --git a/tensorflow/compiler/xla/service/service.h b/tensorflow/compiler/xla/service/service.h index 11e1a79552..95a504ee30 100644 --- a/tensorflow/compiler/xla/service/service.h +++ b/tensorflow/compiler/xla/service/service.h @@ -20,6 +20,7 @@ limitations under the License. #include #include #include +#include #include "absl/types/span.h" #include "tensorflow/compiler/xla/debug_options_flags.h" @@ -61,10 +62,16 @@ class ServiceOptions { ServiceOptions& set_intra_op_parallelism_threads(int num_threads); int intra_op_parallelism_threads() const; + // Sets the allowed_devices set for creation of stream executors. + ServiceOptions& set_allowed_devices(const std::set device_set); + + std::set get_allowed_devices() const; + private: se::Platform* platform_ = nullptr; int number_of_replicas_ = 1; int intra_op_parallelism_threads_ = -1; + std::set allowed_devices_ = {-1}; }; // The XLA service object, which is the same across all platforms. It maintains -- GitLab From 184223ec1652d0d0206e56d062fa12c4c0d9a5a2 Mon Sep 17 00:00:00 2001 From: Dimitris Vardoulakis Date: Tue, 11 Dec 2018 16:20:05 -0800 Subject: [PATCH 388/873] [TF:XLA] Handle more patterns in ArCrsCombiner, and handle sequences of patterns. Now, we optimize any sequence of the form: AR [Bitcast|Transpose|Reshape|Convert|Multiply|Add|Subtract]* CRS PiperOrigin-RevId: 225090998 --- .../compiler/xla/service/ar_crs_combiner.cc | 145 +++++---- .../compiler/xla/service/ar_crs_combiner.h | 9 +- .../xla/service/ar_crs_combiner_test.cc | 306 +++++++++++++++--- .../compiler/xla/service/hlo_instruction.cc | 4 + .../compiler/xla/service/hlo_instruction.h | 5 +- 5 files changed, 357 insertions(+), 112 deletions(-) diff --git a/tensorflow/compiler/xla/service/ar_crs_combiner.cc b/tensorflow/compiler/xla/service/ar_crs_combiner.cc index 362bc44a1c..47d2c7e357 100644 --- a/tensorflow/compiler/xla/service/ar_crs_combiner.cc +++ b/tensorflow/compiler/xla/service/ar_crs_combiner.cc @@ -36,24 +36,40 @@ namespace { namespace m = match; -// If the argument instruction is a CRS in the sequence -// AR -> Convert -> Add -> CRS -// then return the AR in the sequence. -// TODO(b/117554291): Rewrite this to recognize more general patterns, -// not just the specific one of AR -> Add -> Convert -> CRS. -absl::optional MatchesArCrsPattern( - HloInstruction* instruction) { - HloInstruction *ar, *convert, *add, *crs; - if (Match(instruction, - m::CrossReplicaSum( - &crs, m::Add(&add, m::Op(), - m::Convert(&convert, - m::CrossReplicaSum(&ar, m::Op()))))) && - ar->users().size() == 1 && ar->shape().element_type() == BF16 && - convert->shape().element_type() == F32 && !crs->all_reduce_id()) { - return ar; +// Returns true iff the argument instruction is an AllReduce, followed by a +// certain sequence of instructions and then a CRS. It must be possible to move +// the AR past each instruction in the sequence. +bool MatchesArCrsPattern(HloInstruction* instruction) { + auto can_ar_move_past_instruction = [](HloInstruction* instruction) -> bool { + if (instruction->user_count() != 1) { + return false; + } + auto opcode = instruction->opcode(); + return opcode == HloOpcode::kBitcast || opcode == HloOpcode::kTranspose || + opcode == HloOpcode::kReshape || opcode == HloOpcode::kConvert || + opcode == HloOpcode::kAdd || opcode == HloOpcode::kSubtract || + opcode == HloOpcode::kMultiply; + }; + + auto computation_is_addition = [](HloComputation* c) { + return c->instruction_count() == 3 && + Match(c->root_instruction(), m::Add(m::Parameter(), m::Parameter())); + }; + + if (!instruction->IsCrossModuleAllReduce() || + !computation_is_addition(instruction->called_computations()[0]) || + instruction->user_count() != 1) { + return false; } - return absl::optional(); + auto next = instruction->users()[0]; + while (!next->IsCrossReplicaAllReduce()) { + if (can_ar_move_past_instruction(next)) { + next = next->users()[0]; + } else { + return false; + } + } + return computation_is_addition(next->called_computations()[0]); } } // namespace @@ -195,9 +211,8 @@ bool ArCrsCombiner::InstructionsComputeSameValue( void ArCrsCombiner::GroupAllReducesById(HloModule* module) { for (HloComputation* computation : module->MakeNonfusionComputations()) { for (HloInstruction* instruction : computation->instructions()) { - auto ar = MatchesArCrsPattern(instruction); - if (ar) { - all_reduce_map_[*((*ar)->all_reduce_id())].push_back(*ar); + if (MatchesArCrsPattern(instruction)) { + all_reduce_map_[*(instruction->all_reduce_id())].push_back(instruction); } } } @@ -205,21 +220,23 @@ void ArCrsCombiner::GroupAllReducesById(HloModule* module) { void ArCrsCombiner::KeepProvablyEqualInstructionGroups() { for (auto it : all_reduce_map_) { + auto all_reduce_id = it.first; auto instruction_vec = it.second; CHECK_EQ(instruction_vec.size(), num_spatial_partitions_); - auto instr_0 = instruction_vec[0]; - auto add_0 = instr_0->users()[0]->users()[0]; - CHECK_EQ(HloOpcode::kAdd, add_0->opcode()); - for (int i = 1; i < instruction_vec.size(); ++i) { auto instr_i = instruction_vec[i]; - auto add_i = instr_i->users()[0]->users()[0]; - CHECK_EQ(HloOpcode::kAdd, add_i->opcode()); + auto next_0 = instr_0->users()[0]; + auto next_i = instr_i->users()[0]; absl::flat_hash_map visited_pairs; - if (!InstructionsComputeSameValue(add_0, add_i, &visited_pairs)) { - all_reduce_map_.erase(it.first); - } + do { + if (!InstructionsComputeSameValue(next_0, next_i, &visited_pairs)) { + all_reduce_map_.erase(all_reduce_id); + break; + } + next_0 = next_0->users()[0]; + next_i = next_i->users()[0]; + } while (!next_0->IsCrossReplicaAllReduce()); } } } @@ -228,47 +245,51 @@ StatusOr ArCrsCombiner::RewriteGraph() { if (all_reduce_map_.empty()) { return false; } - - auto computation_is_addition = [](HloComputation* c) { - return c->instruction_count() == 3 && - Match(c->root_instruction(), m::Add(m::Parameter(), m::Parameter())); - }; - for (auto it : all_reduce_map_) { auto instruction_vec = it.second; for (auto all_reduce : instruction_vec) { auto parent_computation = all_reduce->parent(); - auto convert = all_reduce->users()[0]; - auto add = convert->users()[0]; - auto crs = add->users()[0]; - - if (!computation_is_addition(all_reduce->called_computations()[0]) || - !computation_is_addition(crs->called_computations()[0])) { - continue; + auto all_reduce_id = all_reduce->all_reduce_id(); + auto prev = all_reduce->mutable_operand(0); + auto next = all_reduce->users()[0]; + TF_CHECK_OK(all_reduce->ReplaceUseWith(next, prev)); + TF_CHECK_OK(parent_computation->RemoveInstruction(all_reduce)); + while (!next->IsCrossReplicaAllReduce()) { + switch (next->opcode()) { + case HloOpcode::kBitcast: + case HloOpcode::kTranspose: + case HloOpcode::kReshape: + case HloOpcode::kConvert: + case HloOpcode::kMultiply: + break; + case HloOpcode::kAdd: + case HloOpcode::kSubtract: { + auto other_operand = (next->operands()[0] == prev) + ? next->operands()[1] + : next->operands()[0]; + // To move the AR past the addition/subtraction, we need to divide + // other_operand by the number of spatial partitions. + auto shape = other_operand->shape(); + Literal lit(shape); + lit.PopulateWithValue(num_spatial_partitions_); + auto divisor = parent_computation->AddInstruction( + HloInstruction::CreateConstant(lit.Clone())); + auto division = + parent_computation->AddInstruction(HloInstruction::CreateBinary( + shape, HloOpcode::kDivide, other_operand, divisor)); + TF_CHECK_OK(other_operand->ReplaceUseWith(next, division)); + break; + } + default: + LOG(FATAL) << "Unexpected instruction: " << next->ToShortString(); + } + prev = next; + next = next->users()[0]; } - HloInstruction* other_summand = (add->operands()[0] == convert) - ? add->operands()[1] - : add->operands()[0]; - // To move the AR past the addition, we need to divide other_summand by - // the number of spatial partitions. - CHECK_EQ(all_reduce->user_count(), 1); - TF_CHECK_OK( - all_reduce->ReplaceAllUsesWith(all_reduce->mutable_operand(0))); - auto shape = other_summand->shape(); - Literal lit(shape); - lit.PopulateWithValue(num_spatial_partitions_); - auto divisor = parent_computation->AddInstruction( - HloInstruction::CreateConstant(lit.Clone())); - auto division = - parent_computation->AddInstruction(HloInstruction::CreateBinary( - shape, HloOpcode::kDivide, other_summand, divisor)); - TF_CHECK_OK(other_summand->ReplaceUseWith(add, division)); // The AllReduce and the CRS are combined to an all-core AllReduce. - crs->set_all_reduce_id(all_reduce->all_reduce_id()); - TF_CHECK_OK(parent_computation->RemoveInstruction(all_reduce)); + next->set_all_reduce_id(all_reduce_id); } } - return true; } diff --git a/tensorflow/compiler/xla/service/ar_crs_combiner.h b/tensorflow/compiler/xla/service/ar_crs_combiner.h index f6a7ef76ec..6be7e1002d 100644 --- a/tensorflow/compiler/xla/service/ar_crs_combiner.h +++ b/tensorflow/compiler/xla/service/ar_crs_combiner.h @@ -25,9 +25,12 @@ limitations under the License. namespace xla { -// Combine an AllReduce and a CrossReplicaSum when they are close to each other -// in the graph, to use an efficient CrossReplicaSum implementation that -// fully utilizes the interconnect bandwidth. +// When the HLO graph contains an AllReduce, followed by some simple linear +// operations, followed by a CrossReplicaSum, we can combine the AR and the CRS, +// to use an efficient CrossReplicaSum implementation that fully utilizes the +// interconnect bandwidth. +// Such sequences appear in spatially partitioned models. +// This pass must run right after spatial partitioning. class ArCrsCombiner : public HloModulePass { public: ArCrsCombiner(int num_spatial_partitions) diff --git a/tensorflow/compiler/xla/service/ar_crs_combiner_test.cc b/tensorflow/compiler/xla/service/ar_crs_combiner_test.cc index 10171835d8..2f7a53bfc8 100644 --- a/tensorflow/compiler/xla/service/ar_crs_combiner_test.cc +++ b/tensorflow/compiler/xla/service/ar_crs_combiner_test.cc @@ -326,11 +326,27 @@ ENTRY %WhileLoop () -> (f32[2,2], f32[2,2]) { EXPECT_FALSE(ArCrsCombiner::TestInstructionsComputeSameValue(i1, i2)); } -TEST_F(ArCrsCombinerTest, RewritePatternArConvertAddCrs) { +void CompareReplicaGroups(const std::vector& groups_before, + const std::vector& groups_after) { + ASSERT_EQ(groups_before.size(), groups_after.size()); + for (int i = 0; i < groups_before.size(); ++i) { + // Somewhat verbose way to compare the replica_ids, because EqualsProto + // is not available in the open-source build. + auto group_before = groups_before[i]; + std::vector ids_before(group_before.replica_ids().begin(), + group_before.replica_ids().end()); + auto group_after = groups_after[i]; + std::vector ids_after(group_after.replica_ids().begin(), + group_after.replica_ids().end()); + EXPECT_EQ(ids_before, ids_after); + } +} + +TEST_F(ArCrsCombinerTest, RewriteArConvertCrs) { const char* module_str = R"( HloModule foobar -%binary_add (a: bf16[], b: bf16[]) -> bf16[] { +%sum.bf16 (a: bf16[], b: bf16[]) -> bf16[] { %a = bf16[] parameter(0) %b = bf16[] parameter(1) ROOT %add = bf16[] add(%a, %b) @@ -342,48 +358,257 @@ HloModule foobar ROOT %add = f32[] add(%x, %y) } -ENTRY %entrycomp (p: f32[2,2]) -> (f32[2,2], f32[2,2]) { - %p = f32[2,2] parameter(0) - %constant.bf16 = bf16[2,2] constant(bf16[2,2] {{1, 2}, {3, 4}}) - %constant.f32 = f32[2,2] constant(f32[2,2] {{1, 2}, {3, 4}}) +ENTRY %entrycomp (p: bf16[]) -> (f32[], f32[]) { + %p = bf16[] parameter(0) + + %cross-replica-sum.ar.1 = bf16[] + cross-replica-sum(%p), + replica_groups={{0},{1}}, + all_reduce_id=1, + to_apply=%sum.bf16, + sharding={maximal device=0} + %convert.1 = f32[] + convert(%cross-replica-sum.ar.1), + sharding={maximal device=0} + %cross-replica-sum.1 = f32[] + cross-replica-sum(%convert.1), + replica_groups={{0,1}}, + to_apply=%sum.f32, + sharding={maximal device=0} + + %cross-replica-sum.ar.2 = bf16[] + cross-replica-sum(%p), + replica_groups={{0},{1}}, + all_reduce_id=1, + to_apply=%sum.bf16, + sharding={maximal device=1} + %convert.2 = f32[] + convert(%cross-replica-sum.ar.2), + sharding={maximal device=1} + %cross-replica-sum.2 = f32[] + cross-replica-sum(%convert.2), + replica_groups={{0,1}}, + to_apply=%sum.f32, + sharding={maximal device=1} + + ROOT %tuple = (f32[], f32[]) + tuple(%cross-replica-sum.1, %cross-replica-sum.2), + sharding={{maximal device=0}, {maximal device=1}} +} +)"; + + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, + ParseAndReturnVerifiedModule(module_str)); + auto crs_before = + module->entry_computation()->root_instruction()->operands()[0]; + auto replica_groups_before = crs_before->replica_groups(); + ArCrsCombiner combiner(2); + auto changed = combiner.Run(module.get()).ValueOrDie(); + EXPECT_TRUE(changed); + EXPECT_THAT(module->entry_computation()->root_instruction(), + op::Tuple(op::CrossReplicaSum(op::Convert(op::Parameter())), + op::CrossReplicaSum(op::Convert(op::Parameter())))); + auto crs_after = + module->entry_computation()->root_instruction()->operands()[0]; + auto replica_groups_after = crs_after->replica_groups(); + CompareReplicaGroups(replica_groups_before, replica_groups_after); +} + +TEST_F(ArCrsCombinerTest, RewriteArBitcastCrs) { + const char* module_str = R"( +HloModule foobar + +%sum.1 (a: f32[2,1], b: f32[2,1]) -> f32[2,1] { + %a = f32[2,1] parameter(0) + %b = f32[2,1] parameter(1) + ROOT %add = f32[2,1] add(%a, %b) +} + +%sum.2 (x: f32[2], y: f32[2]) -> f32[2] { + %x = f32[2] parameter(0) + %y = f32[2] parameter(1) + ROOT %add = f32[2] add(%x, %y) +} + +ENTRY %entrycomp (p: f32[2,1]) -> (f32[2], f32[2]) { + %p = f32[2,1] parameter(0) + + %cross-replica-sum.ar.1 = f32[2,1] + cross-replica-sum(%p), + replica_groups={{0},{1}}, + all_reduce_id=1, + to_apply=%sum.1, + sharding={maximal device=0} + %bitcast.1 = f32[2]{0} bitcast(f32[2,1]{1,0} %cross-replica-sum.ar.1) + %cross-replica-sum.1 = f32[2] + cross-replica-sum(%bitcast.1), + replica_groups={{0,1}}, + to_apply=%sum.2, + sharding={maximal device=0} + + %cross-replica-sum.ar.2 = f32[2,1] + cross-replica-sum(%p), + replica_groups={{0},{1}}, + all_reduce_id=1, + to_apply=%sum.1, + sharding={maximal device=1} + %bitcast.2 = f32[2]{0} bitcast(f32[2,1]{1,0} %cross-replica-sum.ar.2) + %cross-replica-sum.2 = f32[2] + cross-replica-sum(%bitcast.2), + replica_groups={{0,1}}, + to_apply=%sum.2, + sharding={maximal device=1} + + ROOT %tuple = (f32[], f32[]) + tuple(%cross-replica-sum.1, %cross-replica-sum.2), + sharding={{maximal device=0}, {maximal device=1}} +} +)"; + + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, + ParseAndReturnVerifiedModule(module_str)); + auto crs_before = + module->entry_computation()->root_instruction()->operands()[0]; + auto replica_groups_before = crs_before->replica_groups(); + ArCrsCombiner combiner(2); + auto changed = combiner.Run(module.get()).ValueOrDie(); + EXPECT_TRUE(changed); + EXPECT_THAT(module->entry_computation()->root_instruction(), + op::Tuple(op::CrossReplicaSum(op::Bitcast(op::Parameter())), + op::CrossReplicaSum(op::Bitcast(op::Parameter())))); + auto crs_after = + module->entry_computation()->root_instruction()->operands()[0]; + auto replica_groups_after = crs_after->replica_groups(); + CompareReplicaGroups(replica_groups_before, replica_groups_after); +} - %cross-replica-sum.ar.1 = bf16[2,2] +TEST_F(ArCrsCombinerTest, RewriteArMultiplyCrs) { + const char* module_str = R"( +HloModule foobar + +%sum.f32 (x: f32[], y: f32[]) -> f32[] { + %x = f32[] parameter(0) + %y = f32[] parameter(1) + ROOT %add = f32[] add(%x, %y) +} + +ENTRY %entrycomp (p: f32[]) -> (f32[], f32[]) { + %p = f32[] parameter(0) + %constant.f32 = f32[] constant(123) + + %cross-replica-sum.ar.1 = f32[] + cross-replica-sum(%p), + replica_groups={{0},{1}}, + all_reduce_id=1, + to_apply=%sum.f32, + sharding={maximal device=0} + %multiply.1 = f32[] + multiply(%cross-replica-sum.ar.1, %constant.f32), + sharding={maximal device=0} + %cross-replica-sum.1 = f32[] + cross-replica-sum(%multiply.1), + replica_groups={{0,1}}, + to_apply=%sum.f32, + sharding={maximal device=0} + + %cross-replica-sum.ar.2 = f32[] + cross-replica-sum(%p), + replica_groups={{0},{1}}, + all_reduce_id=1, + to_apply=%sum.f32, + sharding={maximal device=1} + %multiply.2 = f32[] + multiply(%cross-replica-sum.ar.2, %constant.f32), + sharding={maximal device=1} + %cross-replica-sum.2 = f32[] + cross-replica-sum(%multiply.2), + replica_groups={{0,1}}, + to_apply=%sum.f32, + sharding={maximal device=1} + + ROOT %tuple = (f32[], f32[]) + tuple(%cross-replica-sum.1, %cross-replica-sum.2), + sharding={{maximal device=0}, {maximal device=1}} +} +)"; + + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, + ParseAndReturnVerifiedModule(module_str)); + auto crs_before = + module->entry_computation()->root_instruction()->operands()[0]; + auto replica_groups_before = crs_before->replica_groups(); + ArCrsCombiner combiner(2); + auto changed = combiner.Run(module.get()).ValueOrDie(); + EXPECT_TRUE(changed); + EXPECT_THAT( + module->entry_computation()->root_instruction(), + op::Tuple( + op::CrossReplicaSum(op::Multiply(op::Parameter(), op::Constant())), + op::CrossReplicaSum(op::Multiply(op::Parameter(), op::Constant())))); + auto crs_after = + module->entry_computation()->root_instruction()->operands()[0]; + auto replica_groups_after = crs_after->replica_groups(); + CompareReplicaGroups(replica_groups_before, replica_groups_after); +} + +TEST_F(ArCrsCombinerTest, RewriteArConvertAddCrs) { + const char* module_str = R"( +HloModule foobar + +%sum.bf16 (a: bf16[], b: bf16[]) -> bf16[] { + %a = bf16[] parameter(0) + %b = bf16[] parameter(1) + ROOT %add = bf16[] add(%a, %b) +} + +%sum.f32 (x: f32[], y: f32[]) -> f32[] { + %x = f32[] parameter(0) + %y = f32[] parameter(1) + ROOT %add = f32[] add(%x, %y) +} + +ENTRY %entrycomp (p: f32[]) -> (f32[], f32[]) { + %p = f32[] parameter(0) + %constant.bf16 = bf16[] constant(1) + %constant.f32 = f32[] constant(2) + + %cross-replica-sum.ar.1 = bf16[] cross-replica-sum(%constant.bf16), replica_groups={{0},{1}}, all_reduce_id=1, - to_apply=%binary_add, + to_apply=%sum.bf16, sharding={maximal device=0} - %convert.1 = f32[2,2] + %convert.1 = f32[] convert(%cross-replica-sum.ar.1), sharding={maximal device=0} - %add.1 = f32[2,2] + %add.1 = f32[] add(%constant.f32, %convert.1), sharding={maximal device=0} - %cross-replica-sum.1 = f32[2,2] + %cross-replica-sum.1 = f32[] cross-replica-sum(%add.1), replica_groups={{0,1}}, to_apply=%sum.f32, sharding={maximal device=0} - %cross-replica-sum.ar.2 = bf16[2,2] + %cross-replica-sum.ar.2 = bf16[] cross-replica-sum(%constant.bf16), replica_groups={{0},{1}}, all_reduce_id=1, - to_apply=%binary_add, + to_apply=%sum.bf16, sharding={maximal device=1} - %convert.2 = f32[2,2] + %convert.2 = f32[] convert(%cross-replica-sum.ar.2), sharding={maximal device=1} - %add.2 = f32[2,2] + %add.2 = f32[] add(%constant.f32, %convert.2), sharding={maximal device=1} - %cross-replica-sum.2 = f32[2,2] + %cross-replica-sum.2 = f32[] cross-replica-sum(%add.2), replica_groups={{0,1}}, to_apply=%sum.f32, sharding={maximal device=1} - ROOT %tuple = (f32[2,2], f32[2,2]) + ROOT %tuple = (f32[], f32[]) tuple(%cross-replica-sum.1, %cross-replica-sum.2), sharding={{maximal device=0}, {maximal device=1}} } @@ -407,25 +632,14 @@ ENTRY %entrycomp (p: f32[2,2]) -> (f32[2,2], f32[2,2]) { auto crs_after = module->entry_computation()->root_instruction()->operands()[0]; auto replica_groups_after = crs_after->replica_groups(); - ASSERT_EQ(replica_groups_before.size(), replica_groups_after.size()); - for (int i = 0; i < replica_groups_before.size(); ++i) { - // Somewhat verbose way to compare the replica_ids, because EqualsProto - // is not available in the open-source build. - auto group_before = replica_groups_before[i]; - std::vector ids_before(group_before.replica_ids().begin(), - group_before.replica_ids().end()); - auto group_after = replica_groups_after[i]; - std::vector ids_after(group_after.replica_ids().begin(), - group_after.replica_ids().end()); - EXPECT_EQ(ids_before, ids_after); - } + CompareReplicaGroups(replica_groups_before, replica_groups_after); } TEST_F(ArCrsCombinerTest, OtherSummandNotTheSameDontRewrite) { const char* module_str = R"( HloModule foobar -%binary_add (a: bf16[], b: bf16[]) -> bf16[] { +%sum.bf16 (a: bf16[], b: bf16[]) -> bf16[] { %a = bf16[] parameter(0) %b = bf16[] parameter(1) ROOT %add = bf16[] add(%a, %b) @@ -437,49 +651,49 @@ HloModule foobar ROOT %add = f32[] add(%x, %y) } -ENTRY %entrycomp (p: f32[2,2]) -> (f32[2,2], f32[2,2]) { - %p = f32[2,2] parameter(0) - %constant.bf16 = bf16[2,2] constant(bf16[2,2] {{1, 2}, {3, 4}}) - %constant.f32.1 = f32[2,2] constant(f32[2,2] {{1, 2}, {3, 4}}) - %constant.f32.2 = f32[2,2] constant(f32[2,2] {{3, 4}, {5, 6}}) +ENTRY %entrycomp (p: f32[]) -> (f32[], f32[]) { + %p = f32[] parameter(0) + %constant.bf16 = bf16[] constant(1) + %constant.f32.1 = f32[] constant(2) + %constant.f32.2 = f32[] constant(3) - %cross-replica-sum.ar.1 = bf16[2,2] + %cross-replica-sum.ar.1 = bf16[] cross-replica-sum(%constant.bf16), replica_groups={{0},{1}}, all_reduce_id=1, - to_apply=%binary_add, + to_apply=%sum.bf16, sharding={maximal device=0} - %convert.1 = f32[2,2] + %convert.1 = f32[] convert(%cross-replica-sum.ar.1), sharding={maximal device=0} - %add.1 = f32[2,2] + %add.1 = f32[] add(%constant.f32.1, %convert.1), sharding={maximal device=0} - %cross-replica-sum.1 = f32[2,2] + %cross-replica-sum.1 = f32[] cross-replica-sum(%add.1), replica_groups={{0,1}}, to_apply=%sum.f32, sharding={maximal device=0} - %cross-replica-sum.ar.2 = bf16[2,2] + %cross-replica-sum.ar.2 = bf16[] cross-replica-sum(%constant.bf16), replica_groups={{0},{1}}, all_reduce_id=1, - to_apply=%binary_add, + to_apply=%sum.bf16, sharding={maximal device=1} - %convert.2 = f32[2,2] + %convert.2 = f32[] convert(%cross-replica-sum.ar.2), sharding={maximal device=1} - %add.2 = f32[2,2] + %add.2 = f32[] add(%constant.f32.2, %convert.2), sharding={maximal device=1} - %cross-replica-sum.2 = f32[2,2] + %cross-replica-sum.2 = f32[] cross-replica-sum(%add.2), replica_groups={{0,1}}, to_apply=%sum.f32, sharding={maximal device=1} - ROOT %tuple = (f32[2,2], f32[2,2]) + ROOT %tuple = (f32[], f32[]) tuple(%cross-replica-sum.1, %cross-replica-sum.2), sharding={{maximal device=0}, {maximal device=1}} } diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc index 152a451c18..c57d9c1e86 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction.cc @@ -2060,6 +2060,10 @@ bool HloInstruction::IsCrossModuleAllReduce() const { return opcode() == HloOpcode::kCrossReplicaSum && all_reduce_id(); } +bool HloInstruction::IsCrossReplicaAllReduce() const { + return opcode() == HloOpcode::kCrossReplicaSum && !all_reduce_id(); +} + string HloInstruction::ToStringWithCanonicalNameMap( const HloPrintOptions& options, CanonicalNameMap* canonical_name_map) const { diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h index a54716217d..a312b6bf0d 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.h +++ b/tensorflow/compiler/xla/service/hlo_instruction.h @@ -1174,9 +1174,12 @@ class HloInstruction { // Returns true if this instruction is elementwise on all its operands. bool IsElementwise() const; - // Returns true if this is an cross module all-reduce instrucion. + // Returns true if this is a cross module all-reduce instruction. bool IsCrossModuleAllReduce() const; + // Returns true if this is a cross-replica all-reduce instruction. + bool IsCrossReplicaAllReduce() const; + // Returns true if this elementwise instruction implicitly broadcasts operand // `operand_idx`. // -- GitLab From 33bc0b978858bafa56cce5679dc41f4ab408b77c Mon Sep 17 00:00:00 2001 From: Anna R Date: Tue, 11 Dec 2018 16:20:16 -0800 Subject: [PATCH 389/873] Internal change. PiperOrigin-RevId: 225091038 --- third_party/gpus/crosstool/BUILD.tpl | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/third_party/gpus/crosstool/BUILD.tpl b/third_party/gpus/crosstool/BUILD.tpl index c8812fab33..1260b265ab 100644 --- a/third_party/gpus/crosstool/BUILD.tpl +++ b/third_party/gpus/crosstool/BUILD.tpl @@ -22,6 +22,7 @@ cc_toolchain_suite( "local|compiler": ":cc-compiler-local", "darwin|compiler": ":cc-compiler-darwin", "x64_windows|msvc-cl": ":cc-compiler-windows", + "x64_windows": ":cc-compiler-windows", }, ) @@ -41,6 +42,7 @@ cc_toolchain( # last on the command line and contain all shared libraries to link, so all # regular options will be left of them. supports_param_files = 1, + toolchain_identifier = "local_linux", ) cc_toolchain( @@ -55,6 +57,7 @@ cc_toolchain( static_runtime_libs = [":empty"], strip_files = ":empty", supports_param_files = 0, + toolchain_identifier = "local_darwin", ) cc_toolchain( @@ -69,6 +72,7 @@ cc_toolchain( static_runtime_libs = [":empty"], strip_files = ":empty", supports_param_files = 1, + toolchain_identifier = "local_windows", ) filegroup( -- GitLab From bafb8747983fbcf186ffb063ed39dbb0a18e3c8e Mon Sep 17 00:00:00 2001 From: Artem Belevich Date: Tue, 11 Dec 2018 16:24:24 -0800 Subject: [PATCH 390/873] Improve CUDA runtime dependencies search. tensorflow::CudaRoot() now may return multiple possible locations of the CUDA root. PiperOrigin-RevId: 225091635 --- .../xla/service/gpu/nvptx_compiler.cc | 82 ++++++++++--------- .../compiler/xla/service/gpu/nvptx_compiler.h | 2 +- tensorflow/core/BUILD | 16 +--- .../core/platform/cuda_libdevice_path.cc | 26 ------ .../core/platform/cuda_libdevice_path.h | 10 +-- .../core/platform/cuda_libdevice_path_test.cc | 35 -------- .../platform/default/cuda_libdevice_path.cc | 5 +- 7 files changed, 53 insertions(+), 123 deletions(-) delete mode 100644 tensorflow/core/platform/cuda_libdevice_path.cc delete mode 100644 tensorflow/core/platform/cuda_libdevice_path_test.cc diff --git a/tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc b/tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc index f3e17d8882..60f2116e60 100644 --- a/tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc +++ b/tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc @@ -108,27 +108,33 @@ namespace { namespace tracing = tensorflow::tracing; -// Returns the directory containing nvvm libdevice files. config_cuda_data_dir -// should be equal to config().debug_options().xla_gpu_cuda_data_dir() of the -// HloModule being compiled. -string GetLibdeviceDir(const string& config_cuda_data_dir) { - std::vector potential_libdevice_dirs; - if (!config_cuda_data_dir.empty()) { - potential_libdevice_dirs.push_back(config_cuda_data_dir); - } - potential_libdevice_dirs.push_back(tensorflow::LibdeviceRoot()); - - // Tries all potential libdevice directories in the order they are inserted. - // Returns the first directory that exists in the file system. - for (const string& potential_libdevice_dir : potential_libdevice_dirs) { - if (tensorflow::Env::Default()->IsDirectory(potential_libdevice_dir).ok()) { - VLOG(2) << "Found libdevice dir " << potential_libdevice_dir; - return potential_libdevice_dir; +// Returns a vector of potential locations of the CUDA root directory. +std::vector GetCudaRootCandidates( + const HloModuleConfig& hlo_module_config) { + std::vector potential_cuda_roots = tensorflow::CandidateCudaRoots(); + + // CUDA location explicitly specified by user via --xla_gpu_cuda_data_dir has + // highest priority. + string xla_gpu_cuda_data_dir = + hlo_module_config.debug_options().xla_gpu_cuda_data_dir(); + if (!xla_gpu_cuda_data_dir.empty()) { + potential_cuda_roots.insert(potential_cuda_roots.begin(), + xla_gpu_cuda_data_dir); + } + return potential_cuda_roots; +} + +// Returns the directory containing nvvm libdevice files. +string GetLibdeviceDir(const HloModuleConfig& hlo_module_config) { + for (const string& cuda_root : GetCudaRootCandidates(hlo_module_config)) { + string libdevice_dir = + tensorflow::io::JoinPath(cuda_root, "nvvm", "libdevice"); + VLOG(2) << "Looking for libdevice at " << libdevice_dir; + if (tensorflow::Env::Default()->IsDirectory(libdevice_dir).ok()) { + VLOG(2) << "Found libdevice dir " << libdevice_dir; + return libdevice_dir; } - VLOG(2) << "Unable to find potential libdevice dir " - << potential_libdevice_dir; } - LOG(WARNING) << "Unable to find libdevice dir. Using '.'"; // Last resort: maybe in the current folder. return "."; @@ -478,14 +484,19 @@ void WarnIfBadDriverJITVersion() { // Compiles the given PTX string using ptxas and returns the resulting machine // code (i.e. a cubin) as a byte array. -StatusOr> CompilePtx(const string& ptx, int cc_major, - int cc_minor, - bool disable_ptx_optimizations) { +StatusOr> CompilePtx( + const string& ptx, int cc_major, int cc_minor, + const HloModuleConfig& hlo_module_config) { tracing::ScopedActivity activity("Compile PTX", /*is_expensive=*/true); - const string ptxas_path = - tensorflow::io::JoinPath(tensorflow::CudaRoot(), "bin", "ptxas"); - VLOG(2) << "Checking ptxas at " << ptxas_path; auto env = tensorflow::Env::Default(); + string ptxas_path; + for (const string& cuda_root : GetCudaRootCandidates(hlo_module_config)) { + ptxas_path = tensorflow::io::JoinPath(cuda_root, "bin", "ptxas"); + VLOG(2) << "Looking for ptxas at " << ptxas_path; + if (env->FileExists(ptxas_path).ok()) { + break; + } + } TF_RETURN_IF_ERROR(env->FileExists(ptxas_path)); VLOG(2) << "Using ptxas at " << ptxas_path; @@ -520,7 +531,7 @@ StatusOr> CompilePtx(const string& ptx, int cc_major, if (VLOG_IS_ON(2)) { ptxas_args.push_back("-v"); } - if (disable_ptx_optimizations) { + if (hlo_module_config.debug_options().xla_gpu_disable_ptxas_optimizations()) { ptxas_args.push_back("-O0"); } ptxas_info_dumper.SetProgram(ptxas_path, ptxas_args); @@ -685,12 +696,8 @@ StatusOr> NVPTXCompiler::RunBackend( // Find the directory containing libdevice. To avoid searching for it every // time, we have a one-element cache, keyed on the module's config's // cuda_data_dir. - const auto& config_cuda_data_dir = - module->config().debug_options().xla_gpu_cuda_data_dir(); - if (cached_libdevice_dir_.empty() || - cached_cuda_data_dir_ != config_cuda_data_dir) { - cached_cuda_data_dir_ = config_cuda_data_dir; - cached_libdevice_dir_ = GetLibdeviceDir(config_cuda_data_dir); + if (cached_libdevice_dir_.empty()) { + cached_libdevice_dir_ = GetLibdeviceDir(module->config()); } libdevice_dir = cached_libdevice_dir_; } @@ -743,9 +750,8 @@ StatusOr> NVPTXCompiler::RunBackend( } } - const std::vector cubin = CompilePtxOrGetCachedResult( - ptx, cc_major, cc_minor, - module->config().debug_options().xla_gpu_disable_ptxas_optimizations()); + const std::vector cubin = + CompilePtxOrGetCachedResult(ptx, cc_major, cc_minor, module->config()); auto thunk_schedule = absl::make_unique( ir_emitter.ConsumeThunkSequence(), std::move(stream_assignment), @@ -779,7 +785,7 @@ StatusOr> NVPTXCompiler::RunBackend( std::vector NVPTXCompiler::CompilePtxOrGetCachedResult( const string& ptx, int cc_major, int cc_minor, - bool disable_ptx_optimizations) { + const HloModuleConfig& hlo_module_config) { XLA_SCOPED_LOGGING_TIMER("NVPTXCompiler::CompilePtxOrGetCachedResult"); tracing::ScopedActivity activity("PTX->CUBIN", /*is_expensive=*/true); bool inserted; @@ -807,8 +813,8 @@ std::vector NVPTXCompiler::CompilePtxOrGetCachedResult( if (inserted) { CHECK(!cache_value->compilation_done); if (!ptx.empty()) { - StatusOr> maybe_cubin = CompilePtx( - *cache_ptx, cc_major, cc_minor, disable_ptx_optimizations); + StatusOr> maybe_cubin = + CompilePtx(*cache_ptx, cc_major, cc_minor, hlo_module_config); if (maybe_cubin.ok()) { cache_value->cubin_data = std::move(maybe_cubin).ValueOrDie(); VLOG(2) << "Compiled PTX size:" << ptx.size() diff --git a/tensorflow/compiler/xla/service/gpu/nvptx_compiler.h b/tensorflow/compiler/xla/service/gpu/nvptx_compiler.h index be5e31a501..b2077f42fd 100644 --- a/tensorflow/compiler/xla/service/gpu/nvptx_compiler.h +++ b/tensorflow/compiler/xla/service/gpu/nvptx_compiler.h @@ -99,7 +99,7 @@ class NVPTXCompiler : public LLVMCompiler { // compiled cubin. If compilation was unsuccessful, returns an empty vector. std::vector CompilePtxOrGetCachedResult( const string& ptx, int cc_major, int cc_minor, - bool disable_ptx_optimizations); + const HloModuleConfig& hlo_module_config); // The compilation_cache_ map is a cache from {ptx string, cc_major, cc_minor} // -> cubin so we don't recompile the same ptx twice. This is important for diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 5f5ca63540..d92f0ba655 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -4062,20 +4062,6 @@ tf_cuda_cc_test( ], ) -tf_cc_test_gpu( - name = "cuda_libdevice_path_test", - size = "small", - srcs = ["platform/cuda_libdevice_path_test.cc"], - linkstatic = tf_kernel_tests_linkstatic(), - tags = tf_cuda_tests_tags(), - deps = [ - ":cuda_libdevice_path", - ":lib", - ":test", - ":test_main", - ], -) - tf_cuda_only_cc_test( name = "util_cuda_kernel_helper_test", srcs = [ @@ -4931,7 +4917,7 @@ filegroup( cc_library( name = "cuda_libdevice_path", - srcs = ["platform/cuda_libdevice_path.cc"] + tf_additional_libdevice_srcs(), + srcs = tf_additional_libdevice_srcs(), hdrs = ["platform/cuda_libdevice_path.h"], copts = tf_copts(), data = tf_additional_libdevice_data(), diff --git a/tensorflow/core/platform/cuda_libdevice_path.cc b/tensorflow/core/platform/cuda_libdevice_path.cc deleted file mode 100644 index 4d6532b983..0000000000 --- a/tensorflow/core/platform/cuda_libdevice_path.cc +++ /dev/null @@ -1,26 +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. -==============================================================================*/ - -#include "tensorflow/core/platform/cuda_libdevice_path.h" - -#include "tensorflow/core/lib/io/path.h" - -namespace tensorflow { - -string LibdeviceRoot() { - return tensorflow::io::JoinPath(tensorflow::CudaRoot(), "nvvm/libdevice"); -} - -} // namespace tensorflow diff --git a/tensorflow/core/platform/cuda_libdevice_path.h b/tensorflow/core/platform/cuda_libdevice_path.h index 6ef565ecd3..f2dbff9043 100644 --- a/tensorflow/core/platform/cuda_libdevice_path.h +++ b/tensorflow/core/platform/cuda_libdevice_path.h @@ -16,16 +16,14 @@ limitations under the License. #ifndef TENSORFLOW_CORE_PLATFORM_CUDA_LIBDEVICE_PATH_H_ #define TENSORFLOW_CORE_PLATFORM_CUDA_LIBDEVICE_PATH_H_ +#include #include "tensorflow/core/platform/types.h" namespace tensorflow { -// Returns the root directory of the CUDA SDK, which contains sub-folders such -// as bin, lib64, and nvvm. -string CudaRoot(); - -// Returns the directory that contains nvvm libdevice files in the CUDA SDK. -string LibdeviceRoot(); +// Returns, in order of preference, potential locations of the root directory of +// the CUDA SDK, which contains sub-folders such as bin, lib64, and nvvm. +std::vector CandidateCudaRoots(); } // namespace tensorflow diff --git a/tensorflow/core/platform/cuda_libdevice_path_test.cc b/tensorflow/core/platform/cuda_libdevice_path_test.cc deleted file mode 100644 index 2d34239a99..0000000000 --- a/tensorflow/core/platform/cuda_libdevice_path_test.cc +++ /dev/null @@ -1,35 +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. -==============================================================================*/ - -#include "tensorflow/core/platform/cuda_libdevice_path.h" - -#include "tensorflow/core/lib/core/status_test_util.h" -#include "tensorflow/core/lib/io/path.h" -#include "tensorflow/core/platform/env.h" -#include "tensorflow/core/platform/test.h" - -namespace tensorflow { - -#if GOOGLE_CUDA -TEST(CudaLibdevicePathTest, LibdevicePath) { - VLOG(2) << "Libdevice root = " << LibdeviceRoot(); - std::vector libdevice_files; - TF_EXPECT_OK(Env::Default()->GetMatchingPaths( - io::JoinPath(LibdeviceRoot(), "libdevice.*.bc"), &libdevice_files)); - EXPECT_LT(0, libdevice_files.size()); -} -#endif - -} // namespace tensorflow diff --git a/tensorflow/core/platform/default/cuda_libdevice_path.cc b/tensorflow/core/platform/default/cuda_libdevice_path.cc index 20ee3ad621..a8b2e7202a 100644 --- a/tensorflow/core/platform/default/cuda_libdevice_path.cc +++ b/tensorflow/core/platform/default/cuda_libdevice_path.cc @@ -16,6 +16,7 @@ limitations under the License. #include "tensorflow/core/platform/cuda_libdevice_path.h" #include +#include #if !defined(PLATFORM_GOOGLE) #include "cuda/cuda_config.h" @@ -24,9 +25,9 @@ limitations under the License. namespace tensorflow { -string CudaRoot() { +std::vector CandidateCudaRoots() { VLOG(3) << "CUDA root = " << TF_CUDA_TOOLKIT_PATH; - return TF_CUDA_TOOLKIT_PATH; + return {TF_CUDA_TOOLKIT_PATH}; } } // namespace tensorflow -- GitLab From b3bd83e826af4019f6d958b2f7d36853082120ca Mon Sep 17 00:00:00 2001 From: Sami Kama Date: Tue, 11 Dec 2018 16:35:10 -0800 Subject: [PATCH 391/873] Fix clang-format issues --- tensorflow/compiler/jit/xla_device.h | 1 - tensorflow/compiler/jit/xla_gpu_device.cc | 2 +- tensorflow/compiler/xla/client/client_library.h | 6 +++--- tensorflow/compiler/xla/service/backend.h | 6 +++--- tensorflow/compiler/xla/service/platform_util.h | 2 +- tensorflow/compiler/xla/service/service.cc | 12 ++++++------ tensorflow/compiler/xla/service/service.h | 2 +- 7 files changed, 15 insertions(+), 16 deletions(-) diff --git a/tensorflow/compiler/jit/xla_device.h b/tensorflow/compiler/jit/xla_device.h index 90d544e2a1..f1bc882f2c 100644 --- a/tensorflow/compiler/jit/xla_device.h +++ b/tensorflow/compiler/jit/xla_device.h @@ -168,7 +168,6 @@ class XlaDevice : public LocalDevice { bool RequiresSyncOnCompletion() const override LOCKS_EXCLUDED(mu_); - // A simple RAII handle. On construction the device's // outstanding_asynchronous_operations_ field is incremented; on destruction // it is decremented. diff --git a/tensorflow/compiler/jit/xla_gpu_device.cc b/tensorflow/compiler/jit/xla_gpu_device.cc index 877af52dde..e84a784607 100644 --- a/tensorflow/compiler/jit/xla_gpu_device.cc +++ b/tensorflow/compiler/jit/xla_gpu_device.cc @@ -100,7 +100,7 @@ Status XlaGpuDeviceFactory::CreateDevices( options.device_ordinal = i; options.compilation_device_name = DEVICE_GPU_XLA_JIT; options.use_multiple_streams = true; - options.allowed_devices=gpu_ids; + options.allowed_devices = gpu_ids; auto device = absl::make_unique(session_options, options); Status status = device->UseGpuDeviceInfo(); diff --git a/tensorflow/compiler/xla/client/client_library.h b/tensorflow/compiler/xla/client/client_library.h index d180c8f7ca..1e6e4c6bf3 100644 --- a/tensorflow/compiler/xla/client/client_library.h +++ b/tensorflow/compiler/xla/client/client_library.h @@ -23,9 +23,9 @@ limitations under the License. #include #include +#include #include #include -#include #include "tensorflow/compiler/xla/client/compile_only_client.h" #include "tensorflow/compiler/xla/client/local_client.h" @@ -81,8 +81,8 @@ class ClientLibrary { // // platform : The platform the underlying XLA service should target. If // null then default platform is used. - // device_set: Set of device IDs for which the stream executor will be created - // for, for the given platform. + // device_set: Set of device IDs for which the stream executor will be + // created for, for the given platform. static StatusOr GetOrCreateLocalClient( se::Platform* platform = nullptr, const std::set device_set = {-1}); static StatusOr GetOrCreateLocalClient( diff --git a/tensorflow/compiler/xla/service/backend.h b/tensorflow/compiler/xla/service/backend.h index 26bae43c1a..3fc0f8de62 100644 --- a/tensorflow/compiler/xla/service/backend.h +++ b/tensorflow/compiler/xla/service/backend.h @@ -18,9 +18,9 @@ limitations under the License. #include #include +#include #include #include -#include #include "absl/strings/str_cat.h" #include "absl/types/span.h" @@ -114,8 +114,8 @@ class Backend { se::StreamExecutor* default_stream_executor() const { CHECK(!stream_executors_.empty()); - for(se::StreamExecutor* e :stream_executors_){ - if(e){ + for (se::StreamExecutor* e : stream_executors_) { + if (e) { return e; } } diff --git a/tensorflow/compiler/xla/service/platform_util.h b/tensorflow/compiler/xla/service/platform_util.h index 04af762fb6..89291e8b74 100644 --- a/tensorflow/compiler/xla/service/platform_util.h +++ b/tensorflow/compiler/xla/service/platform_util.h @@ -16,9 +16,9 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_PLATFORM_UTIL_H_ #define TENSORFLOW_COMPILER_XLA_SERVICE_PLATFORM_UTIL_H_ +#include #include #include -#include #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/types.h" diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc index b9e5be486a..06a16e8c79 100644 --- a/tensorflow/compiler/xla/service/service.cc +++ b/tensorflow/compiler/xla/service/service.cc @@ -160,13 +160,13 @@ Service::Service(const ServiceOptions& options, LOG(INFO) << StrFormat( "XLA service %p executing computations on platform %s. Devices:", this, execute_backend_->platform()->Name()); - auto stream_executors=execute_backend_->stream_executors(); + auto stream_executors = execute_backend_->stream_executors(); for (int i = 0; i < execute_backend_->device_count(); ++i) { - se::StreamExecutor* executor =stream_executors.at(i); - const auto& description = executor->GetDeviceDescription(); - LOG(INFO) << StrFormat(" StreamExecutor device (%d): %s, %s", i, - description.name(), - description.platform_version()); + se::StreamExecutor* executor = stream_executors.at(i); + const auto& description = executor->GetDeviceDescription(); + LOG(INFO) << StrFormat(" StreamExecutor device (%d): %s, %s", i, + description.name(), + description.platform_version()); } } else { VLOG(1) << "XLA compile-only service constructed"; diff --git a/tensorflow/compiler/xla/service/service.h b/tensorflow/compiler/xla/service/service.h index 95a504ee30..b6c0039ccc 100644 --- a/tensorflow/compiler/xla/service/service.h +++ b/tensorflow/compiler/xla/service/service.h @@ -18,9 +18,9 @@ limitations under the License. #include #include +#include #include #include -#include #include "absl/types/span.h" #include "tensorflow/compiler/xla/debug_options_flags.h" -- GitLab From fc220a61b71bd3e348aee311bff3b25117550865 Mon Sep 17 00:00:00 2001 From: Martin Wicke Date: Tue, 11 Dec 2018 16:30:04 -0800 Subject: [PATCH 392/873] Remove deprecated tf.substr PiperOrigin-RevId: 225092500 --- tensorflow/python/ops/string_ops.py | 9 +++++---- tensorflow/tools/api/golden/v2/tensorflow.pbtxt | 4 ---- tensorflow/tools/compatibility/reorders_v2.py | 1 + tensorflow/tools/compatibility/tf_upgrade_v2.py | 7 +++++-- tensorflow/tools/compatibility/tf_upgrade_v2_test.py | 7 +++++++ 5 files changed, 18 insertions(+), 10 deletions(-) diff --git a/tensorflow/python/ops/string_ops.py b/tensorflow/python/ops/string_ops.py index 046459706c..9967f48060 100644 --- a/tensorflow/python/ops/string_ops.py +++ b/tensorflow/python/ops/string_ops.py @@ -367,7 +367,7 @@ def string_length_v2(input, unit="BYTE", name=None): string_length.__doc__ = gen_string_ops.string_length.__doc__ -@tf_export("substr") +@tf_export(v1=["substr"]) @deprecation.deprecated(None, "Use `tf.strings.substr` instead of `tf.substr`.") def substr_deprecated(input, pos, len, name=None, unit="BYTE"): return substr(input, pos, len, name=name, unit=unit) @@ -380,14 +380,15 @@ substr_deprecated.__doc__ = gen_string_ops.substr.__doc__ def substr(input, pos, len, name=None, unit="BYTE"): return gen_string_ops.substr(input, pos, len, unit=unit, name=name) +substr.__doc__ = gen_string_ops.substr.__doc__ + @tf_export("strings.substr", v1=[]) @dispatch.add_dispatch_support def substr_v2(input, pos, len, unit="BYTE", name=None): - return substr(input, pos, len, name=name, unit=unit) - + return gen_string_ops.substr(input, pos, len, unit=unit, name=name) -substr.__doc__ = gen_string_ops.substr.__doc__ +substr_v2.__doc__ = gen_string_ops.substr.__doc__ ops.NotDifferentiable("RegexReplace") diff --git a/tensorflow/tools/api/golden/v2/tensorflow.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.pbtxt index ee81e86fd5..574b6778fa 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.pbtxt @@ -980,10 +980,6 @@ tf_module { name: "string_split" argspec: "args=[\'source\', \'delimiter\', \'skip_empty\'], varargs=None, keywords=None, defaults=[\' \', \'True\'], " } - member_method { - name: "substr" - argspec: "args=[\'input\', \'pos\', \'len\', \'name\', \'unit\'], varargs=None, keywords=None, defaults=[\'None\', \'BYTE\'], " - } member_method { name: "subtract" argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " diff --git a/tensorflow/tools/compatibility/reorders_v2.py b/tensorflow/tools/compatibility/reorders_v2.py index 44494ac148..1c9fb92db0 100644 --- a/tensorflow/tools/compatibility/reorders_v2.py +++ b/tensorflow/tools/compatibility/reorders_v2.py @@ -109,6 +109,7 @@ reorders = { 'tf.strings.length': ['input', 'name', 'unit'], 'tf.strings.reduce_join': ['inputs', 'axis', 'keep_dims', 'separator', 'name', 'reduction_indices'], 'tf.strings.substr': ['input', 'pos', 'len', 'name', 'unit'], + 'tf.substr': ['input', 'pos', 'len', 'name', 'unit'], 'tf.transpose': ['a', 'perm', 'name', 'conjugate'], 'tf.tuple': ['tensors', 'name', 'control_inputs'], 'tf.while_loop': ['cond', 'body', 'loop_vars', 'shape_invariants', 'parallel_iterations', 'back_prop', 'swap_memory', 'name', 'maximum_iterations', 'return_same_structure'] diff --git a/tensorflow/tools/compatibility/tf_upgrade_v2.py b/tensorflow/tools/compatibility/tf_upgrade_v2.py index ea86da42f6..427e22b721 100644 --- a/tensorflow/tools/compatibility/tf_upgrade_v2.py +++ b/tensorflow/tools/compatibility/tf_upgrade_v2.py @@ -492,6 +492,8 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec): "tf.sparse.reduce_max", "tf.random.stateless_multinomial": "tf.random.stateless_categorical", + "tf.substr": + "tf.strings.substr", "tf.string_to_hash_bucket": "tf.strings.to_hash_bucket", "tf.string_to_number": @@ -600,9 +602,10 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec): "tf.sparse.reduce_max", "tf.sparse_reduce_max", "tf.io.decode_csv", - "tf.strings.substr", - "tf.strings.reduce_join", "tf.strings.length", + "tf.strings.reduce_join", + "tf.strings.substr", + "tf.substr", "tf.transpose", "tf.tuple", "tf.parse_example", diff --git a/tensorflow/tools/compatibility/tf_upgrade_v2_test.py b/tensorflow/tools/compatibility/tf_upgrade_v2_test.py index 2cc874fe7f..484900d000 100644 --- a/tensorflow/tools/compatibility/tf_upgrade_v2_test.py +++ b/tensorflow/tools/compatibility/tf_upgrade_v2_test.py @@ -443,6 +443,13 @@ bazel-bin/tensorflow/tools/compatibility/update/generate_v2_reorders_map ) self.assertEqual(new_text, expected_text) + def test_substr(self): + text = "tf.substr(input, pos, len, name, unit)\n" + _, unused_report, errors, new_text = self._upgrade(text) + self.assertEqual("tf.strings.substr(input=input, pos=pos, len=len, " + "name=name, unit=unit)\n", new_text) + self.assertEqual(errors, []) + def testColocateGradientsWithOps(self): text = "tf.gradients(a, foo=False)\n" _, unused_report, errors, new_text = self._upgrade(text) -- GitLab From 9b8005ece04fc815b84fbd032c3374ab82976360 Mon Sep 17 00:00:00 2001 From: Scott Zhu Date: Tue, 11 Dec 2018 16:40:04 -0800 Subject: [PATCH 393/873] Cleanup some duplicated methods for UnifiedLSTM. The methods in the parent class should work the same way. PiperOrigin-RevId: 225094141 --- tensorflow/python/keras/layers/recurrent.py | 43 +-------------------- 1 file changed, 1 insertion(+), 42 deletions(-) diff --git a/tensorflow/python/keras/layers/recurrent.py b/tensorflow/python/keras/layers/recurrent.py index 1c6f2bd3f8..93cb805d08 100644 --- a/tensorflow/python/keras/layers/recurrent.py +++ b/tensorflow/python/keras/layers/recurrent.py @@ -2530,6 +2530,7 @@ class LSTM(RNN): config['implementation'] = 1 return cls(**config) + @tf_export('keras.layers.LSTM', v1=[]) class UnifiedLSTM(LSTM): """Long Short-Term Memory layer - Hochreiter 1997. @@ -2655,8 +2656,6 @@ class UnifiedLSTM(LSTM): self.state_spec = [ InputSpec(shape=(None, dim)) for dim in (self.units, self.units) ] - self._num_constants = None - self._num_inputs = None self._dropout_mask = None self.could_use_cudnn = ( activation == 'tanh' and recurrent_activation == 'sigmoid' and @@ -2775,46 +2774,6 @@ class UnifiedLSTM(LSTM): else: return output - @property - def trainable_weights(self): - if self.trainable: - weights = [] - weights += self.cell.trainable_weights - return weights - return [] - - @property - def non_trainable_weights(self): - if not self.trainable: - weights = [] - weights += self.cell.non_trainable_weights - return weights - return [] - - @property - def losses(self): - losses = [] - losses += self.cell.losses - return losses + self._losses - - @property - def updates(self): - updates = [] - updates += self.cell.updates - return updates + self._updates - - def get_weights(self): - weights = [] - weights += self.cell.weights - return K.batch_get_value(weights) - - def set_weights(self, weights): - tuples = [] - cell_weights = weights[:len(self.cell.weights)] - if cell_weights: - tuples.append((self.cell.weights, cell_weights)) - K.batch_set_value(tuples) - def _canonical_to_params(weights, biases, shape, transpose_weights=False): """Utility function convert variable to CuDNN compatible parameter. -- GitLab From a54fd6b71313ccf22c9fe115bceb256dcef27435 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Tue, 11 Dec 2018 16:42:40 -0800 Subject: [PATCH 394/873] [TF:XLA] Bump open source abseil revision to 455dc17ba1af9635f0b60155bc565bc572a1e722 PiperOrigin-RevId: 225094534 --- tensorflow/workspace.bzl | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 5210df240d..f8b6bd1a3f 100755 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -123,11 +123,11 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""): tf_http_archive( name = "com_google_absl", build_file = clean_dep("//third_party:com_google_absl.BUILD"), - sha256 = "3ad76de484192b2d5afd49d90492b5ed0bc59eb1a4e8e0deecc7a2a077a90251", - strip_prefix = "abseil-cpp-f197d7c72a54064cfde5a2058f1513a4a0ee36fb", + sha256 = "be91500afe4d2768a7aeeeae616d9f7fc4fe237a1493b630883dbf8f20d4682d", + strip_prefix = "abseil-cpp-455dc17ba1af9635f0b60155bc565bc572a1e722", urls = [ - "https://mirror.bazel.build/github.com/abseil/abseil-cpp/archive/f197d7c72a54064cfde5a2058f1513a4a0ee36fb.tar.gz", - "https://github.com/abseil/abseil-cpp/archive/f197d7c72a54064cfde5a2058f1513a4a0ee36fb.tar.gz", + "https://mirror.bazel.build/github.com/abseil/abseil-cpp/archive/455dc17ba1af9635f0b60155bc565bc572a1e722.tar.gz", + "https://github.com/abseil/abseil-cpp/archive/455dc17ba1af9635f0b60155bc565bc572a1e722.tar.gz", ], ) -- GitLab From e8c65fa77fb7473d95988fa23e51c906a428b27a Mon Sep 17 00:00:00 2001 From: Francois Chollet Date: Tue, 11 Dec 2018 16:45:06 -0800 Subject: [PATCH 395/873] Small refactor to improve the readability of the Model class for those who use the code as documentation. General idea: most important methods come first, private utilities are moved to the bottom of the class. Also use a single method for `_standardize_user_data` (previously split into 2 methods that did not reflect two separate sets of actions). PiperOrigin-RevId: 225094903 --- tensorflow/python/keras/engine/training.py | 4363 ++++++++--------- .../keras/engine/training_distributed.py | 7 +- 2 files changed, 2158 insertions(+), 2212 deletions(-) diff --git a/tensorflow/python/keras/engine/training.py b/tensorflow/python/keras/engine/training.py index fe44bc20a1..75d6496988 100644 --- a/tensorflow/python/keras/engine/training.py +++ b/tensorflow/python/keras/engine/training.py @@ -128,272 +128,411 @@ class Model(Network): self.run_eagerly = None - def _set_sample_weight_attributes(self, sample_weight_mode, - skip_target_weighing_indices): - """Sets sample weight related attributes on the model.""" - sample_weights, sample_weight_modes = training_utils.prepare_sample_weights( - self.output_names, sample_weight_mode, skip_target_weighing_indices) - self.sample_weights = sample_weights - self.sample_weight_modes = sample_weight_modes - self._feed_sample_weight_modes = [ - sample_weight_modes[i] - for i in range(len(self.outputs)) - if i not in skip_target_weighing_indices - ] - self._feed_sample_weights = [ - sample_weights[i] - for i in range(len(sample_weights)) - if i not in skip_target_weighing_indices - ] - - def _cache_output_metric_attributes(self, metrics, weighted_metrics): - """Caches metric name and function attributes for every model output.""" - output_shapes = [ - None if output is None else output.get_shape().as_list() - for output in self.outputs - ] - self._per_output_metrics = training_utils.collect_per_output_metric_info( - metrics, self.output_names, output_shapes, self.loss_functions) - self._per_output_weighted_metrics = \ - training_utils.collect_per_output_metric_info( - weighted_metrics, self.output_names, output_shapes, - self.loss_functions, self.sample_weights) - - def _add_unique_metric_name(self, metric_name, output_index): - """Makes the metric name unique and adds it to the model's metric name list. - - If there are multiple outputs for which the metrics are calculated, the - metric names have to be made unique by appending an integer. + @checkpointable.no_automatic_dependency_tracking + def compile(self, + optimizer, + loss=None, + metrics=None, + loss_weights=None, + sample_weight_mode=None, + weighted_metrics=None, + target_tensors=None, + distribute=None, + **kwargs): + """Configures the model for training. Arguments: - metric_name: Metric name that corresponds to the metric specified by the - user. For example: 'acc'. - output_index: The index of the model output for which the metric name is - being added. + optimizer: String (name of optimizer) or optimizer instance. + See [optimizers](/api_docs/python/tf/keras/optimizers). + loss: String (name of objective function) or objective function. + See [losses](/api_docs/python/tf/losses). + If the model has multiple outputs, you can use a different loss + on each output by passing a dictionary or a list of losses. + The loss value that will be minimized by the model + will then be the sum of all individual losses. + metrics: List of metrics to be evaluated by the model + during training and testing. + Typically you will use `metrics=['accuracy']`. + To specify different metrics for different outputs of a + multi-output model, you could also pass a dictionary, + such as `metrics={'output_a': 'accuracy'}`. + loss_weights: Optional list or dictionary specifying scalar + coefficients (Python floats) to weight the loss contributions + of different model outputs. + The loss value that will be minimized by the model + will then be the *weighted sum* of all individual losses, + weighted by the `loss_weights` coefficients. + If a list, it is expected to have a 1:1 mapping + to the model's outputs. If a tensor, it is expected to map + output names (strings) to scalar coefficients. + sample_weight_mode: If you need to do timestep-wise + sample weighting (2D weights), set this to `"temporal"`. + `None` defaults to sample-wise weights (1D). + If the model has multiple outputs, you can use a different + `sample_weight_mode` on each output by passing a + dictionary or a list of modes. + weighted_metrics: List of metrics to be evaluated and weighted + by sample_weight or class_weight during training and testing. + target_tensors: By default, Keras will create placeholders for the + model's target, which will be fed with the target data during + training. If instead you would like to use your own + target tensors (in turn, Keras will not expect external + Numpy data for these targets at training time), you + can specify them via the `target_tensors` argument. It can be + a single tensor (for a single-output model), a list of tensors, + or a dict mapping output names to target tensors. + distribute: The DistributionStrategy instance that we want to use to + distribute the training of the model. + **kwargs: These arguments are passed to `tf.Session.run`. - Returns: - string, name of the model's unique metric name + Raises: + ValueError: In case of invalid arguments for + `optimizer`, `loss`, `metrics` or `sample_weight_mode`. """ - if len(self.output_names) > 1: - metric_name = '%s_%s' % (self.output_names[output_index], metric_name) - j = 1 - base_metric_name = metric_name - while metric_name in self._compile_metrics_names: - metric_name = '%s_%d' % (base_metric_name, j) - j += 1 + run_eagerly = kwargs.pop('run_eagerly', None) + self._run_eagerly = run_eagerly - return metric_name + # Validate that arguments passed by the user to `compile` are supported by + # DistributionStrategy. + if distribute: + if not isinstance( + optimizer, (tf_optimizer_module.Optimizer, optimizers.TFOptimizer)): + raise NotImplementedError( + 'optimizer must be an instance of ' + 'tf.train.Optimizer, not a %s' % type(optimizer)) + if sample_weight_mode: + raise NotImplementedError('sample_weight_mode is not supported with ' + 'DistributionStrategy.') + if weighted_metrics: + raise NotImplementedError('weighted_metrics is not supported with ' + 'DistributionStrategy.') + if target_tensors: + raise ValueError('target_tensors is not supported with ' + 'DistributionStrategy.') - @property - def metrics(self): - """Returns the model's metrics added using `compile`, `add_metric` APIs.""" - metrics = [] - if self._is_compiled: - metrics += self._compile_stateful_metric_functions - return metrics + super(Model, self).metrics + loss = loss or {} + if self.run_eagerly and not isinstance( + optimizer, (tf_optimizer_module.Optimizer, optimizers.TFOptimizer)): + raise ValueError( + 'When running a model in eager execution, the optimizer must be an ' + 'instance of tf.train.Optimizer. Received: ' + '%s' % optimizer) - @property - def metrics_names(self): - """Returns the model's display labels for all outputs.""" - metrics_names = [] - if self._is_compiled: - metrics_names += self._compile_metrics_names # Includes names of losses. + self.optimizer = optimizers.get(optimizer) + # We've disabled automatic dependency tracking for this method, but do want + # to add a checkpoint dependency on the optimizer if it's checkpointable. + if isinstance(self.optimizer, checkpointable.CheckpointableBase): + self._track_checkpointable( + self.optimizer, name='optimizer', overwrite=True) + self.loss = loss + self._compile_metrics = metrics or [] + self.loss_weights = loss_weights + self.sample_weight_mode = sample_weight_mode + self._compile_weighted_metrics = weighted_metrics + if self.run_eagerly and target_tensors is not None: + raise ValueError( + 'target_tensors argument is not supported when ' + 'running a model eagerly.') + self.target_tensors = target_tensors - # Add metric names from layers. - for layer in self.layers: - metrics_names += [m.name for m in layer._metrics] # pylint: disable=protected-access - metrics_names += [m.name for m in self._metrics] - return metrics_names + # Set DistributionStrategy specific parameters. + self._distribution_strategy = distribute + # Reset the value of grouped_model + self._grouped_model = None + if self._distribution_strategy is not None: + distributed_training_utils.configure_and_create_session( + self._distribution_strategy) + # Initialize model metric attributes. + self._init_metric_attributes() + if not self.built: + # Model is not compilable because it does not know its number of inputs + # and outputs, nor their shapes and names. We will compile after the first + # time the model gets called on training data. + return + self._is_compiled = True - @property - def _all_metrics_tensors(self): - """Returns the network's symbolic metric tensors.""" - metrics_tensors = {} - if self._is_compiled: - metrics_tensors.update(self._compile_metrics_tensors) - metrics_tensors.update(super(Model, self)._all_metrics_tensors) - return metrics_tensors + # Prepare loss functions. + if isinstance(loss, dict): + for name in loss: + if name not in self.output_names: + raise ValueError( + 'Unknown entry in loss ' + 'dictionary: "' + name + '". ' + 'Only expected the following keys: ' + str(self.output_names)) + loss_functions = [] + for name in self.output_names: + if name not in loss: + logging.warning( + 'Output "' + name + + '" missing from loss dictionary. We assume ' + 'this was done on purpose. The fit and evaluate APIs will not be ' + 'expecting any data to be passed to "' + name + '".') + loss_functions.append(training_utils.get_loss_function(loss.get(name))) + elif isinstance(loss, list): + if len(loss) != len(self.outputs): + raise ValueError('When passing a list as loss, ' + 'it should have one entry per model outputs. ' + 'The model has ' + str(len(self.outputs)) + + ' outputs, but you passed loss=' + str(loss)) + loss_functions = [training_utils.get_loss_function(l) for l in loss] + else: + loss_function = training_utils.get_loss_function(loss) + loss_functions = [loss_function for _ in range(len(self.outputs))] + self.loss_functions = loss_functions - @property - def _all_stateful_metrics_tensors(self): - """Returns the network's symbolic metric tensors.""" - metrics_tensors = {} - if self._is_compiled: - metrics_tensors.update(self._compile_stateful_metrics_tensors) - metrics_tensors.update(super(Model, self)._all_metrics_tensors) - return metrics_tensors + skip_target_indices = [] + skip_target_weighing_indices = [] + self._feed_outputs = [] + self._feed_output_names = [] + self._feed_output_shapes = [] + self._feed_loss_fns = [] + for i in range(len(loss_functions)): + if loss_functions[i] is None: + skip_target_indices.append(i) + skip_target_weighing_indices.append(i) - def _init_metric_attributes(self): - """Initialized model metric attributes.""" - # List of all metric names in the model. - self._compile_metrics_names = ['loss'] - # List of stateful metric functions. Used for resetting metric state during - # training/eval. - # This includes loss functions when there are multiple outputs. - self._compile_stateful_metric_functions = [] - # Dict of all aggregated metric result tensors. This includes aggregated - # loss result tensors when there are multiple outputs. - self._compile_stateful_metrics_tensors = {} - # Dict of all metric result tensors (aggregated or not - based on the - # values given in compile.). This includes aggregated loss result tensors - # when there are multiple outputs. - self._compile_metrics_tensors = {} + # Prepare output masks. + if not self.run_eagerly: + masks = [getattr(x, '_keras_mask', None) for x in self.outputs] + if not isinstance(masks, list): + masks = [masks] - def _set_per_output_metric_attributes(self, metrics_dict, output_index): - """Sets the metric attributes on the model for the given output. + # Prepare loss weights. + if loss_weights is None: + loss_weights_list = [1. for _ in range(len(self.outputs))] + elif isinstance(loss_weights, dict): + for name in loss_weights: + if name not in self.output_names: + raise ValueError( + 'Unknown entry in loss_weights ' + 'dictionary: "' + name + '". ' + 'Only expected the following keys: ' + str(self.output_names)) + loss_weights_list = [] + for name in self.output_names: + loss_weights_list.append(loss_weights.get(name, 1.)) + elif isinstance(loss_weights, list): + if len(loss_weights) != len(self.outputs): + raise ValueError( + 'When passing a list as loss_weights, ' + 'it should have one entry per model output. ' + 'The model has ' + str(len(self.outputs)) + + ' outputs, but you passed loss_weights=' + str(loss_weights)) + loss_weights_list = loss_weights + else: + raise TypeError('Could not interpret loss_weights argument: ' + + str(loss_weights) + ' - expected a list of dicts.') + self.loss_weights_list = loss_weights_list - Arguments: - metrics_dict: A dict with metric names as keys and metric fns as values. - output_index: The index of the model output for which the metric - attributes are added. + # Initialization for Eager mode execution. + if self.run_eagerly: + # Prepare sample weights. + self._set_sample_weight_attributes(sample_weight_mode, + skip_target_weighing_indices) + # Save all metric attributes per output of the model. + self._cache_output_metric_attributes(metrics, weighted_metrics) - Returns: - Metrics dict updated with unique metric names as keys. - """ - updated_metrics_dict = collections.OrderedDict() - for metric_name, (metric_fn, stateful_metric_fn) in metrics_dict.items(): - metric_name = self._add_unique_metric_name(metric_name, output_index) - updated_metrics_dict[metric_name] = (metric_fn, stateful_metric_fn) - # Keep track of metric name, function and stateful function. - self._compile_metrics_names.append(metric_name) - self._compile_stateful_metric_functions.append(stateful_metric_fn) - return updated_metrics_dict + if target_tensors is not None: + raise ValueError('target_tensors are not currently supported in Eager ' + 'mode.') + self.total_loss = None + for i in range(len(self.outputs)): + if len(self.outputs) > 1: + self._compile_metrics_names.append(self.output_names[i] + '_loss') - def _set_metric_attributes(self, outputs, skip_target_indices=None): - """Sets the metric attributes on the model for all the model outputs.""" - skip_target_indices = skip_target_indices or [] - updated_per_output_metrics = [] - updated_per_output_weighted_metrics = [] - for i in range(len(outputs)): - if i in skip_target_indices: - updated_per_output_metrics.append(self._per_output_metrics[i]) - updated_per_output_weighted_metrics.append( - self._per_output_weighted_metrics[i]) - continue - updated_per_output_metrics.append( - self._set_per_output_metric_attributes(self._per_output_metrics[i], - i)) - updated_per_output_weighted_metrics.append( - self._set_per_output_metric_attributes( - self._per_output_weighted_metrics[i], i)) + # Set metric attributes on model. + self._set_metric_attributes( + self.outputs, + skip_target_indices=skip_target_indices, + ) - self._per_output_metrics = updated_per_output_metrics - self._per_output_weighted_metrics = updated_per_output_weighted_metrics + self.targets = [] + for i in range(len(self.outputs)): + self._feed_output_names.append(self.output_names[i]) + self._collected_trainable_weights = self.trainable_weights + return - def _handle_per_output_metrics(self, - metrics_dict, - y_true, - y_pred, - mask, - weights=None, - return_stateful_result=True): - """Calls metric functions for a single output. + with K.get_graph().as_default(): + # Prepare targets of model. + self.targets = [] + self._feed_targets = [] + if target_tensors not in (None, []): + if isinstance(target_tensors, list): + if len(target_tensors) != len(self.outputs): + raise ValueError( + 'When passing a list as `target_tensors`, ' + 'it should have one entry per model output. ' + 'The model has %s outputs, but you passed target_tensors=%s' % + (len(self.outputs), target_tensors)) + elif isinstance(target_tensors, dict): + for name in target_tensors: + if name not in self.output_names: + raise ValueError( + 'Unknown entry in `target_tensors` ' + 'dictionary: "' + name + '". ' + 'Only expected the following keys: ' + str(self.output_names)) + tmp_target_tensors = [] + for name in self.output_names: + tmp_target_tensors.append(target_tensors.get(name, None)) + target_tensors = tmp_target_tensors + elif tensor_util.is_tensor(target_tensors): + target_tensors = [target_tensors] + else: + raise TypeError('Expected `target_tensors` to be a list or tuple or ' + 'dict or a single tensor, but got:', target_tensors) - Arguments: - metrics_dict: A dict with metric names as keys and metric fns as values. - y_true: Target output. - y_pred: Predicted output. - mask: Computed mask value for the current output. - weights: Weights to be applied on the current output. - return_stateful_result: Boolean, indicates whether the stateful - (aggregated)/stateless metric result should be returned. + for i in range(len(self.outputs)): + if i in skip_target_indices: + self.targets.append(None) + else: + shape = K.int_shape(self.outputs[i]) + name = self.output_names[i] + if target_tensors not in (None, []): + target = target_tensors[i] + else: + target = None + if target is None or K.is_placeholder(target): + if target is None: + target_dtype = losses.LABEL_DTYPES_FOR_LOSSES.get( + self.loss_functions[i], + K.dtype(self.outputs[i])) - Returns: - A list of metric result tensors. - """ - metric_results = [] - for metric_name, (metric_fn, stateful_fn) in metrics_dict.items(): - with K.name_scope(metric_name): + target = K.placeholder( + ndim=len(shape), + name=name + '_target', + sparse=K.is_sparse(self.outputs[i]), + dtype=target_dtype) + self._feed_targets.append(target) + self._feed_outputs.append(self.outputs[i]) + self._feed_output_names.append(name) + self._feed_output_shapes.append(shape) + self._feed_loss_fns.append(self.loss_functions[i]) + else: + skip_target_weighing_indices.append(i) + self.targets.append(target) - def _call_stateful_fn(fn): - return training_utils.call_metric_function( - fn, y_true, y_pred, weights=weights, mask=mask) + # Prepare sample weights. + self._set_sample_weight_attributes(sample_weight_mode, + skip_target_weighing_indices) + # Save all metric attributes per output of the model. + self._cache_output_metric_attributes(metrics, weighted_metrics) - def _call_stateless_fn(fn): - weighted_metric_fn = training_utils.weighted_masked_objective(fn) - return weighted_metric_fn(y_true, y_pred, weights=weights, mask=mask) + # Compute total loss. + total_loss = None + with K.name_scope('loss'): + for i in range(len(self.outputs)): + if i in skip_target_indices: + continue + y_true = self.targets[i] + y_pred = self.outputs[i] + loss_fn = loss_functions[i] + sample_weight = self.sample_weights[i] + mask = masks[i] + loss_weight = loss_weights_list[i] + with K.name_scope(self.output_names[i] + '_loss'): + if isinstance(loss_fn, losses.Loss): + if mask is not None: + mask = math_ops.cast(mask, y_pred.dtype) + # Update weights with mask. + if sample_weight is None: + sample_weight = mask + else: + # Update dimensions of weights to match with mask if possible. + mask, _, sample_weight = squeeze_or_expand_dimensions( + mask, None, sample_weight) + sample_weight *= mask + output_loss = loss_fn(y_true, y_pred, sample_weight=sample_weight) + else: + weighted_loss = training_utils.weighted_masked_objective(loss_fn) + output_loss = weighted_loss(y_true, y_pred, sample_weight, mask) - def _track_metric_tensors(name, stateless_result, stateful_result): - self._compile_metrics_tensors[name] = stateless_result - self._compile_stateful_metrics_tensors[name] = stateful_result + if len(self.outputs) > 1: + # Keep track of the un-aggregated loss result tensor. + self._compile_metrics_tensors[self.output_names[i] + + '_loss'] = output_loss - if isinstance(metric_fn, metrics_module.Metric): - # If the given metric fn is stateful, call the fn and return result. - metric_result = _call_stateful_fn(metric_fn) - metric_results.append(metric_result) - if not self.run_eagerly: - _track_metric_tensors(metric_name, metric_result, metric_result) - elif self.run_eagerly: - # In eager mode, if the given metric fn is not stateful, we invoke the - # given fn or its stateful version based on the given flag. - if return_stateful_result: - metric_result = _call_stateful_fn(stateful_fn) - else: - metric_result = _call_stateless_fn(metric_fn) - metric_results.append(metric_result) - else: - # In graph mode, we build the sub-graph for both the stateful and the - # stateless fns. - stateful_metric_result = _call_stateful_fn(stateful_fn) - metric_result = _call_stateless_fn(metric_fn) - _track_metric_tensors(metric_name, metric_result, - stateful_metric_result) + # Keep track of stateful result tensor and function for the loss. + loss_name = loss_fn.name if isinstance( + loss_fn, losses.Loss) else loss_fn.__name__ + mean_wrapped_loss = metrics_module.MeanMetricWrapper( + loss_fn, name=loss_name) + result_tensor = training_utils.call_metric_function( + mean_wrapped_loss, + y_true, + y_pred, + weights=sample_weight, + mask=mask) + self._compile_stateful_metrics_tensors[self.output_names[i] + + '_loss'] = result_tensor + self._compile_stateful_metric_functions.append(mean_wrapped_loss) - return metric_results + self._compile_metrics_names.append(self.output_names[i] + '_loss') + if total_loss is None: + total_loss = loss_weight * output_loss + else: + total_loss += loss_weight * output_loss + if total_loss is None: + if not self.losses: + raise ValueError('The model cannot be compiled ' + 'because it has no loss to optimize.') + else: + total_loss = 0. - def _handle_metrics(self, - outputs, - skip_target_indices=None, - targets=None, - sample_weights=None, - masks=None, - return_stateful_result=True): - """Handles calling metric functions. + # Add regularization penalties + # and other layer-specific losses. + for loss_tensor in self.losses: + total_loss += loss_tensor - Arguments: - outputs: List of outputs (predictions). - skip_target_indices: Optional. List of target ids to skip. - targets: List of targets. - sample_weights: Optional list of sample weight arrays. - masks: List of computed output mask values. - return_stateful_result: Boolean, indicates whether the stateful - (aggregated)/stateless metric result should be returned. + # Set metric attributes on model. + self._set_metric_attributes( + self.outputs, + skip_target_indices=skip_target_indices, + ) + # Invoke metric functions for all the outputs. + self._handle_metrics( + self.outputs, + masks=masks, + targets=self.targets, + skip_target_indices=skip_target_indices, + sample_weights=self.sample_weights) - Returns: - A list of metric result tensors. - """ - skip_target_indices = skip_target_indices or [] - metric_results = [] - with K.name_scope('metrics'): - # Invoke all metrics added using `compile`. - for i in range(len(outputs)): - if i in skip_target_indices: - continue - output = outputs[i] if outputs else None - target = targets[i] if targets else None - output_mask = masks[i] if masks else None - metric_results.extend( - self._handle_per_output_metrics( - self._per_output_metrics[i], - target, - output, - output_mask, - return_stateful_result=return_stateful_result)) - metric_results.extend( - self._handle_per_output_metrics( - self._per_output_weighted_metrics[i], - target, - output, - output_mask, - weights=sample_weights[i], - return_stateful_result=return_stateful_result)) + # Prepare gradient updates and state updates. + self.total_loss = total_loss - # Add metric results from the `add_metric` metrics in eager mode. - if context.executing_eagerly(): - for m in self.metrics: - if m not in self._compile_stateful_metric_functions: - metric_results.append(m.result()) - return metric_results + # Functions for train, test and predict will + # be compiled lazily when required. + # This saves time when the user is not using all functions. + self._function_kwargs = kwargs + + self._fit_function = None + self._eval_function = None + self.train_function = None + self.test_function = None + self.predict_function = None + + # Collected trainable weights, sorted in topological order. + trainable_weights = self.trainable_weights + self._collected_trainable_weights = trainable_weights + + @property + def metrics(self): + """Returns the model's metrics added using `compile`, `add_metric` APIs.""" + metrics = [] + if self._is_compiled: + metrics += self._compile_stateful_metric_functions + return metrics + super(Model, self).metrics + + @property + def metrics_names(self): + """Returns the model's display labels for all outputs.""" + metrics_names = [] + if self._is_compiled: + metrics_names += self._compile_metrics_names # Includes names of losses. + + # Add metric names from layers. + for layer in self.layers: + metrics_names += [m.name for m in layer._metrics] # pylint: disable=protected-access + metrics_names += [m.name for m in self._metrics] + return metrics_names @property def run_eagerly(self): @@ -429,2159 +568,1969 @@ class Model(Network): def run_eagerly(self, value): self._run_eagerly = value - @checkpointable.no_automatic_dependency_tracking - def compile(self, - optimizer, - loss=None, - metrics=None, - loss_weights=None, - sample_weight_mode=None, - weighted_metrics=None, - target_tensors=None, - distribute=None, - **kwargs): - """Configures the model for training. + def fit(self, + x=None, + y=None, + batch_size=None, + epochs=1, + verbose=1, + callbacks=None, + validation_split=0., + validation_data=None, + shuffle=True, + class_weight=None, + sample_weight=None, + initial_epoch=0, + steps_per_epoch=None, + validation_steps=None, + max_queue_size=10, + workers=1, + use_multiprocessing=False, + **kwargs): + """Trains the model for a fixed number of epochs (iterations on a dataset). Arguments: - optimizer: String (name of optimizer) or optimizer instance. - See [optimizers](/api_docs/python/tf/keras/optimizers). - loss: String (name of objective function) or objective function. - See [losses](/api_docs/python/tf/losses). - If the model has multiple outputs, you can use a different loss - on each output by passing a dictionary or a list of losses. - The loss value that will be minimized by the model - will then be the sum of all individual losses. - metrics: List of metrics to be evaluated by the model - during training and testing. - Typically you will use `metrics=['accuracy']`. - To specify different metrics for different outputs of a - multi-output model, you could also pass a dictionary, - such as `metrics={'output_a': 'accuracy'}`. - loss_weights: Optional list or dictionary specifying scalar - coefficients (Python floats) to weight the loss contributions - of different model outputs. - The loss value that will be minimized by the model - will then be the *weighted sum* of all individual losses, - weighted by the `loss_weights` coefficients. - If a list, it is expected to have a 1:1 mapping - to the model's outputs. If a tensor, it is expected to map - output names (strings) to scalar coefficients. - sample_weight_mode: If you need to do timestep-wise - sample weighting (2D weights), set this to `"temporal"`. - `None` defaults to sample-wise weights (1D). - If the model has multiple outputs, you can use a different - `sample_weight_mode` on each output by passing a - dictionary or a list of modes. - weighted_metrics: List of metrics to be evaluated and weighted - by sample_weight or class_weight during training and testing. - target_tensors: By default, Keras will create placeholders for the - model's target, which will be fed with the target data during - training. If instead you would like to use your own - target tensors (in turn, Keras will not expect external - Numpy data for these targets at training time), you - can specify them via the `target_tensors` argument. It can be - a single tensor (for a single-output model), a list of tensors, - or a dict mapping output names to target tensors. - distribute: The DistributionStrategy instance that we want to use to - distribute the training of the model. - **kwargs: These arguments are passed to `tf.Session.run`. - - Raises: - ValueError: In case of invalid arguments for - `optimizer`, `loss`, `metrics` or `sample_weight_mode`. - """ - run_eagerly = kwargs.pop('run_eagerly', None) - self._run_eagerly = run_eagerly - - # Validate that arguments passed by the user to `compile` are supported by - # DistributionStrategy. - if distribute: - if not isinstance( - optimizer, (tf_optimizer_module.Optimizer, optimizers.TFOptimizer)): - raise NotImplementedError( - 'optimizer must be an instance of ' - 'tf.train.Optimizer, not a %s' % type(optimizer)) - if sample_weight_mode: - raise NotImplementedError('sample_weight_mode is not supported with ' - 'DistributionStrategy.') - if weighted_metrics: - raise NotImplementedError('weighted_metrics is not supported with ' - 'DistributionStrategy.') - if target_tensors: - raise ValueError('target_tensors is not supported with ' - 'DistributionStrategy.') - - loss = loss or {} - if self.run_eagerly and not isinstance( - optimizer, (tf_optimizer_module.Optimizer, optimizers.TFOptimizer)): - raise ValueError( - 'When running a model in eager execution, the optimizer must be an ' - 'instance of tf.train.Optimizer. Received: ' - '%s' % optimizer) - - self.optimizer = optimizers.get(optimizer) - # We've disabled automatic dependency tracking for this method, but do want - # to add a checkpoint dependency on the optimizer if it's checkpointable. - if isinstance(self.optimizer, checkpointable.CheckpointableBase): - self._track_checkpointable( - self.optimizer, name='optimizer', overwrite=True) - self.loss = loss - self._compile_metrics = metrics or [] - self.loss_weights = loss_weights - self.sample_weight_mode = sample_weight_mode - self._compile_weighted_metrics = weighted_metrics - if self.run_eagerly and target_tensors is not None: - raise ValueError( - 'target_tensors argument is not supported when ' - 'running a model eagerly.') - self.target_tensors = target_tensors - - # Set DistributionStrategy specific parameters. - self._distribution_strategy = distribute - # Reset the value of grouped_model - self._grouped_model = None - if self._distribution_strategy is not None: - distributed_training_utils.configure_and_create_session( - self._distribution_strategy) - # Initialize model metric attributes. - self._init_metric_attributes() - if not self.built: - # Model is not compilable because it does not know its number of inputs - # and outputs, nor their shapes and names. We will compile after the first - # time the model gets called on training data. - return - self._is_compiled = True - - # Prepare loss functions. - if isinstance(loss, dict): - for name in loss: - if name not in self.output_names: - raise ValueError( - 'Unknown entry in loss ' - 'dictionary: "' + name + '". ' - 'Only expected the following keys: ' + str(self.output_names)) - loss_functions = [] - for name in self.output_names: - if name not in loss: - logging.warning( - 'Output "' + name + - '" missing from loss dictionary. We assume ' - 'this was done on purpose. The fit and evaluate APIs will not be ' - 'expecting any data to be passed to "' + name + '".') - loss_functions.append(training_utils.get_loss_function(loss.get(name))) - elif isinstance(loss, list): - if len(loss) != len(self.outputs): - raise ValueError('When passing a list as loss, ' - 'it should have one entry per model outputs. ' - 'The model has ' + str(len(self.outputs)) + - ' outputs, but you passed loss=' + str(loss)) - loss_functions = [training_utils.get_loss_function(l) for l in loss] - else: - loss_function = training_utils.get_loss_function(loss) - loss_functions = [loss_function for _ in range(len(self.outputs))] - self.loss_functions = loss_functions - - skip_target_indices = [] - skip_target_weighing_indices = [] - self._feed_outputs = [] - self._feed_output_names = [] - self._feed_output_shapes = [] - self._feed_loss_fns = [] - for i in range(len(loss_functions)): - if loss_functions[i] is None: - skip_target_indices.append(i) - skip_target_weighing_indices.append(i) - - # Prepare output masks. - if not self.run_eagerly: - masks = [getattr(x, '_keras_mask', None) for x in self.outputs] - if not isinstance(masks, list): - masks = [masks] - - # Prepare loss weights. - if loss_weights is None: - loss_weights_list = [1. for _ in range(len(self.outputs))] - elif isinstance(loss_weights, dict): - for name in loss_weights: - if name not in self.output_names: - raise ValueError( - 'Unknown entry in loss_weights ' - 'dictionary: "' + name + '". ' - 'Only expected the following keys: ' + str(self.output_names)) - loss_weights_list = [] - for name in self.output_names: - loss_weights_list.append(loss_weights.get(name, 1.)) - elif isinstance(loss_weights, list): - if len(loss_weights) != len(self.outputs): - raise ValueError( - 'When passing a list as loss_weights, ' - 'it should have one entry per model output. ' - 'The model has ' + str(len(self.outputs)) + - ' outputs, but you passed loss_weights=' + str(loss_weights)) - loss_weights_list = loss_weights - else: - raise TypeError('Could not interpret loss_weights argument: ' + - str(loss_weights) + ' - expected a list of dicts.') - self.loss_weights_list = loss_weights_list - - # Initialization for Eager mode execution. - if self.run_eagerly: - # Prepare sample weights. - self._set_sample_weight_attributes(sample_weight_mode, - skip_target_weighing_indices) - # Save all metric attributes per output of the model. - self._cache_output_metric_attributes(metrics, weighted_metrics) - - if target_tensors is not None: - raise ValueError('target_tensors are not currently supported in Eager ' - 'mode.') - self.total_loss = None - for i in range(len(self.outputs)): - if len(self.outputs) > 1: - self._compile_metrics_names.append(self.output_names[i] + '_loss') + x: Input data. It could be: + - A Numpy array (or array-like), or a list of arrays + (in case the model has multiple inputs). + - A TensorFlow tensor, or a list of tensors + (in case the model has multiple inputs). + - A dict mapping input names to the corresponding array/tensors, + if the model has named inputs. + - A `tf.data` dataset or a dataset iterator. Should return a tuple + of either `(inputs, targets)` or + `(inputs, targets, sample_weights)`. + - A generator or `keras.utils.Sequence` returning `(inputs, targets)` + or `(inputs, targets, sample weights)`. + y: Target data. Like the input data `x`, + it could be either Numpy array(s) or TensorFlow tensor(s). + It should be consistent with `x` (you cannot have Numpy inputs and + tensor targets, or inversely). If `x` is a dataset, dataset + iterator, generator, or `keras.utils.Sequence` instance, `y` should + not be specified (since targets will be obtained from `x`). + batch_size: Integer or `None`. + Number of samples per gradient update. + If unspecified, `batch_size` will default to 32. + Do not specify the `batch_size` if your data is in the + form of symbolic tensors, dataset, dataset iterators, + generators, or `keras.utils.Sequence` instances (since they generate + batches). + epochs: Integer. Number of epochs to train the model. + An epoch is an iteration over the entire `x` and `y` + data provided. + Note that in conjunction with `initial_epoch`, + `epochs` is to be understood as "final epoch". + The model is not trained for a number of iterations + given by `epochs`, but merely until the epoch + of index `epochs` is reached. + verbose: Integer. 0, 1, or 2. Verbosity mode. + 0 = silent, 1 = progress bar, 2 = one line per epoch. + callbacks: List of `keras.callbacks.Callback` instances. + List of callbacks to apply during training. + See [callbacks](/api_docs/python/tf/keras/callbacks). + validation_split: Float between 0 and 1. + Fraction of the training data to be used as validation data. + The model will set apart this fraction of the training data, + will not train on it, and will evaluate + the loss and any model metrics + on this data at the end of each epoch. + The validation data is selected from the last samples + in the `x` and `y` data provided, before shuffling. This argument is + not supported when `x` is a dataset, dataset iterator, generator or + `keras.utils.Sequence` instance. + validation_data: Data on which to evaluate + the loss and any model metrics at the end of each epoch. + The model will not be trained on this data. + `validation_data` will override `validation_split`. + `validation_data` could be: + - tuple `(x_val, y_val)` of Numpy arrays or tensors + - tuple `(x_val, y_val, val_sample_weights)` of Numpy arrays + - dataset or a dataset iterator + For the first two cases, `batch_size` must be provided. + For the last case, `validation_steps` must be provided. + shuffle: Boolean (whether to shuffle the training data + before each epoch) or str (for 'batch'). + 'batch' is a special option for dealing with the + limitations of HDF5 data; it shuffles in batch-sized chunks. + Has no effect when `steps_per_epoch` is not `None`. + class_weight: Optional dictionary mapping class indices (integers) + to a weight (float) value, used for weighting the loss function + (during training only). + This can be useful to tell the model to + "pay more attention" to samples from + an under-represented class. + sample_weight: Optional Numpy array of weights for + the training samples, used for weighting the loss function + (during training only). You can either pass a flat (1D) + Numpy array with the same length as the input samples + (1:1 mapping between weights and samples), + or in the case of temporal data, + you can pass a 2D array with shape + `(samples, sequence_length)`, + to apply a different weight to every timestep of every sample. + In this case you should make sure to specify + `sample_weight_mode="temporal"` in `compile()`. This argument is not + supported when `x` is a dataset, dataset iterator, generator, or + `keras.utils.Sequence` instance, instead provide the sample_weights + as the third element of `x`. + initial_epoch: Integer. + Epoch at which to start training + (useful for resuming a previous training run). + steps_per_epoch: Integer or `None`. + Total number of steps (batches of samples) + before declaring one epoch finished and starting the + next epoch. When training with input tensors such as + TensorFlow data tensors, the default `None` is equal to + the number of samples in your dataset divided by + the batch size, or 1 if that cannot be determined. + validation_steps: Only relevant if `validation_data` is provided and + is a dataset or dataset iterator. Total number of steps (batches of + samples) to draw before stopping when performing validation + at the end of every epoch. + max_queue_size: Integer. Used for generator or `keras.utils.Sequence` + input only. Maximum size for the generator queue. + If unspecified, `max_queue_size` will default to 10. + workers: Integer. Used for generator or `keras.utils.Sequence` input + only. Maximum number of processes to spin up + when using process-based threading. If unspecified, `workers` + will default to 1. If 0, will execute the generator on the main + thread. + use_multiprocessing: Boolean. Used for generator or + `keras.utils.Sequence` input only. If `True`, use process-based + threading. If unspecified, `use_multiprocessing` will default to + `False`. Note that because this implementation relies on + multiprocessing, you should not pass non-picklable arguments to + the generator as they can't be passed easily to children processes. + **kwargs: Used for backwards compatibility. - # Set metric attributes on model. - self._set_metric_attributes( - self.outputs, - skip_target_indices=skip_target_indices, - ) + Returns: + A `History` object. Its `History.history` attribute is + a record of training loss values and metrics values + at successive epochs, as well as validation loss values + and validation metrics values (if applicable). - self.targets = [] - for i in range(len(self.outputs)): - self._feed_output_names.append(self.output_names[i]) - self._collected_trainable_weights = self.trainable_weights - return + Raises: + RuntimeError: If the model was never compiled. + ValueError: In case of mismatch between the provided input data + and what the model expects. + """ + # TODO(fchollet): this method may be creating reference cycles, which would + # lead to accumulating garbage in memory when called in a loop. Investigate. + if data_utils.is_generator_or_sequence(x): + training_utils.check_generator_arguments(y, sample_weight) + return self.fit_generator( + x, + steps_per_epoch=steps_per_epoch, + epochs=epochs, + verbose=verbose, + callbacks=callbacks, + validation_data=validation_data, + validation_steps=validation_steps, + class_weight=class_weight, + max_queue_size=max_queue_size, + workers=workers, + use_multiprocessing=use_multiprocessing, + shuffle=shuffle, + initial_epoch=initial_epoch) - with K.get_graph().as_default(): - # Prepare targets of model. - self.targets = [] - self._feed_targets = [] - if target_tensors not in (None, []): - if isinstance(target_tensors, list): - if len(target_tensors) != len(self.outputs): - raise ValueError( - 'When passing a list as `target_tensors`, ' - 'it should have one entry per model output. ' - 'The model has %s outputs, but you passed target_tensors=%s' % - (len(self.outputs), target_tensors)) - elif isinstance(target_tensors, dict): - for name in target_tensors: - if name not in self.output_names: - raise ValueError( - 'Unknown entry in `target_tensors` ' - 'dictionary: "' + name + '". ' - 'Only expected the following keys: ' + str(self.output_names)) - tmp_target_tensors = [] - for name in self.output_names: - tmp_target_tensors.append(target_tensors.get(name, None)) - target_tensors = tmp_target_tensors - elif tensor_util.is_tensor(target_tensors): - target_tensors = [target_tensors] - else: - raise TypeError('Expected `target_tensors` to be a list or tuple or ' - 'dict or a single tensor, but got:', target_tensors) + # Legacy support + if 'nb_epoch' in kwargs: + logging.warning( + 'The `nb_epoch` argument in `fit` ' + 'has been renamed `epochs`.') + epochs = kwargs.pop('nb_epoch') + if kwargs: + raise TypeError('Unrecognized keyword arguments: ' + str(kwargs)) - for i in range(len(self.outputs)): - if i in skip_target_indices: - self.targets.append(None) - else: - shape = K.int_shape(self.outputs[i]) - name = self.output_names[i] - if target_tensors not in (None, []): - target = target_tensors[i] - else: - target = None - if target is None or K.is_placeholder(target): - if target is None: - target_dtype = losses.LABEL_DTYPES_FOR_LOSSES.get( - self.loss_functions[i], - K.dtype(self.outputs[i])) + # Validate and standardize user data. + if self._distribution_strategy: + distributed_training_utils.validate_callbacks(callbacks, self.optimizer, + self._distribution_strategy) - target = K.placeholder( - ndim=len(shape), - name=name + '_target', - sparse=K.is_sparse(self.outputs[i]), - dtype=target_dtype) - self._feed_targets.append(target) - self._feed_outputs.append(self.outputs[i]) - self._feed_output_names.append(name) - self._feed_output_shapes.append(shape) - self._feed_loss_fns.append(self.loss_functions[i]) - else: - skip_target_weighing_indices.append(i) - self.targets.append(target) + distributed_training_utils.validate_inputs( + x, y, self._distribution_strategy) - # Prepare sample weights. - self._set_sample_weight_attributes(sample_weight_mode, - skip_target_weighing_indices) - # Save all metric attributes per output of the model. - self._cache_output_metric_attributes(metrics, weighted_metrics) + first_x_value = nest.flatten(x)[0] + if isinstance(first_x_value, np.ndarray): + steps_per_epoch, batch_size = ( + distributed_training_utils.get_input_params( + self._distribution_strategy, first_x_value, steps_per_epoch, + batch_size, is_training=True)) - # Compute total loss. - total_loss = None - with K.name_scope('loss'): - for i in range(len(self.outputs)): - if i in skip_target_indices: - continue - y_true = self.targets[i] - y_pred = self.outputs[i] - loss_fn = loss_functions[i] - sample_weight = self.sample_weights[i] - mask = masks[i] - loss_weight = loss_weights_list[i] - with K.name_scope(self.output_names[i] + '_loss'): - if isinstance(loss_fn, losses.Loss): - if mask is not None: - mask = math_ops.cast(mask, y_pred.dtype) - # Update weights with mask. - if sample_weight is None: - sample_weight = mask - else: - # Update dimensions of weights to match with mask if possible. - mask, _, sample_weight = squeeze_or_expand_dimensions( - mask, None, sample_weight) - sample_weight *= mask - output_loss = loss_fn(y_true, y_pred, sample_weight=sample_weight) - else: - weighted_loss = training_utils.weighted_masked_objective(loss_fn) - output_loss = weighted_loss(y_true, y_pred, sample_weight, mask) + batch_size = self._validate_or_infer_batch_size(batch_size, steps_per_epoch, + x) - if len(self.outputs) > 1: - # Keep track of the un-aggregated loss result tensor. - self._compile_metrics_tensors[self.output_names[i] + - '_loss'] = output_loss + x, y, sample_weights = self._standardize_user_data( + x, + y, + sample_weight=sample_weight, + class_weight=class_weight, + batch_size=batch_size, + check_steps=True, + steps_name='steps_per_epoch', + steps=steps_per_epoch, + validation_split=validation_split, + shuffle=shuffle) - # Keep track of stateful result tensor and function for the loss. - loss_name = loss_fn.name if isinstance( - loss_fn, losses.Loss) else loss_fn.__name__ - mean_wrapped_loss = metrics_module.MeanMetricWrapper( - loss_fn, name=loss_name) - result_tensor = training_utils.call_metric_function( - mean_wrapped_loss, - y_true, - y_pred, - weights=sample_weight, - mask=mask) - self._compile_stateful_metrics_tensors[self.output_names[i] + - '_loss'] = result_tensor - self._compile_stateful_metric_functions.append(mean_wrapped_loss) + # Prepare validation data. + if validation_data: + if (isinstance(validation_data, iterator_ops.Iterator) or + isinstance(validation_data, iterator_ops.EagerIterator) or + isinstance(validation_data, dataset_ops.DatasetV2)): + val_x = validation_data + val_y = None + val_sample_weight = None + elif len(validation_data) == 2: + val_x, val_y = validation_data # pylint: disable=unpacking-non-sequence + val_sample_weight = None + elif len(validation_data) == 3: + val_x, val_y, val_sample_weight = validation_data # pylint: disable=unpacking-non-sequence + else: + raise ValueError( + 'When passing a `validation_data` argument, ' + 'it must contain either 2 items (x_val, y_val), ' + 'or 3 items (x_val, y_val, val_sample_weights), ' + 'or alternatively it could be a dataset or a ' + 'dataset or a dataset iterator. ' + 'However we received `validation_data=%s`' % validation_data) - self._compile_metrics_names.append(self.output_names[i] + '_loss') - if total_loss is None: - total_loss = loss_weight * output_loss - else: - total_loss += loss_weight * output_loss - if total_loss is None: - if not self.losses: - raise ValueError('The model cannot be compiled ' - 'because it has no loss to optimize.') - else: - total_loss = 0. + # Validate and standardize validation data. + if self._distribution_strategy: + distributed_training_utils.validate_inputs( + val_x, val_y, self._distribution_strategy) + first_valx_value = nest.flatten(val_x)[0] + if isinstance(first_valx_value, np.ndarray): + validation_steps, _ = distributed_training_utils.get_input_params( + self._distribution_strategy, first_valx_value, validation_steps, + batch_size) - # Add regularization penalties - # and other layer-specific losses. - for loss_tensor in self.losses: - total_loss += loss_tensor + val_x, val_y, val_sample_weights = self._standardize_user_data( + val_x, + val_y, + sample_weight=val_sample_weight, + batch_size=batch_size, + steps=validation_steps) - # Set metric attributes on model. - self._set_metric_attributes( - self.outputs, - skip_target_indices=skip_target_indices, - ) - # Invoke metric functions for all the outputs. - self._handle_metrics( - self.outputs, - masks=masks, - targets=self.targets, - skip_target_indices=skip_target_indices, - sample_weights=self.sample_weights) + elif validation_split and 0. < validation_split < 1.: + if training_utils.has_symbolic_tensors(x): + raise ValueError('If your data is in the form of symbolic tensors, ' + 'you cannot use `validation_split`.') + if hasattr(x[0], 'shape'): + split_at = int(x[0].shape[0] * (1. - validation_split)) + else: + split_at = int(len(x[0]) * (1. - validation_split)) + x, val_x = (slice_arrays(x, 0, split_at), slice_arrays(x, split_at)) + y, val_y = (slice_arrays(y, 0, split_at), slice_arrays(y, split_at)) + sample_weights, val_sample_weights = (slice_arrays( + sample_weights, 0, split_at), slice_arrays(sample_weights, split_at)) + elif validation_steps: + val_x = [] + val_y = [] + val_sample_weights = [] + else: + val_x = None + val_y = None + val_sample_weights = None - # Prepare gradient updates and state updates. - self.total_loss = total_loss + if (self.run_eagerly or (isinstance(x, iterator_ops.EagerIterator) and + not self._distribution_strategy)): + return training_generator.fit_generator( + self, (x, y, sample_weights), + steps_per_epoch=steps_per_epoch, + batch_size=batch_size, + epochs=epochs, + shuffle=shuffle, + verbose=verbose, + callbacks=callbacks, + validation_data=validation_data, + validation_steps=validation_steps, + workers=0, + initial_epoch=initial_epoch) + elif distributed_training_utils.is_tpu_strategy( + self._distribution_strategy): + return training_distributed.experimental_fit_loop( + self, + x, + epochs=epochs, + verbose=verbose, + callbacks=callbacks, + val_iterator=val_x, + initial_epoch=initial_epoch, + steps_per_epoch=steps_per_epoch, + validation_steps=validation_steps) + else: + return training_arrays.fit_loop( + self, + x, + y, + sample_weights=sample_weights, + batch_size=batch_size, + epochs=epochs, + verbose=verbose, + callbacks=callbacks, + val_inputs=val_x, + val_targets=val_y, + val_sample_weights=val_sample_weights, + shuffle=shuffle, + initial_epoch=initial_epoch, + steps_per_epoch=steps_per_epoch, + validation_steps=validation_steps) - # Functions for train, test and predict will - # be compiled lazily when required. - # This saves time when the user is not using all functions. - self._function_kwargs = kwargs + def evaluate(self, + x=None, + y=None, + batch_size=None, + verbose=1, + sample_weight=None, + steps=None, + max_queue_size=10, + workers=1, + use_multiprocessing=False): + """Returns the loss value & metrics values for the model in test mode. - self._fit_function = None - self._eval_function = None - self.train_function = None - self.test_function = None - self.predict_function = None + Computation is done in batches. - # Collected trainable weights, sorted in topological order. - trainable_weights = self.trainable_weights - self._collected_trainable_weights = trainable_weights + Arguments: + x: Input data. It could be: + - A Numpy array (or array-like), or a list of arrays + (in case the model has multiple inputs). + - A TensorFlow tensor, or a list of tensors + (in case the model has multiple inputs). + - A dict mapping input names to the corresponding array/tensors, + if the model has named inputs. + - A `tf.data` dataset or a dataset iterator. + - A generator or `keras.utils.Sequence` instance. + y: Target data. Like the input data `x`, + it could be either Numpy array(s) or TensorFlow tensor(s). + It should be consistent with `x` (you cannot have Numpy inputs and + tensor targets, or inversely). + If `x` is a dataset, dataset iterator, generator or + `keras.utils.Sequence` instance, `y` should not be specified (since + targets will be obtained from the iterator/dataset). + batch_size: Integer or `None`. + Number of samples per gradient update. + If unspecified, `batch_size` will default to 32. + Do not specify the `batch_size` is your data is in the + form of symbolic tensors, dataset, dataset iterators, + generators, or `keras.utils.Sequence` instances (since they generate + batches). + verbose: 0 or 1. Verbosity mode. + 0 = silent, 1 = progress bar. + sample_weight: Optional Numpy array of weights for + the test samples, used for weighting the loss function. + You can either pass a flat (1D) + Numpy array with the same length as the input samples + (1:1 mapping between weights and samples), + or in the case of temporal data, + you can pass a 2D array with shape + `(samples, sequence_length)`, + to apply a different weight to every timestep of every sample. + In this case you should make sure to specify + `sample_weight_mode="temporal"` in `compile()`. This argument is not + supported when `x` is a dataset or a dataset iterator, instead pass + sample weights as the third element of `x`. + steps: Integer or `None`. + Total number of steps (batches of samples) + before declaring the evaluation round finished. + Ignored with the default value of `None`. + max_queue_size: Integer. Used for generator or `keras.utils.Sequence` + input only. Maximum size for the generator queue. + If unspecified, `max_queue_size` will default to 10. + workers: Integer. Used for generator or `keras.utils.Sequence` input + only. Maximum number of processes to spin up when using + process-based threading. If unspecified, `workers` will default + to 1. If 0, will execute the generator on the main thread. + use_multiprocessing: Boolean. Used for generator or + `keras.utils.Sequence` input only. If `True`, use process-based + threading. If unspecified, `use_multiprocessing` will default to + `False`. Note that because this implementation relies on + multiprocessing, you should not pass non-picklable arguments to + the generator as they can't be passed easily to children processes. - def _check_trainable_weights_consistency(self): - """Check trainable weights count consistency. + Returns: + Scalar test loss (if the model has a single output and no metrics) + or list of scalars (if the model has multiple outputs + and/or metrics). The attribute `model.metrics_names` will give you + the display labels for the scalar outputs. - This will raise a warning if `trainable_weights` and - `_collected_trainable_weights` are inconsistent (i.e. have different - number of parameters). - Inconsistency will typically arise when one modifies `model.trainable` - without calling `model.compile` again. + Raises: + ValueError: in case of invalid arguments. """ - if not hasattr(self, '_collected_trainable_weights'): - return - - if len(self.trainable_weights) != len(self._collected_trainable_weights): - logging.log_first_n( - logging.WARN, 'Discrepancy between trainable weights and collected' - ' trainable weights, did you set `model.trainable`' - ' without calling `model.compile` after ?', 1) - - def _make_train_function_helper(self, fn_name, outputs, metric_updates=None): - if not hasattr(self, fn_name): - raise RuntimeError('You must compile your model before using it.') - self._check_trainable_weights_consistency() - if getattr(self, fn_name) is None: - inputs = (self._feed_inputs + - self._feed_targets + - self._feed_sample_weights) - if not isinstance(K.symbolic_learning_phase(), int): - inputs += [K.symbolic_learning_phase()] + if data_utils.is_generator_or_sequence(x): + training_utils.check_generator_arguments(y, sample_weight) + return self.evaluate_generator( + x, + steps=steps, + verbose=verbose, + max_queue_size=max_queue_size, + workers=workers, + use_multiprocessing=use_multiprocessing) + # Validate and standardize user data. + if self._distribution_strategy: + distributed_training_utils.validate_inputs( + x, y, self._distribution_strategy) + first_x_value = nest.flatten(x)[0] + if isinstance(first_x_value, np.ndarray): + steps, batch_size = distributed_training_utils.get_input_params( + self._distribution_strategy, first_x_value, steps, batch_size) - with K.get_graph().as_default(): - with K.name_scope('training'): - with K.name_scope(self.optimizer.__class__.__name__): - # Training updates - updates = self.optimizer.get_updates( - params=self._collected_trainable_weights, loss=self.total_loss) - # Unconditional updates - updates += self.get_updates_for(None) - # Conditional updates relevant to this model - updates += self.get_updates_for(self.inputs) - # Add stateful metrics updates. - if metric_updates is not None: - updates += metric_updates + batch_size = self._validate_or_infer_batch_size(batch_size, steps, x) - with K.name_scope('training'): - # Gets loss and metrics. Updates weights at each call. - fn = K.function( - inputs, - outputs, - updates=updates, - name='train_function', - **self._function_kwargs) - setattr(self, fn_name, fn) + x, y, sample_weights = self._standardize_user_data( + x, + y, + sample_weight=sample_weight, + batch_size=batch_size, + check_steps=True, + steps_name='steps', + steps=steps) - def _make_train_function(self): - metrics_tensors = [ - self._all_metrics_tensors[m] for m in self.metrics_names[1:] - ] - self._make_train_function_helper('train_function', - [self.total_loss] + metrics_tensors) + if (self.run_eagerly or (isinstance(x, iterator_ops.EagerIterator) and + not self._distribution_strategy)): + return training_generator.evaluate_generator( + self, (x, y, sample_weights), + steps=steps, + batch_size=batch_size, + verbose=verbose, + workers=0) + elif distributed_training_utils.is_tpu_strategy( + self._distribution_strategy): + return training_distributed.experimental_test_loop( + self, iterator=x, verbose=verbose, steps=steps) + else: + return training_arrays.test_loop( + self, + inputs=x, + targets=y, + sample_weights=sample_weights, + batch_size=batch_size, + verbose=verbose, + steps=steps) - def _make_fit_function(self): - metrics_tensors = [ - self._all_stateful_metrics_tensors[m] for m in self.metrics_names[1:] - ] - self._make_train_function_helper( - '_fit_function', [self.total_loss] + metrics_tensors) + def predict(self, + x, + batch_size=None, + verbose=0, + steps=None, + max_queue_size=10, + workers=1, + use_multiprocessing=False): + """Generates output predictions for the input samples. - def _make_test_function_helper(self, fn_name, outputs, metric_updates=None): - if not hasattr(self, fn_name): - raise RuntimeError('You must compile your model before using it.') - if getattr(self, fn_name) is None: - inputs = (self._feed_inputs + - self._feed_targets + - self._feed_sample_weights) + Computation is done in batches. - with K.name_scope('evaluation'): - updates = self.state_updates - # Add stateful metrics updates. - if metric_updates is not None: - updates += metric_updates - # Return loss and metrics, no gradient updates. - # Does update the network states. - fn = K.function( - inputs, - outputs, - updates=updates, - name='test_function', - **self._function_kwargs) - setattr(self, fn_name, fn) + Arguments: + x: Input samples. It could be: + - A Numpy array (or array-like), or a list of arrays + (in case the model has multiple inputs). + - A TensorFlow tensor, or a list of tensors + (in case the model has multiple inputs). + - A `tf.data` dataset or a dataset iterator. + - A generator or `keras.utils.Sequence` instance. + batch_size: Integer or `None`. + Number of samples per gradient update. + If unspecified, `batch_size` will default to 32. + Do not specify the `batch_size` is your data is in the + form of symbolic tensors, dataset, dataset iterators, + generators, or `keras.utils.Sequence` instances (since they generate + batches). + verbose: Verbosity mode, 0 or 1. + steps: Total number of steps (batches of samples) + before declaring the prediction round finished. + Ignored with the default value of `None`. + max_queue_size: Integer. Used for generator or `keras.utils.Sequence` + input only. Maximum size for the generator queue. + If unspecified, `max_queue_size` will default to 10. + workers: Integer. Used for generator or `keras.utils.Sequence` input + only. Maximum number of processes to spin up when using + process-based threading. If unspecified, `workers` will default + to 1. If 0, will execute the generator on the main thread. + use_multiprocessing: Boolean. Used for generator or + `keras.utils.Sequence` input only. If `True`, use process-based + threading. If unspecified, `use_multiprocessing` will default to + `False`. Note that because this implementation relies on + multiprocessing, you should not pass non-picklable arguments to + the generator as they can't be passed easily to children processes. - def _make_test_function(self): - metrics_tensors = [ - self._all_metrics_tensors[m] for m in self.metrics_names[1:] - ] - self._make_test_function_helper('test_function', - [self.total_loss] + metrics_tensors) - def _make_eval_function(self): - metrics_tensors = [ - self._all_stateful_metrics_tensors[m] for m in self.metrics_names[1:] - ] - self._make_test_function_helper( - '_eval_function', [self.total_loss] + metrics_tensors) + Returns: + Numpy array(s) of predictions. - def _make_predict_function(self): - if not hasattr(self, 'predict_function'): - self.predict_function = None - if self.predict_function is None: - inputs = self._feed_inputs - # Gets network outputs. Does not update weights. - # Does update the network states. - kwargs = getattr(self, '_function_kwargs', {}) - with K.name_scope('predict'): - self.predict_function = K.function( - inputs, - self.outputs, - updates=self.state_updates, - name='predict_function', - **kwargs) + Raises: + ValueError: In case of mismatch between the provided + input data and the model's expectations, + or in case a stateful model receives a number of samples + that is not a multiple of the batch size. + """ + if data_utils.is_generator_or_sequence(x): + return self.predict_generator( + x, + steps=steps, + verbose=verbose, + max_queue_size=max_queue_size, + workers=workers, + use_multiprocessing=use_multiprocessing) + if self._distribution_strategy: + distributed_training_utils.validate_inputs( + x, None, self._distribution_strategy) + first_x_value = nest.flatten(x)[0] + if isinstance(first_x_value, np.ndarray): + steps, batch_size = distributed_training_utils.get_input_params( + self._distribution_strategy, first_x_value, steps, batch_size) - def _make_execution_function(self, mode): - if mode == 'train': - self._make_fit_function() - return self._fit_function - if mode == 'test': - self._make_eval_function() - return self._eval_function - if mode == 'predict': - self._make_predict_function() - return self.predict_function + batch_size = self._validate_or_infer_batch_size(batch_size, steps, x) - def _get_iterator_get_next_tensors(self, iterator): - get_next_op = self._iterator_get_next.get(iterator, None) - if get_next_op is None: - get_next_op = iterator.get_next() - self._iterator_get_next[iterator] = get_next_op - return get_next_op + # Validate and standardize user data. + if self._distribution_strategy: + x, _, _ = self._standardize_user_data( + x, check_steps=True, steps_name='steps', steps=steps, + batch_size=batch_size) + else: + # TODO(anjalisridhar): We don't pass batch_size here for some reason. This + # means we need to special case distribution strategy which needs the + # batch size. + x, _, _ = self._standardize_user_data( + x, check_steps=True, steps_name='steps', steps=steps) - def _distribution_standardize_user_data(self, - x, - y=None, - sample_weight=None, - class_weight=None, - batch_size=None, - check_steps=False, - steps_name='steps', - steps=None, - validation_split=0, - shuffle=False): - """Runs validation checks on input and target data passed by the user. + if (self.run_eagerly or (isinstance(x, iterator_ops.EagerIterator) and + not self._distribution_strategy)): + return training_generator.predict_generator( + self, + x, + steps=steps, + batch_size=batch_size, + verbose=verbose, + workers=0) + elif distributed_training_utils.is_tpu_strategy( + self._distribution_strategy): + return training_distributed.experimental_predict_loop( + self, x, verbose=verbose, steps=steps) + else: + return training_arrays.predict_loop( + self, x, batch_size=batch_size, verbose=verbose, steps=steps) - This is called when using DistributionStrategy to train, evaluate or serve - the model. + def reset_metrics(self): + """Resets the state of metrics.""" + if hasattr(self, 'metrics'): + for m in self.metrics: + m.reset_states() + if self._distribution_strategy: + training_distributed._reset_metrics(self) # pylint: disable=protected-access - Args: - x: Input data. A numpy array or `tf.data` dataset. - y: Target data. A numpy array or None if x is a `tf.data` dataset. - sample_weight: An optional sample-weight array passed by the user to - weight the importance of each sample in `x`. - class_weight: An optional class-weight array by the user to - weight the importance of samples in `x` based on the class they belong - to, as conveyed by `y`. - batch_size: Integer batch size. If provided, it is used to run additional - validation checks on stateful models. - check_steps: boolean, True if we want to check for validity of `steps` and - False, otherwise. - steps_name: The public API's parameter name for `steps`. - steps: Integer or `None`. Total number of steps (batches of samples) to - execute. - validation_split: Float between 0 and 1. - Fraction of the training data to be used as validation data. - shuffle: Boolean whether to shuffle the training data before each epoch. + def train_on_batch(self, + x, + y=None, + sample_weight=None, + class_weight=None, + reset_metrics=True): + """Runs a single gradient update on a single batch of data. + + Arguments: + x: Input data. It could be: + - A Numpy array (or array-like), or a list of arrays + (in case the model has multiple inputs). + - A TensorFlow tensor, or a list of tensors + (in case the model has multiple inputs). + - A dict mapping input names to the corresponding array/tensors, + if the model has named inputs. + - A `tf.data` dataset or a dataset iterator. + y: Target data. Like the input data `x`, it could be either Numpy + array(s) or TensorFlow tensor(s). It should be consistent with `x` + (you cannot have Numpy inputs and tensor targets, or inversely). If + `x` is a dataset or a dataset iterator, `y` should not be specified + (since targets will be obtained from the iterator). + sample_weight: Optional array of the same length as x, containing + weights to apply to the model's loss for each sample. In the case of + temporal data, you can pass a 2D array with shape (samples, + sequence_length), to apply a different weight to every timestep of + every sample. In this case you should make sure to specify + sample_weight_mode="temporal" in compile(). This argument is not + supported when `x` is a dataset or a dataset iterator. + class_weight: Optional dictionary mapping class indices (integers) to a + weight (float) to apply to the model's loss for the samples from this + class during training. This can be useful to tell the model to "pay + more attention" to samples from an under-represented class. + reset_metrics: If `True`, the metrics returned will be only for this + batch. If `False`, the metrics will be statefully accumulated across + batches. Returns: - Iterator for reading the dataset `x`. + Scalar training loss + (if the model has a single output and no metrics) + or list of scalars (if the model has multiple outputs + and/or metrics). The attribute `model.metrics_names` will give you + the display labels for the scalar outputs. Raises: - ValueError: In case of invalid user-provided data. - RuntimeError: If the model was never compiled. + ValueError: In case of invalid user-provided arguments. """ - if class_weight: - raise NotImplementedError('`class_weight` is currently not supported ' - 'when using DistributionStrategy.') - - if (sample_weight is not None and sample_weight.all() and - distributed_training_utils.is_tpu_strategy( - self._distribution_strategy)): - raise NotImplementedError('`sample_weight` is currently not supported ' - 'when using TPUStrategy.') + if self._distribution_strategy: + raise NotImplementedError('`train_on_batch` is not supported for models ' + 'compiled with DistributionStrategy.') + # Validate and standardize user data. + x, y, sample_weights = self._standardize_user_data( + x, y, sample_weight=sample_weight, class_weight=class_weight) - # Validates `steps` argument right at the beginning since we use it to - # construct the dataset object. - # TODO(anjalisridhar): Remove this check once we refactor the - # _standardize_user_data code path. This check is already present elsewhere - # in the codebase. - if check_steps and isinstance(x, dataset_ops.DatasetV2) and steps is None: - raise ValueError('When using Datasets as input, ' - 'you should specify the `{steps_name}` argument.' - .format(steps_name=steps_name)) + if self.run_eagerly: + outputs = training_eager.train_on_batch( + self, x, y, sample_weights=sample_weights) + else: + if not isinstance(K.symbolic_learning_phase(), int): + ins = x + y + sample_weights + [True] + else: + ins = x + y + sample_weights - first_x_value = nest.flatten(x)[0] - if isinstance(first_x_value, np.ndarray): - # We need to use the drop_remainder argument to allow for a static - # input shape which is required for TPUs. - drop_remainder = self._distribution_strategy.require_static_shapes - if y is not None: - var_x = distributed_training_utils.get_var_for_numpy( - self._distribution_strategy, x) - var_y = distributed_training_utils.get_var_for_numpy( - self._distribution_strategy, y) - if sample_weight is not None: - var_sample_weights = distributed_training_utils.get_var_for_numpy( - self._distribution_strategy, sample_weight) + if reset_metrics: + self._make_train_function() + outputs = self.train_function(ins) # pylint: disable=not-callable + else: + self._make_fit_function() + outputs = self._fit_function(ins) # pylint: disable=not-callable - x = dataset_ops.Dataset.from_tensor_slices((var_x, var_y, - var_sample_weights)) - else: - x = dataset_ops.Dataset.from_tensor_slices((var_x, var_y)) + if reset_metrics: + self.reset_metrics() - x = dataset_ops.Dataset.from_tensor_slices((var_x, var_y)) - if shuffle: - # 1024 is a good buffer size since it is much larger than the average - # batch size provided by the user and provides sufficient randomness. - # One thing to keep in mind is the memory usage based on the size of - # each sample. - x = x.shuffle(1024) - x = x.repeat() - x = x.batch(batch_size, drop_remainder=drop_remainder) - y = None - sample_weight = None - else: - # This case is for the predict call where the dataset only contains - # inputs and no targets, i.e. it does not return a tuple - var_x = distributed_training_utils.get_var_for_numpy( - self._distribution_strategy, x) - x = dataset_ops.Dataset.from_tensor_slices(var_x) - x = x.batch(batch_size, drop_remainder=drop_remainder) + if len(outputs) == 1: + return outputs[0] + return outputs - assert isinstance(x, dataset_ops.DatasetV2) + def test_on_batch(self, x, y=None, sample_weight=None, reset_metrics=True): + """Test the model on a single batch of samples. - with self._distribution_strategy.scope(): - iterator = self._distribution_strategy.make_dataset_iterator(x) - init_op = iterator.initialize() - if not context.executing_eagerly(): - K.get_session().run(init_op) + Arguments: + x: Input data. It could be: + - A Numpy array (or array-like), or a list of arrays + (in case the model has multiple inputs). + - A TensorFlow tensor, or a list of tensors + (in case the model has multiple inputs). + - A dict mapping input names to the corresponding array/tensors, + if the model has named inputs. + - A `tf.data` dataset or a dataset iterator. + y: Target data. Like the input data `x`, + it could be either Numpy array(s) or TensorFlow tensor(s). + It should be consistent with `x` (you cannot have Numpy inputs and + tensor targets, or inversely). If `x` is a dataset or a + dataset iterator, `y` should not be specified + (since targets will be obtained from the iterator). + sample_weight: Optional array of the same length as x, containing + weights to apply to the model's loss for each sample. + In the case of temporal data, you can pass a 2D array + with shape (samples, sequence_length), + to apply a different weight to every timestep of every sample. + In this case you should make sure to specify + sample_weight_mode="temporal" in compile(). This argument is not + supported when `x` is a dataset or a dataset iterator. + reset_metrics: If `True`, the metrics returned will be only for this + batch. If `False`, the metrics will be statefully accumulated across + batches. - training_utils.validate_iterator_input(x, y, sample_weight, - validation_split) - return iterator + Returns: + Scalar test loss (if the model has a single output and no metrics) + or list of scalars (if the model has multiple outputs + and/or metrics). The attribute `model.metrics_names` will give you + the display labels for the scalar outputs. - def _standardize_user_data(self, - x, - y=None, - sample_weight=None, - class_weight=None, - batch_size=None, - check_steps=False, - steps_name='steps', - steps=None, - validation_split=0, - shuffle=False): - """Runs validation checks on input and target data passed by the user. + Raises: + ValueError: In case of invalid user-provided arguments. + """ + if self._distribution_strategy: + raise NotImplementedError('`test_on_batch` is not supported for models ' + 'compiled with DistributionStrategy.') + # Validate and standardize user data. + x, y, sample_weights = self._standardize_user_data( + x, y, sample_weight=sample_weight) - Also standardizes the data to lists of arrays, in order. + if self.run_eagerly: + outputs = training_eager.test_on_batch( + self, x, y, sample_weights=sample_weights) + else: + inputs = x + y + sample_weights + if reset_metrics: + self._make_test_function() + outputs = self.test_function(inputs) # pylint: disable=not-callable + else: + self._make_eval_function() + outputs = self._eval_function(inputs) # pylint: disable=not-callable - Also builds and compiles the model on the fly if it is a subclassed model - that has never been called before (and thus has no inputs/outputs). + if reset_metrics: + self.reset_metrics() - This is a purely internal method, subject to refactoring at any time. + if len(outputs) == 1: + return outputs[0] + return outputs - Args: - x: Input data. It could be: - - A Numpy array (or array-like), or a list of arrays - (in case the model has multiple inputs). - - A TensorFlow tensor, or a list of tensors - (in case the model has multiple inputs). - - A dict mapping input names to the corresponding array/tensors, - if the model has named inputs. - - A `tf.data` dataset or a dataset iterator. - y: Target data. Like the input data `x`, - it could be either Numpy array(s) or TensorFlow tensor(s). - It should be consistent with `x` (you cannot have Numpy inputs and - tensor targets, or inversely). If `x` is a dataset or a - dataset iterator, `y` should not be specified - (since targets will be obtained from the iterator). - sample_weight: An optional sample-weight array passed by the user to - weight the importance of each sample in `x`. - class_weight: An optional class-weight array by the user to - weight the importance of samples in `x` based on the class they belong - to, as conveyed by `y`. - batch_size: Integer batch size. If provided, it is used to run additional - validation checks on stateful models. - check_steps: boolean, True if we want to check for validity of `steps` and - False, otherwise. For example, when we are standardizing one batch of - data for train_on_batch/predict_on_batch/test_on_batch APIs, `steps` - value is not required and we should not check for its validity in these - cases. - steps_name: The public API's parameter name for `steps`. - steps: Integer or `None`. Total number of steps (batches of samples) to - execute. - validation_split: Float between 0 and 1. - Fraction of the training data to be used as validation data. - shuffle: Boolean whether to shuffle the training data before each epoch. + def predict_on_batch(self, x): + """Returns predictions for a single batch of samples. + + Arguments: + x: Input data. It could be: + - A Numpy array (or array-like), or a list of arrays + (in case the model has multiple inputs). + - A TensorFlow tensor, or a list of tensors + (in case the model has multiple inputs). + - A `tf.data` dataset or a dataset iterator. Returns: - A tuple of 3: inputs (arrays or dicts, depending on whether `x` was a dict - or not), target arrays, sample-weight arrays. - If the model's input and targets are symbolic, these lists are empty - (since the model takes no user-provided data, instead the data comes - from the symbolic inputs/targets). + Numpy array(s) of predictions. Raises: - ValueError: In case of invalid user-provided data. - RuntimeError: If the model was never compiled. + ValueError: In case of mismatch between given number of inputs and + expectations of the model. """ if self._distribution_strategy: - iterator = self._distribution_standardize_user_data( - x, - y, - sample_weight=sample_weight, - class_weight=class_weight, - batch_size=batch_size, - check_steps=check_steps, - steps_name=steps_name, - steps=steps, - validation_split=validation_split, - shuffle=shuffle) - return iterator, None, None + raise NotImplementedError('`predict_on_batch` is not supported for ' + 'models compiled with DistributionStrategy.') + # Validate and standardize user data. + inputs, _, _ = self._standardize_user_data(x) + if self.run_eagerly: + if (isinstance(inputs, iterator_ops.EagerIterator) or + (isinstance(inputs, dataset_ops.DatasetV2))): + inputs = training_utils.cast_if_floating_dtype(inputs) + elif isinstance(inputs, collections.Sequence): + inputs = [ + ops.convert_to_tensor(val, dtype=K.floatx()) for val in inputs] + return self(inputs) # pylint: disable=not-callable - if isinstance(x, dataset_ops.DatasetV2): - if context.executing_eagerly(): - x = iter(x) - else: - if x in self._dataset_iterator_cache: - x = self._dataset_iterator_cache[x] - else: - iterator = dataset_ops.make_initializable_iterator(x) - self._dataset_iterator_cache[x] = iterator - x = iterator - K.get_session().run(x.initializer) + self._make_predict_function() + outputs = self.predict_function(inputs) - # Validates `steps` argument based on x's type. - if check_steps: - training_utils.check_steps_argument(x, steps, steps_name) + if len(outputs) == 1: + return outputs[0] + return outputs - is_x_eager_iterator = isinstance(x, iterator_ops.EagerIterator) - is_x_iterator = isinstance(x, iterator_ops.Iterator) + def fit_generator(self, + generator, + steps_per_epoch=None, + epochs=1, + verbose=1, + callbacks=None, + validation_data=None, + validation_steps=None, + class_weight=None, + max_queue_size=10, + workers=1, + use_multiprocessing=False, + shuffle=True, + initial_epoch=0): + """Fits the model on data yielded batch-by-batch by a Python generator. + + The generator is run in parallel to the model, for efficiency. + For instance, this allows you to do real-time data augmentation + on images on CPU in parallel to training your model on GPU. + + The use of `keras.utils.Sequence` guarantees the ordering + and guarantees the single use of every input per epoch when + using `use_multiprocessing=True`. + + Arguments: + generator: A generator or an instance of `Sequence` + (`keras.utils.Sequence`) + object in order to avoid duplicate data + when using multiprocessing. + The output of the generator must be either + - a tuple `(inputs, targets)` + - a tuple `(inputs, targets, sample_weights)`. + This tuple (a single output of the generator) makes a single batch. + Therefore, all arrays in this tuple must have the same length (equal + to the size of this batch). Different batches may have different + sizes. + For example, the last batch of the epoch is commonly smaller than + the + others, if the size of the dataset is not divisible by the batch + size. + The generator is expected to loop over its data + indefinitely. An epoch finishes when `steps_per_epoch` + batches have been seen by the model. + steps_per_epoch: Total number of steps (batches of samples) + to yield from `generator` before declaring one epoch + finished and starting the next epoch. It should typically + be equal to the number of samples of your dataset + divided by the batch size. + Optional for `Sequence`: if unspecified, will use + the `len(generator)` as a number of steps. + epochs: Integer, total number of iterations on the data. + verbose: Verbosity mode, 0, 1, or 2. + callbacks: List of callbacks to be called during training. + validation_data: This can be either + - a generator for the validation data + - a tuple (inputs, targets) + - a tuple (inputs, targets, sample_weights). + validation_steps: Only relevant if `validation_data` + is a generator. Total number of steps (batches of samples) + to yield from `generator` before stopping. + Optional for `Sequence`: if unspecified, will use + the `len(validation_data)` as a number of steps. + class_weight: Dictionary mapping class indices to a weight + for the class. + max_queue_size: Integer. Maximum size for the generator queue. + If unspecified, `max_queue_size` will default to 10. + workers: Integer. Maximum number of processes to spin up + when using process-based threading. + If unspecified, `workers` will default to 1. If 0, will + execute the generator on the main thread. + use_multiprocessing: Boolean. + If `True`, use process-based threading. + If unspecified, `use_multiprocessing` will default to `False`. + Note that because this implementation relies on multiprocessing, + you should not pass non-picklable arguments to the generator + as they can't be passed easily to children processes. + shuffle: Boolean. Whether to shuffle the order of the batches at + the beginning of each epoch. Only used with instances + of `Sequence` (`keras.utils.Sequence`). + Has no effect when `steps_per_epoch` is not `None`. + initial_epoch: Epoch at which to start training + (useful for resuming a previous training run) - # Validate user inputs when data is given as a dataset or dataset iterator. - if is_x_iterator or is_x_eager_iterator: - training_utils.validate_iterator_input(x, y, sample_weight, - validation_split) + Returns: + A `History` object. - # For eager iterators, when we have to process multiple batches of samples, - # we will standardize the data when we actually loop over iterator and get - # the batches. For now, we just return the iterator as is. - if is_x_eager_iterator: - return x, y, sample_weight + Example: - # If input data is a dataset iterator in graph mode or if it is an eager - # iterator and only one batch of samples is required, we fetch the data - # tensors from the iterator and then standardize them. - if is_x_iterator or is_x_eager_iterator: - try: - if is_x_iterator: - next_element = self._get_iterator_get_next_tensors(x) - else: - next_element = x.get_next() - except errors.OutOfRangeError: - raise RuntimeError('Your dataset iterator ran out of data; ' - 'Make sure that your dataset can generate ' - 'required number of samples.') + ```python + def generate_arrays_from_file(path): + while 1: + f = open(path) + for line in f: + # create numpy arrays of input data + # and labels, from each line in the file + x1, x2, y = process_line(line) + yield ({'input_1': x1, 'input_2': x2}, {'output': y}) + f.close() - if isinstance(next_element, (list, tuple)): - if len(next_element) not in [2, 3]: - raise ValueError( - 'Please provide model inputs as a list or tuple of 2 or 3' - 'elements: (input, target) or (input, target, sample_weights)' - 'Received %s' % next_element) - if len(next_element) == 2: - x, y = next_element - else: - x, y, sample_weight = next_element - else: - x = next_element - x, y, sample_weights = self._standardize_weights( - x, y, sample_weight, class_weight, batch_size, is_x_iterator) - return x, y, sample_weights + model.fit_generator(generate_arrays_from_file('/my_file.txt'), + steps_per_epoch=10000, epochs=10) + ``` + Raises: + ValueError: In case the generator yields data in an invalid format. + """ + if self._distribution_strategy: + raise NotImplementedError('`fit_generator` is not supported for ' + 'models compiled with DistributionStrategy.') + return training_generator.fit_generator( + self, + generator, + steps_per_epoch=steps_per_epoch, + epochs=epochs, + verbose=verbose, + callbacks=callbacks, + validation_data=validation_data, + validation_steps=validation_steps, + class_weight=class_weight, + max_queue_size=max_queue_size, + workers=workers, + use_multiprocessing=use_multiprocessing, + shuffle=shuffle, + initial_epoch=initial_epoch) - def _standardize_weights(self, - x, - y, - sample_weight=None, - class_weight=None, - batch_size=None, - from_iterator=False): - """Standardize input data, target data, and weight values. + def evaluate_generator(self, + generator, + steps=None, + max_queue_size=10, + workers=1, + use_multiprocessing=False, + verbose=0): + """Evaluates the model on a data generator. - This method reformats all data passed to the model to an ordered list of - array/tensors, matching the order expected by the model. This also validates - the input and target data shapes. + The generator should return the same kind of data + as accepted by `test_on_batch`. - Args: - x: Input data. It could be: - - A Numpy array (or array-like), or a list of arrays - (in case the model has multiple inputs). - - A TensorFlow tensor, or a list of tensors - (in case the model has multiple inputs). - - A dict mapping input names to the corresponding array/tensors, - if the model has named inputs. - x cannot not be an iterator. - y: Target data. Like the input data `x`, - it could be either Numpy array(s) or TensorFlow tensor(s). - It should be consistent with `x` (you cannot have Numpy inputs and - tensor targets, or inversely). - sample_weight: An optional sample-weight array passed by the user to - weight the importance of each sample in `x`. - class_weight: An optional class-weight array by the user to - weight the importance of samples in `x` based on the class they belong - to, as conveyed by `y`. - batch_size: Integer batch size. If provided, it is used to run additional - validation checks on stateful models. - from_iterator: Whether x and y were obtained from an iterator. + Arguments: + generator: Generator yielding tuples (inputs, targets) + or (inputs, targets, sample_weights) + or an instance of `keras.utils.Sequence` + object in order to avoid duplicate data + when using multiprocessing. + steps: Total number of steps (batches of samples) + to yield from `generator` before stopping. + Optional for `Sequence`: if unspecified, will use + the `len(generator)` as a number of steps. + max_queue_size: maximum size for the generator queue + workers: Integer. Maximum number of processes to spin up + when using process-based threading. + If unspecified, `workers` will default to 1. If 0, will + execute the generator on the main thread. + use_multiprocessing: Boolean. + If `True`, use process-based threading. + If unspecified, `use_multiprocessing` will default to `False`. + Note that because this implementation relies on multiprocessing, + you should not pass non-picklable arguments to the generator + as they can't be passed easily to children processes. + verbose: Verbosity mode, 0 or 1. Returns: - Tuple of standardized data that will be fed to the model: - (input data, target data, sample weights) + Scalar test loss (if the model has a single output and no metrics) + or list of scalars (if the model has multiple outputs + and/or metrics). The attribute `model.metrics_names` will give you + the display labels for the scalar outputs. Raises: - RuntimeError: If target data is provided, but the model has not yet been - compiled. - ValueError: If the input data, target data, and batch size have invalid - shapes or formats (e.g. the model expects input to be a list of three - tensors, but x is a list with two tensors). Error is also raised if the - input and target data are not both arrays or tensors. - """ - # TODO(sourabhbajaj): Split input validation from weight standardization. - if sample_weight is not None and class_weight is not None: - logging.warning( - 'Received both a `sample_weight` and `class_weight` argument. ' - 'The `class_weight` argument will be ignored.') - # First, we build/compile the model on the fly if necessary. - all_inputs = [] - is_build_called = False - is_compile_called = False - # Whether this is a subclassed model that expects dictionary inputs - # rather than list inputs (e.g. FeatureColumn-based models). - dict_inputs = False - if not self.inputs: - # We need to use `x` to set the model inputs. - # We type-check that `x` and `y` are either single arrays - # or lists of arrays. - if isinstance(x, (list, tuple)): - if not all(isinstance(v, np.ndarray) or - tensor_util.is_tensor(v) for v in x): - raise ValueError('Please provide as model inputs either a single ' - 'array or a list of arrays. You passed: x=' + str(x)) - all_inputs += list(x) - elif isinstance(x, dict): - dict_inputs = True - keys = sorted(x.keys()) - all_inputs = [x[k] for k in keys] - else: - if not isinstance(x, np.ndarray) and not tensor_util.is_tensor(x): - raise ValueError('Please provide as model inputs either a single ' - 'array or a list of arrays. You passed: x=' + str(x)) - all_inputs.append(x) - - # Build the model using the retrieved inputs (value or symbolic). - # If values or generated from a dataset, then in symbolic-mode - # placeholders will be created to match the value shapes. - if not self.inputs: - is_build_called = True - if from_iterator: - cast_inputs = nest.map_structure(lambda v: v.shape, x) - elif training_utils.has_tensors(x): - cast_inputs = training_utils.cast_if_floating_dtype(x) - else: - cast_inputs = x - self._set_inputs(cast_inputs) - else: - dict_inputs = isinstance(self.inputs, dict) - if dict_inputs and context.executing_eagerly(): - # No support for graph functions when the model expects dictionary inputs - # (i.e. FeatureColumn-based models). - self.run_eagerly = True - - if y is not None: - if not self.optimizer: - raise RuntimeError('You must compile a model before ' - 'training/testing. ' - 'Use `model.compile(optimizer, loss)`.') - if not self._is_compiled: - # On-the-fly compilation of the model. - # We need to use `y` to set the model targets. - if training_utils.has_tensors(y): - y = training_utils.cast_if_floating_dtype(y) - if isinstance(y, (list, tuple)): - if not all(isinstance(v, np.ndarray) or - tensor_util.is_tensor(v) for v in y): - raise ValueError('Please provide as model targets either a single ' - 'array or a list of arrays. ' - 'You passed: y=' + str(y)) - all_inputs += list(y) - elif isinstance(y, dict): - raise ValueError('Please do not pass a dictionary as model targets.') - else: - if not isinstance(y, np.ndarray) and not tensor_util.is_tensor(y): - raise ValueError('Please provide as model targets either a single ' - 'array or a list of arrays. ' - 'You passed: y=' + str(y)) - all_inputs.append(y) - - # Typecheck that all inputs are *either* value *or* symbolic. - # TODO(fchollet): this check could be removed in Eager mode? - if any(tensor_util.is_tensor(v) for v in all_inputs): - if not all(tensor_util.is_tensor(v) for v in all_inputs): - raise ValueError('Do not pass inputs that mix Numpy arrays and ' - 'TensorFlow tensors. ' - 'You passed: x=' + str(x) + '; y=' + str(y)) + ValueError: in case of invalid arguments. - if self.run_eagerly or from_iterator: - target_tensors = None - else: - # Handle target tensors if any passed. - if not isinstance(y, (list, tuple)): - y = [y] - target_tensors = [v for v in y if _is_symbolic_tensor(v)] - is_compile_called = True - self.compile( - optimizer=self.optimizer, - loss=self.loss, - metrics=self._compile_metrics, - weighted_metrics=self._compile_weighted_metrics, - loss_weights=self.loss_weights, - target_tensors=target_tensors, - run_eagerly=self.run_eagerly) + Raises: + ValueError: In case the generator yields data in an invalid format. + """ + if self._distribution_strategy: + raise NotImplementedError('`evaluate_generator` is not supported for ' + 'models compiled with DistributionStrategy.') + return training_generator.evaluate_generator( + self, + generator, + steps=steps, + max_queue_size=max_queue_size, + workers=workers, + use_multiprocessing=use_multiprocessing, + verbose=verbose) - # In graph mode, if we had just set inputs and targets as symbolic tensors - # by invoking build and compile on the model respectively, we do not have to - # feed anything to the model. Model already has input and target data as - # part of the graph. - # Note: in this case, `any` and `all` are equivalent since we disallow - # mixed symbolic/value inputs. - if (not self.run_eagerly and is_build_called and is_compile_called and - not from_iterator and any(_is_symbolic_tensor(v) for v in all_inputs)): - return [], [], [] + def predict_generator(self, + generator, + steps=None, + max_queue_size=10, + workers=1, + use_multiprocessing=False, + verbose=0): + """Generates predictions for the input samples from a data generator. - # What follows is input validation and standardization to list format, - # in the case where all inputs are value arrays. + The generator should return the same kind of data as accepted by + `predict_on_batch`. - if self.run_eagerly: - # In eager mode, do not do shape validation - # since the network has no input nodes (placeholders) to be fed. - feed_input_names = self.input_names - feed_input_shapes = None - elif not self._is_graph_network: - # Case: symbolic-mode subclassed network. Do not do shape validation. - feed_input_names = self._feed_input_names - feed_input_shapes = None - else: - # Case: symbolic-mode graph network. - # In this case, we run extensive shape validation checks. - feed_input_names = self._feed_input_names - feed_input_shapes = self._feed_input_shapes + Arguments: + generator: Generator yielding batches of input samples + or an instance of `keras.utils.Sequence` object in order to + avoid duplicate data when using multiprocessing. + steps: Total number of steps (batches of samples) + to yield from `generator` before stopping. + Optional for `Sequence`: if unspecified, will use + the `len(generator)` as a number of steps. + max_queue_size: Maximum size for the generator queue. + workers: Integer. Maximum number of processes to spin up + when using process-based threading. + If unspecified, `workers` will default to 1. If 0, will + execute the generator on the main thread. + use_multiprocessing: Boolean. + If `True`, use process-based threading. + If unspecified, `use_multiprocessing` will default to `False`. + Note that because this implementation relies on multiprocessing, + you should not pass non-picklable arguments to the generator + as they can't be passed easily to children processes. + verbose: verbosity mode, 0 or 1. - # Standardize the inputs. - x = training_utils.standardize_input_data( - x, - feed_input_names, - feed_input_shapes, - check_batch_axis=False, # Don't enforce the batch size. - exception_prefix='input') + Returns: + Numpy array(s) of predictions. - if y is not None: - if not self._is_graph_network: - feed_output_names = self._feed_output_names - feed_output_shapes = None - # Sample weighting not supported in this case. - # TODO(fchollet): consider supporting it. - feed_sample_weight_modes = [None for _ in self.outputs] - else: - feed_output_names = self._feed_output_names - feed_sample_weight_modes = self._feed_sample_weight_modes - feed_output_shapes = [] - for output_shape, loss_fn in zip(self._feed_output_shapes, - self._feed_loss_fns): - if loss_fn is losses.sparse_categorical_crossentropy: - if K.image_data_format() == 'channels_first': - feed_output_shapes.append( - (output_shape[0], 1) + output_shape[2:]) - else: - feed_output_shapes.append(output_shape[:-1] + (1,)) - elif (not hasattr(loss_fn, '__name__') or - getattr(losses, loss_fn.__name__, None) is None): - # If `loss_fn` is not a function (e.g. callable class) - # or if it not in the `losses` module, then - # it is a user-defined loss and we make no assumptions - # about it. - feed_output_shapes.append(None) - else: - feed_output_shapes.append(output_shape) + Raises: + ValueError: In case the generator yields data in an invalid format. + """ + if self._distribution_strategy: + raise NotImplementedError('`predict_generator` is not supported for ' + 'models compiled with DistributionStrategy.') + return training_generator.predict_generator( + self, + generator, + steps=steps, + max_queue_size=max_queue_size, + workers=workers, + use_multiprocessing=use_multiprocessing, + verbose=verbose) - # Standardize the outputs. - y = training_utils.standardize_input_data( - y, - feed_output_names, - # Don't enforce target shapes to match output shapes. - # Precise checks will be run in `check_loss_and_target_compatibility`. - shapes=None, - check_batch_axis=False, # Don't enforce the batch size. - exception_prefix='target') + def _get_callback_model(self): + """Returns the Callback Model for this Model.""" - # Generate sample-wise weight values given the `sample_weight` and - # `class_weight` arguments. - sample_weights = training_utils.standardize_sample_weights( - sample_weight, feed_output_names) - class_weights = training_utils.standardize_class_weights( - class_weight, feed_output_names) - sample_weights = [ - training_utils.standardize_weights(ref, sw, cw, mode) - for (ref, sw, cw, mode) in zip(y, sample_weights, class_weights, - feed_sample_weight_modes) - ] - # Check that all arrays have the same length. - if not self._distribution_strategy: - training_utils.check_array_lengths(x, y, sample_weights) - if self._is_graph_network and not self.run_eagerly: - # Additional checks to avoid users mistakenly using improper loss fns. - training_utils.check_loss_and_target_compatibility( - y, self._feed_loss_fns, feed_output_shapes) - else: - y = [] - sample_weights = [] + if hasattr(self, '_replicated_model') and self._replicated_model: + # When using training_distributed, we set the callback model + # to an instance of the `DistributedModel` that we create in + # the `compile` call. The `DistributedModel` is initialized + # with the first replicated model. We need to set the callback + # model to a DistributedModel to allow us to override saving + # and loading weights when we checkpoint the model during training. + return self._replicated_model + if hasattr(self, 'callback_model') and self.callback_model: + return self.callback_model + return self - if self.stateful and batch_size: - # Check that for stateful networks, number of samples is a multiple - # of the static batch size. - if x[0].shape[0] % batch_size != 0: - raise ValueError('In a stateful network, ' - 'you should only pass inputs with ' - 'a number of samples that can be ' - 'divided by the batch size. Found: ' + - str(x[0].shape[0]) + ' samples') + def _make_callback_model(self, grouped_model): + first_replicated_model = self._distribution_strategy.unwrap( + grouped_model)[0] + # We initialize the callback model with the first replicated model. + self._replicated_model = DistributedCallbackModel(first_replicated_model) + self._replicated_model.set_original_model(self) - # If dictionary inputs were provided, we return a dictionary as well. - if dict_inputs: - x = dict(zip(feed_input_names, x)) - return x, y, sample_weights + def _validate_or_infer_batch_size(self, batch_size, steps, x): + """Validates that the `batch_size` provided is consistent with InputLayer. - @checkpointable.no_automatic_dependency_tracking - def _set_inputs(self, inputs, outputs=None, training=None): - """Set model's input and output specs based on the input data received. + It's possible that the user specified a static batch size in their + InputLayer. If so, this method checks the provided `batch_size` and `x` + arguments are consistent with this static batch size. Also, if + `batch_size` is `None`, this method will attempt to infer the batch size + from the static batch size of the InputLayer. - This is to be used for Model subclasses, which do not know at instantiation - time what their inputs look like. + Arguments: + batch_size: The batch_size provided as an argument to + fit/evaluate/predict. + steps: The steps provided as an argument to fit/evaluate/predict. + x: The data passed as `x` to fit/evaluate/predict. - Args: - inputs: Single array, or list of arrays. The arrays could be placeholders, - Numpy arrays, data tensors, or TensorShapes. - - if placeholders: the model is built on top of these placeholders, - and we expect Numpy data to be fed for them when calling `fit`/etc. - - if Numpy data or TensorShapes: we create placeholders matching the - TensorShapes or shapes of the Numpy arrays. We expect Numpy data to be - fed for these placeholders when calling `fit`/etc. - - if data tensors: the model is built on top of these tensors. - We do not expect any Numpy data to be provided when calling `fit`/etc. - outputs: None, a data tensor, or a list of tensors. If None, the - outputs will be determined by invoking `self.call()`, otherwise the - provided value will be used. - training: Boolean or None. Only relevant in symbolic mode. Specifies - whether to build the model's graph in inference mode (False), training - mode (True), or using the Keras learning phase (None). - Raises: - ValueError: If dict inputs are passed to a Sequential Model where the - first layer isn't FeatureLayer. + Returns: + The validated batch_size, auto-inferred from the first layer if not + provided. """ - if self.inputs: - raise ValueError('Model inputs are already set.') + layers = super(Model, self).layers # Avoids the override in Sequential. + if layers: + first_layer = layers[0] + static_batch_size = training_utils.get_static_batch_size(first_layer) + if static_batch_size is not None: - if self.__class__.__name__ == 'Sequential' and not self.built: - if tensor_util.is_tensor(inputs): - input_shape = (None,) + tuple(inputs.shape.as_list()[1:]) - elif isinstance(inputs, tensor_shape.TensorShape): - input_shape = (None,) + tuple(inputs.as_list()[1:]) - elif isinstance(inputs, dict): - # We assert that the first layer is a FeatureLayer. - if not training_utils.is_feature_layer(self.layers[0]): - raise ValueError('Passing a dictionary input to a Sequential Model ' - 'which doesn\'t have FeatureLayer as the first layer' - ' is an error.') - input_shape = (None,) - else: - input_shape = (None,) + tuple(inputs.shape[1:]) - self._build_input_shape = input_shape + # Check `batch_size` argument is consistent with InputLayer. + if batch_size is not None and batch_size != static_batch_size: + raise ValueError('The `batch_size` argument value {} is incompatible ' + 'with the specified batch size of your Input Layer: ' + '{}'.format(batch_size, static_batch_size)) - # On-the-fly setting of symbolic model inputs (either by using the tensor - # provided, or by creating a placeholder if Numpy data was provided). - model_inputs = training_utils.ModelInputs(inputs) - inputs = model_inputs.get_symbolic_inputs() - self.inputs = model_inputs.get_symbolic_inputs(return_single_as_list=True) - self.input_names = model_inputs.get_input_names() + # Check Dataset/Iterator batch size is consistent with InputLayer. + if isinstance(x, (dataset_ops.DatasetV2, iterator_ops.Iterator, + iterator_ops.EagerIterator)): + ds_batch_size = tensor_shape.as_dimension( + nest.flatten(x.output_shapes)[0][0]).value + if ds_batch_size is not None and ds_batch_size != static_batch_size: + raise ValueError('The batch output shape of your `Dataset` is {}, ' + 'which is incompatible with the specified batch ' + 'size of your Input Layer: {}'.format( + ds_batch_size, static_batch_size)) - self._feed_inputs = [] - self._feed_input_names = [] - self._feed_input_shapes = [] + # Set inferred batch size from the InputLayer. + if steps is None: + batch_size = static_batch_size - for k, v in model_inputs.as_dict(): - if K.is_placeholder(v): - self._feed_inputs.append(v) - self._feed_input_names.append(k) - self._feed_input_shapes.append(K.int_shape(v)) + if batch_size is None and steps is None: + # Backwards compatibility + batch_size = 32 + return batch_size + + @property + def _default_save_signature(self): + return training_utils.trace_model_call(self) - # TODO(fchollet): consider calling `_maybe_build` before calling the model. + def _set_sample_weight_attributes(self, sample_weight_mode, + skip_target_weighing_indices): + """Sets sample weight related attributes on the model.""" + sample_weights, sample_weight_modes = training_utils.prepare_sample_weights( + self.output_names, sample_weight_mode, skip_target_weighing_indices) + self.sample_weights = sample_weights + self.sample_weight_modes = sample_weight_modes + self._feed_sample_weight_modes = [ + sample_weight_modes[i] + for i in range(len(self.outputs)) + if i not in skip_target_weighing_indices + ] + self._feed_sample_weights = [ + sample_weights[i] + for i in range(len(sample_weights)) + if i not in skip_target_weighing_indices + ] - if outputs is None: - # Obtain symbolic outputs by calling the model. - with K.get_graph().as_default(): - if self._expects_training_arg: - outputs = self.call(inputs, training=training) - else: - outputs = self.call(inputs) + def _cache_output_metric_attributes(self, metrics, weighted_metrics): + """Caches metric name and function attributes for every model output.""" + output_shapes = [ + None if output is None else output.get_shape().as_list() + for output in self.outputs + ] + self._per_output_metrics = training_utils.collect_per_output_metric_info( + metrics, self.output_names, output_shapes, self.loss_functions) + self._per_output_weighted_metrics = \ + training_utils.collect_per_output_metric_info( + weighted_metrics, self.output_names, output_shapes, + self.loss_functions, self.sample_weights) - outputs = nest.flatten(outputs) - self.outputs = outputs - self.output_names = training_utils.generic_output_names(outputs) - self.built = True + def _add_unique_metric_name(self, metric_name, output_index): + """Makes the metric name unique and adds it to the model's metric name list. - def fit(self, - x=None, - y=None, - batch_size=None, - epochs=1, - verbose=1, - callbacks=None, - validation_split=0., - validation_data=None, - shuffle=True, - class_weight=None, - sample_weight=None, - initial_epoch=0, - steps_per_epoch=None, - validation_steps=None, - max_queue_size=10, - workers=1, - use_multiprocessing=False, - **kwargs): - """Trains the model for a fixed number of epochs (iterations on a dataset). + If there are multiple outputs for which the metrics are calculated, the + metric names have to be made unique by appending an integer. Arguments: - x: Input data. It could be: - - A Numpy array (or array-like), or a list of arrays - (in case the model has multiple inputs). - - A TensorFlow tensor, or a list of tensors - (in case the model has multiple inputs). - - A dict mapping input names to the corresponding array/tensors, - if the model has named inputs. - - A `tf.data` dataset or a dataset iterator. Should return a tuple - of either `(inputs, targets)` or - `(inputs, targets, sample_weights)`. - - A generator or `keras.utils.Sequence` returning `(inputs, targets)` - or `(inputs, targets, sample weights)`. - y: Target data. Like the input data `x`, - it could be either Numpy array(s) or TensorFlow tensor(s). - It should be consistent with `x` (you cannot have Numpy inputs and - tensor targets, or inversely). If `x` is a dataset, dataset - iterator, generator, or `keras.utils.Sequence` instance, `y` should - not be specified (since targets will be obtained from `x`). - batch_size: Integer or `None`. - Number of samples per gradient update. - If unspecified, `batch_size` will default to 32. - Do not specify the `batch_size` if your data is in the - form of symbolic tensors, dataset, dataset iterators, - generators, or `keras.utils.Sequence` instances (since they generate - batches). - epochs: Integer. Number of epochs to train the model. - An epoch is an iteration over the entire `x` and `y` - data provided. - Note that in conjunction with `initial_epoch`, - `epochs` is to be understood as "final epoch". - The model is not trained for a number of iterations - given by `epochs`, but merely until the epoch - of index `epochs` is reached. - verbose: Integer. 0, 1, or 2. Verbosity mode. - 0 = silent, 1 = progress bar, 2 = one line per epoch. - callbacks: List of `keras.callbacks.Callback` instances. - List of callbacks to apply during training. - See [callbacks](/api_docs/python/tf/keras/callbacks). - validation_split: Float between 0 and 1. - Fraction of the training data to be used as validation data. - The model will set apart this fraction of the training data, - will not train on it, and will evaluate - the loss and any model metrics - on this data at the end of each epoch. - The validation data is selected from the last samples - in the `x` and `y` data provided, before shuffling. This argument is - not supported when `x` is a dataset, dataset iterator, generator or - `keras.utils.Sequence` instance. - validation_data: Data on which to evaluate - the loss and any model metrics at the end of each epoch. - The model will not be trained on this data. - `validation_data` will override `validation_split`. - `validation_data` could be: - - tuple `(x_val, y_val)` of Numpy arrays or tensors - - tuple `(x_val, y_val, val_sample_weights)` of Numpy arrays - - dataset or a dataset iterator - For the first two cases, `batch_size` must be provided. - For the last case, `validation_steps` must be provided. - shuffle: Boolean (whether to shuffle the training data - before each epoch) or str (for 'batch'). - 'batch' is a special option for dealing with the - limitations of HDF5 data; it shuffles in batch-sized chunks. - Has no effect when `steps_per_epoch` is not `None`. - class_weight: Optional dictionary mapping class indices (integers) - to a weight (float) value, used for weighting the loss function - (during training only). - This can be useful to tell the model to - "pay more attention" to samples from - an under-represented class. - sample_weight: Optional Numpy array of weights for - the training samples, used for weighting the loss function - (during training only). You can either pass a flat (1D) - Numpy array with the same length as the input samples - (1:1 mapping between weights and samples), - or in the case of temporal data, - you can pass a 2D array with shape - `(samples, sequence_length)`, - to apply a different weight to every timestep of every sample. - In this case you should make sure to specify - `sample_weight_mode="temporal"` in `compile()`. This argument is not - supported when `x` is a dataset, dataset iterator, generator, or - `keras.utils.Sequence` instance, instead provide the sample_weights - as the third element of `x`. - initial_epoch: Integer. - Epoch at which to start training - (useful for resuming a previous training run). - steps_per_epoch: Integer or `None`. - Total number of steps (batches of samples) - before declaring one epoch finished and starting the - next epoch. When training with input tensors such as - TensorFlow data tensors, the default `None` is equal to - the number of samples in your dataset divided by - the batch size, or 1 if that cannot be determined. - validation_steps: Only relevant if `validation_data` is provided and - is a dataset or dataset iterator. Total number of steps (batches of - samples) to draw before stopping when performing validation - at the end of every epoch. - max_queue_size: Integer. Used for generator or `keras.utils.Sequence` - input only. Maximum size for the generator queue. - If unspecified, `max_queue_size` will default to 10. - workers: Integer. Used for generator or `keras.utils.Sequence` input - only. Maximum number of processes to spin up - when using process-based threading. If unspecified, `workers` - will default to 1. If 0, will execute the generator on the main - thread. - use_multiprocessing: Boolean. Used for generator or - `keras.utils.Sequence` input only. If `True`, use process-based - threading. If unspecified, `use_multiprocessing` will default to - `False`. Note that because this implementation relies on - multiprocessing, you should not pass non-picklable arguments to - the generator as they can't be passed easily to children processes. - **kwargs: Used for backwards compatibility. + metric_name: Metric name that corresponds to the metric specified by the + user. For example: 'acc'. + output_index: The index of the model output for which the metric name is + being added. Returns: - A `History` object. Its `History.history` attribute is - a record of training loss values and metrics values - at successive epochs, as well as validation loss values - and validation metrics values (if applicable). - - Raises: - RuntimeError: If the model was never compiled. - ValueError: In case of mismatch between the provided input data - and what the model expects. + string, name of the model's unique metric name """ - # TODO(fchollet): this method may be creating reference cycles, which would - # lead to accumulating garbage in memory when called in a loop. Investigate. - if data_utils.is_generator_or_sequence(x): - training_utils.check_generator_arguments(y, sample_weight) - return self.fit_generator( - x, - steps_per_epoch=steps_per_epoch, - epochs=epochs, - verbose=verbose, - callbacks=callbacks, - validation_data=validation_data, - validation_steps=validation_steps, - class_weight=class_weight, - max_queue_size=max_queue_size, - workers=workers, - use_multiprocessing=use_multiprocessing, - shuffle=shuffle, - initial_epoch=initial_epoch) + if len(self.output_names) > 1: + metric_name = '%s_%s' % (self.output_names[output_index], metric_name) + j = 1 + base_metric_name = metric_name + while metric_name in self._compile_metrics_names: + metric_name = '%s_%d' % (base_metric_name, j) + j += 1 - # Legacy support - if 'nb_epoch' in kwargs: - logging.warning( - 'The `nb_epoch` argument in `fit` ' - 'has been renamed `epochs`.') - epochs = kwargs.pop('nb_epoch') - if kwargs: - raise TypeError('Unrecognized keyword arguments: ' + str(kwargs)) + return metric_name - # Validate and standardize user data. - if self._distribution_strategy: - distributed_training_utils.validate_callbacks(callbacks, self.optimizer, - self._distribution_strategy) + @property + def _all_metrics_tensors(self): + """Returns the network's symbolic metric tensors.""" + metrics_tensors = {} + if self._is_compiled: + metrics_tensors.update(self._compile_metrics_tensors) + metrics_tensors.update(super(Model, self)._all_metrics_tensors) + return metrics_tensors - distributed_training_utils.validate_inputs( - x, y, self._distribution_strategy) + @property + def _all_stateful_metrics_tensors(self): + """Returns the network's symbolic metric tensors.""" + metrics_tensors = {} + if self._is_compiled: + metrics_tensors.update(self._compile_stateful_metrics_tensors) + metrics_tensors.update(super(Model, self)._all_metrics_tensors) + return metrics_tensors - first_x_value = nest.flatten(x)[0] - if isinstance(first_x_value, np.ndarray): - steps_per_epoch, batch_size = ( - distributed_training_utils.get_input_params( - self._distribution_strategy, first_x_value, steps_per_epoch, - batch_size, is_training=True)) + def _init_metric_attributes(self): + """Initialized model metric attributes.""" + # List of all metric names in the model. + self._compile_metrics_names = ['loss'] + # List of stateful metric functions. Used for resetting metric state during + # training/eval. + # This includes loss functions when there are multiple outputs. + self._compile_stateful_metric_functions = [] + # Dict of all aggregated metric result tensors. This includes aggregated + # loss result tensors when there are multiple outputs. + self._compile_stateful_metrics_tensors = {} + # Dict of all metric result tensors (aggregated or not - based on the + # values given in compile.). This includes aggregated loss result tensors + # when there are multiple outputs. + self._compile_metrics_tensors = {} - batch_size = self._validate_or_infer_batch_size(batch_size, steps_per_epoch, - x) + def _set_per_output_metric_attributes(self, metrics_dict, output_index): + """Sets the metric attributes on the model for the given output. - x, y, sample_weights = self._standardize_user_data( - x, - y, - sample_weight=sample_weight, - class_weight=class_weight, - batch_size=batch_size, - check_steps=True, - steps_name='steps_per_epoch', - steps=steps_per_epoch, - validation_split=validation_split, - shuffle=shuffle) + Arguments: + metrics_dict: A dict with metric names as keys and metric fns as values. + output_index: The index of the model output for which the metric + attributes are added. - # Prepare validation data. - if validation_data: - if (isinstance(validation_data, iterator_ops.Iterator) or - isinstance(validation_data, iterator_ops.EagerIterator) or - isinstance(validation_data, dataset_ops.DatasetV2)): - val_x = validation_data - val_y = None - val_sample_weight = None - elif len(validation_data) == 2: - val_x, val_y = validation_data # pylint: disable=unpacking-non-sequence - val_sample_weight = None - elif len(validation_data) == 3: - val_x, val_y, val_sample_weight = validation_data # pylint: disable=unpacking-non-sequence - else: - raise ValueError( - 'When passing a `validation_data` argument, ' - 'it must contain either 2 items (x_val, y_val), ' - 'or 3 items (x_val, y_val, val_sample_weights), ' - 'or alternatively it could be a dataset or a ' - 'dataset or a dataset iterator. ' - 'However we received `validation_data=%s`' % validation_data) + Returns: + Metrics dict updated with unique metric names as keys. + """ + updated_metrics_dict = collections.OrderedDict() + for metric_name, (metric_fn, stateful_metric_fn) in metrics_dict.items(): + metric_name = self._add_unique_metric_name(metric_name, output_index) + updated_metrics_dict[metric_name] = (metric_fn, stateful_metric_fn) + # Keep track of metric name, function and stateful function. + self._compile_metrics_names.append(metric_name) + self._compile_stateful_metric_functions.append(stateful_metric_fn) + return updated_metrics_dict - # Validate and standardize validation data. - if self._distribution_strategy: - distributed_training_utils.validate_inputs( - val_x, val_y, self._distribution_strategy) - first_valx_value = nest.flatten(val_x)[0] - if isinstance(first_valx_value, np.ndarray): - validation_steps, _ = distributed_training_utils.get_input_params( - self._distribution_strategy, first_valx_value, validation_steps, - batch_size) + def _set_metric_attributes(self, outputs, skip_target_indices=None): + """Sets the metric attributes on the model for all the model outputs.""" + skip_target_indices = skip_target_indices or [] + updated_per_output_metrics = [] + updated_per_output_weighted_metrics = [] + for i in range(len(outputs)): + if i in skip_target_indices: + updated_per_output_metrics.append(self._per_output_metrics[i]) + updated_per_output_weighted_metrics.append( + self._per_output_weighted_metrics[i]) + continue + updated_per_output_metrics.append( + self._set_per_output_metric_attributes(self._per_output_metrics[i], + i)) + updated_per_output_weighted_metrics.append( + self._set_per_output_metric_attributes( + self._per_output_weighted_metrics[i], i)) - val_x, val_y, val_sample_weights = self._standardize_user_data( - val_x, - val_y, - sample_weight=val_sample_weight, - batch_size=batch_size, - steps=validation_steps) + self._per_output_metrics = updated_per_output_metrics + self._per_output_weighted_metrics = updated_per_output_weighted_metrics - elif validation_split and 0. < validation_split < 1.: - if training_utils.has_symbolic_tensors(x): - raise ValueError('If your data is in the form of symbolic tensors, ' - 'you cannot use `validation_split`.') - if hasattr(x[0], 'shape'): - split_at = int(x[0].shape[0] * (1. - validation_split)) - else: - split_at = int(len(x[0]) * (1. - validation_split)) - x, val_x = (slice_arrays(x, 0, split_at), slice_arrays(x, split_at)) - y, val_y = (slice_arrays(y, 0, split_at), slice_arrays(y, split_at)) - sample_weights, val_sample_weights = (slice_arrays( - sample_weights, 0, split_at), slice_arrays(sample_weights, split_at)) - elif validation_steps: - val_x = [] - val_y = [] - val_sample_weights = [] - else: - val_x = None - val_y = None - val_sample_weights = None + def _handle_per_output_metrics(self, + metrics_dict, + y_true, + y_pred, + mask, + weights=None, + return_stateful_result=True): + """Calls metric functions for a single output. - if (self.run_eagerly or (isinstance(x, iterator_ops.EagerIterator) and - not self._distribution_strategy)): - return training_generator.fit_generator( - self, (x, y, sample_weights), - steps_per_epoch=steps_per_epoch, - batch_size=batch_size, - epochs=epochs, - shuffle=shuffle, - verbose=verbose, - callbacks=callbacks, - validation_data=validation_data, - validation_steps=validation_steps, - workers=0, - initial_epoch=initial_epoch) - elif distributed_training_utils.is_tpu_strategy( - self._distribution_strategy): - return training_distributed.experimental_fit_loop( - self, - x, - epochs=epochs, - verbose=verbose, - callbacks=callbacks, - val_iterator=val_x, - initial_epoch=initial_epoch, - steps_per_epoch=steps_per_epoch, - validation_steps=validation_steps) - else: - return training_arrays.fit_loop( - self, - x, - y, - sample_weights=sample_weights, - batch_size=batch_size, - epochs=epochs, - verbose=verbose, - callbacks=callbacks, - val_inputs=val_x, - val_targets=val_y, - val_sample_weights=val_sample_weights, - shuffle=shuffle, - initial_epoch=initial_epoch, - steps_per_epoch=steps_per_epoch, - validation_steps=validation_steps) + Arguments: + metrics_dict: A dict with metric names as keys and metric fns as values. + y_true: Target output. + y_pred: Predicted output. + mask: Computed mask value for the current output. + weights: Weights to be applied on the current output. + return_stateful_result: Boolean, indicates whether the stateful + (aggregated)/stateless metric result should be returned. - def evaluate(self, - x=None, - y=None, - batch_size=None, - verbose=1, - sample_weight=None, - steps=None, - max_queue_size=10, - workers=1, - use_multiprocessing=False): - """Returns the loss value & metrics values for the model in test mode. + Returns: + A list of metric result tensors. + """ + metric_results = [] + for metric_name, (metric_fn, stateful_fn) in metrics_dict.items(): + with K.name_scope(metric_name): - Computation is done in batches. + def _call_stateful_fn(fn): + return training_utils.call_metric_function( + fn, y_true, y_pred, weights=weights, mask=mask) - Arguments: - x: Input data. It could be: - - A Numpy array (or array-like), or a list of arrays - (in case the model has multiple inputs). - - A TensorFlow tensor, or a list of tensors - (in case the model has multiple inputs). - - A dict mapping input names to the corresponding array/tensors, - if the model has named inputs. - - A `tf.data` dataset or a dataset iterator. - - A generator or `keras.utils.Sequence` instance. - y: Target data. Like the input data `x`, - it could be either Numpy array(s) or TensorFlow tensor(s). - It should be consistent with `x` (you cannot have Numpy inputs and - tensor targets, or inversely). - If `x` is a dataset, dataset iterator, generator or - `keras.utils.Sequence` instance, `y` should not be specified (since - targets will be obtained from the iterator/dataset). - batch_size: Integer or `None`. - Number of samples per gradient update. - If unspecified, `batch_size` will default to 32. - Do not specify the `batch_size` is your data is in the - form of symbolic tensors, dataset, dataset iterators, - generators, or `keras.utils.Sequence` instances (since they generate - batches). - verbose: 0 or 1. Verbosity mode. - 0 = silent, 1 = progress bar. - sample_weight: Optional Numpy array of weights for - the test samples, used for weighting the loss function. - You can either pass a flat (1D) - Numpy array with the same length as the input samples - (1:1 mapping between weights and samples), - or in the case of temporal data, - you can pass a 2D array with shape - `(samples, sequence_length)`, - to apply a different weight to every timestep of every sample. - In this case you should make sure to specify - `sample_weight_mode="temporal"` in `compile()`. This argument is not - supported when `x` is a dataset or a dataset iterator, instead pass - sample weights as the third element of `x`. - steps: Integer or `None`. - Total number of steps (batches of samples) - before declaring the evaluation round finished. - Ignored with the default value of `None`. - max_queue_size: Integer. Used for generator or `keras.utils.Sequence` - input only. Maximum size for the generator queue. - If unspecified, `max_queue_size` will default to 10. - workers: Integer. Used for generator or `keras.utils.Sequence` input - only. Maximum number of processes to spin up when using - process-based threading. If unspecified, `workers` will default - to 1. If 0, will execute the generator on the main thread. - use_multiprocessing: Boolean. Used for generator or - `keras.utils.Sequence` input only. If `True`, use process-based - threading. If unspecified, `use_multiprocessing` will default to - `False`. Note that because this implementation relies on - multiprocessing, you should not pass non-picklable arguments to - the generator as they can't be passed easily to children processes. + def _call_stateless_fn(fn): + weighted_metric_fn = training_utils.weighted_masked_objective(fn) + return weighted_metric_fn(y_true, y_pred, weights=weights, mask=mask) + + def _track_metric_tensors(name, stateless_result, stateful_result): + self._compile_metrics_tensors[name] = stateless_result + self._compile_stateful_metrics_tensors[name] = stateful_result + + if isinstance(metric_fn, metrics_module.Metric): + # If the given metric fn is stateful, call the fn and return result. + metric_result = _call_stateful_fn(metric_fn) + metric_results.append(metric_result) + if not self.run_eagerly: + _track_metric_tensors(metric_name, metric_result, metric_result) + elif self.run_eagerly: + # In eager mode, if the given metric fn is not stateful, we invoke the + # given fn or its stateful version based on the given flag. + if return_stateful_result: + metric_result = _call_stateful_fn(stateful_fn) + else: + metric_result = _call_stateless_fn(metric_fn) + metric_results.append(metric_result) + else: + # In graph mode, we build the sub-graph for both the stateful and the + # stateless fns. + stateful_metric_result = _call_stateful_fn(stateful_fn) + metric_result = _call_stateless_fn(metric_fn) + _track_metric_tensors(metric_name, metric_result, + stateful_metric_result) + + return metric_results + + def _handle_metrics(self, + outputs, + skip_target_indices=None, + targets=None, + sample_weights=None, + masks=None, + return_stateful_result=True): + """Handles calling metric functions. + + Arguments: + outputs: List of outputs (predictions). + skip_target_indices: Optional. List of target ids to skip. + targets: List of targets. + sample_weights: Optional list of sample weight arrays. + masks: List of computed output mask values. + return_stateful_result: Boolean, indicates whether the stateful + (aggregated)/stateless metric result should be returned. Returns: - Scalar test loss (if the model has a single output and no metrics) - or list of scalars (if the model has multiple outputs - and/or metrics). The attribute `model.metrics_names` will give you - the display labels for the scalar outputs. + A list of metric result tensors. + """ + skip_target_indices = skip_target_indices or [] + metric_results = [] + with K.name_scope('metrics'): + # Invoke all metrics added using `compile`. + for i in range(len(outputs)): + if i in skip_target_indices: + continue + output = outputs[i] if outputs else None + target = targets[i] if targets else None + output_mask = masks[i] if masks else None + metric_results.extend( + self._handle_per_output_metrics( + self._per_output_metrics[i], + target, + output, + output_mask, + return_stateful_result=return_stateful_result)) + metric_results.extend( + self._handle_per_output_metrics( + self._per_output_weighted_metrics[i], + target, + output, + output_mask, + weights=sample_weights[i], + return_stateful_result=return_stateful_result)) - Raises: - ValueError: in case of invalid arguments. + # Add metric results from the `add_metric` metrics in eager mode. + if context.executing_eagerly(): + for m in self.metrics: + if m not in self._compile_stateful_metric_functions: + metric_results.append(m.result()) + return metric_results + + def _check_trainable_weights_consistency(self): + """Check trainable weights count consistency. + + This will raise a warning if `trainable_weights` and + `_collected_trainable_weights` are inconsistent (i.e. have different + number of parameters). + Inconsistency will typically arise when one modifies `model.trainable` + without calling `model.compile` again. """ - if data_utils.is_generator_or_sequence(x): - training_utils.check_generator_arguments(y, sample_weight) - return self.evaluate_generator( - x, - steps=steps, - verbose=verbose, - max_queue_size=max_queue_size, - workers=workers, - use_multiprocessing=use_multiprocessing) - # Validate and standardize user data. - if self._distribution_strategy: - distributed_training_utils.validate_inputs( - x, y, self._distribution_strategy) - first_x_value = nest.flatten(x)[0] - if isinstance(first_x_value, np.ndarray): - steps, batch_size = distributed_training_utils.get_input_params( - self._distribution_strategy, first_x_value, steps, batch_size) + if not hasattr(self, '_collected_trainable_weights'): + return - batch_size = self._validate_or_infer_batch_size(batch_size, steps, x) + if len(self.trainable_weights) != len(self._collected_trainable_weights): + logging.log_first_n( + logging.WARN, 'Discrepancy between trainable weights and collected' + ' trainable weights, did you set `model.trainable`' + ' without calling `model.compile` after ?', 1) - x, y, sample_weights = self._standardize_user_data( - x, - y, - sample_weight=sample_weight, - batch_size=batch_size, - check_steps=True, - steps_name='steps', - steps=steps) + def _make_train_function_helper(self, fn_name, outputs, metric_updates=None): + if not hasattr(self, fn_name): + raise RuntimeError('You must compile your model before using it.') + self._check_trainable_weights_consistency() + if getattr(self, fn_name) is None: + inputs = (self._feed_inputs + + self._feed_targets + + self._feed_sample_weights) + if not isinstance(K.symbolic_learning_phase(), int): + inputs += [K.symbolic_learning_phase()] - if (self.run_eagerly or (isinstance(x, iterator_ops.EagerIterator) and - not self._distribution_strategy)): - return training_generator.evaluate_generator( - self, (x, y, sample_weights), - steps=steps, - batch_size=batch_size, - verbose=verbose, - workers=0) - elif distributed_training_utils.is_tpu_strategy( - self._distribution_strategy): - return training_distributed.experimental_test_loop( - self, iterator=x, verbose=verbose, steps=steps) - else: - return training_arrays.test_loop( - self, - inputs=x, - targets=y, - sample_weights=sample_weights, - batch_size=batch_size, - verbose=verbose, - steps=steps) + with K.get_graph().as_default(): + with K.name_scope('training'): + with K.name_scope(self.optimizer.__class__.__name__): + # Training updates + updates = self.optimizer.get_updates( + params=self._collected_trainable_weights, loss=self.total_loss) + # Unconditional updates + updates += self.get_updates_for(None) + # Conditional updates relevant to this model + updates += self.get_updates_for(self.inputs) + # Add stateful metrics updates. + if metric_updates is not None: + updates += metric_updates - def predict(self, - x, - batch_size=None, - verbose=0, - steps=None, - max_queue_size=10, - workers=1, - use_multiprocessing=False): - """Generates output predictions for the input samples. + with K.name_scope('training'): + # Gets loss and metrics. Updates weights at each call. + fn = K.function( + inputs, + outputs, + updates=updates, + name='train_function', + **self._function_kwargs) + setattr(self, fn_name, fn) - Computation is done in batches. + def _make_train_function(self): + metrics_tensors = [ + self._all_metrics_tensors[m] for m in self.metrics_names[1:] + ] + self._make_train_function_helper('train_function', + [self.total_loss] + metrics_tensors) - Arguments: - x: Input samples. It could be: - - A Numpy array (or array-like), or a list of arrays - (in case the model has multiple inputs). - - A TensorFlow tensor, or a list of tensors - (in case the model has multiple inputs). - - A `tf.data` dataset or a dataset iterator. - - A generator or `keras.utils.Sequence` instance. - batch_size: Integer or `None`. - Number of samples per gradient update. - If unspecified, `batch_size` will default to 32. - Do not specify the `batch_size` is your data is in the - form of symbolic tensors, dataset, dataset iterators, - generators, or `keras.utils.Sequence` instances (since they generate - batches). - verbose: Verbosity mode, 0 or 1. - steps: Total number of steps (batches of samples) - before declaring the prediction round finished. - Ignored with the default value of `None`. - max_queue_size: Integer. Used for generator or `keras.utils.Sequence` - input only. Maximum size for the generator queue. - If unspecified, `max_queue_size` will default to 10. - workers: Integer. Used for generator or `keras.utils.Sequence` input - only. Maximum number of processes to spin up when using - process-based threading. If unspecified, `workers` will default - to 1. If 0, will execute the generator on the main thread. - use_multiprocessing: Boolean. Used for generator or - `keras.utils.Sequence` input only. If `True`, use process-based - threading. If unspecified, `use_multiprocessing` will default to - `False`. Note that because this implementation relies on - multiprocessing, you should not pass non-picklable arguments to - the generator as they can't be passed easily to children processes. + def _make_fit_function(self): + metrics_tensors = [ + self._all_stateful_metrics_tensors[m] for m in self.metrics_names[1:] + ] + self._make_train_function_helper( + '_fit_function', [self.total_loss] + metrics_tensors) + + def _make_test_function_helper(self, fn_name, outputs, metric_updates=None): + if not hasattr(self, fn_name): + raise RuntimeError('You must compile your model before using it.') + if getattr(self, fn_name) is None: + inputs = (self._feed_inputs + + self._feed_targets + + self._feed_sample_weights) + with K.name_scope('evaluation'): + updates = self.state_updates + # Add stateful metrics updates. + if metric_updates is not None: + updates += metric_updates + # Return loss and metrics, no gradient updates. + # Does update the network states. + fn = K.function( + inputs, + outputs, + updates=updates, + name='test_function', + **self._function_kwargs) + setattr(self, fn_name, fn) - Returns: - Numpy array(s) of predictions. + def _make_test_function(self): + metrics_tensors = [ + self._all_metrics_tensors[m] for m in self.metrics_names[1:] + ] + self._make_test_function_helper('test_function', + [self.total_loss] + metrics_tensors) - Raises: - ValueError: In case of mismatch between the provided - input data and the model's expectations, - or in case a stateful model receives a number of samples - that is not a multiple of the batch size. - """ - if data_utils.is_generator_or_sequence(x): - return self.predict_generator( - x, - steps=steps, - verbose=verbose, - max_queue_size=max_queue_size, - workers=workers, - use_multiprocessing=use_multiprocessing) - if self._distribution_strategy: - distributed_training_utils.validate_inputs( - x, None, self._distribution_strategy) - first_x_value = nest.flatten(x)[0] - if isinstance(first_x_value, np.ndarray): - steps, batch_size = distributed_training_utils.get_input_params( - self._distribution_strategy, first_x_value, steps, batch_size) + def _make_eval_function(self): + metrics_tensors = [ + self._all_stateful_metrics_tensors[m] for m in self.metrics_names[1:] + ] + self._make_test_function_helper( + '_eval_function', [self.total_loss] + metrics_tensors) - batch_size = self._validate_or_infer_batch_size(batch_size, steps, x) + def _make_predict_function(self): + if not hasattr(self, 'predict_function'): + self.predict_function = None + if self.predict_function is None: + inputs = self._feed_inputs + # Gets network outputs. Does not update weights. + # Does update the network states. + kwargs = getattr(self, '_function_kwargs', {}) + with K.name_scope('predict'): + self.predict_function = K.function( + inputs, + self.outputs, + updates=self.state_updates, + name='predict_function', + **kwargs) - # Validate and standardize user data. - if self._distribution_strategy: - x, _, _ = self._standardize_user_data( - x, check_steps=True, steps_name='steps', steps=steps, - batch_size=batch_size) - else: - # TODO(anjalisridhar): We don't pass batch_size here for some reason. This - # means we need to special case distribution strategy which needs the - # batch size. - x, _, _ = self._standardize_user_data( - x, check_steps=True, steps_name='steps', steps=steps) + def _make_execution_function(self, mode): + if mode == 'train': + self._make_fit_function() + return self._fit_function + if mode == 'test': + self._make_eval_function() + return self._eval_function + if mode == 'predict': + self._make_predict_function() + return self.predict_function - if (self.run_eagerly or (isinstance(x, iterator_ops.EagerIterator) and - not self._distribution_strategy)): - return training_generator.predict_generator( - self, - x, - steps=steps, - batch_size=batch_size, - verbose=verbose, - workers=0) - elif distributed_training_utils.is_tpu_strategy( - self._distribution_strategy): - return training_distributed.experimental_predict_loop( - self, x, verbose=verbose, steps=steps) - else: - return training_arrays.predict_loop( - self, x, batch_size=batch_size, verbose=verbose, steps=steps) + def _get_iterator_get_next_tensors(self, iterator): + get_next_op = self._iterator_get_next.get(iterator, None) + if get_next_op is None: + get_next_op = iterator.get_next() + self._iterator_get_next[iterator] = get_next_op + return get_next_op - def reset_metrics(self): - """Resets the state of metrics.""" - if hasattr(self, 'metrics'): - for m in self.metrics: - m.reset_states() - if self._distribution_strategy: - training_distributed._reset_metrics(self) # pylint: disable=protected-access + def _distribution_standardize_user_data(self, + x, + y=None, + sample_weight=None, + class_weight=None, + batch_size=None, + check_steps=False, + steps_name='steps', + steps=None, + validation_split=0, + shuffle=False): + """Runs validation checks on input and target data passed by the user. - def train_on_batch(self, - x, - y=None, - sample_weight=None, - class_weight=None, - reset_metrics=True): - """Runs a single gradient update on a single batch of data. + This is called when using DistributionStrategy to train, evaluate or serve + the model. - Arguments: - x: Input data. It could be: - - A Numpy array (or array-like), or a list of arrays - (in case the model has multiple inputs). - - A TensorFlow tensor, or a list of tensors - (in case the model has multiple inputs). - - A dict mapping input names to the corresponding array/tensors, - if the model has named inputs. - - A `tf.data` dataset or a dataset iterator. - y: Target data. Like the input data `x`, it could be either Numpy - array(s) or TensorFlow tensor(s). It should be consistent with `x` - (you cannot have Numpy inputs and tensor targets, or inversely). If - `x` is a dataset or a dataset iterator, `y` should not be specified - (since targets will be obtained from the iterator). - sample_weight: Optional array of the same length as x, containing - weights to apply to the model's loss for each sample. In the case of - temporal data, you can pass a 2D array with shape (samples, - sequence_length), to apply a different weight to every timestep of - every sample. In this case you should make sure to specify - sample_weight_mode="temporal" in compile(). This argument is not - supported when `x` is a dataset or a dataset iterator. - class_weight: Optional dictionary mapping class indices (integers) to a - weight (float) to apply to the model's loss for the samples from this - class during training. This can be useful to tell the model to "pay - more attention" to samples from an under-represented class. - reset_metrics: If `True`, the metrics returned will be only for this - batch. If `False`, the metrics will be statefully accumulated across - batches. + Args: + x: Input data. A numpy array or `tf.data` dataset. + y: Target data. A numpy array or None if x is a `tf.data` dataset. + sample_weight: An optional sample-weight array passed by the user to + weight the importance of each sample in `x`. + class_weight: An optional class-weight array by the user to + weight the importance of samples in `x` based on the class they belong + to, as conveyed by `y`. + batch_size: Integer batch size. If provided, it is used to run additional + validation checks on stateful models. + check_steps: boolean, True if we want to check for validity of `steps` and + False, otherwise. + steps_name: The public API's parameter name for `steps`. + steps: Integer or `None`. Total number of steps (batches of samples) to + execute. + validation_split: Float between 0 and 1. + Fraction of the training data to be used as validation data. + shuffle: Boolean whether to shuffle the training data before each epoch. Returns: - Scalar training loss - (if the model has a single output and no metrics) - or list of scalars (if the model has multiple outputs - and/or metrics). The attribute `model.metrics_names` will give you - the display labels for the scalar outputs. + Iterator for reading the dataset `x`. Raises: - ValueError: In case of invalid user-provided arguments. + ValueError: In case of invalid user-provided data. + RuntimeError: If the model was never compiled. """ - if self._distribution_strategy: - raise NotImplementedError('`train_on_batch` is not supported for models ' - 'compiled with DistributionStrategy.') - # Validate and standardize user data. - x, y, sample_weights = self._standardize_user_data( - x, y, sample_weight=sample_weight, class_weight=class_weight) + if class_weight: + raise NotImplementedError('`class_weight` is currently not supported ' + 'when using DistributionStrategy.') - if self.run_eagerly: - outputs = training_eager.train_on_batch( - self, x, y, sample_weights=sample_weights) - else: - if not isinstance(K.symbolic_learning_phase(), int): - ins = x + y + sample_weights + [True] - else: - ins = x + y + sample_weights + if (sample_weight is not None and sample_weight.all() and + distributed_training_utils.is_tpu_strategy( + self._distribution_strategy)): + raise NotImplementedError('`sample_weight` is currently not supported ' + 'when using TPUStrategy.') - if reset_metrics: - self._make_train_function() - outputs = self.train_function(ins) # pylint: disable=not-callable - else: - self._make_fit_function() - outputs = self._fit_function(ins) # pylint: disable=not-callable + # Validates `steps` argument right at the beginning since we use it to + # construct the dataset object. + # TODO(anjalisridhar): Remove this check once we refactor the + # _standardize_user_data code path. This check is already present elsewhere + # in the codebase. + if check_steps and isinstance(x, dataset_ops.DatasetV2) and steps is None: + raise ValueError('When using Datasets as input, ' + 'you should specify the `{steps_name}` argument.' + .format(steps_name=steps_name)) - if reset_metrics: - self.reset_metrics() + first_x_value = nest.flatten(x)[0] + if isinstance(first_x_value, np.ndarray): + # We need to use the drop_remainder argument to allow for a static + # input shape which is required for TPUs. + drop_remainder = self._distribution_strategy.require_static_shapes + if y is not None: + var_x = distributed_training_utils.get_var_for_numpy( + self._distribution_strategy, x) + var_y = distributed_training_utils.get_var_for_numpy( + self._distribution_strategy, y) + if sample_weight is not None: + var_sample_weights = distributed_training_utils.get_var_for_numpy( + self._distribution_strategy, sample_weight) - if len(outputs) == 1: - return outputs[0] - return outputs + x = dataset_ops.Dataset.from_tensor_slices((var_x, var_y, + var_sample_weights)) + else: + x = dataset_ops.Dataset.from_tensor_slices((var_x, var_y)) - def test_on_batch(self, x, y=None, sample_weight=None, reset_metrics=True): - """Test the model on a single batch of samples. + x = dataset_ops.Dataset.from_tensor_slices((var_x, var_y)) + if shuffle: + # 1024 is a good buffer size since it is much larger than the average + # batch size provided by the user and provides sufficient randomness. + # One thing to keep in mind is the memory usage based on the size of + # each sample. + x = x.shuffle(1024) + x = x.repeat() + x = x.batch(batch_size, drop_remainder=drop_remainder) + y = None + sample_weight = None + else: + # This case is for the predict call where the dataset only contains + # inputs and no targets, i.e. it does not return a tuple + var_x = distributed_training_utils.get_var_for_numpy( + self._distribution_strategy, x) + x = dataset_ops.Dataset.from_tensor_slices(var_x) + x = x.batch(batch_size, drop_remainder=drop_remainder) - Arguments: - x: Input data. It could be: - - A Numpy array (or array-like), or a list of arrays - (in case the model has multiple inputs). - - A TensorFlow tensor, or a list of tensors - (in case the model has multiple inputs). - - A dict mapping input names to the corresponding array/tensors, - if the model has named inputs. - - A `tf.data` dataset or a dataset iterator. - y: Target data. Like the input data `x`, - it could be either Numpy array(s) or TensorFlow tensor(s). - It should be consistent with `x` (you cannot have Numpy inputs and - tensor targets, or inversely). If `x` is a dataset or a - dataset iterator, `y` should not be specified - (since targets will be obtained from the iterator). - sample_weight: Optional array of the same length as x, containing - weights to apply to the model's loss for each sample. - In the case of temporal data, you can pass a 2D array - with shape (samples, sequence_length), - to apply a different weight to every timestep of every sample. - In this case you should make sure to specify - sample_weight_mode="temporal" in compile(). This argument is not - supported when `x` is a dataset or a dataset iterator. - reset_metrics: If `True`, the metrics returned will be only for this - batch. If `False`, the metrics will be statefully accumulated across - batches. + assert isinstance(x, dataset_ops.DatasetV2) - Returns: - Scalar test loss (if the model has a single output and no metrics) - or list of scalars (if the model has multiple outputs - and/or metrics). The attribute `model.metrics_names` will give you - the display labels for the scalar outputs. + with self._distribution_strategy.scope(): + iterator = self._distribution_strategy.make_dataset_iterator(x) + init_op = iterator.initialize() + if not context.executing_eagerly(): + K.get_session().run(init_op) - Raises: - ValueError: In case of invalid user-provided arguments. - """ - if self._distribution_strategy: - raise NotImplementedError('`test_on_batch` is not supported for models ' - 'compiled with DistributionStrategy.') - # Validate and standardize user data. - x, y, sample_weights = self._standardize_user_data( - x, y, sample_weight=sample_weight) + training_utils.validate_iterator_input(x, y, sample_weight, + validation_split) + return iterator - if self.run_eagerly: - outputs = training_eager.test_on_batch( - self, x, y, sample_weights=sample_weights) - else: - inputs = x + y + sample_weights - if reset_metrics: - self._make_test_function() - outputs = self.test_function(inputs) # pylint: disable=not-callable - else: - self._make_eval_function() - outputs = self._eval_function(inputs) # pylint: disable=not-callable + def _standardize_user_data(self, + x, + y=None, + sample_weight=None, + class_weight=None, + batch_size=None, + check_steps=False, + steps_name='steps', + steps=None, + validation_split=0, + shuffle=False): + """Runs validation checks on input and target data passed by the user. - if reset_metrics: - self.reset_metrics() + Also standardizes the data to lists of arrays, in order. - if len(outputs) == 1: - return outputs[0] - return outputs + Also builds and compiles the model on the fly if it is a subclassed model + that has never been called before (and thus has no inputs/outputs). - def predict_on_batch(self, x): - """Returns predictions for a single batch of samples. + This is a purely internal method, subject to refactoring at any time. - Arguments: - x: Input data. It could be: - - A Numpy array (or array-like), or a list of arrays - (in case the model has multiple inputs). - - A TensorFlow tensor, or a list of tensors - (in case the model has multiple inputs). - - A `tf.data` dataset or a dataset iterator. + Args: + x: Input data. It could be: + - A Numpy array (or array-like), or a list of arrays + (in case the model has multiple inputs). + - A TensorFlow tensor, or a list of tensors + (in case the model has multiple inputs). + - A dict mapping input names to the corresponding array/tensors, + if the model has named inputs. + - A `tf.data` dataset or a dataset iterator. + y: Target data. Like the input data `x`, + it could be either Numpy array(s) or TensorFlow tensor(s). + It should be consistent with `x` (you cannot have Numpy inputs and + tensor targets, or inversely). If `x` is a dataset or a + dataset iterator, `y` should not be specified + (since targets will be obtained from the iterator). + sample_weight: An optional sample-weight array passed by the user to + weight the importance of each sample in `x`. + class_weight: An optional class-weight array by the user to + weight the importance of samples in `x` based on the class they belong + to, as conveyed by `y`. + batch_size: Integer batch size. If provided, it is used to run additional + validation checks on stateful models. + check_steps: boolean, True if we want to check for validity of `steps` and + False, otherwise. For example, when we are standardizing one batch of + data for train_on_batch/predict_on_batch/test_on_batch APIs, `steps` + value is not required and we should not check for its validity in these + cases. + steps_name: The public API's parameter name for `steps`. + steps: Integer or `None`. Total number of steps (batches of samples) to + execute. + validation_split: Float between 0 and 1. + Fraction of the training data to be used as validation data. + shuffle: Boolean whether to shuffle the training data before each epoch. Returns: - Numpy array(s) of predictions. + A tuple of 3: inputs (arrays or dicts, depending on whether `x` was a dict + or not), target arrays, sample-weight arrays. + If the model's input and targets are symbolic, these lists are empty + (since the model takes no user-provided data, instead the data comes + from the symbolic inputs/targets). Raises: - ValueError: In case of mismatch between given number of inputs and - expectations of the model. + ValueError: In case of invalid user-provided data. + RuntimeError: If the model was never compiled. """ if self._distribution_strategy: - raise NotImplementedError('`predict_on_batch` is not supported for ' - 'models compiled with DistributionStrategy.') - # Validate and standardize user data. - inputs, _, _ = self._standardize_user_data(x) - if self.run_eagerly: - if (isinstance(inputs, iterator_ops.EagerIterator) or - (isinstance(inputs, dataset_ops.DatasetV2))): - inputs = training_utils.cast_if_floating_dtype(inputs) - elif isinstance(inputs, collections.Sequence): - inputs = [ - ops.convert_to_tensor(val, dtype=K.floatx()) for val in inputs] - return self(inputs) # pylint: disable=not-callable - - self._make_predict_function() - outputs = self.predict_function(inputs) - - if len(outputs) == 1: - return outputs[0] - return outputs - - def fit_generator(self, - generator, - steps_per_epoch=None, - epochs=1, - verbose=1, - callbacks=None, - validation_data=None, - validation_steps=None, - class_weight=None, - max_queue_size=10, - workers=1, - use_multiprocessing=False, - shuffle=True, - initial_epoch=0): - """Fits the model on data yielded batch-by-batch by a Python generator. - - The generator is run in parallel to the model, for efficiency. - For instance, this allows you to do real-time data augmentation - on images on CPU in parallel to training your model on GPU. - - The use of `keras.utils.Sequence` guarantees the ordering - and guarantees the single use of every input per epoch when - using `use_multiprocessing=True`. + iterator = self._distribution_standardize_user_data( + x, + y, + sample_weight=sample_weight, + class_weight=class_weight, + batch_size=batch_size, + check_steps=check_steps, + steps_name=steps_name, + steps=steps, + validation_split=validation_split, + shuffle=shuffle) + return iterator, None, None - Arguments: - generator: A generator or an instance of `Sequence` - (`keras.utils.Sequence`) - object in order to avoid duplicate data - when using multiprocessing. - The output of the generator must be either - - a tuple `(inputs, targets)` - - a tuple `(inputs, targets, sample_weights)`. - This tuple (a single output of the generator) makes a single batch. - Therefore, all arrays in this tuple must have the same length (equal - to the size of this batch). Different batches may have different - sizes. - For example, the last batch of the epoch is commonly smaller than - the - others, if the size of the dataset is not divisible by the batch - size. - The generator is expected to loop over its data - indefinitely. An epoch finishes when `steps_per_epoch` - batches have been seen by the model. - steps_per_epoch: Total number of steps (batches of samples) - to yield from `generator` before declaring one epoch - finished and starting the next epoch. It should typically - be equal to the number of samples of your dataset - divided by the batch size. - Optional for `Sequence`: if unspecified, will use - the `len(generator)` as a number of steps. - epochs: Integer, total number of iterations on the data. - verbose: Verbosity mode, 0, 1, or 2. - callbacks: List of callbacks to be called during training. - validation_data: This can be either - - a generator for the validation data - - a tuple (inputs, targets) - - a tuple (inputs, targets, sample_weights). - validation_steps: Only relevant if `validation_data` - is a generator. Total number of steps (batches of samples) - to yield from `generator` before stopping. - Optional for `Sequence`: if unspecified, will use - the `len(validation_data)` as a number of steps. - class_weight: Dictionary mapping class indices to a weight - for the class. - max_queue_size: Integer. Maximum size for the generator queue. - If unspecified, `max_queue_size` will default to 10. - workers: Integer. Maximum number of processes to spin up - when using process-based threading. - If unspecified, `workers` will default to 1. If 0, will - execute the generator on the main thread. - use_multiprocessing: Boolean. - If `True`, use process-based threading. - If unspecified, `use_multiprocessing` will default to `False`. - Note that because this implementation relies on multiprocessing, - you should not pass non-picklable arguments to the generator - as they can't be passed easily to children processes. - shuffle: Boolean. Whether to shuffle the order of the batches at - the beginning of each epoch. Only used with instances - of `Sequence` (`keras.utils.Sequence`). - Has no effect when `steps_per_epoch` is not `None`. - initial_epoch: Epoch at which to start training - (useful for resuming a previous training run) + if isinstance(x, dataset_ops.DatasetV2): + if context.executing_eagerly(): + x = iter(x) + else: + if x in self._dataset_iterator_cache: + x = self._dataset_iterator_cache[x] + else: + iterator = dataset_ops.make_initializable_iterator(x) + self._dataset_iterator_cache[x] = iterator + x = iterator + K.get_session().run(x.initializer) - Returns: - A `History` object. + # Validates `steps` argument based on x's type. + if check_steps: + training_utils.check_steps_argument(x, steps, steps_name) - Example: + is_x_eager_iterator = isinstance(x, iterator_ops.EagerIterator) + is_x_iterator = isinstance(x, iterator_ops.Iterator) - ```python - def generate_arrays_from_file(path): - while 1: - f = open(path) - for line in f: - # create numpy arrays of input data - # and labels, from each line in the file - x1, x2, y = process_line(line) - yield ({'input_1': x1, 'input_2': x2}, {'output': y}) - f.close() + # Validate user inputs when data is given as a dataset or dataset iterator. + if is_x_iterator or is_x_eager_iterator: + training_utils.validate_iterator_input(x, y, sample_weight, + validation_split) - model.fit_generator(generate_arrays_from_file('/my_file.txt'), - steps_per_epoch=10000, epochs=10) - ``` - Raises: - ValueError: In case the generator yields data in an invalid format. - """ - if self._distribution_strategy: - raise NotImplementedError('`fit_generator` is not supported for ' - 'models compiled with DistributionStrategy.') - return training_generator.fit_generator( - self, - generator, - steps_per_epoch=steps_per_epoch, - epochs=epochs, - verbose=verbose, - callbacks=callbacks, - validation_data=validation_data, - validation_steps=validation_steps, - class_weight=class_weight, - max_queue_size=max_queue_size, - workers=workers, - use_multiprocessing=use_multiprocessing, - shuffle=shuffle, - initial_epoch=initial_epoch) + # For eager iterators, when we have to process multiple batches of samples, + # we will standardize the data when we actually loop over iterator and get + # the batches. For now, we just return the iterator as is. + if is_x_eager_iterator: + return x, y, sample_weight - def evaluate_generator(self, - generator, - steps=None, - max_queue_size=10, - workers=1, - use_multiprocessing=False, - verbose=0): - """Evaluates the model on a data generator. + # If input data is a dataset iterator in graph mode or if it is an eager + # iterator and only one batch of samples is required, we fetch the data + # tensors from the iterator and then standardize them. + if is_x_iterator or is_x_eager_iterator: + try: + if is_x_iterator: + next_element = self._get_iterator_get_next_tensors(x) + else: + next_element = x.get_next() + except errors.OutOfRangeError: + raise RuntimeError('Your dataset iterator ran out of data; ' + 'Make sure that your dataset can generate ' + 'required number of samples.') - The generator should return the same kind of data - as accepted by `test_on_batch`. + if isinstance(next_element, (list, tuple)): + if len(next_element) not in [2, 3]: + raise ValueError( + 'Please provide model inputs as a list or tuple of 2 or 3' + 'elements: (input, target) or (input, target, sample_weights)' + 'Received %s' % next_element) + if len(next_element) == 2: + x, y = next_element + else: + x, y, sample_weight = next_element + else: + x = next_element - Arguments: - generator: Generator yielding tuples (inputs, targets) - or (inputs, targets, sample_weights) - or an instance of `keras.utils.Sequence` - object in order to avoid duplicate data - when using multiprocessing. - steps: Total number of steps (batches of samples) - to yield from `generator` before stopping. - Optional for `Sequence`: if unspecified, will use - the `len(generator)` as a number of steps. - max_queue_size: maximum size for the generator queue - workers: Integer. Maximum number of processes to spin up - when using process-based threading. - If unspecified, `workers` will default to 1. If 0, will - execute the generator on the main thread. - use_multiprocessing: Boolean. - If `True`, use process-based threading. - If unspecified, `use_multiprocessing` will default to `False`. - Note that because this implementation relies on multiprocessing, - you should not pass non-picklable arguments to the generator - as they can't be passed easily to children processes. - verbose: Verbosity mode, 0 or 1. + if sample_weight is not None and class_weight is not None: + logging.warning( + 'Received both a `sample_weight` and `class_weight` argument. ' + 'The `class_weight` argument will be ignored.') + # First, we build/compile the model on the fly if necessary. + all_inputs = [] + is_build_called = False + is_compile_called = False + # Whether this is a subclassed model that expects dictionary inputs + # rather than list inputs (e.g. FeatureColumn-based models). + dict_inputs = False + if not self.inputs: + # We need to use `x` to set the model inputs. + # We type-check that `x` and `y` are either single arrays + # or lists of arrays. + if isinstance(x, (list, tuple)): + if not all(isinstance(v, np.ndarray) or + tensor_util.is_tensor(v) for v in x): + raise ValueError('Please provide as model inputs either a single ' + 'array or a list of arrays. You passed: x=' + str(x)) + all_inputs += list(x) + elif isinstance(x, dict): + dict_inputs = True + keys = sorted(x.keys()) + all_inputs = [x[k] for k in keys] + else: + if not isinstance(x, np.ndarray) and not tensor_util.is_tensor(x): + raise ValueError('Please provide as model inputs either a single ' + 'array or a list of arrays. You passed: x=' + str(x)) + all_inputs.append(x) - Returns: - Scalar test loss (if the model has a single output and no metrics) - or list of scalars (if the model has multiple outputs - and/or metrics). The attribute `model.metrics_names` will give you - the display labels for the scalar outputs. + # Build the model using the retrieved inputs (value or symbolic). + # If values or generated from a dataset, then in symbolic-mode + # placeholders will be created to match the value shapes. + if not self.inputs: + is_build_called = True + if is_x_iterator: + cast_inputs = nest.map_structure(lambda v: v.shape, x) + elif training_utils.has_tensors(x): + cast_inputs = training_utils.cast_if_floating_dtype(x) + else: + cast_inputs = x + self._set_inputs(cast_inputs) + else: + dict_inputs = isinstance(self.inputs, dict) + if dict_inputs and context.executing_eagerly(): + # No support for graph functions when the model expects dictionary inputs + # (i.e. FeatureColumn-based models). + self.run_eagerly = True - Raises: - ValueError: in case of invalid arguments. + if y is not None: + if not self.optimizer: + raise RuntimeError('You must compile a model before ' + 'training/testing. ' + 'Use `model.compile(optimizer, loss)`.') + if not self._is_compiled: + # On-the-fly compilation of the model. + # We need to use `y` to set the model targets. + if training_utils.has_tensors(y): + y = training_utils.cast_if_floating_dtype(y) + if isinstance(y, (list, tuple)): + if not all(isinstance(v, np.ndarray) or + tensor_util.is_tensor(v) for v in y): + raise ValueError('Please provide as model targets either a single ' + 'array or a list of arrays. ' + 'You passed: y=' + str(y)) + all_inputs += list(y) + elif isinstance(y, dict): + raise ValueError('Please do not pass a dictionary as model targets.') + else: + if not isinstance(y, np.ndarray) and not tensor_util.is_tensor(y): + raise ValueError('Please provide as model targets either a single ' + 'array or a list of arrays. ' + 'You passed: y=' + str(y)) + all_inputs.append(y) - Raises: - ValueError: In case the generator yields data in an invalid format. - """ - if self._distribution_strategy: - raise NotImplementedError('`evaluate_generator` is not supported for ' - 'models compiled with DistributionStrategy.') - return training_generator.evaluate_generator( - self, - generator, - steps=steps, - max_queue_size=max_queue_size, - workers=workers, - use_multiprocessing=use_multiprocessing, - verbose=verbose) + # Typecheck that all inputs are *either* value *or* symbolic. + # TODO(fchollet): this check could be removed in Eager mode? + if any(tensor_util.is_tensor(v) for v in all_inputs): + if not all(tensor_util.is_tensor(v) for v in all_inputs): + raise ValueError('Do not pass inputs that mix Numpy arrays and ' + 'TensorFlow tensors. ' + 'You passed: x=' + str(x) + '; y=' + str(y)) - def predict_generator(self, - generator, - steps=None, - max_queue_size=10, - workers=1, - use_multiprocessing=False, - verbose=0): - """Generates predictions for the input samples from a data generator. + if self.run_eagerly or is_x_iterator: + target_tensors = None + else: + # Handle target tensors if any passed. + if not isinstance(y, (list, tuple)): + y = [y] + target_tensors = [v for v in y if _is_symbolic_tensor(v)] + is_compile_called = True + self.compile( + optimizer=self.optimizer, + loss=self.loss, + metrics=self._compile_metrics, + weighted_metrics=self._compile_weighted_metrics, + loss_weights=self.loss_weights, + target_tensors=target_tensors, + run_eagerly=self.run_eagerly) + + # In graph mode, if we had just set inputs and targets as symbolic tensors + # by invoking build and compile on the model respectively, we do not have to + # feed anything to the model. Model already has input and target data as + # part of the graph. + # Note: in this case, `any` and `all` are equivalent since we disallow + # mixed symbolic/value inputs. + if (not self.run_eagerly and is_build_called and is_compile_called and + not is_x_iterator and any(_is_symbolic_tensor(v) for v in all_inputs)): + return [], [], [] + + # What follows is input validation and standardization to list format, + # in the case where all inputs are value arrays. + + if self.run_eagerly: + # In eager mode, do not do shape validation + # since the network has no input nodes (placeholders) to be fed. + feed_input_names = self.input_names + feed_input_shapes = None + elif not self._is_graph_network: + # Case: symbolic-mode subclassed network. Do not do shape validation. + feed_input_names = self._feed_input_names + feed_input_shapes = None + else: + # Case: symbolic-mode graph network. + # In this case, we run extensive shape validation checks. + feed_input_names = self._feed_input_names + feed_input_shapes = self._feed_input_shapes - The generator should return the same kind of data as accepted by - `predict_on_batch`. + # Standardize the inputs. + x = training_utils.standardize_input_data( + x, + feed_input_names, + feed_input_shapes, + check_batch_axis=False, # Don't enforce the batch size. + exception_prefix='input') - Arguments: - generator: Generator yielding batches of input samples - or an instance of `keras.utils.Sequence` object in order to - avoid duplicate data when using multiprocessing. - steps: Total number of steps (batches of samples) - to yield from `generator` before stopping. - Optional for `Sequence`: if unspecified, will use - the `len(generator)` as a number of steps. - max_queue_size: Maximum size for the generator queue. - workers: Integer. Maximum number of processes to spin up - when using process-based threading. - If unspecified, `workers` will default to 1. If 0, will - execute the generator on the main thread. - use_multiprocessing: Boolean. - If `True`, use process-based threading. - If unspecified, `use_multiprocessing` will default to `False`. - Note that because this implementation relies on multiprocessing, - you should not pass non-picklable arguments to the generator - as they can't be passed easily to children processes. - verbose: verbosity mode, 0 or 1. + if y is not None: + if not self._is_graph_network: + feed_output_names = self._feed_output_names + feed_output_shapes = None + # Sample weighting not supported in this case. + # TODO(fchollet): consider supporting it. + feed_sample_weight_modes = [None for _ in self.outputs] + else: + feed_output_names = self._feed_output_names + feed_sample_weight_modes = self._feed_sample_weight_modes + feed_output_shapes = [] + for output_shape, loss_fn in zip(self._feed_output_shapes, + self._feed_loss_fns): + if loss_fn is losses.sparse_categorical_crossentropy: + if K.image_data_format() == 'channels_first': + feed_output_shapes.append( + (output_shape[0], 1) + output_shape[2:]) + else: + feed_output_shapes.append(output_shape[:-1] + (1,)) + elif (not hasattr(loss_fn, '__name__') or + getattr(losses, loss_fn.__name__, None) is None): + # If `loss_fn` is not a function (e.g. callable class) + # or if it not in the `losses` module, then + # it is a user-defined loss and we make no assumptions + # about it. + feed_output_shapes.append(None) + else: + feed_output_shapes.append(output_shape) - Returns: - Numpy array(s) of predictions. + # Standardize the outputs. + y = training_utils.standardize_input_data( + y, + feed_output_names, + # Don't enforce target shapes to match output shapes. + # Precise checks will be run in `check_loss_and_target_compatibility`. + shapes=None, + check_batch_axis=False, # Don't enforce the batch size. + exception_prefix='target') - Raises: - ValueError: In case the generator yields data in an invalid format. - """ - if self._distribution_strategy: - raise NotImplementedError('`predict_generator` is not supported for ' - 'models compiled with DistributionStrategy.') - return training_generator.predict_generator( - self, - generator, - steps=steps, - max_queue_size=max_queue_size, - workers=workers, - use_multiprocessing=use_multiprocessing, - verbose=verbose) + # Generate sample-wise weight values given the `sample_weight` and + # `class_weight` arguments. + sample_weights = training_utils.standardize_sample_weights( + sample_weight, feed_output_names) + class_weights = training_utils.standardize_class_weights( + class_weight, feed_output_names) + sample_weights = [ + training_utils.standardize_weights(ref, sw, cw, mode) + for (ref, sw, cw, mode) in zip(y, sample_weights, class_weights, + feed_sample_weight_modes) + ] + # Check that all arrays have the same length. + if not self._distribution_strategy: + training_utils.check_array_lengths(x, y, sample_weights) + if self._is_graph_network and not self.run_eagerly: + # Additional checks to avoid users mistakenly using improper loss fns. + training_utils.check_loss_and_target_compatibility( + y, self._feed_loss_fns, feed_output_shapes) + else: + y = [] + sample_weights = [] - def _get_callback_model(self): - """Returns the Callback Model for this Model.""" + if self.stateful and batch_size: + # Check that for stateful networks, number of samples is a multiple + # of the static batch size. + if x[0].shape[0] % batch_size != 0: + raise ValueError('In a stateful network, ' + 'you should only pass inputs with ' + 'a number of samples that can be ' + 'divided by the batch size. Found: ' + + str(x[0].shape[0]) + ' samples') - if hasattr(self, '_replicated_model') and self._replicated_model: - # When using training_distributed, we set the callback model - # to an instance of the `DistributedModel` that we create in - # the `compile` call. The `DistributedModel` is initialized - # with the first replicated model. We need to set the callback - # model to a DistributedModel to allow us to override saving - # and loading weights when we checkpoint the model during training. - return self._replicated_model - if hasattr(self, 'callback_model') and self.callback_model: - return self.callback_model - return self + # If dictionary inputs were provided, we return a dictionary as well. + if dict_inputs: + x = dict(zip(feed_input_names, x)) + return x, y, sample_weights - def _make_callback_model(self, grouped_model): - first_replicated_model = self._distribution_strategy.unwrap( - grouped_model)[0] - # We initialize the callback model with the first replicated model. - self._replicated_model = DistributedCallbackModel(first_replicated_model) - self._replicated_model.set_original_model(self) + @checkpointable.no_automatic_dependency_tracking + def _set_inputs(self, inputs, outputs=None, training=None): + """Set model's input and output specs based on the input data received. - def _validate_or_infer_batch_size(self, batch_size, steps, x): - """Validates that the `batch_size` provided is consistent with InputLayer. + This is to be used for Model subclasses, which do not know at instantiation + time what their inputs look like. - It's possible that the user specified a static batch size in their - InputLayer. If so, this method checks the provided `batch_size` and `x` - arguments are consistent with this static batch size. Also, if - `batch_size` is `None`, this method will attempt to infer the batch size - from the static batch size of the InputLayer. + Args: + inputs: Single array, or list of arrays. The arrays could be placeholders, + Numpy arrays, data tensors, or TensorShapes. + - if placeholders: the model is built on top of these placeholders, + and we expect Numpy data to be fed for them when calling `fit`/etc. + - if Numpy data or TensorShapes: we create placeholders matching the + TensorShapes or shapes of the Numpy arrays. We expect Numpy data to be + fed for these placeholders when calling `fit`/etc. + - if data tensors: the model is built on top of these tensors. + We do not expect any Numpy data to be provided when calling `fit`/etc. + outputs: None, a data tensor, or a list of tensors. If None, the + outputs will be determined by invoking `self.call()`, otherwise the + provided value will be used. + training: Boolean or None. Only relevant in symbolic mode. Specifies + whether to build the model's graph in inference mode (False), training + mode (True), or using the Keras learning phase (None). + Raises: + ValueError: If dict inputs are passed to a Sequential Model where the + first layer isn't FeatureLayer. + """ + if self.inputs: + raise ValueError('Model inputs are already set.') - Arguments: - batch_size: The batch_size provided as an argument to - fit/evaluate/predict. - steps: The steps provided as an argument to fit/evaluate/predict. - x: The data passed as `x` to fit/evaluate/predict. + if self.__class__.__name__ == 'Sequential' and not self.built: + if tensor_util.is_tensor(inputs): + input_shape = (None,) + tuple(inputs.shape.as_list()[1:]) + elif isinstance(inputs, tensor_shape.TensorShape): + input_shape = (None,) + tuple(inputs.as_list()[1:]) + elif isinstance(inputs, dict): + # We assert that the first layer is a FeatureLayer. + if not training_utils.is_feature_layer(self.layers[0]): + raise ValueError('Passing a dictionary input to a Sequential Model ' + 'which doesn\'t have FeatureLayer as the first layer' + ' is an error.') + input_shape = (None,) + else: + input_shape = (None,) + tuple(inputs.shape[1:]) + self._build_input_shape = input_shape - Returns: - The validated batch_size, auto-inferred from the first layer if not - provided. - """ - layers = super(Model, self).layers # Avoids the override in Sequential. - if layers: - first_layer = layers[0] - static_batch_size = training_utils.get_static_batch_size(first_layer) - if static_batch_size is not None: + # On-the-fly setting of symbolic model inputs (either by using the tensor + # provided, or by creating a placeholder if Numpy data was provided). + model_inputs = training_utils.ModelInputs(inputs) + inputs = model_inputs.get_symbolic_inputs() + self.inputs = model_inputs.get_symbolic_inputs(return_single_as_list=True) + self.input_names = model_inputs.get_input_names() - # Check `batch_size` argument is consistent with InputLayer. - if batch_size is not None and batch_size != static_batch_size: - raise ValueError('The `batch_size` argument value {} is incompatible ' - 'with the specified batch size of your Input Layer: ' - '{}'.format(batch_size, static_batch_size)) + self._feed_inputs = [] + self._feed_input_names = [] + self._feed_input_shapes = [] - # Check Dataset/Iterator batch size is consistent with InputLayer. - if isinstance(x, (dataset_ops.DatasetV2, iterator_ops.Iterator, - iterator_ops.EagerIterator)): - ds_batch_size = tensor_shape.as_dimension( - nest.flatten(x.output_shapes)[0][0]).value - if ds_batch_size is not None and ds_batch_size != static_batch_size: - raise ValueError('The batch output shape of your `Dataset` is {}, ' - 'which is incompatible with the specified batch ' - 'size of your Input Layer: {}'.format( - ds_batch_size, static_batch_size)) + for k, v in model_inputs.as_dict(): + if K.is_placeholder(v): + self._feed_inputs.append(v) + self._feed_input_names.append(k) + self._feed_input_shapes.append(K.int_shape(v)) - # Set inferred batch size from the InputLayer. - if steps is None: - batch_size = static_batch_size + # TODO(fchollet): consider calling `_maybe_build` before calling the model. - if batch_size is None and steps is None: - # Backwards compatibility - batch_size = 32 - return batch_size + if outputs is None: + # Obtain symbolic outputs by calling the model. + with K.get_graph().as_default(): + if self._expects_training_arg: + outputs = self.call(inputs, training=training) + else: + outputs = self.call(inputs) - @property - def _default_save_signature(self): - return training_utils.trace_model_call(self) + outputs = nest.flatten(outputs) + self.outputs = outputs + self.output_names = training_utils.generic_output_names(outputs) + self.built = True class DistributedCallbackModel(Model): diff --git a/tensorflow/python/keras/engine/training_distributed.py b/tensorflow/python/keras/engine/training_distributed.py index d20d092d8e..ffb0266911 100644 --- a/tensorflow/python/keras/engine/training_distributed.py +++ b/tensorflow/python/keras/engine/training_distributed.py @@ -570,11 +570,8 @@ def _get_input_from_iterator(iterator, model): # Validate that all the elements in x and y are of the same type and shape. # We can then pass the first element of x and y to `_standardize_weights` # below and be confident of the output. - x_values, y_values, sample_weights_values = distributed_training_utils.\ - validate_distributed_dataset_inputs(model._distribution_strategy, x, y, - sample_weights) - model._standardize_weights(x_values, y_values, - sample_weight=sample_weights_values) + distributed_training_utils.validate_distributed_dataset_inputs( + model._distribution_strategy, x, y, sample_weights) return x, y, sample_weights -- GitLab From 8e7339b501b23e128572e984a25986c308adb0ed Mon Sep 17 00:00:00 2001 From: Sami Kama Date: Tue, 11 Dec 2018 16:57:35 -0800 Subject: [PATCH 396/873] Fix include ordering in backend.cc --- tensorflow/compiler/xla/service/backend.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/compiler/xla/service/backend.cc b/tensorflow/compiler/xla/service/backend.cc index 385c5eefa0..99e963b929 100644 --- a/tensorflow/compiler/xla/service/backend.cc +++ b/tensorflow/compiler/xla/service/backend.cc @@ -22,7 +22,6 @@ limitations under the License. #include #include "absl/memory/memory.h" -#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/compiler/xla/service/compiler.h" #include "tensorflow/compiler/xla/service/platform_util.h" #include "tensorflow/compiler/xla/status_macros.h" @@ -37,6 +36,7 @@ limitations under the License. #include "tensorflow/core/platform/env.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/stream_executor_no_cuda.h" +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" namespace xla { -- GitLab From 45a6696c0ad95d6953c43da2352d297ea61916e3 Mon Sep 17 00:00:00 2001 From: Yuefeng Zhou Date: Tue, 11 Dec 2018 16:55:34 -0800 Subject: [PATCH 397/873] Fix the bug in collective_all_reduce_strategy that wrong cross device op is used. PiperOrigin-RevId: 225096446 --- .../distribute/python/collective_all_reduce_strategy.py | 8 ++++++-- .../python/collective_all_reduce_strategy_test.py | 2 +- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/distribute/python/collective_all_reduce_strategy.py b/tensorflow/contrib/distribute/python/collective_all_reduce_strategy.py index 5c50a20490..346513dc58 100644 --- a/tensorflow/contrib/distribute/python/collective_all_reduce_strategy.py +++ b/tensorflow/contrib/distribute/python/collective_all_reduce_strategy.py @@ -70,6 +70,8 @@ class CollectiveAllReduceExtended(mirrored_strategy.MirroredExtended): self._cross_device_ops = None self._num_gpus_per_worker = num_gpus_per_worker self._initialize_local_worker(num_gpus_per_worker) + assert isinstance(self._get_cross_device_ops(), + cross_device_ops_lib.CollectiveAllReduce) def _initialize_local_worker(self, num_gpus_per_worker): """Initializes the object for local training.""" @@ -86,7 +88,7 @@ class CollectiveAllReduceExtended(mirrored_strategy.MirroredExtended): self._collective_keys = cross_device_utils.CollectiveKeys() self._initialize_local(local_devices) - self._cross_tower_ops = cross_device_ops_lib.CollectiveAllReduce( + self._cross_device_ops = cross_device_ops_lib.CollectiveAllReduce( num_workers=self._num_workers, num_gpus_per_worker=num_gpus_per_worker, collective_keys=self._collective_keys) @@ -128,7 +130,7 @@ class CollectiveAllReduceExtended(mirrored_strategy.MirroredExtended): self._collective_keys = cross_device_utils.CollectiveKeys() self._initialize_local(local_devices) - self._cross_tower_ops = cross_device_ops_lib.CollectiveAllReduce( + self._cross_device_ops = cross_device_ops_lib.CollectiveAllReduce( num_workers=self._num_workers, num_gpus_per_worker=num_gpus_per_worker, collective_keys=self._collective_keys) @@ -267,6 +269,8 @@ class CollectiveAllReduceExtended(mirrored_strategy.MirroredExtended): # already been initialized with a `cluster_spec`. self._initialize_multi_worker(self._num_gpus_per_worker, cluster_spec, task_type, task_id) + assert isinstance(self._get_cross_device_ops(), + cross_device_ops_lib.CollectiveAllReduce) if session_config: session_config.CopyFrom(self._update_config_proto(session_config)) diff --git a/tensorflow/contrib/distribute/python/collective_all_reduce_strategy_test.py b/tensorflow/contrib/distribute/python/collective_all_reduce_strategy_test.py index 8a9e583f0a..6d7cd14ed5 100644 --- a/tensorflow/contrib/distribute/python/collective_all_reduce_strategy_test.py +++ b/tensorflow/contrib/distribute/python/collective_all_reduce_strategy_test.py @@ -82,7 +82,7 @@ class CollectiveAllReduceStrategyTestBase( instance_key_with_id_start=num_gpus * 10000 + CollectiveAllReduceStrategyTestBase.collective_key_base) distribution.extended._collective_keys = collective_keys - distribution.extended._inferred_cross_device_ops._collective_keys = ( + distribution.extended._cross_device_ops._collective_keys = ( collective_keys) if task_type and task_id is not None: return distribution, 'grpc://' + self._cluster_spec[task_type][ -- GitLab From db340f9efc3dee7f7a7e931db8f2f36104daa446 Mon Sep 17 00:00:00 2001 From: Allen Lavoie Date: Tue, 11 Dec 2018 16:57:56 -0800 Subject: [PATCH 398/873] Fix wrap_function on empty arguments Caused by an implicit boolean check which should have been an explicit None check PiperOrigin-RevId: 225096833 --- tensorflow/python/eager/function.py | 2 +- tensorflow/python/eager/wrap_function_test.py | 9 +++++++++ 2 files changed, 10 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/eager/function.py b/tensorflow/python/eager/function.py index 0de0cd96ac..f3480ebb56 100644 --- a/tensorflow/python/eager/function.py +++ b/tensorflow/python/eager/function.py @@ -340,7 +340,7 @@ class Function(object): TypeError: For invalid positional/keyword argument combinations. """ if self._arg_keywords is None or self._num_positional_args is None: - if self._signature: + if self._signature is not None: if kwargs: raise NotImplementedError( "Keyword arguments not supported when calling a " diff --git a/tensorflow/python/eager/wrap_function_test.py b/tensorflow/python/eager/wrap_function_test.py index b32b6ca426..d34e9228f3 100644 --- a/tensorflow/python/eager/wrap_function_test.py +++ b/tensorflow/python/eager/wrap_function_test.py @@ -19,6 +19,7 @@ from __future__ import print_function from tensorflow.python.eager import wrap_function +from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_spec @@ -70,6 +71,14 @@ class WrapFunctionTest(test.TestCase): f_pruned = f_wrapped.prune(x_in[0], [x_out[0]]) self.assertAllEqual(f_pruned(ops.convert_to_tensor(2.0)), [4.0]) + def testNoArguments(self): + + def f(): + return constant_op.constant(1.) + + f_wrapped = wrap_function.wrap_function(f, []) + self.assertAllEqual(1.0, f_wrapped()) + if __name__ == '__main__': ops.enable_eager_execution() -- GitLab From 5fecd1ead795ec7ddc5d9ede0f8c3b386a1ca8f0 Mon Sep 17 00:00:00 2001 From: Jian Li Date: Tue, 11 Dec 2018 17:03:32 -0800 Subject: [PATCH 399/873] Fix build error PiperOrigin-RevId: 225097826 --- .../microfrontend/audio_microfrontend.cc | 10 ++++---- .../microfrontend/audio_microfrontend_test.cc | 9 ++++--- .../microfrontend/lib/fft_test.cc | 3 ++- .../microfrontend/lib/filterbank_io.c | 3 ++- .../microfrontend/lib/filterbank_test.cc | 24 ++++++++++++------- .../microfrontend/lib/frontend_test.cc | 6 +++-- .../microfrontend/lib/log_scale.c | 3 ++- .../microfrontend/lib/log_scale_test.cc | 6 +++-- .../microfrontend/lib/noise_reduction_test.cc | 6 +++-- .../microfrontend/lib/pcan_gain_control.c | 3 ++- .../lib/pcan_gain_control_test.cc | 3 ++- .../lib/pcan_gain_control_util.c | 3 ++- .../microfrontend/lib/window_io.c | 3 ++- .../microfrontend/lib/window_test.cc | 12 ++++++---- .../ops/audio_microfrontend_op.cc | 10 ++++---- 15 files changed, 68 insertions(+), 36 deletions(-) diff --git a/tensorflow/lite/experimental/microfrontend/audio_microfrontend.cc b/tensorflow/lite/experimental/microfrontend/audio_microfrontend.cc index 4367fe74a4..84ab164d2c 100644 --- a/tensorflow/lite/experimental/microfrontend/audio_microfrontend.cc +++ b/tensorflow/lite/experimental/microfrontend/audio_microfrontend.cc @@ -142,7 +142,8 @@ void GenerateFeatures(TfLiteAudioMicrofrontendParams* data, if (output.values != nullptr) { frame_buffer[frame_index].reserve(output.size); - for (int i = 0; i < output.size; ++i) { + int i; + for (i = 0; i < output.size; ++i) { frame_buffer[frame_index].push_back(static_cast(output.values[i]) / data->out_scale); } @@ -152,9 +153,10 @@ void GenerateFeatures(TfLiteAudioMicrofrontendParams* data, int index = 0; std::vector pad(data->state->filterbank.num_channels, 0); - for (int anchor = 0; anchor < frame_buffer.size(); - anchor += data->frame_stride) { - for (int frame = anchor - data->left_context; + int anchor; + for (anchor = 0; anchor < frame_buffer.size(); anchor += data->frame_stride) { + int frame; + for (frame = anchor - data->left_context; frame <= anchor + data->right_context; ++frame) { std::vector* feature; if (data->zero_padding && (frame < 0 || frame >= frame_buffer.size())) { diff --git a/tensorflow/lite/experimental/microfrontend/audio_microfrontend_test.cc b/tensorflow/lite/experimental/microfrontend/audio_microfrontend_test.cc index a9119d0183..e3a0e06f7b 100644 --- a/tensorflow/lite/experimental/microfrontend/audio_microfrontend_test.cc +++ b/tensorflow/lite/experimental/microfrontend/audio_microfrontend_test.cc @@ -140,13 +140,16 @@ class BaseMicroFrontendTest : public ::testing::Test { // Mimic padding behaviour with zero_padding = true. std::vector output_flattened; - for (int anchor = 0; anchor < output.size(); + int anchor; + for (anchor = 0; anchor < output.size(); anchor += micro_frontend->num_frame_stride()) { - for (int frame = anchor - micro_frontend->num_left_context(); + int frame; + for (frame = anchor - micro_frontend->num_left_context(); frame <= anchor + micro_frontend->num_right_context(); ++frame) { if (frame < 0 || frame >= output.size()) { // Padding with zeros. - for (int j = 0; j < num_frequency_per_frame; ++j) { + int j; + for (j = 0; j < num_frequency_per_frame; ++j) { output_flattened.push_back(0.0); } } else { diff --git a/tensorflow/lite/experimental/microfrontend/lib/fft_test.cc b/tensorflow/lite/experimental/microfrontend/lib/fft_test.cc index 7c1ee2d852..1b754c1b4c 100644 --- a/tensorflow/lite/experimental/microfrontend/lib/fft_test.cc +++ b/tensorflow/lite/experimental/microfrontend/lib/fft_test.cc @@ -38,7 +38,8 @@ TEST(FftTest, CheckOutputValues) { {-887, 0}, {3000, 3000}, {0, -6401}, {-3000, 3000}, {886, 0}, {118, 119}, {0, 25}, {9, -10}, {19, 0}, {9, 9}, {0, 0}}; ASSERT_EQ(state.fft_size / 2 + 1, sizeof(expected) / sizeof(expected[0])); - for (int i = 0; i <= state.fft_size / 2; ++i) { + int i; + for (i = 0; i <= state.fft_size / 2; ++i) { EXPECT_EQ(state.output[i].real, expected[i].real); EXPECT_EQ(state.output[i].imag, expected[i].imag); } diff --git a/tensorflow/lite/experimental/microfrontend/lib/filterbank_io.c b/tensorflow/lite/experimental/microfrontend/lib/filterbank_io.c index 2dbb4b3bf0..6ce4c7c796 100644 --- a/tensorflow/lite/experimental/microfrontend/lib/filterbank_io.c +++ b/tensorflow/lite/experimental/microfrontend/lib/filterbank_io.c @@ -17,7 +17,8 @@ limitations under the License. static void PrintArray(FILE* fp, const char* name, const int16_t* values, size_t size) { fprintf(fp, "static int16_t filterbank_%s[] = {", name); - for (int i = 0; i < size; ++i) { + int i; + for (i = 0; i < size; ++i) { fprintf(fp, "%d", values[i]); if (i < size - 1) { fprintf(fp, ", "); diff --git a/tensorflow/lite/experimental/microfrontend/lib/filterbank_test.cc b/tensorflow/lite/experimental/microfrontend/lib/filterbank_test.cc index 808d527186..41f0064d4f 100644 --- a/tensorflow/lite/experimental/microfrontend/lib/filterbank_test.cc +++ b/tensorflow/lite/experimental/microfrontend/lib/filterbank_test.cc @@ -71,7 +71,8 @@ TEST_F(FilterbankTest, CheckChannelFrequencyStarts) { const int16_t expected[] = {0, 4, 8}; ASSERT_EQ(state.num_channels + 1, sizeof(expected) / sizeof(expected[0])); - for (int i = 0; i <= state.num_channels; ++i) { + int i; + for (i = 0; i <= state.num_channels; ++i) { EXPECT_EQ(state.channel_frequency_starts[i], expected[i]); } @@ -85,7 +86,8 @@ TEST_F(FilterbankTest, CheckChannelWeightStarts) { const int16_t expected[] = {0, 8, 16}; ASSERT_EQ(state.num_channels + 1, sizeof(expected) / sizeof(expected[0])); - for (int i = 0; i <= state.num_channels; ++i) { + int i; + for (i = 0; i <= state.num_channels; ++i) { EXPECT_EQ(state.channel_weight_starts[i], expected[i]); } @@ -99,7 +101,8 @@ TEST_F(FilterbankTest, CheckChannelWidths) { const int16_t expected[] = {8, 8, 8}; ASSERT_EQ(state.num_channels + 1, sizeof(expected) / sizeof(expected[0])); - for (int i = 0; i <= state.num_channels; ++i) { + int i; + for (i = 0; i <= state.num_channels; ++i) { EXPECT_EQ(state.channel_widths[i], expected[i]); } @@ -117,7 +120,8 @@ TEST_F(FilterbankTest, CheckWeights) { ASSERT_EQ(state.channel_weight_starts[state.num_channels] + state.channel_widths[state.num_channels], sizeof(expected) / sizeof(expected[0])); - for (int i = 0; i < sizeof(expected) / sizeof(expected[0]); ++i) { + int i; + for (i = 0; i < sizeof(expected) / sizeof(expected[0]); ++i) { EXPECT_EQ(state.weights[i], expected[i]); } @@ -135,7 +139,8 @@ TEST_F(FilterbankTest, CheckUnweights) { ASSERT_EQ(state.channel_weight_starts[state.num_channels] + state.channel_widths[state.num_channels], sizeof(expected) / sizeof(expected[0])); - for (int i = 0; i < sizeof(expected) / sizeof(expected[0]); ++i) { + int i; + for (i = 0; i < sizeof(expected) / sizeof(expected[0]); ++i) { EXPECT_EQ(state.unweights[i], expected[i]); } @@ -154,7 +159,8 @@ TEST_F(FilterbankTest, CheckConvertFftComplexToEnergy) { int32_t* energy = reinterpret_cast(fake_fft); FilterbankConvertFftComplexToEnergy(&state, fake_fft, energy); - for (int i = state.start_index; i < state.end_index; ++i) { + int i; + for (i = state.start_index; i < state.end_index; ++i) { EXPECT_EQ(energy[i], kEnergy[i]); } } @@ -167,7 +173,8 @@ TEST_F(FilterbankTest, CheckAccumulateChannels) { FilterbankAccumulateChannels(&state, kEnergy); ASSERT_EQ(state.num_channels + 1, sizeof(kWork) / sizeof(kWork[0])); - for (int i = 0; i <= state.num_channels; ++i) { + int i; + for (i = 0; i <= state.num_channels; ++i) { EXPECT_EQ(state.work[i], kWork[i]); } @@ -184,7 +191,8 @@ TEST_F(FilterbankTest, CheckSqrt) { const uint32_t expected[] = {247311, 508620}; ASSERT_EQ(state.num_channels, sizeof(expected) / sizeof(expected[0])); - for (int i = 0; i < state.num_channels; ++i) { + int i; + for (i = 0; i < state.num_channels; ++i) { EXPECT_EQ(scaled_filterbank[i], expected[i]); } diff --git a/tensorflow/lite/experimental/microfrontend/lib/frontend_test.cc b/tensorflow/lite/experimental/microfrontend/lib/frontend_test.cc index 993e866cc0..a6faa1fc1f 100644 --- a/tensorflow/lite/experimental/microfrontend/lib/frontend_test.cc +++ b/tensorflow/lite/experimental/microfrontend/lib/frontend_test.cc @@ -64,7 +64,8 @@ TEST_F(FrontendTest, CheckOutputValues) { const uint16_t expected[] = {479, 425}; ASSERT_EQ(output.size, sizeof(expected) / sizeof(expected[0])); - for (int i = 0; i < output.size; ++i) { + int i; + for (i = 0; i < output.size; ++i) { EXPECT_EQ(output.values[i], expected[i]); } @@ -86,7 +87,8 @@ TEST_F(FrontendTest, CheckConsecutiveWindow) { const int16_t expected[] = {436, 378}; ASSERT_EQ(output.size, sizeof(expected) / sizeof(expected[0])); - for (int i = 0; i < output.size; ++i) { + int i; + for (i = 0; i < output.size; ++i) { EXPECT_EQ(output.values[i], expected[i]); } diff --git a/tensorflow/lite/experimental/microfrontend/lib/log_scale.c b/tensorflow/lite/experimental/microfrontend/lib/log_scale.c index 54f370e7d9..149ec7cfba 100644 --- a/tensorflow/lite/experimental/microfrontend/lib/log_scale.c +++ b/tensorflow/lite/experimental/microfrontend/lib/log_scale.c @@ -63,7 +63,8 @@ uint16_t* LogScaleApply(struct LogScaleState* state, uint32_t* signal, const int scale_shift = state->scale_shift; uint16_t* output = (uint16_t*) signal; uint16_t* ret = output; - for (int i = 0; i < signal_size; ++i) { + int i; + for (i = 0; i < signal_size; ++i) { uint32_t value = *signal++; if (state->enable_log) { if (correction_bits < 0) { diff --git a/tensorflow/lite/experimental/microfrontend/lib/log_scale_test.cc b/tensorflow/lite/experimental/microfrontend/lib/log_scale_test.cc index 91ca657e54..1ea0842ec2 100644 --- a/tensorflow/lite/experimental/microfrontend/lib/log_scale_test.cc +++ b/tensorflow/lite/experimental/microfrontend/lib/log_scale_test.cc @@ -34,7 +34,8 @@ TEST(LogScaleTest, CheckOutputValues) { kCorrectionBits); const uint16_t expected[] = {479, 425}; - for (int i = 0; i < sizeof(expected) / sizeof(expected[0]); ++i) { + int i; + for (i = 0; i < sizeof(expected) / sizeof(expected[0]); ++i) { EXPECT_EQ(output[i], expected[i]); } } @@ -50,7 +51,8 @@ TEST(LogScaleTest, CheckOutputValuesNoLog) { kCorrectionBits); const uint16_t expected[] = {65535, 45998}; - for (int i = 0; i < sizeof(expected) / sizeof(expected[0]); ++i) { + int i; + for (i = 0; i < sizeof(expected) / sizeof(expected[0]); ++i) { EXPECT_EQ(output[i], expected[i]); } } diff --git a/tensorflow/lite/experimental/microfrontend/lib/noise_reduction_test.cc b/tensorflow/lite/experimental/microfrontend/lib/noise_reduction_test.cc index 1614056487..13d58b2476 100644 --- a/tensorflow/lite/experimental/microfrontend/lib/noise_reduction_test.cc +++ b/tensorflow/lite/experimental/microfrontend/lib/noise_reduction_test.cc @@ -44,7 +44,8 @@ TEST_F(NoiseReductionTest, TestNoiseReductionEstimate) { const uint32_t expected[] = {6321887, 31248341}; ASSERT_EQ(state.num_channels, sizeof(expected) / sizeof(expected[0])); - for (int i = 0; i < state.num_channels; ++i) { + int i; + for (i = 0; i < state.num_channels; ++i) { EXPECT_EQ(state.estimate[i], expected[i]); } @@ -60,7 +61,8 @@ TEST_F(NoiseReductionTest, TestNoiseReduction) { const uint32_t expected[] = {241137, 478104}; ASSERT_EQ(state.num_channels, sizeof(expected) / sizeof(expected[0])); - for (int i = 0; i < state.num_channels; ++i) { + int i; + for (i = 0; i < state.num_channels; ++i) { EXPECT_EQ(signal[i], expected[i]); } diff --git a/tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control.c b/tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control.c index b49eb30137..8ccc2fde98 100644 --- a/tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control.c +++ b/tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control.c @@ -47,7 +47,8 @@ uint32_t PcanShrink(const uint32_t x) { void PcanGainControlApply(struct PcanGainControlState* state, uint32_t* signal) { - for (int i = 0; i < state->num_channels; ++i) { + int i; + for (i = 0; i < state->num_channels; ++i) { const uint32_t gain = WideDynamicFunction(state->noise_estimate[i], state->gain_lut); const uint32_t snr = ((uint64_t) signal[i] * gain) >> state->snr_shift; diff --git a/tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control_test.cc b/tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control_test.cc index 830db89edd..7c92d2d29d 100644 --- a/tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control_test.cc +++ b/tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control_test.cc @@ -49,7 +49,8 @@ TEST_F(PcanGainControlTest, TestPcanGainControl) { const uint32_t expected[] = {3578, 1533}; ASSERT_EQ(state.num_channels, sizeof(expected) / sizeof(expected[0])); - for (int i = 0; i < state.num_channels; ++i) { + int i; + for (i = 0; i < state.num_channels; ++i) { EXPECT_EQ(signal[i], expected[i]); } diff --git a/tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control_util.c b/tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control_util.c index dbe44c494a..5201cf045b 100644 --- a/tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control_util.c +++ b/tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control_util.c @@ -62,7 +62,8 @@ int PcanGainControlPopulateState(const struct PcanGainControlConfig* config, state->gain_lut[0] = PcanGainLookupFunction(config, input_bits, 0); state->gain_lut[1] = PcanGainLookupFunction(config, input_bits, 1); state->gain_lut -= 6; - for (int interval = 2; interval <= kWideDynamicFunctionBits; ++interval) { + int interval; + for (interval = 2; interval <= kWideDynamicFunctionBits; ++interval) { const uint32_t x0 = (uint32_t) 1 << (interval - 1); const uint32_t x1 = x0 + (x0 >> 1); const uint32_t x2 = (interval == kWideDynamicFunctionBits) diff --git a/tensorflow/lite/experimental/microfrontend/lib/window_io.c b/tensorflow/lite/experimental/microfrontend/lib/window_io.c index ed4ac5eb11..d12cac2c85 100644 --- a/tensorflow/lite/experimental/microfrontend/lib/window_io.c +++ b/tensorflow/lite/experimental/microfrontend/lib/window_io.c @@ -16,7 +16,8 @@ limitations under the License. void WindowWriteMemmapPreamble(FILE* fp, const struct WindowState* state) { fprintf(fp, "static int16_t window_coefficients[] = {\n"); - for (int i = 0; i < state->size; ++i) { + int i; + for (i = 0; i < state->size; ++i) { fprintf(fp, "%d", state->coefficients[i]); if (i < state->size - 1) { fprintf(fp, ", "); diff --git a/tensorflow/lite/experimental/microfrontend/lib/window_test.cc b/tensorflow/lite/experimental/microfrontend/lib/window_test.cc index 8c6c19188d..60f11440f5 100644 --- a/tensorflow/lite/experimental/microfrontend/lib/window_test.cc +++ b/tensorflow/lite/experimental/microfrontend/lib/window_test.cc @@ -48,7 +48,8 @@ TEST_F(WindowTest, CheckCoefficients) { 3843, 3541, 3145, 2681, 2177, 1664, 1176, 743, 391, 144, 16}; ASSERT_EQ(state.size, sizeof(expected) / sizeof(expected[0])); - for (int i = 0; i < state.size; ++i) { + int i; + for (i = 0; i < state.size; ++i) { EXPECT_EQ(state.coefficients[i], expected[i]); } @@ -64,7 +65,8 @@ TEST_F(WindowTest, CheckResidualInput) { &state, kFakeAudioData, sizeof(kFakeAudioData) / sizeof(kFakeAudioData[0]), &num_samples_read)); - for (int i = kStepSamples; i < kWindowSamples; ++i) { + int i; + for (i = kStepSamples; i < kWindowSamples; ++i) { EXPECT_EQ(state.input[i - kStepSamples], kFakeAudioData[i]); } @@ -84,7 +86,8 @@ TEST_F(WindowTest, CheckOutputValues) { 0, 1151, 0, -5944, 0, 13311, 0, -21448, 0, 28327, 0, -32256, 0, 32255, 0, -28328, 0, 21447, 0, -13312, 0, 5943, 0, -1152, 0}; ASSERT_EQ(state.size, sizeof(expected) / sizeof(expected[0])); - for (int i = 0; i < state.size; ++i) { + int i; + for (i = 0; i < state.size; ++i) { EXPECT_EQ(state.output[i], expected[i]); } @@ -122,7 +125,8 @@ TEST_F(WindowTest, CheckConsecutiveWindow) { 0, -1152, 0, 5943, 0, -13312, 0, 21447, 0, -28328, 0, 32255, 0, -32256, 0, 28327, 0, -21448, 0, 13311, 0, -5944, 0, 1151, 0}; ASSERT_EQ(state.size, sizeof(expected) / sizeof(expected[0])); - for (int i = 0; i < state.size; ++i) { + int i; + for (i = 0; i < state.size; ++i) { EXPECT_EQ(state.output[i], expected[i]); } diff --git a/tensorflow/lite/experimental/microfrontend/ops/audio_microfrontend_op.cc b/tensorflow/lite/experimental/microfrontend/ops/audio_microfrontend_op.cc index 51094a976d..9f2ea7eee6 100644 --- a/tensorflow/lite/experimental/microfrontend/ops/audio_microfrontend_op.cc +++ b/tensorflow/lite/experimental/microfrontend/ops/audio_microfrontend_op.cc @@ -250,7 +250,8 @@ class AudioMicrofrontendOp : public OpKernel { if (output.values != nullptr) { frame_buffer[frame_index].reserve(output.size); - for (int i = 0; i < output.size; ++i) { + int i; + for (i = 0; i < output.size; ++i) { frame_buffer[frame_index].push_back(static_cast(output.values[i]) / out_scale_); } @@ -261,9 +262,10 @@ class AudioMicrofrontendOp : public OpKernel { int index = 0; std::vector pad(config_.filterbank.num_channels, 0); - for (int anchor = 0; anchor < frame_buffer.size(); - anchor += frame_stride_) { - for (int frame = anchor - left_context_; frame <= anchor + right_context_; + int anchor; + for (anchor = 0; anchor < frame_buffer.size(); anchor += frame_stride_) { + int frame; + for (frame = anchor - left_context_; frame <= anchor + right_context_; ++frame) { std::vector* feature; if (zero_padding_ && (frame < 0 || frame >= frame_buffer.size())) { -- GitLab From 00426b0db4f2a9b0cc47d76724b024d5e9183f64 Mon Sep 17 00:00:00 2001 From: Scott Zhu Date: Tue, 11 Dec 2018 17:05:55 -0800 Subject: [PATCH 400/873] Minor cleanup for the import of the tests. PiperOrigin-RevId: 225098257 --- .../python/keras/layers/unified_lstm_test.py | 77 ++++++++++--------- 1 file changed, 39 insertions(+), 38 deletions(-) diff --git a/tensorflow/python/keras/layers/unified_lstm_test.py b/tensorflow/python/keras/layers/unified_lstm_test.py index 0219e5e426..6662bb8c04 100644 --- a/tensorflow/python/keras/layers/unified_lstm_test.py +++ b/tensorflow/python/keras/layers/unified_lstm_test.py @@ -34,8 +34,6 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import test_util from tensorflow.python.keras import testing_utils -from tensorflow.python.keras.layers.cudnn_recurrent import CuDNNLSTM -from tensorflow.python.keras.layers.recurrent import UnifiedLSTM from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import gen_math_ops @@ -75,7 +73,7 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): num_classes=output_shape) y_train = keras.utils.to_categorical(y_train, output_shape) - layer = UnifiedLSTM(rnn_state_size, return_runtime=True) + layer = keras.layers.UnifiedLSTM(rnn_state_size, return_runtime=True) inputs = array_ops.placeholder( dtypes.float32, shape=(None, timestep, input_shape), name='inputs') @@ -122,7 +120,7 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): num_classes=output_shape) y_train = keras.utils.to_categorical(y_train, output_shape) - layer = UnifiedLSTM(rnn_state_size, return_runtime=True) + layer = keras.layers.UnifiedLSTM(rnn_state_size, return_runtime=True) inputs = array_ops.placeholder( dtypes.float32, shape=(None, timestep, input_shape), name='inputs') @@ -172,13 +170,14 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): def test_could_use_defun_backend(self, activation, recurrent_activation, recurrent_dropout, unroll, use_bias, bias_regularizer): - layer = UnifiedLSTM(1, - activation=activation, - recurrent_activation=recurrent_activation, - recurrent_dropout=recurrent_dropout, - unroll=unroll, - use_bias=use_bias, - bias_regularizer=bias_regularizer) + layer = keras.layers.UnifiedLSTM( + 1, + activation=activation, + recurrent_activation=recurrent_activation, + recurrent_dropout=recurrent_dropout, + unroll=unroll, + use_bias=use_bias, + bias_regularizer=bias_regularizer) self.assertFalse(layer.could_use_cudnn) def test_unified_lstm_feature_parity_with_canonical_lstm(self): @@ -272,14 +271,14 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): inputs = keras.layers.Input( shape=[timestep, input_shape], dtype=dtypes.float32) with test_util.device(use_gpu=False): - layer = UnifiedLSTM(rnn_state_size) + layer = keras.layers.UnifiedLSTM(rnn_state_size) output = layer(inputs) cpu_model = keras.models.Model(inputs, output) weights = cpu_model.get_weights() y_1 = cpu_model.predict(x_train) with test_util.device(use_gpu=True): - layer = UnifiedLSTM(rnn_state_size) + layer = keras.layers.UnifiedLSTM(rnn_state_size) output = layer(inputs) gpu_model = keras.models.Model(inputs, output) gpu_model.set_weights(weights) @@ -359,7 +358,7 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): num_classes=output_shape) y_train = keras.utils.to_categorical(y_train, output_shape) - layer = UnifiedLSTM(rnn_state_size) + layer = keras.layers.UnifiedLSTM(rnn_state_size) inputs = keras.layers.Input( shape=[timestep, input_shape], dtype=dtypes.float32) @@ -378,7 +377,7 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): embedding_dim = 4 units = 2 testing_utils.layer_test( - UnifiedLSTM, + keras.layers.UnifiedLSTM, kwargs={ 'units': units, 'return_sequences': True @@ -396,7 +395,7 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): inputs = keras.layers.Dense( embedding_dim, input_shape=(timesteps, embedding_dim)) model.add(inputs) - layer = UnifiedLSTM(units, return_sequences=True) + layer = keras.layers.UnifiedLSTM(units, return_sequences=True) model.add(layer) outputs = model.layers[-1].output self.assertEqual(outputs.get_shape().as_list(), [None, timesteps, units]) @@ -407,7 +406,7 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): timesteps = 3 embedding_dim = 4 units = 2 - layer = UnifiedLSTM(units, input_shape=(None, embedding_dim)) + layer = keras.layers.UnifiedLSTM(units, input_shape=(None, embedding_dim)) model = keras.models.Sequential() model.add(layer) model.compile(gradient_descent.GradientDescentOptimizer(0.001), 'mse') @@ -422,7 +421,7 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): embedding_dim = 4 units = 2 testing_utils.layer_test( - UnifiedLSTM, + keras.layers.UnifiedLSTM, kwargs={ 'units': units, 'dropout': 0.1, @@ -438,7 +437,7 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): embedding_dim = 4 units = 2 testing_utils.layer_test( - UnifiedLSTM, + keras.layers.UnifiedLSTM, kwargs={ 'units': units, 'implementation': implementation_mode @@ -448,7 +447,7 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): @test_util.run_in_graph_and_eager_modes(config=_config) def test_constraints_LSTM(self): embedding_dim = 4 - layer_class = UnifiedLSTM + layer_class = keras.layers.UnifiedLSTM k_constraint = keras.constraints.max_norm(0.01) r_constraint = keras.constraints.max_norm(0.01) b_constraint = keras.constraints.max_norm(0.01) @@ -467,7 +466,7 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): @test_util.run_in_graph_and_eager_modes(config=_config) def test_with_masking_layer_LSTM(self): - layer_class = UnifiedLSTM + layer_class = keras.layers.UnifiedLSTM inputs = np.random.random((2, 3, 4)) targets = np.abs(np.random.random((2, 3, 5))) targets /= targets.sum(axis=-1, keepdims=True) @@ -485,8 +484,8 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): targets = np.abs(np.random.random((2, 3, 5))) targets /= targets.sum(axis=-1, keepdims=True) model = keras.models.Sequential() - model.add(UnifiedLSTM(10, return_sequences=True, unroll=False)) - model.add(UnifiedLSTM(5, return_sequences=True, unroll=False)) + model.add(keras.layers.UnifiedLSTM(10, return_sequences=True, unroll=False)) + model.add(keras.layers.UnifiedLSTM(5, return_sequences=True, unroll=False)) model.compile( loss='categorical_crossentropy', optimizer=gradient_descent.GradientDescentOptimizer(0.01)) @@ -499,8 +498,8 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): targets /= targets.sum(axis=-1, keepdims=True) model = keras.models.Sequential() model.add(keras.layers.Masking(input_shape=(3, 4))) - model.add(UnifiedLSTM(10, return_sequences=True, unroll=False)) - model.add(UnifiedLSTM(5, return_sequences=True, unroll=False)) + model.add(keras.layers.UnifiedLSTM(10, return_sequences=True, unroll=False)) + model.add(keras.layers.UnifiedLSTM(5, return_sequences=True, unroll=False)) model.compile( loss='categorical_crossentropy', optimizer=gradient_descent.GradientDescentOptimizer(0.01)) @@ -508,7 +507,7 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): @test_util.run_in_graph_and_eager_modes(config=_config) def test_from_config_LSTM(self): - layer_class = UnifiedLSTM + layer_class = keras.layers.UnifiedLSTM for stateful in (False, True): l1 = layer_class(units=1, stateful=stateful) l2 = layer_class.from_config(l1.get_config()) @@ -525,7 +524,7 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): # Test with Keras tensor inputs = keras.Input((timesteps, embedding_dim)) initial_state = [keras.Input((units,)) for _ in range(num_states)] - layer = UnifiedLSTM(units) + layer = keras.layers.UnifiedLSTM(units) if len(initial_state) == 1: output = layer(inputs, initial_state=initial_state[0]) else: @@ -558,7 +557,7 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): keras.backend.random_normal_variable((num_samples, units), 0, 1) for _ in range(num_states) ] - layer = UnifiedLSTM(units) + layer = keras.layers.UnifiedLSTM(units) output = layer(inputs, initial_state=initial_state) model = keras.models.Model(inputs, output) @@ -578,7 +577,7 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): units = 3 num_samples = 2 - layer = UnifiedLSTM(units, stateful=True) + layer = keras.layers.UnifiedLSTM(units, stateful=True) layer.build((num_samples, timesteps, embedding_dim)) layer.reset_states() assert len(layer.states) == num_states @@ -612,7 +611,8 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): inputs = keras.Input((timesteps, embedding_dim)) _ = keras.layers.Masking()(inputs) initial_state = [keras.Input((units,)) for _ in range(num_states)] - output = UnifiedLSTM(units)(inputs, initial_state=initial_state) + output = keras.layers.UnifiedLSTM(units)( + inputs, initial_state=initial_state) model = keras.models.Model([inputs] + initial_state, output) model.compile( @@ -635,7 +635,7 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): num_samples = 2 inputs = keras.Input(batch_shape=(num_samples, timesteps, embedding_dim)) - layer = UnifiedLSTM(units, return_state=True, stateful=True) + layer = keras.layers.UnifiedLSTM(units, return_state=True, stateful=True) outputs = layer(inputs) state = outputs[1:] assert len(state) == num_states @@ -653,10 +653,11 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): num_samples = 2 inputs = keras.Input(batch_shape=(num_samples, timesteps, embedding_dim)) - layer = UnifiedLSTM(units, return_state=True, return_sequences=True) + layer = keras.layers.UnifiedLSTM( + units, return_state=True, return_sequences=True) outputs = layer(inputs) output, state = outputs[0], outputs[1:] - output = UnifiedLSTM(units)(output, initial_state=state) + output = keras.layers.UnifiedLSTM(units)(output, initial_state=state) model = keras.models.Model(inputs, output) inputs = np.random.random((num_samples, timesteps, embedding_dim)) @@ -669,7 +670,7 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): units = 3 num_samples = 2 num_states = 2 - layer_class = UnifiedLSTM + layer_class = keras.layers.UnifiedLSTM # Test with Keras tensor main_inputs = keras.Input((timesteps, embedding_dim)) @@ -701,7 +702,7 @@ class LSTMLayerGraphOnlyTest(test.TestCase): timesteps = 3 embedding_dim = 4 units = 2 - layer_class = UnifiedLSTM + layer_class = keras.layers.UnifiedLSTM with self.cached_session(config=_config): model = keras.models.Sequential() model.add( @@ -761,7 +762,7 @@ class LSTMLayerGraphOnlyTest(test.TestCase): def test_regularizers_LSTM(self): embedding_dim = 4 - layer_class = UnifiedLSTM + layer_class = keras.layers.UnifiedLSTM with self.cached_session(config=_config): layer = layer_class( 5, @@ -799,7 +800,7 @@ class UnifiedLSTMPerformanceTest(test.Benchmark): rnn_state_size = test_config['rnn_state_size'] timestep = test_config['timestep'] - cudnn_lstm_layer = CuDNNLSTM(rnn_state_size) + cudnn_lstm_layer = keras.layers.CuDNNLSTM(rnn_state_size) inputs = keras.layers.Input( shape=[timestep, input_shape], dtype=dtypes.float32) @@ -820,7 +821,7 @@ class UnifiedLSTMPerformanceTest(test.Benchmark): rnn_state_size = test_config['rnn_state_size'] timestep = test_config['timestep'] - layer = UnifiedLSTM(rnn_state_size) + layer = keras.layers.UnifiedLSTM(rnn_state_size) inputs = keras.layers.Input( shape=[timestep, input_shape], dtype=dtypes.float32) -- GitLab From 5269f8acf996052f8fcf2587f4f929d9de67b6e4 Mon Sep 17 00:00:00 2001 From: Jeremy Lau Date: Tue, 11 Dec 2018 17:06:42 -0800 Subject: [PATCH 401/873] Set infinite GRPC watchdog timeout. Under heavy network load, the GRPC watchdog timer can be "stuck" behind other pending RPCs and timeout actively running workers. Disable this for now. PiperOrigin-RevId: 225098378 --- .../core/distributed_runtime/rpc/grpc_channel.cc | 1 + .../core/distributed_runtime/rpc/grpc_server_lib.cc | 10 ++++++---- 2 files changed, 7 insertions(+), 4 deletions(-) diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_channel.cc b/tensorflow/core/distributed_runtime/rpc/grpc_channel.cc index 1420589f82..e5634d38bd 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_channel.cc +++ b/tensorflow/core/distributed_runtime/rpc/grpc_channel.cc @@ -61,6 +61,7 @@ Status ValidateHostPortPair(const string& host_port) { ::grpc::ChannelArguments args; args.SetInt(GRPC_ARG_MAX_MESSAGE_LENGTH, std::numeric_limits::max()); args.SetInt(GRPC_ARG_KEEPALIVE_TIME_MS, std::numeric_limits::max()); + args.SetInt(GRPC_ARG_KEEPALIVE_TIMEOUT_MS, std::numeric_limits::max()); // NOTE(mrry): Some versions of gRPC use a 20-second minimum backoff // on connection failure, which makes our tests time out. args.SetInt("grpc.testing.fixed_reconnect_backoff_ms", 1000); diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc b/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc index 33ff8e1ac4..08518606f6 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc +++ b/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc @@ -110,10 +110,7 @@ GrpcServer::~GrpcServer() { // - worker_env_.compute_pool } -void GrpcServer::MaybeMutateBuilder(::grpc::ServerBuilder* builder) { - builder->AddChannelArgument(GRPC_ARG_KEEPALIVE_TIME_MS, - std::numeric_limits::max()); -} +void GrpcServer::MaybeMutateBuilder(::grpc::ServerBuilder* builder) {} Status GrpcServer::Init( ServiceInitFunction service_func, @@ -196,6 +193,11 @@ Status GrpcServer::Init( builder.AddListeningPort(strings::StrCat("0.0.0.0:", requested_port), GetServerCredentials(server_def_), &bound_port_); builder.SetMaxMessageSize(std::numeric_limits::max()); + builder.AddChannelArgument(GRPC_ARG_KEEPALIVE_TIME_MS, + std::numeric_limits::max()); + builder.AddChannelArgument(GRPC_ARG_KEEPALIVE_TIMEOUT_MS, + std::numeric_limits::max()); + builder.SetOption( std::unique_ptr<::grpc::ServerBuilderOption>(new NoReusePortOption)); // Allow subclasses to specify more args to pass to the gRPC server. -- GitLab From dcd966eaba1661315828bf9141512c1bdc0b827b Mon Sep 17 00:00:00 2001 From: Eugene Zhulenev Date: Tue, 11 Dec 2018 17:41:45 -0800 Subject: [PATCH 402/873] Runtime flag to disable MKL-DNN contraction kernels PiperOrigin-RevId: 225102856 --- tensorflow/core/kernels/BUILD | 1 + .../core/kernels/eigen_contraction_kernel.cc | 55 +++++++++++++++++++ .../core/kernels/eigen_contraction_kernel.h | 48 +++++++++++++++- 3 files changed, 101 insertions(+), 3 deletions(-) create mode 100644 tensorflow/core/kernels/eigen_contraction_kernel.cc diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD index e8b1dd270f..d62992233b 100644 --- a/tensorflow/core/kernels/BUILD +++ b/tensorflow/core/kernels/BUILD @@ -592,6 +592,7 @@ cc_library( # #endif cc_library( name = "eigen_contraction_kernel", + srcs = ["eigen_contraction_kernel.cc"], hdrs = ["eigen_contraction_kernel.h"], defines = select({ ":mkldnn_contraction_kernel": [ diff --git a/tensorflow/core/kernels/eigen_contraction_kernel.cc b/tensorflow/core/kernels/eigen_contraction_kernel.cc new file mode 100644 index 0000000000..da42001781 --- /dev/null +++ b/tensorflow/core/kernels/eigen_contraction_kernel.cc @@ -0,0 +1,55 @@ +/* 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/eigen_contraction_kernel.h" + +#include // NOLINT(build/c++11) + +// We need a pair of compile time and runtime flags to disable compilation of +// custom contraction kernels for unsupported architectures (e.g. Android, +// iOS, ARM and PPC CPUs, etc...), and to be able to fallback on default Eigen +// matrix multiplication at runtime. +// +// It's not allowed to use absl flags library in Tensorflow, so we have to pass +// the configuration through the environment variable. +// +// Example: +// bazel test --test_env=TENSORFLOW_USE_CUSTOM_CONTRACTION_KERNEL=false //test + +#if defined(TENSORFLOW_USE_CUSTOM_CONTRACTION_KERNEL) + +namespace Eigen { +namespace internal { + +// TODO(ezhulenev): This is a temporary workaround for disabling custom kernels +// at runtime in tests. We should always rely on compile time flags for that. +// Example: ... --test_env=TENSORFLOW_USE_CUSTOM_CONTRACTION_KERNEL=false //test +bool UseCustomContractionKernels() { + static bool use_custom_contraction_kernel = true; + + static std::once_flag initialized; + std::call_once(initialized, [&] { + char* flag = std::getenv("TENSORFLOW_USE_CUSTOM_CONTRACTION_KERNEL"); + if (flag && (strcmp(flag, "false") == 0 || strcmp(flag, "0") == 0)) { + use_custom_contraction_kernel = false; + } + }); + + return use_custom_contraction_kernel; +} + +} // namespace internal +} // namespace Eigen +#endif diff --git a/tensorflow/core/kernels/eigen_contraction_kernel.h b/tensorflow/core/kernels/eigen_contraction_kernel.h index 66e93a83af..3d8e52ca0e 100644 --- a/tensorflow/core/kernels/eigen_contraction_kernel.h +++ b/tensorflow/core/kernels/eigen_contraction_kernel.h @@ -33,11 +33,20 @@ limitations under the License. // #endif #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" + +#if defined(TENSORFLOW_USE_MKLDNN_CONTRACTION_KERNEL) #include "mkldnn.h" +#endif namespace Eigen { namespace internal { +#if defined(TENSORFLOW_USE_CUSTOM_CONTRACTION_KERNEL) +// Returns `true` iff we can use custom contraction kernels. This is a runtime +// check, that uses environment variables. +bool UseCustomContractionKernels(); +#endif // TENSORFLOW_USE_CUSTOM_CONTRACTION_KERNEL + // Enabled by build option: "--define tensorflow_mkldnn_contraction_kernel=1" #if defined(TENSORFLOW_USE_MKLDNN_CONTRACTION_KERNEL) @@ -170,6 +179,10 @@ class TensorContractionBlocking(mc_ * kScaleM), kUnrollM) * @@ -211,23 +224,52 @@ struct TensorContractionKernel; using GemmKernel = mkldnn_gemm_kernel; + // Fallback on default Eigen pack and GEBP kernel if custom contraction + // kernels disabled at runtime. + using EigenLhsPacker = + gemm_pack_lhs; + using EigenRhsPacker = + gemm_pack_rhs; + using GebpKernel = + gebp_kernel; + EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE static void packLhs( Scalar* lhsBlock, const typename LhsMapper::SubMapper& data_mapper, const StorageIndex depth, const StorageIndex rows) { - LhsPacker()(lhsBlock, data_mapper, rows, depth); + if (UseCustomContractionKernels()) { + LhsPacker()(lhsBlock, data_mapper, rows, depth); + } else { + EigenLhsPacker()(lhsBlock, data_mapper, depth, rows, /*stride*/ 0, + /*offset*/ 0); + } } EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE static void packRhs( Scalar* rhsBlock, const typename RhsMapper::SubMapper& data_mapper, const StorageIndex depth, const StorageIndex cols) { - RhsPacker()(rhsBlock, data_mapper, depth, cols); + if (UseCustomContractionKernels()) { + RhsPacker()(rhsBlock, data_mapper, depth, cols); + } else { + EigenRhsPacker()(rhsBlock, data_mapper, depth, cols); + } } EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE static void invoke( const OutputMapper& output_mapper, const Scalar* lhsBlock, const Scalar* rhsBlock, const StorageIndex rows, const StorageIndex depth, const StorageIndex cols, const Scalar alpha) { - GemmKernel()(output_mapper, lhsBlock, rhsBlock, rows, depth, cols, alpha); + if (UseCustomContractionKernels()) { + GemmKernel()(output_mapper, lhsBlock, rhsBlock, rows, depth, cols, alpha); + } else { + GebpKernel()(output_mapper, lhsBlock, rhsBlock, rows, depth, cols, alpha, + /*strideA*/ -1, /*strideB*/ -1, + /*offsetA*/ 0, /*offsetB*/ 0); + } } }; -- GitLab From 0bdd941c2adca373b91b74925ccd3528a565b8bc Mon Sep 17 00:00:00 2001 From: Zhenyu Tan Date: Tue, 11 Dec 2018 17:42:38 -0800 Subject: [PATCH 403/873] expose v2 api for optimizers and migrate away from keras v1 optimizers. PiperOrigin-RevId: 225102983 --- .../compiler/tf2xla/kernels/training_ops.cc | 59 ++++ .../tf2xla/resource_operation_table.cc | 1 + .../python/keras_optimizer_v2_test.py | 112 +------- .../contrib/tpu/python/tpu/keras_support.py | 4 + .../tpu/python/tpu/keras_tpu_variables.py | 1 + tensorflow/python/keras/engine/training.py | 6 +- .../python/keras/optimizer_v2/adadelta.py | 4 +- .../keras/optimizer_v2/adadelta_test.py | 15 +- .../python/keras/optimizer_v2/adagrad.py | 4 +- .../python/keras/optimizer_v2/adagrad_test.py | 23 +- tensorflow/python/keras/optimizer_v2/adam.py | 14 +- .../python/keras/optimizer_v2/adam_test.py | 12 +- .../python/keras/optimizer_v2/adamax.py | 2 + .../python/keras/optimizer_v2/adamax_test.py | 12 +- tensorflow/python/keras/optimizer_v2/ftrl.py | 2 + .../python/keras/optimizer_v2/ftrl_test.py | 7 +- .../keras/optimizer_v2/gradient_descent.py | 16 +- .../optimizer_v2/gradient_descent_test.py | 34 ++- tensorflow/python/keras/optimizer_v2/nadam.py | 3 + .../python/keras/optimizer_v2/nadam_test.py | 12 + .../python/keras/optimizer_v2/optimizer_v2.py | 253 ++++++++++++------ .../keras/optimizer_v2/optimizer_v2_test.py | 66 ++--- .../python/keras/optimizer_v2/rmsprop.py | 14 +- .../python/keras/optimizer_v2/rmsprop_test.py | 23 +- tensorflow/python/keras/optimizers.py | 7 - ...ensorflow.keras.optimizers.-adadelta.pbtxt | 41 ++- ...tensorflow.keras.optimizers.-adagrad.pbtxt | 41 ++- .../tensorflow.keras.optimizers.-adam.pbtxt | 41 ++- .../tensorflow.keras.optimizers.-adamax.pbtxt | 42 ++- ...nsorflow.keras.optimizers.-optimizer.pbtxt | 39 ++- ...nsorflow.keras.optimizers.-r-m-sprop.pbtxt | 41 ++- .../tensorflow.keras.optimizers.-s-g-d.pbtxt | 41 ++- ...ensorflow.keras.optimizers.-adadelta.pbtxt | 41 ++- ...tensorflow.keras.optimizers.-adagrad.pbtxt | 41 ++- .../tensorflow.keras.optimizers.-adam.pbtxt | 41 ++- .../tensorflow.keras.optimizers.-adamax.pbtxt | 42 ++- ...nsorflow.keras.optimizers.-optimizer.pbtxt | 39 ++- ...nsorflow.keras.optimizers.-r-m-sprop.pbtxt | 41 ++- .../tensorflow.keras.optimizers.-s-g-d.pbtxt | 41 ++- 39 files changed, 942 insertions(+), 336 deletions(-) diff --git a/tensorflow/compiler/tf2xla/kernels/training_ops.cc b/tensorflow/compiler/tf2xla/kernels/training_ops.cc index 960c1462ce..26d4214099 100644 --- a/tensorflow/compiler/tf2xla/kernels/training_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/training_ops.cc @@ -172,6 +172,65 @@ class ResourceApplyMomentum : public XlaOpKernel { REGISTER_XLA_OP(Name("ResourceApplyMomentum").TypeConstraint("T", kFloatTypes), ResourceApplyMomentum); +class ResourceApplyKerasMomentum : public XlaOpKernel { + public: + explicit ResourceApplyKerasMomentum(OpKernelConstruction* ctx) + : XlaOpKernel(ctx) { + OP_REQUIRES_OK(ctx, ctx->GetAttr("use_nesterov", &use_nesterov_)); + } + + void Compile(XlaOpKernelContext* ctx) override { + DataType type = ctx->input_type(2); + + TensorShape var_shape, accum_shape; + xla::XlaOp var, accum; + OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(0, type, &var_shape, &var)); + OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(1, type, &accum_shape, &accum)); + + OP_REQUIRES(ctx, var_shape.IsSameSize(accum_shape), + errors::InvalidArgument( + "var and accum do not have the same shape", + var_shape.DebugString(), " ", accum_shape.DebugString())); + + TensorShape lr_shape = ctx->InputShape(2); + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(lr_shape), + errors::InvalidArgument("lr is not a scalar: ", + lr_shape.DebugString())); + + TensorShape grad_shape = ctx->InputShape(3); + OP_REQUIRES(ctx, var_shape.IsSameSize(grad_shape), + errors::InvalidArgument( + "var and grad do not have the same shape", + var_shape.DebugString(), " ", grad_shape.DebugString())); + + TensorShape momentum_shape = ctx->InputShape(4); + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(momentum_shape), + errors::InvalidArgument("momentum is not a scalar: ", + momentum_shape.DebugString())); + + xla::XlaOp lr = ctx->Input(2); + xla::XlaOp grad = ctx->Input(3); + xla::XlaOp momentum = ctx->Input(4); + + accum = accum * momentum - grad * lr; + if (use_nesterov_) { + // See https://github.com/tensorflow/tensorflow/pull/2798 for an + // explanation of the reparameterization used here. + var = var + accum * momentum - grad * lr; + } else { + var = var + accum; + } + OP_REQUIRES_OK(ctx, ctx->AssignVariable(0, type, var)); + OP_REQUIRES_OK(ctx, ctx->AssignVariable(1, type, accum)); + } + + private: + bool use_nesterov_; +}; +REGISTER_XLA_OP( + Name("ResourceApplyKerasMomentum").TypeConstraint("T", kFloatTypes), + ResourceApplyKerasMomentum); + class ResourceApplyAdagrad : public XlaOpKernel { public: explicit ResourceApplyAdagrad(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} diff --git a/tensorflow/compiler/tf2xla/resource_operation_table.cc b/tensorflow/compiler/tf2xla/resource_operation_table.cc index 72b240996f..ff9f1b9ccb 100644 --- a/tensorflow/compiler/tf2xla/resource_operation_table.cc +++ b/tensorflow/compiler/tf2xla/resource_operation_table.cc @@ -65,6 +65,7 @@ CreateResourceOpInfoMap() { add("ResourceApplyFtrlV2" , kReadWrite, kVariable); add("ResourceApplyGradientDescent" , kReadWrite, kVariable); add("ResourceApplyMomentum" , kReadWrite, kVariable); + add("ResourceApplyKerasMomentum" , kReadWrite, kVariable); add("ResourceApplyPowerSign" , kReadWrite, kVariable); add("ResourceApplyProximalAdagrad" , kReadWrite, kVariable); add("ResourceApplyProximalGradientDescent" , kReadWrite, kVariable); diff --git a/tensorflow/contrib/distribute/python/keras_optimizer_v2_test.py b/tensorflow/contrib/distribute/python/keras_optimizer_v2_test.py index 6dfd85bcc4..8c596549c4 100644 --- a/tensorflow/contrib/distribute/python/keras_optimizer_v2_test.py +++ b/tensorflow/contrib/distribute/python/keras_optimizer_v2_test.py @@ -18,24 +18,12 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import shutil -import tempfile from absl.testing import parameterized import numpy as np -import six from tensorflow.contrib.distribute.python import combinations -from tensorflow.core.protobuf import config_pb2 from tensorflow.python import keras -from tensorflow.python.data.ops import dataset_ops from tensorflow.python.distribute import distribution_strategy_context as ds_context -from tensorflow.python.estimator import run_config -from tensorflow.python.estimator import training -from tensorflow.python.estimator.canned import dnn_linear_combined -from tensorflow.python.estimator.canned import prediction_keys -from tensorflow.python.estimator.export import export -from tensorflow.python.estimator.inputs import numpy_io -from tensorflow.python.feature_column import feature_column_lib as feature_column from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -44,103 +32,7 @@ from tensorflow.python.keras.optimizer_v2 import gradient_descent from tensorflow.python.ops import math_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables -from tensorflow.python.platform import gfile from tensorflow.python.platform import test -from tensorflow.python.summary.writer import writer_cache - - -class KerasOptimizerV2IntegrationTest(test.TestCase, parameterized.TestCase): - - def setUp(self): - self._model_dir = tempfile.mkdtemp() - - def dataset_input_fn(self, x, y, batch_size): - - def input_fn(): - dataset = dataset_ops.Dataset.from_tensor_slices((x, y)) - dataset = dataset.repeat(1).batch(batch_size) - return dataset - - return input_fn - - @combinations.generate( - combinations.combine( - mode=['graph'], - distribution=[ - combinations.one_device_strategy, - combinations.mirrored_strategy_with_gpu_and_cpu, - combinations.mirrored_strategy_with_two_gpus, - combinations.core_mirrored_strategy_with_gpu_and_cpu, - combinations.core_mirrored_strategy_with_two_gpus - ], - use_train_and_evaluate=[True, False])) - def test_complete_flow_with_mode(self, distribution, use_train_and_evaluate): - label_dimension = 2 - input_dimension = label_dimension - batch_size = 10 - data = np.linspace(0., 2., batch_size * label_dimension, dtype=np.float32) - data = data.reshape(batch_size, label_dimension) - train_input_fn = self.dataset_input_fn( - x={'x': data}, - y=data, - batch_size=batch_size // distribution.num_replicas_in_sync) - eval_input_fn = self.dataset_input_fn( - x={'x': data}, - y=data, - batch_size=batch_size // distribution.num_replicas_in_sync) - predict_input_fn = numpy_io.numpy_input_fn( - x={'x': data}, batch_size=batch_size, shuffle=False) - - linear_feature_columns = [ - feature_column.numeric_column('x', shape=(input_dimension,)) - ] - dnn_feature_columns = [ - feature_column.numeric_column('x', shape=(input_dimension,)) - ] - feature_columns = linear_feature_columns + dnn_feature_columns - session_config = config_pb2.ConfigProto( - log_device_placement=True, allow_soft_placement=True) - estimator = dnn_linear_combined.DNNLinearCombinedRegressor( - linear_feature_columns=linear_feature_columns, - dnn_hidden_units=(2, 2), - dnn_feature_columns=dnn_feature_columns, - label_dimension=label_dimension, - model_dir=self._model_dir, - dnn_optimizer=adam.Adam(0.001), - linear_optimizer=adam.Adam(0.001), - config=run_config.RunConfig( - train_distribute=distribution, - eval_distribute=distribution, - session_config=session_config)) - - num_steps = 2 - if use_train_and_evaluate: - scores, _ = training.train_and_evaluate( - estimator, training.TrainSpec(train_input_fn, max_steps=num_steps), - training.EvalSpec(eval_input_fn)) - else: - estimator.train(train_input_fn, steps=num_steps) - scores = estimator.evaluate(eval_input_fn) - - self.assertIn('loss', six.iterkeys(scores)) - - predictions = np.array([ - x[prediction_keys.PredictionKeys.PREDICTIONS] - for x in estimator.predict(predict_input_fn) - ]) - self.assertAllEqual((batch_size, label_dimension), predictions.shape) - - feature_spec = feature_column.make_parse_example_spec(feature_columns) - serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn( - feature_spec) - export_dir = estimator.export_savedmodel(tempfile.mkdtemp(), - serving_input_receiver_fn) - self.assertTrue(gfile.Exists(export_dir)) - - def tearDown(self): - if self._model_dir: - writer_cache.FileWriterCache.clear() - shutil.rmtree(self._model_dir) def get_model(): @@ -162,7 +54,9 @@ class MirroredStrategyOptimizerV2Test(test.TestCase, parameterized.TestCase): var = variables.Variable( 2.0, name='var', aggregation=variable_scope.VariableAggregation.SUM) # grad for cpu is 1, grad for gpu is 2, avg grad is 1.5. - loss = math_ops.cast(_replica_id() + 1, dtype=dtypes.float32) * var + def loss(): + return math_ops.cast(_replica_id() + 1, dtype=dtypes.float32) * var + optimizer = adam.Adam(learning_rate=0.01, beta_1=0.2, beta_2=0.2) train_op = optimizer.minimize(loss, var_list=[var]) m = optimizer.get_slot(var, 'm') diff --git a/tensorflow/contrib/tpu/python/tpu/keras_support.py b/tensorflow/contrib/tpu/python/tpu/keras_support.py index 4ce1945903..cf9672f8d8 100644 --- a/tensorflow/contrib/tpu/python/tpu/keras_support.py +++ b/tensorflow/contrib/tpu/python/tpu/keras_support.py @@ -2069,6 +2069,8 @@ class KerasTPUModel(models.Model): # tpu_model may not be compiled, e.g., loading weights and then predict. return for k, v in six.iteritems(cpu_optimizer_config): + if k == 'name': + continue opt_var = getattr(self._tpu_model.optimizer, k) if isinstance(opt_var, variables.Variable): logging.info('CPU -> TPU %s: %s {%s}', k, v, K.get_value(opt_var)) @@ -2097,6 +2099,8 @@ class KerasTPUModel(models.Model): self._cpu_model.set_weights(tpu_weights) for k, v in six.iteritems(tpu_optimizer_config): logging.info('TPU -> CPU %s: %s', k, v) + if k == 'name': + continue opt_var = getattr(self.cpu_optimizer, k) if isinstance(opt_var, variables.Variable): K.get_session().run(opt_var.assign(v)) diff --git a/tensorflow/contrib/tpu/python/tpu/keras_tpu_variables.py b/tensorflow/contrib/tpu/python/tpu/keras_tpu_variables.py index 8b0b240dc7..de425626c8 100644 --- a/tensorflow/contrib/tpu/python/tpu/keras_tpu_variables.py +++ b/tensorflow/contrib/tpu/python/tpu/keras_tpu_variables.py @@ -69,6 +69,7 @@ class ReplicatedVariable(object): def __init__(self, name, variables): self._name = name self._primary_var = variables[0] + self._common_name = self._primary_var.name.split(":")[0] self._vars = variables self._cached_value = None self._dtype = variables[0].dtype diff --git a/tensorflow/python/keras/engine/training.py b/tensorflow/python/keras/engine/training.py index 75d6496988..de929f2d3c 100644 --- a/tensorflow/python/keras/engine/training.py +++ b/tensorflow/python/keras/engine/training.py @@ -40,6 +40,7 @@ from tensorflow.python.keras.engine import training_eager from tensorflow.python.keras.engine import training_generator from tensorflow.python.keras.engine import training_utils from tensorflow.python.keras.engine.network import Network +from tensorflow.python.keras.optimizer_v2 import optimizer_v2 from tensorflow.python.keras.utils import data_utils from tensorflow.python.keras.utils.generic_utils import slice_arrays from tensorflow.python.keras.utils.losses_utils import squeeze_or_expand_dimensions @@ -195,8 +196,9 @@ class Model(Network): # Validate that arguments passed by the user to `compile` are supported by # DistributionStrategy. if distribute: - if not isinstance( - optimizer, (tf_optimizer_module.Optimizer, optimizers.TFOptimizer)): + if not isinstance(optimizer, + (tf_optimizer_module.Optimizer, optimizers.TFOptimizer, + optimizer_v2.OptimizerV2)): raise NotImplementedError( 'optimizer must be an instance of ' 'tf.train.Optimizer, not a %s' % type(optimizer)) diff --git a/tensorflow/python/keras/optimizer_v2/adadelta.py b/tensorflow/python/keras/optimizer_v2/adadelta.py index 55b4eba105..8985325056 100644 --- a/tensorflow/python/keras/optimizer_v2/adadelta.py +++ b/tensorflow/python/keras/optimizer_v2/adadelta.py @@ -22,8 +22,10 @@ import numpy as np from tensorflow.python.keras.optimizer_v2 import optimizer_v2 from tensorflow.python.training import training_ops +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.optimizers.Adadelta') class Adadelta(optimizer_v2.OptimizerV2): r"""Optimizer that implements the Adadelta algorithm. @@ -85,7 +87,7 @@ class Adadelta(optimizer_v2.OptimizerV2): @end_compatibility """ super(Adadelta, self).__init__(name, **kwargs) - self._set_hyper('learning_rate', learning_rate) + self._set_hyper('learning_rate', kwargs.get('lr', learning_rate)) self._set_hyper('decay', self._initial_decay) self._set_hyper('rho', rho) self._set_hyper('epsilon', epsilon) diff --git a/tensorflow/python/keras/optimizer_v2/adadelta_test.py b/tensorflow/python/keras/optimizer_v2/adadelta_test.py index 0fb67d0cd1..c95af6a8ad 100644 --- a/tensorflow/python/keras/optimizer_v2/adadelta_test.py +++ b/tensorflow/python/keras/optimizer_v2/adadelta_test.py @@ -153,8 +153,11 @@ class AdadeltaOptimizerTest(test.TestCase): with self.cached_session(): var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype) x = constant_op.constant([[4.0], [5.0]], dtype=dtype) - pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) - loss = pred * pred + + def loss(): + pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) # pylint: disable=cell-var-from-loop + return pred * pred + sgd_op = adadelta.Adadelta(1.0, 1.0, 1.0).minimize( loss, var_list=[var0]) variables.global_variables_initializer().run() @@ -165,6 +168,14 @@ class AdadeltaOptimizerTest(test.TestCase): # Validate updated params self.assertAllCloseAccordingToType([[-111, -138]], self.evaluate(var0)) + def testConstructAdadeltaWithLR(self): + opt = adadelta.Adadelta(lr=1.0, rho=0.9, epsilon=1.) + self.assertEqual(opt.lr, 1.0) + opt_2 = adadelta.Adadelta(learning_rate=0.1, rho=0.9, epsilon=1., lr=1.0) + self.assertEqual(opt_2.lr, 1.0) + opt_3 = adadelta.Adadelta(learning_rate=0.1, rho=0.9, epsilon=1.) + self.assertEqual(opt_3.lr, 0.1) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/keras/optimizer_v2/adagrad.py b/tensorflow/python/keras/optimizer_v2/adagrad.py index 670cad70e6..6faf7fc2f4 100644 --- a/tensorflow/python/keras/optimizer_v2/adagrad.py +++ b/tensorflow/python/keras/optimizer_v2/adagrad.py @@ -27,8 +27,10 @@ from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import state_ops +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.optimizers.Adagrad') class Adagrad(optimizer_v2.OptimizerV2): r"""Optimizer that implements the Adagrad algorithm. @@ -86,7 +88,7 @@ class Adagrad(optimizer_v2.OptimizerV2): if epsilon < 1e-7: raise ValueError('epsilon must be larger than 1e-7: %s' % epsilon) super(Adagrad, self).__init__(name, **kwargs) - self._set_hyper('learning_rate', learning_rate) + self._set_hyper('learning_rate', kwargs.get('lr', learning_rate)) self._set_hyper('decay', self._initial_decay) self._initial_accumulator_value = initial_accumulator_value self._set_hyper('epsilon', epsilon) diff --git a/tensorflow/python/keras/optimizer_v2/adagrad_test.py b/tensorflow/python/keras/optimizer_v2/adagrad_test.py index b2c290178f..cf6f6a7832 100644 --- a/tensorflow/python/keras/optimizer_v2/adagrad_test.py +++ b/tensorflow/python/keras/optimizer_v2/adagrad_test.py @@ -167,8 +167,11 @@ class AdagradOptimizerTest(test.TestCase): var0 = resource_variable_ops.ResourceVariable( [[1.0, 2.0], [3.0, 4.0]], dtype=dtype) x = constant_op.constant([[4.0], [5.0]], dtype=dtype) - pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) - loss = pred * pred + + def loss(): + pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) # pylint: disable=cell-var-from-loop + return pred * pred + sgd_op = adagrad.Adagrad(1.0).minimize(loss, var_list=[var0]) variables.global_variables_initializer().run() # Fetch params to validate initial values @@ -297,12 +300,12 @@ class AdagradOptimizerTest(test.TestCase): with self.cached_session(): var_repeated = resource_variable_ops.ResourceVariable( [1.0, 2.0], dtype=dtype) - loss_repeated = math_ops.reduce_sum( - embedding_ops.embedding_lookup(var_repeated, [0, 0])) + loss_repeated = lambda: math_ops.reduce_sum( # pylint: disable=g-long-lambda + embedding_ops.embedding_lookup(var_repeated, [0, 0])) # pylint: disable=cell-var-from-loop var_aggregated = resource_variable_ops.ResourceVariable( [1.0, 2.0], dtype=dtype) - loss_aggregated = 2 * math_ops.reduce_sum( - embedding_ops.embedding_lookup(var_aggregated, [0])) + loss_aggregated = lambda: 2 * math_ops.reduce_sum( # pylint: disable=g-long-lambda + embedding_ops.embedding_lookup(var_aggregated, [0])) # pylint: disable=cell-var-from-loop update_op_repeated = adagrad.Adagrad(2.0).minimize( loss_repeated, var_list=[var_repeated]) update_op_aggregated = adagrad.Adagrad(2.0).minimize( @@ -395,6 +398,14 @@ class AdagradOptimizerTest(test.TestCase): self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0)) self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1)) + def testConstructAdagradWithLR(self): + opt = adagrad.Adagrad(lr=1.0) + self.assertEqual(opt.lr, 1.0) + opt_2 = adagrad.Adagrad(learning_rate=0.1, lr=1.0) + self.assertEqual(opt_2.lr, 1.0) + opt_3 = adagrad.Adagrad(learning_rate=0.1) + self.assertEqual(opt_3.lr, 0.1) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/keras/optimizer_v2/adam.py b/tensorflow/python/keras/optimizer_v2/adam.py index ef3d783f89..c99468f8cf 100644 --- a/tensorflow/python/keras/optimizer_v2/adam.py +++ b/tensorflow/python/keras/optimizer_v2/adam.py @@ -24,8 +24,10 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import state_ops from tensorflow.python.training import training_ops +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.optimizers.Adam') class Adam(optimizer_v2.OptimizerV2): """Optimizer that implements the Adam algorithm. @@ -127,12 +129,12 @@ class Adam(optimizer_v2.OptimizerV2): """ super(Adam, self).__init__(name, **kwargs) - self._set_hyper('learning_rate', learning_rate) + self._set_hyper('learning_rate', kwargs.get('lr', learning_rate)) self._set_hyper('decay', self._initial_decay) self._set_hyper('beta_1', beta_1) self._set_hyper('beta_2', beta_2) self._set_hyper('epsilon', epsilon) - self._amsgrad = amsgrad + self.amsgrad = amsgrad def _create_slots(self, var_list): # Create slots for the first and second moments. @@ -141,7 +143,7 @@ class Adam(optimizer_v2.OptimizerV2): self.add_slot(var, 'm') for var in var_list: self.add_slot(var, 'v') - if self._amsgrad: + if self.amsgrad: for var in var_list: self.add_slot(var, 'vhat') @@ -166,7 +168,7 @@ class Adam(optimizer_v2.OptimizerV2): local_step = math_ops.cast(self.iterations + 1, var_dtype) beta_1_power = math_ops.pow(beta_1_t, local_step) beta_2_power = math_ops.pow(beta_2_t, local_step) - if not self._amsgrad: + if not self.amsgrad: return training_ops.resource_apply_adam( var.handle, m.handle, @@ -220,7 +222,7 @@ class Adam(optimizer_v2.OptimizerV2): with ops.control_dependencies([v_t]): v_t = self._resource_scatter_add(v, indices, v_scaled_g_values) - if not self._amsgrad: + if not self.amsgrad: v_sqrt = math_ops.sqrt(v_t) var_update = state_ops.assign_sub( var, lr * m_t / (v_sqrt + epsilon_t), use_locking=self._use_locking) @@ -251,6 +253,6 @@ class Adam(optimizer_v2.OptimizerV2): 'beta_1': self._serialize_hyperparameter('beta_1'), 'beta_2': self._serialize_hyperparameter('beta_2'), 'epsilon': self._serialize_hyperparameter('epsilon'), - 'amsgrad': self._amsgrad, + 'amsgrad': self.amsgrad, }) return config diff --git a/tensorflow/python/keras/optimizer_v2/adam_test.py b/tensorflow/python/keras/optimizer_v2/adam_test.py index 3bbafe12f8..49a9de41cd 100644 --- a/tensorflow/python/keras/optimizer_v2/adam_test.py +++ b/tensorflow/python/keras/optimizer_v2/adam_test.py @@ -162,9 +162,9 @@ class AdamOptimizerTest(test.TestCase): # it (i.e. they have GPU kernels). var = variables.Variable([[1.0], [2.0]]) indices = constant_op.constant([0, 1], dtype=index_dtype) - gathered_sum = math_ops.reduce_sum(array_ops.gather(var, indices)) + g_sum = lambda: math_ops.reduce_sum(array_ops.gather(var, indices)) # pylint: disable=cell-var-from-loop optimizer = adam.Adam(3.0) - minimize_op = optimizer.minimize(gathered_sum, var_list=[var]) + minimize_op = optimizer.minimize(g_sum, var_list=[var]) variables.global_variables_initializer().run() minimize_op.run() @@ -503,6 +503,14 @@ class AdamOptimizerTest(test.TestCase): self.assertEqual( self.evaluate(keras_v1_iteration), self.evaluate(keras_v2_iteration)) + def testConstructAdamWithLR(self): + opt = adam.Adam(lr=1.0) + self.assertEqual(opt.lr, 1.0) + opt_2 = adam.Adam(learning_rate=0.1, lr=1.0) + self.assertEqual(opt_2.lr, 1.0) + opt_3 = adam.Adam(learning_rate=0.1) + self.assertEqual(opt_3.lr, 0.1) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/keras/optimizer_v2/adamax.py b/tensorflow/python/keras/optimizer_v2/adamax.py index ddd78584f8..920a6c0fd3 100644 --- a/tensorflow/python/keras/optimizer_v2/adamax.py +++ b/tensorflow/python/keras/optimizer_v2/adamax.py @@ -25,8 +25,10 @@ from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.training import training_ops +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.optimizers.Adamax') class Adamax(adam.Adam): """Optimizer that implements the Adamax algorithm. diff --git a/tensorflow/python/keras/optimizer_v2/adamax_test.py b/tensorflow/python/keras/optimizer_v2/adamax_test.py index baf131fbb0..339c0fe6e6 100644 --- a/tensorflow/python/keras/optimizer_v2/adamax_test.py +++ b/tensorflow/python/keras/optimizer_v2/adamax_test.py @@ -136,9 +136,9 @@ class AdamaxOptimizerTest(test.TestCase): # it (i.e. they have GPU kernels). var = variables.Variable([[1.0], [2.0]]) indices = constant_op.constant([0, 1], dtype=index_dtype) - gathered_sum = math_ops.reduce_sum(array_ops.gather(var, indices)) + g_sum = lambda: math_ops.reduce_sum(array_ops.gather(var, indices)) # pylint: disable=cell-var-from-loop optimizer = adamax.Adamax(3.0) - minimize_op = optimizer.minimize(gathered_sum, var_list=[var]) + minimize_op = optimizer.minimize(g_sum, var_list=[var]) variables.global_variables_initializer().run() minimize_op.run() @@ -362,6 +362,14 @@ class AdamaxOptimizerTest(test.TestCase): # There should be iteration, and two unique slot variables for v1 and v2. self.assertEqual(5, len(set(opt.variables()))) + def testConstructAdamaxWithLR(self): + opt = adamax.Adamax(lr=1.0) + self.assertEqual(opt.lr, 1.0) + opt_2 = adamax.Adamax(learning_rate=0.1, lr=1.0) + self.assertEqual(opt_2.lr, 1.0) + opt_3 = adamax.Adamax(learning_rate=0.1) + self.assertEqual(opt_3.lr, 0.1) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/keras/optimizer_v2/ftrl.py b/tensorflow/python/keras/optimizer_v2/ftrl.py index e278e352f5..365bd68220 100644 --- a/tensorflow/python/keras/optimizer_v2/ftrl.py +++ b/tensorflow/python/keras/optimizer_v2/ftrl.py @@ -21,8 +21,10 @@ from tensorflow.python.keras.optimizer_v2 import optimizer_v2 from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.training import training_ops +from tensorflow.python.util.tf_export import tf_export +@tf_export('keras.optimizers.Ftrl') class Ftrl(optimizer_v2.OptimizerV2): """Optimizer that implements the FTRL algorithm. diff --git a/tensorflow/python/keras/optimizer_v2/ftrl_test.py b/tensorflow/python/keras/optimizer_v2/ftrl_test.py index bec400e8cb..f0f07e9d03 100644 --- a/tensorflow/python/keras/optimizer_v2/ftrl_test.py +++ b/tensorflow/python/keras/optimizer_v2/ftrl_test.py @@ -113,8 +113,11 @@ class FtrlOptimizerTest(test.TestCase): with self.cached_session(): var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype) x = constant_op.constant([[4.0], [5.0]], dtype=dtype) - pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) - loss = pred * pred + + def loss(): + pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) # pylint: disable=cell-var-from-loop + return pred * pred + sgd_op = ftrl.Ftrl(1.0).minimize(loss, var_list=[var0]) variables.global_variables_initializer().run() # Fetch params to validate initial values diff --git a/tensorflow/python/keras/optimizer_v2/gradient_descent.py b/tensorflow/python/keras/optimizer_v2/gradient_descent.py index 2b82b5e78d..a77ae30551 100644 --- a/tensorflow/python/keras/optimizer_v2/gradient_descent.py +++ b/tensorflow/python/keras/optimizer_v2/gradient_descent.py @@ -1,4 +1,4 @@ -# Copyright 2015 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. @@ -21,8 +21,10 @@ from tensorflow.python.framework import ops from tensorflow.python.keras.optimizer_v2 import optimizer_v2 from tensorflow.python.ops import resource_variable_ops from tensorflow.python.training import training_ops +from tensorflow.python.util.tf_export import tf_export +@tf_export("keras.optimizers.SGD") class SGD(optimizer_v2.OptimizerV2): """Stochastic gradient descent and momentum optimizer. @@ -32,7 +34,7 @@ class SGD(optimizer_v2.OptimizerV2): gradient is evaluated at theta(t). ``` - or Computes (if `use_nesterov = False`): + or Computes (if `nesterov = False`): ``` v(t+1) = momentum * v(t) - learning_rate * gradient theta(t+1) = theta(t) + v(t+1) @@ -75,7 +77,7 @@ class SGD(optimizer_v2.OptimizerV2): **kwargs: keyword arguments. Allowed to be {`decay`} """ super(SGD, self).__init__(name, **kwargs) - self._set_hyper("learning_rate", learning_rate) + self._set_hyper("learning_rate", kwargs.get("lr", learning_rate)) self._set_hyper("decay", self._initial_decay) self._momentum = False @@ -85,7 +87,7 @@ class SGD(optimizer_v2.OptimizerV2): raise ValueError("`momentum` must be between [0, 1].") self._set_hyper("momentum", momentum) - self._nesterov = nesterov + self.nesterov = nesterov def _create_slots(self, var_list): if self._momentum: @@ -104,7 +106,7 @@ class SGD(optimizer_v2.OptimizerV2): grad, self._get_hyper("momentum", var_dtype), use_locking=self._use_locking, - use_nesterov=self._nesterov) + use_nesterov=self.nesterov) else: return training_ops.resource_apply_gradient_descent( var.handle, lr_t, grad, use_locking=self._use_locking) @@ -132,7 +134,7 @@ class SGD(optimizer_v2.OptimizerV2): indices, self._get_hyper("momentum", var_dtype), use_locking=self._use_locking, - use_nesterov=self._nesterov) + use_nesterov=self.nesterov) def get_config(self): config = super(SGD, self).get_config() @@ -140,6 +142,6 @@ class SGD(optimizer_v2.OptimizerV2): "learning_rate": self._serialize_hyperparameter("learning_rate"), "decay": self._serialize_hyperparameter("decay"), "momentum": self._serialize_hyperparameter("momentum"), - "nesterov": self._nesterov, + "nesterov": self.nesterov, }) return config diff --git a/tensorflow/python/keras/optimizer_v2/gradient_descent_test.py b/tensorflow/python/keras/optimizer_v2/gradient_descent_test.py index 0c64202da8..9a4178db46 100644 --- a/tensorflow/python/keras/optimizer_v2/gradient_descent_test.py +++ b/tensorflow/python/keras/optimizer_v2/gradient_descent_test.py @@ -122,8 +122,6 @@ class GradientDescentOptimizerTest(test.TestCase): var1 = resource_variable_ops.ResourceVariable([3.0], dtype=dtype) x = constant_op.constant([[4.0], [5.0]], dtype=dtype) loss = lambda: math_ops.matmul(var0, x) + var1 # pylint: disable=cell-var-from-loop - if not context.executing_eagerly(): - loss = loss() sgd = gradient_descent.SGD(1.0) sgd_op = sgd.minimize(loss, [var0, var1]) self.evaluate(variables.global_variables_initializer()) @@ -141,9 +139,12 @@ class GradientDescentOptimizerTest(test.TestCase): var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype) var1 = resource_variable_ops.ResourceVariable([3.0], dtype=dtype) x = constant_op.constant([[4.0], [5.0]], dtype=dtype) - pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) - pred += var1 - loss = pred * pred + + def loss(): + pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) # pylint: disable=cell-var-from-loop + pred += var1 # pylint: disable=cell-var-from-loop + return pred * pred + sgd_op = gradient_descent.SGD(1.0).minimize(loss, [var0, var1]) self.evaluate(variables.global_variables_initializer()) # Run 1 step of sgd @@ -181,7 +182,8 @@ class GradientDescentOptimizerTest(test.TestCase): opt = gradient_descent.SGD(3.0) values = [1.0, 3.0] vars_ = [variables.Variable([v], dtype=dtype) for v in values] - grads_and_vars = opt.compute_gradients(vars_[0] + vars_[1], vars_) + loss = lambda: vars_[0] + vars_[1] # pylint: disable=cell-var-from-loop + grads_and_vars = opt._compute_gradients(loss, vars_) self.evaluate(variables.global_variables_initializer()) for grad, _ in grads_and_vars: self.assertAllCloseAccordingToType([1.0], self.evaluate(grad)) @@ -259,6 +261,14 @@ class GradientDescentOptimizerTest(test.TestCase): # be an EagerTensor once again, not a graph Tensor. self.assertEqual(float(step()), -1.0) + def testConstructSGDWithLR(self): + opt = gradient_descent.SGD(lr=1.0) + self.assertEqual(opt.lr, 1.0) + opt_2 = gradient_descent.SGD(learning_rate=0.1, lr=1.0) + self.assertEqual(opt_2.lr, 1.0) + opt_3 = gradient_descent.SGD(learning_rate=0.1) + self.assertEqual(opt_3.lr, 0.1) + class MomentumOptimizerTest(test.TestCase): @@ -346,7 +356,7 @@ class MomentumOptimizerTest(test.TestCase): var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype) accum0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) accum1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) - loss = 5 * var0 * var0 + 3 * var1 + loss = lambda: 5 * var0 * var0 + 3 * var1 # pylint: disable=cell-var-from-loop mom_op = gradient_descent.SGD( learning_rate=2.0, momentum=0.9, nesterov=True) opt_op = mom_op.minimize(loss, [var0, var1]) @@ -677,12 +687,20 @@ class MomentumOptimizerTest(test.TestCase): opt3._get_hyper("momentum")) # self.assertEqual( # self.evaluate(opt._get_hyper("decay")), opt3._get_hyper("decay")) - self.assertTrue(opt3._nesterov) + self.assertTrue(opt3.nesterov) def testNesterovWithoutMomentum(self): with self.assertRaisesRegexp(ValueError, "must be between"): gradient_descent.SGD(learning_rate=1.0, momentum=2.0) + def testConstructMomentumWithLR(self): + opt = gradient_descent.SGD(lr=1.0, momentum=0.9) + self.assertEqual(opt.lr, 1.0) + opt_2 = gradient_descent.SGD(learning_rate=0.1, momentum=0.9, lr=1.0) + self.assertEqual(opt_2.lr, 1.0) + opt_3 = gradient_descent.SGD(learning_rate=0.1, momentum=0.9) + self.assertEqual(opt_3.lr, 0.1) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/keras/optimizer_v2/nadam.py b/tensorflow/python/keras/optimizer_v2/nadam.py index 00b095e0dc..afa74c8de3 100644 --- a/tensorflow/python/keras/optimizer_v2/nadam.py +++ b/tensorflow/python/keras/optimizer_v2/nadam.py @@ -74,6 +74,9 @@ class Nadam(adam.Adam): **kwargs: keyword arguments. Allowed to be {`decay`} """ + # Backwards compatiblity with keras NAdam optimizer. + if 'schedule_decay' in kwargs: + kwargs['decay'] = kwargs.pop('schedule_decay') # pylint: disable=useless-super-delegation super(Nadam, self).__init__( learning_rate=learning_rate, diff --git a/tensorflow/python/keras/optimizer_v2/nadam_test.py b/tensorflow/python/keras/optimizer_v2/nadam_test.py index d991e3117c..73568e81f0 100644 --- a/tensorflow/python/keras/optimizer_v2/nadam_test.py +++ b/tensorflow/python/keras/optimizer_v2/nadam_test.py @@ -208,6 +208,18 @@ class NadamOptimizerTest(test.TestCase): self.assertAllCloseAccordingToType(var0_np, var0.eval()) self.assertAllCloseAccordingToType(var1_np, var1.eval()) + def testConstructNAdamWithLR(self): + opt = nadam.Nadam(lr=1.0) + self.assertEqual(opt.lr, 1.0) + opt_2 = nadam.Nadam(learning_rate=0.1, lr=1.0) + self.assertEqual(opt_2.lr, 1.0) + opt_3 = nadam.Nadam(learning_rate=0.1) + self.assertEqual(opt_3.lr, 0.1) + + def testConstructNAdamWithScheduleDecay(self): + opt = nadam.Nadam(schedule_decay=0.2) + self.assertEqual(opt.decay, 0.2) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/keras/optimizer_v2/optimizer_v2.py b/tensorflow/python/keras/optimizer_v2/optimizer_v2.py index 15f3009a4a..a130e1d0c3 100644 --- a/tensorflow/python/keras/optimizer_v2/optimizer_v2.py +++ b/tensorflow/python/keras/optimizer_v2/optimizer_v2.py @@ -28,22 +28,45 @@ from tensorflow.python.distribute import distribute_lib from tensorflow.python.distribute import distribution_strategy_context as distribute_ctx from tensorflow.python.distribute import reduce_util as ds_reduce_util from tensorflow.python.eager import backprop -from tensorflow.python.eager import context from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.keras import backend from tensorflow.python.keras import initializers from tensorflow.python.keras.engine import base_layer_utils +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import clip_ops from tensorflow.python.ops import gradients from tensorflow.python.ops import math_ops from tensorflow.python.ops import variables as tf_variables from tensorflow.python.platform import tf_logging as logging -from tensorflow.python.training import optimizer as optimizer_v1 +from tensorflow.python.training.checkpointable import base as checkpointable from tensorflow.python.util import nest +from tensorflow.python.util.tf_export import tf_export + + +def _deduplicate_indexed_slices(values, indices): + """Sums `values` associated with any non-unique `indices`. + + Args: + values: A `Tensor` with rank >= 1. + indices: A one-dimensional integer `Tensor`, indexing into the first + dimension of `values` (as in an IndexedSlices object). + + Returns: + A tuple of (`summed_values`, `unique_indices`) where `unique_indices` is a + de-duplicated version of `indices` and `summed_values` contains the sum of + `values` slices associated with each unique index. + """ + unique_indices, new_index_positions = array_ops.unique(indices) + summed_values = math_ops.unsorted_segment_sum( + values, new_index_positions, + array_ops.shape(unique_indices)[0]) + return (summed_values, unique_indices) @six.add_metaclass(abc.ABCMeta) -class OptimizerV2(optimizer_v1.Optimizer): +@tf_export("keras.optimizers.Optimizer") +class OptimizerV2(checkpointable.CheckpointableBase): """Updated base class for optimizers. This class defines the API to add Ops to train a model. You never use this @@ -138,7 +161,7 @@ class OptimizerV2(optimizer_v1.Optimizer): _create_vars. """ self._use_locking = True - super(OptimizerV2, self).__init__(self._use_locking, name) + self._name = name self._hyper = {} # dict: {variable name : {slot name : variable}} self._slots = {} @@ -148,16 +171,11 @@ class OptimizerV2(optimizer_v1.Optimizer): if decay < 0.: raise ValueError("decay cannot be less than 0: {}".format(decay)) self._initial_decay = decay + self.__dict__.update(kwargs) self._prepared = False - def minimize(self, - loss, - var_list, - aggregation_method=None, - colocate_gradients_with_ops=False, - name=None, - grad_loss=None): + def minimize(self, loss, var_list, grad_loss=None, name=None): """Add operations to minimize `loss` by updating `var_list`. This method simply combines calls `compute_gradients()` and @@ -166,15 +184,11 @@ class OptimizerV2(optimizer_v1.Optimizer): of using this function. Args: - loss: A `Tensor` containing the value to minimize. + loss: A callable taking no arguments which returns the value to minimize. var_list: list or tuple of `Variable` objects to update to minimize `loss`. - aggregation_method: Specifies the method used to combine gradient terms. - Valid values are defined in the class `AggregationMethod`. - colocate_gradients_with_ops: If True, try colocating gradients with the - corresponding op. - name: Optional name for the returned operation. grad_loss: Optional. A `Tensor` holding the gradient computed for `loss`. + name: Optional name for the returned operation. Returns: An Operation that updates the variables in `var_list`. If `global_step` @@ -186,29 +200,16 @@ class OptimizerV2(optimizer_v1.Optimizer): @compatibility(eager) When eager execution is enabled, `loss` should be a Python function that takes no arguments and computes the value to be minimized. Minimization (and - gradient computation) is done with respect to the elements of `var_list` if - not None, else with respect to any trainable variables created during the - execution of the `loss` function. `gate_gradients`, `aggregation_method`, - `colocate_gradients_with_ops` and `grad_loss` are ignored when eager - execution is enabled. + gradient computation) is done with respect to the elements of `var_list`. + `grad_loss` is ignored when eager execution is enabled. @end_compatibility """ - grads_and_vars = self.compute_gradients( - loss, - var_list=var_list, - aggregation_method=aggregation_method, - colocate_gradients_with_ops=colocate_gradients_with_ops, - grad_loss=grad_loss) + grads_and_vars = self._compute_gradients( + loss, var_list=var_list, grad_loss=grad_loss) return self.apply_gradients(grads_and_vars, name=name) - def compute_gradients(self, - loss, - var_list, - aggregation_method=None, - colocate_gradients_with_ops=False, - grad_loss=None, - stop_gradients=None): + def _compute_gradients(self, loss, var_list, grad_loss=None): """Compute gradients of `loss` for the variables in `var_list`. This is the first part of `minimize()`. It returns a list @@ -218,19 +219,11 @@ class OptimizerV2(optimizer_v1.Optimizer): given variable. Args: - loss: A Tensor containing the value to minimize or a callable taking no - arguments which returns the value to minimize. When eager execution is - enabled it must be a callable. - var_list: Optional list or tuple of `tf.Variable` to update to minimize + loss: A callable taking no arguments which returns the value to minimize. + var_list: List or tuple of `tf.Variable` to update to minimize `loss`. Defaults to the list of variables collected in the graph under the key `GraphKeys.TRAINABLE_VARIABLES`. - aggregation_method: Specifies the method used to combine gradient terms. - Valid values are defined in the class `AggregationMethod`. - colocate_gradients_with_ops: If True, try colocating gradients with the - corresponding op. grad_loss: Optional. A `Tensor` holding the gradient computed for `loss`. - stop_gradients: Optional. A Tensor or list of tensors not to differentiate - through. Returns: A list of (gradient, variable) pairs. Variable is always present, but @@ -239,38 +232,22 @@ class OptimizerV2(optimizer_v1.Optimizer): Raises: TypeError: If `var_list` contains anything else than `Variable` objects. ValueError: If some arguments are invalid, or var_list is None. - RuntimeError: If called with eager execution enabled and `loss` is - not callable. - - @compatibility(eager) - When eager execution is enabled, `aggregation_method`, and - `colocate_gradients_with_ops` are ignored. - @end_compatibility """ var_list = nest.flatten(var_list) # TODO(josh11b): Test that we handle weight decay in a reasonable way. - if callable(loss): - with backprop.GradientTape() as tape: - tape.watch(var_list) - loss_value = loss() - loss_value = self._scale_loss(loss_value) - grads = tape.gradient(loss_value, var_list, grad_loss) - else: - if context.executing_eagerly(): - raise RuntimeError("`loss` passed to Optimizer.compute_gradients " - "should be a function when eager execution is " - "enabled.") - loss = self._scale_loss(loss) - self._assert_valid_dtypes([loss]) - if grad_loss is not None: - self._assert_valid_dtypes([grad_loss]) - grads = gradients.gradients( - loss, - var_list, - grad_ys=grad_loss, - aggregation_method=aggregation_method, - colocate_gradients_with_ops=colocate_gradients_with_ops, - stop_gradients=stop_gradients) + with backprop.GradientTape() as tape: + tape.watch(var_list) + loss_value = loss() + loss_value = self._scale_loss(loss_value) + grads = tape.gradient(loss_value, var_list, grad_loss) + + if hasattr(self, "clipnorm"): + grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g in grads] + if hasattr(self, "clipvalue"): + grads = [ + clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) + for g in grads + ] grads_and_vars = list(zip(grads, var_list)) self._assert_valid_dtypes([ @@ -289,6 +266,37 @@ class OptimizerV2(optimizer_v1.Optimizer): loss_value *= (1. / num_replicas) return loss_value + def get_gradients(self, loss, params): + """Returns gradients of `loss` with respect to `params`. + + Arguments: + loss: Loss tensor. + params: List of variables. + + Returns: + List of gradient tensors. + + Raises: + ValueError: In case any gradient cannot be computed (e.g. if gradient + function not implemented). + """ + loss = self._scale_loss(loss) + grads = gradients.gradients(loss, params) + if None in grads: + raise ValueError("An operation has `None` for gradient. " + "Please make sure that all of your ops have a " + "gradient defined (i.e. are differentiable). " + "Common ops without gradient: " + "K.argmax, K.round, K.eval.") + if hasattr(self, "clipnorm"): + grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g in grads] + if hasattr(self, "clipvalue"): + grads = [ + clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) + for g in grads + ] + return grads + def apply_gradients(self, grads_and_vars, name=None): """Apply gradients to variables. @@ -351,7 +359,13 @@ class OptimizerV2(optimizer_v1.Optimizer): return apply_updates def get_updates(self, loss, params): - return [self.minimize(loss, params)] + grads = self.get_gradients(loss, params) + grads_and_vars = list(zip(grads, params)) + self._assert_valid_dtypes([ + v for g, v in grads_and_vars + if g is not None and v.dtype != dtypes.resource + ]) + return [self.apply_gradients(grads_and_vars)] def _set_hyper(self, name, value): """set hyper `name` to value. value can be callable, tensor, numeric.""" @@ -575,6 +589,95 @@ class OptimizerV2(optimizer_v1.Optimizer): return variable + def _assert_valid_dtypes(self, tensors): + """Asserts tensors are all valid types (see `_valid_dtypes`). + + Args: + tensors: Tensors to check. + + Raises: + ValueError: If any tensor is not a valid type. + """ + valid_dtypes = self._valid_dtypes() + for t in tensors: + dtype = t.dtype.base_dtype + if dtype not in valid_dtypes: + raise ValueError("Invalid type %r for %s, expected: %s." % + (dtype, t.name, [v for v in valid_dtypes])) + + def _valid_dtypes(self): + """Valid types for loss, variables and gradients. + + Subclasses should override to allow other float types. + + Returns: + Valid types for loss, variables and gradients. + """ + return set( + [dtypes.float16, dtypes.bfloat16, dtypes.float32, dtypes.float64]) + + def _call_if_callable(self, param): + """Call the function if param is callable.""" + return param() if callable(param) else param + + def _resource_apply_dense(self, grad, handle): + """Add ops to apply dense gradients to the variable `handle`. + + Args: + grad: a `Tensor` representing the gradient. + handle: a `Tensor` of dtype `resource` which points to the variable to be + updated. + + Returns: + An `Operation` which updates the value of the variable. + """ + raise NotImplementedError() + + def _resource_apply_sparse_duplicate_indices(self, grad, handle, indices): + """Add ops to apply sparse gradients to `handle`, with repeated indices. + + Optimizers which override this method must deal with repeated indices. See + the docstring of `_apply_sparse_duplicate_indices` for details. By default + the correct behavior, to sum non-unique indices and their associated + gradients, is enforced by first pre-processing `grad` and `indices` and + passing them on to `_resource_apply_sparse`. Optimizers which deal correctly + with duplicate indices may instead override this method to avoid the + overhead of summing. + + Args: + grad: a `Tensor` representing the gradient for the affected indices. + handle: a `Tensor` of dtype `resource` which points to the variable to be + updated. + indices: a `Tensor` of integral type representing the indices for which + the gradient is nonzero. Indices may be repeated. + + Returns: + An `Operation` which updates the value of the variable. + """ + summed_grad, unique_indices = _deduplicate_indexed_slices( + values=grad, indices=indices) + return self._resource_apply_sparse(summed_grad, handle, unique_indices) + + def _resource_apply_sparse(self, grad, handle, indices): + """Add ops to apply sparse gradients to the variable `handle`. + + Similar to `_apply_sparse`, the `indices` argument to this method has been + de-duplicated. Optimizers which deal correctly with non-unique indices may + instead override `_resource_apply_sparse_duplicate_indices` to avoid this + overhead. + + Args: + grad: a `Tensor` representing the gradient for the affected indices. + handle: a `Tensor` of dtype `resource` which points to the variable to be + updated. + indices: a `Tensor` of integral type representing the indices for which + the gradient is nonzero. Indices are unique. + + Returns: + An `Operation` which updates the value of the variable. + """ + raise NotImplementedError() + def _filter_grads(grads_and_vars): """Filter out iterable with grad equal to None.""" diff --git a/tensorflow/python/keras/optimizer_v2/optimizer_v2_test.py b/tensorflow/python/keras/optimizer_v2/optimizer_v2_test.py index 158577fe64..8b2865e2aa 100644 --- a/tensorflow/python/keras/optimizer_v2/optimizer_v2_test.py +++ b/tensorflow/python/keras/optimizer_v2/optimizer_v2_test.py @@ -46,7 +46,6 @@ from tensorflow.python.keras.optimizer_v2 import gradient_descent from tensorflow.python.keras.optimizer_v2 import optimizer_v2 from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops -from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import state_ops from tensorflow.python.ops import variables @@ -64,8 +63,6 @@ class OptimizerTest(test.TestCase): var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype) loss = lambda: 5 * var0 + 3 * var1 # pylint: disable=cell-var-from-loop - if not context.executing_eagerly(): - loss = loss() sgd = gradient_descent.SGD(3.0) self.evaluate(variables.global_variables_initializer()) @@ -116,33 +113,6 @@ class OptimizerTest(test.TestCase): # var1 = [0., 1.] - 0.5 * [3, 3] self.assertAllClose([-1.5, -0.5], self.evaluate(var1)) - @test_util.run_in_graph_and_eager_modes - def testAggregationMethod(self): - for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: - with self.cached_session(): - var0 = variables.Variable([1.0, 2.0], dtype=dtype) - var1 = variables.Variable([3.0, 4.0], dtype=dtype) - loss = lambda: 5 * var0 + 3 * var1 # pylint: disable=cell-var-from-loop - if not context.executing_eagerly(): - loss = loss() - sgd = gradient_descent.SGD(3.0) - - 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)) - # Run 1 step of sgd through optimizer - opt_op = sgd.minimize( - loss, - var_list=[var0, var1], - aggregation_method=gradients_impl.AggregationMethod - .EXPERIMENTAL_ACCUMULATE_N) - self.evaluate(variables.global_variables_initializer()) - self.evaluate(opt_op) - # Validate updated params - self.assertAllClose([-14., -13.], self.evaluate(var0)) - self.assertAllClose([-6., -5.], self.evaluate(var1)) - @test_util.run_in_graph_and_eager_modes def testPrecomputedGradient(self): for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: @@ -150,8 +120,6 @@ class OptimizerTest(test.TestCase): var0 = variables.Variable([1.0, 2.0], dtype=dtype) var1 = variables.Variable([3.0, 4.0], dtype=dtype) loss = lambda: 5 * var0 + 3 * var1 # pylint: disable=cell-var-from-loop - if not context.executing_eagerly(): - loss = loss() grad_loss = constant_op.constant([42, -42], dtype=dtype) sgd = gradient_descent.SGD(3.0) @@ -176,8 +144,6 @@ class OptimizerTest(test.TestCase): var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype) loss = lambda: 5 * var0 # pylint: disable=cell-var-from-loop - if not context.executing_eagerly(): - loss = loss() sgd_op = gradient_descent.SGD(3.0) with self.assertRaisesRegexp(ValueError, 'No gradients'): # var1 has no gradient @@ -190,8 +156,6 @@ class OptimizerTest(test.TestCase): var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype) loss = lambda: constant_op.constant(5.0) - if not context.executing_eagerly(): - loss = loss() sgd_op = gradient_descent.SGD(3.0) with self.assertRaisesRegexp(ValueError, @@ -216,11 +180,9 @@ class OptimizerTest(test.TestCase): var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype) loss = lambda: 5 * var0 + 3 * var1 # pylint: disable=cell-var-from-loop - if not context.executing_eagerly(): - loss = loss() sgd = gradient_descent.SGD(3.0) - grads_and_vars = sgd.compute_gradients(loss, [var0, var1]) + grads_and_vars = sgd._compute_gradients(loss, [var0, var1]) # Convert gradients to tf.Variables converted_grads = [ resource_variable_ops.ResourceVariable( @@ -259,7 +221,7 @@ class OptimizerTest(test.TestCase): return x * x sgd = gradient_descent.SGD(3.0) - grads_and_vars = sgd.compute_gradients(f, [x]) + grads_and_vars = sgd._compute_gradients(f, [x]) self.assertEqual(1, len(grads_and_vars)) grad, x_as_var = grads_and_vars[0] self.assertIs(x, x_as_var) @@ -278,8 +240,6 @@ class OptimizerTest(test.TestCase): var1 = variables.Variable([3.0, 4.0], constraint=constraint_0) loss = lambda: 5 * var0 + 3 * var1 - if not context.executing_eagerly(): # pylint: disable=cell-var-from-loop - loss = loss() sgd = gradient_descent.SGD(3.0) self.evaluate(variables.global_variables_initializer()) @@ -338,6 +298,28 @@ class OptimizerTest(test.TestCase): self.evaluate(opt._get_hyper('learning_rate')), opt3._get_hyper('learning_rate')) + @test_util.run_in_graph_and_eager_modes + def testGradClipValue(self): + with self.cached_session(): + var = resource_variable_ops.ResourceVariable([1.0, 2.0]) + loss = lambda: 3 * var + opt = gradient_descent.SGD(learning_rate=1.0, clipvalue=1.0) + opt_op = opt.minimize(loss, [var]) + self.evaluate(variables.global_variables_initializer()) + self.evaluate(opt_op) + self.assertAllClose([0., 1.], self.evaluate(var)) + + @test_util.run_in_graph_and_eager_modes + def testGradClipNorm(self): + with self.cached_session(): + var = resource_variable_ops.ResourceVariable([1.0]) + loss = lambda: 3 * var + opt = gradient_descent.SGD(learning_rate=1.0, clipnorm=1.0) + opt_op = opt.minimize(loss, [var]) + self.evaluate(variables.global_variables_initializer()) + self.evaluate(opt_op) + self.assertAllClose([0.], self.evaluate(var)) + @test_util.run_in_graph_and_eager_modes def testWeights(self): with self.cached_session(): diff --git a/tensorflow/python/keras/optimizer_v2/rmsprop.py b/tensorflow/python/keras/optimizer_v2/rmsprop.py index 6a5b334fc4..634111b470 100644 --- a/tensorflow/python/keras/optimizer_v2/rmsprop.py +++ b/tensorflow/python/keras/optimizer_v2/rmsprop.py @@ -20,8 +20,10 @@ from __future__ import print_function from tensorflow.python.framework import ops from tensorflow.python.keras.optimizer_v2 import optimizer_v2 from tensorflow.python.training import training_ops +from tensorflow.python.util.tf_export import tf_export +@tf_export("keras.optimizers.RMSprop") class RMSprop(optimizer_v2.OptimizerV2): r"""Optimizer that implements the RMSprop algorithm. @@ -91,7 +93,7 @@ class RMSprop(optimizer_v2.OptimizerV2): **kwargs: keyword arguments. Allowed to be {`decay`} """ super(RMSprop, self).__init__(name, **kwargs) - self._set_hyper("learning_rate", learning_rate) + self._set_hyper("learning_rate", kwargs.get("lr", learning_rate)) self._set_hyper("decay", self._initial_decay) self._set_hyper("rho", rho) @@ -103,13 +105,13 @@ class RMSprop(optimizer_v2.OptimizerV2): self._set_hyper("momentum", momentum) self._set_hyper("epsilon", epsilon) - self._centered = centered + self.centered = centered def _create_slots(self, var_list): for var in var_list: self.add_slot(var, "rms") self.add_slot(var, "momentum") - if self._centered: + if self.centered: self.add_slot(var, "mg") def _resource_apply_dense(self, grad, var): @@ -120,7 +122,7 @@ class RMSprop(optimizer_v2.OptimizerV2): rho = self._get_hyper("rho", var_dtype) momentum = self._get_hyper("momentum", var_dtype) epsilon = self._get_hyper("epsilon", var_dtype) - if self._centered: + if self.centered: mg = self.get_slot(var, "mg") return training_ops.resource_apply_centered_rms_prop( var.handle, @@ -153,7 +155,7 @@ class RMSprop(optimizer_v2.OptimizerV2): rho = self._get_hyper("rho", var_dtype) momentum = self._get_hyper("momentum", var_dtype) epsilon = self._get_hyper("epsilon", var_dtype) - if self._centered: + if self.centered: mg = self.get_slot(var, "mg") return training_ops.resource_sparse_apply_centered_rms_prop( var.handle, @@ -188,7 +190,7 @@ class RMSprop(optimizer_v2.OptimizerV2): "rho": self._serialize_hyperparameter("rho"), "momentum": self._serialize_hyperparameter("momentum"), "epsilon": self._serialize_hyperparameter("epsilon"), - "centered": self._centered, + "centered": self.centered, }) return config diff --git a/tensorflow/python/keras/optimizer_v2/rmsprop_test.py b/tensorflow/python/keras/optimizer_v2/rmsprop_test.py index a8658a8550..4d61cfbbc5 100644 --- a/tensorflow/python/keras/optimizer_v2/rmsprop_test.py +++ b/tensorflow/python/keras/optimizer_v2/rmsprop_test.py @@ -233,8 +233,11 @@ class RMSpropOptimizerTest(test.TestCase): with self.cached_session(): var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype) x = constant_op.constant([[4.0], [5.0]], dtype=dtype) - pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) - loss = pred * pred + + def loss(): + pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) # pylint: disable=cell-var-from-loop + return pred * pred + sgd_op = rmsprop.RMSprop( learning_rate=1.0, rho=0.0, @@ -258,8 +261,12 @@ class RMSpropOptimizerTest(test.TestCase): with self.cached_session(): var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype) x = constant_op.constant([[4.0], [5.0]], dtype=dtype) - pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) - loss = pred * pred + + def loss(): + pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) # pylint: disable=cell-var-from-loop + return pred * pred + + # loss = lambda: pred * pred # pylint: disable=cell-var-from-loop sgd_op = rmsprop.RMSprop( learning_rate=1.0, rho=0.0, @@ -405,6 +412,14 @@ class RMSpropOptimizerTest(test.TestCase): (0.01 * 2.0 / math.sqrt(0.00001 * 0.9 + 1e-5 + 1.0)) ]), self.evaluate(var1)) + def testConstructRMSpropWithLR(self): + opt = rmsprop.RMSprop(lr=1.0) + self.assertEqual(opt.lr, 1.0) + opt_2 = rmsprop.RMSprop(learning_rate=0.1, lr=1.0) + self.assertEqual(opt_2.lr, 1.0) + opt_3 = rmsprop.RMSprop(learning_rate=0.1) + self.assertEqual(opt_3.lr, 0.1) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/keras/optimizers.py b/tensorflow/python/keras/optimizers.py index ee6dbba5ad..a558c2532b 100644 --- a/tensorflow/python/keras/optimizers.py +++ b/tensorflow/python/keras/optimizers.py @@ -45,7 +45,6 @@ from tensorflow.python.training.checkpointable import base as checkpointable from tensorflow.python.util.tf_export import tf_export -@tf_export('keras.optimizers.Optimizer') class Optimizer(object): """Abstract optimizer base class. @@ -159,7 +158,6 @@ class Optimizer(object): return cls(**config) -@tf_export('keras.optimizers.SGD') class SGD(Optimizer): """Stochastic gradient descent optimizer. @@ -224,7 +222,6 @@ class SGD(Optimizer): return dict(list(base_config.items()) + list(config.items())) -@tf_export('keras.optimizers.RMSprop') class RMSprop(Optimizer): """RMSProp optimizer. @@ -291,7 +288,6 @@ class RMSprop(Optimizer): return dict(list(base_config.items()) + list(config.items())) -@tf_export('keras.optimizers.Adagrad') class Adagrad(Optimizer): """Adagrad optimizer. @@ -358,7 +354,6 @@ class Adagrad(Optimizer): return dict(list(base_config.items()) + list(config.items())) -@tf_export('keras.optimizers.Adadelta') class Adadelta(Optimizer): """Adadelta optimizer. @@ -442,7 +437,6 @@ class Adadelta(Optimizer): return dict(list(base_config.items()) + list(config.items())) -@tf_export('keras.optimizers.Adam') class Adam(Optimizer): """Adam optimizer. @@ -539,7 +533,6 @@ class Adam(Optimizer): return dict(list(base_config.items()) + list(config.items())) -@tf_export('keras.optimizers.Adamax') class Adamax(Optimizer): """Adamax optimizer from Adam paper's Section 7. diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adadelta.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adadelta.pbtxt index b9ce154bdd..00cd5aca4c 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adadelta.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adadelta.pbtxt @@ -1,15 +1,36 @@ path: "tensorflow.keras.optimizers.Adadelta" tf_class { - is_instance: "" - is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" + member { + name: "iterations" + mtype: "" + } + member { + name: "weights" + mtype: "" + } member_method { name: "__init__" - argspec: "args=[\'self\', \'lr\', \'rho\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'1.0\', \'0.95\', \'None\', \'0.0\'], " + argspec: "args=[\'self\', \'learning_rate\', \'rho\', \'epsilon\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.95\', \'1e-07\', \'Adadelta\'], " + } + member_method { + name: "add_slot" + argspec: "args=[\'self\', \'var\', \'slot_name\', \'initializer\'], varargs=None, keywords=None, defaults=[\'zeros\'], " + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'trainable\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'zeros\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], " + } + member_method { + name: "apply_gradients" + argspec: "args=[\'self\', \'grads_and_vars\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "from_config" - argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "get_config" @@ -19,6 +40,10 @@ tf_class { name: "get_gradients" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "get_slot" + argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "get_updates" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" @@ -27,8 +52,16 @@ tf_class { name: "get_weights" argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "minimize" + argspec: "args=[\'self\', \'loss\', \'var_list\', \'grad_loss\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } member_method { name: "set_weights" argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } } diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adagrad.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adagrad.pbtxt index d0dc9e37a3..6d47fe310d 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adagrad.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adagrad.pbtxt @@ -1,15 +1,36 @@ path: "tensorflow.keras.optimizers.Adagrad" tf_class { - is_instance: "" - is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" + member { + name: "iterations" + mtype: "" + } + member { + name: "weights" + mtype: "" + } member_method { name: "__init__" - argspec: "args=[\'self\', \'lr\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'0.01\', \'None\', \'0.0\'], " + argspec: "args=[\'self\', \'learning_rate\', \'initial_accumulator_value\', \'epsilon\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.1\', \'1e-07\', \'Adagrad\'], " + } + member_method { + name: "add_slot" + argspec: "args=[\'self\', \'var\', \'slot_name\', \'initializer\'], varargs=None, keywords=None, defaults=[\'zeros\'], " + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'trainable\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'zeros\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], " + } + member_method { + name: "apply_gradients" + argspec: "args=[\'self\', \'grads_and_vars\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "from_config" - argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "get_config" @@ -19,6 +40,10 @@ tf_class { name: "get_gradients" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "get_slot" + argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "get_updates" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" @@ -27,8 +52,16 @@ tf_class { name: "get_weights" argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "minimize" + argspec: "args=[\'self\', \'loss\', \'var_list\', \'grad_loss\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } member_method { name: "set_weights" argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } } diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adam.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adam.pbtxt index 06815fa99a..417362d211 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adam.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adam.pbtxt @@ -1,15 +1,36 @@ path: "tensorflow.keras.optimizers.Adam" tf_class { - is_instance: "" - is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" + member { + name: "iterations" + mtype: "" + } + member { + name: "weights" + mtype: "" + } member_method { name: "__init__" - argspec: "args=[\'self\', \'lr\', \'beta_1\', \'beta_2\', \'epsilon\', \'decay\', \'amsgrad\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.9\', \'0.999\', \'None\', \'0.0\', \'False\'], " + argspec: "args=[\'self\', \'learning_rate\', \'beta_1\', \'beta_2\', \'epsilon\', \'amsgrad\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.9\', \'0.999\', \'1e-07\', \'False\', \'Adam\'], " + } + member_method { + name: "add_slot" + argspec: "args=[\'self\', \'var\', \'slot_name\', \'initializer\'], varargs=None, keywords=None, defaults=[\'zeros\'], " + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'trainable\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'zeros\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], " + } + member_method { + name: "apply_gradients" + argspec: "args=[\'self\', \'grads_and_vars\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "from_config" - argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "get_config" @@ -19,6 +40,10 @@ tf_class { name: "get_gradients" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "get_slot" + argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "get_updates" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" @@ -27,8 +52,16 @@ tf_class { name: "get_weights" argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "minimize" + argspec: "args=[\'self\', \'loss\', \'var_list\', \'grad_loss\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } member_method { name: "set_weights" argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } } diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adamax.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adamax.pbtxt index 47b55fdb44..7b43abee23 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adamax.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adamax.pbtxt @@ -1,15 +1,37 @@ path: "tensorflow.keras.optimizers.Adamax" tf_class { - is_instance: "" - is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" + member { + name: "iterations" + mtype: "" + } + member { + name: "weights" + mtype: "" + } member_method { name: "__init__" - argspec: "args=[\'self\', \'lr\', \'beta_1\', \'beta_2\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'0.002\', \'0.9\', \'0.999\', \'None\', \'0.0\'], " + argspec: "args=[\'self\', \'learning_rate\', \'beta_1\', \'beta_2\', \'epsilon\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.9\', \'0.999\', \'1e-07\', \'Adamax\'], " + } + member_method { + name: "add_slot" + argspec: "args=[\'self\', \'var\', \'slot_name\', \'initializer\'], varargs=None, keywords=None, defaults=[\'zeros\'], " + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'trainable\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'zeros\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], " + } + member_method { + name: "apply_gradients" + argspec: "args=[\'self\', \'grads_and_vars\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "from_config" - argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "get_config" @@ -19,6 +41,10 @@ tf_class { name: "get_gradients" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "get_slot" + argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "get_updates" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" @@ -27,8 +53,16 @@ tf_class { name: "get_weights" argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "minimize" + argspec: "args=[\'self\', \'loss\', \'var_list\', \'grad_loss\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } member_method { name: "set_weights" argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } } diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-optimizer.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-optimizer.pbtxt index 53d64dae93..a996746dac 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-optimizer.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-optimizer.pbtxt @@ -1,14 +1,35 @@ path: "tensorflow.keras.optimizers.Optimizer" tf_class { - is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" + member { + name: "iterations" + mtype: "" + } + member { + name: "weights" + mtype: "" + } member_method { name: "__init__" - argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=kwargs, defaults=None" + } + member_method { + name: "add_slot" + argspec: "args=[\'self\', \'var\', \'slot_name\', \'initializer\'], varargs=None, keywords=None, defaults=[\'zeros\'], " + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'trainable\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'zeros\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], " + } + member_method { + name: "apply_gradients" + argspec: "args=[\'self\', \'grads_and_vars\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "from_config" - argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "get_config" @@ -18,6 +39,10 @@ tf_class { name: "get_gradients" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "get_slot" + argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "get_updates" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" @@ -26,8 +51,16 @@ tf_class { name: "get_weights" argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "minimize" + argspec: "args=[\'self\', \'loss\', \'var_list\', \'grad_loss\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } member_method { name: "set_weights" argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } } diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-r-m-sprop.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-r-m-sprop.pbtxt index a1e9b8cceb..bfc9d67a47 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-r-m-sprop.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-r-m-sprop.pbtxt @@ -1,15 +1,36 @@ path: "tensorflow.keras.optimizers.RMSprop" tf_class { - is_instance: "" - is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" + member { + name: "iterations" + mtype: "" + } + member { + name: "weights" + mtype: "" + } member_method { name: "__init__" - argspec: "args=[\'self\', \'lr\', \'rho\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.9\', \'None\', \'0.0\'], " + argspec: "args=[\'self\', \'learning_rate\', \'rho\', \'momentum\', \'epsilon\', \'centered\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.9\', \'0.0\', \'1e-07\', \'False\', \'RMSprop\'], " + } + member_method { + name: "add_slot" + argspec: "args=[\'self\', \'var\', \'slot_name\', \'initializer\'], varargs=None, keywords=None, defaults=[\'zeros\'], " + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'trainable\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'zeros\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], " + } + member_method { + name: "apply_gradients" + argspec: "args=[\'self\', \'grads_and_vars\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "from_config" - argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "get_config" @@ -19,6 +40,10 @@ tf_class { name: "get_gradients" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "get_slot" + argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "get_updates" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" @@ -27,8 +52,16 @@ tf_class { name: "get_weights" argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "minimize" + argspec: "args=[\'self\', \'loss\', \'var_list\', \'grad_loss\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } member_method { name: "set_weights" argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } } diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-s-g-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-s-g-d.pbtxt index a67fefb1ba..3f3d57962b 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-s-g-d.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-s-g-d.pbtxt @@ -1,15 +1,36 @@ path: "tensorflow.keras.optimizers.SGD" tf_class { - is_instance: "" - is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" + member { + name: "iterations" + mtype: "" + } + member { + name: "weights" + mtype: "" + } member_method { name: "__init__" - argspec: "args=[\'self\', \'lr\', \'momentum\', \'decay\', \'nesterov\'], varargs=None, keywords=kwargs, defaults=[\'0.01\', \'0.0\', \'0.0\', \'False\'], " + argspec: "args=[\'self\', \'learning_rate\', \'momentum\', \'nesterov\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.0\', \'False\', \'SGD\'], " + } + member_method { + name: "add_slot" + argspec: "args=[\'self\', \'var\', \'slot_name\', \'initializer\'], varargs=None, keywords=None, defaults=[\'zeros\'], " + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'trainable\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'zeros\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], " + } + member_method { + name: "apply_gradients" + argspec: "args=[\'self\', \'grads_and_vars\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "from_config" - argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "get_config" @@ -19,6 +40,10 @@ tf_class { name: "get_gradients" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "get_slot" + argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "get_updates" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" @@ -27,8 +52,16 @@ tf_class { name: "get_weights" argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "minimize" + argspec: "args=[\'self\', \'loss\', \'var_list\', \'grad_loss\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } member_method { name: "set_weights" argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } } diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adadelta.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adadelta.pbtxt index b9ce154bdd..00cd5aca4c 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adadelta.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adadelta.pbtxt @@ -1,15 +1,36 @@ path: "tensorflow.keras.optimizers.Adadelta" tf_class { - is_instance: "" - is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" + member { + name: "iterations" + mtype: "" + } + member { + name: "weights" + mtype: "" + } member_method { name: "__init__" - argspec: "args=[\'self\', \'lr\', \'rho\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'1.0\', \'0.95\', \'None\', \'0.0\'], " + argspec: "args=[\'self\', \'learning_rate\', \'rho\', \'epsilon\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.95\', \'1e-07\', \'Adadelta\'], " + } + member_method { + name: "add_slot" + argspec: "args=[\'self\', \'var\', \'slot_name\', \'initializer\'], varargs=None, keywords=None, defaults=[\'zeros\'], " + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'trainable\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'zeros\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], " + } + member_method { + name: "apply_gradients" + argspec: "args=[\'self\', \'grads_and_vars\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "from_config" - argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "get_config" @@ -19,6 +40,10 @@ tf_class { name: "get_gradients" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "get_slot" + argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "get_updates" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" @@ -27,8 +52,16 @@ tf_class { name: "get_weights" argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "minimize" + argspec: "args=[\'self\', \'loss\', \'var_list\', \'grad_loss\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } member_method { name: "set_weights" argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } } diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adagrad.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adagrad.pbtxt index d0dc9e37a3..6d47fe310d 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adagrad.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adagrad.pbtxt @@ -1,15 +1,36 @@ path: "tensorflow.keras.optimizers.Adagrad" tf_class { - is_instance: "" - is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" + member { + name: "iterations" + mtype: "" + } + member { + name: "weights" + mtype: "" + } member_method { name: "__init__" - argspec: "args=[\'self\', \'lr\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'0.01\', \'None\', \'0.0\'], " + argspec: "args=[\'self\', \'learning_rate\', \'initial_accumulator_value\', \'epsilon\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.1\', \'1e-07\', \'Adagrad\'], " + } + member_method { + name: "add_slot" + argspec: "args=[\'self\', \'var\', \'slot_name\', \'initializer\'], varargs=None, keywords=None, defaults=[\'zeros\'], " + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'trainable\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'zeros\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], " + } + member_method { + name: "apply_gradients" + argspec: "args=[\'self\', \'grads_and_vars\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "from_config" - argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "get_config" @@ -19,6 +40,10 @@ tf_class { name: "get_gradients" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "get_slot" + argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "get_updates" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" @@ -27,8 +52,16 @@ tf_class { name: "get_weights" argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "minimize" + argspec: "args=[\'self\', \'loss\', \'var_list\', \'grad_loss\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } member_method { name: "set_weights" argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } } diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adam.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adam.pbtxt index 06815fa99a..417362d211 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adam.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adam.pbtxt @@ -1,15 +1,36 @@ path: "tensorflow.keras.optimizers.Adam" tf_class { - is_instance: "" - is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" + member { + name: "iterations" + mtype: "" + } + member { + name: "weights" + mtype: "" + } member_method { name: "__init__" - argspec: "args=[\'self\', \'lr\', \'beta_1\', \'beta_2\', \'epsilon\', \'decay\', \'amsgrad\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.9\', \'0.999\', \'None\', \'0.0\', \'False\'], " + argspec: "args=[\'self\', \'learning_rate\', \'beta_1\', \'beta_2\', \'epsilon\', \'amsgrad\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.9\', \'0.999\', \'1e-07\', \'False\', \'Adam\'], " + } + member_method { + name: "add_slot" + argspec: "args=[\'self\', \'var\', \'slot_name\', \'initializer\'], varargs=None, keywords=None, defaults=[\'zeros\'], " + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'trainable\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'zeros\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], " + } + member_method { + name: "apply_gradients" + argspec: "args=[\'self\', \'grads_and_vars\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "from_config" - argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "get_config" @@ -19,6 +40,10 @@ tf_class { name: "get_gradients" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "get_slot" + argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "get_updates" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" @@ -27,8 +52,16 @@ tf_class { name: "get_weights" argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "minimize" + argspec: "args=[\'self\', \'loss\', \'var_list\', \'grad_loss\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } member_method { name: "set_weights" argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } } diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adamax.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adamax.pbtxt index 47b55fdb44..7b43abee23 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adamax.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adamax.pbtxt @@ -1,15 +1,37 @@ path: "tensorflow.keras.optimizers.Adamax" tf_class { - is_instance: "" - is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" + member { + name: "iterations" + mtype: "" + } + member { + name: "weights" + mtype: "" + } member_method { name: "__init__" - argspec: "args=[\'self\', \'lr\', \'beta_1\', \'beta_2\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'0.002\', \'0.9\', \'0.999\', \'None\', \'0.0\'], " + argspec: "args=[\'self\', \'learning_rate\', \'beta_1\', \'beta_2\', \'epsilon\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.9\', \'0.999\', \'1e-07\', \'Adamax\'], " + } + member_method { + name: "add_slot" + argspec: "args=[\'self\', \'var\', \'slot_name\', \'initializer\'], varargs=None, keywords=None, defaults=[\'zeros\'], " + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'trainable\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'zeros\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], " + } + member_method { + name: "apply_gradients" + argspec: "args=[\'self\', \'grads_and_vars\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "from_config" - argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "get_config" @@ -19,6 +41,10 @@ tf_class { name: "get_gradients" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "get_slot" + argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "get_updates" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" @@ -27,8 +53,16 @@ tf_class { name: "get_weights" argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "minimize" + argspec: "args=[\'self\', \'loss\', \'var_list\', \'grad_loss\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } member_method { name: "set_weights" argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } } diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-optimizer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-optimizer.pbtxt index 53d64dae93..a996746dac 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-optimizer.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-optimizer.pbtxt @@ -1,14 +1,35 @@ path: "tensorflow.keras.optimizers.Optimizer" tf_class { - is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" + member { + name: "iterations" + mtype: "" + } + member { + name: "weights" + mtype: "" + } member_method { name: "__init__" - argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=kwargs, defaults=None" + } + member_method { + name: "add_slot" + argspec: "args=[\'self\', \'var\', \'slot_name\', \'initializer\'], varargs=None, keywords=None, defaults=[\'zeros\'], " + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'trainable\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'zeros\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], " + } + member_method { + name: "apply_gradients" + argspec: "args=[\'self\', \'grads_and_vars\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "from_config" - argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "get_config" @@ -18,6 +39,10 @@ tf_class { name: "get_gradients" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "get_slot" + argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "get_updates" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" @@ -26,8 +51,16 @@ tf_class { name: "get_weights" argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "minimize" + argspec: "args=[\'self\', \'loss\', \'var_list\', \'grad_loss\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } member_method { name: "set_weights" argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } } diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-r-m-sprop.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-r-m-sprop.pbtxt index a1e9b8cceb..bfc9d67a47 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-r-m-sprop.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-r-m-sprop.pbtxt @@ -1,15 +1,36 @@ path: "tensorflow.keras.optimizers.RMSprop" tf_class { - is_instance: "" - is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" + member { + name: "iterations" + mtype: "" + } + member { + name: "weights" + mtype: "" + } member_method { name: "__init__" - argspec: "args=[\'self\', \'lr\', \'rho\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.9\', \'None\', \'0.0\'], " + argspec: "args=[\'self\', \'learning_rate\', \'rho\', \'momentum\', \'epsilon\', \'centered\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.9\', \'0.0\', \'1e-07\', \'False\', \'RMSprop\'], " + } + member_method { + name: "add_slot" + argspec: "args=[\'self\', \'var\', \'slot_name\', \'initializer\'], varargs=None, keywords=None, defaults=[\'zeros\'], " + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'trainable\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'zeros\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], " + } + member_method { + name: "apply_gradients" + argspec: "args=[\'self\', \'grads_and_vars\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "from_config" - argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "get_config" @@ -19,6 +40,10 @@ tf_class { name: "get_gradients" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "get_slot" + argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "get_updates" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" @@ -27,8 +52,16 @@ tf_class { name: "get_weights" argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "minimize" + argspec: "args=[\'self\', \'loss\', \'var_list\', \'grad_loss\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } member_method { name: "set_weights" argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } } diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-s-g-d.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-s-g-d.pbtxt index a67fefb1ba..3f3d57962b 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-s-g-d.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-s-g-d.pbtxt @@ -1,15 +1,36 @@ path: "tensorflow.keras.optimizers.SGD" tf_class { - is_instance: "" - is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" is_instance: "" + member { + name: "iterations" + mtype: "" + } + member { + name: "weights" + mtype: "" + } member_method { name: "__init__" - argspec: "args=[\'self\', \'lr\', \'momentum\', \'decay\', \'nesterov\'], varargs=None, keywords=kwargs, defaults=[\'0.01\', \'0.0\', \'0.0\', \'False\'], " + argspec: "args=[\'self\', \'learning_rate\', \'momentum\', \'nesterov\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.0\', \'False\', \'SGD\'], " + } + member_method { + name: "add_slot" + argspec: "args=[\'self\', \'var\', \'slot_name\', \'initializer\'], varargs=None, keywords=None, defaults=[\'zeros\'], " + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'trainable\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'zeros\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], " + } + member_method { + name: "apply_gradients" + argspec: "args=[\'self\', \'grads_and_vars\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "from_config" - argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "get_config" @@ -19,6 +40,10 @@ tf_class { name: "get_gradients" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "get_slot" + argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "get_updates" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" @@ -27,8 +52,16 @@ tf_class { name: "get_weights" argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "minimize" + argspec: "args=[\'self\', \'loss\', \'var_list\', \'grad_loss\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } member_method { name: "set_weights" argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } } -- GitLab From 9b21247408beba5325aeaf11e906f4a5e879ec12 Mon Sep 17 00:00:00 2001 From: Pan Daoxin Date: Wed, 12 Dec 2018 10:03:35 +0800 Subject: [PATCH 404/873] More modifications to comments. --- tensorflow/core/kernels/mkl_slice_op.cc | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/tensorflow/core/kernels/mkl_slice_op.cc b/tensorflow/core/kernels/mkl_slice_op.cc index 577aa5c8db..e2cbeec2d2 100644 --- a/tensorflow/core/kernels/mkl_slice_op.cc +++ b/tensorflow/core/kernels/mkl_slice_op.cc @@ -190,7 +190,10 @@ class MklSlicePrimitive : public MklPrimitive { context_.dst_mem->set_data_handle(sliceParams.to->get_data_handle()); context_.slice_stream->submit(context_.slice_primitives); - // For safety guard, so that data_handle wouldn't be rewritten. + // We should set it back to DummyData so as to make the primitive + // in cache pool stateless. Otherwise, if the result for previous + // iteration is kept, problems of current iteration won't be + // thrown immediately, and wrong data would be reused. context_.src_mem->set_data_handle(DummyData); context_.dst_mem->set_data_handle(DummyData); return; @@ -214,7 +217,8 @@ class MklSlicePrimitive : public MklPrimitive { engine cpu_engine_ = engine(engine::cpu, 0); void Setup(const MklSliceParams& sliceParams) { - // Just create the memory primitive, fill with dummy. + // Actually, this DummyData will not be used in computation, + // because the real data will be filled before real execution. context_.src_mem.reset( new memory({sliceParams.from->get_primitive_desc().desc(), cpu_engine_}, DummyData)); -- GitLab From 02101df8e851aad6e9788f82aa51b4f5281b9e75 Mon Sep 17 00:00:00 2001 From: Billy Lamberta Date: Tue, 11 Dec 2018 18:19:05 -0800 Subject: [PATCH 405/873] Docs: Convert markdown links to backtick auto-link format in keras/engine. PiperOrigin-RevId: 225107457 --- tensorflow/python/keras/engine/training.py | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/tensorflow/python/keras/engine/training.py b/tensorflow/python/keras/engine/training.py index de929f2d3c..65a5d00d74 100644 --- a/tensorflow/python/keras/engine/training.py +++ b/tensorflow/python/keras/engine/training.py @@ -144,12 +144,11 @@ class Model(Network): Arguments: optimizer: String (name of optimizer) or optimizer instance. - See [optimizers](/api_docs/python/tf/keras/optimizers). + See `tf.keras.optimizers`. loss: String (name of objective function) or objective function. - See [losses](/api_docs/python/tf/losses). - If the model has multiple outputs, you can use a different loss - on each output by passing a dictionary or a list of losses. - The loss value that will be minimized by the model + See `tf.losses`. If the model has multiple outputs, you can use a + different loss on each output by passing a dictionary or a list of + losses. The loss value that will be minimized by the model will then be the sum of all individual losses. metrics: List of metrics to be evaluated by the model during training and testing. @@ -629,7 +628,7 @@ class Model(Network): 0 = silent, 1 = progress bar, 2 = one line per epoch. callbacks: List of `keras.callbacks.Callback` instances. List of callbacks to apply during training. - See [callbacks](/api_docs/python/tf/keras/callbacks). + See `tf.keras.callbacks`. validation_split: Float between 0 and 1. Fraction of the training data to be used as validation data. The model will set apart this fraction of the training data, -- GitLab From ae244e6dabeb6b879c5adb9ca4c2a85cb4722dc5 Mon Sep 17 00:00:00 2001 From: Dan Moldovan Date: Tue, 11 Dec 2018 18:22:21 -0800 Subject: [PATCH 406/873] Update the function API doc to cover the autograph functionality. Minor other formatting fixes. PiperOrigin-RevId: 225107801 --- tensorflow/python/eager/def_function.py | 55 ++++++++++++++++++++----- 1 file changed, 44 insertions(+), 11 deletions(-) diff --git a/tensorflow/python/eager/def_function.py b/tensorflow/python/eager/def_function.py index cdbf39ddd5..a12f9ed765 100644 --- a/tensorflow/python/eager/def_function.py +++ b/tensorflow/python/eager/def_function.py @@ -553,12 +553,33 @@ def function(func=None, assert f(x, y).numpy() == g(x, y).numpy() # Tensors and tf.Variables used by the Python function are captured in the - # traced graph. + # graph. @tf.function def h(): return f(x, y) assert (h().numpy() == f(x, y).numpy()).all() + + # Data-dependent control flow is also captured in the graph. Supported + # control flow statements include `if`, `for`, `break`, `continue`, `return`. + @tf.function + def g(x): + if tf.reduce_sum(x) > 0: + return x * x + else: + return -x // 2 + + # print and TensorFlow side effects are supported, but exercise caution when + # using Python side effects like mutating objects, saving to files, etc. + l = [] + + @tf.function + def g(x): + for i in x: + print(i) # Works + tf.assign(v, i) # Works + tf.py_func(lambda i: l.append(i))(i) # Works + l.append(i) # Caution! Doesn't work. ``` _Referencing `tf.Variable`s_ @@ -630,6 +651,7 @@ def function(func=None, ``` _Input Signatures_ + `function` instantiates a separate graph for every unique set of input shapes and datatypes. For example, the following code snippet will result in three distinct graphs being traced, as each input has a different @@ -663,9 +685,15 @@ def function(func=None, When an `input_signature` is specified, the callable will only accept `Tensor` (or NumPy `ndarray`) objects as arguments. - _Tracing_ - Note that `function` only traces TensorFlow operations, all the other - Python code that `func` executes will shape the _construction_ of the graph. + _Tracing and staging_ + + When `autograph` is `True`, all Python code that depends on `Tensor` values is + staged into a TensorFlow graph. When `autograph` is `False`, the function is + traced and control flow is not allowed to depend on data. + + Note that `function` only stages TensorFlow operations, all Python code that + `func` executes and does not depend on data will shape the _construction_ of + the graph. For example, consider the following: ```python @@ -678,21 +706,26 @@ def function(func=None, ``` `add_noise()` will return a different output every time it is invoked. - However, `traced` will return the same value every time it is called, since a - particular random value generated by the `np.random.randn` call will be - inserted in the traced TensorFlow graph as a constant. In this particular - example, replacing `np.random.randn(5, 5)` with `tf.random_normal((5, 5))` - will result in the same behavior for `add_noise()` and `traced()`. + However, `add_noise` will return the same value every time it is called, + since a particular random value generated by the `np.random.randn` call will + be inserted in the traced/staged TensorFlow graph as a constant. In this + particular example, replacing `np.random.randn(5, 5)` with + `tf.random_normal((5, 5))` will result in the same behavior for `add_noise()` + and `traced()`. _Python Side-Effects_ + A corollary of the previous discussion on tracing is the following: If a Python function `func` has Python side-effects, then executing `func` multiple - times - may not be semantically equivalent to executing `F = tf.function(func)` + times may not be semantically equivalent to executing `F = tf.function(func)` multiple times; this difference is due to the fact that `function` only captures the subgraph of TensorFlow operations that is constructed when `func` is invoked to trace a graph. + The same is true if code with Python side effects is used inside control flow, + such as a loop. If your code uses side effects that are not intended to + control graph construction, wrap them inside `tf.py_func`. + Args: func: function to be compiled. If `func` is None, returns a decorator that can be invoked with a single argument - `func`. The end result is -- GitLab From 04e8759ee2416baac1f31f6a27cb49a8b6051e19 Mon Sep 17 00:00:00 2001 From: Andy Ly Date: Tue, 11 Dec 2018 18:36:46 -0800 Subject: [PATCH 407/873] [Grappler] Add helper functions to GraphView. PiperOrigin-RevId: 225109110 --- tensorflow/core/grappler/graph_view.h | 60 ++++++++++++++------- tensorflow/core/grappler/graph_view_test.cc | 34 ++++++++++++ tensorflow/core/grappler/utils.cc | 7 ++- tensorflow/core/grappler/utils.h | 4 ++ tensorflow/core/grappler/utils_test.cc | 7 +++ 5 files changed, 92 insertions(+), 20 deletions(-) diff --git a/tensorflow/core/grappler/graph_view.h b/tensorflow/core/grappler/graph_view.h index 0a47b22565..16156d0f20 100644 --- a/tensorflow/core/grappler/graph_view.h +++ b/tensorflow/core/grappler/graph_view.h @@ -111,32 +111,37 @@ class GraphViewInternal { GraphDefT* graph() const { return graph_; } - // Find a node by name or return `nullptr` if it's not in a graph view. + // Finds a node by name or return `nullptr` if it's not in the graph view. NodeDefT* GetNode(absl::string_view node_name) const { return gtl::FindWithDefault(nodes_, node_name, nullptr); } - // Get the specified input port. Note that the special '-1' port_id can be + // Checks if a node by name is in the graph view. + bool HasNode(absl::string_view node_name) const { + return GetNode(node_name) != nullptr; + } + + // Gets the specified input port. Note that the special '-1' port_id can be // used to access the controlling nodes (i.e. the nodes connected to node_name // through an incoming control dependency). InputPort GetInputPort(absl::string_view node_name, int port_id) const { return InputPort(GetNode(node_name), port_id); } - // Get the specified output port. Note that the special '-1' port_id can be + // Gets the specified output port. Note that the special '-1' port_id can be // used to access the controlled nodes (i.e. the nodes connected to node_name // through an outgoing control dependency). OutputPort GetOutputPort(absl::string_view node_name, int port_id) const { return OutputPort(GetNode(node_name), port_id); } - // Get the input (resp. output) port(s) in the immediate fanout (resp. fanin) - // of an output (resp. input) port. + // Gets the input port(s) in the immediate fanout of an output port. const absl::flat_hash_set& GetFanout( const OutputPort& port) const { return gtl::FindWithDefault(fanouts_, port, fanout_not_found_value_); } + // Gets the output port(s) in the immediate fanin of an input port. absl::flat_hash_set GetFanin(const InputPort& port) const { if (port.port_id >= 0) return {GetRegularFanin(port)}; @@ -162,9 +167,22 @@ class GraphViewInternal { return GetOutputPort(tensor_id.node(), tensor_id.index()); } - // Get all the input (resp. output) ports in the immediate fanout (resp - // fanin) of a node. Include the controlling nodes iff - // include_controlling_nodes is true. + // Checks if a tensor id is a fanin of the node. + bool HasFanin(const NodeDef& node, const TensorId& fanin) const { + if (fanin.index() < -1) { + return false; + } + string fanin_string = TensorIdToString(fanin); + for (int i = 0; i < node.input_size(); ++i) { + if (node.input(i) == fanin_string) { + return true; + } + } + return false; + } + + // Gets all the input ports in the immediate fanout of a node. Include the + // controlled nodes iff include_controlled_nodes is true. absl::flat_hash_set GetFanouts( const NodeDef& node, bool include_controlled_nodes) const { absl::flat_hash_set result; @@ -185,6 +203,8 @@ class GraphViewInternal { return result; } + // Gets all the output ports in the immediate fanin of a node. Include the + // controlling nodes iff include_controlling_nodes is true. absl::flat_hash_set GetFanins( const NodeDef& node, bool include_controlling_nodes) const { absl::flat_hash_set result; @@ -198,7 +218,7 @@ class GraphViewInternal { return result; } - // Get the number of ports in the immediate fanin of a node. Count the + // Gets the number of ports in the immediate fanin of a node. Count the // controlling nodes iff include_controlling_nodes is true. int NumFanins(const NodeDef& node, bool include_controlling_nodes) const { int count = 0; @@ -211,14 +231,14 @@ class GraphViewInternal { return count; } - // Get the number of ports in the immediate fanout of a node. Count the - // controlling nodes iff include_controlling_nodes is true. - int NumFanouts(const NodeDef& node, bool include_controlling_nodes) const { + // Gets the number of ports in the immediate fanout of a node. Count the + // controlled nodes iff include_controlled_nodes is true. + int NumFanouts(const NodeDef& node, bool include_controlled_nodes) const { int count = 0; OutputPort port; port.node = const_cast(&node); - const int first_port_id = include_controlling_nodes ? -1 : 0; + const int first_port_id = include_controlled_nodes ? -1 : 0; const int last_port_id = gtl::FindWithDefault(max_regular_output_port_, port.node, -1); @@ -231,8 +251,8 @@ class GraphViewInternal { return count; } - // Get all the edges in the immediate fanout (resp fanin) of a node. - // Include the control edges iff include_controlling_edges is true. + // Gets all the edges in the immediate fanout of a node. Include the + // controlled edges iff include_controlled_edges is true. absl::flat_hash_set GetFanoutEdges( const NodeDef& node, bool include_controlled_edges) const { absl::flat_hash_set result; @@ -248,14 +268,16 @@ class GraphViewInternal { auto it = fanouts_.find(port); if (it != fanouts_.end()) { for (auto itr = it->second.begin(); itr != it->second.end(); ++itr) { - result.emplace(/*src*/ OutputPort(const_cast(&node), i), - /*dst*/ *itr); + result.emplace(/*src=*/OutputPort(const_cast(&node), i), + /*dst=*/*itr); } } } return result; } + // Gets all the edges in the immediate fanin of a node. Include the + // controlling edges iff include_controlling_edges is true. absl::flat_hash_set GetFaninEdges( const NodeDef& node, bool include_controlling_edges) const { absl::flat_hash_set result; @@ -265,8 +287,8 @@ class GraphViewInternal { auto it = nodes_.find(tensor_id.node()); if (it != nodes_.end()) { - result.emplace(/*src*/ OutputPort(it->second, tensor_id.index()), - /*dst*/ InputPort(const_cast(&node), i)); + result.emplace(/*src=*/OutputPort(it->second, tensor_id.index()), + /*dst=*/InputPort(const_cast(&node), i)); } } return result; diff --git a/tensorflow/core/grappler/graph_view_test.cc b/tensorflow/core/grappler/graph_view_test.cc index cbf859a4a9..404dcd30c1 100644 --- a/tensorflow/core/grappler/graph_view_test.cc +++ b/tensorflow/core/grappler/graph_view_test.cc @@ -230,6 +230,40 @@ TEST_F(GraphViewTest, ControlDependencies) { EXPECT_EQ(0, (*fanin.begin()).port_id); } +TEST_F(GraphViewTest, HasNode) { + tensorflow::Scope s = tensorflow::Scope::NewRootScope(); + Output a = ops::Const(s.WithOpName("a"), 0.0f, {10, 10}); + + GrapplerItem item; + TF_CHECK_OK(s.ToGraphDef(&item.graph)); + GraphView graph(&item.graph); + + EXPECT_EQ(true, graph.HasNode("a")); + EXPECT_EQ(false, graph.HasNode("b")); +} + +TEST_F(GraphViewTest, HasFanin) { + tensorflow::Scope s = tensorflow::Scope::NewRootScope(); + Output a = ops::Const(s.WithOpName("a"), 0.0f, {10, 10}); + Output b = ops::Square(s.WithOpName("b"), {a}); + Output c = ops::Sqrt(s.WithOpName("c"), {b}); + Output d = ops::AddN(s.WithOpName("d").WithControlDependencies(a), {b, c}); + + GrapplerItem item; + TF_CHECK_OK(s.ToGraphDef(&item.graph)); + GraphView graph(&item.graph); + + const NodeDef* d_node = graph.GetNode("d"); + EXPECT_NE(nullptr, d_node); + + EXPECT_EQ(true, graph.HasFanin(*d_node, {"a", Graph::kControlSlot})); + EXPECT_EQ(false, graph.HasFanin(*d_node, {"a", 0})); + EXPECT_EQ(true, graph.HasFanin(*d_node, {"b", 0})); + EXPECT_EQ(false, graph.HasFanin(*d_node, {"b", Graph::kControlSlot})); + EXPECT_EQ(true, graph.HasFanin(*d_node, {"c", 0})); + EXPECT_EQ(false, graph.HasFanin(*d_node, {"c", Graph::kControlSlot})); +} + } // namespace } // namespace grappler } // namespace tensorflow diff --git a/tensorflow/core/grappler/utils.cc b/tensorflow/core/grappler/utils.cc index 2977544262..90ad04cf47 100644 --- a/tensorflow/core/grappler/utils.cc +++ b/tensorflow/core/grappler/utils.cc @@ -144,11 +144,16 @@ void NodeMap::UpdateOutput(const string& node_name, outputs.insert(nodes_[NodeName(new_output_name)]); } +string TensorIdToString(const TensorId& tensor_id) { + return tensor_id.index() == 0 ? string(tensor_id.node()) + : tensor_id.ToString(); +} + bool IsSameInput(const string& name1, const string& name2) { if (name1 == name2) return true; TensorId tensor1 = ParseTensorName(name1); TensorId tensor2 = ParseTensorName(name2); - return tensor1.node() == tensor2.node() && tensor1.index() == tensor2.index(); + return tensor1 == tensor2; } bool IsControlInput(const string& name) { diff --git a/tensorflow/core/grappler/utils.h b/tensorflow/core/grappler/utils.h index b1e2d4e9cb..89a87af323 100644 --- a/tensorflow/core/grappler/utils.h +++ b/tensorflow/core/grappler/utils.h @@ -100,6 +100,10 @@ class SetVector { std::vector vector_; }; +// Returns formatted string from TensorId specific to grappler. Specifically, +// for the 0 port (first output), only the node name is returned. +string TensorIdToString(const TensorId& tensor_id); + // True iff 'name' refers to a control inputs, i.e. a node name prefixed with // the ^ character. bool IsControlInput(const string& name); diff --git a/tensorflow/core/grappler/utils_test.cc b/tensorflow/core/grappler/utils_test.cc index e993391b51..f5ae39867a 100644 --- a/tensorflow/core/grappler/utils_test.cc +++ b/tensorflow/core/grappler/utils_test.cc @@ -464,6 +464,13 @@ TEST_F(UtilsTest, SetTensorValueBFloat16IntMin) { Tensor(bfloat16(std::numeric_limits::min())), t); } +TEST_F(UtilsTest, TensorIdToString) { + EXPECT_EQ("^foo", TensorIdToString({"foo", -1})); + EXPECT_EQ("foo", TensorIdToString({"foo", 0})); + EXPECT_EQ("foo:1", TensorIdToString({"foo", 1})); + EXPECT_EQ("foo:2", TensorIdToString({"foo", 2})); +} + } // namespace } // namespace grappler } // namespace tensorflow -- GitLab From e4e9409b3de9a8d12a56fc0e2fa7270bffd0d41a Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 11 Dec 2018 18:56:53 -0800 Subject: [PATCH 408/873] Fix internal type mismatch in ragged.map_fn PiperOrigin-RevId: 225110815 --- tensorflow/python/ops/ragged/BUILD | 4 ++-- .../python/ops/ragged/ragged_map_fn_op_test.py | 13 +++++++++++++ tensorflow/python/ops/ragged/ragged_map_ops.py | 4 +++- 3 files changed, 18 insertions(+), 3 deletions(-) diff --git a/tensorflow/python/ops/ragged/BUILD b/tensorflow/python/ops/ragged/BUILD index c0db8bfbb5..440d9db824 100644 --- a/tensorflow/python/ops/ragged/BUILD +++ b/tensorflow/python/ops/ragged/BUILD @@ -263,17 +263,17 @@ py_library( srcs = ["ragged_map_ops.py"], srcs_version = "PY2AND3", deps = [ - ":ragged_array_ops", - ":ragged_factory_ops", ":ragged_tensor", "//tensorflow/python:array_ops", "//tensorflow/python:constant_op", "//tensorflow/python:control_flow_ops", "//tensorflow/python:dtypes", "//tensorflow/python:framework_ops", + "//tensorflow/python:math_ops", "//tensorflow/python:platform", "//tensorflow/python:sparse_tensor", "//tensorflow/python:tensor_array_ops", + "//tensorflow/python:tensor_shape", "//tensorflow/python:util", "//tensorflow/python:variable_scope", "//tensorflow/python/eager:context", diff --git a/tensorflow/python/ops/ragged/ragged_map_fn_op_test.py b/tensorflow/python/ops/ragged/ragged_map_fn_op_test.py index 49c0996b24..171cb347de 100644 --- a/tensorflow/python/ops/ragged/ragged_map_fn_op_test.py +++ b/tensorflow/python/ops/ragged/ragged_map_fn_op_test.py @@ -21,6 +21,7 @@ from absl.testing import parameterized import numpy as np from tensorflow.python.framework import dtypes +from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import test_util from tensorflow.python.keras import backend from tensorflow.python.ops import array_ops @@ -270,6 +271,18 @@ class RaggedMapOpTest(ragged_test_util.RaggedTensorTestCase, elems, dtype=ragged.RaggedTensorType(dtype=dtypes.int64, ragged_rank=10)) + def testMapOnSparseTensor(self): + s = sparse_tensor.SparseTensor( + indices=[[0, 0], [0, 1], [1, 0], [1, 1]], + values=[0, 5, 0, 4], + dense_shape=[2, 2], + ) + t2 = ragged.RaggedTensor.from_sparse(s) + id_t2 = ragged.map_fn( + lambda x: x, t2, + ) + self.assertRaggedEqual(id_t2, [[0, 5], [0, 4]]) + if __name__ == '__main__': googletest.main() diff --git a/tensorflow/python/ops/ragged/ragged_map_ops.py b/tensorflow/python/ops/ragged/ragged_map_ops.py index af40352b1d..fbe188bd1a 100644 --- a/tensorflow/python/ops/ragged/ragged_map_ops.py +++ b/tensorflow/python/ops/ragged/ragged_map_ops.py @@ -30,6 +30,7 @@ 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 control_flow_ops +from tensorflow.python.ops import math_ops from tensorflow.python.ops import tensor_array_ops from tensorflow.python.ops import variable_scope as vs from tensorflow.python.ops.ragged import ragged_tensor @@ -238,6 +239,7 @@ def map_fn(fn, n = (tensor_shape.dimension_value(static_shape[0]) or array_ops.shape(elems_flat[0])[0]) + n = math_ops.cast(n, dtype=dtypes.int32) # Create a flat list of TAs. # Flatten the dtype structure to a list. @@ -254,7 +256,7 @@ def map_fn(fn, for t in dtype_components_flat ] - i = constant_op.constant(0) + i = constant_op.constant(0, dtype=dtypes.int32) def compute(i, tas): """The loop body of map_fn. -- GitLab From 4b974cf1c1c072338f9c420b9149840780907443 Mon Sep 17 00:00:00 2001 From: Yifei Feng Date: Tue, 11 Dec 2018 18:59:00 -0800 Subject: [PATCH 409/873] Remove some extra cuda header includes. PiperOrigin-RevId: 225110993 --- tensorflow/core/grappler/costs/BUILD | 3 --- tensorflow/core/grappler/costs/utils.cc | 6 ------ tensorflow/core/util/cuda_launch_config.h | 1 - tensorflow/core/util/port.cc | 3 --- tensorflow/stream_executor/cuda/cuda_helpers.h | 1 - 5 files changed, 14 deletions(-) diff --git a/tensorflow/core/grappler/costs/BUILD b/tensorflow/core/grappler/costs/BUILD index 5090e62b2c..f8af1232f7 100644 --- a/tensorflow/core/grappler/costs/BUILD +++ b/tensorflow/core/grappler/costs/BUILD @@ -132,9 +132,6 @@ tf_cuda_library( name = "utils", srcs = ["utils.cc"], hdrs = ["utils.h"], - cuda_deps = [ - "@local_config_cuda//cuda:cudnn_header", - ], visibility = ["//visibility:public"], deps = [ "//third_party/eigen3", diff --git a/tensorflow/core/grappler/costs/utils.cc b/tensorflow/core/grappler/costs/utils.cc index 7d868a3679..d45bb14e07 100644 --- a/tensorflow/core/grappler/costs/utils.cc +++ b/tensorflow/core/grappler/costs/utils.cc @@ -20,12 +20,6 @@ limitations under the License. #include "third_party/eigen3/Eigen/Core" -#if GOOGLE_CUDA -#include "cuda/include/cuda.h" -#include "cuda/include/cuda_runtime_api.h" -#include "cuda/include/cudnn.h" -#endif - #include "tensorflow/core/common_runtime/gpu/gpu_id.h" #include "tensorflow/core/common_runtime/gpu/gpu_id_manager.h" #include "tensorflow/core/framework/allocation_description.pb.h" diff --git a/tensorflow/core/util/cuda_launch_config.h b/tensorflow/core/util/cuda_launch_config.h index 080d4067ce..c0ae6349f7 100644 --- a/tensorflow/core/util/cuda_launch_config.h +++ b/tensorflow/core/util/cuda_launch_config.h @@ -21,7 +21,6 @@ limitations under the License. #include #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" -#include "cuda/include/cuda.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/stream_executor.h" diff --git a/tensorflow/core/util/port.cc b/tensorflow/core/util/port.cc index e01058dff6..7dc8ddda06 100644 --- a/tensorflow/core/util/port.cc +++ b/tensorflow/core/util/port.cc @@ -15,9 +15,6 @@ limitations under the License. #include "tensorflow/core/util/port.h" -#if GOOGLE_CUDA -#include "cuda/include/cuda.h" -#endif namespace tensorflow { diff --git a/tensorflow/stream_executor/cuda/cuda_helpers.h b/tensorflow/stream_executor/cuda/cuda_helpers.h index d55706c66a..dc0dc694cd 100644 --- a/tensorflow/stream_executor/cuda/cuda_helpers.h +++ b/tensorflow/stream_executor/cuda/cuda_helpers.h @@ -25,7 +25,6 @@ limitations under the License. #include #include "cuda/include/cuComplex.h" -#include "cuda/include/cuda.h" namespace stream_executor { -- GitLab From ab3db8c3001002592044c95dfa1ad042f2286149 Mon Sep 17 00:00:00 2001 From: Smit Hinsu <1990079+smit-hinsu@users.noreply.github.com> Date: Tue, 11 Dec 2018 19:08:24 -0800 Subject: [PATCH 410/873] Update tensorflow/contrib/tensorrt/convert/convert_nodes.cc Co-Authored-By: trevor-m --- tensorflow/contrib/tensorrt/convert/convert_nodes.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index ba1c2e80b2..6e411a21f8 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -2992,7 +2992,7 @@ tensorflow::Status ConvertFusedBatchNorm(OpConverterParams* params) { << "are using Keras, please call " << "keras.backend.set_learning_phase(0) before constructing " << "your model. At " - << node_def.name()); + << node_def.name(); return tensorflow::errors::Unimplemented( node_def.op(), " only supports is_training=false, at ", node_def.name()); -- GitLab From 8ac99aa0ec18f65f9976af0eb0e3fc2fef6536c4 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 11 Dec 2018 19:09:14 -0800 Subject: [PATCH 411/873] Enhance the Tensor-Tracer in the following ways: (1) Able to trace tensors when the model is executed on the CPU. (previously, it could only trace when the model is executed on TPU) (2) Allow the user to specify the op-names and op-types to be excluded or included for tracing via regular expressions. (3) Two new trace modes: (1) tracing the vector norm of the tensor and (2) tracing the maximum of the absolute values of all elements in the tensor. (4) Attach the replica-ID to a traced tensor value so that the post-processing tool (Tensor-Inspector) can reconstruct the whole tensor from all replicas. (5) An API to trace tensors programmatically. (6) Allow writing the trace to stdout (previously, it must be written to a file). PiperOrigin-RevId: 225112219 --- .../contrib/tpu/python/tpu/tensor_tracer.py | 553 +++++++++++++++--- .../contrib/tpu/python/tpu/tpu_estimator.py | 10 + 2 files changed, 486 insertions(+), 77 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/tensor_tracer.py b/tensorflow/contrib/tpu/python/tpu/tensor_tracer.py index 70baea203c..a1494e3660 100644 --- a/tensorflow/contrib/tpu/python/tpu/tensor_tracer.py +++ b/tensorflow/contrib/tpu/python/tpu/tensor_tracer.py @@ -21,44 +21,56 @@ from __future__ import print_function import os import os.path import re +import sys from tensorflow.contrib.tpu.python.ops import tpu_ops from tensorflow.contrib.tpu.python.tpu import tpu from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_util from tensorflow.python.ops import gen_math_ops +from tensorflow.python.ops import linalg_ops from tensorflow.python.ops import logging_ops from tensorflow.python.ops import math_ops +from tensorflow.python.platform import gfile from tensorflow.python.platform import tf_logging as logging _TRACER_LOG_PREFIX = ' [>>>TT>>>]' _DEVICE_TYPE_TPU = 'tpu' _DEVICE_TYPE_CPU = 'cpu' -_GLOBAL_STEP_OP_NAME = 'GLOBAL-STEP' _TRACE_MODE_NAN_INF = 'nan-inf' _TRACE_MODE_PART_TENSOR = 'part-tensor' _TRACE_MODE_PART_TENSOR_SIZE = 3 _TRACE_MODE_FULL_TENSOR = 'full-tensor' -_RECORD_OUTSIDE_OP_RANGE = 'not-traced-outside-op-range' -_RECORD_SHOULD_NOT_TRACE = 'not-traced-should-not-trace' -_RECORD_FILTERED_OUT = 'not-traced-filtered-out' -_RECORD_SCALAR = 'not-traced-scalar' -_RECORD_DYNAMIC_SHAPE = 'not-traced-dynamic-shape' -_RECORD_GET_TRACED = 'get-traced' +_TRACE_MODE_NORM = 'norm' +_TRACE_MODE_MAX_ABS = 'max-abs' +_REASON_OUTSIDE_OP_RANGE = 'not-traced-outside-op-range' +_REASON_UNSAFE_OP = 'not-traced-unsafe-op' +_REASON_UNSAFE_SCALAR = 'not-traced-unsafe-scalar' +_REASON_LESS_INTERESTING_OP = 'not-traced-less-interesting-op' +_REASON_DEVICE_MISMATCH = 'not-traced-device-mismatch' +_REASON_DYNAMIC_SHAPE = 'not-traced-dynamic-shape' +_REASON_SCALAR_GET_TRACED = 'traced-scalar' +_REASON_TENSOR_GET_TRACED = 'traced-tensor' +_REASON_USER_INCLUDED = 'traced-user-included' +_REASON_USER_EXCLUDED = 'not-traced-user-excluded' +_REASON_NON_NUMERIC_TENSOR = 'not-traced-non-numeric-tensor' _MARKER_SECTION_BEGIN = '!!!!!!! section-begin:' _MARKER_SECTION_END = '!!!!!!! section-end:' _SECTION_NAME_CONFIG = 'configuration' _SECTION_NAME_REASON = 'reason' _SECTION_NAME_OP_LIST = 'op-list' +_SECTION_NAME_TENSOR_LIST = 'tensor-list' _SECTION_NAME_GRAPH = 'graph' _FIELD_NAME_VERSION = 'version:' _FIELD_NAME_DEVICE = 'device:' _FIELD_NAME_TRACE_MODE = 'trace-mode:' _FIELD_NAME_NUM_REPLICAS = 'num-replicas:' _FIELD_NAME_NUM_OPS = 'number-of-ops:' +_FIELD_NAME_NUM_TENSORS = 'number-of-tensors:' _FIELD_NAME_TOPOLOGICAL_SORT_SUCCEED = 'topological-sort-succeed:' _FLAGS_ENV_VAR = 'TENSOR_TRACER_FLAGS' _FLAG_SINGLE_QUOTE_PAT = re.compile(r"\s*--([^=]+)='([^']*)'") @@ -66,13 +78,72 @@ _FLAG_DOUBLE_QUOTE_PAT = re.compile(r'\s*--([^=]+)="([^"]*)"') _FLAG_NO_QUOTE_PAT = re.compile(r'\s*--([^=]+)=(\S*)') _FLAG_NAME_ENABLE = 'enable' _FLAG_NAME_TRACE_MODE = 'trace_mode' -_FLAG_NAME_INTERESTING_OPS = 'interesting_ops' +_FLAG_NAME_INCLUDE_LESS_INTERESTING_OPS = 'include_less_interesting_ops' +_FLAG_NAME_EXCLUDED_OPNAMES = 'excluded_opnames' +_FLAG_NAME_EXCLUDED_OPTYPES = 'excluded_optypes' +_FLAG_NAME_INCLUDED_OPNAMES = 'included_opnames' +_FLAG_NAME_INCLUDED_OPTYPES = 'included_optypes' _FLAG_NAME_TRACE_FILE = 'trace_file_path' +_FLAG_NAME_REPORT_FILE = 'report_file_path' _FLAG_NAME_USE_TEST_UNDECLARED_OUTPUTS_DIR = 'use_test_undeclared_outputs_dir' _FLAG_NAME_OP_RANGE = 'op_range' _OP_RANGE_PAT = re.compile(r'(\d+):(\d+)') _OUTPUT_STREAM_ESCAPE = 'file://' _TEST_UNDECLARED_OUTPUTS_DIR_ENV_VAR = 'TEST_UNDECLARED_OUTPUTS_DIR' +_TENSOR_TRACER_COLLECTION = 'tensor_tracer_variables' +_TENSOR_TRACER_CHECKPOINT = 'tensor_tracer_checkpoint' + + +def tensor_checkpoint(tensor, checkpoint_name): + """Adds a checkpoint with the given checkpoint name for the given tensor. + + The tensor will be added to the list of tensors that will be traced by the + tensor tracer. + + Args: + tensor: the tensor object for which the tracing is requested. + checkpoint_name: a string name for the checkpoint. This name has to be a + unique name if used within model comparison. The tensors that have the same + checkpoint identifier is compared in model comparison. + Returns: + The provided tensor. + """ + + tensor.graph.get_collection(_TENSOR_TRACER_COLLECTION) + tensor.graph.add_to_collection(_TENSOR_TRACER_COLLECTION, + (tensor, checkpoint_name)) + return tensor + + +def keras_layer_checkpoint(layer, checkpoint_name): + """An interface for adding the tensor outputs of a keras layer. + + Encapsulates tensor_checkpoint. + + Args: + layer: A keras layer. + checkpoint_name: a string name for the checkpoint. This name has to be a + unique name if used within model comparison. The tensors that have the same + checkpoint identifier is compared in model comparison. + + Returns: + The provided layer. + """ + try: + outputs = layer.output + if tensor_util.is_tensor(outputs): + tensor_checkpoint(outputs, '%s' % (checkpoint_name)) + else: + idx = 0 + for output_tensor in outputs: + if tensor_util.is_tensor(outputs): + tensor_checkpoint(output_tensor, '%s_%d' % (checkpoint_name, idx)) + idx += 1 + except AttributeError: + pass + except RuntimeError: + pass + return layer class TensorTracer(object): @@ -105,6 +176,34 @@ class TensorTracer(object): match = _FLAG_NO_QUOTE_PAT.match(flags, pos) return match + @staticmethod + def validate_flag_names(): + """Validates if the TensorTrace flags passed are valid.""" + valid_flag_names = [_FLAG_NAME_ENABLE, _FLAG_NAME_TRACE_MODE, + _FLAG_NAME_EXCLUDED_OPNAMES, + _FLAG_NAME_EXCLUDED_OPTYPES, + _FLAG_NAME_INCLUDED_OPNAMES, + _FLAG_NAME_INCLUDED_OPTYPES, + _FLAG_NAME_TRACE_FILE, _FLAG_NAME_REPORT_FILE, + _FLAG_NAME_USE_TEST_UNDECLARED_OUTPUTS_DIR, + _FLAG_NAME_INCLUDE_LESS_INTERESTING_OPS, + _FLAG_NAME_OP_RANGE] + tensor_tracer_flags = os.environ.get(_FLAGS_ENV_VAR) + if not tensor_tracer_flags: + return + pos = 0 + while True: + match = TensorTracer._match_next_flag(tensor_tracer_flags, pos) + if not match: + break + flag_name = match.group(1) + if flag_name not in valid_flag_names: + raise ValueError( + 'The flag name "%s" passed via the environment variable "%s" ' + 'is invalid. Valid flag names are:' + '\n%s'%(flag_name, _FLAGS_ENV_VAR, valid_flag_names)) + pos = match.end() + @staticmethod def print_flag_values(): """Prints all TensorTracer flags passed via environment variables.""" @@ -146,6 +245,20 @@ class TensorTracer(object): pos = match.end() return '' + @staticmethod + def flag_value_to_re_list(flag_name): + """Converts list of strings to compiled RE.""" + + re_list = [] + flag_value = TensorTracer.get_flag_value(flag_name) + if not flag_value: + return re_list + list_of_values = flag_value.split() + for v in list_of_values: + r = re.compile(v) + re_list.append(r) + return re_list + @staticmethod def is_enabled(): """Returns True if TensorTracer is enabled.""" @@ -186,29 +299,67 @@ class TensorTracer(object): """Checks if the given trace mode is valid.""" valid_trace_modes = [_TRACE_MODE_NAN_INF, _TRACE_MODE_PART_TENSOR, - _TRACE_MODE_FULL_TENSOR] + _TRACE_MODE_FULL_TENSOR, _TRACE_MODE_NORM, + _TRACE_MODE_MAX_ABS] if trace_mode not in valid_trace_modes: raise ValueError('Invalid trace mode "%s" given to the Tensor_Tracer.' 'Valid trace modes are: %s'%(trace_mode, valid_trace_modes)) @staticmethod - def should_trace(device_type, op): - """Returns True if the given Op should be traced.""" + def unsafe_op(op): + """Returns True if this op is not safe to be traced.""" - if device_type != _DEVICE_TYPE_TPU: - raise ValueError('Non TPU device type is not supported') if control_flow_util.IsInCond(op): + return True + # Reasons for not including following op types: + # Assign: cause incorrect result with CPU tracing. + # others: compilation problems. + if op.type in ['Assign', 'Pack', 'Shape', 'Reshape', 'ArgMin', 'ArgMax']: + return True + return False + + @staticmethod + def device_mismatch(device_type, op): + if device_type == _DEVICE_TYPE_TPU: + # pylint: disable=protected-access + return tpu._TPU_REPLICATE_ATTR not in op.node_def.attr + # pylint: enable=protected-access + return False + + @staticmethod + def unsafe_scalar_trace(op): + """Return true if scalar output tensor from Op is not safe to be traced.""" + + # Tracing the following causes cycle in the graph on TPU. + if op.type in ['LoopCond', 'Enter', 'Merge', 'Const', + 'Switch', 'Less', 'ReadVariableOp']: + return True + # Tracing the following will cause casting-issue + # with the norm tracing mode or other compilation issues on CPU. + if op.type in ['VarHandleOp', 'IteratorToStringHandle', + 'IteratorGetNext', 'OneShotIterator', + 'IteratorV2', 'MakeIterator', + 'BatchDatasetV2', 'MapDataset', + 'FixedLengthRecordDataset', 'TakeDataset', 'ZipDataset', + 'Placeholder', 'PlaceholderWithDefault', 'StridedSlice']: + return True + return False + + @staticmethod + def less_interesting_op(op): + """Returns True if the given Op is not an interesting one to be traced.""" + + include_less_interesting = TensorTracer.get_flag_value( + _FLAG_NAME_INCLUDE_LESS_INTERESTING_OPS) + if include_less_interesting: return False - if op.type in ['Reshape', 'ArgMin', 'ArgMax']: - return False - # pylint: disable=protected-access - return tpu._TPU_REPLICATE_ATTR in op.node_def.attr - # pylint: enable=protected-access + return op.type in ['Const', 'Identity', 'Cast', 'Shape'] @staticmethod def reason(op_idx, details): - """Returns why the Op at op_idx is traced or not.""" + """Returns reason why the Op at op_idx is traced or not.""" + return '%d %s'%(op_idx, details) @staticmethod @@ -274,6 +425,33 @@ class TensorTracer(object): assert len(unsorted_ops) == len(sorted_ops) return (True, sorted_ops) + @staticmethod + def _make_op_and_tensor_maps(op_list): + """Creates various maps and lists from op_list. + + Args: + op_list: a list of Ops + + Returns: + opname_idx_map: a map from Op's name to its index in op_list. + tensor_list: a list of output tensors of the Ops in op_list. + tensorname_idx_map: a map from output tensor name to its index + in tensor_list. + """ + + opname_idx_map = {} + tensor_list = [] + tensorname_idx_map = {} + for op_id, op in enumerate(op_list): + if op.name in opname_idx_map: + raise ValueError('Duplicated Op name: %s'%op.name) + opname_idx_map[op.name] = op_id + for output_tensor in op.outputs: + if output_tensor.name not in tensorname_idx_map: + tensor_list.append(output_tensor) + tensorname_idx_map[output_tensor.name] = len(tensor_list)-1 + return (opname_idx_map, tensor_list, tensorname_idx_map) + def __init__(self): """Initializes a TensorTracer. @@ -281,16 +459,20 @@ class TensorTracer(object): """ self._version = 'use-outside-compilation' self._device_type = None + TensorTracer.validate_flag_names() self._trace_mode = TensorTracer.get_flag_value(_FLAG_NAME_TRACE_MODE) if not self._trace_mode: self._trace_mode = _TRACE_MODE_NAN_INF TensorTracer.check_trace_mode(self._trace_mode) self._part_tensor_size = _TRACE_MODE_PART_TENSOR_SIZE self._instrument_records = {} - interesting_ops = TensorTracer.get_flag_value(_FLAG_NAME_INTERESTING_OPS) - self._selected_ops = interesting_ops.split() self._set_trace_file_path() + self._set_report_file() self._set_op_range() + self._set_excluded_opnames() + self._set_excluded_optypes() + self._set_included_opnames() + self._set_included_optypes() self._num_replicas = None self._replica_id = None @@ -318,10 +500,7 @@ class TensorTracer(object): """Sets the path of the output trace file.""" self._trace_file_path = TensorTracer.get_flag_value(_FLAG_NAME_TRACE_FILE) - if not self._trace_file_path: - raise ValueError('--%s is not set in the environment variable %s' - %(_FLAG_NAME_TRACE_FILE, _FLAGS_ENV_VAR)) - elif TensorTracer.use_test_undeclared_outputs_dir(): + if self._trace_file_path and TensorTracer.use_test_undeclared_outputs_dir(): if os.path.isabs(self._trace_file_path): raise ValueError('If use_test_undeclared_outputs_dir is set,' 'trace_file_path cannot be an absolute path (%s)' @@ -330,6 +509,22 @@ class TensorTracer(object): self._trace_file_path = os.path.join(outputs_dir, self._trace_file_path) + def _set_report_file(self): + """Sets the path of the output report file.""" + + self._report_file_path = TensorTracer.get_flag_value(_FLAG_NAME_REPORT_FILE) + if not self._report_file_path: + self._report_file = None + return + try: + self._report_file = gfile.Open(self._report_file_path, 'w') + except IOError as e: + raise e + + def _close_report_file(self): + if self._report_file: + self._report_file.close() + def _set_op_range(self): """Sets the index range of the Ops that we will consider tracing.""" @@ -350,19 +545,48 @@ class TensorTracer(object): return False return self._op_range[1] < 0 or idx <= self._op_range[1] - def _write_report(self, content): - """Writes the given content to the report.""" + def _set_excluded_opnames(self): + self._excluded_opname_re_list = TensorTracer.flag_value_to_re_list( + _FLAG_NAME_EXCLUDED_OPNAMES) + + def _set_excluded_optypes(self): + self._excluded_optype_re_list = TensorTracer.flag_value_to_re_list( + _FLAG_NAME_EXCLUDED_OPTYPES) + + def _set_included_opnames(self): + self._included_opname_re_list = TensorTracer.flag_value_to_re_list( + _FLAG_NAME_INCLUDED_OPNAMES) + + def _set_included_optypes(self): + self._included_optype_re_list = TensorTracer.flag_value_to_re_list( + _FLAG_NAME_INCLUDED_OPTYPES) + + def _is_user_included_op(self, op): + for opname_re in self._included_opname_re_list: + if opname_re.match(op.name): + return True + for optype_re in self._included_optype_re_list: + if optype_re.match(op.type): + return True + return False - logging.info('%s %s'%(_TRACER_LOG_PREFIX, content)) + def _is_user_excluded_op(self, op): + for opname_re in self._excluded_opname_re_list: + if opname_re.match(op.name): + return True + for optype_re in self._excluded_optype_re_list: + if optype_re.match(op.type): + return True + return False - def _is_selected_op(self, op_name): - """Returns True if the Op with op_name is selected to be traced.""" + def _write_report(self, content): + """Writes the given content to the report.""" - if not self._selected_ops: - return True - if op_name in self._selected_ops: - return True - return False + line = '%s %s'%(_TRACER_LOG_PREFIX, content) + if self._report_file: + self._report_file.write(line) + else: + logging.info(line) def _write_config_section(self): """Writes the config section of the report.""" @@ -382,15 +606,42 @@ class TensorTracer(object): self._write_report('"%s" %s\n'%(key, self._instrument_records[key])) self._write_report('%s %s\n'%(_MARKER_SECTION_END, _SECTION_NAME_REASON)) - def _write_op_list_section(self, op_list): + def _write_op_list_section(self, op_list, tensorname_idx_map): """Writes the Op-list section of the report.""" self._write_report('%s %s\n'%(_MARKER_SECTION_BEGIN, _SECTION_NAME_OP_LIST)) self._write_report('%s %d\n'%(_FIELD_NAME_NUM_OPS, len(op_list))) for i in range(0, len(op_list)): - self._write_report('%d "%s" %s\n'%(i, op_list[i].name, op_list[i].type)) + op = op_list[i] + line = '%d "%s" %s'%(i, op.name, op.type) + for out_tensor in op.outputs: + if out_tensor.name not in tensorname_idx_map: + raise ValueError( + 'out_tensor %s is not in tensorname_idx_map'%out_tensor.name) + line += ' %d'%tensorname_idx_map[out_tensor.name] + line += '\n' + self._write_report(line) self._write_report('%s %s\n'%(_MARKER_SECTION_END, _SECTION_NAME_OP_LIST)) + def _write_tensor_list_section(self, tensor_list, opname_idx_map): + """Writes the tensor-list section of the report.""" + + self._write_report('%s %s\n'%(_MARKER_SECTION_BEGIN, + _SECTION_NAME_TENSOR_LIST)) + self._write_report('%s %d\n'%(_FIELD_NAME_NUM_TENSORS, len(tensor_list))) + for i in range(0, len(tensor_list)): + tensor = tensor_list[i] + line = '%d "%s"'%(i, tensor.name) + for consumer_op in tensor.consumers(): + if consumer_op.name not in opname_idx_map: + raise ValueError( + 'consumer_op %s is not in opname_idx_map'%consumer_op.name) + line += ' %d'%opname_idx_map[consumer_op.name] + line += '\n' + self._write_report(line) + self._write_report('%s %s\n'%(_MARKER_SECTION_END, + _SECTION_NAME_TENSOR_LIST)) + def _write_graph_section(self, succeed, sorted_or_cycle): """Writes the graph section of the report.""" @@ -422,7 +673,7 @@ class TensorTracer(object): Args: op_name: the name of the Op that outputs the tensor to be printed. output_idx: which output of the Op it is (0 means the first output). - num_elements: number of elements to print. + num_elements: number of elements to print (-1 means print all). tensor: the tensor needs to be returned. output_tensor: the tensor needs to be printed. @@ -430,10 +681,13 @@ class TensorTracer(object): The same tensor passed via the "tensor" argument. """ msg = '"%s:%d" '%(op_name, output_idx) - output_stream = _OUTPUT_STREAM_ESCAPE + self._trace_file_path + if self._trace_file_path: + output_stream = _OUTPUT_STREAM_ESCAPE + self._trace_file_path + else: + output_stream = sys.stderr print_op = logging_ops.print_v2(msg, array_ops.shape(output_tensor), ' @', self._replica_id, - '\n', output_tensor, + '\n', output_tensor, '\n', summarize=num_elements, output_stream=output_stream) with ops.control_dependencies([print_op]): @@ -442,7 +696,8 @@ class TensorTracer(object): def _detect_nan_inf(tensor): """Trace function for detecting any NaN/Inf in the tensor.""" - if tensor.dtype.is_floating: + if tensor.dtype.__eq__(dtypes.bfloat16) or tensor.dtype.__eq__( + dtypes.float16): # Since host can't handle bf16, always convert tensor to f32. tensor = math_ops.cast(tensor, dtypes.float32) output_tensor = math_ops.reduce_any( @@ -450,12 +705,19 @@ class TensorTracer(object): gen_math_ops.is_inf(tensor))) else: output_tensor = constant_op.constant(0) - return _print_tensor(op_name, output_idx, 1, tensor, output_tensor) + return _print_tensor(op_name, output_idx, -1, tensor, output_tensor) - def _show_global_step(tensor): - """Trace function for printing the global step count.""" + def _show_norm(tensor): + tensor = math_ops.cast(tensor, dtypes.float64) + output_tensor = linalg_ops.norm(tensor) + return _print_tensor(op_name, output_idx, -1, tensor, output_tensor) - return _print_tensor(op_name, output_idx, 1, tensor, tensor) + def _show_max_abs(tensor): + output_tensor = math_ops.cast(math_ops.reduce_max(math_ops.abs(tensor)), + dtypes.float64) + zero = constant_op.constant(0, dtypes.float64) + output_tensor = gen_math_ops.maximum(zero, output_tensor) + return _print_tensor(op_name, output_idx, -1, tensor, output_tensor) def _show_part_tensor(tensor): """Trace function for printing part of the tensor.""" @@ -468,23 +730,139 @@ class TensorTracer(object): return _print_tensor(op_name, output_idx, -1, tensor, tensor) - if op_name == _GLOBAL_STEP_OP_NAME: - return _show_global_step if self._trace_mode == _TRACE_MODE_NAN_INF: return _detect_nan_inf if self._trace_mode == _TRACE_MODE_PART_TENSOR: return _show_part_tensor if self._trace_mode == _TRACE_MODE_FULL_TENSOR: return _show_full_tensor + if self._trace_mode == _TRACE_MODE_NORM: + return _show_norm + if self._trace_mode == _TRACE_MODE_MAX_ABS: + return _show_max_abs raise RuntimeError('Tensor trace fun for %s is not yet implemented' %self._trace_mode) + def _skip_op(self, op_id, op, user_included, user_excluded): + """Returns True if we should not trace Op.""" + + if user_included: + self._instrument_records[op.name] = TensorTracer.reason( + op_id, _REASON_USER_INCLUDED) + return False + if user_excluded: + self._instrument_records[op.name] = TensorTracer.reason( + op_id, _REASON_USER_EXCLUDED) + return True + if not self._inside_op_range(op_id): + self._instrument_records[op.name] = TensorTracer.reason( + op_id, _REASON_OUTSIDE_OP_RANGE) + return True + if TensorTracer.unsafe_op(op): + self._instrument_records[op.name] = TensorTracer.reason( + op_id, _REASON_UNSAFE_OP) + return True + if TensorTracer.device_mismatch(self._device_type, op): + self._instrument_records[op.name] = TensorTracer.reason( + op_id, _REASON_DEVICE_MISMATCH) + return True + if TensorTracer.less_interesting_op(op): + self._instrument_records[op.name] = TensorTracer.reason( + op_id, _REASON_LESS_INTERESTING_OP) + return True + return False + + def _skip_tensor(self, op_id, out_tensor, user_included, + user_excluded): + """Returns True if we should not trace out_tensor.""" + + # Skips a tensor if the tensor has a non-numeric type. + # Note: we cannot use check_ops.is_numeric_tensor(out_tensor) + # because it also excludes tensors with dtypes, bool, and + # float32_ref, which we actually want to trace. + non_numeric_tensor_types = set([dtypes.variant, dtypes.resource, + dtypes.string]) + if out_tensor.dtype in non_numeric_tensor_types: + self._instrument_records[out_tensor.name] = TensorTracer.reason( + op_id, _REASON_NON_NUMERIC_TENSOR) + return True + + if user_included: + self._instrument_records[out_tensor.name] = TensorTracer.reason( + op_id, _REASON_USER_INCLUDED) + return False + if user_excluded: + self._instrument_records[out_tensor.name] = TensorTracer.reason( + op_id, _REASON_USER_EXCLUDED) + return True + if not out_tensor.get_shape().is_fully_defined(): + self._instrument_records[out_tensor.name] = TensorTracer.reason( + op_id, _REASON_DYNAMIC_SHAPE) + return True + rank = len(out_tensor.shape) + if rank < 1: + # scalar + if TensorTracer.unsafe_scalar_trace(out_tensor.op): + self._instrument_records[out_tensor.name] = TensorTracer.reason( + op_id, _REASON_UNSAFE_SCALAR) + return True + else: + self._instrument_records[out_tensor.name] = TensorTracer.reason( + op_id, _REASON_SCALAR_GET_TRACED) + return False + else: + # tensor + self._instrument_records[out_tensor.name] = TensorTracer.reason( + op_id, _REASON_TENSOR_GET_TRACED) + return False + + def _pre_tracing(self, graph): + """Work needs to be done prior to TPU or CPU tracing.""" + + operations = graph.get_operations() + (opname_idx_map, tensor_list, tensorname_idx_map) = ( + TensorTracer._make_op_and_tensor_maps(operations)) + self._write_config_section() + self._write_op_list_section(operations, tensorname_idx_map) + self._write_tensor_list_section(tensor_list, opname_idx_map) + # Does the topological sort before adding any nodes to the graph. + (succeed, sorted_or_cycle) = TensorTracer.topological_sort(graph) + return (operations, succeed, sorted_or_cycle) + + def _post_tracing(self, succeed, sorted_or_cycle): + """Work needs to be done after TPU or CPU tracing.""" + + self._write_reason_section() + self._write_graph_section(succeed, sorted_or_cycle) + self._close_report_file() + + def _get_checkpoints(self, graph): + """Returns the list of Ops that produce the tensors traced with API. + + Args: + graph: the graph of Ops. + + Returns: + A set of operation names which should be traced. + """ + + self._write_report('%s %s\n'%(_MARKER_SECTION_BEGIN, + _TENSOR_TRACER_CHECKPOINT)) + checkpoint_operations = set() + tensor_tracer_variables = graph.get_collection(_TENSOR_TRACER_COLLECTION) + for (tensor, checkpoint_name) in tensor_tracer_variables: + self._write_report('%s %s\n'%(tensor.name, checkpoint_name)) + checkpoint_operations.add(tensor.op.name) + self._write_report('%s %s\n'%(_MARKER_SECTION_END, + _TENSOR_TRACER_CHECKPOINT)) + return checkpoint_operations + def trace_tpu(self, graph, result_tensor, num_replicas=None): """Traces the tensors generated by TPU Ops in a TF graph. Args: - graph: the graph of Ops. + graph: the graph of Ops executed on the TPU. result_tensor: a result tensor of evaluating the graph. num_replicas: number of replicas used on the TPU. @@ -502,38 +880,22 @@ class TensorTracer(object): TensorTracer.check_device_type(self._device_type) result_tensor_copy = self._add_replica_id_to_graph(num_replicas, result_tensor) - self._write_config_section() + (operations, succeed, sorted_or_cycle) = self._pre_tracing(graph) tracing_ops = [] - operations = graph.get_operations() - self._write_op_list_section(operations) - # Does the topological sort before adding any nodes to the graph. - (succeed, sorted_or_cycle) = TensorTracer.topological_sort(graph) + checkpoint_operations = self._get_checkpoints(graph) + for op_id, op in enumerate(operations): - if not self._inside_op_range(op_id): - self._instrument_records[op.name] = TensorTracer.reason( - op_id, _RECORD_OUTSIDE_OP_RANGE) + if checkpoint_operations and op.name not in checkpoint_operations: continue - if not TensorTracer.should_trace(self._device_type, op): - self._instrument_records[op.name] = TensorTracer.reason( - op_id, _RECORD_SHOULD_NOT_TRACE) - continue - if not self._is_selected_op(op.name): - self._instrument_records[op.name] = TensorTracer.reason( - op_id, _RECORD_FILTERED_OUT) + user_included = self._is_user_included_op(op) + user_excluded = self._is_user_excluded_op(op) + if self._skip_op(op_id, op, user_included, user_excluded): continue for i in range(len(op.outputs)): out_tensor = op.outputs[i] - if not out_tensor.get_shape().is_fully_defined(): - self._instrument_records[out_tensor.name] = TensorTracer.reason( - op_id, _RECORD_DYNAMIC_SHAPE) - continue # cannot trace tensors with dynamic shape. - rank = len(out_tensor.shape) - if rank < 1: - self._instrument_records[out_tensor.name] = TensorTracer.reason( - op_id, _RECORD_SCALAR) - continue # cannot trace scalar. - self._instrument_records[out_tensor.name] = TensorTracer.reason( - op_id, _RECORD_GET_TRACED) + if self._skip_tensor(op_id, out_tensor, user_included, + user_excluded): + continue consumers = out_tensor.consumers() trace_op = tpu.outside_compilation( self._make_tensor_trace_fun(op.name, i), out_tensor) @@ -546,8 +908,45 @@ class TensorTracer(object): # if there is no consumer, we will add the control dependence later # when we add the control dependency to the output operations. tracing_ops.append(trace_op) + self._post_tracing(succeed, sorted_or_cycle) + return (result_tensor_copy, tracing_ops) - self._write_reason_section() - self._write_graph_section(succeed, sorted_or_cycle) + def trace_cpu(self, graph): + """Traces the tensors generated by CPU Ops in a TF graph. - return (result_tensor_copy, tracing_ops) + Args: + graph: the graph of Ops executed on the CPU. + + Returns: + tracing_calls: a map from keys to trace calls. + A key is constructed from an Op's name. + A trace call consists of a function and a tensor ( + the function will be invoked with the tensor). + """ + + self._device_type = _DEVICE_TYPE_CPU + TensorTracer.check_device_type(self._device_type) + self._num_replicas = 1 + self._replica_id = 0 + (operations, succeed, sorted_or_cycle) = self._pre_tracing(graph) + tracing_calls = {} + checkpoint_operations = self._get_checkpoints(graph) + + for op_id, op in enumerate(operations): + if checkpoint_operations and op.name not in checkpoint_operations: + continue + user_included = self._is_user_included_op(op) + user_excluded = self._is_user_excluded_op(op) + if self._skip_op(op_id, op, user_included, user_excluded): + continue + for i in range(len(op.outputs)): + out_tensor = op.outputs[i] + if self._skip_tensor(op_id, out_tensor, user_included, + user_excluded): + continue + trace_fun = self._make_tensor_trace_fun(op.name, i) + trace_call = (trace_fun, [out_tensor]) + trace_call_key = 'tensor_tracing_cpu-%s:%d'%(op.name, i) + tracing_calls[trace_call_key] = trace_call + self._post_tracing(succeed, sorted_or_cycle) + return tracing_calls diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index 84816d70d0..fe2ac61bf9 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -336,6 +336,16 @@ class TPUEstimatorSpec(model_fn_lib._TPUEstimatorSpec): # pylint: disable=prote hooks = None if self.host_call is not None: hooks = [_OutfeedHostCallHook(host_call_ret['host_call'])] + if tensor_tracer.TensorTracer.is_enabled(): + tt = tensor_tracer.TensorTracer() + tracing_calls = tt.trace_cpu(ops.get_default_graph()) + tracing_call_ret = _OutfeedHostCall.create_cpu_hostcall(tracing_calls) + tracing_functions = tracing_call_ret.values() + if tracing_functions: + if hooks: + hooks.extend([_OutfeedHostCallHook(tracing_functions)]) + else: + hooks = [_OutfeedHostCallHook(tracing_functions)] hooks = tuple(hooks or []) scaffold = self.scaffold_fn() if self.scaffold_fn else None return model_fn_lib.EstimatorSpec( -- GitLab From c6245fa0b4efaf5e75b12e8aea4588c0d25c5519 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 11 Dec 2018 20:15:05 -0800 Subject: [PATCH 412/873] RELNOTES: Add an ignore_unknown argument to parse_values which suppresses ValueError for unknown hyperparameter types. Such hyperparameter are ignored. parse_values('a=1,b=foo', {a: int}) Raises a ValueError parse_values('a=1,b=foo', {a: int}, ignore_unknown=True) does not raise a ValueError, and returns {'a': 1} PiperOrigin-RevId: 225117666 --- .../training/python/training/hparam.py | 7 +- .../training/python/training/hparam_test.py | 69 +++++++++++++++++++ 2 files changed, 75 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/training/python/training/hparam.py b/tensorflow/contrib/training/python/training/hparam.py index 3beb7bfe30..bcc177601b 100644 --- a/tensorflow/contrib/training/python/training/hparam.py +++ b/tensorflow/contrib/training/python/training/hparam.py @@ -187,7 +187,7 @@ def _cast_to_type_if_compatible(name, param_type, value): return param_type(value) -def parse_values(values, type_map): +def parse_values(values, type_map, ignore_unknown=False): """Parses hyperparameter values from a string into a python map. `values` is a string containing comma-separated `name=value` pairs. @@ -233,6 +233,9 @@ def parse_values(values, type_map): type T if either V has type T, or V is a list of elements of type T. Hence, for a multidimensional parameter 'x' taking float values, 'x=[0.1,0.2]' will parse successfully if type_map['x'] = float. + ignore_unknown: Bool. Whether values that are missing a type in type_map + should be ignored. If set to True, a ValueError will not be raised for + unknown hyperparameter type. Returns: A python map mapping each name to either: @@ -260,6 +263,8 @@ def parse_values(values, type_map): m_dict = m.groupdict() name = m_dict['name'] if name not in type_map: + if ignore_unknown: + continue raise ValueError('Unknown hyperparameter type for %s' % name) type_ = type_map[name] diff --git a/tensorflow/contrib/training/python/training/hparam_test.py b/tensorflow/contrib/training/python/training/hparam_test.py index 660c97f25e..a990e04711 100644 --- a/tensorflow/contrib/training/python/training/hparam_test.py +++ b/tensorflow/contrib/training/python/training/hparam_test.py @@ -216,6 +216,14 @@ class HParamsTest(test.TestCase): self.assertTrue(isinstance(parse_dict['arr'], dict)) self.assertDictEqual(parse_dict['arr'], {1: 10}) + def testParseValuesWithIndexAssigment1_IgnoreUnknown(self): + """Assignment to an index position.""" + parse_dict = hparam.parse_values( + 'arr[1]=10,b=5', {'arr': int}, ignore_unknown=True) + self.assertEqual(len(parse_dict), 1) + self.assertTrue(isinstance(parse_dict['arr'], dict)) + self.assertDictEqual(parse_dict['arr'], {1: 10}) + def testParseValuesWithIndexAssigment2(self): """Assignment to multiple index positions.""" parse_dict = hparam.parse_values('arr[0]=10,arr[5]=20', {'arr': int}) @@ -223,6 +231,14 @@ class HParamsTest(test.TestCase): self.assertTrue(isinstance(parse_dict['arr'], dict)) self.assertDictEqual(parse_dict['arr'], {0: 10, 5: 20}) + def testParseValuesWithIndexAssigment2_IgnoreUnknown(self): + """Assignment to multiple index positions.""" + parse_dict = hparam.parse_values( + 'arr[0]=10,arr[5]=20,foo=bar', {'arr': int}, ignore_unknown=True) + self.assertEqual(len(parse_dict), 1) + self.assertTrue(isinstance(parse_dict['arr'], dict)) + self.assertDictEqual(parse_dict['arr'], {0: 10, 5: 20}) + def testParseValuesWithIndexAssigment3(self): """Assignment to index positions in multiple names.""" parse_dict = hparam.parse_values('arr[0]=10,arr[1]=20,L[5]=100,L[10]=200', @@ -234,6 +250,17 @@ class HParamsTest(test.TestCase): self.assertTrue(isinstance(parse_dict['L'], dict)) self.assertDictEqual(parse_dict['L'], {5: 100, 10: 200}) + def testParseValuesWithIndexAssigment3_IgnoreUnknown(self): + """Assignment to index positions in multiple names.""" + parse_dict = hparam.parse_values( + 'arr[0]=10,C=5,arr[1]=20,B[0]=kkk,L[5]=100,L[10]=200', + {'arr': int, 'L': int}, ignore_unknown=True) + self.assertEqual(len(parse_dict), 2) + self.assertTrue(isinstance(parse_dict['arr'], dict)) + self.assertDictEqual(parse_dict['arr'], {0: 10, 1: 20}) + self.assertTrue(isinstance(parse_dict['L'], dict)) + self.assertDictEqual(parse_dict['L'], {5: 100, 10: 200}) + def testParseValuesWithIndexAssigment4(self): """Assignment of index positions and scalars.""" parse_dict = hparam.parse_values('x=10,arr[1]=20,y=30', @@ -246,6 +273,17 @@ class HParamsTest(test.TestCase): self.assertEqual(parse_dict['x'], 10) self.assertEqual(parse_dict['y'], 30) + def testParseValuesWithIndexAssigment4_IgnoreUnknown(self): + """Assignment of index positions and scalars.""" + parse_dict = hparam.parse_values( + 'x=10,foo[0]=bar,arr[1]=20,zzz=78,y=30', + {'x': int, 'y': int, 'arr': int}, ignore_unknown=True) + self.assertEqual(len(parse_dict), 3) + self.assertTrue(isinstance(parse_dict['arr'], dict)) + self.assertDictEqual(parse_dict['arr'], {1: 20}) + self.assertEqual(parse_dict['x'], 10) + self.assertEqual(parse_dict['y'], 30) + def testParseValuesWithIndexAssigment5(self): """Different variable types.""" parse_dict = hparam.parse_values('a[0]=5,b[1]=true,c[2]=abc,d[3]=3.14', { @@ -264,24 +302,55 @@ class HParamsTest(test.TestCase): self.assertTrue(isinstance(parse_dict['d'], dict)) self.assertDictEqual(parse_dict['d'], {3: 3.14}) + def testParseValuesWithIndexAssigment5_IgnoreUnknown(self): + """Different variable types.""" + parse_dict = hparam.parse_values( + 'a[0]=5,cc=4,b[1]=true,c[2]=abc,mm=2,d[3]=3.14', + {'a': int, 'b': bool, 'c': str, 'd': float}, + ignore_unknown=True) + self.assertEqual(set(parse_dict.keys()), {'a', 'b', 'c', 'd'}) + self.assertTrue(isinstance(parse_dict['a'], dict)) + self.assertDictEqual(parse_dict['a'], {0: 5}) + self.assertTrue(isinstance(parse_dict['b'], dict)) + self.assertDictEqual(parse_dict['b'], {1: True}) + self.assertTrue(isinstance(parse_dict['c'], dict)) + self.assertDictEqual(parse_dict['c'], {2: 'abc'}) + self.assertTrue(isinstance(parse_dict['d'], dict)) + self.assertDictEqual(parse_dict['d'], {3: 3.14}) + def testParseValuesWithBadIndexAssigment1(self): """Reject assignment of list to variable type.""" with self.assertRaisesRegexp(ValueError, r'Assignment of a list to a list index.'): hparam.parse_values('arr[1]=[1,2,3]', {'arr': int}) + def testParseValuesWithBadIndexAssigment1_IgnoreUnknown(self): + """Reject assignment of list to variable type.""" + with self.assertRaisesRegexp(ValueError, + r'Assignment of a list to a list index.'): + hparam.parse_values( + 'arr[1]=[1,2,3],c=8', {'arr': int}, ignore_unknown=True) + def testParseValuesWithBadIndexAssigment2(self): """Reject if type missing.""" with self.assertRaisesRegexp(ValueError, r'Unknown hyperparameter type for arr'): hparam.parse_values('arr[1]=5', {}) + def testParseValuesWithBadIndexAssigment2_IgnoreUnknown(self): + """Ignore missing type.""" + hparam.parse_values('arr[1]=5', {}, ignore_unknown=True) + def testParseValuesWithBadIndexAssigment3(self): """Reject type of the form name[index].""" with self.assertRaisesRegexp(ValueError, 'Unknown hyperparameter type for arr'): hparam.parse_values('arr[1]=1', {'arr[1]': int}) + def testParseValuesWithBadIndexAssigment3_IgnoreUnknown(self): + """Ignore type of the form name[index].""" + hparam.parse_values('arr[1]=1', {'arr[1]': int}, ignore_unknown=True) + def testWithReusedVariables(self): with self.assertRaisesRegexp(ValueError, 'Multiple assignments to variable \'x\''): -- GitLab From 23810678737d40c00227252e5efffaaaa8fc94d6 Mon Sep 17 00:00:00 2001 From: Mark Heffernan Date: Tue, 11 Dec 2018 20:56:59 -0800 Subject: [PATCH 413/873] Replace Layout and Tile protos with C++ classes in XLA. No functional change. Rename the proto message Layout to LayoutProto, and Tile to TileProto. Define in-place replacement C++ classes named Layout and Tile with an interface which mirrors the protobuf generated code interface. Having these data structures as C++ classes enables greater flexibility in the interface, enables enforcement of invariants, and potential performance improvements. PiperOrigin-RevId: 225121052 --- tensorflow/compiler/xla/BUILD | 18 ++ tensorflow/compiler/xla/client/client.cc | 2 +- tensorflow/compiler/xla/layout.cc | 96 +++++++++ tensorflow/compiler/xla/layout.h | 187 ++++++++++++++++++ tensorflow/compiler/xla/layout_test.cc | 104 ++++++++++ tensorflow/compiler/xla/layout_util.cc | 34 +--- tensorflow/compiler/xla/layout_util.h | 3 +- tensorflow/compiler/xla/layout_util_test.cc | 11 -- .../compiler/xla/packed_literal_reader.cc | 3 +- .../xla/service/gpu/stream_executor_util.h | 1 + tensorflow/compiler/xla/service/service.cc | 8 +- tensorflow/compiler/xla/shape.cc | 4 +- tensorflow/compiler/xla/shape.h | 26 +-- tensorflow/compiler/xla/shape_util.cc | 8 +- tensorflow/compiler/xla/tests/copy_test.cc | 4 +- tensorflow/compiler/xla/xla.proto | 2 +- tensorflow/compiler/xla/xla_data.proto | 13 +- 17 files changed, 450 insertions(+), 74 deletions(-) create mode 100644 tensorflow/compiler/xla/layout.cc create mode 100644 tensorflow/compiler/xla/layout.h create mode 100644 tensorflow/compiler/xla/layout_test.cc diff --git a/tensorflow/compiler/xla/BUILD b/tensorflow/compiler/xla/BUILD index 4360e08579..19f12569ff 100644 --- a/tensorflow/compiler/xla/BUILD +++ b/tensorflow/compiler/xla/BUILD @@ -224,6 +224,7 @@ cc_library( name = "shape_util", srcs = [ "index_util.cc", + "layout.cc", "layout_util.cc", "primitive_util.cc", "shape.cc", @@ -231,6 +232,7 @@ cc_library( ], hdrs = [ "index_util.h", + "layout.h", "layout_util.h", "primitive_util.h", "shape.h", @@ -301,6 +303,22 @@ tf_cc_test( ], ) +tf_cc_test( + name = "layout_test", + srcs = ["layout_test.cc"], + deps = [ + ":shape_util", + ":status_macros", + ":test", + ":test_helpers", + ":types", + ":util", + ":xla_data_proto", + "//tensorflow/core:test_main", + "@com_google_absl//absl/strings", + ], +) + tf_cc_test( name = "index_util_test", srcs = ["index_util_test.cc"], diff --git a/tensorflow/compiler/xla/client/client.cc b/tensorflow/compiler/xla/client/client.cc index 74b76f9299..43127cae1e 100644 --- a/tensorflow/compiler/xla/client/client.cc +++ b/tensorflow/compiler/xla/client/client.cc @@ -186,7 +186,7 @@ StatusOr Client::ComputeConstant(const XlaComputation& computation, ComputeConstantGraphRequest request; *request.mutable_computation() = computation.proto(); if (output_layout != nullptr) { - *request.mutable_output_layout() = *output_layout; + *request.mutable_output_layout() = output_layout->ToProto(); } ComputeConstantResponse response; diff --git a/tensorflow/compiler/xla/layout.cc b/tensorflow/compiler/xla/layout.cc new file mode 100644 index 0000000000..e3b5fcd527 --- /dev/null +++ b/tensorflow/compiler/xla/layout.cc @@ -0,0 +1,96 @@ +/* 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/layout.h" + +#include "absl/strings/str_cat.h" +#include "absl/strings/str_join.h" +#include "tensorflow/compiler/xla/layout_util.h" + +namespace xla { + +TileProto Tile::ToProto() const { + TileProto tile_proto; + for (int64 i : dimensions()) { + tile_proto.add_dimensions(i); + } + return tile_proto; +} + +string Tile::ToString() const { + return absl::StrCat("(", absl::StrJoin(dimensions(), ","), ")"); +} + +/* static */ Layout Layout::CreateFromProto(const LayoutProto& proto) { + Layout layout; + layout.set_format(proto.format()); + layout.minor_to_major_.reserve(proto.minor_to_major_size()); + for (const int64 dimension : proto.minor_to_major()) { + layout.add_minor_to_major(dimension); + } + layout.set_max_sparse_elements(proto.max_sparse_elements()); + for (const TileProto& tile_proto : proto.tiles()) { + *layout.add_tiles() = Tile::CreateFromProto(tile_proto); + } + layout.set_element_size_in_bits(proto.element_size_in_bits()); + return layout; +} + +LayoutProto Layout::ToProto() const { + LayoutProto proto; + proto.set_format(format_); + proto.mutable_minor_to_major()->Reserve(minor_to_major_size()); + for (const int64 dimension : minor_to_major()) { + proto.add_minor_to_major(dimension); + } + proto.set_max_sparse_elements(max_sparse_elements_); + for (const Tile& tile : tiles()) { + *proto.add_tiles() = tile.ToProto(); + } + proto.set_element_size_in_bits(element_size_in_bits()); + return proto; +} + +string Layout::ToString() const { + // TODO(b/119839262): Emit tiles in string. + if (format() == SPARSE) { + return absl::StrCat("sparse{", max_sparse_elements(), "}"); + } else if (format() == DENSE) { + return absl::StrCat("{", absl::StrJoin(minor_to_major(), ","), "}"); + } else { + CHECK_EQ(format(), INVALID_FORMAT); + return "invalid{}"; + } +} + +bool Layout::operator==(const Layout& other) const { + return (other.format() == format() && + other.minor_to_major() == minor_to_major() && + other.element_size_in_bits() == element_size_in_bits() && + other.max_sparse_elements() == max_sparse_elements() && + other.tiles() == tiles()); +} + +std::ostream& operator<<(std::ostream& out, const Tile& tile) { + out << tile.ToString(); + return out; +} + +std::ostream& operator<<(std::ostream& out, const Layout& layout) { + out << layout.ToString(); + return out; +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/layout.h b/tensorflow/compiler/xla/layout.h new file mode 100644 index 0000000000..313368c39e --- /dev/null +++ b/tensorflow/compiler/xla/layout.h @@ -0,0 +1,187 @@ +/* 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_LAYOUT_H_ +#define TENSORFLOW_COMPILER_XLA_LAYOUT_H_ + +#include + +#include "absl/types/span.h" + +#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/compiler/xla/util.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/platform/types.h" + +namespace xla { + +// Describes a tile used in tiling-based layout. Refer to +// g3doc/third_party/tensorflow/compiler/xla/g3doc/layout_with_tiling.md for +// details. +class Tile { + public: + Tile() = default; + explicit Tile(absl::Span dimensions) + : dimensions_(dimensions.begin(), dimensions.end()) {} + + // De/Serialize a Tile to and from a TileProto. + static Tile CreateFromProto(const TileProto& tile_proto) { + return Tile(AsInt64Slice(tile_proto.dimensions())); + } + TileProto ToProto() const; + + bool operator==(const Tile& other) const { + return dimensions() == other.dimensions(); + } + bool operator!=(const Tile& other) const { return !(*this == other); } + + string ToString() const; + + // Returns the bound of the tile in the given dimension index. + int64 dimension(int i) const { return dimensions_.at(i); } + + // Returns the dimensions of the tile. + const std::vector& dimensions() const { return dimensions_; } + + private: + // The bounds of the tile. + std::vector dimensions_; +}; + +class Layout { + public: + Layout() = default; + + // Constructs a dense layout with the given minor-to-major order. + explicit Layout(absl::Span minor_to_major) + : format_(DENSE), + minor_to_major_(minor_to_major.begin(), minor_to_major.end()) {} + + // Constructs a dense tiled layout with the given minor-to-major order and + // tiles. + Layout(absl::Span minor_to_major, absl::Span tiles) + : format_(DENSE), + minor_to_major_(minor_to_major.begin(), minor_to_major.end()), + tiles_(tiles.begin(), tiles.end()) {} + + // Construct a shape from a LayoutProto. + static Layout CreateFromProto(const LayoutProto& proto); + + // Returns a LayoutProto representation of the Layout. + LayoutProto ToProto() const; + + // Returns a human-readable string that represents this layout. + string ToString() const; + + bool operator==(const Layout& other) const; + bool operator!=(const Layout& other) const { return !(*this == other); } + + // The following methods mirror the protobuf generated code interface for the + // message LayoutProto. This enabled easy migration of this data structure + // from a proto to a proper C++ class. + // + // TODO(b/29771030): Replace or augment these methods with a more ergonomic + // interface. + + // Methods for accessing the format. + Format format() const { return format_; } + Layout& set_format(Format value) { + format_ = value; + return *this; + } + + // Methods for accessing the minor-to-major array. + int minor_to_major_size() const { return minor_to_major_.size(); } + int64 minor_to_major(int index) const { return minor_to_major_.at(index); } + Layout& set_minor_to_major(int index, int64 value) { + minor_to_major_.at(index) = value; + return *this; + } + Layout& add_minor_to_major(int64 value) { + minor_to_major_.push_back(value); + return *this; + } + Layout& clear_minor_to_major() { + minor_to_major_.clear(); + return *this; + } + const std::vector& minor_to_major() const { return minor_to_major_; } + std::vector* mutable_minor_to_major() { return &minor_to_major_; } + + // Methods for accessing the tile field. + int tiles_size() const { return tiles_.size(); } + const Tile& tiles(int index) const { return tiles_.at(index); } + Tile* mutable_tiles(int index) { return &tiles_.at(index); } + Tile* add_tiles() { + tiles_.push_back(Tile()); + return &tiles_.back(); + } + Layout& clear_tiles() { + tiles_.clear(); + return *this; + } + const std::vector& tiles() const { return tiles_; } + std::vector* mutable_tiles() { return &tiles_; } + + // Methods for accessing the int64 fields. + int64 max_sparse_elements() const { return max_sparse_elements_; } + Layout& set_max_sparse_elements(int64 value) { + max_sparse_elements_ = value; + return *this; + } + int64 element_size_in_bits() const { return element_size_in_bits_; } + Layout& set_element_size_in_bits(int64 value) { + element_size_in_bits_ = value; + return *this; + } + + void Swap(Layout* other) { + using std::swap; + swap(*this, *other); + } + + void Clear() { + format_ = INVALID_FORMAT; + minor_to_major_.clear(); + max_sparse_elements_ = 0; + element_size_in_bits_ = 0; + } + + public: + // The format of this layout. + Format format_ = INVALID_FORMAT; + + // Sequence of dimension numbers, from minor (fastest varying index) to major + // (slowest varying index). + std::vector minor_to_major_; + + // The maximum number of elements that can be stored for SPARSE formats. This + // can be used to determine the maximum size in bytes of arrays stored in + // memory. This field must be zero unless the format is SPARSE. + int64 max_sparse_elements_ = 0; + + // The number of bits used to store an individual array element. + int64 element_size_in_bits_ = 0; + + // The tiles used in tiling-based layout. + std::vector tiles_; +}; + +std::ostream& operator<<(std::ostream& out, const Tile& Tile); +std::ostream& operator<<(std::ostream& out, const Layout& layout); + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_LAYOUT_H_ diff --git a/tensorflow/compiler/xla/layout_test.cc b/tensorflow/compiler/xla/layout_test.cc new file mode 100644 index 0000000000..fb6abd3f65 --- /dev/null +++ b/tensorflow/compiler/xla/layout_test.cc @@ -0,0 +1,104 @@ +/* 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/layout.h" + +#include "absl/strings/str_cat.h" +#include "absl/strings/str_join.h" +#include "tensorflow/compiler/xla/layout_util.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/status_macros.h" +#include "tensorflow/compiler/xla/test.h" +#include "tensorflow/compiler/xla/test_helpers.h" +#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/compiler/xla/util.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" + +namespace xla { +namespace { + +class LayoutTest : public ::testing::Test {}; + +TEST_F(LayoutTest, ToString) { + EXPECT_EQ(Layout().ToString(), "invalid{}"); + EXPECT_EQ(Layout({4, 5, 6}).ToString(), "{4,5,6}"); + EXPECT_EQ(Layout().set_format(SPARSE).set_max_sparse_elements(123).ToString(), + "sparse{123}"); + EXPECT_EQ(Layout({4, 5, 6}).ToString(), "{4,5,6}"); + EXPECT_EQ(Layout({3, 2, 1, 0}, {Tile({42, 123}), Tile({4, 5})}).ToString(), + "{3,2,1,0}"); + EXPECT_EQ( + Layout({1, 0}, {Tile({2, 55})}).set_element_size_in_bits(42).ToString(), + "{1,0}"); +} + +TEST_F(LayoutTest, StreamOut) { + { + std::ostringstream oss; + oss << Tile({7, 8}); + EXPECT_EQ(oss.str(), "(7,8)"); + } + + { + std::ostringstream oss; + oss << Layout({0, 1, 2}); + EXPECT_EQ(oss.str(), "{0,1,2}"); + } +} + +TEST_F(LayoutTest, SparseLayoutMaxElements) { + EXPECT_EQ(LayoutUtil::MaxSparseElements(LayoutUtil::MakeSparseLayout(101)), + 101); +} + +TEST_F(LayoutTest, Equality) { + EXPECT_EQ(Layout(), Layout()); + const std::vector empty_dims; + EXPECT_EQ(Layout(empty_dims), Layout(empty_dims)); + EXPECT_NE(Layout(), Layout(empty_dims)); + EXPECT_EQ(Layout({0, 1, 2, 3}), Layout({0, 1, 2, 3})); + EXPECT_NE(Layout({0, 1, 2, 3}), Layout({0, 1, 2})); + EXPECT_EQ(Layout({0, 1, 2}, {Tile({42, 44})}), + Layout({0, 1, 2}, {Tile({42, 44})})); + EXPECT_NE(Layout({0, 1, 2}, {Tile({42, 44})}), + Layout({0, 1, 2}, {Tile({42, 45})})); + EXPECT_NE(Layout({0, 1, 2}, {Tile({42, 44})}), Layout({0, 1, 2, 3})); + EXPECT_EQ(Layout({0, 1, 2}).set_element_size_in_bits(33), + Layout({0, 1, 2}).set_element_size_in_bits(33)); + EXPECT_NE(Layout({0, 1, 2}).set_element_size_in_bits(33), + Layout({0, 1, 2}).set_element_size_in_bits(7)); + EXPECT_EQ(Layout().set_format(SPARSE), Layout().set_format(SPARSE)); + EXPECT_EQ(Layout().set_format(SPARSE).set_max_sparse_elements(42), + Layout().set_format(SPARSE).set_max_sparse_elements(42)); + EXPECT_NE(Layout().set_format(SPARSE).set_max_sparse_elements(42), + Layout().set_format(SPARSE).set_max_sparse_elements(24)); +} + +TEST_F(LayoutTest, LayoutToFromProto) { + // Round-trips a Layout through proto de/serialization. + auto expect_unchanged = [](const Layout& layout) { + EXPECT_EQ(layout, Layout::CreateFromProto(layout.ToProto())); + }; + + expect_unchanged(Layout()); + expect_unchanged(Layout({1, 3, 2, 0})); + expect_unchanged(Layout().set_format(SPARSE)); + expect_unchanged(Layout().set_format(SPARSE).set_max_sparse_elements(123)); + expect_unchanged(Layout({0, 1}).set_element_size_in_bits(42)); + expect_unchanged(Layout({3, 2, 1, 0}, {Tile({42, 123}), Tile({4, 5})})); +} + +} // namespace +} // namespace xla diff --git a/tensorflow/compiler/xla/layout_util.cc b/tensorflow/compiler/xla/layout_util.cc index dbb81381ac..ddccd8c798 100644 --- a/tensorflow/compiler/xla/layout_util.cc +++ b/tensorflow/compiler/xla/layout_util.cc @@ -41,15 +41,13 @@ namespace { // Internal helper for GetDefaultLayoutForShape and SetToDefaultLayout. Sets // minor_to_major to the value that represents the default layout. -void SetDefaultLayoutToContainer( - tensorflow::protobuf::RepeatedField* - minor_to_major) { +void SetDefaultLayoutToContainer(std::vector* minor_to_major) { // The default XLA layout is major-to-minor (dim 0 is major). // For more information on XLA layouts, see: // https://www.tensorflow.org/performance/xla/shapes const int64 size = minor_to_major->size(); for (int64 i = 0; i < size; ++i) { - minor_to_major->Set(i, size - 1 - i); + (*minor_to_major)[i] = size - 1 - i; } } @@ -94,9 +92,8 @@ namespace { Layout CreateDefaultLayoutForRank(int64 rank) { Layout layout; layout.set_format(DENSE); - tensorflow::protobuf::RepeatedField* - minor_to_major = layout.mutable_minor_to_major(); - minor_to_major->Resize(rank, 0); + std::vector* minor_to_major = layout.mutable_minor_to_major(); + minor_to_major->resize(rank, 0); SetDefaultLayoutToContainer(minor_to_major); return layout; } @@ -139,9 +136,8 @@ Layout CreateDefaultLayoutForRank(int64 rank) { shape->clear_layout(); } else if (ShapeUtil::IsArray(*shape)) { shape->mutable_layout()->set_format(DENSE); - tensorflow::protobuf::RepeatedField* - minor_to_major = shape->mutable_layout()->mutable_minor_to_major(); - minor_to_major->Resize(shape->dimensions_size(), 0); + auto* minor_to_major = shape->mutable_layout()->mutable_minor_to_major(); + minor_to_major->resize(shape->dimensions_size(), 0); SetDefaultLayoutToContainer(minor_to_major); } else { // Opaque, token types etc. have no layout. @@ -210,9 +206,8 @@ Layout CreateDefaultLayoutForRank(int64 rank) { } if (layout.format() == INVALID_FORMAT || !Format_IsValid(layout.format())) { - return InvalidArgument( - "Layout has an invalid format (%d) in layout {%s}, shape {%s}", - layout.format(), layout.ShortDebugString(), shape.ShortDebugString()); + return InvalidArgument("Layout has an invalid format (%d)", + layout.format()); } if (layout.format() == DENSE) { @@ -316,7 +311,7 @@ Layout CreateDefaultLayoutForRank(int64 rank) { } /* static */ bool LayoutUtil::Equal(const Layout& lhs, const Layout& rhs) { - return protobuf_util::ProtobufEquals(lhs, rhs); + return lhs == rhs; } /* static */ absl::Span LayoutUtil::MinorToMajor( @@ -358,11 +353,7 @@ Layout CreateDefaultLayoutForRank(int64 rank) { } /* static */ string LayoutUtil::HumanString(const Layout& layout) { - if (IsSparse(layout)) { - return absl::StrCat("sparse{", layout.max_sparse_elements(), "}"); - } - CHECK(IsDense(layout)); - return absl::StrCat("{", absl::StrJoin(layout.minor_to_major(), ","), "}"); + return layout.ToString(); } namespace { @@ -444,11 +435,6 @@ Status LayoutUtil::CopyLayoutBetweenShapes(const Shape& src, Shape* dst) { return true; } -std::ostream& operator<<(std::ostream& out, const Layout& layout) { - out << LayoutUtil::HumanString(layout); - return out; -} - /*static*/ size_t LayoutUtil::Hash(const Layout& layout) { using tensorflow::hash; using tensorflow::Hash64Combine; diff --git a/tensorflow/compiler/xla/layout_util.h b/tensorflow/compiler/xla/layout_util.h index 6c298e5725..609dba67bc 100644 --- a/tensorflow/compiler/xla/layout_util.h +++ b/tensorflow/compiler/xla/layout_util.h @@ -21,6 +21,7 @@ limitations under the License. #include #include "absl/types/span.h" +#include "tensorflow/compiler/xla/layout.h" #include "tensorflow/compiler/xla/shape.h" #include "tensorflow/compiler/xla/status.h" #include "tensorflow/compiler/xla/types.h" @@ -195,8 +196,6 @@ class LayoutUtil { TF_DISALLOW_COPY_AND_ASSIGN(LayoutUtil); }; -std::ostream& operator<<(std::ostream& out, const Layout& layout); - } // namespace xla #endif // TENSORFLOW_COMPILER_XLA_LAYOUT_UTIL_H_ diff --git a/tensorflow/compiler/xla/layout_util_test.cc b/tensorflow/compiler/xla/layout_util_test.cc index 12ce2d2d7c..4cc94c270c 100644 --- a/tensorflow/compiler/xla/layout_util_test.cc +++ b/tensorflow/compiler/xla/layout_util_test.cc @@ -317,17 +317,6 @@ TEST_F(LayoutUtilTest, DefaultLayoutGettersMajorToMinor) { ShapeUtil::MakeShape(F32, {10, 20, 30, 15, 25})))); } -TEST_F(LayoutUtilTest, SparseLayoutMaxElements) { - EXPECT_EQ(LayoutUtil::MaxSparseElements(LayoutUtil::MakeSparseLayout(101)), - 101); -} - -TEST_F(LayoutUtilTest, StreamOut) { - std::ostringstream oss; - oss << LayoutUtil::MakeLayout({0, 1, 2}); - EXPECT_EQ(oss.str(), "{0,1,2}"); -} - TEST_F(LayoutUtilTest, ValidateLayout_ValidArrayLayout) { Shape shape = ShapeUtil::MakeShapeWithLayout(F32, {2, 3}, {0, 1}); auto status = diff --git a/tensorflow/compiler/xla/packed_literal_reader.cc b/tensorflow/compiler/xla/packed_literal_reader.cc index 0f86f9f35e..339660cf44 100644 --- a/tensorflow/compiler/xla/packed_literal_reader.cc +++ b/tensorflow/compiler/xla/packed_literal_reader.cc @@ -42,8 +42,7 @@ PackedLiteralReader::~PackedLiteralReader() { delete file_; } StatusOr PackedLiteralReader::Read(const Shape& shape, const Layout* layout) { VLOG(3) << "reading shape from file: " << ShapeUtil::HumanString(shape) - << " layout: " - << (layout == nullptr ? "" : layout->ShortDebugString()); + << " layout: " << (layout == nullptr ? "" : layout->ToString()); Shape literal_shape = shape; if (layout != nullptr) { TF_RETURN_IF_ERROR( diff --git a/tensorflow/compiler/xla/service/gpu/stream_executor_util.h b/tensorflow/compiler/xla/service/gpu/stream_executor_util.h index 1fc46bafa1..92e4d6dbbc 100644 --- a/tensorflow/compiler/xla/service/gpu/stream_executor_util.h +++ b/tensorflow/compiler/xla/service/gpu/stream_executor_util.h @@ -16,6 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_GPU_STREAM_EXECUTOR_UTIL_H_ #define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_STREAM_EXECUTOR_UTIL_H_ +#include "tensorflow/compiler/xla/layout.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/xla_data.pb.h" diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc index 5ec7fe2ade..ae5bd93e7c 100644 --- a/tensorflow/compiler/xla/service/service.cc +++ b/tensorflow/compiler/xla/service/service.cc @@ -1078,9 +1078,11 @@ Status Service::ComputeConstantGraph(const ComputeConstantGraphRequest* arg, ProgramShape program_shape(arg->computation().host_program_shape()); TF_DCHECK_OK(ShapeUtil::ValidateShape(program_shape.result())); + absl::optional output_layout; if (arg->has_output_layout()) { + output_layout = Layout::CreateFromProto(arg->output_layout()); TF_RETURN_IF_ERROR(LayoutUtil::ValidateLayoutForShape( - arg->output_layout(), program_shape.result())); + *output_layout, program_shape.result())); } HloModuleConfig config(program_shape); @@ -1096,8 +1098,8 @@ Status Service::ComputeConstantGraph(const ComputeConstantGraphRequest* arg, // relayout here. // // TODO(b/77824332): Make HloEvaluator take care of the re-layout. - if (arg->has_output_layout()) { - result_literal = result_literal.Relayout(arg->output_layout()); + if (output_layout.has_value()) { + result_literal = result_literal.Relayout(*output_layout); } *result->mutable_literal() = result_literal.ToProto(); diff --git a/tensorflow/compiler/xla/shape.cc b/tensorflow/compiler/xla/shape.cc index 746ab9e997..b206345db2 100644 --- a/tensorflow/compiler/xla/shape.cc +++ b/tensorflow/compiler/xla/shape.cc @@ -32,7 +32,7 @@ Shape::Shape(const ShapeProto& shape_proto) { *add_tuple_shapes() = Shape(element_shape); } if (shape_proto.has_layout()) { - *mutable_layout() = shape_proto.layout(); + *mutable_layout() = Layout::CreateFromProto(shape_proto.layout()); } } @@ -48,7 +48,7 @@ ShapeProto Shape::ToProto() const { *proto.add_tuple_shapes() = shape.ToProto(); } if (has_layout()) { - *proto.mutable_layout() = layout(); + *proto.mutable_layout() = layout().ToProto(); } return proto; } diff --git a/tensorflow/compiler/xla/shape.h b/tensorflow/compiler/xla/shape.h index 7f6b14ab42..7643f64d8a 100644 --- a/tensorflow/compiler/xla/shape.h +++ b/tensorflow/compiler/xla/shape.h @@ -20,6 +20,7 @@ limitations under the License. #include #include "absl/types/optional.h" +#include "tensorflow/compiler/xla/layout.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/platform/types.h" @@ -76,21 +77,10 @@ class Shape { std::vector* mutable_tuple_shapes() { return &tuple_shapes_; } // Methods for accessing the layout field. - bool has_layout() const { return layout_.has_value(); } - const Layout& layout() const { - if (layout_.has_value()) { - return *layout_; - } else { - return Layout::default_instance(); - } - } - Layout* mutable_layout() { - if (!layout_.has_value()) { - layout_ = Layout(); - } - return &layout_.value(); - } - void clear_layout() { layout_.reset(); } + bool has_layout() const { return layout_.format() != INVALID_FORMAT; } + const Layout& layout() const { return layout_; } + Layout* mutable_layout() { return &layout_; } + void clear_layout() { layout_.Clear(); } void Swap(Shape* other) { using std::swap; @@ -101,7 +91,7 @@ class Shape { element_type_ = PRIMITIVE_TYPE_INVALID; dimensions_.clear(); tuple_shapes_.clear(); - layout_.reset(); + clear_layout(); } string SerializeAsString() const { return ToProto().SerializeAsString(); } @@ -118,8 +108,8 @@ class Shape { // The tuple element subshapes. This is nonempty only for tuple shapes. std::vector tuple_shapes_; - // The array layout of the shape. This is present only for array shapes. - absl::optional layout_; + // The layout of the shape. Only relevant for arrays. + Layout layout_; }; // Shape of the parameters and output of an XLA computation. This is analogous diff --git a/tensorflow/compiler/xla/shape_util.cc b/tensorflow/compiler/xla/shape_util.cc index a4d4e1e53e..eef2dc913d 100644 --- a/tensorflow/compiler/xla/shape_util.cc +++ b/tensorflow/compiler/xla/shape_util.cc @@ -164,9 +164,9 @@ StatusOr MakeShapeWithLayoutInternal( TF_ASSIGN_OR_RETURN(Shape shape, ShapeUtil::MakeValidatedShape(element_type, dimensions)); auto min2maj = shape.mutable_layout()->mutable_minor_to_major(); - min2maj->Clear(); + min2maj->clear(); for (int64 value : minor_to_major) { - min2maj->Add(value); + min2maj->push_back(value); } if (!shape.has_layout()) { return InvalidArgument("Shape has no layout."); @@ -1618,10 +1618,10 @@ ShapeUtil::DimensionsUnmodifiedByReshape(const Shape& input_shape, if (LayoutUtil::HasLayout(shape)) { Layout* layout = shape.mutable_layout(); layout->set_format(DENSE); - for (size_t i = 0; i < layout->minor_to_major().size();) { + for (int64 i = 0; i < layout->minor_to_major().size();) { if (layout->minor_to_major(i) == dim_to_delete) { layout->mutable_minor_to_major()->erase( - layout->minor_to_major().begin() + i); + layout->mutable_minor_to_major()->begin() + i); continue; } if (layout->minor_to_major(i) > dim_to_delete) { diff --git a/tensorflow/compiler/xla/tests/copy_test.cc b/tensorflow/compiler/xla/tests/copy_test.cc index 3622f2c1e8..df005a6709 100644 --- a/tensorflow/compiler/xla/tests/copy_test.cc +++ b/tensorflow/compiler/xla/tests/copy_test.cc @@ -133,7 +133,9 @@ XLA_TEST_F(CopyOpTest, CopyConstantR2DifferentLayouts) { // Reverse the minor-to-major order of the literal. Layout* literal_layout = literal.mutable_shape_do_not_use()->mutable_layout(); ASSERT_EQ(2, literal_layout->minor_to_major_size()); - literal_layout->mutable_minor_to_major()->SwapElements(0, 1); + // Swap the first and second elements. + *literal_layout->mutable_minor_to_major() = { + literal_layout->minor_to_major(1), literal_layout->minor_to_major(0)}; HloInstruction* constant = builder.AddInstruction( HloInstruction::CreateConstant(std::move(literal))); diff --git a/tensorflow/compiler/xla/xla.proto b/tensorflow/compiler/xla/xla.proto index 32b51c104c..238312e36b 100644 --- a/tensorflow/compiler/xla/xla.proto +++ b/tensorflow/compiler/xla/xla.proto @@ -399,7 +399,7 @@ message WaitForExecutionResponse { message ComputeConstantGraphRequest { HloModuleProto computation = 1; - Layout output_layout = 2; + LayoutProto output_layout = 2; } message ComputeConstantResponse { diff --git a/tensorflow/compiler/xla/xla_data.proto b/tensorflow/compiler/xla/xla_data.proto index 85ec83437a..e9c86abe50 100644 --- a/tensorflow/compiler/xla/xla_data.proto +++ b/tensorflow/compiler/xla/xla_data.proto @@ -100,6 +100,8 @@ message PaddingConfig { // A format specifies the method used by a layout to store an array in memory. enum Format { + // TODO(b/120869032): Rename this to FORMAT_NONE or something else which + // better corresponds to its meaning. INVALID_FORMAT = 0; // The default layout, with exactly one storage location per element. DENSE = 1; @@ -109,8 +111,9 @@ enum Format { } // Describes a tile used in tiling-based layout. Refer to -// g3doc/layout_with_tiling.md for details about tiling-based layout. -message Tile { +// g3doc/third_party/tensorflow/compiler/xla/g3doc/layout_with_tiling.md for +// details about tiling-based layout. +message TileProto { // Number of elements in each dimension of the tile. It's ordered from the // most major dimension of the tile to the most minor dimension of the tile. // The dimensions correspond to a suffix of the dimensions of the shape being @@ -128,7 +131,7 @@ message Tile { // See the XLA documentation for more information on shapes and layouts. // // LINT.IfChange -message Layout { +message LayoutProto { // The method used to store the data in memory. The format determines which of // the other fields are used by the layout. Format format = 4; @@ -153,7 +156,7 @@ message Layout { // // TODO(b/119839262): implement tiling in each backend or add Unimplemented // error. - repeated Tile tiles = 6; + repeated TileProto tiles = 6; // Bit size of each element. If the size is bigger than what the element // type requires, the value is stored in the least significant @@ -196,7 +199,7 @@ message ShapeProto { repeated ShapeProto tuple_shapes = 4; // The layout used to back this shape. - Layout layout = 5; + LayoutProto layout = 5; // Important: if any field is added, be sure to modify ShapeUtil::Equal(), // ShapeUtil::Compatible() and ShapeUtil::Hash() appropriately to account for -- GitLab From 92f67536b78895c47065fb9b35f775ee4326f9e1 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 11 Dec 2018 22:00:58 -0800 Subject: [PATCH 414/873] Pack supports input dimensions >= 4. PiperOrigin-RevId: 225125955 --- tensorflow/lite/kernels/pack.cc | 1 - tensorflow/lite/kernels/pack_test.cc | 13 +++++++++++++ 2 files changed, 13 insertions(+), 1 deletion(-) diff --git a/tensorflow/lite/kernels/pack.cc b/tensorflow/lite/kernels/pack.cc index 479495c875..d15a5a08af 100644 --- a/tensorflow/lite/kernels/pack.cc +++ b/tensorflow/lite/kernels/pack.cc @@ -35,7 +35,6 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); const TfLiteTensor* input0 = GetInput(context, node, 0); - TF_LITE_ENSURE(context, NumDimensions(input0) < 4); TF_LITE_ENSURE(context, NumDimensions(input0) >= data->axis); // TODO(renjieliu): Support negative axis. TF_LITE_ENSURE(context, data->axis >= 0); diff --git a/tensorflow/lite/kernels/pack_test.cc b/tensorflow/lite/kernels/pack_test.cc index 4f58debc5c..530cc2e50f 100644 --- a/tensorflow/lite/kernels/pack_test.cc +++ b/tensorflow/lite/kernels/pack_test.cc @@ -82,6 +82,19 @@ TEST(PackOpTest, FloatMultilDimensions) { ElementsAreArray({1, 2, 3, 7, 8, 9, 4, 5, 6, 10, 11, 12})); } +TEST(PackOpTest, FloatFiveDimensions) { + PackOpModel model({TensorType_FLOAT32, {2, 2, 2, 2}}, 1, 2); + model.SetInput(0, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}); + model.SetInput( + 1, {17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32}); + model.Invoke(); + EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 2, 2, 2, 2)); + EXPECT_THAT(model.GetOutput(), + ElementsAreArray({1, 2, 3, 4, 5, 6, 7, 8, 17, 18, 19, + 20, 21, 22, 23, 24, 9, 10, 11, 12, 13, 14, + 15, 16, 25, 26, 27, 28, 29, 30, 31, 32})); +} + // int32 tests. TEST(PackOpTest, Int32ThreeInputs) { PackOpModel model({TensorType_INT32, {2}}, 0, 3); -- GitLab From 413551b9537565b5c918085951c8c0bde315f46c Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Tue, 11 Dec 2018 22:22:07 -0800 Subject: [PATCH 415/873] [XLA:CPU] Make instruction order compulsory in IrEmitter::EmitComputation; NFC PiperOrigin-RevId: 225127595 --- .../compiler/xla/service/cpu/cpu_compiler.cc | 17 ++++++++--------- .../compiler/xla/service/cpu/ir_emitter.cc | 11 +++-------- .../compiler/xla/service/cpu/ir_emitter.h | 2 +- .../compiler/xla/service/hlo_computation.cc | 6 +++--- .../compiler/xla/service/hlo_computation.h | 2 +- 5 files changed, 16 insertions(+), 22 deletions(-) diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc index 6374822c81..f3dfa4d642 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc @@ -635,18 +635,17 @@ StatusOr> CpuCompiler::RunBackend( .EmitComputation( embedded_computation, embedded_computation->name(), /*is_top_level_computation=*/false, - &schedule.sequence(embedded_computation).instructions()) + schedule.sequence(embedded_computation).instructions()) .status()); } string function_name_prefix = entry_computation->name().empty() ? "__compute" : entry_computation->name(); - TF_ASSIGN_OR_RETURN( - llvm::Function * entry_function, - ir_emitter.EmitComputation( - entry_computation, function_name_prefix, - /*is_top_level_computation=*/true, - &schedule.sequence(entry_computation).instructions())); + TF_ASSIGN_OR_RETURN(llvm::Function * entry_function, + ir_emitter.EmitComputation( + entry_computation, function_name_prefix, + /*is_top_level_computation=*/true, + schedule.sequence(entry_computation).instructions())); string function_name = [&]() { llvm::SmallVector function_name_vector; @@ -835,7 +834,7 @@ CpuCompiler::CompileAheadOfTime(std::unique_ptr module_group, .EmitComputation( embedded_computation, embedded_computation->name(), /*is_top_level_computation=*/false, - &schedule.sequence(embedded_computation).instructions()) + schedule.sequence(embedded_computation).instructions()) .status()); } const string& entry_point_name = options.entry_point_name(); @@ -843,7 +842,7 @@ CpuCompiler::CompileAheadOfTime(std::unique_ptr module_group, ir_emitter.EmitComputation( computation, entry_point_name, /*is_top_level_computation=*/true, - &schedule.sequence(computation).instructions())); + schedule.sequence(computation).instructions())); CHECK(entry_function->getName() == llvm_ir::AsStringRef(entry_point_name)); diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc index 38ab5b78d2..62a4e8d350 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc @@ -111,10 +111,9 @@ IrEmitter::IrEmitter( StatusOr IrEmitter::EmitComputation( HloComputation* computation, const string& function_name_prefix, bool is_top_level_computation, - const std::vector* instruction_order) { + absl::Span instruction_order) { string function_name = name_uniquer_.GetUniqueName(function_name_prefix); - VLOG(2) << "Emitting IR for CPU function [" << function_name_prefix - << "]; ordered? " << (instruction_order != nullptr); + VLOG(2) << "Emitting IR for CPU function [" << function_name_prefix << "]"; is_top_level_computation_ = is_top_level_computation; num_dynamic_loop_bounds_ = 0; if (!computation->root_instruction()->outer_dimension_partitions().empty()) { @@ -141,11 +140,7 @@ StatusOr IrEmitter::EmitComputation( bool use_rdtscp = arch_type_ == llvm::Triple::ArchType::x86 || arch_type_ == llvm::Triple::ArchType::x86_64; profiling_state_ = ProfilingState(use_rdtscp); - if (instruction_order == nullptr) { - TF_RETURN_IF_ERROR(computation->Accept(this)); - } else { - TF_RETURN_IF_ERROR(computation->AcceptOrdered(this, *instruction_order)); - } + TF_RETURN_IF_ERROR(computation->AcceptOrdered(this, instruction_order)); llvm::Function* ir_function = compute_function_->function(); InsertOrDie(&emitted_functions_, computation, ir_function); // Delete 'compute_function', finalizing 'ir_function' and restoring caller diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.h b/tensorflow/compiler/xla/service/cpu/ir_emitter.h index 559a8162a2..1db75cc8be 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.h +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.h @@ -101,7 +101,7 @@ class IrEmitter : public DfsHloVisitorWithDefault, StatusOr EmitComputation( HloComputation* computation, const string& function_name_prefix, bool is_top_level_computation, - const std::vector* instruction_order); + absl::Span instruction_order); llvm::IRBuilder<>* b() { return &b_; } diff --git a/tensorflow/compiler/xla/service/hlo_computation.cc b/tensorflow/compiler/xla/service/hlo_computation.cc index ff122b529b..80f7247048 100644 --- a/tensorflow/compiler/xla/service/hlo_computation.cc +++ b/tensorflow/compiler/xla/service/hlo_computation.cc @@ -797,7 +797,7 @@ Status HloComputation::AcceptWithOperandOrder( template Status HloComputation::AcceptOrdered( DfsHloVisitorBase* visitor, - const std::vector& order) const { + absl::Span order) const { VLOG(3) << "Accepting visitor with order."; for (HloInstruction* root : CollectUnreachableRoots()) { TF_RET_CHECK(std::find(order.begin(), order.end(), root) != order.end()) @@ -827,9 +827,9 @@ Status HloComputation::AcceptOrdered( // Explicit instantiations. template Status HloComputation::AcceptOrdered( - DfsHloVisitor*, const std::vector&) const; + DfsHloVisitor*, absl::Span) const; template Status HloComputation::AcceptOrdered( - ConstDfsHloVisitor*, const std::vector&) const; + ConstDfsHloVisitor*, absl::Span) const; Status HloComputation::Accept( const std::function& visitor_func) { diff --git a/tensorflow/compiler/xla/service/hlo_computation.h b/tensorflow/compiler/xla/service/hlo_computation.h index c584e4c7ca..da8a5320bb 100644 --- a/tensorflow/compiler/xla/service/hlo_computation.h +++ b/tensorflow/compiler/xla/service/hlo_computation.h @@ -307,7 +307,7 @@ class HloComputation { // be a topological sort of all instructions in the computation. template Status AcceptOrdered(DfsHloVisitorBase* visitor, - const std::vector& order) const; + absl::Span order) const; // Same as Accept() above, but the visitor is given as a function. Status Accept(const std::function& visitor_func); -- GitLab From b4c28561416e97f8029ad0c009cbe15e4fb75563 Mon Sep 17 00:00:00 2001 From: Skye Wanderman-Milne Date: Tue, 11 Dec 2018 23:14:27 -0800 Subject: [PATCH 416/873] Make AddWhileInputHack handle control inputs correctly. PiperOrigin-RevId: 225131361 --- tensorflow/core/graph/graph.cc | 8 +++++++- tensorflow/python/framework/ops_test.py | 3 +++ 2 files changed, 10 insertions(+), 1 deletion(-) diff --git a/tensorflow/core/graph/graph.cc b/tensorflow/core/graph/graph.cc index 550e3ef915..223fc85f9f 100644 --- a/tensorflow/core/graph/graph.cc +++ b/tensorflow/core/graph/graph.cc @@ -555,7 +555,13 @@ Status Graph::AddWhileInputHack(Node* new_src, int new_src_index, Node* dst) { dst->DebugString()); } TF_RETURN_IF_ERROR(IsValidOutputTensor(new_src, new_src_index)); - int dst_index = dst->in_edges().size(); + // Find the current number of data inputs. We'll add the new edge to the next + // missing data input. + int dst_index = 0; + for (const Edge* edge : dst->in_edges()) { + if (edge->IsControlEdge()) continue; + ++dst_index; + } TF_RETURN_IF_ERROR(IsValidInputTensor(dst, dst_index)); AddEdge(new_src, new_src_index, dst, dst_index); dst->MaybeCopyOnWrite(); diff --git a/tensorflow/python/framework/ops_test.py b/tensorflow/python/framework/ops_test.py index 0fcbcd6ee4..2d7ee1a99e 100644 --- a/tensorflow/python/framework/ops_test.py +++ b/tensorflow/python/framework/ops_test.py @@ -615,6 +615,9 @@ class OperationTest(test_util.TensorFlowTestCase): self.assertEqual(while_op.type, "While") orig_num_inputs = len(while_op.inputs) + # Make sure we can handle the while op having a control input. + while_op._add_control_input(constant_op.constant(0).op) + new_input1 = constant_op.constant(1.0) new_input2 = constant_op.constant(True) -- GitLab From 9585202ed095ec63c1a6f947a0197fce852e9036 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 12 Dec 2018 00:01:07 -0800 Subject: [PATCH 417/873] Remove :android_tensorflow_lib_selective_registration* aliases, targets using selective registration can now use the :android_tensorflow_lib_lite* targets. PiperOrigin-RevId: 225134497 --- tensorflow/core/BUILD | 23 +---------------------- 1 file changed, 1 insertion(+), 22 deletions(-) diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index d92f0ba655..276005038c 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -49,7 +49,7 @@ # filegroup ":android_proto_srcs" - Protos # filegroup ":android_srcs" - Core sources # cc_library ":android_tensorflow_lib" - Native library -# cc_library ":android_tensorflow_lib_selective_registration" - Native library +# cc_library ":android_tensorflow_lib_lite" - Native library, without ops, # supporting SELECTIVE_REGISTRATION feature. # portable_proto_library ":android_proto_lib" (Google-internal) # @@ -1832,27 +1832,6 @@ cc_library( alwayslink = 1, ) -# Android library for use with the SELECTIVE_REGISTRATION feature. -# Does not contain operators. In contrast to android_tensorflow_lib_lite, -# this links in framework support for all types, relying on selective -# registration of ops to prune code size. -# -# TODO(gonnet): Move all users of these aliases to the corresponding -# :android_tensorflow_lib_lite* targets and remove. -alias( - name = "android_tensorflow_lib_selective_registration", - actual = ":android_tensorflow_lib_lite", - visibility = ["//visibility:public"], -) - -# Android library for use with the SELECTIVE_REGISTRATION feature with -# no proto_rtti. -alias( - name = "android_tensorflow_lib_selective_registration_nortti", - actual = ":android_tensorflow_lib_lite_nortti", - visibility = ["//visibility:public"], -) - filegroup( name = "android_op_registrations_and_gradients", srcs = glob( -- GitLab From d856a3ca443599feb7d577ba17943a76ffa65c1e Mon Sep 17 00:00:00 2001 From: avijit-nervana Date: Wed, 12 Dec 2018 00:33:46 -0800 Subject: [PATCH 418/873] Upgraded to v0.9.0 and fixed the broken MacOS build --- tensorflow/tensorflow.bzl | 3 +-- tensorflow/workspace.bzl | 32 +++++++++++++++--------------- third_party/ngraph/ngraph.BUILD | 18 +++++++++++++---- third_party/ngraph/ngraph_tf.BUILD | 2 ++ 4 files changed, 33 insertions(+), 22 deletions(-) diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl index ed1de5a31c..d93e0df5e4 100644 --- a/tensorflow/tensorflow.bzl +++ b/tensorflow/tensorflow.bzl @@ -1666,8 +1666,7 @@ def tf_py_wrap_cc( ) extra_linkopts = select({ "@local_config_cuda//cuda:darwin": [ - "-Wl,-exported_symbols_list", - "$(location %s.lds)" % vscriptname, + "-Wl,-exported_symbols_list,$(location %s.lds)" % vscriptname, ], clean_dep("//tensorflow:windows"): [], "//conditions:default": [ diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index f8b6bd1a3f..9ed668e1c5 100755 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -828,44 +828,44 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""): tf_http_archive( name = "tbb", build_file = clean_dep("//third_party/ngraph:tbb.BUILD"), - sha256 = "724686f90bcda78f13b76f297d964008737ccd6399328143c1c0093e73ae6a13", - strip_prefix = "tbb-tbb_2018", + sha256 = "c3245012296f09f1418b78a8c2f17df5188b3bd0db620f7fd5fabe363320805a", + strip_prefix = "tbb-2019_U1", urls = [ - "https://mirror.bazel.build/github.com/01org/tbb/archive/tbb_2018.zip", - "https://github.com/01org/tbb/archive/tbb_2018.zip", + "https://mirror.bazel.build/github.com/01org/tbb/archive/2019_U1.zip", + "https://github.com/01org/tbb/archive/2019_U1.zip", ], ) tf_http_archive( name = "ngraph", build_file = clean_dep("//third_party/ngraph:ngraph.BUILD"), - sha256 = "2b28f9c9f063b96825a96d56d7f7978c9a1c55c9b25175c20dd49a8a77cb0305", - strip_prefix = "ngraph-0.9.1", + sha256 = "a1780f24a1381fc25e323b4b2d08b6ef5129f42e011305b2a34dcf43a48030d5", + strip_prefix = "ngraph-0.11.0", urls = [ - "https://mirror.bazel.build/github.com/NervanaSystems/ngraph/archive/v0.9.1.tar.gz", - "https://github.com/NervanaSystems/ngraph/archive/v0.9.1.tar.gz", + "https://mirror.bazel.build/github.com/NervanaSystems/ngraph/archive/v0.11.0.tar.gz", + "https://github.com/NervanaSystems/ngraph/archive/v0.11.0.tar.gz" ], ) tf_http_archive( name = "nlohmann_json_lib", build_file = clean_dep("//third_party/ngraph:nlohmann_json.BUILD"), - sha256 = "9f3549824af3ca7e9707a2503959886362801fb4926b869789d6929098a79e47", - strip_prefix = "json-3.1.1", + sha256 = "c377963a95989270c943d522bfefe7b889ef5ed0e1e15d535fd6f6f16ed70732", + strip_prefix = "json-3.4.0", urls = [ - "https://mirror.bazel.build/github.com/nlohmann/json/archive/v3.1.1.tar.gz", - "https://github.com/nlohmann/json/archive/v3.1.1.tar.gz", + "https://mirror.bazel.build/github.com/nlohmann/json/archive/v3.4.0.tar.gz", + "https://github.com/nlohmann/json/archive/v3.4.0.tar.gz", ], ) tf_http_archive( name = "ngraph_tf", build_file = clean_dep("//third_party/ngraph:ngraph_tf.BUILD"), - sha256 = "89accbc702e68a09775f1011a99dd16561038fd1ce59d566d64450176abaae5c", - strip_prefix = "ngraph-tf-0.7.0", + sha256 = "742a642d2c6622277df4c902b6830d616d0539cc8cd843d6cdb899bb99e66e36", + strip_prefix = "ngraph-tf-0.9.0", urls = [ - "https://mirror.bazel.build/github.com/NervanaSystems/ngraph-tf/archive/v0.7.0.tar.gz", - "https://github.com/NervanaSystems/ngraph-tf/archive/v0.7.0.tar.gz", + "https://mirror.bazel.build/github.com/NervanaSystems/ngraph-tf/archive/v0.9.0.zip", + "https://github.com/NervanaSystems/ngraph-tf/archive/v0.9.0.zip" ], ) diff --git a/third_party/ngraph/ngraph.BUILD b/third_party/ngraph/ngraph.BUILD index 63e9548c53..bb1f65d347 100644 --- a/third_party/ngraph/ngraph.BUILD +++ b/third_party/ngraph/ngraph.BUILD @@ -56,14 +56,16 @@ cc_library( "src/ngraph/runtime/cpu/cpu_backend.cpp", "src/ngraph/runtime/cpu/cpu_builder.cpp", "src/ngraph/runtime/cpu/cpu_call_frame.cpp", + "src/ngraph/runtime/cpu/cpu_cse.cpp", + "src/ngraph/runtime/cpu/cpu_executor.cpp", "src/ngraph/runtime/cpu/cpu_external_function.cpp", "src/ngraph/runtime/cpu/cpu_kernels.cpp", "src/ngraph/runtime/cpu/cpu_layout_descriptor.cpp", + "src/ngraph/runtime/cpu/cpu_op_annotations.cpp", "src/ngraph/runtime/cpu/cpu_tensor_view.cpp", "src/ngraph/runtime/cpu/cpu_tensor_view_wrapper.cpp", "src/ngraph/runtime/cpu/cpu_tracing.cpp", "src/ngraph/runtime/cpu/cpu_visualize_tree.cpp", - "src/ngraph/runtime/cpu/kernel/eigen_thread_pool.cpp", "src/ngraph/runtime/cpu/kernel/pad.cpp", "src/ngraph/runtime/cpu/kernel/reduce_max.cpp", "src/ngraph/runtime/cpu/kernel/reduce_sum.cpp", @@ -79,21 +81,27 @@ cc_library( "src/ngraph/runtime/cpu/op/conv_relu.cpp", "src/ngraph/runtime/cpu/op/convert_layout.cpp", "src/ngraph/runtime/cpu/op/group_conv.cpp", + "src/ngraph/runtime/cpu/op/group_conv_bias.cpp", + "src/ngraph/runtime/cpu/op/halide_op.cpp", + "src/ngraph/runtime/cpu/op/leaky_relu.cpp", "src/ngraph/runtime/cpu/op/loop_kernel.cpp", "src/ngraph/runtime/cpu/op/lstm.cpp", "src/ngraph/runtime/cpu/op/matmul_bias.cpp", "src/ngraph/runtime/cpu/op/max_pool_with_indices.cpp", "src/ngraph/runtime/cpu/op/rnn.cpp", "src/ngraph/runtime/cpu/op/sigmoid_mul.cpp", + "src/ngraph/runtime/cpu/op/update_slice.cpp", "src/ngraph/runtime/cpu/pass/cpu_assignment.cpp", "src/ngraph/runtime/cpu/pass/cpu_collapse_dims.cpp", - "src/ngraph/runtime/cpu/pass/cpu_concat_inputs.cpp", "src/ngraph/runtime/cpu/pass/cpu_fusion.cpp", + "src/ngraph/runtime/cpu/pass/cpu_horizontal_fusion.cpp", "src/ngraph/runtime/cpu/pass/cpu_layout.cpp", "src/ngraph/runtime/cpu/pass/cpu_loop_kernel_fusion.cpp", "src/ngraph/runtime/cpu/pass/cpu_mat_fusion.cpp", + "src/ngraph/runtime/cpu/pass/cpu_memory_optimization.cpp", "src/ngraph/runtime/cpu/pass/cpu_post_layout_optimizations.cpp", "src/ngraph/runtime/cpu/pass/cpu_rnn_fusion.cpp", + "src/ngraph/runtime/cpu/pass/cpu_reshape_sinking.cpp", "src/ngraph/runtime/cpu/pass/cpu_workspace_insertion.cpp", ], hdrs = glob(["src/ngraph/runtime/cpu/**/*.hpp"]) + glob([]), @@ -101,7 +109,7 @@ cc_library( "-I external/ngraph/src", "-I external/nlohmann_json_lib/include/", '-D SHARED_LIB_EXT=\\".so\\"', - '-D NGRAPH_VERSION=\\"0.9.1\\"', + '-D NGRAPH_VERSION=\\"0.11.0\\"', "-D NGRAPH_DEX_ONLY", '-D PROJECT_ROOT_DIR=\\"\\"', ], @@ -124,11 +132,13 @@ cc_library( "src/ngraph/builder/*.cpp", "src/ngraph/descriptor/*.cpp", "src/ngraph/descriptor/layout/*.cpp", + "src/ngraph/op/experimental/generate_mask.cpp", "src/ngraph/op/experimental/quantized_avg_pool.cpp", "src/ngraph/op/experimental/quantized_conv_bias.cpp", "src/ngraph/op/experimental/quantized_conv_relu.cpp", "src/ngraph/op/experimental/quantized_conv.cpp", "src/ngraph/op/experimental/quantized_max_pool.cpp", + "src/ngraph/op/experimental/shape_of.cpp", "src/ngraph/op/*.cpp", "src/ngraph/op/util/*.cpp", "src/ngraph/pattern/*.cpp", @@ -142,7 +152,7 @@ cc_library( "-I external/ngraph/src", "-I external/nlohmann_json_lib/include/", '-D SHARED_LIB_EXT=\\".so\\"', - '-D NGRAPH_VERSION=\\"0.9.1\\"', + '-D NGRAPH_VERSION=\\"0.11.0\\"', '-D PROJECT_ROOT_DIR=\\"\\"', ], visibility = ["//visibility:public"], diff --git a/third_party/ngraph/ngraph_tf.BUILD b/third_party/ngraph/ngraph_tf.BUILD index db9a66f9b5..d4619395f8 100644 --- a/third_party/ngraph/ngraph_tf.BUILD +++ b/third_party/ngraph/ngraph_tf.BUILD @@ -18,6 +18,8 @@ cc_library( "src/ngraph_api.h", "src/ngraph_assign_clusters.cc", "src/ngraph_assign_clusters.h", + "src/ngraph_backend_manager.h", + "src/ngraph_backend_manager.cc", "src/ngraph_builder.cc", "src/ngraph_builder.h", "src/ngraph_capture_variables.cc", -- GitLab From a0d9780dfbc3ab7a18e58affc5aaafc5c19f419e Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 12 Dec 2018 00:34:52 -0800 Subject: [PATCH 419/873] Use proper iso8601 time format in log. Format in the previous state didn't give the timezone. PiperOrigin-RevId: 225138116 --- tensorflow/python/training/evaluation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/training/evaluation.py b/tensorflow/python/training/evaluation.py index a10178f8cf..37d46795b1 100644 --- a/tensorflow/python/training/evaluation.py +++ b/tensorflow/python/training/evaluation.py @@ -253,7 +253,7 @@ def _evaluate_once(checkpoint_path, if isinstance(h, (_StopAfterNEvalsHook, _MultiStepStopAfterNEvalsHook)): h._set_evals_completed_tensor(eval_step_value) # pylint: disable=protected-access - logging.info('Starting evaluation at ' + time.strftime('%Y-%m-%d-%H:%M:%S', + logging.info('Starting evaluation at ' + time.strftime('%Y-%m-%dT%H:%M:%SZ', time.gmtime())) # Prepare the session creator. -- GitLab From 25337d2065bd3ef79b9018714c0cb5af46ca06dc Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 12 Dec 2018 01:04:38 -0800 Subject: [PATCH 420/873] compat: Update forward compatibility horizon to 2018-12-12 PiperOrigin-RevId: 225140840 --- tensorflow/python/compat/compat.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/compat/compat.py b/tensorflow/python/compat/compat.py index 679dcf9696..57a4c8be7d 100644 --- a/tensorflow/python/compat/compat.py +++ b/tensorflow/python/compat/compat.py @@ -32,7 +32,7 @@ from tensorflow.python.ops import variable_scope from tensorflow.python.util import tf_contextlib from tensorflow.python.util.tf_export import tf_export -_FORWARD_COMPATIBILITY_HORIZON = datetime.date(2018, 12, 11) +_FORWARD_COMPATIBILITY_HORIZON = datetime.date(2018, 12, 12) @tf_export("compat.forward_compatible") -- GitLab From 1068d773964b06b0a086714aad9bc2760d649c24 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 12 Dec 2018 07:05:09 -0800 Subject: [PATCH 421/873] Docstring fixes PiperOrigin-RevId: 225178266 --- tensorflow/python/ops/ragged/ragged_factory_ops.py | 6 +++--- tensorflow/python/ops/ragged/ragged_functional_ops.py | 6 +++--- tensorflow/python/ops/ragged/ragged_getitem.py | 2 +- tensorflow/python/ops/ragged/ragged_tensor.py | 2 +- tensorflow/python/ops/ragged/ragged_tensor_value.py | 5 ++++- tensorflow/python/ops/ragged/segment_id_ops.py | 4 ++-- 6 files changed, 14 insertions(+), 11 deletions(-) diff --git a/tensorflow/python/ops/ragged/ragged_factory_ops.py b/tensorflow/python/ops/ragged/ragged_factory_ops.py index 695accc652..8cda98765b 100644 --- a/tensorflow/python/ops/ragged/ragged_factory_ops.py +++ b/tensorflow/python/ops/ragged/ragged_factory_ops.py @@ -80,9 +80,9 @@ def constant(pylist, dtype=None, ragged_rank=None, inner_shape=None, name=None): def constant_value(pylist, dtype=None, ragged_rank=None, inner_shape=None): """Constructs a RaggedTensorValue from a nested Python list. - > Warning: This function returns a `RaggedTensorValue`, not a `RaggedTensor`. - > If you wish to construct a constant `RaggedTensor`, use - > [`ragged.constant(...)`](constant.md) instead. + Warning: This function returns a `RaggedTensorValue`, not a `RaggedTensor`. + If you wish to construct a constant `RaggedTensor`, use + [`ragged.constant(...)`](constant.md) instead. Example: diff --git a/tensorflow/python/ops/ragged/ragged_functional_ops.py b/tensorflow/python/ops/ragged/ragged_functional_ops.py index 7344c96465..b6937a1c37 100644 --- a/tensorflow/python/ops/ragged/ragged_functional_ops.py +++ b/tensorflow/python/ops/ragged/ragged_functional_ops.py @@ -26,15 +26,15 @@ from tensorflow.python.util.tf_export import tf_export @tf_export("ragged.map_flat_values") def map_flat_values(op, *args, **kwargs): - """Applies `op` to the inner values of one or more RaggedTensors. + """Applies `op` to the values of one or more RaggedTensors. Replaces any `RaggedTensor` in `args` or `kwargs` with its `flat_values` tensor, and then calls `op`. Returns a `RaggedTensor` that is constructed - from the input `RaggedTensor`s' `splits` and the value returned by + from the input `RaggedTensor`s' `nested_row_splits` and the value returned by the `op`. If the input arguments contain multiple `RaggedTensor`s, then they must have - identical `splits`. + identical `nested_row_splits`. Examples: diff --git a/tensorflow/python/ops/ragged/ragged_getitem.py b/tensorflow/python/ops/ragged/ragged_getitem.py index 0fa72a3658..001a400596 100644 --- a/tensorflow/python/ops/ragged/ragged_getitem.py +++ b/tensorflow/python/ops/ragged/ragged_getitem.py @@ -38,7 +38,7 @@ def ragged_tensor_getitem(self, key): IndexError; (2) use a default value; or (3) skip that value and return a tensor with fewer rows than we started with. Following the guiding principles of Python ("In the face of ambiguity, refuse the temptation to - guess" ), we simply disallow this operation. + guess"), we simply disallow this operation. Any dimensions added by `array_ops.newaxis` will be ragged if the following dimension is ragged. diff --git a/tensorflow/python/ops/ragged/ragged_tensor.py b/tensorflow/python/ops/ragged/ragged_tensor.py index acf3a3841d..fd334e6cc7 100644 --- a/tensorflow/python/ops/ragged/ragged_tensor.py +++ b/tensorflow/python/ops/ragged/ragged_tensor.py @@ -46,7 +46,7 @@ _eval_using_default_session = ops._eval_using_default_session @tf_export("RaggedTensor") class RaggedTensor(object): - """Represents a ragged tensor (go/ragged). + """Represents a ragged tensor. A `RaggedTensor` is a tensor with one or more *ragged dimensions*, which are dimensions whose slices may have different lengths. For example, the inner diff --git a/tensorflow/python/ops/ragged/ragged_tensor_value.py b/tensorflow/python/ops/ragged/ragged_tensor_value.py index 1162487f0f..c5e498e95f 100644 --- a/tensorflow/python/ops/ragged/ragged_tensor_value.py +++ b/tensorflow/python/ops/ragged/ragged_tensor_value.py @@ -27,7 +27,10 @@ from tensorflow.python.util.tf_export import tf_export class RaggedTensorValue(object): """Represents the value of a `RaggedTensor`. - See `RaggedTensor` for a description of ragged tensors. + Warning: `RaggedTensorValue` should only be used in graph mode; in + eager mode, the `tf.RaggedTensor` class contains its value directly. + + See `tf.RaggedTensor` for a description of ragged tensors. """ def __init__(self, values, row_splits): diff --git a/tensorflow/python/ops/ragged/segment_id_ops.py b/tensorflow/python/ops/ragged/segment_id_ops.py index ee17e4d636..42dc13223b 100644 --- a/tensorflow/python/ops/ragged/segment_id_ops.py +++ b/tensorflow/python/ops/ragged/segment_id_ops.py @@ -32,7 +32,7 @@ from tensorflow.python.util.tf_export import tf_export # https://www.tensorflow.org/api_guides/python/math_ops#Segmentation @tf_export("ragged.row_splits_to_segment_ids") def row_splits_to_segment_ids(splits, name=None): - """Generates the segmentation corresponding to a RaggedTensor `splits` vector. + """Generates the segmentation corresponding to a RaggedTensor `row_splits`. Returns an integer vector `segment_ids`, where `segment_ids[i] == j` if `splits[j] <= i < splits[j+1]`. Example: @@ -67,7 +67,7 @@ def row_splits_to_segment_ids(splits, name=None): # https://www.tensorflow.org/api_guides/python/math_ops#Segmentation @tf_export("ragged.segment_ids_to_row_splits") def segment_ids_to_row_splits(segment_ids, num_segments=None, name=None): - """Generates the RaggedTensor `splits` vector corresponding to a segmentation. + """Generates the RaggedTensor `row_splits` corresponding to a segmentation. Returns an integer vector `splits`, where `splits[0] = 0` and `splits[i] = splits[i-1] + count(segment_ids==i)`. Example: -- GitLab From bf16a7511a2d29c460d4e1a771d53ef692a2d32b Mon Sep 17 00:00:00 2001 From: Gaurav Jain Date: Wed, 12 Dec 2018 07:21:17 -0800 Subject: [PATCH 422/873] Wrap global_variables_initializer with self.evaluate() In addition, fix a few eval() calls as well as remove some @test_util.run_v1_only annotations. PiperOrigin-RevId: 225180248 --- .../distribute/distribute_coordinator_test.py | 4 +- tensorflow/python/eager/function_test.py | 2 +- .../framework/auto_control_deps_test.py | 16 +- .../kernel_tests/checkpoint_ops_test.py | 7 +- .../conditional_accumulator_test.py | 2 +- .../kernel_tests/control_flow_ops_py_test.py | 139 ++++++------ .../dense_update_ops_no_tsan_test.py | 13 +- .../kernel_tests/functional_ops_test.py | 2 +- .../partitioned_variables_test.py | 52 ++--- .../resource_variable_ops_test.py | 6 +- .../kernel_tests/tensor_array_ops_test.py | 8 +- .../python/kernel_tests/variables_test.py | 81 ++++--- .../python/ops/control_flow_ops_test.py | 2 +- tensorflow/python/ops/gradients_test.py | 2 +- .../python/saved_model/saved_model_test.py | 2 +- tensorflow/python/training/adagrad_test.py | 12 +- .../python/training/checkpoint_ops_test.py | 14 +- tensorflow/python/training/input_test.py | 54 ++--- .../python/training/moving_averages_test.py | 12 +- .../python/training/queue_runner_test.py | 14 +- tensorflow/python/training/saver_test.py | 198 +++++++++--------- .../python/training/slot_creator_test.py | 12 +- .../python/training/training_ops_test.py | 12 +- 23 files changed, 322 insertions(+), 344 deletions(-) diff --git a/tensorflow/python/distribute/distribute_coordinator_test.py b/tensorflow/python/distribute/distribute_coordinator_test.py index 7598c105c2..dbed3e7f59 100644 --- a/tensorflow/python/distribute/distribute_coordinator_test.py +++ b/tensorflow/python/distribute/distribute_coordinator_test.py @@ -230,7 +230,7 @@ class DistributeCoordinatorTestBase(test.TestCase): with ops.device("/job:worker/task:0"): result = math_ops.add_n(xs) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) result_value = sess.run(result) self.assertEqual(result_value, expected) if result_value == expected: @@ -278,7 +278,7 @@ class DistributeCoordinatorTestBase(test.TestCase): train_op = control_flow_ops.group([x_add, y_sub]) if context.is_chief: - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) # Synchronize workers after initializaton. if context.has_barrier: diff --git a/tensorflow/python/eager/function_test.py b/tensorflow/python/eager/function_test.py index 8d1f8c21d9..2697ab5b17 100644 --- a/tensorflow/python/eager/function_test.py +++ b/tensorflow/python/eager/function_test.py @@ -579,7 +579,7 @@ class FunctionTest(test.TestCase, parameterized.TestCase): return self.v * 2 o = HasAVar() - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) call = def_function.function(o.call) op = call() self.assertAllEqual(self.evaluate(op), 2.0) diff --git a/tensorflow/python/framework/auto_control_deps_test.py b/tensorflow/python/framework/auto_control_deps_test.py index 5f5de45b9e..d81adef26a 100644 --- a/tensorflow/python/framework/auto_control_deps_test.py +++ b/tensorflow/python/framework/auto_control_deps_test.py @@ -39,7 +39,7 @@ class AutomaticControlDependenciesTest(test.TestCase): def testBasic(self): with context.graph_mode(), self.cached_session(): v = resource_variable_ops.ResourceVariable(1.0) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) with acd.AutomaticControlDependencies() as c: v.assign(v + 1) v.assign(2 * v) @@ -51,7 +51,7 @@ class AutomaticControlDependenciesTest(test.TestCase): def testCondMustRun(self): with context.graph_mode(), self.cached_session(): v = resource_variable_ops.ResourceVariable(1.0) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) p = array_ops.placeholder(dtype=dtypes.bool) with acd.AutomaticControlDependencies() as c: @@ -73,7 +73,7 @@ class AutomaticControlDependenciesTest(test.TestCase): def testCondMustRunSeparateRead(self): with context.graph_mode(), self.cached_session(): v = resource_variable_ops.ResourceVariable(1.0) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) p = array_ops.placeholder(dtype=dtypes.bool) with acd.AutomaticControlDependencies() as c: @@ -97,7 +97,7 @@ class AutomaticControlDependenciesTest(test.TestCase): def testCondNested(self): with context.graph_mode(), self.cached_session(): v = resource_variable_ops.ResourceVariable(1.0) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) p = array_ops.placeholder(dtype=dtypes.bool) q = array_ops.placeholder(dtype=dtypes.bool) with acd.AutomaticControlDependencies() as c: @@ -132,7 +132,7 @@ class AutomaticControlDependenciesTest(test.TestCase): def testCondOneBranch(self): with context.graph_mode(), self.cached_session(): v = resource_variable_ops.ResourceVariable(1.0) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) p = array_ops.placeholder(dtype=dtypes.bool) with acd.AutomaticControlDependencies() as c: @@ -153,7 +153,7 @@ class AutomaticControlDependenciesTest(test.TestCase): def testCondOneBranchUpdateBefore(self): with context.graph_mode(), self.cached_session(): v = resource_variable_ops.ResourceVariable(1.0) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) p = array_ops.placeholder(dtype=dtypes.bool) with acd.AutomaticControlDependencies() as c: v.assign(v * 2) @@ -175,7 +175,7 @@ class AutomaticControlDependenciesTest(test.TestCase): def testCondOneBranchUpdateAfter(self): with context.graph_mode(), self.cached_session(): v = resource_variable_ops.ResourceVariable(1.0) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) p = array_ops.placeholder(dtype=dtypes.bool) with acd.AutomaticControlDependencies() as c: @@ -211,7 +211,7 @@ class AutomaticControlDependenciesTest(test.TestCase): def testDecorator(self): with context.graph_mode(), self.cached_session(): v = resource_variable_ops.ResourceVariable(1.0) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) @acd.automatic_control_dependencies def f(): diff --git a/tensorflow/python/kernel_tests/checkpoint_ops_test.py b/tensorflow/python/kernel_tests/checkpoint_ops_test.py index 6e289bf9b7..dd5ac1f763 100644 --- a/tensorflow/python/kernel_tests/checkpoint_ops_test.py +++ b/tensorflow/python/kernel_tests/checkpoint_ops_test.py @@ -105,7 +105,6 @@ class GenerateVocabRemappingTest(test.TestCase): self.assertAllEqual(expected_num_present, self.evaluate(num_present)) -@test_util.run_v1_only('b/120545219') class LoadAndRemapMatrixTest(test.TestCase): """Tests for the load_and_remap_matrix() op.""" @@ -126,7 +125,7 @@ class LoadAndRemapMatrixTest(test.TestCase): save = saver.Saver([matrix]) with self.cached_session() as sess: - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.bundle_file = os.path.join(test.get_temp_dir(), 'bundle_checkpoint') save.save(sess, self.bundle_file) @@ -231,6 +230,7 @@ class LoadAndRemapMatrixTest(test.TestCase): np.reshape(initializing_values, (num_rows, num_cols)), self.evaluate(remapped_matrix)) + @test_util.run_v1_only('b/120545219') def test_load_and_remap_invalid_remapping(self): """Tests that errors are raised when an ID maps to multiple new IDs. @@ -262,6 +262,7 @@ class LoadAndRemapMatrixTest(test.TestCase): with self.cached_session(), self.assertRaises(errors.UnimplementedError): self.evaluate(remapped_matrix) + @test_util.run_v1_only('b/120545219') def test_load_and_remap_incorrect_initializing_values(self): """Tests that errors are raised with incorrect number of init values.""" remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix( @@ -313,7 +314,7 @@ class LoadAndRemapMatrixWithMaxRowsTest(test.TestCase): with self.cached_session() as sess: ckpt_path = os.path.join(test.get_temp_dir(), 'temp_ckpt') save = saver.Saver([matrix]) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) save.save(sess, ckpt_path) num_rows, num_cols = np_value.shape diff --git a/tensorflow/python/kernel_tests/conditional_accumulator_test.py b/tensorflow/python/kernel_tests/conditional_accumulator_test.py index ce34201706..32a2058750 100644 --- a/tensorflow/python/kernel_tests/conditional_accumulator_test.py +++ b/tensorflow/python/kernel_tests/conditional_accumulator_test.py @@ -408,7 +408,7 @@ class ConditionalAccumulatorTest(test.TestCase): set_global_step_op = q.set_global_step(new_global_step) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) for _ in range(3): set_global_step_op.run() self.evaluate(inc_global_step) diff --git a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py index f4a7d5bec9..42cfe9e237 100644 --- a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py +++ b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py @@ -140,7 +140,7 @@ class ControlFlowTest(test.TestCase): v2 = control_flow_ops.with_dependencies([op], v) self.assertTrue(isinstance(v2, ops.Tensor)) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertEqual(9, self.evaluate(v2)) @test_util.run_v1_only("b/120545219") @@ -154,7 +154,7 @@ class ControlFlowTest(test.TestCase): op = state_ops.assign(enter_v, enter_nine) v2 = control_flow_ops.with_dependencies([op], enter_v) v3 = control_flow_ops.exit(v2) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertEqual(9, self.evaluate(v3)) @test_util.run_v1_only("b/120545219") @@ -165,7 +165,7 @@ class ControlFlowTest(test.TestCase): p = constant_op.constant(True) v1 = control_flow_ops._SwitchRefOrTensor(v._ref(), p) # pylint: disable=protected-access v2 = state_ops.assign(v1[1], 9) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertEqual(9, self.evaluate(v2)) def testEnterMulExit(self): @@ -205,8 +205,8 @@ class ControlFlowTest(test.TestCase): switch_op = control_flow_ops.switch(data, pred) merge_op = control_flow_ops.merge(switch_op)[0] - val = merge_op.values.eval() - ind = merge_op.indices.eval() + val = merge_op.values + ind = merge_op.indices self.assertAllEqual(np.arange(1, 7), val) self.assertAllEqual(np.arange(0, 12, 2), ind) @@ -418,8 +418,8 @@ class ControlFlowTest(test.TestCase): fn2 = lambda: ops.IndexedSlices(math_ops.subtract(x.values, 1), indices) r = control_flow_ops.cond(pred, fn1, fn2) - val = r.values.eval() - ind = r.indices.eval() + val = r.values + ind = r.indices self.assertAllEqual(11, val) self.assertAllEqual(0, ind) @@ -437,8 +437,8 @@ class ControlFlowTest(test.TestCase): fn2 = lambda: sparse_tensor.SparseTensor( indices, x.values - 1, dense_shape=shape) r = control_flow_ops.cond(pred, fn1, fn2) - self.assertAllEqual([3.0, 5.0], r.values.eval()) - self.assertAllEqual([[1], [4]], r.indices.eval()) + self.assertAllEqual([3.0, 5.0], r.values) + self.assertAllEqual([[1], [4]], r.indices) self.assertAllEqual(r.values.get_shape(), (2,)) @test_util.run_v1_only("b/120545219") @@ -446,7 +446,7 @@ class ControlFlowTest(test.TestCase): with self.cached_session(): rv = resource_variable_ops.ResourceVariable(True) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) t = ops.convert_to_tensor(1.0) def case(): @@ -454,7 +454,8 @@ class ControlFlowTest(test.TestCase): with ops.control_dependencies([assign]): return array_ops.identity(t) - self.assertEqual(1.0, control_flow_ops.cond(rv, case, lambda: t).eval()) + self.assertEqual( + 1.0, self.evaluate(control_flow_ops.cond(rv, case, lambda: t))) @test_util.run_v1_only("b/120545219") def testCondWithTensorArrayGrad(self): @@ -483,8 +484,8 @@ class ControlFlowTest(test.TestCase): fn2 = lambda: ops.IndexedSlices(math_ops.subtract(x.values, 1), i_64) r = control_flow_ops.cond(pred, fn1, fn2) - val = r.values.eval() - ind = r.indices.eval() + val = r.values + ind = r.indices self.assertAllEqual(11, val) self.assertAllEqual(0, ind) self.assertTrue(ind.dtype == np.int64) @@ -565,8 +566,8 @@ class ControlFlowTest(test.TestCase): if not context.executing_eagerly(): with self.cached_session(): - variables.global_variables_initializer().run() - result = f().eval() + self.evaluate(variables.global_variables_initializer()) + result = self.evaluate(f()) self.assertEqual(True, result) # Only second cond result was fetched, so v1 assign shouldn't run. self.assertEqual(7, self.evaluate(v1)) @@ -605,7 +606,7 @@ class ControlFlowTest(test.TestCase): fn2 = lambda: v1 r = control_flow_ops.cond(pred, fn1, fn2) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) result = self.evaluate(r) self.assertAllEqual(np.array([7]), result) @@ -895,7 +896,7 @@ class ControlFlowTest(test.TestCase): fn2 = lambda: array_ops.gather(v1, [1, 1]) r = control_flow_ops.cond(pred, fn1, fn2) grad = gradients_impl.gradients(r, [v1])[0] - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) # Should just be [1, 1], but possibly a sparse representation gv, gi = sess.run([grad.values, grad.indices], feed_dict={c: 1}) dense_gv = [ @@ -942,11 +943,11 @@ class ControlFlowTest(test.TestCase): if not context.executing_eagerly(): with self.cached_session(): with self.captureWritesToStream(sys.stderr) as printed: - self.assertEqual(build_cond().eval(), 10) + self.assertEqual(build_cond(), 10) self.assertEqual(printed.contents(), "C\n") with self.captureWritesToStream(sys.stderr) as printed: - self.assertEqual(build_nested_cond().eval(), 10) + self.assertEqual(build_nested_cond(), 10) self.assertEqual(printed.contents(), "C\n") # In defuns, all prints should execute in program order. @@ -996,11 +997,11 @@ class ControlFlowTest(test.TestCase): if not context.executing_eagerly(): with self.cached_session(): with self.captureWritesToStream(sys.stderr) as printed: - self.assertEqual(build_while()[0].eval(), 2) + self.assertEqual(build_while()[0], 2) self.assertEqual(printed.contents(), "D\nD\n") with self.captureWritesToStream(sys.stderr) as printed: - self.assertEqual(build_nested_while()[0].eval(), 2) + self.assertEqual(build_nested_while()[0], 2) self.assertEqual(printed.contents(), "D\nD\n") # In defuns, all prints should execute in program order. @@ -1049,8 +1050,8 @@ class ControlFlowTest(test.TestCase): result = control_flow_ops.while_loop(cond=lambda i: i < 2, body=body_fn, loop_vars=[1]) - self.assertAllEqual(result.eval(), 2) - self.assertAllEqual(v.eval(), 1.0) + self.assertAllEqual(result, 2) + self.assertAllEqual(v.read_value(), 1.0) @test_util.disable_control_flow_v2("b/79881896 (control deps)") @test_util.run_v1_only("b/120545219") @@ -1067,7 +1068,7 @@ class ControlFlowTest(test.TestCase): result = control_flow_ops.while_loop(cond=lambda i: i < 5, body=body_fn, loop_vars=[0]) self.evaluate(result) - self.assertAllEqual(v.eval(), 1.0) + self.assertAllEqual(self.evaluate(v), 1.0) @test_util.disable_control_flow_v2("b/113324949 (RefVariable)") @test_util.run_v1_only("b/120545219") @@ -1085,7 +1086,7 @@ class ControlFlowTest(test.TestCase): r = control_flow_ops.while_loop(c, b, [i, x], parallel_iterations=5) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertEqual(r[0].dtype, dtypes.int32) self.assertEqual(r[1].dtype, dtypes.int32_ref) @@ -1333,7 +1334,7 @@ class ControlFlowTest(test.TestCase): d = ops.convert_to_tensor(100) r = control_flow_ops.while_loop(lambda i, m, c, o: math_ops.less(i, d), compute, [i, m, c, o]) - result = r[3].eval() + result = r[3] self.assertAllEqual(10100, result) @test_util.run_deprecated_v1 @@ -1355,7 +1356,7 @@ class ControlFlowTest(test.TestCase): s = array_ops.size(x) r = control_flow_ops.while_loop(lambda i, m, c, o: math_ops.less(i, s), compute, [i, m, c, o]) - result = r[3].eval() + result = r[3] self.assertAllEqual(42, result) @test_util.run_v1_only("b/120545219") @@ -1380,7 +1381,7 @@ class ControlFlowTest(test.TestCase): tensor_shape.unknown_shape(), tensor_shape.unknown_shape() ]) - result = r[2].eval() + result = r[2] self.assertAllEqual(np.array([0, 1, 2, 3, 4, 5, 6]), result) @test_util.disable_control_flow_v2("b/116338794 (buffer_reuse)") @@ -1634,7 +1635,7 @@ class ControlFlowTest(test.TestCase): res = control_flow_ops.while_loop( condition, body, [n, r], parallel_iterations=1) - self.assertAllEqual(12, res[1].eval()) + self.assertAllEqual(12, res[1]) @test_util.run_deprecated_v1 def testWhileWithControl_2(self): @@ -1721,7 +1722,7 @@ class ControlFlowTest(test.TestCase): return i + 1 r = control_flow_ops.while_loop(loop_condition, loop_body, (i0,)) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertEqual(4, self.evaluate(r)) self.assertAllClose(65536.0, self.evaluate(v)) @@ -1747,7 +1748,7 @@ class ControlFlowTest(test.TestCase): r = control_flow_ops.cond( constant_op.constant(False), lambda: constant_op.constant(1.0), false_branch) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertEqual(6.0, self.evaluate(r)) self.assertEqual(99, self.evaluate(v)) @@ -1890,7 +1891,7 @@ class ControlFlowTest(test.TestCase): r = control_flow_ops.while_loop( loop_iterator, loop_body, [n], parallel_iterations=1) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertEqual(3, self.evaluate(r)) result = self.evaluate(select) self.assertAllClose(np.array([10.0, 10.0, 10.0]), result) @@ -1916,7 +1917,7 @@ class ControlFlowTest(test.TestCase): r = control_flow_ops.while_loop( loop_iterator, loop_body, [n], parallel_iterations=1) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertEqual(3, self.evaluate(r)) result1 = self.evaluate(select1) self.assertAllClose(np.array([10.0, 10.0, 10.0]), result1) @@ -1942,8 +1943,8 @@ class ControlFlowTest(test.TestCase): loop_iterator, loop_body, [n, array_ops.identity(select)], parallel_iterations=1) - variables.global_variables_initializer().run() - result = r[1].eval() + self.evaluate(variables.global_variables_initializer()) + result = r[1] self.assertAllClose(np.array([10.0, 10.0, 10.0]), result) @test_util.disable_control_flow_v2("b/113324949 (RefVariable)") @@ -1952,7 +1953,7 @@ class ControlFlowTest(test.TestCase): with self.cached_session(): var_a = variables.Variable(0, name="a") var_b = variables.Variable(0, name="b") - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) c = constant_op.constant(0, name="c") asn1 = state_ops.assign_add(var_a, 1, name="a_add") @@ -1982,7 +1983,7 @@ class ControlFlowTest(test.TestCase): # Create some variables. var_a = variables.Variable(0, name="a") var_b = variables.Variable(0, name="b") - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) # Change condition to check var_b def pred(_): @@ -2014,7 +2015,7 @@ class ControlFlowTest(test.TestCase): var_a = variables.Variable(0, name="a") var_b = variables.Variable(0, name="b") c = constant_op.constant(0) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) # Loop condition def pred(i): @@ -2054,7 +2055,7 @@ class ControlFlowTest(test.TestCase): r = control_flow_ops.while_loop(c, b, [i], parallel_iterations=1) self.assertEqual([10], self.evaluate(r)) for i in xrange(10): - self.assertEqual([i], q.dequeue().eval()) + self.assertEqual([i], self.evaluate(q.dequeue())) @test_util.run_v1_only("b/120545219") def testWhileTimeOut(self): @@ -2272,8 +2273,8 @@ class ControlFlowTest(test.TestCase): r = control_flow_ops.while_loop(c, b, [v], parallel_iterations=1) r = gradients_impl.gradients(r, a) - variables.global_variables_initializer().run() - self.assertAllClose(216.0, r[0].eval()) + self.evaluate(variables.global_variables_initializer()) + self.assertAllClose(216.0, r[0]) @test_util.run_deprecated_v1 def testWhileGrad_ResourceVariable(self): @@ -2285,8 +2286,8 @@ class ControlFlowTest(test.TestCase): r = control_flow_ops.while_loop(c, b, [v], parallel_iterations=1) g = gradients_impl.gradients(r, a) - variables.global_variables_initializer().run() - self.assertAllClose(216.0, g[0].eval()) + self.evaluate(variables.global_variables_initializer()) + self.assertAllClose(216.0, g[0]) @test_util.run_v1_only("b/120545219") def testWhileGradInCond(self): @@ -2463,13 +2464,13 @@ class ControlFlowTest(test.TestCase): rx, ry = control_flow_ops.while_loop(c, b, [x, y], parallel_iterations=1) r = gradients_impl.gradients([rx, ry], x) - self.assertAllClose(304.0, r[0].eval()) + self.assertAllClose(304.0, r[0]) r = gradients_impl.gradients([rx, ry], y) - self.assertAllClose(124.0, r[0].eval()) + self.assertAllClose(124.0, r[0]) r = gradients_impl.gradients([rx], x) - self.assertAllClose(295.0, r[0].eval()) + self.assertAllClose(295.0, r[0]) r = gradients_impl.gradients([rx], y) - self.assertAllClose(120.0, r[0].eval()) + self.assertAllClose(120.0, r[0]) @test_util.run_deprecated_v1 def testWhileGrad_Dependency(self): @@ -2487,9 +2488,9 @@ class ControlFlowTest(test.TestCase): ri, rx = control_flow_ops.while_loop(c, b, [i, x], parallel_iterations=1) r = gradients_impl.gradients([ri, rx], x) - self.assertAllClose(1024.0, r[0].eval()) + self.assertAllClose(1024.0, r[0]) r = gradients_impl.gradients([rx], x) - self.assertAllClose(1024.0, r[0].eval()) + self.assertAllClose(1024.0, r[0]) @test_util.disable_control_flow_v2("b/116355153 (back_prop flag)") @test_util.run_v1_only("b/120545219") @@ -2501,7 +2502,7 @@ class ControlFlowTest(test.TestCase): r = control_flow_ops.while_loop(c, b, [v], back_prop=False) r = math_ops.add(r, v) r = gradients_impl.gradients(r, v) - self.assertAllClose(1.0, r[0].eval()) + self.assertAllClose(1.0, r[0]) @test_util.disable_control_flow_v2("b/113324949 (RefVariable)") @test_util.run_v1_only("b/120545219") @@ -2522,7 +2523,7 @@ class ControlFlowTest(test.TestCase): cond=cond, body=body, loop_vars=loop_vars) cost = math_ops.reduce_sum(tensors[2]) grad = gradients_impl.gradients(cost, [variable]) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllClose(np.ones([2, 3]), sess.run(grad[0])) @test_util.run_deprecated_v1 @@ -2562,7 +2563,7 @@ class ControlFlowTest(test.TestCase): _, rx = control_flow_ops.while_loop(c, b, [i, rx], parallel_iterations=1) r = gradients_impl.gradients([rx], x) - self.assertAllClose(1024.0, r[0].eval()) + self.assertAllClose(1024.0, r[0]) @test_util.run_v1_only("b/120545219") def testWhileGrad_ParallelTwoLoops(self): @@ -2582,7 +2583,7 @@ class ControlFlowTest(test.TestCase): rx = math_ops.add(r1, r2) r = gradients_impl.gradients([rx], x) - self.assertAllClose(64.0, r[0].eval()) + self.assertAllClose(64.0, r[0]) @test_util.run_v1_only("b/120545219") def testWhileGrad_OneOutputWithControlDependencyOnSecond(self): @@ -2697,7 +2698,7 @@ class ControlFlowTest(test.TestCase): train_op = optimizer.minimize(math_ops.reduce_mean(math_ops.square(res))) self.evaluate(variables.global_variables_initializer()) self.evaluate(train_op) - self.assertAllClose(2.999, self.evaluate(var)) + self.assertAllClose(2.999, var.read_value()) def _testWhileCondGrad_Simple(self, use_gpu): with self.cached_session(use_gpu=use_gpu): @@ -2784,7 +2785,7 @@ class ControlFlowTest(test.TestCase): grad_ys = [variables.VariableV1(73)._ref()] # pylint: disable=protected-access grad = gradients_impl.gradients([r[1]], [x], grad_ys=grad_ys) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertEqual(r[0].dtype, dtypes.int32) self.assertEqual(r[1].dtype, dtypes.float32_ref) @@ -3093,7 +3094,7 @@ class ControlFlowTest(test.TestCase): grads = linalg_ops.norm(gradients_impl.gradients(r, vars_)[0]) z = math_ops.add(r, array_ops.stop_gradient(math_ops.reduce_sum(grads))) result = gradients_impl.gradients(z, vars_)[0] - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertEqual(5.0, self.evaluate(result)) @test_util.run_v1_only("b/120545219") @@ -3145,14 +3146,14 @@ class ControlFlowTest(test.TestCase): x < y: f1, x > z: f2 }, default=f3, exclusive=True) - self.assertAllEqual(r1.eval(), 17) + self.assertAllEqual(r1, 17) r2 = control_flow_ops.case([(y > z, f1), (y > x, f2)], default=f3) - self.assertAllEqual(r2.eval(), 23) + self.assertAllEqual(r2, 23) # Duplicate events can happen, first one is selected r3 = control_flow_ops.case([(x < y, f1), (x < y, f2)], default=f3) - self.assertAllEqual(r3.eval(), 17) + self.assertAllEqual(r3, 17) # Duplicate events cause an error if exclusive = True r4 = control_flow_ops.case( @@ -3162,7 +3163,7 @@ class ControlFlowTest(test.TestCase): # Check that the default is called if none of the others are r5 = control_flow_ops.case({x > y: f1}, default=f3) - self.assertAllEqual(r5.eval(), -1) + self.assertAllEqual(r5, -1) ran_once = [False, False, False] @@ -3181,7 +3182,7 @@ class ControlFlowTest(test.TestCase): [(x < y, break_run_twice(0)), (x > y, break_run_twice(1))], default=lambda: constant_op.constant(2)) - self.assertAllEqual(r6.eval(), 0) + self.assertAllEqual(r6, 0) @test_util.run_v1_only("b/120545219") def testCaseSideEffects(self): @@ -3204,17 +3205,17 @@ class ControlFlowTest(test.TestCase): r2 = control_flow_ops.case( ((x > y, a), (x > y, b)), default=c, exclusive=True) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllEqual(self.evaluate([v0, v1, v2]), [-1] * 3) self.assertEqual(2, self.evaluate(r2)) self.assertAllEqual(self.evaluate([v0, v1, v2]), [-1, -1, 2]) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllEqual(self.evaluate([v0, v1, v2]), [-1] * 3) self.assertEqual(1, self.evaluate(r1)) self.assertAllEqual(self.evaluate([v0, v1, v2]), [-1, 1, -1]) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllEqual(self.evaluate([v0, v1, v2]), [-1] * 3) self.assertEqual(0, self.evaluate(r0)) self.assertAllEqual(self.evaluate([v0, v1, v2]), [0, -1, -1]) @@ -3237,7 +3238,7 @@ class ControlFlowTest(test.TestCase): i = control_flow_ops.cond(p, a, b) self.assertTrue(isinstance(i, ops.Tensor)) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertEqual(0, self.evaluate(v)) @@ -3495,7 +3496,7 @@ class ControlFlowTest(test.TestCase): lambda i, v: [i + 1, script_ops.py_func(func, [v], [dtypes.float32])[0]], [constant_op.constant(0), constant_op.constant(2.0, dtypes.float32)], [tensor_shape.unknown_shape(), tensor_shape.unknown_shape()]) - self.assertEqual(r[1].eval(), 65536.0) + self.assertEqual(self.evaluate(r[1]), 65536.0) @test_util.run_v1_only("b/120545219") def testWhileFuncBasic(self): @@ -3512,8 +3513,8 @@ class ControlFlowTest(test.TestCase): [tensor_shape.unknown_shape(), tensor_shape.unknown_shape()]) grad = gradients_impl.gradients(r, x)[0] - self.assertEqual(r[1].eval(), 65536.0) - self.assertEqual(grad.eval(), 524288.0) + self.assertEqual(self.evaluate(r[1]), 65536.0) + self.assertEqual(self.evaluate(grad), 524288.0) # while_v2 does not have stacks. if not control_flow_util.ENABLE_CONTROL_FLOW_V2: self.assertEqual( @@ -3877,7 +3878,7 @@ class WhileOpBenchmark(test.Benchmark): with session.Session() as sess, ops.device(default_device): # Get the initial id i, input x, and kernel. i, x, kernel = self._getInitVariables() - self.evaluate(variables.global_variables_initializer()) + variables.global_variables_initializer().run() if static_unroll: for _ in xrange(steps): diff --git a/tensorflow/python/kernel_tests/dense_update_ops_no_tsan_test.py b/tensorflow/python/kernel_tests/dense_update_ops_no_tsan_test.py index 4e3da068b8..a778bf231b 100644 --- a/tensorflow/python/kernel_tests/dense_update_ops_no_tsan_test.py +++ b/tensorflow/python/kernel_tests/dense_update_ops_no_tsan_test.py @@ -20,7 +20,6 @@ from __future__ import print_function import numpy as np -from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import state_ops @@ -33,7 +32,6 @@ class AssignOpTest(test.TestCase): # NOTE(mrry): We exclude thess tests from the TSAN TAP target, because they # contain benign and deliberate data races when multiple threads update # the same parameters without a lock. - @test_util.run_v1_only("b/120545219") def testParallelUpdateWithoutLocking(self): with self.cached_session() as sess: ones_t = array_ops.fill([1024, 1024], 1.0) @@ -42,7 +40,7 @@ class AssignOpTest(test.TestCase): state_ops.assign_add( p, ones_t, use_locking=False) for _ in range(20) ] - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) def run_add(add_op): self.evaluate(add_op) @@ -61,7 +59,6 @@ class AssignOpTest(test.TestCase): self.assertTrue((vals >= ones).all()) self.assertTrue((vals <= ones * 20).all()) - @test_util.run_v1_only("b/120545219") def testParallelAssignWithoutLocking(self): with self.cached_session() as sess: ones_t = array_ops.fill([1024, 1024], float(1)) @@ -70,7 +67,7 @@ class AssignOpTest(test.TestCase): state_ops.assign(p, math_ops.multiply(ones_t, float(i)), False) for i in range(1, 21) ] - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) def run_assign(assign_op): self.evaluate(assign_op) @@ -94,7 +91,6 @@ class AssignOpTest(test.TestCase): # contain non-benign but known data races between the variable assignment and # returning the output tensors. This issue will be resolved with the new # resource variables. - @test_util.run_v1_only("b/120545219") def testParallelUpdateWithLocking(self): with self.cached_session() as sess: zeros_t = array_ops.fill([1024, 1024], 0.0) @@ -104,7 +100,7 @@ class AssignOpTest(test.TestCase): state_ops.assign_add( p, ones_t, use_locking=True) for _ in range(20) ] - p.initializer.run() + self.evaluate(p.initializer) def run_add(add_op): self.evaluate(add_op) @@ -122,7 +118,6 @@ class AssignOpTest(test.TestCase): ones = np.ones((1024, 1024)).astype(np.float32) self.assertAllEqual(vals, ones * 20) - @test_util.run_v1_only("b/120545219") def testParallelAssignWithLocking(self): with self.cached_session() as sess: zeros_t = array_ops.fill([1024, 1024], 0.0) @@ -133,7 +128,7 @@ class AssignOpTest(test.TestCase): p, math_ops.multiply(ones_t, float(i)), use_locking=True) for i in range(1, 21) ] - p.initializer.run() + self.evaluate(p.initializer) def run_assign(assign_op): self.evaluate(assign_op) diff --git a/tensorflow/python/kernel_tests/functional_ops_test.py b/tensorflow/python/kernel_tests/functional_ops_test.py index 95ee454614..0d6a3cbd35 100644 --- a/tensorflow/python/kernel_tests/functional_ops_test.py +++ b/tensorflow/python/kernel_tests/functional_ops_test.py @@ -466,7 +466,7 @@ class FunctionalOpsTest(test.TestCase): loss = l0 + array_ops.stop_gradient(l1) grad = gradients_impl.gradients(ys=[loss], xs=[a, b]) with self.test_session(use_gpu=True) as sess: - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.evaluate(grad) @test_util.run_in_graph_and_eager_modes diff --git a/tensorflow/python/kernel_tests/partitioned_variables_test.py b/tensorflow/python/kernel_tests/partitioned_variables_test.py index da79b4ecfc..edcbc2967e 100644 --- a/tensorflow/python/kernel_tests/partitioned_variables_test.py +++ b/tensorflow/python/kernel_tests/partitioned_variables_test.py @@ -323,26 +323,24 @@ class PartitionedVariablesTestCase(test.TestCase): for i in xrange(len(expected_specs)): self.assertEquals(expected_specs[i], slices[i]._save_slice_info.spec) - @test_util.run_deprecated_v1 def testVecConstantInit(self): with self.cached_session(): rnd_par = constant_op.constant([1, 2, 3, 4]) vs = partitioned_variables.create_partitioned_variables([4], [4], rnd_par) - variables.global_variables_initializer().run() - val = array_ops.concat(vs, 0).eval() + self.evaluate(variables.global_variables_initializer()) + val = array_ops.concat(vs, 0) rnd = self.evaluate(rnd_par) self.assertAllClose(rnd, val) self.assertEqual([dtypes.int32] * 4, [v.dtype.base_dtype for v in vs]) self._TestSaveSpec(vs, ["4 0,1", "4 1,1", "4 2,1", "4 3,1"]) - @test_util.run_deprecated_v1 def testConstantInit(self): with self.cached_session(): rnd_par = constant_op.constant([[1, 2, 3, 4], [5, 6, 7, 8]]) vs = partitioned_variables.create_partitioned_variables([2, 4], [1, 2], rnd_par) - variables.global_variables_initializer().run() - val = array_ops.concat(vs, 1).eval() + self.evaluate(variables.global_variables_initializer()) + val = array_ops.concat(vs, 1) rnd = self.evaluate(rnd_par) self.assertAllClose(rnd, val) self.assertEqual([dtypes.int32] * 2, [v.dtype.base_dtype for v in vs]) @@ -356,7 +354,7 @@ class PartitionedVariablesTestCase(test.TestCase): rnd_par) vs2 = partitioned_variables.create_partitioned_variables([2, 4], [1, 2], rnd_par) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) var1_name = vs1[0]._save_slice_info.full_name var2_name = vs2[0]._save_slice_info.full_name self.assertEqual("hi/PartitionedVariable", var1_name) @@ -376,7 +374,7 @@ class PartitionedVariablesTestCase(test.TestCase): vs, reuse=True, use_resource=use_resource): vs2 = partitioned_variables.create_partitioned_variables( [2, 4], [1, 2], rnd_par, dtype=dtypes.int32) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) var1_name = vs1[0]._save_slice_info.full_name var2_name = vs2[0]._save_slice_info.full_name self.assertEqual("hola/PartitionedVariable", var1_name) @@ -393,7 +391,7 @@ class PartitionedVariablesTestCase(test.TestCase): rnd_par) vs2 = partitioned_variables.create_partitioned_variables([2, 4], [1, 2], rnd_par) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) var1_name = vs1[0]._save_slice_info.full_name var2_name = vs2[0]._save_slice_info.full_name # Currently, the name scope 'ola' has no effect. @@ -408,18 +406,16 @@ class PartitionedVariablesTestCase(test.TestCase): def testName(self): self._testNameHelper(use_resource=False) - @test_util.run_deprecated_v1 def testResourceName(self): self._testNameHelper(use_resource=True) - @test_util.run_v1_only("b/120545219") def testRandomInitValue(self): with self.cached_session(): rnd = variables.Variable(random_ops.random_uniform([200, 40])) vs = partitioned_variables.create_partitioned_variables( rnd.get_shape(), [1, 10], rnd.initialized_value()) - variables.global_variables_initializer().run() - val = array_ops.concat(vs, 1).eval() + self.evaluate(variables.global_variables_initializer()) + val = array_ops.concat(vs, 1) rnd = self.evaluate(rnd) self.assertAllClose(rnd, val) self.assertEqual([dtypes.float32] * 10, [v.dtype.base_dtype for v in vs]) @@ -430,7 +426,6 @@ class PartitionedVariablesTestCase(test.TestCase): "200 40 0,200:36,4" ]) - @test_util.run_v1_only("b/120545219") def testRandomInitUnevenPartitions(self): with self.cached_session(): rnd = variables.Variable( @@ -440,7 +435,7 @@ class PartitionedVariablesTestCase(test.TestCase): rnd.get_shape(), [1, i], rnd.initialized_value()) for i in xrange(1, 10) ] - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) rnd_val = self.evaluate(rnd) # Only check the slice save specs for the first 5 tf. save_specs = [ @@ -462,33 +457,31 @@ class PartitionedVariablesTestCase(test.TestCase): ] ] for i, vs in enumerate(var_lists): - var_val = array_ops.concat(vs, 1).eval() + var_val = array_ops.concat(vs, 1) self.assertAllClose(rnd_val, var_val) self.assertEqual([dtypes.float64] * len(vs), [v.dtype.base_dtype for v in vs]) if i < len(save_specs): self._TestSaveSpec(vs, save_specs[i]) - @test_util.run_v1_only("b/120545219") def testDegenerate(self): with self.cached_session(): rnd = variables.Variable(random_ops.random_uniform([10, 43])) vs = partitioned_variables.create_partitioned_variables( rnd.get_shape(), [1, 1], rnd.initialized_value()) - variables.global_variables_initializer().run() - val = array_ops.concat(vs, 0).eval() + self.evaluate(variables.global_variables_initializer()) + val = array_ops.concat(vs, 0) rnd = self.evaluate(rnd) self.assertAllClose(rnd, val) self._TestSaveSpec(vs, ["10 43 0,10:0,43"]) - @test_util.run_v1_only("b/120545219") def testSliceSizeOne(self): with self.cached_session(): rnd = variables.Variable(random_ops.random_uniform([10, 43])) vs = partitioned_variables.create_partitioned_variables( rnd.get_shape(), [10, 1], rnd.initialized_value()) - variables.global_variables_initializer().run() - val = array_ops.concat(vs, 0).eval() + self.evaluate(variables.global_variables_initializer()) + val = array_ops.concat(vs, 0) rnd = self.evaluate(rnd) self.assertAllClose(rnd, val) self._TestSaveSpec(vs, [ @@ -497,7 +490,6 @@ class PartitionedVariablesTestCase(test.TestCase): "10 43 6,1:0,43", "10 43 7,1:0,43", "10 43 8,1:0,43", "10 43 9,1:0,43" ]) - @test_util.run_deprecated_v1 def testIotaInitializer(self): self.assertAllClose([0., 1., 2., 3.], _IotaInitializer([4])) self.assertAllClose([[0., 1.], [0., 10.], [0., 100.], [0., 1000.]], @@ -505,11 +497,11 @@ class PartitionedVariablesTestCase(test.TestCase): with self.cached_session(): vs = partitioned_variables.create_partitioned_variables([13, 5], [3, 1], _IotaInitializer) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) slice0 = _IotaInitializer([5, 5]) slice1 = _IotaInitializer([4, 5]) slice2 = _IotaInitializer([4, 5]) - val = array_ops.concat(vs, 0).eval() + val = array_ops.concat(vs, 0) self.assertAllClose(slice0 + slice1 + slice2, val) self._TestSaveSpec(vs, ["13 5 0,5:0,5", "13 5 5,4:0,5", "13 5 9,4:0,5"]) @@ -520,7 +512,7 @@ class PartitionedVariablesTestCase(test.TestCase): with self.cached_session(): var0, var1 = partitioned_variables.create_partitioned_variables( [20, 12], [1, 2], init_ops.random_uniform_initializer()) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) val0, val1 = self.evaluate(var0).flatten(), self.evaluate(var1).flatten() self.assertTrue(np.linalg.norm(val0 - val1) > 1e-6) # Negative test that proves that slices have the same values if @@ -528,7 +520,7 @@ class PartitionedVariablesTestCase(test.TestCase): with self.cached_session(): var0, var1 = partitioned_variables.create_partitioned_variables( [20, 12], [1, 2], init_ops.random_uniform_initializer(seed=201)) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) val0, val1 = self.evaluate(var0).flatten(), self.evaluate(var1).flatten() self.assertAllClose(val0, val1) @@ -607,8 +599,8 @@ class PartitionedVariablesTestCase(test.TestCase): self.assertTrue( c.op in concat_control_inputs, "var_x._concat() should get control dependencies from its scope.") - variables.global_variables_initializer().run() - self.assertAllClose(value.eval(), var_x.as_tensor().eval()) + self.evaluate(variables.global_variables_initializer()) + self.assertAllClose(value, var_x.as_tensor()) def testMetaGraphSaveLoad(self): save_prefix = os.path.join(self.get_temp_dir(), "ckpt") @@ -623,7 +615,7 @@ class PartitionedVariablesTestCase(test.TestCase): v0_part = v0._get_partitions() self.assertEqual(len(v0_list), 5) self.assertAllEqual(v0_part, (5, 1)) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) save_graph.get_collection_ref("partvar").append(v0) saver = saver_lib.Saver() diff --git a/tensorflow/python/kernel_tests/resource_variable_ops_test.py b/tensorflow/python/kernel_tests/resource_variable_ops_test.py index 1dabcbb5c3..df7b686165 100644 --- a/tensorflow/python/kernel_tests/resource_variable_ops_test.py +++ b/tensorflow/python/kernel_tests/resource_variable_ops_test.py @@ -689,7 +689,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): def testToFromProto(self): with self.cached_session(): v = resource_variable_ops.ResourceVariable(1.0) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) w = resource_variable_ops.ResourceVariable.from_proto(v.to_proto()) self.assertEquals(2, math_ops.add(w, 1).eval()) @@ -793,11 +793,11 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): with self.assertRaises(ValueError): _ = w.value().op.get_attr("_class") - @test_util.run_v1_only("b/120545219") + @test_util.run_deprecated_v1 def testSharedName(self): with self.cached_session(): v = resource_variable_ops.ResourceVariable(300.0, name="var4") - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) w = resource_variable_ops.var_handle_op( dtype=v.dtype.base_dtype, shape=v.get_shape(), shared_name="var4", diff --git a/tensorflow/python/kernel_tests/tensor_array_ops_test.py b/tensorflow/python/kernel_tests/tensor_array_ops_test.py index 6d8e3e8356..147e7fde57 100644 --- a/tensorflow/python/kernel_tests/tensor_array_ops_test.py +++ b/tensorflow/python/kernel_tests/tensor_array_ops_test.py @@ -425,7 +425,6 @@ class TensorArrayTest(test.TestCase): self.assertAllEqual(t_g_ta_0, t_g_ta_1) self.assertAllEqual([[4.0, 5.0]], d_r1_0) - @test_util.run_v1_only("b/120545219") def testTensorArrayWriteWrongIndexOrDataTypeFails(self): with self.session(use_gpu=True): ta = _make_ta(3, "foo", dtype=dtypes.float32) @@ -459,7 +458,6 @@ class TensorArrayTest(test.TestCase): with self.assertRaisesOpError(error_msg): self.evaluate(ta.write(3, 3.0).flow) - @test_util.run_v1_only("b/120545219") def testTensorArrayReadWrongIndexOrDataTypeFails(self): with self.session(use_gpu=True): ta = _make_ta(3, "foo", dtype=dtypes.float32) @@ -505,7 +503,6 @@ class TensorArrayTest(test.TestCase): "it has already been written to."): self.evaluate(ta.write(2, 3.0).write(2, 3.0).flow) - @test_util.run_v1_only("b/120545219") def testTensorArrayConcatIncompatibleShapesFails(self): with self.session(use_gpu=True): ta = tensor_array_ops.TensorArray( @@ -537,7 +534,6 @@ class TensorArrayTest(test.TestCase): with self.assertRaisesOpError("shape"): self.evaluate(w3.concat()) - @test_util.run_v1_only("b/120545219") def testTensorArraySplitIncompatibleShapesFails(self): with self.session(use_gpu=True): in_eager_mode = context.executing_eagerly() @@ -959,7 +955,7 @@ class TensorArrayTest(test.TestCase): v0_grad = gradients_impl.gradients([vout], [v0], [grad_val])[0] state0_grad = gradients_impl.gradients([vout], [state0], [grad_val])[0] var_grad = gradients_impl.gradients([vout], [var], [grad_val])[0] - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) state0_t, var_t, v0_t, vout_t, v0_grad_t, var_grad_t, state0_grad_t = ( self.evaluate( @@ -1578,7 +1574,7 @@ class TensorArrayTest(test.TestCase): self.assertEqual(tensor_shape.scalar(), read1.get_shape()) if not context.executing_eagerly(): - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) read0_v, read1_v, size0_v, size1_v = self.evaluate((read0, read1, size0, size1)) diff --git a/tensorflow/python/kernel_tests/variables_test.py b/tensorflow/python/kernel_tests/variables_test.py index 336e9b0bca..07807e89d0 100644 --- a/tensorflow/python/kernel_tests/variables_test.py +++ b/tensorflow/python/kernel_tests/variables_test.py @@ -66,7 +66,7 @@ class VariablesTestCase(test.TestCase): with self.assertRaisesOpError("Attempting to use uninitialized value"): self.evaluate(var1) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllClose(0.0, self.evaluate(var0)) self.assertAllClose(1.1, self.evaluate(var1)) @@ -96,11 +96,11 @@ class VariablesTestCase(test.TestCase): self.assertEqual([3, 6], depdep.get_shape()) self.assertEqual([3, 6], depdep.shape) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) - self.assertAllClose(rnd.eval(), self.evaluate(dep)) - self.assertAllClose(rnd.eval() + self.evaluate(dep) + 2.0, - self.evaluate(depdep)) + self.assertAllClose(self.evaluate(rnd), self.evaluate(dep)) + self.assertAllClose( + self.evaluate(rnd) + self.evaluate(dep) + 2.0, self.evaluate(depdep)) def testIterable(self): with self.assertRaisesRegexp(TypeError, "not iterable"): @@ -117,7 +117,7 @@ class VariablesTestCase(test.TestCase): plus_one = var.assign_add(1.0) minus_one = var.assign_sub(2.0) four = var.assign(4.0) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllClose(0.0, self.evaluate(var)) self.assertAllClose(1.0, self.evaluate(plus_one)) @@ -136,7 +136,7 @@ class VariablesTestCase(test.TestCase): plus_one = var.assign_add(1.0) minus_one = var.assign_sub(2.0) four = var.assign(4.0) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllClose(0.0, self.evaluate(var)) self.evaluate(plus_one) @@ -166,7 +166,7 @@ class VariablesTestCase(test.TestCase): var = variables.Variable(zero) count_up_to = var.count_up_to(3) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertEqual(0, self.evaluate(var)) self.assertEqual(0, self.evaluate(count_up_to)) @@ -264,10 +264,10 @@ class VariablesTestCase(test.TestCase): with self.cached_session(): var_x = variables.Variable(2.0) var_y = variables.Variable(3.0) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllClose(2.0, self.evaluate(var_x)) self.assertAllClose(3.0, self.evaluate(var_y)) - self.assertAllClose(5.0, math_ops.add(var_x, var_y).eval()) + self.assertAllClose(5.0, self.evaluate(math_ops.add(var_x, var_y))) @test_util.run_deprecated_v1 def testZeroSizeVarSameAsConst(self): @@ -277,9 +277,9 @@ class VariablesTestCase(test.TestCase): variable_mul = math_ops.matmul(zero_size_const, zero_size_var) const_mul = math_ops.matmul( zero_size_const, zero_size_const, transpose_b=True) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) variable_output = self.evaluate(variable_mul) - self.assertAllClose(const_mul.eval(), variable_output) + self.assertAllClose(self.evaluate(const_mul), variable_output) self.assertAllClose([[0., 0.], [0., 0.]], variable_output) @test_util.run_deprecated_v1 @@ -372,7 +372,7 @@ class VariablesTestCase(test.TestCase): matmul = var_m.__matmul__([[10.0], [20.0]]) rmatmul = var_m.__rmatmul__([[10.0], [20.0]]) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllClose([2.0], self.evaluate(add)) self.assertAllClose([3.0], self.evaluate(radd)) self.assertAllClose([1.0], self.evaluate(sub)) @@ -409,7 +409,7 @@ class VariablesTestCase(test.TestCase): def testSession(self): with self.cached_session() as sess: var = variables.Variable([1, 12]) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllClose([1, 12], self.evaluate(var)) @test_util.run_v1_only("b/120545219") @@ -431,7 +431,7 @@ class VariablesTestCase(test.TestCase): v1 = variables.Variable(initializer, dtype=dtypes.float32) self.assertEqual(shape, v1.get_shape()) self.assertEqual(shape, v1.shape) - self.assertAllClose(value, v1.initial_value.eval()) + self.assertAllClose(value, self.evaluate(v1.initial_value)) with self.assertRaises(errors_impl.FailedPreconditionError): self.evaluate(v1) @@ -439,11 +439,11 @@ class VariablesTestCase(test.TestCase): math_ops.negative(v1.initialized_value()), dtype=dtypes.float32) self.assertEqual(v1.get_shape(), v2.get_shape()) self.assertEqual(v1.shape, v2.shape) - self.assertAllClose(np.negative(value), v2.initial_value.eval()) + self.assertAllClose(np.negative(value), self.evaluate(v2.initial_value)) with self.assertRaises(errors_impl.FailedPreconditionError): self.evaluate(v2) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllClose(np.negative(value), self.evaluate(v2)) def testConstraintArg(self): @@ -465,10 +465,10 @@ class VariablesTestCase(test.TestCase): a = variables.Variable([1, 2, 3], dtype=dtypes.float32) b = variables.Variable(a.initialized_value() + 2) c = variables.Variable(b.initialized_value() + 2) - variables.global_variables_initializer().run() - self.assertAllEqual(a.eval(), [1, 2, 3]) - self.assertAllEqual(b.eval(), [3, 4, 5]) - self.assertAllEqual(c.eval(), [5, 6, 7]) + self.evaluate(variables.global_variables_initializer()) + self.assertAllEqual(self.evaluate(a), [1, 2, 3]) + self.assertAllEqual(self.evaluate(b), [3, 4, 5]) + self.assertAllEqual(self.evaluate(c), [5, 6, 7]) @test_util.run_deprecated_v1 def testInitializerFunctionDevicePlacement(self): @@ -503,7 +503,7 @@ class VariablesTestCase(test.TestCase): # initialized_value should not rerun the initializer_op if the variable # has already been initialized elsewhere. self.evaluate(v.assign(1.0)) - self.assertEqual(1.0, v.initialized_value().eval()) + self.assertEqual(1.0, self.evaluate(v.initialized_value())) v_def.ClearField("initial_value_name") with ops.Graph().as_default(), self.cached_session() as sess: @@ -537,7 +537,7 @@ class VariablesTestCase(test.TestCase): def testLoad(self): with self.cached_session(): var = variables.Variable(np.zeros((5, 5), np.float32)) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) var.load(np.ones((5, 5), np.float32)) self.assertAllClose(np.ones((5, 5), np.float32), self.evaluate(var)) @@ -573,7 +573,7 @@ class IsInitializedTest(test.TestCase): _ = v, w uninited = variables.report_uninitialized_variables() self.assertAllEqual(np.array([b"v", b"w"]), self.evaluate(uninited)) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertEqual(0, self.evaluate(uninited).size) @test_util.run_v1_only("b/120545219") @@ -601,20 +601,20 @@ class IsInitializedTest(test.TestCase): b = variables.Variable(array_ops.ones([2, 2])) objective = math_ops.reduce_sum(b + math_ops.matmul( a, a, transpose_a=True)) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) do_opt = gradient_descent.GradientDescentOptimizer(0.1).minimize( objective) self.evaluate([do_opt]) self.assertAllClose([[0.9, 0.9], [0.9, 0.9]], self.evaluate(b)) +@test_util.run_v1_only("b/120545219") class ObsoleteIsInitializedTest(test.TestCase): def testNoVars(self): with ops.Graph().as_default(): self.assertEqual(None, variables.assert_variables_initialized()) - @test_util.run_v1_only("b/120545219") def testVariables(self): with ops.Graph().as_default(), self.cached_session() as sess: v = variables.VariableV1([1, 2]) @@ -623,10 +623,9 @@ class ObsoleteIsInitializedTest(test.TestCase): inited = variables.assert_variables_initialized() with self.assertRaisesOpError("Attempting to use uninitialized value"): self.evaluate(inited) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.evaluate(inited) - @test_util.run_v1_only("b/120545219") def testVariableList(self): with ops.Graph().as_default(), self.cached_session() as sess: v = variables.VariableV1([1, 2]) @@ -766,36 +765,36 @@ class PartitionedVariableTest(test.TestCase): assign_list = pv_1.assign([c_0, c_1]) assign_part_value = pv_1.assign_add(assign_ones) assign_part_var = pv_1.assign_sub(pv_0) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) - self.assertEqual([1.0], plus_delta[0].eval()) + self.assertEqual([1.0], self.evaluate(plus_delta[0])) self.assertEqual([1.0], self.evaluate(v0)) - self.assertEqual([3.0], plus_delta[1].eval()) + self.assertEqual([3.0], self.evaluate(plus_delta[1])) self.assertEqual([3.0], self.evaluate(v1)) - self.assertEqual([-2.0], minus_delta[0].eval()) + self.assertEqual([-2.0], self.evaluate(minus_delta[0])) self.assertEqual([-2.0], self.evaluate(v0)) - self.assertEqual([-1.0], minus_delta[1].eval()) + self.assertEqual([-1.0], self.evaluate(minus_delta[1])) self.assertEqual([-1.0], self.evaluate(v1)) - self.assertEqual([1.0], assign_ones[0].eval()) + self.assertEqual([1.0], self.evaluate(assign_ones[0])) self.assertEqual([1.0], self.evaluate(v0)) - self.assertEqual([1.0], assign_ones[1].eval()) + self.assertEqual([1.0], self.evaluate(assign_ones[1])) self.assertEqual([1.0], self.evaluate(v1)) - self.assertEqual([2.0], assign_list[0].eval()) + self.assertEqual([2.0], self.evaluate(assign_list[0])) self.assertEqual([2.0], self.evaluate(v2)) - self.assertEqual([3.0], assign_list[1].eval()) + self.assertEqual([3.0], self.evaluate(assign_list[1])) self.assertEqual([3.0], self.evaluate(v3)) - self.assertEqual([3.0], assign_part_value[0].eval()) + self.assertEqual([3.0], self.evaluate(assign_part_value[0])) self.assertEqual([3.0], self.evaluate(v2)) - self.assertEqual([4.0], assign_part_value[1].eval()) + self.assertEqual([4.0], self.evaluate(assign_part_value[1])) self.assertEqual([4.0], self.evaluate(v3)) - self.assertEqual([2.0], assign_part_var[0].eval()) + self.assertEqual([2.0], self.evaluate(assign_part_var[0])) self.assertEqual([2.0], self.evaluate(v2)) - self.assertEqual([3.0], assign_part_var[1].eval()) + self.assertEqual([3.0], self.evaluate(assign_part_var[1])) self.assertEqual([3.0], self.evaluate(v3)) diff --git a/tensorflow/python/ops/control_flow_ops_test.py b/tensorflow/python/ops/control_flow_ops_test.py index 0c18b7208f..f1dd4f529f 100644 --- a/tensorflow/python/ops/control_flow_ops_test.py +++ b/tensorflow/python/ops/control_flow_ops_test.py @@ -565,7 +565,7 @@ class DataTypesTest(test_util.TensorFlowTestCase): strict=strict) with self.cached_session() as sess: - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) true_feed_dict = {condition: True} true_feed_dict.update(feed_dict) result_cond, result_case = sess.run([output_cond, output_case], diff --git a/tensorflow/python/ops/gradients_test.py b/tensorflow/python/ops/gradients_test.py index abdcbc7a3a..c53afef63b 100644 --- a/tensorflow/python/ops/gradients_test.py +++ b/tensorflow/python/ops/gradients_test.py @@ -1027,7 +1027,7 @@ class CustomGradientTest(test_util.TensorFlowTestCase): conditional, lambda: alpha * 2, lambda: alpha * 3) g, = gradients_impl.gradients(output, alpha) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllEqual(g.eval(), [2.0]) self.assertAllEqual(g.eval(feed_dict={conditional: False}), [3.0]) diff --git a/tensorflow/python/saved_model/saved_model_test.py b/tensorflow/python/saved_model/saved_model_test.py index 8d94c7c989..e36b8b30bf 100644 --- a/tensorflow/python/saved_model/saved_model_test.py +++ b/tensorflow/python/saved_model/saved_model_test.py @@ -1084,7 +1084,7 @@ class SavedModelTest(SavedModelTestBase): # CheckpointedOp is a key-value table that can be saved across sessions. # The table register itself in SAVEABLE_OBJECTS collection. v1 = saver_test_utils.CheckpointedOp(name="v1") - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) v1.insert("k1", 3.0).run() # Once the table is restored, we can access it through this reference. ops.add_to_collection("table_ref", v1.table_ref) diff --git a/tensorflow/python/training/adagrad_test.py b/tensorflow/python/training/adagrad_test.py index 1e2d29b337..3528fdaa8b 100644 --- a/tensorflow/python/training/adagrad_test.py +++ b/tensorflow/python/training/adagrad_test.py @@ -106,7 +106,7 @@ class AdagradOptimizerTest(test.TestCase): pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) loss = pred * pred sgd_op = adagrad.AdagradOptimizer(1.0).minimize(loss) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) # Fetch params to validate initial values self.assertAllCloseAccordingToType([[1.0, 2.0], [3.0, 4.0]], self.evaluate(var0)) @@ -129,7 +129,7 @@ class AdagradOptimizerTest(test.TestCase): constant_op.constant(3.0), initial_accumulator_value=0.1) ada_update = ada_opt.apply_gradients( zip([grads0, grads1], [var0, var1])) - variables.global_variables_initializer().run() + 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)) @@ -163,7 +163,7 @@ class AdagradOptimizerTest(test.TestCase): ada_opt = adagrad.AdagradOptimizer(3.0, initial_accumulator_value=0.1) ada_update = ada_opt.apply_gradients( zip([grads0, grads1], [var0, var1])) - variables.global_variables_initializer().run() + 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)) @@ -198,7 +198,7 @@ class AdagradOptimizerTest(test.TestCase): [(grad_repeated_index, repeated_index_update_var)]) aggregated_update = adagrad.AdagradOptimizer(3.0).apply_gradients( [(grad_aggregated, aggregated_update_var)]) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllClose(aggregated_update_var.eval(), self.evaluate(repeated_index_update_var)) for _ in range(3): @@ -223,7 +223,7 @@ class AdagradOptimizerTest(test.TestCase): 2.0).minimize(loss_repeated) update_op_aggregated = adagrad.AdagradOptimizer( 2.0).minimize(loss_aggregated) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllCloseAccordingToType( self.evaluate(var_repeated), self.evaluate(var_aggregated)) for _ in range(3): @@ -289,7 +289,7 @@ class AdagradOptimizerTest(test.TestCase): self.assertEquals(slot0.get_shape(), var0.get_shape()) slot1 = ada_opt.get_slot(var1, "accumulator") self.assertEquals(slot1.get_shape(), var1.get_shape()) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) # Fetch params to validate initial values. self.assertAllClose([1.0, 2.0], self.evaluate(var0)) diff --git a/tensorflow/python/training/checkpoint_ops_test.py b/tensorflow/python/training/checkpoint_ops_test.py index c481547139..a0fd2dc6ba 100644 --- a/tensorflow/python/training/checkpoint_ops_test.py +++ b/tensorflow/python/training/checkpoint_ops_test.py @@ -154,7 +154,7 @@ class LoadAndRemapWrappersTest(test.TestCase): partitioner=partitioned_variables.fixed_size_partitioner(2)) with self.cached_session(): - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllClose(expected_remapped_matrix, remapped_matrix.as_tensor().eval()) @@ -188,7 +188,7 @@ class LoadAndRemapWrappersTest(test.TestCase): partitioner=partitioned_variables.fixed_size_partitioner(2)) with self.cached_session(): - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllClose(expected_remapped_matrix, remapped_matrix.as_tensor().eval()) @@ -226,7 +226,7 @@ class LoadAndRemapWrappersTest(test.TestCase): partitioner=partitioned_variables.fixed_size_partitioner(2)) with self.cached_session(): - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllClose(expected_remapped_matrix, remapped_matrix.as_tensor().eval()) @@ -262,7 +262,7 @@ class LoadAndRemapWrappersTest(test.TestCase): partitioner=partitioned_variables.fixed_size_partitioner(2)) with self.cached_session(): - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllClose(expected_remapped_matrix, remapped_matrix.as_tensor().eval()) @@ -296,7 +296,7 @@ class LoadAndRemapWrappersTest(test.TestCase): partitioner=partitioned_variables.fixed_size_partitioner(2)) with self.cached_session(): - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllClose(expected_remapped_embeddings, remapped_embeddings.as_tensor().eval()) @@ -342,7 +342,7 @@ class LoadAndRemapWrappersTest(test.TestCase): partitioner=partitioned_variables.fixed_size_partitioner(2)) with self.cached_session(): - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllClose(expected_remapped_embeddings, remapped_embeddings.as_tensor().eval()) @@ -380,7 +380,7 @@ class LoadAndRemapWrappersTest(test.TestCase): partitioner=partitioned_variables.fixed_size_partitioner(2)) with self.cached_session(): - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllClose(expected_remapped_embeddings, remapped_embeddings.as_tensor().eval()) diff --git a/tensorflow/python/training/input_test.py b/tensorflow/python/training/input_test.py index d89f5f3bbd..5efc15d56f 100644 --- a/tensorflow/python/training/input_test.py +++ b/tensorflow/python/training/input_test.py @@ -58,7 +58,7 @@ class MatchFilenamesOnceTest(test_lib.TestCase): question = inp.match_filenames_once( os.path.join(self.get_temp_dir(), "match_filenames.?")) one = inp.match_filenames_once(additional[1]) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) variables.local_variables_initializer().run() self.assertItemsEqual( map(compat.as_bytes, filenames), self.evaluate(star)) @@ -84,7 +84,7 @@ class LimitEpochsTest(test_lib.TestCase): with self.cached_session(): love_me = constant_op.constant("Love Me") love_me_two_times = inp.limit_epochs(love_me, num_epochs=2) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) variables.local_variables_initializer().run() self.assertEqual(b"Love Me", self.evaluate(love_me_two_times)) self.assertEqual(b"Love Me", self.evaluate(love_me_two_times)) @@ -105,7 +105,7 @@ class InputProducerTest(test_lib.TestCase): input_tensor, num_epochs=num_epochs, shuffle=False) dequeue_many = queue.dequeue_many(len(input_tensor) * num_epochs) dequeue = queue.dequeue() - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) variables.local_variables_initializer().run() threads = queue_runner_impl.start_queue_runners() @@ -132,7 +132,7 @@ class InputProducerTest(test_lib.TestCase): input_tensor, element_shape=[4], num_epochs=num_epochs, shuffle=False) dequeue_many = queue.dequeue_many(len(input_value) * num_epochs) dequeue = queue.dequeue() - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) variables.local_variables_initializer().run() threads = queue_runner_impl.start_queue_runners() @@ -163,7 +163,7 @@ class StringInputProducerTest(test_lib.TestCase): strings, num_epochs=num_epochs, shuffle=False) dequeue_many = queue.dequeue_many(len(strings) * num_epochs) dequeue = queue.dequeue() - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) variables.local_variables_initializer().run() threads = queue_runner_impl.start_queue_runners() @@ -186,7 +186,7 @@ class StringInputProducerTest(test_lib.TestCase): strings, num_epochs=num_epochs, shuffle=True, seed=271828) dequeue_many = queue.dequeue_many(len(strings)) dequeue = queue.dequeue() - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) variables.local_variables_initializer().run() threads = queue_runner_impl.start_queue_runners() @@ -234,7 +234,7 @@ class StringInputProducerTest(test_lib.TestCase): constant_op.constant( [], dtype=dtypes.string)) dequeue = queue.dequeue() - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) variables.local_variables_initializer().run() threads = queue_runner_impl.start_queue_runners(coord=coord) with self.assertRaises(errors_impl.OutOfRangeError): @@ -284,7 +284,7 @@ class RangeInputProducerTest(test_lib.TestCase): range_size, num_epochs=num_epochs, shuffle=False) dequeue_many = queue.dequeue_many(range_size * num_epochs) dequeue = queue.dequeue() - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) variables.local_variables_initializer().run() threads = queue_runner_impl.start_queue_runners() @@ -307,7 +307,7 @@ class RangeInputProducerTest(test_lib.TestCase): range_size, num_epochs=num_epochs, shuffle=True, seed=314159) dequeue_many = queue.dequeue_many(range_size) dequeue = queue.dequeue() - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) variables.local_variables_initializer().run() threads = queue_runner_impl.start_queue_runners() @@ -358,7 +358,7 @@ class SliceInputProducerTest(test_lib.TestCase): source_ints = [2, 3, 5, 7] slices = inp.slice_input_producer( [source_strings, source_ints], num_epochs=num_epochs, shuffle=False) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) variables.local_variables_initializer().run() threads = queue_runner_impl.start_queue_runners() @@ -386,7 +386,7 @@ class SliceInputProducerTest(test_lib.TestCase): num_epochs=num_epochs, shuffle=True, seed=161803) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) variables.local_variables_initializer().run() threads = queue_runner_impl.start_queue_runners() @@ -487,7 +487,7 @@ class BatchTest(test_lib.TestCase): batched = inp.batch( [counter, sparse_counter, "string"], batch_size=batch_size) batched_fetch = batched - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) variables.local_variables_initializer().run() threads = queue_runner_impl.start_queue_runners() @@ -555,7 +555,7 @@ class BatchTest(test_lib.TestCase): counter = examples.count_up_to(num_batches * batch_size) string = array_ops.tile(["string"], math_ops.to_int32(array_ops.stack([counter]))) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) variables.local_variables_initializer().run() batched = inp.batch( [counter, string], batch_size=batch_size, dynamic_pad=True) @@ -590,7 +590,7 @@ class BatchTest(test_lib.TestCase): dense_shape=[1]) pre_batched = inp.batch([counter, sparse_counter, "string"], batch_size=2) batched = inp.batch(pre_batched, enqueue_many=True, batch_size=batch_size) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) variables.local_variables_initializer().run() threads = queue_runner_impl.start_queue_runners() @@ -629,7 +629,7 @@ class BatchTest(test_lib.TestCase): [counter, sparse_counter, "string"], batch_size=batch_size, num_threads=4) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) variables.local_variables_initializer().run() threads = queue_runner_impl.start_queue_runners() @@ -672,7 +672,7 @@ class BatchTest(test_lib.TestCase): [counter, sparse_counter, "string"], batch_size=batch_size, allow_smaller_final_batch=True) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) variables.local_variables_initializer().run() threads = queue_runner_impl.start_queue_runners() @@ -730,7 +730,7 @@ class BatchTest(test_lib.TestCase): batch_size=batch_size, num_threads=4, allow_smaller_final_batch=True) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) variables.local_variables_initializer().run() threads = queue_runner_impl.start_queue_runners() @@ -1058,7 +1058,7 @@ class BatchJoinTest(test_lib.TestCase): batched_fetch[1].dense_shape.get_shape().as_list()) self.assertAllEqual((batch_size,), batched_fetch[2].get_shape().as_list()) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) variables.local_variables_initializer().run() threads = queue_runner_impl.start_queue_runners() @@ -1157,7 +1157,7 @@ class BatchJoinTest(test_lib.TestCase): self.assertAllEqual((batch_size,), batched[0].get_shape().as_list()) self.assertAllEqual((batch_size, None), batched[1].get_shape().as_list()) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) variables.local_variables_initializer().run() threads = queue_runner_impl.start_queue_runners() @@ -1244,7 +1244,7 @@ class BatchJoinTest(test_lib.TestCase): self.assertAllEqual((2,), batched[1].dense_shape.get_shape().as_list()) self.assertAllEqual((None,), batched[2].get_shape().as_list()) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) variables.local_variables_initializer().run() threads = queue_runner_impl.start_queue_runners() @@ -1339,7 +1339,7 @@ class BatchJoinTest(test_lib.TestCase): self.assertAllEqual((None,), batched[0].get_shape().as_list()) self.assertAllEqual((None, None), batched[1].get_shape().as_list()) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) variables.local_variables_initializer().run() threads = queue_runner_impl.start_queue_runners() @@ -1644,7 +1644,7 @@ class ShuffleBatchTest(test_lib.TestCase): min_after_dequeue=16, seed=141421) batched_fetch = batched - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) variables.local_variables_initializer().run() threads = queue_runner_impl.start_queue_runners() @@ -1702,7 +1702,7 @@ class ShuffleBatchTest(test_lib.TestCase): seed=141421, allow_smaller_final_batch=True) batched_fetch = batched - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) variables.local_variables_initializer().run() threads = queue_runner_impl.start_queue_runners() @@ -1756,7 +1756,7 @@ class ShuffleBatchTest(test_lib.TestCase): min_after_dequeue=16, seed=173205, num_threads=4) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) variables.local_variables_initializer().run() threads = queue_runner_impl.start_queue_runners() @@ -1807,7 +1807,7 @@ class ShuffleBatchTest(test_lib.TestCase): seed=173205, num_threads=4, allow_smaller_final_batch=True) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) variables.local_variables_initializer().run() threads = queue_runner_impl.start_queue_runners() @@ -2070,7 +2070,7 @@ class ShuffleBatchJoinTest(test_lib.TestCase): batched_fetch[1].dense_shape.get_shape().as_list()) self.assertAllEqual((batch_size,), batched_fetch[2].get_shape().as_list()) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) variables.local_variables_initializer().run() threads = queue_runner_impl.start_queue_runners() @@ -2165,7 +2165,7 @@ class ShuffleBatchJoinTest(test_lib.TestCase): self.assertAllEqual((2,), batched[1].dense_shape.get_shape().as_list()) self.assertAllEqual((None,), batched[2].get_shape().as_list()) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) variables.local_variables_initializer().run() threads = queue_runner_impl.start_queue_runners() diff --git a/tensorflow/python/training/moving_averages_test.py b/tensorflow/python/training/moving_averages_test.py index 03bcde9c84..0a7cff4f56 100644 --- a/tensorflow/python/training/moving_averages_test.py +++ b/tensorflow/python/training/moving_averages_test.py @@ -43,7 +43,7 @@ class MovingAveragesTest(test.TestCase): decay = 0.25 assign = moving_averages.assign_moving_average( var, val, decay, zero_debias=False) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllClose([10.0, 11.0], self.evaluate(var)) assign.op.run() self.assertAllClose( @@ -57,7 +57,7 @@ class MovingAveragesTest(test.TestCase): val = constant_op.constant([1.0, 2.0], dtypes.float32) decay = 0.25 assign = moving_averages.assign_moving_average(var, val, decay) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllClose([0.0, 0.0], self.evaluate(var)) assign.op.run() self.assertAllClose( @@ -98,7 +98,7 @@ class MovingAveragesTest(test.TestCase): val = array_ops.placeholder(dtypes.float32, []) wma = moving_averages.weighted_moving_average(val, decay, weight) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) # Get the first weighted moving average. val_1 = 3.0 @@ -125,7 +125,7 @@ class MovingAveragesTest(test.TestCase): val = array_ops.placeholder(dtypes.bfloat16, []) wma = moving_averages.weighted_moving_average(val, decay, weight) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) # Get the first weighted moving average. val_1 = 3.0 @@ -164,7 +164,7 @@ class ExponentialMovingAverageTest(test.TestCase): thirties = _Repeat(30.0, dim) var0 = variables.Variable(tens, name="v0") var1 = variables.Variable(thirties, name="v1") - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) # Note that tensor2 is not a Variable but just a plain Tensor resulting # from the sum operation. tensor2 = var0 + var1 @@ -178,7 +178,7 @@ class ExponentialMovingAverageTest(test.TestCase): self.assertFalse(avg0 in variables.trainable_variables()) self.assertFalse(avg1 in variables.trainable_variables()) self.assertFalse(avg2 in variables.trainable_variables()) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertEqual("v0/ExponentialMovingAverage:0", avg0.name) self.assertEqual("v1/ExponentialMovingAverage:0", avg1.name) diff --git a/tensorflow/python/training/queue_runner_test.py b/tensorflow/python/training/queue_runner_test.py index 2f6e924f98..c5085079b7 100644 --- a/tensorflow/python/training/queue_runner_test.py +++ b/tensorflow/python/training/queue_runner_test.py @@ -49,7 +49,7 @@ class QueueRunnerTest(test.TestCase): var = variables.VariableV1(zero64) count_up_to = var.count_up_to(3) queue = data_flow_ops.FIFOQueue(10, dtypes.float32) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) qr = queue_runner_impl.QueueRunner(queue, [count_up_to]) threads = qr.create_threads(sess) self.assertEqual(sorted(t.name for t in threads), @@ -77,7 +77,7 @@ class QueueRunnerTest(test.TestCase): self.assertEqual(sorted(t.name for t in threads), ["QueueRunnerThread-fifo_queue-CountUpTo:0", "QueueRunnerThread-fifo_queue-CountUpTo_1:0"]) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) for t in threads: t.start() for t in threads: @@ -93,7 +93,7 @@ class QueueRunnerTest(test.TestCase): qr = queue_runner_impl.QueueRunner(queue, [_MockOp("i fail"), _MockOp("so fail")]) threads = qr.create_threads(sess) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) for t in threads: t.start() for t in threads: @@ -140,7 +140,7 @@ class QueueRunnerTest(test.TestCase): var = variables.VariableV1(zero64) count_up_to = var.count_up_to(3) queue = data_flow_ops.FIFOQueue(10, dtypes.float32) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) qr = queue_runner_impl.QueueRunner(queue, [count_up_to]) # As the coordinator to stop. The queue runner should # finish immediately. @@ -196,7 +196,7 @@ class QueueRunnerTest(test.TestCase): var = variables.VariableV1(zero64) count_up_to = var.count_up_to(3) queue = data_flow_ops.FIFOQueue(10, dtypes.float32) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) coord = coordinator.Coordinator() qr = queue_runner_impl.QueueRunner(queue, [count_up_to]) # NOTE that this test does not actually start the threads. @@ -212,7 +212,7 @@ class QueueRunnerTest(test.TestCase): var = variables.VariableV1(zero64) count_up_to = var.count_up_to(3) queue = data_flow_ops.FIFOQueue(10, dtypes.float32) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) coord = coordinator.Coordinator() qr = queue_runner_impl.QueueRunner(queue, [count_up_to]) threads = [] @@ -229,7 +229,7 @@ class QueueRunnerTest(test.TestCase): var = variables.VariableV1(zero64) count_up_to = var.count_up_to(3) queue = data_flow_ops.FIFOQueue(10, dtypes.float32) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) qr = queue_runner_impl.QueueRunner(queue, [count_up_to, _MockOp("bad_op")]) threads = qr.create_threads(sess, start=True) diff --git a/tensorflow/python/training/saver_test.py b/tensorflow/python/training/saver_test.py index 95c21cb815..d1b51adaa4 100644 --- a/tensorflow/python/training/saver_test.py +++ b/tensorflow/python/training/saver_test.py @@ -124,8 +124,8 @@ class SaverTest(test.TestCase): if not context.executing_eagerly(): self.assertEqual( len(variables.report_uninitialized_variables().eval()), 2) - self.assertEqual(0, len(v2.keys().eval())) - self.assertEqual(0, len(v2.values().eval())) + self.assertEqual(0, len(self.evaluate(v2.keys()))) + self.assertEqual(0, len(self.evaluate(v2.values()))) # Restore the saved values in the parameter nodes. save = saver_module.Saver({"v0": v0, "v1": v1, "v2": v2.saveable}) save.restore(sess, save_path) @@ -331,10 +331,10 @@ class SaverTest(test.TestCase): self.evaluate(init_all_op) # Check that the parameter nodes have been initialized. - self.assertEqual(10.0, v0.eval()) - self.assertEqual(20.0, v1.eval()) - self.assertEqual(b"k1", v2.keys().eval()) - self.assertEqual(30.0, v2.values().eval()) + self.assertEqual(10.0, self.evaluate(v0)) + self.assertEqual(20.0, self.evaluate(v1)) + self.assertEqual(b"k1", self.evaluate(v2.keys())) + self.assertEqual(30.0, self.evaluate(v2.values())) # Save the initialized values in the file at "save_path" val = save.save(sess, save_path1) @@ -360,16 +360,16 @@ class SaverTest(test.TestCase): # Assert that the variables are not initialized. self.assertEqual( len(variables.report_uninitialized_variables().eval()), 2) - self.assertEqual(0, len(v2.keys().eval())) - self.assertEqual(0, len(v2.values().eval())) + self.assertEqual(0, len(self.evaluate(v2.keys()))) + self.assertEqual(0, len(self.evaluate(v2.values()))) # Restore the saved values in the parameter nodes. save.restore(sess, save_path2) # Check that the parameter nodes have been restored. - self.assertEqual(10.0, v0.eval()) - self.assertEqual(20.0, v1.eval()) - self.assertEqual(b"k1", v2.keys().eval()) - self.assertEqual(30.0, v2.values().eval()) + self.assertEqual(10.0, self.evaluate(v0)) + self.assertEqual(20.0, self.evaluate(v1)) + self.assertEqual(b"k1", self.evaluate(v2.keys())) + self.assertEqual(30.0, self.evaluate(v2.values())) @test_util.run_deprecated_v1 def testFilenameTensor(self): @@ -398,7 +398,7 @@ class SaverTest(test.TestCase): # Build a graph with 1 node, and save and restore for them. v = variables.VariableV1(np.int64(15), name="v") save = saver_module.Saver({"v": v}, restore_sequentially=True) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) # Save the initialized values in the file at "save_path" val = save.save(sess, save_path) @@ -416,7 +416,7 @@ class SaverTest(test.TestCase): # Restore the saved values in the parameter nodes. save.restore(sess, save_path) # Check that the parameter nodes have been restored. - self.assertEqual(np.int64(15), v.eval()) + self.assertEqual(np.int64(15), self.evaluate(v)) def testSomeErrors(self): with ops_lib.Graph().as_default(): @@ -478,14 +478,14 @@ class SaverTest(test.TestCase): v2 = saver_test_utils.CheckpointedOp(name="v2") v2_init = v2.insert("k1", 30.0) save = saver_module.Saver([v0, v1, v2.saveable]) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) v2_init.run() # Check that the parameter nodes have been initialized. - self.assertEqual(10.0, v0.eval()) - self.assertEqual(20.0, v1.eval()) - self.assertEqual(b"k1", v2.keys().eval()) - self.assertEqual(30.0, v2.values().eval()) + self.assertEqual(10.0, self.evaluate(v0)) + self.assertEqual(20.0, self.evaluate(v1)) + self.assertEqual(b"k1", self.evaluate(v2.keys())) + self.assertEqual(30.0, self.evaluate(v2.values())) # Save the initialized values in the file at "save_path" val = save.save(sess, save_path) @@ -506,16 +506,16 @@ class SaverTest(test.TestCase): with self.assertRaisesWithPredicateMatch( errors_impl.OpError, lambda e: "uninitialized value v1" in e.message): self.evaluate(v1) - self.assertEqual(0, len(v2.keys().eval())) - self.assertEqual(0, len(v2.values().eval())) + self.assertEqual(0, len(self.evaluate(v2.keys()))) + self.assertEqual(0, len(self.evaluate(v2.values()))) # Restore the saved values in the parameter nodes. save.restore(sess, save_path) # Check that the parameter nodes have been restored. - self.assertEqual(10.0, v0.eval()) - self.assertEqual(20.0, v1.eval()) - self.assertEqual(b"k1", v2.keys().eval()) - self.assertEqual(30.0, v2.values().eval()) + self.assertEqual(10.0, self.evaluate(v0)) + self.assertEqual(20.0, self.evaluate(v1)) + self.assertEqual(b"k1", self.evaluate(v2.keys())) + self.assertEqual(30.0, self.evaluate(v2.values())) # Build another graph with 2 nodes, initialized # differently, and a Restore node for them. @@ -525,20 +525,20 @@ class SaverTest(test.TestCase): v2_2 = saver_test_utils.CheckpointedOp(name="v2") save2 = saver_module.Saver([v0_2, v1_2, v2_2.saveable]) v2_2.insert("k1000", 3000.0).run() - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) # Check that the parameter nodes have been initialized. - self.assertEqual(1000.0, v0_2.eval()) - self.assertEqual(2000.0, v1_2.eval()) - self.assertEqual(b"k1000", v2_2.keys().eval()) - self.assertEqual(3000.0, v2_2.values().eval()) + self.assertEqual(1000.0, self.evaluate(v0_2)) + self.assertEqual(2000.0, self.evaluate(v1_2)) + self.assertEqual(b"k1000", self.evaluate(v2_2.keys())) + self.assertEqual(3000.0, self.evaluate(v2_2.values())) # Restore the values saved earlier in the parameter nodes. save2.restore(sess, save_path) # Check that the parameter nodes have been restored. - self.assertEqual(10.0, v0_2.eval()) - self.assertEqual(20.0, v1_2.eval()) - self.assertEqual(b"k1", v2_2.keys().eval()) - self.assertEqual(30.0, v2_2.values().eval()) + self.assertEqual(10.0, self.evaluate(v0_2)) + self.assertEqual(20.0, self.evaluate(v1_2)) + self.assertEqual(b"k1", self.evaluate(v2_2.keys())) + self.assertEqual(30.0, self.evaluate(v2_2.values())) def _SaveAndLoad(self, var_name, var_value, other_value, save_path): with self.session(graph=ops_lib.Graph()) as sess: @@ -582,14 +582,14 @@ class SaverTest(test.TestCase): with sess.graph.device(test.gpu_device_name()): v0_1 = variables.VariableV1(123.45) save = saver_module.Saver({"v0": v0_1}) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) save.save(sess, save_path) with session.Session("", graph=ops_lib.Graph()) as sess: with sess.graph.device(test.gpu_device_name()): v0_2 = variables.VariableV1(543.21) save = saver_module.Saver({"v0": v0_2}) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) def testSharedServerOnGPU(self): if not test.is_gpu_available(): @@ -599,14 +599,14 @@ class SaverTest(test.TestCase): with sess.graph.device(test.gpu_device_name()): v0_1 = variables.VariableV1(123.45) save = saver_module.Saver({"v0": v0_1}, sharded=True, allow_empty=True) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) save.save(sess, save_path) with session.Session("", graph=ops_lib.Graph()) as sess: with sess.graph.device(test.gpu_device_name()): v0_2 = variables.VariableV1(543.21) save = saver_module.Saver({"v0": v0_2}, sharded=True, allow_empty=True) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) def testVariables(self): save_path = os.path.join(self.get_temp_dir(), "variables") @@ -627,10 +627,10 @@ class SaverTest(test.TestCase): # Saver with no arg, defaults to 'all variables'. save = saver_module.Saver() save.restore(sess, save_path) - self.assertAllClose(1.0, one.eval()) - self.assertAllClose([2.0, 2.0, 2.0], twos.eval()) - self.assertEqual(b"k1", v2.keys().eval()) - self.assertEqual(3.0, v2.values().eval()) + self.assertAllClose(1.0, self.evaluate(one)) + self.assertAllClose([2.0, 2.0, 2.0], self.evaluate(twos)) + self.assertEqual(b"k1", self.evaluate(v2.keys())) + self.assertEqual(3.0, self.evaluate(v2.values())) def testVarListShouldBeEmptyInDeferredBuild(self): with ops_lib.Graph().as_default(): @@ -664,8 +664,8 @@ class SaverTest(test.TestCase): # Saver with no arg, defaults to 'all variables'. save = saver_module.Saver() save.restore(sess, save_path) - self.assertAllClose(1.0, one.eval()) - self.assertAllClose([2.0, 2.0, 2.0], twos.eval()) + self.assertAllClose(1.0, self.evaluate(one)) + self.assertAllClose([2.0, 2.0, 2.0], self.evaluate(twos)) @test_util.run_v1_only("b/120545219") def testReshape(self): @@ -691,7 +691,8 @@ class SaverTest(test.TestCase): var = variables.VariableV1([[0.0, 0.0], [0.0, 0.0], [0.0, 0.0]]) save = saver_module.Saver(reshape=True) save.restore(sess, save_path) - self.assertAllClose([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], var.eval()) + self.assertAllClose([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], + self.evaluate(var)) @test_util.run_in_graph_and_eager_modes def testSaveWithGlobalStep(self, pad_step_number=False): @@ -726,7 +727,6 @@ class SaverTest(test.TestCase): def testSaveWithGlobalStepWithPadding(self): self.testSaveWithGlobalStep(pad_step_number=True) - @test_util.run_v1_only("b/120545219") def testSaveToNonexistingPath(self): file_io.write_string_to_file( os.path.join(self.get_temp_dir(), "actually_a_file"), "") @@ -753,8 +753,8 @@ class SaverTest(test.TestCase): self.evaluate(init_all_op) # Check that the parameter nodes have been initialized. - self.assertEqual(10.0, v0.eval()) - self.assertEqual(20.0, v1.eval()) + self.assertEqual(10.0, self.evaluate(v0)) + self.assertEqual(20.0, self.evaluate(v1)) # Save the graph. save.save(sess, save_path) @@ -763,13 +763,12 @@ class SaverTest(test.TestCase): # Restore the saved values in the parameter nodes. save.restore(sess, save_path) # Check that the parameter nodes have been restored. - self.assertEqual(10.0, v0.eval()) - self.assertEqual(20.0, v1.eval()) + self.assertEqual(10.0, self.evaluate(v0)) + self.assertEqual(20.0, self.evaluate(v1)) except ValueError as exc: error_msg_template = "Parent directory of {} doesn't exist, can't save." self.assertEqual(error_msg_template.format(save_path), str(exc)) - @test_util.run_deprecated_v1 def testSaveToURI(self): # ParseURI functions don't work on Windows yet. # TODO(jhseu): Remove this check when it works. @@ -789,8 +788,8 @@ class SaverTest(test.TestCase): self.evaluate(init_all_op) # Check that the parameter nodes have been initialized. - self.assertEqual(10.0, v0.eval()) - self.assertEqual(20.0, v1.eval()) + self.assertEqual(10.0, self.evaluate(v0)) + self.assertEqual(20.0, self.evaluate(v1)) save.save(sess, save_path) def testSaveRestoreAndValidateVariableDtype(self): @@ -835,7 +834,7 @@ class SaverTest(test.TestCase): orig_vars = _model() self.evaluate(variables.global_variables_initializer()) save = saver_module.Saver(max_to_keep=1) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) save.save(sess, save_dir) orig_vals = self.evaluate(orig_vars) @@ -882,7 +881,7 @@ class SaveRestoreShardedTest(test.TestCase): }, write_version=self._WRITE_VERSION, sharded=True) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) t0.insert("k1", 30.0).run() t1.insert("k2", 40.0).run() val = save.save(sess, save_path) @@ -908,15 +907,15 @@ class SaveRestoreShardedTest(test.TestCase): }, write_version=self._WRITE_VERSION, sharded=True) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) t0.insert("k11", 33.0).run() - self.assertEqual(111, v0.eval()) - self.assertEqual(b"k11", t0.keys().eval()) - self.assertEqual(33.0, t0.values().eval()) + self.assertEqual(111, self.evaluate(v0)) + self.assertEqual(b"k11", self.evaluate(t0.keys())) + self.assertEqual(33.0, self.evaluate(t0.values())) save.restore(sess, save_path + "-00000-of-00002") - self.assertEqual(10, v0.eval()) - self.assertEqual(b"k1", t0.keys().eval()) - self.assertEqual(30.0, t0.values().eval()) + self.assertEqual(10, self.evaluate(v0)) + self.assertEqual(b"k1", self.evaluate(t0.keys())) + self.assertEqual(30.0, self.evaluate(t0.values())) # Restore different ops from shard 1 of the saved files. with session.Session( @@ -932,15 +931,15 @@ class SaveRestoreShardedTest(test.TestCase): }, write_version=self._WRITE_VERSION, sharded=True) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) t1.insert("k22", 44.0).run() - self.assertEqual(222, v1.eval()) - self.assertEqual(b"k22", t1.keys().eval()) - self.assertEqual(44.0, t1.values().eval()) + self.assertEqual(222, self.evaluate(v1)) + self.assertEqual(b"k22", self.evaluate(t1.keys())) + self.assertEqual(44.0, self.evaluate(t1.values())) save.restore(sess, save_path + "-00001-of-00002") - self.assertEqual(20, v1.eval()) - self.assertEqual(b"k2", t1.keys().eval()) - self.assertEqual(40.0, t1.values().eval()) + self.assertEqual(20, self.evaluate(v1)) + self.assertEqual(b"k2", self.evaluate(t1.keys())) + self.assertEqual(40.0, self.evaluate(t1.values())) # Now try a restore with the sharded filename. with session.Session( @@ -961,26 +960,26 @@ class SaveRestoreShardedTest(test.TestCase): }, write_version=self._WRITE_VERSION, sharded=True) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) t0.insert("k11", 33.0).run() t1.insert("k22", 44.0).run() - self.assertEqual(111, v0.eval()) - self.assertEqual(222, v1.eval()) - self.assertEqual(b"k11", t0.keys().eval()) - self.assertEqual(33.0, t0.values().eval()) - self.assertEqual(b"k22", t1.keys().eval()) - self.assertEqual(44.0, t1.values().eval()) + self.assertEqual(111, self.evaluate(v0)) + self.assertEqual(222, self.evaluate(v1)) + self.assertEqual(b"k11", self.evaluate(t0.keys())) + self.assertEqual(33.0, self.evaluate(t0.values())) + self.assertEqual(b"k22", self.evaluate(t1.keys())) + self.assertEqual(44.0, self.evaluate(t1.values())) save_path = os.path.join(self.get_temp_dir(), "sharded_basics") if save._write_version is saver_pb2.SaverDef.V1: save.restore(sess, save_path + "-?????-of-?????") else: save.restore(sess, save_path) - self.assertEqual(10, v0.eval()) - self.assertEqual(20, v1.eval()) - self.assertEqual(b"k1", t0.keys().eval()) - self.assertEqual(30.0, t0.values().eval()) - self.assertEqual(b"k2", t1.keys().eval()) - self.assertEqual(40.0, t1.values().eval()) + self.assertEqual(10, self.evaluate(v0)) + self.assertEqual(20, self.evaluate(v1)) + self.assertEqual(b"k1", self.evaluate(t0.keys())) + self.assertEqual(30.0, self.evaluate(t0.values())) + self.assertEqual(b"k2", self.evaluate(t1.keys())) + self.assertEqual(40.0, self.evaluate(t1.values())) if save._write_version is saver_pb2.SaverDef.V1: self.assertEqual( @@ -1028,7 +1027,7 @@ class SaveRestoreShardedTest(test.TestCase): else: vs = [variables.VariableV1(rnd, name=var_name)] - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) if call_saver_with_dict: saver = saver_module.Saver({var_name: vs[0]}) else: @@ -1056,7 +1055,7 @@ class SaveRestoreShardedTest(test.TestCase): name=var_name) ] - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) if call_saver_with_dict: saver = saver_module.Saver({ var_name: new_vs[0] @@ -1203,7 +1202,7 @@ class MaxToKeepTest(test.TestCase): with self.cached_session() as sess: v = variables.VariableV1(10.0, name="v") save = saver_module.Saver({"v": v}, max_to_keep=2) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertEqual([], save.last_checkpoints) s1 = save.save(sess, os.path.join(save_dir, "s1")) @@ -1388,7 +1387,7 @@ class MaxToKeepTest(test.TestCase): "v0": v0, "v1": v1 }, sharded=True, max_to_keep=2) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertEqual([], save.last_checkpoints) s1 = save.save(sess, os.path.join(save_dir, "s1")) @@ -1434,14 +1433,13 @@ class MaxToKeepTest(test.TestCase): self.assertTrue( gfile.Exists(checkpoint_management.meta_graph_filename(s3))) - @test_util.run_deprecated_v1 def testNoMaxToKeep(self): save_dir = self._get_test_dir("no_max_to_keep") save_dir2 = self._get_test_dir("max_to_keep_0") with self.cached_session() as sess: v = variables.VariableV1(10.0, name="v") - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) # Test max_to_keep being None. save = saver_module.Saver({"v": v}, max_to_keep=None) @@ -1463,14 +1461,13 @@ class MaxToKeepTest(test.TestCase): self.assertEqual([], save2.last_checkpoints) self.assertTrue(checkpoint_management.checkpoint_exists(s2)) - @test_util.run_deprecated_v1 def testNoMetaGraph(self): save_dir = self._get_test_dir("no_meta_graph") with self.cached_session() as sess: v = variables.VariableV1(10.0, name="v") save = saver_module.Saver({"v": v}) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) s1 = save.save(sess, os.path.join(save_dir, "s1"), write_meta_graph=False) self.assertTrue(checkpoint_management.checkpoint_exists(s1)) @@ -1487,7 +1484,6 @@ class KeepCheckpointEveryNHoursTest(test.TestCase): @test_util.run_in_graph_and_eager_modes @test.mock.patch.object(saver_module, "time") - @test_util.run_deprecated_v1 def testNonSharded(self, mock_time): save_dir = self._get_test_dir("keep_checkpoint_every_n_hours") @@ -1607,7 +1603,6 @@ class SaveRestoreWithVariableNameMap(test.TestCase): self.assertEqual(20.0, self.evaluate(v1)) @test_util.run_in_graph_and_eager_modes - @test_util.run_v1_only("b/120545219") def testNonReshapeResourceVariable(self): self._testNonReshape(resource_variable_ops.ResourceVariable) @@ -1714,7 +1709,7 @@ class MetaGraphTest(test.TestCase): saver1 = saver_module.Saver({"v1": v1}, name="saver1") ops_lib.add_to_collection("savers", saver0) ops_lib.add_to_collection("savers", saver1) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) # Saves to different checkpoints. saver0.save(sess, saver0_ckpt) saver1.save(sess, saver1_ckpt) @@ -1760,7 +1755,8 @@ class MetaGraphTest(test.TestCase): new_saver0.restore(sess, saver0_ckpt) v0 = sess.graph.get_tensor_by_name("v0:0") v1 = sess.graph.get_tensor_by_name("v1:0") - self.assertAllEqual([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], v0.eval()) + self.assertAllEqual([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], + self.evaluate(v0)) self.assertEqual([3, 2], v0.get_shape()) self.assertEqual([], v1.get_shape()) with self.assertRaisesWithPredicateMatch( @@ -1770,7 +1766,7 @@ class MetaGraphTest(test.TestCase): new_saver1 = savers[1] new_saver1.restore(sess, saver1_ckpt) v1 = sess.graph.get_tensor_by_name("v1:0") - self.assertEqual(11.0, v1.eval()) + self.assertEqual(11.0, self.evaluate(v1)) @test_util.run_v1_only("b/120545219") def testMultiSaverCollection(self): @@ -1794,7 +1790,7 @@ class MetaGraphTest(test.TestCase): saver1 = saver_module.Saver({"v1": v1}, name="saver1") ops_lib.add_to_collection("savers", saver0) ops_lib.add_to_collection("savers", saver1) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) # Saves to different checkpoints. saver0.save(sess, saver0_ckpt) @@ -1878,7 +1874,7 @@ class MetaGraphTest(test.TestCase): # The names are different and will work. slice_saver = saver_module.Saver({"first": v1, "second": v2}) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) # Exports to meta_graph meta_graph_def = slice_saver.export_meta_graph(filename) @@ -2093,7 +2089,6 @@ class MetaGraphTest(test.TestCase): return i + 1, x + r self._testWhileLoopAndGradientSerDes(body) - @test_util.run_deprecated_v1 def testNestedControlFlowSerDes(self): # Test while loop in a cond in a while loop. # pylint: disable=g-long-lambda @@ -2745,7 +2740,7 @@ class ScopedGraphTest(test.TestCase): graph.add_to_collection(ops_lib.GraphKeys.SAVERS, saver2) with self.session(graph=graph) as sess: - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) saver1.save(sess, saver1_ckpt, write_state=False) saver2.save(sess, saver2_ckpt, write_state=False) @@ -2762,7 +2757,7 @@ class ScopedGraphTest(test.TestCase): with self.session(graph=graph1) as sess: saver_list1[0].restore(sess, saver1_ckpt) - self.assertEqual(1.0, var_dict1["variable1:0"].eval()) + self.assertEqual(1.0, self.evaluate(var_dict1["variable1:0"])) graph2 = ops_lib.Graph() var_dict2 = meta_graph.copy_scoped_meta_graph( @@ -2777,7 +2772,7 @@ class ScopedGraphTest(test.TestCase): with self.session(graph=graph2) as sess: saver_list2[0].restore(sess, saver2_ckpt) - self.assertEqual(2.0, var_dict2["variable2:0"].eval()) + self.assertEqual(2.0, self.evaluate(var_dict2["variable2:0"])) class _OwnsAVariableSimple(checkpointable_base.CheckpointableBase): @@ -3010,7 +3005,6 @@ class CheckpointableCompatibilityTests(test.TestCase): "a mismatch between the current graph and the graph"): a_saver.restore(sess=sess, save_path=save_path) - @test_util.run_v1_only("b/120545219") def testLoadFromObjectBasedGraph(self): checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") diff --git a/tensorflow/python/training/slot_creator_test.py b/tensorflow/python/training/slot_creator_test.py index f1f0d58a69..ec2eec3932 100644 --- a/tensorflow/python/training/slot_creator_test.py +++ b/tensorflow/python/training/slot_creator_test.py @@ -38,7 +38,7 @@ class SlotCreatorTest(test.TestCase): v = variables.Variable([1.0, 2.5], name="var") slot = slot_creator.create_slot(v, v.initialized_value(), name="slot") - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertEqual("var/slot", slot.op.name) self.assertEqual([2], slot.get_shape().as_list()) @@ -51,7 +51,7 @@ class SlotCreatorTest(test.TestCase): v = constant_op.constant([1.0, 2.5], name="const") slot = slot_creator.create_slot(v, v * 2, name="slot") - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertEqual("const/slot", slot.op.name) self.assertEqual([2], slot.get_shape().as_list()) @@ -66,7 +66,7 @@ class SlotCreatorTest(test.TestCase): slot = slot_creator.create_zeros_slot( v, name="slot", dtype=dtypes.float64) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertEqual("var/slot", slot.op.name) self.assertEqual([2], slot.get_shape().as_list()) @@ -88,7 +88,7 @@ class SlotCreatorTest(test.TestCase): slot = slot_creator.create_zeros_slot( v, name="slot", dtype=dtypes.float64) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertEqual("var/slot", slot.op.name) self.assertEqual([2], array_ops.shape(slot).eval()) @@ -102,7 +102,7 @@ class SlotCreatorTest(test.TestCase): with ops.control_dependencies(None): slot = slot_creator.create_zeros_slot(v, name="slot") - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertEqual("const/slot", slot.op.name) self.assertEqual([2], slot.get_shape().as_list()) @@ -118,7 +118,7 @@ class SlotCreatorTest(test.TestCase): slot = slot_creator.create_zeros_slot( v, name="slot", dtype=dtypes.float64) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertEqual("const/slot", slot.op.name) self.assertEqual([2], array_ops.shape(slot).eval()) diff --git a/tensorflow/python/training/training_ops_test.py b/tensorflow/python/training/training_ops_test.py index ba0f40999b..8ba6abdcf9 100644 --- a/tensorflow/python/training/training_ops_test.py +++ b/tensorflow/python/training/training_ops_test.py @@ -53,7 +53,7 @@ class TrainingOpsTest(TensorFlowTestCase): self.setUp() with self.session(use_gpu=use_gpu): var = variables.VariableV1(x) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllCloseAccordingToType(x, self.evaluate(var)) apply_sgd = training_ops.apply_gradient_descent(var, alpha, delta) out = self.evaluate(apply_sgd) @@ -74,7 +74,7 @@ class TrainingOpsTest(TensorFlowTestCase): with self.session(use_gpu=use_gpu): var = variables.VariableV1(x) accum = variables.VariableV1(y) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllCloseAccordingToType(x, self.evaluate(var)) apply_adagrad = training_ops.apply_adagrad(var, accum, lr, grad) @@ -99,7 +99,7 @@ class TrainingOpsTest(TensorFlowTestCase): var = variables.VariableV1(x) accum = variables.VariableV1(y) linear = variables.VariableV1(z) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllCloseAccordingToType(x, self.evaluate(var)) apply_ftrl = training_ops.apply_ftrl(var, accum, linear, grad, lr, l1, l2, @@ -156,7 +156,7 @@ class TrainingOpsTest(TensorFlowTestCase): with self.session(use_gpu=False): var = variables.VariableV1(x) accum = variables.VariableV1(y) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllCloseAccordingToType(x, self.evaluate(var)) sparse_apply_adagrad = training_ops.sparse_apply_adagrad( @@ -187,7 +187,7 @@ class TrainingOpsTest(TensorFlowTestCase): var = variables.VariableV1(x) accum = variables.VariableV1(y) linear = variables.VariableV1(z) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllCloseAccordingToType(x, self.evaluate(var)) sparse_apply_ftrl = training_ops.sparse_apply_ftrl( @@ -285,7 +285,7 @@ class TrainingOpsTest(TensorFlowTestCase): beta2_power_t = variables.VariableV1(beta2_power) lr_t = constant_op.constant(lr, self._toType(var.dtype), []) epsilon_t = constant_op.constant(epsilon, self._toType(var.dtype), []) - variables.global_variables_initializer().run() + self.evaluate(variables.global_variables_initializer()) self.assertAllCloseAccordingToType(var, self.evaluate(var_t)) new_var, _, _ = self._adamUpdateNumpy(var, grad, t, m, v, lr, beta1, -- GitLab From cd0180d075cdffa442361802704f29fb8085cd3b Mon Sep 17 00:00:00 2001 From: Skye Wanderman-Milne Date: Wed, 12 Dec 2018 08:12:47 -0800 Subject: [PATCH 423/873] Don't constant fold LoopCond nodes. Removing the LoopCond of a while_loop can cause the partitioner to fail with: A cross-device loop must have a pivot predicate For some reason this only triggers with while_v2 (the lowered while loop is slightly different than what would be produced by the original while_loop). PiperOrigin-RevId: 225188075 --- tensorflow/core/grappler/optimizers/constant_folding.cc | 6 ++++++ tensorflow/python/eager/function_test.py | 1 + 2 files changed, 7 insertions(+) diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc index 5e3e5d6af9..3882e3b3a9 100644 --- a/tensorflow/core/grappler/optimizers/constant_folding.cc +++ b/tensorflow/core/grappler/optimizers/constant_folding.cc @@ -751,6 +751,12 @@ bool ConstantFolding::IsFoldable(const NodeDef& node) const { if (ModifiesFrameInfo(node)) { return false; } + + // Removing LoopCond nodes can screw up the partitioner. + if (node.op() == "LoopCond") { + return false; + } + // Skip constants, they're already folded if (IsConstant(node)) { return false; diff --git a/tensorflow/python/eager/function_test.py b/tensorflow/python/eager/function_test.py index 2697ab5b17..95777a3a65 100644 --- a/tensorflow/python/eager/function_test.py +++ b/tensorflow/python/eager/function_test.py @@ -462,6 +462,7 @@ class FunctionTest(test.TestCase, parameterized.TestCase): var_t = resource_variable_ops.read_variable_op(var_handle, dtype=v.dtype) self.assertEqual(var_t.shape, tensor_shape.TensorShape([2, 2])) + @test_util.enable_control_flow_v2 def testVariableInLoopInFunction(self): @function.defun -- GitLab From 587cda883091868c1b7ac08dfdceb8e4e57a5593 Mon Sep 17 00:00:00 2001 From: Mihai Maruseac Date: Wed, 12 Dec 2018 08:21:11 -0800 Subject: [PATCH 424/873] Add fuzzer for CheckNumerics. PiperOrigin-RevId: 225189182 --- tensorflow/core/kernels/fuzzing/BUILD | 2 + .../kernels/fuzzing/check_numerics_fuzz.cc | 50 +++++++++++++++++++ 2 files changed, 52 insertions(+) create mode 100644 tensorflow/core/kernels/fuzzing/check_numerics_fuzz.cc diff --git a/tensorflow/core/kernels/fuzzing/BUILD b/tensorflow/core/kernels/fuzzing/BUILD index 2d8b734535..fcaf1a8966 100644 --- a/tensorflow/core/kernels/fuzzing/BUILD +++ b/tensorflow/core/kernels/fuzzing/BUILD @@ -68,3 +68,5 @@ tf_ops_fuzz_target_lib("decode_json_example") tf_oss_fuzz_corpus("decode_json_example") tf_oss_fuzz_dict("decode_json_example") + +tf_ops_fuzz_target_lib("check_numerics") diff --git a/tensorflow/core/kernels/fuzzing/check_numerics_fuzz.cc b/tensorflow/core/kernels/fuzzing/check_numerics_fuzz.cc new file mode 100644 index 0000000000..bcd299e308 --- /dev/null +++ b/tensorflow/core/kernels/fuzzing/check_numerics_fuzz.cc @@ -0,0 +1,50 @@ +/* Copyright 2016 Google Inc. 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/cc/ops/array_ops.h" +#include "tensorflow/cc/ops/standard_ops.h" +#include "tensorflow/core/kernels/fuzzing/fuzz_session.h" + +namespace tensorflow { +namespace fuzzing { + +class FuzzCheckNumerics : public FuzzSession { + void BuildGraph(const Scope& scope) override { + auto input = + tensorflow::ops::Placeholder(scope.WithOpName("input1"), DT_FLOAT); + auto prefix = "Error: "; + (void)tensorflow::ops::CheckNumerics(scope.WithOpName("output"), input, + prefix); + } + + void FuzzImpl(const uint8_t* data, size_t size) override { + size_t ratio = sizeof(float) / sizeof(uint8_t); + size_t num_floats = size / ratio; + const float* float_data = reinterpret_cast(data); + + Tensor input_tensor(tensorflow::DT_FLOAT, + TensorShape({static_cast(size)})); + auto flat_tensor = input_tensor.flat(); + for (size_t i = 0; i < num_floats; i++) { + flat_tensor(i) = float_data[i]; + } + RunOneInput(input_tensor).IgnoreError(); + } +}; + +STANDARD_TF_FUZZ_FUNCTION(FuzzCheckNumerics); + +} // end namespace fuzzing +} // end namespace tensorflow -- GitLab From 51900856e93d9708a602c01e877c1eb6488aa6f1 Mon Sep 17 00:00:00 2001 From: Scott Zhu Date: Wed, 12 Dec 2018 08:53:57 -0800 Subject: [PATCH 425/873] Remove the bias regularizer has the constrain for using cudnn backend. This contrain was originally added due to the different weights format issue between canonical and cudnn (extra input bias). Now since the input bias is feeded as zeros in cudnn mode, and weights are unified into one format. Having bias regularizer should not be a issue. PiperOrigin-RevId: 225193782 --- tensorflow/python/keras/layers/recurrent.py | 3 +-- .../python/keras/layers/unified_lstm_test.py | 17 +++++++---------- 2 files changed, 8 insertions(+), 12 deletions(-) diff --git a/tensorflow/python/keras/layers/recurrent.py b/tensorflow/python/keras/layers/recurrent.py index 93cb805d08..568e879c9c 100644 --- a/tensorflow/python/keras/layers/recurrent.py +++ b/tensorflow/python/keras/layers/recurrent.py @@ -2659,8 +2659,7 @@ class UnifiedLSTM(LSTM): self._dropout_mask = None self.could_use_cudnn = ( activation == 'tanh' and recurrent_activation == 'sigmoid' and - recurrent_dropout == 0 and not unroll and use_bias and - bias_regularizer is None) + recurrent_dropout == 0 and not unroll and use_bias) def call(self, inputs, mask=None, training=None, initial_state=None): # LSTM does not support constants. Ignore it during process. diff --git a/tensorflow/python/keras/layers/unified_lstm_test.py b/tensorflow/python/keras/layers/unified_lstm_test.py index 6662bb8c04..55ccebb43b 100644 --- a/tensorflow/python/keras/layers/unified_lstm_test.py +++ b/tensorflow/python/keras/layers/unified_lstm_test.py @@ -159,25 +159,22 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): existing_loss = loss_value @parameterized.named_parameters( - ('non_tan_activation', 'relu', 'sigmoid', 0, False, True, None), - ('non_sigmoid_recur_activation', 'tanh', 'relu', 0, False, True, None), - ('use_recurrent_dropout', 'tanh', 'sigmoid', 0.1, False, True, None), - ('unroll', 'tanh', 'sigmoid', 0, True, True, None), - ('not_use_bias', 'tanh', 'sigmoid', 0, False, False, None), - ('use_bias_regularizer', 'tanh', 'sigmoid', 0, False, True, 'l2') + ('non_tan_activation', 'relu', 'sigmoid', 0, False, True), + ('non_sigmoid_recur_activation', 'tanh', 'relu', 0, False, True), + ('use_recurrent_dropout', 'tanh', 'sigmoid', 0.1, False, True), + ('unroll', 'tanh', 'sigmoid', 0, True, True), + ('not_use_bias', 'tanh', 'sigmoid', 0, False, False), ) @test_util.run_in_graph_and_eager_modes(config=_config) def test_could_use_defun_backend(self, activation, recurrent_activation, - recurrent_dropout, unroll, use_bias, - bias_regularizer): + recurrent_dropout, unroll, use_bias): layer = keras.layers.UnifiedLSTM( 1, activation=activation, recurrent_activation=recurrent_activation, recurrent_dropout=recurrent_dropout, unroll=unroll, - use_bias=use_bias, - bias_regularizer=bias_regularizer) + use_bias=use_bias) self.assertFalse(layer.could_use_cudnn) def test_unified_lstm_feature_parity_with_canonical_lstm(self): -- GitLab From da29d1c8e796e4e5849d371cd613727769257056 Mon Sep 17 00:00:00 2001 From: Skye Wanderman-Milne Date: Wed, 12 Dec 2018 09:48:44 -0800 Subject: [PATCH 426/873] Control flow v2 should only add control edges if outer graph does too. PiperOrigin-RevId: 225202451 --- .../python/framework/auto_control_deps.py | 9 +++ tensorflow/python/framework/ops.py | 3 + .../kernel_tests/control_flow_ops_py_test.py | 81 +++++++++++++------ tensorflow/python/ops/cond_v2.py | 2 +- tensorflow/python/ops/while_v2.py | 2 +- 5 files changed, 72 insertions(+), 25 deletions(-) diff --git a/tensorflow/python/framework/auto_control_deps.py b/tensorflow/python/framework/auto_control_deps.py index 30dc959e9a..a72ded1131 100644 --- a/tensorflow/python/framework/auto_control_deps.py +++ b/tensorflow/python/framework/auto_control_deps.py @@ -100,6 +100,7 @@ class AutomaticControlDependencies(object): # graph (but that would mess up devices and collections at least, # probably other things as well). self._graph = ops.get_default_graph() + self._graph._add_control_dependencies = True # pylint: disable=protected-access self._n_operations = len(self._graph.get_operations()) return self @@ -170,6 +171,14 @@ class AutomaticControlDependencies(object): raise RuntimeError( "Graph changed while trying to add control dependencies.") + # pylint: disable=protected-access + if hasattr(self._graph, "outer_graph"): + outer_val = self._graph.outer_graph._add_control_dependencies + self._graph._add_control_dependencies = outer_val + else: + self._graph._add_control_dependencies = False + # pylint: enable=protected-access + # map from resource tensor to the last op which used it last_op_using_resource_tensor = {} # set of conditional and loop exits diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py index fa306936d6..27c56ef990 100644 --- a/tensorflow/python/framework/ops.py +++ b/tensorflow/python/framework/ops.py @@ -2896,6 +2896,9 @@ class Graph(object): self._last_loss_reduction = None self._container = "" self._registered_ops = op_def_registry.get_registered_ops() + # Set to True if this graph is being built in an + # AutomaticControlDependencies context. + self._add_control_dependencies = False # TODO(skyewm): fold as much of the above as possible into the C # implementation diff --git a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py index 42cfe9e237..39ceb0d749 100644 --- a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py +++ b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py @@ -33,6 +33,7 @@ from tensorflow.python.client import device_lib from tensorflow.python.client import session from tensorflow.python.eager import context from tensorflow.python.eager import function as eager_function +from tensorflow.python.eager import wrap_function from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors_impl @@ -921,9 +922,8 @@ class ControlFlowTest(test.TestCase): r = control_flow_ops.cond(foo()[1], lambda: 1.0, lambda: 2.0) self.assertEqual(self.evaluate(r), 1.0) - # TODO(b/117945658): reenable @test_util.run_in_graph_and_eager_modes - def DISABLED_testCondAutoControlDeps(self): + def testCondAutoControlDeps(self): def branch_fn(): logging_ops.print_v2("A") @@ -943,11 +943,11 @@ class ControlFlowTest(test.TestCase): if not context.executing_eagerly(): with self.cached_session(): with self.captureWritesToStream(sys.stderr) as printed: - self.assertEqual(build_cond(), 10) + self.assertEqual(self.evaluate(build_cond()), 10) self.assertEqual(printed.contents(), "C\n") with self.captureWritesToStream(sys.stderr) as printed: - self.assertEqual(build_nested_cond(), 10) + self.assertEqual(self.evaluate(build_nested_cond()), 10) self.assertEqual(printed.contents(), "C\n") # In defuns, all prints should execute in program order. @@ -970,9 +970,28 @@ class ControlFlowTest(test.TestCase): self.assertEqual(self.evaluate(nested_cond()), 10) self.assertEqual(printed.contents(), "A\nB\nC\n") - # TODO(b/117945658): reenable + # wrap_function should prune. + def pruned_cond(): + return build_cond() + pruned_cond = wrap_function.wrap_function(pruned_cond, []) + + with self.captureWritesToStream(sys.stderr) as printed: + self.assertEqual(self.evaluate(pruned_cond()), 10) + self.assertEqual(printed.contents(), "C\n") + + def pruned_nested_cond(): + return build_nested_cond() + pruned_nested_cond = wrap_function.wrap_function(pruned_nested_cond, []) + + with self.captureWritesToStream(sys.stderr) as printed: + self.assertEqual(self.evaluate(pruned_nested_cond()), 10) + self.assertEqual(printed.contents(), "C\n") + @test_util.run_in_graph_and_eager_modes - def DISABLED_testWhileAutoControlDeps(self): + def testWhileAutoControlDeps(self): + # Legacy while_loop fails this test because it produces deprecation notices + # in stderr. + if not control_flow_util.ENABLE_CONTROL_FLOW_V2: return def cond(i, unused_x): logging_ops.print_v2("A") @@ -991,40 +1010,56 @@ class ControlFlowTest(test.TestCase): def build_nested_while(): return control_flow_ops.cond( - constant_op.constant(True), build_while, lambda: (0, 0)) + constant_op.constant(True), build_while, lambda: [0, 0]) # In v1 graph mode, pruning should make only "D" print. if not context.executing_eagerly(): with self.cached_session(): with self.captureWritesToStream(sys.stderr) as printed: - self.assertEqual(build_while()[0], 2) + self.assertEqual(self.evaluate(build_while()[0]), 2) self.assertEqual(printed.contents(), "D\nD\n") with self.captureWritesToStream(sys.stderr) as printed: - self.assertEqual(build_nested_while()[0], 2) + self.assertEqual(self.evaluate(build_nested_while()[0]), 2) self.assertEqual(printed.contents(), "D\nD\n") # In defuns, all prints should execute in program order. - # This doesn't work with legacy control flow. - if control_flow_util.ENABLE_CONTROL_FLOW_V2: + @eager_function.defun + def while_loop(): + return build_while()[0] - @eager_function.defun - def while_loop(): - return build_while()[0] + with self.captureWritesToStream(sys.stderr) as printed: + self.assertEqual(self.evaluate(while_loop()), 2) + self.assertEqual(printed.contents(), "A\nB\nC\nD\nA\nB\nC\nD\nA\n") + @eager_function.defun + def nested_while_loop(): + return build_nested_while()[0] + + # TODO(b/117840611): calling nested_while_loop fails in eager + if not context.executing_eagerly(): with self.captureWritesToStream(sys.stderr) as printed: - self.assertEqual(self.evaluate(while_loop()), 2) + self.assertEqual(self.evaluate(nested_while_loop()), 2) self.assertEqual(printed.contents(), "A\nB\nC\nD\nA\nB\nC\nD\nA\n") - @eager_function.defun - def nested_while_loop(): - return build_nested_while()[0] + # wrap_function should prune. + def pruned_while(): + return build_while()[0] + pruned_while = wrap_function.wrap_function(pruned_while, []) - # TODO(b/117840611): calling nested_while_loop fails in eager - if not context.executing_eagerly(): - with self.captureWritesToStream(sys.stderr) as printed: - self.assertEqual(self.evaluate(nested_while_loop()), 2) - self.assertEqual(printed.contents(), "A\nB\nC\nD\nA\nB\nC\nD\nA\n") + with self.captureWritesToStream(sys.stderr) as printed: + self.assertEqual(self.evaluate(pruned_while()), 2) + self.assertEqual(printed.contents(), "D\nD\n") + + def pruned_nested_while(): + return build_nested_while()[0] + pruned_nested_while = wrap_function.wrap_function(pruned_nested_while, []) + + # TODO(b/117840611): calling nested_while_loop fails in eager + if not context.executing_eagerly(): + with self.captureWritesToStream(sys.stderr) as printed: + self.assertEqual(self.evaluate(pruned_nested_while()), 2) + self.assertEqual(printed.contents(), "D\nD\n") # Microbenchmark: 256,000 iterations/s. @test_util.disable_control_flow_v2("b/116630618 (Times out)") diff --git a/tensorflow/python/ops/cond_v2.py b/tensorflow/python/ops/cond_v2.py index abc99c1205..7d09e32e24 100644 --- a/tensorflow/python/ops/cond_v2.py +++ b/tensorflow/python/ops/cond_v2.py @@ -61,7 +61,7 @@ def cond_v2(pred, true_fn, false_fn, name="cond"): # Automatic control dependencies are added in defuns, but not in v1 # graphs. Propagate that behavior here. - add_control_dependencies = util.in_defun() + add_control_dependencies = ops.get_default_graph()._add_control_dependencies pred = ops.convert_to_tensor(pred) true_graph = func_graph_module.func_graph_from_py_func( diff --git a/tensorflow/python/ops/while_v2.py b/tensorflow/python/ops/while_v2.py index f7566bac9b..3e5a8fcdfa 100644 --- a/tensorflow/python/ops/while_v2.py +++ b/tensorflow/python/ops/while_v2.py @@ -99,7 +99,7 @@ def while_loop(cond, # Automatic control dependencies are added in defuns, but not in v1 # graphs. Propagate that behavior here. - add_control_dependencies = util.in_defun() + add_control_dependencies = ops.get_default_graph()._add_control_dependencies # Build a `cond` wrapper that can handle the extra counter loop_var. def wrapped_cond(loop_counter, *args): -- GitLab From 46afcd061ca74564329b418a96a0cfb453dca57e Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 12 Dec 2018 09:57:48 -0800 Subject: [PATCH 427/873] Implement Dequantize Op in XLA. 1. Only MIN_COMBINED mode is supported; 2. Reshape the output to [d0,..., dn * unpack_size] if input shape is [d0, ..., dn]. 3. Only uint32 is supported for the input; 4. Output data type is bfloat16; 5. Only uint8 or uint16 is supported for the original unpacked input. PiperOrigin-RevId: 225203930 --- tensorflow/compiler/xla/client/lib/BUILD | 28 ++ tensorflow/compiler/xla/client/lib/quantize.h | 162 +++++++++++ .../compiler/xla/client/lib/quantize_test.cc | 254 ++++++++++++++++++ 3 files changed, 444 insertions(+) create mode 100644 tensorflow/compiler/xla/client/lib/quantize.h create mode 100644 tensorflow/compiler/xla/client/lib/quantize_test.cc diff --git a/tensorflow/compiler/xla/client/lib/BUILD b/tensorflow/compiler/xla/client/lib/BUILD index 8fc221ee2b..970f00759f 100644 --- a/tensorflow/compiler/xla/client/lib/BUILD +++ b/tensorflow/compiler/xla/client/lib/BUILD @@ -336,6 +336,34 @@ xla_test( ], ) +cc_library( + name = "quantize", + hdrs = ["quantize.h"], + deps = [ + ":constants", + "//tensorflow/compiler/xla:types", + "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/core:lib", + ], +) + +xla_test( + name = "quantize_test", + srcs = ["quantize_test.cc"], + tags = ["enable_for_xla_interpreter"], + deps = [ + ":quantize", + "//tensorflow/compiler/xla:test", + "//tensorflow/compiler/xla:types", + "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/tests:client_library_test_base", + "//tensorflow/compiler/xla/tests:xla_internal_test_main", + ], +) + cc_library( name = "testing", srcs = ["testing.cc"], diff --git a/tensorflow/compiler/xla/client/lib/quantize.h b/tensorflow/compiler/xla/client/lib/quantize.h new file mode 100644 index 0000000000..e002e5e19c --- /dev/null +++ b/tensorflow/compiler/xla/client/lib/quantize.h @@ -0,0 +1,162 @@ +/* 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_CLIENT_LIB_QUANTIZE_H_ +#define TENSORFLOW_COMPILER_XLA_CLIENT_LIB_QUANTIZE_H_ + +#include +#include +#include + +#include "tensorflow/compiler/xla/client/lib/constants.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/compiler/xla/util.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/bfloat16/bfloat16.h" + +namespace xla { + +constexpr int64 kBitsOfByte = 8; + +// Represents the range used for quantization +struct QuantizedRange { + QuantizedRange() = default; + QuantizedRange(float min_in, float max_in) : min(min_in), max(max_in) {} + + bool operator==(const QuantizedRange& rhs) const { + return this->min == rhs.min && this->max == rhs.max; + } + + bool operator!=(const QuantizedRange& rhs) const { return !(*this == rhs); } + + tensorflow::bfloat16 min = tensorflow::bfloat16(0.0f); + tensorflow::bfloat16 max = tensorflow::bfloat16(0.0f); +}; + +template +inline std::vector PackToUint32(absl::Span input) { + const int64 kElementsPerPack = sizeof(uint32) / sizeof(T); + const int64 input_size = input.size(); + const int64 output_size = CeilOfRatio(input_size, kElementsPerPack); + + std::vector output_vec; + constexpr int64 kShiftBits = sizeof(T) / sizeof(uint8) * kBitsOfByte; + + for (int64 i = 0; i < output_size; i++) { + uint32 result = 0; + for (int64 p = 0; p < kElementsPerPack; p++) { + int64 index = i * kElementsPerPack + p; + if (index < input_size) { + int64 total_shift_bits = kShiftBits * (kElementsPerPack - p - 1); + result |= (input[index] << total_shift_bits); + } + } + output_vec.push_back(result); + } + + return output_vec; +} + +// Dequantize the quantized input of packed uint32 to bfloat16. +// Only uint8 or uint16 is supported for the original unpacked input. +// Returns a tensor of shape [d0,..., dn * unpack_size] if +// input shape is [d0, ..., dn], where unpack_size = sizeof(unit32) / sizeof(T). +template +inline XlaOp Dequantize(XlaOp input, const QuantizedRange& range, + absl::string_view mode_string = "MIN_COMBINED") { + XlaBuilder* const builder = input.builder(); + return builder->ReportErrorOrReturn([&]() -> StatusOr { + float half_range = + !std::is_signed::value + ? 0.0f + : (static_cast(std::numeric_limits::max()) - + std::numeric_limits::min() + 1) / + 2.0f; + const int64 unpack_size = sizeof(uint32) / sizeof(T); + TF_ASSIGN_OR_RETURN(Shape shape, builder->GetShape(input)); + + auto element_type = shape.element_type(); + if (element_type != U32) { + return InvalidArgument( + "Only U32 is supported for input type of xla::Dequantize Op."); + } + + auto broadcast_size = shape.dimensions(); + broadcast_size.push_back(unpack_size); + std::vector broadcast_dimensions(shape.dimensions_size()); + std::iota(broadcast_dimensions.begin(), broadcast_dimensions.end(), 0); + // Broadcast the input to [d0, ..., dn, unpack_size] if input size is + // [d0, ..., dn]. + auto broadcast_input = + BroadcastInDim(input, broadcast_size, broadcast_dimensions); + + XlaOp iota_r1 = Iota(builder, U32, unpack_size); + // Highest significant bytes needs to shift more bytes than lower + // significant bytes. + XlaOp shift_bytes = + xla::ConstantR0(builder, unpack_size - 1) - iota_r1; + + const int bytes_of_type = sizeof(T) / sizeof(uint8); + XlaOp shift_bits = shift_bytes * xla::ConstantR0( + builder, kBitsOfByte * bytes_of_type); + + // Make bit_mask for different data type T. + uint32 bit_mask = 0x00000000; + for (int i = 0; i < bytes_of_type; i++) { + bit_mask <<= kBitsOfByte; + bit_mask |= 0x000000ff; + } + + // Shift the input by sizeof(T) bytes and apply bit_mask to unpack. + XlaOp shifted_input = ShiftRightLogical( + broadcast_input, Broadcast(shift_bits, shape.dimensions())); + XlaOp unpack_input = + And(shifted_input, xla::ConstantR0(builder, bit_mask)); + + XlaOp result; + + if (mode_string == "MIN_COMBINED") { + const tensorflow::bfloat16 scale_factor = + (range.max - range.min) / + (static_cast(std::numeric_limits::max() - + std::numeric_limits::min())); + // result = bfloat16(input + half_range) * scale_factor + range.min + XlaOp unpack_input_bf16 = ConvertElementType(unpack_input, BF16); + XlaOp half_range_bf16 = xla::ConstantR0( + builder, static_cast(half_range)); + XlaOp sum = unpack_input_bf16 + half_range_bf16; + + result = + sum * xla::ConstantR0(builder, scale_factor) + + xla::ConstantR0(builder, range.min); + } else { + // TODO(wangtao): support other modes. + return InvalidArgument( + "Only MIN_COMBINED mode is supported in xla::Dequantize Op."); + } + + // Reshape the result to [d0,..., dn * unpack_size] if + // input shape is [d0, ..., dn]. + std::vector result_shape(shape.dimensions()); + result_shape[shape.dimensions_size() - 1] = + shape.dimensions(shape.dimensions_size() - 1) * unpack_size; + return Reshape(result, result_shape); + }); +} + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_CLIENT_LIB_QUANTIZE_H_ diff --git a/tensorflow/compiler/xla/client/lib/quantize_test.cc b/tensorflow/compiler/xla/client/lib/quantize_test.cc new file mode 100644 index 0000000000..f7ff3502d1 --- /dev/null +++ b/tensorflow/compiler/xla/client/lib/quantize_test.cc @@ -0,0 +1,254 @@ +/* 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/client/lib/quantize.h" + +#include + +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/test.h" +#include "tensorflow/compiler/xla/tests/client_library_test_base.h" +#include "tensorflow/compiler/xla/tests/test_macros.h" +#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/compiler/xla/util.h" + +namespace xla { +namespace { + +using bfloat16 = tensorflow::bfloat16; + +template +std::vector GenerateInput() { + std::vector input; + + for (int64 i = std::numeric_limits::min(); + i < std::numeric_limits::max(); ++i) { + input.push_back(static_cast(i)); + } + + return input; +} + +template +Array2D GenerateLargeSizeInput(int num_columns, int num_rows) { + Array2D input(num_columns, num_rows); + + input.FillRandom(6, 128); + + return input; +} + +template +Array2D PackLargeInput(Array2D &input) { + const int64 size_per_pack = sizeof(uint32) / sizeof(NativeT); + int64 width = input.width(); + + int64 padded_output_width = CeilOfRatio(width, size_per_pack); + + Array2D pack_input(input.height(), padded_output_width); + + for (int h = 0; h < input.height(); h++) { + std::vector input_row; + for (int w = 0; w < width; w++) { + input_row.push_back(input({h, w})); + } + + auto pack_input_vec = PackToUint32(input_row); + + for (int w = 0; w < padded_output_width; w++) { + pack_input(h, w) = pack_input_vec[w]; + } + } + + return pack_input; +} + +template +Array2D GenerateLargeSizeMinCombinedOutput( + Array2D &input, const QuantizedRange &range) { + const int64 size_per_pack = sizeof(uint32) / sizeof(NativeT); + int64 width = input.width(); + + int64 padded_output_width = CeilOfRatio(width, size_per_pack) * size_per_pack; + + Array2D output(input.height(), padded_output_width, bfloat16(0.0)); + + float half_range = + !std::is_signed::value + ? 0.0f + : (static_cast(std::numeric_limits::max() - + std::numeric_limits::min() + 1)) / + 2.0f; + const bfloat16 scale_factor = + (range.max - range.min) / + (static_cast(std::numeric_limits::max() - + std::numeric_limits::min())); + + for (int h = 0; h < input.height(); h++) { + std::vector input_row; + for (int w = 0; w < width; w++) { + bfloat16 result = + static_cast(input(h, w) + half_range) * scale_factor + + range.min; + output(h, w) = result; + } + } + + return output; +} + +template +std::vector GenerateMinCombinedOutput(const QuantizedRange &range) { + float half_range = + !std::is_signed::value + ? 0.0f + : (static_cast(std::numeric_limits::max() - + std::numeric_limits::min() + 1)) / + 2.0f; + const bfloat16 scale_factor = + (range.max - range.min) / + (static_cast(std::numeric_limits::max() - + std::numeric_limits::min())); + std::vector output; + for (int64 i = std::numeric_limits::min(); + i < std::numeric_limits::max(); ++i) { + bfloat16 result = + static_cast(i + half_range) * scale_factor + range.min; + output.push_back(result); + } + + const int64 pack_size = sizeof(uint32) / sizeof(NativeT); + const int64 output_size = output.size(); + + int64 num_tailing_zeros = + CeilOfRatio(output_size, pack_size) * pack_size - output_size; + + output.insert(output.end(), num_tailing_zeros, bfloat16(0.0)); + return output; +} + +// TODO(wangtao): add a test to make sure this op is the inverse of the existing +// TF quantize op defined in: third_party/tensorflow/core/kernels/quantize_op.cc + +using DequantizeTest = ClientLibraryTestBase; + +TEST(PackTest, PackUint8ToUint32) { + std::vector input = {0xAB, 0x0B, 0x00, 0xF0, 0x01}; + auto output = PackToUint32(input); + EXPECT_THAT(output, ::testing::ElementsAre(0xAB0B00F0, 0x01000000)); +} + +TEST(PackTest, PackInt8ToUint32) { + std::vector input = {static_cast(0x81), 0x0B, 0x00, 0x20, + 0x01}; + auto output = PackToUint32(input); + EXPECT_THAT(output, ::testing::ElementsAre(0x810B0020, 0x01000000)); +} + +TEST(PackTest, PackUint8ToUint32PerfectSize) { + std::vector input = {3, 2, 1, 0}; + auto output = PackToUint32(input); + EXPECT_THAT(output, ::testing::ElementsAre(0x03020100)); +} + +XLA_TEST_F(DequantizeTest, MinCombinedUint16R1) { + XlaBuilder builder(TestName()); + auto input = GenerateInput(); + auto x = ConstantR1(&builder, PackToUint32(input)); + QuantizedRange range(0, 255.0f); + xla::Dequantize(x, range, "MIN_COMBINED"); + auto expected = GenerateMinCombinedOutput(range); + ComputeAndCompareR1(&builder, expected, {}); +} + +XLA_TEST_F(DequantizeTest, MinCombinedUint8R1) { + XlaBuilder builder(TestName()); + auto input = GenerateInput(); + auto x = ConstantR1(&builder, PackToUint32(input)); + QuantizedRange range(0, 127.0f); + xla::Dequantize(x, range, "MIN_COMBINED"); + auto expected = GenerateMinCombinedOutput(range); + ComputeAndCompareR1(&builder, expected, {}); +} + +XLA_TEST_F(DequantizeTest, MinCombinedUint8R2) { + XlaBuilder builder(TestName()); + std::vector> input = { + {0, 1, 2, 3}, + {4, 5, 6, 7}, + {8, 9, 10, 11}, + {12, 13, 16, 15}, + }; + auto x = ConstantR2(&builder, {{PackToUint32(input[0])[0]}, + {PackToUint32(input[1])[0]}, + {PackToUint32(input[2])[0]}, + {PackToUint32(input[3])[0]}}); + QuantizedRange range(0, 255.0f); + xla::Dequantize(x, range, "MIN_COMBINED"); + const Array2D expected = { + {bfloat16(0.0), bfloat16(1.0), bfloat16(2.0), bfloat16(3.0)}, + {bfloat16(4.0), bfloat16(5.0), bfloat16(6.0), bfloat16(7.0)}, + {bfloat16(8.0), bfloat16(9.0), bfloat16(10.0), bfloat16(11.0)}, + {bfloat16(12.0), bfloat16(13.0), bfloat16(16.0), bfloat16(15.0)}, + }; + ComputeAndCompareR2(&builder, expected, {}); +} + +XLA_TEST_F(DequantizeTest, MinCombinedUint8R2TailingZero) { + XlaBuilder builder(TestName()); + std::vector> input = { + {0, 1, 2, 3, 16}, + {4, 5, 6, 7, 17}, + {8, 9, 10, 11, 18}, + {12, 13, 16, 15, 19}, + }; + auto x = ConstantR2( + &builder, + {{PackToUint32(input[0])[0], PackToUint32(input[0])[1]}, + {PackToUint32(input[1])[0], PackToUint32(input[1])[1]}, + {PackToUint32(input[2])[0], PackToUint32(input[2])[1]}, + {PackToUint32(input[3])[0], PackToUint32(input[3])[1]}}); + QuantizedRange range(0, 255.0f); + xla::Dequantize(x, range, "MIN_COMBINED"); + + const Array2D expected = { + {bfloat16(0.0), bfloat16(1.0), bfloat16(2.0), bfloat16(3.0), + bfloat16(16.0), bfloat16(0.0), bfloat16(0.0), bfloat16(0.0)}, + {bfloat16(4.0), bfloat16(5.0), bfloat16(6.0), bfloat16(7.0), + bfloat16(17.0), bfloat16(0.0), bfloat16(0.0), bfloat16(0.0)}, + {bfloat16(8.0), bfloat16(9.0), bfloat16(10.0), bfloat16(11.0), + bfloat16(18.0), bfloat16(0.0), bfloat16(0.0), bfloat16(0.0)}, + {bfloat16(12.0), bfloat16(13.0), bfloat16(16.0), bfloat16(15.0), + bfloat16(19.0), bfloat16(0.0), bfloat16(0.0), bfloat16(0.0)}, + }; + ComputeAndCompareR2(&builder, expected, {}); +} + +XLA_TEST_F(DequantizeTest, MinCombinedUint8LargeSizeTest) { + XlaBuilder builder(TestName()); + Array2D input = GenerateLargeSizeInput(500, 3547); + Array2D input_packed = PackLargeInput(input); + + auto x = ConstantR2FromArray2D(&builder, input_packed); + QuantizedRange range(0, 255.0f); + xla::Dequantize(x, range, "MIN_COMBINED"); + + const Array2D expected = + GenerateLargeSizeMinCombinedOutput(input, range); + ComputeAndCompareR2(&builder, expected, {}); +} + +} // namespace +} // namespace xla -- GitLab From 4a2abacb2ab8c53245ff293f23b58468f9f52db5 Mon Sep 17 00:00:00 2001 From: Peter Hawkins Date: Wed, 12 Dec 2018 10:06:37 -0800 Subject: [PATCH 428/873] [XLA:Python] Add CustomCall support to Python LocalComputationBuilder. PiperOrigin-RevId: 225205868 --- .../xla/python/local_computation_builder.cc | 15 +++++++++++ .../xla/python/local_computation_builder.h | 6 +++++ .../xla/python/local_computation_builder.i | 1 + tensorflow/compiler/xla/python/xla_client.py | 25 +++++++++++++++++++ .../compiler/xla/service/hlo_verifier.cc | 4 ++- 5 files changed, 50 insertions(+), 1 deletion(-) diff --git a/tensorflow/compiler/xla/python/local_computation_builder.cc b/tensorflow/compiler/xla/python/local_computation_builder.cc index d4d31fb8c0..8e3ac381ce 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.cc +++ b/tensorflow/compiler/xla/python/local_computation_builder.cc @@ -783,6 +783,21 @@ LocalOp LocalComputationBuilder::Call(const LocalComputation& local_computation, return xla::Call(&builder_, local_computation.computation(), xla_ops); } +LocalOp LocalComputationBuilder::CustomCall( + const string& call_target_name, absl::Span operands, + const Shape& shape_with_layout, + const std::vector& operand_shapes_with_layout, + const string& opaque) { + std::vector xla_ops; + xla_ops.reserve(operands.size()); + for (const auto& op : operands) { + xla_ops.push_back(op.op()); + } + return xla::CustomCallWithLayout(&builder_, call_target_name, xla_ops, + shape_with_layout, + operand_shapes_with_layout, opaque); +} + LocalOp LocalComputationBuilder::Transpose( const LocalOp& operand, absl::Span permutation) { return xla::Transpose(operand.op(), permutation); diff --git a/tensorflow/compiler/xla/python/local_computation_builder.h b/tensorflow/compiler/xla/python/local_computation_builder.h index 7647ef44ad..eebbe674e5 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.h +++ b/tensorflow/compiler/xla/python/local_computation_builder.h @@ -352,6 +352,12 @@ class LocalComputationBuilder { LocalOp Call(const LocalComputation& local_computation, absl::Span operands); + LocalOp CustomCall(const string& call_target_name, + absl::Span operands, + const Shape& shape_with_layout, + const std::vector& operand_shapes_with_layout, + const string& opaque); + LocalOp Transpose(const LocalOp& operand, absl::Span permutation); diff --git a/tensorflow/compiler/xla/python/local_computation_builder.i b/tensorflow/compiler/xla/python/local_computation_builder.i index 82d25304f0..db7e0458f4 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.i +++ b/tensorflow/compiler/xla/python/local_computation_builder.i @@ -1147,6 +1147,7 @@ tensorflow::ImportNumpy(); %unignore xla::swig::LocalComputationBuilder::Cholesky; %unignore xla::swig::LocalComputationBuilder::QR; %unignore xla::swig::LocalComputationBuilder::TriangularSolve; +%unignore xla::swig::LocalComputationBuilder::CustomCall; %unignore xla::swig::DeleteLocalComputation; %unignore xla::swig::DestructureLocalShapedBufferTuple; %unignore xla::swig::DestructureXrtAllocationTuple; diff --git a/tensorflow/compiler/xla/python/xla_client.py b/tensorflow/compiler/xla/python/xla_client.py index 3366a83543..cd85713d72 100644 --- a/tensorflow/compiler/xla/python/xla_client.py +++ b/tensorflow/compiler/xla/python/xla_client.py @@ -1102,6 +1102,31 @@ class ComputationBuilder(object): """ return self._client.Call(computation_to_apply.computation, operands) + def CustomCall(self, + call_target_name, + operands, + shape_with_layout, + operand_shapes_with_layout, + opaque=None): + """Enqueues a custom call operation onto the computation. + + Args: + call_target_name: the name of the function to call. + operands: an iterable of LocalOp. The number and types of operands must + match the arity of `operand_shapes_with_layout`. + shape_with_layout: the shape of the operator's output, with layout. + operand_shapes_with_layout: the shapes of `operands`, including the + expected layouts. + opaque: an opaque string passed to the backend. + + Returns: + A LocalOp representing the added custom call op. + """ + opaque = opaque or '' + return self._client.CustomCall(call_target_name, operands, + shape_with_layout, + operand_shapes_with_layout, opaque) + def Map(self, operands, computation_to_apply, dimensions): """Enqueues a map operation onto the computation. diff --git a/tensorflow/compiler/xla/service/hlo_verifier.cc b/tensorflow/compiler/xla/service/hlo_verifier.cc index 77db7b098a..ace854ed6a 100644 --- a/tensorflow/compiler/xla/service/hlo_verifier.cc +++ b/tensorflow/compiler/xla/service/hlo_verifier.cc @@ -481,7 +481,9 @@ Status ShapeVerifier::HandleCustomCall(HloInstruction* instruction) { const Shape& operand_shape_with_layout = custom_call->operand_shapes_with_layout()[i]; TF_RET_CHECK(ShapeUtil::Compatible(custom_call->operand(i)->shape(), - operand_shape_with_layout)); + operand_shape_with_layout)) + << custom_call->operand(i)->shape().ToString() << " operand " + << operand_shape_with_layout.ToString(); TF_RET_CHECK(LayoutUtil::HasLayout(operand_shape_with_layout)); } } -- GitLab From c5b7754ce1fa9577be9613b417a85b48669b78ba Mon Sep 17 00:00:00 2001 From: Artem Belevich Date: Wed, 12 Dec 2018 10:19:48 -0800 Subject: [PATCH 429/873] Render HLO graph as HTML. PiperOrigin-RevId: 225208397 --- .../compiler/xla/debug_options_flags.cc | 6 + tensorflow/compiler/xla/service/BUILD | 1 + .../compiler/xla/service/hlo_graph_dumper.cc | 143 +++++++++++++++++- .../compiler/xla/service/hlo_graph_dumper.h | 6 + .../xla/service/hlo_graph_html_renderer.cc | 43 ++++++ tensorflow/compiler/xla/xla.proto | 5 +- 6 files changed, 201 insertions(+), 3 deletions(-) create mode 100644 tensorflow/compiler/xla/service/hlo_graph_html_renderer.cc diff --git a/tensorflow/compiler/xla/debug_options_flags.cc b/tensorflow/compiler/xla/debug_options_flags.cc index c55ebcd066..a9a91648ac 100644 --- a/tensorflow/compiler/xla/debug_options_flags.cc +++ b/tensorflow/compiler/xla/debug_options_flags.cc @@ -33,6 +33,7 @@ DebugOptions DefaultDebugOptionsIgnoringFlags() { opts.set_xla_cpu_multi_thread_eigen(true); opts.set_xla_gpu_cuda_data_dir("./cuda_sdk_lib"); opts.set_xla_eliminate_hlo_implicit_broadcast(true); + opts.set_xla_hlo_dump_as_html(false); #ifdef INTEL_MKL opts.set_xla_cpu_use_mkl_dnn(true); #endif // INTEL_MKL @@ -132,6 +133,11 @@ static void AllocateFlags() { bool_setter_for(&DebugOptions::set_xla_hlo_dump_as_graphdef), flag_values->xla_hlo_dump_as_graphdef(), "Dump HLO graphs as TensorFlow GraphDefs."), + tensorflow::Flag("xla_hlo_dump_as_html", + bool_setter_for(&DebugOptions::set_xla_hlo_dump_as_html), + flag_values->xla_hlo_dump_as_html(), + "Dump HLO graphs as an HTML (DOT rendered into SVG " + "inlined in HTML)."), tensorflow::Flag( "xla_hlo_graph_sharding_color", bool_setter_for(&DebugOptions::set_xla_hlo_graph_sharding_color), diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD index 4c21ae2a42..8ed9a7bea2 100644 --- a/tensorflow/compiler/xla/service/BUILD +++ b/tensorflow/compiler/xla/service/BUILD @@ -3163,6 +3163,7 @@ cc_library( name = "hlo_graph_dumper", srcs = [ "hlo_graph_dumper.cc", + "hlo_graph_html_renderer.cc", ], hdrs = ["hlo_graph_dumper.h"], deps = [ diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc index 302eca656b..5db21e47ca 100644 --- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc +++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc @@ -1474,14 +1474,15 @@ string ExportGraph(const string& graph, GraphRendererInterface::GraphKind graph_kind, const DebugOptions& debug_options) { string path = debug_options.xla_hlo_graph_path(); - if (!path.empty()) { + if (!path.empty() && !debug_options.xla_hlo_dump_as_html()) { return SaveGraph(graph, graph_kind, path); } else { auto graph_renderer = GraphRendererRegistry::Default()->GetDefaultRenderer(); CHECK(graph_renderer != nullptr) << "No registered renderer for the HLO graph. " - "Use --xla_hlo_graph_path=PATH to export to local file system"; + "Use --xla_hlo_graph_path=PATH --xla_hlo_dump_as_html=false to " + "export to local file system"; return graph_renderer->RenderGraph(graph, graph_kind, debug_options); } } @@ -1589,5 +1590,143 @@ string MaybeDumpHloModule(const HloModule& module, const string& label, return graph_url; } +string WrapDotInHTML(const string& dot) { + static const char html_prefix[] = R"html( + + + + + + + + + + + +
+ + + +)html"; + + return html_prefix + dot + html_suffix; +} + +string RenderDotAsHTMLFile(const string& dot, + const DebugOptions& debug_options) { + string html = WrapDotInHTML(dot); + + auto env = tensorflow::Env::Default(); + std::vector dirs; + string output_dir = debug_options.xla_hlo_graph_path(); + if (output_dir.empty()) { + env->GetLocalTempDirectories(&dirs); + } else { + dirs.push_back(output_dir); + } + // Try each directory, as they might be full, have inappropriate + // permissions or have different problems at times. + string output; + for (const string& dir : dirs) { + string filename = tensorflow::io::JoinPath(dir, "graph-"); + if (env->CreateUniqueFileName(&filename, ".html")) { + output = filename; + break; + } + } + if (output.empty()) { + LOG(FATAL) << "Failed to create unique output file name."; + } + TF_CHECK_OK(tensorflow::WriteStringToFile(env, output, html)); + return "file://" + output; +} + } // namespace hlo_graph_dumper } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.h b/tensorflow/compiler/xla/service/hlo_graph_dumper.h index de1eefab77..8e51454ef1 100644 --- a/tensorflow/compiler/xla/service/hlo_graph_dumper.h +++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.h @@ -81,6 +81,12 @@ string DumpAllPathsFromTo(const HloInstruction& from, const HloInstruction& to, void DumpText(const HloModule& module, const string& label, const string& directory_path, bool do_prefix = true); +// Renders DOT graph as inline SVG and saves it in an HTML file in a temprary +// directory or directory specified via --xla_hlo_graph_path. Returns the file +// URI pointing to the file. +string RenderDotAsHTMLFile(const string& dot, + const DebugOptions& debug_options); + // Graph renderers may be added using a registration mechanism, e.g.: // XLA_REGISTER_GRAPH_RENDERER(AGraphRendererClass, 100) // The renderer with the highest numeric priority value is used. diff --git a/tensorflow/compiler/xla/service/hlo_graph_html_renderer.cc b/tensorflow/compiler/xla/service/hlo_graph_html_renderer.cc new file mode 100644 index 0000000000..84c4cf18df --- /dev/null +++ b/tensorflow/compiler/xla/service/hlo_graph_html_renderer.cc @@ -0,0 +1,43 @@ +/* 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. +==============================================================================*/ + +// Implementation of an DOT graph renderer that uses Javascript to render DOT to +// SVG in a browser. + +#include "tensorflow/compiler/xla/service/hlo_graph_dumper.h" +#include "tensorflow/compiler/xla/service/hlo_module.h" + +namespace xla { +namespace hlo_graph_dumper { +namespace { + +class GraphHtmlRenderer : public GraphRendererInterface { + public: + string RenderGraph(const string& graph, GraphKind graph_kind, + const DebugOptions& debug_options) override { + switch (graph_kind) { + case DOT_GRAPH: + return RenderDotAsHTMLFile(graph, debug_options); + default: + LOG(FATAL) << "Only DOT graphs can be rendered"; + } + } +}; + +XLA_REGISTER_GRAPH_RENDERER(GraphHtmlRenderer); + +} // namespace +} // namespace hlo_graph_dumper +} // namespace xla diff --git a/tensorflow/compiler/xla/xla.proto b/tensorflow/compiler/xla/xla.proto index 238312e36b..8b894cc769 100644 --- a/tensorflow/compiler/xla/xla.proto +++ b/tensorflow/compiler/xla/xla.proto @@ -224,7 +224,10 @@ message DebugOptions { // If set to true XLA:GPU invokes `ptxas` with -O0 (default is -O3). bool xla_gpu_disable_ptxas_optimizations = 103; - // Next id: 105 + // Dump HLO graphs as an HTML (DOT -> SVG inlined in HTML) + bool xla_hlo_dump_as_html = 105; + + // Next id: 106 // Extra options to pass to the compilation backend (e.g. LLVM); specific // interpretation of these values is left to the backend. -- GitLab From 373a764c3812b1f8a3b655b63256f14e541be185 Mon Sep 17 00:00:00 2001 From: Gaurav Jain Date: Wed, 12 Dec 2018 10:32:08 -0800 Subject: [PATCH 430/873] Fix null context construction PiperOrigin-RevId: 225210711 --- tensorflow/python/keras/layers/normalization.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/keras/layers/normalization.py b/tensorflow/python/keras/layers/normalization.py index 75b10222ed..ee37e8a242 100644 --- a/tensorflow/python/keras/layers/normalization.py +++ b/tensorflow/python/keras/layers/normalization.py @@ -418,7 +418,7 @@ class BatchNormalizationV2(Layer): # because of a bug which leads cond_v2 to skip rewriting them creating # conflicts. if tf2.enabled(): - cm = contextlib.contextmanager(lambda: (yield)) + cm = contextlib.contextmanager(lambda: (yield))() else: cm = ops.colocate_with(variable) with cm: -- GitLab From 57eb92b7781d46f22f57f89f75010b898e236c42 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 12 Dec 2018 10:38:08 -0800 Subject: [PATCH 431/873] Internal Change PiperOrigin-RevId: 225212001 --- tensorflow/python/BUILD | 1 + tensorflow/python/ops/ragged/__init__.py | 15 ++++++++++++++- tensorflow/python/ops/ragged/ragged_dispatch.py | 11 +++++++++-- tensorflow/python/ops/standard_ops.py | 6 ++++++ 4 files changed, 30 insertions(+), 3 deletions(-) diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index 8a7c001321..c11df5534d 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -3033,6 +3033,7 @@ py_library( "//tensorflow/python/eager:wrap_function", "//tensorflow/python/ops/distributions", "//tensorflow/python/ops/linalg", + "//tensorflow/python/ops/ragged", ], ) diff --git a/tensorflow/python/ops/ragged/__init__.py b/tensorflow/python/ops/ragged/__init__.py index 3d915ee269..f23f506e06 100644 --- a/tensorflow/python/ops/ragged/__init__.py +++ b/tensorflow/python/ops/ragged/__init__.py @@ -66,6 +66,15 @@ class documentation. @@RaggedTensorDynamicShape @@broadcast_to @@broadcast_dynamic_shape + + +@@ragged_dispatch +@@ragged_factory_ops +@@ragged_operators +@@ragged_string_ops +@@ragged_tensor +@@ragged_tensor_value +@@ragged_util """ from __future__ import absolute_import @@ -73,8 +82,12 @@ from __future__ import division from __future__ import print_function from tensorflow.python.ops.ragged import ragged_dispatch +from tensorflow.python.ops.ragged import ragged_factory_ops from tensorflow.python.ops.ragged import ragged_operators from tensorflow.python.ops.ragged import ragged_string_ops +from tensorflow.python.ops.ragged import ragged_tensor +from tensorflow.python.ops.ragged import ragged_tensor_value +from tensorflow.python.ops.ragged import ragged_util from tensorflow.python.ops.ragged.ragged_array_ops import batch_gather from tensorflow.python.ops.ragged.ragged_array_ops import boolean_mask @@ -133,7 +146,7 @@ from tensorflow.python.util import all_util as _all_util # Register OpDispatchers that override standard TF ops to work w/ RaggedTensors. -__doc__ += ragged_dispatch.register_dispatchers() # pylint: disable=redefined-builtin +__doc__ += ragged_dispatch.ragged_op_list() # pylint: disable=redefined-builtin # Any symbol that is not referenced (with "@@name") in the module docstring # above will be removed. diff --git a/tensorflow/python/ops/ragged/ragged_dispatch.py b/tensorflow/python/ops/ragged/ragged_dispatch.py index f334f1fc8e..77990a8b18 100644 --- a/tensorflow/python/ops/ragged/ragged_dispatch.py +++ b/tensorflow/python/ops/ragged/ragged_dispatch.py @@ -447,10 +447,17 @@ def register_dispatchers(): for (original_op, ragged_op, args) in _RAGGED_DISPATCH_OPS: RaggedDispatcher(original_op, ragged_op, args).register(original_op) - docstring = ( + +def ragged_op_list(): + """Returns a string listing operators that have dispathers registered.""" + op_list = ( + _UNARY_ELEMENTWISE_OPS + _UNARY_LIST_ELEMENTWISE_OPS + + _BINARY_ELEMENTWISE_OPS + [x[0] for x in _RAGGED_DISPATCH_OPS]) + return ( '\n\n### Additional ops that support `RaggedTensor`\n\n' + '\n'.join([ '* `tf.%s`' % tf_export.get_canonical_name_for_symbol(op) for op in op_list ])) - return docstring + +register_dispatchers() diff --git a/tensorflow/python/ops/standard_ops.py b/tensorflow/python/ops/standard_ops.py index 8ef0fe8070..ba3bd09492 100644 --- a/tensorflow/python/ops/standard_ops.py +++ b/tensorflow/python/ops/standard_ops.py @@ -71,6 +71,8 @@ from tensorflow.python.ops.math_ops import * from tensorflow.python.ops.numerics import * from tensorflow.python.ops.parsing_ops import * from tensorflow.python.ops.partitioned_variables import * +from tensorflow.python.ops.ragged import ragged_dispatch as _ragged_dispatch +from tensorflow.python.ops.ragged import ragged_operators as _ragged_operators from tensorflow.python.ops.random_ops import * from tensorflow.python.ops.script_ops import py_func from tensorflow.python.ops.session_ops import * @@ -102,3 +104,7 @@ from tensorflow.python.ops.variable_scope import * from tensorflow.python.ops.variables import * # pylint: enable=wildcard-import # pylint: enable=g-bad-import-order + + +# These modules were imported to set up RaggedTensor operators and dispatchers: +del _ragged_dispatch, _ragged_operators -- GitLab From 6939c38130f6cdaea01a4db7cd2db1d2297eb9e8 Mon Sep 17 00:00:00 2001 From: Zhenyu Tan Date: Wed, 12 Dec 2018 11:06:59 -0800 Subject: [PATCH 432/873] Internal Cleanup. PiperOrigin-RevId: 225217785 --- tensorflow/core/kernels/training_ops_test.cc | 44 -------------------- 1 file changed, 44 deletions(-) diff --git a/tensorflow/core/kernels/training_ops_test.cc b/tensorflow/core/kernels/training_ops_test.cc index 09804f95dc..2dcc4a500e 100644 --- a/tensorflow/core/kernels/training_ops_test.cc +++ b/tensorflow/core/kernels/training_ops_test.cc @@ -194,50 +194,6 @@ static void BM_Adam(int iters, int params) { } BENCHMARK(BM_Adam)->Arg(128 << 10)->Arg(256 << 10); -static void AdamWithAmsgrad(int32 n, Graph** init_g, Graph** train_g) { - TensorShape shape({n}); - { - Graph* g = new Graph(OpRegistry::Global()); - auto var = Var(g, n); - auto m = Var(g, n); - auto v = Var(g, n); - auto zero = Zeros(g, n); - test::graph::Assign(g, var, zero); - test::graph::Assign(g, m, zero); - test::graph::Assign(g, v, zero); - *init_g = g; - } - { - Graph* g = new Graph(OpRegistry::Global()); - auto var = Var(g, n); - auto m = Var(g, n); - auto v = Var(g, n); - auto vhat = Var(g, n); - auto beta1_power = Scalar(g, 0.9); - auto beta2_power = Scalar(g, 0.99); - auto lr = Scalar(g, 0.01); - auto beta1 = Scalar(g, 0.9); - auto beta2 = Scalar(g, 0.99); - auto epsilon = Scalar(g, 1e-8); - auto grad = Random(g, n); - test::graph::Multi(g, "ApplyAdamWithAmsgrad", - {var, m, v, vhat, beta1_power, beta2_power, lr, beta1, - beta2, epsilon, grad}); - *train_g = g; - } -} - -static void BM_AdamWithAmsgrad(int iters, int params) { - const int64 tot = static_cast(iters) * params; - testing::ItemsProcessed(tot); - testing::BytesProcessed(tot * sizeof(float)); - Graph* init; - Graph* train; - AdamWithAmsgrad(params, &init, &train); - test::Benchmark("cpu", train, GetOptions(), init).Run(iters); -} -BENCHMARK(BM_AdamWithAmsgrad)->Arg(128 << 10)->Arg(256 << 10); - static void RMSProp(int32 n, Graph** init_g, Graph** train_g) { TensorShape shape({n}); { -- GitLab From 3ae0654d41b74538920d1d1cf812f83e35895fc6 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 12 Dec 2018 11:08:26 -0800 Subject: [PATCH 433/873] Fix and re-enable three tests under LossWeightingTest in training_test.py. These tests share the same assertion: that weighting a particular class's loss over other classes (by passing in `sample_weight` into `model.fit`) leads to a lower evaluation loss when evaluating test data limited to that class compared to evaluating all test data. My theory is that the models in these tests are not trained enough for that assumption to always hold true, which is why they are flaky. Increased the weight from 2 to 10 and the training epochs from 5 to 10. PiperOrigin-RevId: 225218063 --- .../python/keras/engine/training_test.py | 25 +++++++++---------- 1 file changed, 12 insertions(+), 13 deletions(-) diff --git a/tensorflow/python/keras/engine/training_test.py b/tensorflow/python/keras/engine/training_test.py index 91a0c7cc2f..a61e2edcd3 100644 --- a/tensorflow/python/keras/engine/training_test.py +++ b/tensorflow/python/keras/engine/training_test.py @@ -793,12 +793,12 @@ class TestExceptionsAndWarnings(keras_parameterized.TestCase): class LossWeightingTest(keras_parameterized.TestCase): @keras_parameterized.run_all_keras_modes - # TODO(b/120562577): Test failing with assertion error. - def DISABLED_test_class_weights(self): + def test_class_weights(self): num_classes = 5 batch_size = 5 - epochs = 5 + epochs = 10 weighted_class = 3 + weight = 10. train_samples = 1000 test_samples = 1000 input_dim = 5 @@ -827,7 +827,7 @@ class LossWeightingTest(keras_parameterized.TestCase): test_ids = np.where(int_y_test == np.array(weighted_class))[0] class_weight = dict([(i, 1.) for i in range(num_classes)]) - class_weight[weighted_class] = 2. + class_weight[weighted_class] = weight sample_weight = np.ones((y_train.shape[0])) sample_weight[int_y_train == weighted_class] = 2. @@ -864,12 +864,12 @@ class LossWeightingTest(keras_parameterized.TestCase): self.assertLess(score[0], ref_score[0]) @keras_parameterized.run_all_keras_modes - @tf_test_util.run_v1_only('b/120545219') def test_sample_weights(self): num_classes = 5 batch_size = 5 - epochs = 5 + epochs = 10 weighted_class = 3 + weight = 10. train_samples = 1000 test_samples = 1000 input_dim = 5 @@ -898,7 +898,7 @@ class LossWeightingTest(keras_parameterized.TestCase): test_ids = np.where(int_y_test == np.array(weighted_class))[0] sample_weight = np.ones((y_train.shape[0])) - sample_weight[int_y_train == weighted_class] = 2. + sample_weight[int_y_train == weighted_class] = weight model.fit( x_train, @@ -962,13 +962,12 @@ class LossWeightingTest(keras_parameterized.TestCase): self.assertTrue(msg_found) @keras_parameterized.run_all_keras_modes - @tf_test_util.run_v1_only('b/120545219') - # TODO(b/120562577): Test failing with assertion error. - def DISABLED_test_temporal_sample_weights(self): + def test_temporal_sample_weights(self): num_classes = 5 batch_size = 5 - epochs = 5 + epochs = 10 weighted_class = 3 + weight = 10. train_samples = 1000 test_samples = 1000 input_dim = 5 @@ -997,7 +996,7 @@ class LossWeightingTest(keras_parameterized.TestCase): test_ids = np.where(int_y_test == np.array(weighted_class))[0] sample_weight = np.ones((y_train.shape[0])) - sample_weight[int_y_train == weighted_class] = 2. + sample_weight[int_y_train == weighted_class] = weight temporal_x_train = np.reshape(x_train, (len(x_train), 1, x_train.shape[1])) @@ -1018,7 +1017,7 @@ class LossWeightingTest(keras_parameterized.TestCase): model.compile( RMSPropOptimizer(learning_rate=learning_rate), - loss='binary_crossentropy', + loss='categorical_crossentropy', metrics=['acc', metrics_module.CategoricalAccuracy()], weighted_metrics=['mae', metrics_module.CategoricalAccuracy()], sample_weight_mode='temporal', -- GitLab From 350791003de42dbb17c53474a677b108f473b0ba Mon Sep 17 00:00:00 2001 From: Dan Moldovan Date: Wed, 12 Dec 2018 11:52:20 -0800 Subject: [PATCH 434/873] Reduce the cost of serializing ConversionOptions to code, by using a more efficient inspect.util.getqualifiedname, reducing its max_depth and falling back to caching the value in the namespace. The latter step makes it more difficult to run the generated code afterwards, but it should in turn speed up the conversion process. This also adds an extra check to tf_decorator to improve robustness. PiperOrigin-RevId: 225226256 --- .../python/autograph/converters/call_trees.py | 2 +- tensorflow/python/autograph/core/converter.py | 18 ++++-- tensorflow/python/autograph/impl/api.py | 3 + .../python/autograph/pyct/inspect_utils.py | 25 +++++--- .../autograph/pyct/inspect_utils_test.py | 57 +++++++++++++++++++ tensorflow/python/util/tf_decorator.py | 5 ++ 6 files changed, 94 insertions(+), 16 deletions(-) diff --git a/tensorflow/python/autograph/converters/call_trees.py b/tensorflow/python/autograph/converters/call_trees.py index 3e0b40290f..b1bfe04347 100644 --- a/tensorflow/python/autograph/converters/call_trees.py +++ b/tensorflow/python/autograph/converters/call_trees.py @@ -261,7 +261,7 @@ class CallTreeTransformer(converter.Base): func=func, owner=owner, options=self.ctx.program.options.to_ast( - self.ctx.info.namespace, + self.ctx, internal_convert_user_code=self.ctx.program.options.recursive), args=node.args) # TODO(mdan): Improve the template mechanism to better support this. diff --git a/tensorflow/python/autograph/core/converter.py b/tensorflow/python/autograph/core/converter.py index eea2621056..b9c2449566 100644 --- a/tensorflow/python/autograph/core/converter.py +++ b/tensorflow/python/autograph/core/converter.py @@ -179,15 +179,14 @@ class ConversionOptions(object): return (Feature.ALL in self.optional_features or feature in self.optional_features) - def to_ast(self, namespace, internal_convert_user_code=None): + def to_ast(self, ctx, internal_convert_user_code=None): """Returns a representation of this object as an AST node. The AST node encodes a constructor that would create an object with the same contents. Args: - namespace: Dict[str, Any], the namespace to use when serializing values to - names. + ctx: EntityContext, the entity with which this AST needs to be consistent. internal_convert_user_code: Optional[bool], allows ovrriding the corresponding value. @@ -205,10 +204,11 @@ class ConversionOptions(object): """ def as_qualified_name(o): - name = inspect_utils.getqualifiedname(namespace, o) + name = inspect_utils.getqualifiedname(ctx.info.namespace, o, max_depth=1) if not name: - raise ValueError('Could not locate entity {} in {}'.format( - o, namespace)) + # TODO(mdan): This needs to account for the symbols defined locally. + name = ctx.namer.new_symbol(o.__name__, ()) + ctx.program.add_symbol(name, o) return name def list_of_names(values): @@ -279,6 +279,7 @@ class ProgramContext(object): self.dependency_cache = {} self.additional_imports = set() self.name_map = {} + self.additional_symbols = {} @property def required_imports(self): @@ -321,6 +322,11 @@ class ProgramContext(object): else: self.name_map[o] = name + def add_symbol(self, name, value): + if name in self.additional_symbols: + assert self.additional_symbols[name] is value + self.additional_symbols[name] = value + def add_to_cache(self, original_entity, converted_ast): self.conversion_order.append(original_entity) self.dependency_cache[original_entity] = converted_ast diff --git a/tensorflow/python/autograph/impl/api.py b/tensorflow/python/autograph/impl/api.py index 54b46b1efd..a20ad71c97 100644 --- a/tensorflow/python/autograph/impl/api.py +++ b/tensorflow/python/autograph/impl/api.py @@ -424,6 +424,9 @@ def to_graph(entity, # Avoid overwriting entities that have been transformed. if key not in compiled_module.__dict__: compiled_module.__dict__[key] = val + for key, val in program_ctx.additional_symbols.items(): + if key not in compiled_module.__dict__: + compiled_module.__dict__[key] = val compiled = getattr(compiled_module, name) if tf_inspect.isfunction(entity): diff --git a/tensorflow/python/autograph/pyct/inspect_utils.py b/tensorflow/python/autograph/pyct/inspect_utils.py index 7c819f364f..56945b464b 100644 --- a/tensorflow/python/autograph/pyct/inspect_utils.py +++ b/tensorflow/python/autograph/pyct/inspect_utils.py @@ -101,7 +101,7 @@ def getnamespace(f): return namespace -def getqualifiedname(namespace, object_, max_depth=2): +def getqualifiedname(namespace, object_, max_depth=7, visited=None): """Returns the name by which a value can be referred to in a given namespace. If the object defines a parent module, the function attempts to use it to @@ -115,16 +115,20 @@ def getqualifiedname(namespace, object_, max_depth=2): object_: Any, the value to search. max_depth: Optional[int], a limit to the recursion depth when searching inside modules. + visited: Optional[Set[int]], ID of modules to avoid visiting. Returns: Union[str, None], the fully-qualified name that resolves to the value o, or None if it couldn't be found. """ - for name, value in namespace.items(): + if visited is None: + visited = set() + + for name in namespace: # The value may be referenced by more than one symbol, case in which # any symbol will be fine. If the program contains symbol aliases that # change over time, this may capture a symbol that will later point to # something else. # TODO(mdan): Prefer the symbol that matches the value type name. - if object_ is value: + if object_ is namespace[name]: return name # If an object is not found, try to search its parent modules. @@ -132,22 +136,25 @@ def getqualifiedname(namespace, object_, max_depth=2): if (parent is not None and parent is not object_ and parent is not namespace): # No limit to recursion depth because of the guard above. - parent_name = getqualifiedname(namespace, parent, max_depth=0) + parent_name = getqualifiedname( + namespace, parent, max_depth=0, visited=visited) if parent_name is not None: - name_in_parent = getqualifiedname(parent.__dict__, object_, max_depth=0) + name_in_parent = getqualifiedname( + parent.__dict__, object_, max_depth=0, visited=visited) assert name_in_parent is not None, ( 'An object should always be found in its owner module') return '{}.{}'.format(parent_name, name_in_parent) - # TODO(mdan): Use breadth-first search and avoid visiting modules twice. if max_depth: # Iterating over a copy prevents "changed size due to iteration" errors. # It's unclear why those occur - suspecting new modules may load during # iteration. - for name, value in namespace.copy().items(): - if tf_inspect.ismodule(value): + for name in tuple(namespace.keys()): + value = namespace[name] + if tf_inspect.ismodule(value) and id(value) not in visited: + visited.add(id(value)) name_in_module = getqualifiedname(value.__dict__, object_, - max_depth - 1) + max_depth - 1, visited) if name_in_module is not None: return '{}.{}'.format(name, name_in_module) return None diff --git a/tensorflow/python/autograph/pyct/inspect_utils_test.py b/tensorflow/python/autograph/pyct/inspect_utils_test.py index a2c39056d1..420a20c22f 100644 --- a/tensorflow/python/autograph/pyct/inspect_utils_test.py +++ b/tensorflow/python/autograph/pyct/inspect_utils_test.py @@ -183,6 +183,63 @@ class InspectUtilsTest(test.TestCase): self.assertEqual(inspect_utils.getqualifiedname(ns, bar), 'bar') self.assertEqual(inspect_utils.getqualifiedname(ns, baz), 'bar.baz') + def test_getqualifiedname_efficiency(self): + foo = object() + + # We create a densely connected graph consisting of a relatively small + # number of modules and hide our symbol in one of them. The path to the + # symbol is at least 10, and each node has about 10 neighbors. However, + # by skipping visited modules, the search should take much less. + ns = {} + prev_level = [] + for i in range(10): + current_level = [] + for j in range(10): + mod_name = 'mod_{}_{}'.format(i, j) + mod = imp.new_module(mod_name) + current_level.append(mod) + if i == 9 and j == 9: + mod.foo = foo + if prev_level: + # All modules at level i refer to all modules at level i+1 + for prev in prev_level: + for mod in current_level: + prev.__dict__[mod.__name__] = mod + else: + for mod in current_level: + ns[mod.__name__] = mod + prev_level = current_level + + self.assertIsNone(inspect_utils.getqualifiedname(ns, inspect_utils)) + self.assertIsNotNone( + inspect_utils.getqualifiedname(ns, foo, max_depth=10000000000)) + + def test_getqualifiedname_cycles(self): + foo = object() + + # We create a graph of modules that contains circular references. The + # search process should avoid them. The searched object is hidden at the + # bottom of a path of length roughly 10. + ns = {} + mods = [] + for i in range(10): + mod = imp.new_module('mod_{}'.format(i)) + if i == 9: + mod.foo = foo + # Module i refers to module i+1 + if mods: + mods[-1].__dict__[mod.__name__] = mod + else: + ns[mod.__name__] = mod + # Module i refers to all modules j < i. + for prev in mods: + mod.__dict__[prev.__name__] = prev + mods.append(mod) + + self.assertIsNone(inspect_utils.getqualifiedname(ns, inspect_utils)) + self.assertIsNotNone( + inspect_utils.getqualifiedname(ns, foo, max_depth=10000000000)) + def test_getqualifiedname_finds_via_parent_module(self): # TODO(mdan): This test is vulnerable to change in the lib module. # A better way to forge modules should be found. diff --git a/tensorflow/python/util/tf_decorator.py b/tensorflow/python/util/tf_decorator.py index 0cfc836246..f018e1a1bd 100644 --- a/tensorflow/python/util/tf_decorator.py +++ b/tensorflow/python/util/tf_decorator.py @@ -98,6 +98,9 @@ def make_decorator(target, if hasattr(target, '__doc__'): decorator_func.__doc__ = decorator.__doc__ decorator_func.__wrapped__ = target + # Keeping a second handle to `target` allows callers to detect whether the + # decorator was modified using `rewrap`. + decorator_func.__original_wrapped__ = target return decorator_func @@ -173,6 +176,8 @@ def unwrap(maybe_tf_decorator): decorators.append(getattr(cur, '_tf_decorator')) else: break + if not hasattr(decorators[-1], 'decorated_target'): + break cur = decorators[-1].decorated_target return decorators, cur -- GitLab From 16069bf8745b029a82ee8eae194909f578b3dea0 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 12 Dec 2018 12:05:32 -0800 Subject: [PATCH 435/873] Allow RandomUniform to be quantized. As the RandomUniform operator is a custom op, it is up to the tf-lite user to provide the implementation. Best to assume it exists so the user can implement. PiperOrigin-RevId: 225228337 --- tensorflow/lite/toco/graph_transformations/quantize.cc | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/lite/toco/graph_transformations/quantize.cc b/tensorflow/lite/toco/graph_transformations/quantize.cc index 1146078c30..2fa80f2eda 100644 --- a/tensorflow/lite/toco/graph_transformations/quantize.cc +++ b/tensorflow/lite/toco/graph_transformations/quantize.cc @@ -64,6 +64,7 @@ bool SupportsQuantization(const Operator& op) { type == OperatorType::kRelu1 || type == OperatorType::kRelu6 || type == OperatorType::kShape || type == OperatorType::kExpandDims || type == OperatorType::kPack || type == OperatorType::kTopK_V2 || + type == OperatorType::kRandomUniform || type == OperatorType::kResizeNearestNeighbor || type == OperatorType::kPRelu; } -- GitLab From 250ab666f6b6fe78818bd040da0a57e6fddd9f89 Mon Sep 17 00:00:00 2001 From: Francois Chollet Date: Wed, 12 Dec 2018 12:23:40 -0800 Subject: [PATCH 436/873] Make core layers tests run in graph and eager mode. PiperOrigin-RevId: 225231668 --- tensorflow/python/keras/BUILD | 2 +- tensorflow/python/keras/layers/core_test.py | 323 ++++++++++---------- 2 files changed, 155 insertions(+), 170 deletions(-) diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD index 36fea36389..3c390cb2b0 100755 --- a/tensorflow/python/keras/BUILD +++ b/tensorflow/python/keras/BUILD @@ -401,7 +401,7 @@ py_test( name = "core_test", size = "medium", srcs = ["layers/core_test.py"], - shard_count = 2, + shard_count = 3, srcs_version = "PY2AND3", deps = [ ":keras", diff --git a/tensorflow/python/keras/layers/core_test.py b/tensorflow/python/keras/layers/core_test.py index f138adf760..9df40f806f 100644 --- a/tensorflow/python/keras/layers/core_test.py +++ b/tensorflow/python/keras/layers/core_test.py @@ -22,43 +22,36 @@ import numpy as np from tensorflow.python import keras from tensorflow.python.eager import context -from tensorflow.python.framework import test_util as tf_test_util +from tensorflow.python.keras import keras_parameterized from tensorflow.python.keras import testing_utils from tensorflow.python.ops import math_ops from tensorflow.python.platform import test -class CoreLayersTest(test.TestCase): - - def test_masking(self): - with self.cached_session(): - testing_utils.layer_test( - keras.layers.Masking, kwargs={}, input_shape=(3, 2, 3)) +@keras_parameterized.run_all_keras_modes +class DropoutLayersTest(keras_parameterized.TestCase): def test_dropout(self): - with self.cached_session(): - testing_utils.layer_test( - keras.layers.Dropout, kwargs={'rate': 0.5}, input_shape=(3, 2)) + testing_utils.layer_test( + keras.layers.Dropout, kwargs={'rate': 0.5}, input_shape=(3, 2)) - with self.cached_session(): - testing_utils.layer_test( - keras.layers.Dropout, - kwargs={'rate': 0.5, - 'noise_shape': [3, 1]}, - input_shape=(3, 2)) - - # https://github.com/tensorflow/tensorflow/issues/14819 - with self.cached_session(): - dropout = keras.layers.Dropout(0.5) - self.assertEqual(True, dropout.supports_masking) - - @tf_test_util.run_in_graph_and_eager_modes - def test_spatial_dropout(self): + testing_utils.layer_test( + keras.layers.Dropout, + kwargs={'rate': 0.5, + 'noise_shape': [3, 1]}, + input_shape=(3, 2)) + + def test_dropout_supports_masking(self): + dropout = keras.layers.Dropout(0.5) + self.assertEqual(True, dropout.supports_masking) + + def test_spatial_dropout_1d(self): testing_utils.layer_test( keras.layers.SpatialDropout1D, kwargs={'rate': 0.5}, input_shape=(2, 3, 4)) + def test_spatial_dropout_2d(self): testing_utils.layer_test( keras.layers.SpatialDropout2D, kwargs={'rate': 0.5}, @@ -69,6 +62,7 @@ class CoreLayersTest(test.TestCase): kwargs={'rate': 0.5, 'data_format': 'channels_first'}, input_shape=(2, 3, 4, 5)) + def test_spatial_dropout_3d(self): testing_utils.layer_test( keras.layers.SpatialDropout3D, kwargs={'rate': 0.5}, @@ -79,7 +73,122 @@ class CoreLayersTest(test.TestCase): kwargs={'rate': 0.5, 'data_format': 'channels_first'}, input_shape=(2, 3, 4, 4, 5)) - @tf_test_util.run_in_graph_and_eager_modes + +@keras_parameterized.run_all_keras_modes +class LambdaLayerTest(keras_parameterized.TestCase): + + def test_lambda(self): + testing_utils.layer_test( + keras.layers.Lambda, + kwargs={'function': lambda x: x + 1}, + input_shape=(3, 2)) + + testing_utils.layer_test( + keras.layers.Lambda, + kwargs={ + 'function': lambda x, a, b: x * a + b, + 'arguments': { + 'a': 0.6, + 'b': 0.4 + } + }, + input_shape=(3, 2)) + + # test serialization with function + def f(x): + return x + 1 + + ld = keras.layers.Lambda(f) + config = ld.get_config() + ld = keras.layers.deserialize({ + 'class_name': 'Lambda', + 'config': config + }) + + # test with lambda + ld = keras.layers.Lambda( + lambda x: keras.backend.concatenate([math_ops.square(x), x])) + config = ld.get_config() + ld = keras.layers.Lambda.from_config(config) + + def test_lambda_multiple_inputs(self): + ld = keras.layers.Lambda(lambda x: x[0], output_shape=lambda x: x[0]) + x1 = np.ones([3, 2], np.float32) + x2 = np.ones([3, 5], np.float32) + out = ld([x1, x2]) + self.assertAllEqual(out.shape, [3, 2]) + + def test_lambda_output_shape(self): + l = keras.layers.Lambda(lambda x: x + 1, output_shape=(1, 1)) + l(keras.backend.variable(np.ones((1, 1)))) + self.assertEqual((1, 1), l.get_config()['output_shape']) + + def test_lambda_output_shape_function(self): + def get_output_shape(input_shape): + return 1 * input_shape + + l = keras.layers.Lambda(lambda x: x + 1, output_shape=get_output_shape) + l(keras.backend.variable(np.ones((1, 1)))) + self.assertEqual('lambda', l.get_config()['output_shape_type']) + + def test_lambda_output_shape_autocalculate_multiple_inputs(self): + + def lambda_fn(x): + return math_ops.matmul(x[0], x[1]) + + l = keras.layers.Lambda(lambda_fn) + output_shape = l.compute_output_shape([(10, 10), (10, 20)]) + self.assertAllEqual((10, 20), output_shape) + + def test_lambda_output_shape_list_multiple_outputs(self): + + def lambda_fn(x): + return x + + l = keras.layers.Lambda(lambda_fn, output_shape=[(10,), (20,)]) + output_shape = l.compute_output_shape([(10, 10), (10, 20)]) + self.assertAllEqual([(10, 10), (10, 20)], output_shape) + + def test_lambda_output_shape_tuple_with_none(self): + + def lambda_fn(x): + return x + + l = keras.layers.Lambda(lambda_fn, output_shape=(None, 10)) + output_shape = l.compute_output_shape((5, 10, 20)) + self.assertAllEqual([5, None, 10], output_shape.as_list()) + + def test_lambda_output_shape_function_multiple_outputs(self): + + def lambda_fn(x): + return x + + def output_shape_fn(input_shape): + return input_shape + + l = keras.layers.Lambda(lambda_fn, output_shape=output_shape_fn) + output_shape = l.compute_output_shape([(10, 10), (10, 20)]) + self.assertAllEqual([(10, 10), (10, 20)], output_shape) + + def test_lambda_config_serialization(self): + # Test serialization with output_shape and output_shape_type + layer = keras.layers.Lambda(lambda x: x + 1, output_shape=(1, 1)) + layer(keras.backend.variable(np.ones((1, 1)))) + config = layer.get_config() + layer = keras.layers.deserialize({ + 'class_name': 'Lambda', + 'config': config + }) + layer = keras.layers.Lambda.from_config(config) + + +@keras_parameterized.run_all_keras_modes +class CoreLayersTest(keras_parameterized.TestCase): + + def test_masking(self): + testing_utils.layer_test( + keras.layers.Masking, kwargs={}, input_shape=(3, 2, 3)) + def test_activation(self): # with string argument testing_utils.layer_test( @@ -93,7 +202,6 @@ class CoreLayersTest(test.TestCase): kwargs={'activation': keras.backend.relu}, input_shape=(3, 2)) - @tf_test_util.run_in_graph_and_eager_modes def test_reshape(self): testing_utils.layer_test( keras.layers.Reshape, @@ -115,26 +223,22 @@ class CoreLayersTest(test.TestCase): kwargs={'target_shape': (-1, 1)}, input_shape=(None, None, 2)) - @tf_test_util.run_in_graph_and_eager_modes def test_permute(self): testing_utils.layer_test( keras.layers.Permute, kwargs={'dims': (2, 1)}, input_shape=(3, 2, 4)) - @tf_test_util.run_in_graph_and_eager_modes def test_permute_errors_on_invalid_starting_dims_index(self): with self.assertRaisesRegexp(ValueError, r'Invalid permutation .*dims.*'): testing_utils.layer_test( keras.layers.Permute, kwargs={'dims': (0, 1, 2)}, input_shape=(3, 2, 4)) - @tf_test_util.run_in_graph_and_eager_modes def test_permute_errors_on_invalid_set_of_dims_indices(self): with self.assertRaisesRegexp(ValueError, r'Invalid permutation .*dims.*'): testing_utils.layer_test( keras.layers.Permute, kwargs={'dims': (1, 4, 2)}, input_shape=(3, 2, 4)) - @tf_test_util.run_in_graph_and_eager_modes def test_flatten(self): testing_utils.layer_test( keras.layers.Flatten, kwargs={}, input_shape=(3, 2, 4)) @@ -149,7 +253,6 @@ class CoreLayersTest(test.TestCase): np.transpose(inputs, (0, 2, 3, 1)), (-1, 5 * 5 * 3)) self.assertAllClose(outputs, target_outputs) - @tf_test_util.run_in_graph_and_eager_modes def test_flatten_scalar_channels(self): testing_utils.layer_test( keras.layers.Flatten, kwargs={}, input_shape=(3,)) @@ -163,54 +266,10 @@ class CoreLayersTest(test.TestCase): target_outputs = np.expand_dims(inputs, -1) self.assertAllClose(outputs, target_outputs) - @tf_test_util.run_in_graph_and_eager_modes def test_repeat_vector(self): testing_utils.layer_test( keras.layers.RepeatVector, kwargs={'n': 3}, input_shape=(3, 2)) - def test_lambda(self): - testing_utils.layer_test( - keras.layers.Lambda, - kwargs={'function': lambda x: x + 1}, - input_shape=(3, 2)) - - testing_utils.layer_test( - keras.layers.Lambda, - kwargs={ - 'function': lambda x, a, b: x * a + b, - 'arguments': { - 'a': 0.6, - 'b': 0.4 - } - }, - input_shape=(3, 2)) - - # test serialization with function - def f(x): - return x + 1 - - ld = keras.layers.Lambda(f) - config = ld.get_config() - ld = keras.layers.deserialize({ - 'class_name': 'Lambda', - 'config': config - }) - - # test with lambda - ld = keras.layers.Lambda( - lambda x: keras.backend.concatenate([math_ops.square(x), x])) - config = ld.get_config() - ld = keras.layers.Lambda.from_config(config) - - @tf_test_util.run_in_graph_and_eager_modes - def test_lambda_multiple_inputs(self): - ld = keras.layers.Lambda(lambda x: x[0], output_shape=lambda x: x[0]) - x1 = np.ones([3, 2], np.float32) - x2 = np.ones([3, 5], np.float32) - out = ld([x1, x2]) - self.assertAllEqual(out.shape, [3, 2]) - - @tf_test_util.run_in_graph_and_eager_modes def test_dense(self): testing_utils.layer_test( keras.layers.Dense, kwargs={'units': 3}, input_shape=(3, 2)) @@ -225,105 +284,31 @@ class CoreLayersTest(test.TestCase): keras.layers.Dense, kwargs={'units': 3}, input_shape=(3, 4, 5, 2)) def test_dense_regularization(self): - with self.cached_session(): - layer = keras.layers.Dense( - 3, - kernel_regularizer=keras.regularizers.l1(0.01), - bias_regularizer='l1', - activity_regularizer='l2', - name='dense_reg') - layer(keras.backend.variable(np.ones((2, 4)))) - self.assertEqual(3, len(layer.losses)) + layer = keras.layers.Dense( + 3, + kernel_regularizer=keras.regularizers.l1(0.01), + bias_regularizer='l1', + activity_regularizer='l2', + name='dense_reg') + layer(keras.backend.variable(np.ones((2, 4)))) + self.assertEqual(3, len(layer.losses)) def test_dense_constraints(self): - with self.cached_session(): - k_constraint = keras.constraints.max_norm(0.01) - b_constraint = keras.constraints.max_norm(0.01) - layer = keras.layers.Dense( - 3, kernel_constraint=k_constraint, bias_constraint=b_constraint) - layer(keras.backend.variable(np.ones((2, 4)))) - self.assertEqual(layer.kernel.constraint, k_constraint) - self.assertEqual(layer.bias.constraint, b_constraint) + k_constraint = keras.constraints.max_norm(0.01) + b_constraint = keras.constraints.max_norm(0.01) + layer = keras.layers.Dense( + 3, kernel_constraint=k_constraint, bias_constraint=b_constraint) + layer(keras.backend.variable(np.ones((2, 4)))) + self.assertEqual(layer.kernel.constraint, k_constraint) + self.assertEqual(layer.bias.constraint, b_constraint) def test_activity_regularization(self): - with self.cached_session(): - layer = keras.layers.ActivityRegularization(l1=0.1) - layer(keras.backend.variable(np.ones((2, 4)))) - self.assertEqual(1, len(layer.losses)) - _ = layer.get_config() - - def test_lambda_output_shape(self): - with self.cached_session(): - l = keras.layers.Lambda(lambda x: x + 1, output_shape=(1, 1)) - l(keras.backend.variable(np.ones((1, 1)))) - self.assertEqual((1, 1), l.get_config()['output_shape']) + layer = keras.layers.ActivityRegularization(l1=0.1) + layer(keras.backend.variable(np.ones((2, 4)))) + self.assertEqual(1, len(layer.losses)) + config = layer.get_config() + self.assertEqual(config.pop('l1'), 0.1) - def test_lambda_output_shape_function(self): - def get_output_shape(input_shape): - return 1 * input_shape - - with self.cached_session(): - l = keras.layers.Lambda(lambda x: x + 1, output_shape=get_output_shape) - l(keras.backend.variable(np.ones((1, 1)))) - self.assertEqual('lambda', l.get_config()['output_shape_type']) - - @tf_test_util.run_in_graph_and_eager_modes - def test_lambda_output_shape_autocalculate_multiple_inputs(self): - - def lambda_fn(x): - return math_ops.matmul(x[0], x[1]) - - l = keras.layers.Lambda(lambda_fn) - output_shape = l.compute_output_shape([(10, 10), (10, 20)]) - self.assertAllEqual((10, 20), output_shape) - - @tf_test_util.run_in_graph_and_eager_modes - def test_lambda_output_shape_list_multiple_outputs(self): - - def lambda_fn(x): - return x - - l = keras.layers.Lambda(lambda_fn, output_shape=[(10,), (20,)]) - output_shape = l.compute_output_shape([(10, 10), (10, 20)]) - self.assertAllEqual([(10, 10), (10, 20)], output_shape) - - @tf_test_util.run_in_graph_and_eager_modes - def test_lambda_output_shape_tuple_with_none(self): - - def lambda_fn(x): - return x - - l = keras.layers.Lambda(lambda_fn, output_shape=(None, 10)) - output_shape = l.compute_output_shape((5, 10, 20)) - self.assertAllEqual([5, None, 10], output_shape.as_list()) - - @tf_test_util.run_in_graph_and_eager_modes - def test_lambda_output_shape_function_multiple_outputs(self): - - def lambda_fn(x): - return x - - def output_shape_fn(input_shape): - return input_shape - - l = keras.layers.Lambda(lambda_fn, output_shape=output_shape_fn) - output_shape = l.compute_output_shape([(10, 10), (10, 20)]) - self.assertAllEqual([(10, 10), (10, 20)], output_shape) - - def test_lambda_config_serialization(self): - with self.cached_session(): - # test serialization with output_shape and output_shape_type - layer = keras.layers.Lambda(lambda x: x + 1, output_shape=(1, 1)) - layer(keras.backend.variable(np.ones((1, 1)))) - config = layer.get_config() - layer = keras.layers.deserialize({ - 'class_name': 'Lambda', - 'config': config - }) - - layer = keras.layers.Lambda.from_config(config) - - @tf_test_util.run_in_graph_and_eager_modes def test_numpy_inputs(self): if context.executing_eagerly(): layer = keras.layers.RepeatVector(2) -- GitLab From 27d89c6b8e2fcb56b3d560196e4dc5c11121fafc Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 12 Dec 2018 12:54:39 -0800 Subject: [PATCH 437/873] Automated rollback of commit d6a46850353acfe26625c5ab1ffe7bd5c5a4aaf0 PiperOrigin-RevId: 225236744 --- third_party/nccl/archive.BUILD | 154 +++++---- third_party/nccl/build_defs.bzl.tpl | 467 ++++++++++------------------ 2 files changed, 270 insertions(+), 351 deletions(-) diff --git a/third_party/nccl/archive.BUILD b/third_party/nccl/archive.BUILD index 22b9728017..7a08f97ef3 100644 --- a/third_party/nccl/archive.BUILD +++ b/third_party/nccl/archive.BUILD @@ -1,110 +1,157 @@ # NVIDIA NCCL 2 # A package of optimized primitives for collective multi-GPU communication. -licenses(["notice"]) +licenses(["restricted"]) exports_files(["LICENSE.txt"]) load( "@local_config_nccl//:build_defs.bzl", - "cuda_rdc_library", - "gen_device_srcs", - "process_srcs", + "gen_nccl_h", + "nccl_library", + "rdc_copts", + "rdc_library", +) +load( + "@local_config_cuda//cuda:build_defs.bzl", + "cuda_default_copts", ) -load("@org_tensorflow//tensorflow:tensorflow.bzl", "tf_cuda_library") -process_srcs( - name = "process_srcs", - srcs = glob([ - "**/*.cc", - "**/*.h", - ]), +# Generate the nccl.h header file. +gen_nccl_h( + name = "nccl_h", + output = "src/nccl.h", + template = "src/nccl.h.in", ) -cc_library( +nccl_library( name = "src_hdrs", hdrs = [ - "src/collectives/collectives.h", "src/nccl.h", + # src/include/common_coll.h #includes "collectives/collectives.h". + # All other #includes of collectives.h are patched in process_srcs. + "src/collectives/collectives.h", ], - data = [":process_srcs"], strip_include_prefix = "src", ) -cc_library( +nccl_library( name = "include_hdrs", hdrs = glob(["src/include/*.h"]), - data = [":process_srcs"], strip_include_prefix = "src/include", ) -cc_library( +filegroup( name = "device_hdrs", - hdrs = glob(["src/collectives/device/*.h"]), - strip_include_prefix = "src/collectives/device", + srcs = glob(["src/collectives/device/*.h"]), ) filegroup( name = "device_srcs", srcs = [ - "src/collectives/device/all_gather.cu.cc", - "src/collectives/device/all_reduce.cu.cc", - "src/collectives/device/broadcast.cu.cc", - "src/collectives/device/reduce.cu.cc", - "src/collectives/device/reduce_scatter.cu.cc", + "src/collectives/device/all_gather.cu", + "src/collectives/device/all_reduce.cu", + "src/collectives/device/broadcast.cu", + "src/collectives/device/reduce.cu", + "src/collectives/device/reduce_scatter.cu", ], ) -# NCCL compiles the same source files with different NCCL_OP defines. RDC -# compilation requires that each compiled module has a unique ID. Clang derives -# the module ID from the path only so we need to rename the files to get -# different IDs for different parts of compilation. NVCC does not have that -# problem because it generates IDs based on preprocessed content. -gen_device_srcs( +nccl_library( name = "sum", - srcs = [":device_srcs"], - NCCL_OP = 0, + srcs = [ + ":device_hdrs", + ":device_srcs", + ], + copts = ["-DNCCL_OP=0"] + rdc_copts(), + linkstatic = True, + prefix = "sum_", + deps = [ + ":include_hdrs", + ":src_hdrs", + "@local_config_cuda//cuda:cuda_headers", + ], ) -gen_device_srcs( +nccl_library( name = "prod", - srcs = [":device_srcs"], - NCCL_OP = 1, + srcs = [ + ":device_hdrs", + ":device_srcs", + ], + copts = ["-DNCCL_OP=1"] + rdc_copts(), + linkstatic = True, + prefix = "_prod", + deps = [ + ":include_hdrs", + ":src_hdrs", + "@local_config_cuda//cuda:cuda_headers", + ], ) -gen_device_srcs( +nccl_library( name = "min", - srcs = [":device_srcs"], - NCCL_OP = 2, + srcs = [ + ":device_hdrs", + ":device_srcs", + ], + copts = ["-DNCCL_OP=2"] + rdc_copts(), + linkstatic = True, + prefix = "min_", + deps = [ + ":include_hdrs", + ":src_hdrs", + "@local_config_cuda//cuda:cuda_headers", + ], ) -gen_device_srcs( +nccl_library( name = "max", - srcs = [":device_srcs"], - NCCL_OP = 3, + srcs = [ + ":device_hdrs", + ":device_srcs", + ], + copts = ["-DNCCL_OP=3"] + rdc_copts(), + linkstatic = True, + prefix = "max_", + deps = [ + ":include_hdrs", + ":src_hdrs", + "@local_config_cuda//cuda:cuda_headers", + ], ) -cuda_rdc_library( - name = "device", +nccl_library( + name = "functions", srcs = [ - "src/collectives/device/functions.cu.cc", - ":max", - ":min", - ":prod", - ":sum", + "src/collectives/device/functions.cu", + ":device_hdrs", ], + copts = rdc_copts(), + linkstatic = True, deps = [ - ":device_hdrs", ":include_hdrs", ":src_hdrs", + "@local_config_cuda//cuda:cuda_headers", + ], +) + +rdc_library( + name = "device_code", + deps = [ + ":functions", + ":max", + ":min", + ":prod", + ":sum", ], ) # Primary NCCL target. -tf_cuda_library( +nccl_library( name = "nccl", srcs = glob( - include = ["src/**/*.cu.cc"], + include = ["src/**/*.cu"], # Exclude device-library code. exclude = ["src/collectives/device/**"], ) + [ @@ -115,14 +162,13 @@ tf_cuda_library( "src/nccl.h", ], hdrs = ["src/nccl.h"], - copts = ["-Wno-vla"], + copts = cuda_default_copts(), include_prefix = "third_party/nccl", strip_include_prefix = "src", visibility = ["//visibility:public"], deps = [ - ":device", + ":device_code", ":include_hdrs", ":src_hdrs", - "@local_config_cuda//cuda:cudart_static", ], ) diff --git a/third_party/nccl/build_defs.bzl.tpl b/third_party/nccl/build_defs.bzl.tpl index fe16f10432..42de79c411 100644 --- a/third_party/nccl/build_defs.bzl.tpl +++ b/third_party/nccl/build_defs.bzl.tpl @@ -1,86 +1,87 @@ """Repository rule for NCCL.""" -load("@org_tensorflow//tensorflow:tensorflow.bzl", "tf_cuda_library") -load("@bazel_tools//tools/cpp:toolchain_utils.bzl", "find_cpp_toolchain") +load("@local_config_cuda//cuda:build_defs.bzl", "cuda_default_copts") -def _process_srcs_impl(ctx): - """Appends .cc to .cu files, patches include directives.""" - files = [] - for src in ctx.files.srcs: +def _gen_nccl_h_impl(ctx): + """Creates nccl.h from a template.""" + ctx.actions.expand_template( + output = ctx.outputs.output, + template = ctx.file.template, substitutions = { - "\"collectives.h": "\"collectives/collectives.h", - "\"../collectives.h": "\"collectives/collectives.h", - # Clang does not define __CUDACC_VER_*__, use CUDA_VERSION instead. - # TODO(csigg): Apply substitutions upstream and remove here. - "#if __CUDACC_VER_MAJOR__ >= 10 || (__CUDACC_VER_MAJOR__ >= 9 && __CUDACC_VER_MINOR__ >= 2)": "#if CUDA_VERSION >= 9200", - "#if __CUDACC_VER_MAJOR__ >= 10": "#if CUDA_VERSION >= 10000", - "#if __CUDACC_VER_MAJOR__ >= 9": "#if CUDA_VERSION >= 9000", - "#if __CUDACC_VER_MAJOR__ < 9": "#if CUDA_VERSION < 9000", - "nullptr_t": "std::nullptr_t", - } - name = src.basename - if name == "nccl.in.h": - name = "nccl.h" - substitutions.update({ - "${nccl:Major}": "2", - "${nccl:Minor}": "3", - "${nccl:Patch}": "5", - "${nccl:Suffix}": "", - "${nccl:Version}": "2305", - }) - if name == "functions.cu": - # Don't try to initialize the host shadow copy of this device-side - # global variable. There is no host pointer to a device-side - # function, which confuses clang. - # TODO(csigg): remove when fixed in clang. - substitutions.update({ - "NCCL_FUNCS2B(ncclBroadcast),": "#if __CUDA_ARCH__\nNCCL_FUNCS2B(ncclBroadcast),", - "NCCL_FUNCS2A(ncclAllReduce)": "NCCL_FUNCS2A(ncclAllReduce)\n#endif", - }) - if src.extension == "cu": - name += ".cc" - file = ctx.actions.declare_file(name, sibling = src) - ctx.actions.expand_template( - output = file, - template = src, - substitutions = substitutions, - ) - files.append(file) - return [DefaultInfo(files = depset(files))] + "${nccl:Major}": "2", + "${nccl:Minor}": "3", + "${nccl:Patch}": "5", + "${nccl:Suffix}": "", + "${nccl:Version}": "2305", + }, + ) -process_srcs = rule( - implementation = _process_srcs_impl, +gen_nccl_h = rule( + implementation = _gen_nccl_h_impl, attrs = { - "srcs": attr.label_list(allow_files = True), + "template": attr.label(allow_single_file = True), + "output": attr.output(), }, ) -"""Processes the NCCL srcs so they can be compiled with bazel and clang.""" +"""Creates the NCCL header file.""" -def _gen_device_srcs_impl(ctx): +def _process_srcs_impl(ctx): + """Appends .cc to .cu files, patches include directives.""" files = [] for src in ctx.files.srcs: - name = "%s_%s" % (ctx.attr.name, src.basename) + if not src.is_source: + # Process only once, specifically "src/nccl.h". + files.append(src) + continue + name = src.basename + if src.extension == "cu": + name = ctx.attr.prefix + name + ".cc" file = ctx.actions.declare_file(name, sibling = src) ctx.actions.expand_template( output = file, template = src, substitutions = { - "#define UNROLL 4": "#define UNROLL 4\n#define NCCL_OP %d" % ctx.attr.NCCL_OP, + "\"collectives.h": "\"collectives/collectives.h", + "\"../collectives.h": "\"collectives/collectives.h", + "#if __CUDACC_VER_MAJOR__": "#if defined __CUDACC_VER_MAJOR__ && __CUDACC_VER_MAJOR__", + # Substitutions are applied in order. + "std::nullptr_t": "nullptr_t", + "nullptr_t": "std::nullptr_t", }, ) files.append(file) return [DefaultInfo(files = depset(files))] -gen_device_srcs = rule( - implementation = _gen_device_srcs_impl, +_process_srcs = rule( + implementation = _process_srcs_impl, attrs = { "srcs": attr.label_list(allow_files = True), - "NCCL_OP": attr.int(), + "prefix": attr.string(default = ""), }, ) -"""Adds prefix to each file name in srcs and adds #define NCCL_OP.""" +"""Processes the NCCL srcs so they can be compiled with bazel and clang.""" + +def nccl_library(name, srcs = None, hdrs = None, prefix = None, **kwargs): + """Processes the srcs and hdrs and creates a cc_library.""" + + _process_srcs( + name = name + "_srcs", + srcs = srcs, + prefix = prefix, + ) + _process_srcs( + name = name + "_hdrs", + srcs = hdrs, + ) + + native.cc_library( + name = name, + srcs = [name + "_srcs"] if srcs else [], + hdrs = [name + "_hdrs"] if hdrs else [], + **kwargs + ) -def _rdc_copts(): +def rdc_copts(): """Returns copts for compiling relocatable device code.""" # The global functions can not have a lower register count than the @@ -88,7 +89,7 @@ def _rdc_copts(): # https://github.com/NVIDIA/nccl/blob/f93fe9bfd94884cec2ba711897222e0df5569a53/makefiles/common.mk#L48 maxrregcount = "-maxrregcount=96" - return select({ + return cuda_default_copts() + select({ "@local_config_cuda//cuda:using_nvcc": [ "-nvcc_options", "relocatable-device-code=true", @@ -99,255 +100,118 @@ def _rdc_copts(): "-fcuda-rdc", "-Xcuda-ptxas", maxrregcount, - # Work around for clang bug (fixed in r348662), declaring - # '__device__ operator delete(void*, std::size_t)' non-inline. - # TODO(csigg): Only add this option for older clang versions. - "-std=gnu++11", ], "//conditions:default": [], - }) - -def _lookup_file(filegroup, path): - """Extracts file at (relative) path in filegroup.""" - for file in filegroup.files: - if file.path.endswith(path): - return file - return None - -def _pic_only(files): - """Returns the PIC files if there are any in 'files', otherwise 'files'.""" - pic_only = [f for f in files if f.basename.find(".pic.") >= 0] - return pic_only if pic_only else files + }) + ["-fvisibility=hidden"] -def _device_link_impl(ctx): - if not ctx.attr.gpu_archs: - fail("No GPU architecture specified. NCCL requires --config=cuda or similar.") - - inputs = [] - for dep in ctx.attr.deps: - inputs += dep.files.to_list() - inputs = _pic_only(inputs) - - # Device-link to cubins for each architecture. - name = ctx.attr.name - register_h = None - cubins = [] - images = [] - for arch in ctx.attr.gpu_archs: - cubin = ctx.actions.declare_file("%s_%s.cubin" % (name, arch)) - register_h = ctx.actions.declare_file("%s_register_%s.h" % (name, arch)) - ctx.actions.run( - outputs = [register_h, cubin], - inputs = inputs, - executable = ctx.file._nvlink, - arguments = ctx.attr.nvlink_args + [ - "--arch=%s" % arch, - "--register-link-binaries=%s" % register_h.path, - "--output-file=%s" % cubin.path, - ] + [file.path for file in inputs], - mnemonic = "nvlink", - ) - cubins.append(cubin) - images.append("--image=profile=%s,file=%s" % (arch, cubin.path)) +def _filter_impl(ctx): + suffix = ctx.attr.suffix + files = [src for src in ctx.files.srcs if src.path.endswith(suffix)] + return [DefaultInfo(files = depset(files))] - # Generate fatbin header from all cubins. - tmp_fatbin = ctx.actions.declare_file("%s.fatbin" % name) - fatbin_h = ctx.actions.declare_file("%s_fatbin.h" % name) - bin2c = ctx.file._bin2c - ctx.actions.run( - outputs = [tmp_fatbin, fatbin_h], - inputs = cubins, - executable = ctx.file._fatbinary, - arguments = [ - "-64", - "--cmdline=--compile-only", - "--link", - "--compress-all", - "--bin2c-path=%s" % bin2c.dirname, - "--create=%s" % tmp_fatbin.path, - "--embedded-fatbin=%s" % fatbin_h.path, - ] + images, - tools = [bin2c], - mnemonic = "fatbinary", - ) +_filter = rule( + implementation = _filter_impl, + attrs = { + "srcs": attr.label_list(allow_files = True), + "suffix": attr.string(), + }, +) +"""Filters the srcs to the ones ending with suffix.""" - # Generate the source file #including the headers generated above. +def _gen_link_src_impl(ctx): ctx.actions.expand_template( - output = ctx.outputs.out, - template = ctx.file._link_stub, + output = ctx.outputs.output, + template = ctx.file.template, substitutions = { - "REGISTERLINKBINARYFILE": '"%s"' % register_h.short_path, - "FATBINFILE": '"%s"' % fatbin_h.short_path, + "REGISTERLINKBINARYFILE": '"%s"' % ctx.file.register_hdr.short_path, + "FATBINFILE": '"%s"' % ctx.file.fatbin_hdr.short_path, }, ) - return [DefaultInfo(files = depset([register_h, fatbin_h]))] - -_device_link = rule( - implementation = _device_link_impl, +_gen_link_src = rule( + implementation = _gen_link_src_impl, attrs = { - "deps": attr.label_list(), - "out": attr.output(mandatory = True), - "gpu_archs": attr.string_list(), - "nvlink_args": attr.string_list(), - "_nvlink": attr.label( - default = Label("@local_config_nccl//:nvlink"), - allow_single_file = True, - executable = True, - cfg = "host", - ), - "_fatbinary": attr.label( - default = Label("@local_config_nccl//:cuda/bin/fatbinary"), - allow_single_file = True, - executable = True, - cfg = "host", - ), - "_bin2c": attr.label( - default = Label("@local_config_nccl//:cuda/bin/bin2c"), - allow_single_file = True, - executable = True, - cfg = "host", - ), - "_link_stub": attr.label( - default = Label("@local_config_nccl//:cuda/bin/crt/link.stub"), - allow_single_file = True, - ), + "register_hdr": attr.label(allow_single_file = True), + "fatbin_hdr": attr.label(allow_single_file = True), + "template": attr.label(allow_single_file = True), + "output": attr.output(), }, ) -"""Links device code and generates source code for kernel registration.""" - -def _merge_archive_impl(ctx): - # Generate an mri script to the merge archives in srcs and pass it to 'ar'. - # See https://stackoverflow.com/a/23621751. - files = _pic_only(ctx.files.srcs) - mri_script = "create " + ctx.outputs.out.path - for f in files: - mri_script += "\\naddlib " + f.path - mri_script += "\\nsave\\nend" - - cc_toolchain = find_cpp_toolchain(ctx) - ctx.actions.run_shell( - inputs = ctx.files.srcs, # + ctx.files._crosstool, - outputs = [ctx.outputs.out], - command = ("printf \"%s\" " % mri_script + - "| %s -M" % cc_toolchain.ar_executable), +"""Patches the include directives for the link.stub file.""" + +def rdc_library(name, deps): + """Produces a cc_library from deps containing relocatable device code.""" + + # From .a and .pic.a archives, just use the latter. Otherwise we get + # multiply defined symbols. + # TODO(csigg): C++ Sandwich once available should allow passing this target + # to a cc_library dependency, which would avoid the linking order issue. + _filter( + name = name + "_deps_a", + srcs = deps, + suffix = ".pic.a", ) -_merge_archive = rule( - implementation = _merge_archive_impl, - attrs = { - "srcs": attr.label_list(mandatory = True, allow_files = True), - "_cc_toolchain": attr.label(default = "@bazel_tools//tools/cpp:current_cc_toolchain"), - # "_crosstool": attr.label_list(cfg = "host", default = ["@bazel_tools//tools/cpp:crosstool"]), - }, - outputs = {"out": "lib%{name}.a"}, -) -"""Merges srcs into a single archive.""" - -def cuda_rdc_library(name, hdrs = None, copts = None, linkstatic = True, **kwargs): - """Produces a cuda_library using separate compilation and linking. - - CUDA separate compilation and linking allows device function calls across - translation units. This is different from the normal whole program - compilation where each translation unit contains all device code. For more - background, see - https://devblogs.nvidia.com/separate-compilation-linking-cuda-device-code/, - https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html#nvcc-options-for-separate-compilation - - During separate compilation, the different CUDA source files are compiled - to 'relocatable device code' (RDC) and embedded in the host object files. - When using nvcc, linking the device code for each supported GPU - architecture and generating kernel registration code for the CUDA runtime - is handled automatically. Clang supports generating relocatable device - code, but it can't link it. We therefore rely on tools provided by the CUDA - SDK to link the device code and generate the host code to register the - kernels. - - The nvlink tool extracts the RDC code from the object files and links it - into cubin files, one per GPU architecture. It also produces a header file - with a list of kernel names to register. The cubins are merged into a - binary blob using the fatbinary tool, and converted to a C header file with - the help of the bin2c tool. The registration header file, the fatbinary - header file, and the link.stub file (shipped with the CUDA SDK) are - compiled as ordinary host code. - - Here is a diagram of the CUDA separate compilation trajectory: - - x.cu.cc y.cu.cc - \ / cc_library (compile RDC and archive) - xy.a - / \ * nvlink - register.h xy.cubin - : | * fatbinary and bin2c - : xy.fatbin.h - : : * #include - dlink.cc * Expanded from crt/dlink.stub template - | cc_library (host compile and archive) - dlink.a - - The steps marked with '*' are implemented in the _device_link rule. - - The object files in both xy.a and dlink.a reference symbols defined in the - other archive. The separate archives are a side effect of using two - cc_library targets to implement a single compilation trajectory. We could - fix this once bazel supports C++ sandwich. For now, we just merge the two - archives to avoid unresolved symbols: - - xy.a dlink.a - \ / merge archive - xy_dlink.a - | cc_library (or alternatively, cc_import) - final target - - Another complication is that cc_library produces (depending on the - configuration) both PIC and non-PIC archives, but the distinction - is hidden from Starlark until C++ sandwich becomes available. We work - around this by dropping the non-PIC files if PIC files are available. - - Args: - name: Target name. - hdrs: Header files. - copts: Compiler options. - linkstatic: Must be true. - **kwargs: Any other arguments. - """ - - if not hdrs: - hdrs = [] - if not copts: - copts = [] + # Device-link to cubins for each architecture. + images = [] + cubins = [] + for arch in %{gpu_architectures}: + cubin = "%s_%s.cubin" % (name, arch) + register_hdr = "%s_%s.h" % (name, arch) + nvlink = "@local_config_nccl//:nvlink" + cmd = ("$(location %s) " % nvlink + + select({ + # NCCL is only supported on Linux. + "@org_tensorflow//tensorflow:linux_x86_64": "--cpu-arch=X86_64 ", + "@org_tensorflow//tensorflow:linux_ppc64le": "--cpu-arch=PPC64LE ", + "//conditions:default": "", + }) + + "--arch=%s $(SRCS) " % arch + + "--register-link-binaries=$(location %s) " % register_hdr + + "--output-file=$(location %s)" % cubin) + native.genrule( + name = "%s_%s" % (name, arch), + outs = [register_hdr, cubin], + srcs = [name + "_deps_a"], + cmd = cmd, + tools = [nvlink], + ) + images.append("--image=profile=%s,file=$(location %s)" % (arch, cubin)) + cubins.append(cubin) - # Compile host and device code into library. - lib = name + "_lib" - tf_cuda_library( - name = lib, - hdrs = hdrs, - copts = _rdc_copts() + copts, - linkstatic = linkstatic, - **kwargs + # Generate fatbin header from all cubins. + fatbin_hdr = name + ".fatbin.h" + fatbinary = "@local_config_nccl//:cuda/bin/fatbinary" + bin2c = "@local_config_nccl//:cuda/bin/bin2c" + cmd = ("$(location %s) -64 --cmdline=--compile-only " % fatbinary + + "--link --bin2c-path $$(dirname $(location %s)) " % bin2c + + "--compress-all %s --create=%%{name}.fatbin " % " ".join(images) + + "--embedded-fatbin=$@") + native.genrule( + name = name + "_fatbin_h", + outs = [fatbin_hdr], + srcs = cubins, + cmd = cmd, + tools = [fatbinary, bin2c], ) - # Generate source file containing linked device code. - dlink_hdrs = name + "_dlink_hdrs" - dlink_cc = name + "_dlink.cc" - _device_link( - name = dlink_hdrs, - deps = [lib], - out = dlink_cc, - gpu_archs = %{gpu_architectures}, - nvlink_args = select({ - "@org_tensorflow//tensorflow:linux_x86_64": ["--cpu-arch=X86_64"], - "@org_tensorflow//tensorflow:linux_ppc64le": ["--cpu-arch=PPC64LE"], - "//conditions:default": [], - }), + # Generate the source file #including the headers generated above. + _gen_link_src( + name = name + "_dlink_src", + # Include just the last one, they are equivalent. + register_hdr = register_hdr, + fatbin_hdr = fatbin_hdr, + template = "@local_config_nccl//:cuda/bin/crt/link.stub", + output = name + ".cc", ) - # Compile the source file into a library. - dlink = name + "_dlink" + # Compile the source file into the cc_library. native.cc_library( - name = dlink, - srcs = [dlink_cc], - textual_hdrs = [dlink_hdrs], + name = name + "_dlink_a", + srcs = [ + name + "_dlink_src", + ], + textual_hdrs = [register_hdr, fatbin_hdr], deps = [ "@local_config_cuda//cuda:cuda_headers", ], @@ -358,22 +222,31 @@ def cuda_rdc_library(name, hdrs = None, copts = None, linkstatic = True, **kwarg "__NV_EXTRA_INITIALIZATION=", "__NV_EXTRA_FINALIZATION=", ], - linkstatic = linkstatic, + linkstatic = True, ) - # Repackage the two libs into a single archive. This is required because - # both libs reference symbols defined in the other one. For details, see + # Repackage deps into a single archive. This avoid unresolved symbols when + # the archives happen to be linked in the wrong order. For more details, see # https://eli.thegreenplace.net/2013/07/09/library-order-in-static-linking - archive = name + "_a" - _merge_archive( - name = archive, - srcs = [lib, dlink], + native.genrule( + name = name + "_a", + srcs = [ + name + "_deps_a", + name + "_dlink_a", + ], + outs = [name + ".a"], + # See https://stackoverflow.com/a/23621751 + cmd = """ +addlibs=$$(echo $(SRCS) | sed "s/[^ ]* */\\naddlib &/g") +printf "create $@$${addlibs}\\nsave\\nend" | $(AR) -M +""", ) - # Create cc target from archive. native.cc_library( name = name, - srcs = [archive], - hdrs = hdrs, - linkstatic = linkstatic, + srcs = [name + "_a"], + deps = [ + "@local_config_cuda//cuda:cudart_static", + ], + linkstatic = True, ) -- GitLab From 00151d81f4fc5928f4ce75250d20567558fe1634 Mon Sep 17 00:00:00 2001 From: Katherine Wu Date: Wed, 12 Dec 2018 13:40:20 -0800 Subject: [PATCH 438/873] Run all tests in both graph and eager mode. PiperOrigin-RevId: 225245412 --- tensorflow/python/keras/activations_test.py | 121 +++++++++----------- tensorflow/python/keras/constraints_test.py | 79 ++++++------- 2 files changed, 91 insertions(+), 109 deletions(-) diff --git a/tensorflow/python/keras/activations_test.py b/tensorflow/python/keras/activations_test.py index 6b7bfb698b..33001f419e 100644 --- a/tensorflow/python/keras/activations_test.py +++ b/tensorflow/python/keras/activations_test.py @@ -31,6 +31,7 @@ def _ref_softmax(values): return e / np.sum(e) +@test_util.run_all_in_graph_and_eager_modes class KerasActivationsTest(test.TestCase): def test_serialization(self): @@ -46,12 +47,11 @@ class KerasActivationsTest(test.TestCase): assert fn == ref_fn def test_softmax(self): - with self.cached_session(): - x = keras.backend.placeholder(ndim=2) - f = keras.backend.function([x], [keras.activations.softmax(x)]) - test_values = np.random.random((2, 5)) + x = keras.backend.placeholder(ndim=2) + f = keras.backend.function([x], [keras.activations.softmax(x)]) + test_values = np.random.random((2, 5)) - result = f([test_values])[0] + result = f([test_values])[0] expected = _ref_softmax(test_values[0]) self.assertAllClose(result[0], expected, rtol=1e-05) @@ -60,40 +60,36 @@ class KerasActivationsTest(test.TestCase): keras.activations.softmax(x) def test_temporal_softmax(self): - with self.cached_session(): - x = keras.backend.placeholder(shape=(2, 2, 3)) - f = keras.backend.function([x], [keras.activations.softmax(x)]) - test_values = np.random.random((2, 2, 3)) * 10 - result = f([test_values])[0] + x = keras.backend.placeholder(shape=(2, 2, 3)) + f = keras.backend.function([x], [keras.activations.softmax(x)]) + test_values = np.random.random((2, 2, 3)) * 10 + result = f([test_values])[0] expected = _ref_softmax(test_values[0, 0]) self.assertAllClose(result[0, 0], expected, rtol=1e-05) - @test_util.run_deprecated_v1 def test_selu(self): x = keras.backend.placeholder(ndim=2) f = keras.backend.function([x], [keras.activations.selu(x)]) alpha = 1.6732632423543772848170429916717 scale = 1.0507009873554804934193349852946 - with self.cached_session(): - positive_values = np.array([[1, 2]], dtype=keras.backend.floatx()) - result = f([positive_values])[0] - self.assertAllClose(result, positive_values * scale, rtol=1e-05) + positive_values = np.array([[1, 2]], dtype=keras.backend.floatx()) + result = f([positive_values])[0] + self.assertAllClose(result, positive_values * scale, rtol=1e-05) - negative_values = np.array([[-1, -2]], dtype=keras.backend.floatx()) - result = f([negative_values])[0] - true_result = (np.exp(negative_values) - 1) * scale * alpha - self.assertAllClose(result, true_result) + negative_values = np.array([[-1, -2]], dtype=keras.backend.floatx()) + result = f([negative_values])[0] + true_result = (np.exp(negative_values) - 1) * scale * alpha + self.assertAllClose(result, true_result) def test_softplus(self): def softplus(x): return np.log(np.ones_like(x) + np.exp(x)) - with self.cached_session(): - x = keras.backend.placeholder(ndim=2) - f = keras.backend.function([x], [keras.activations.softplus(x)]) - test_values = np.random.random((2, 5)) - result = f([test_values])[0] + x = keras.backend.placeholder(ndim=2) + f = keras.backend.function([x], [keras.activations.softplus(x)]) + test_values = np.random.random((2, 5)) + result = f([test_values])[0] expected = softplus(test_values) self.assertAllClose(result, expected, rtol=1e-05) @@ -101,11 +97,10 @@ class KerasActivationsTest(test.TestCase): def softsign(x): return np.divide(x, np.ones_like(x) + np.absolute(x)) - with self.cached_session(): - x = keras.backend.placeholder(ndim=2) - f = keras.backend.function([x], [keras.activations.softsign(x)]) - test_values = np.random.random((2, 5)) - result = f([test_values])[0] + x = keras.backend.placeholder(ndim=2) + f = keras.backend.function([x], [keras.activations.softsign(x)]) + test_values = np.random.random((2, 5)) + result = f([test_values])[0] expected = softsign(test_values) self.assertAllClose(result, expected, rtol=1e-05) @@ -118,68 +113,60 @@ class KerasActivationsTest(test.TestCase): return z / (1 + z) sigmoid = np.vectorize(ref_sigmoid) - with self.cached_session(): - x = keras.backend.placeholder(ndim=2) - f = keras.backend.function([x], [keras.activations.sigmoid(x)]) - test_values = np.random.random((2, 5)) - result = f([test_values])[0] + x = keras.backend.placeholder(ndim=2) + f = keras.backend.function([x], [keras.activations.sigmoid(x)]) + test_values = np.random.random((2, 5)) + result = f([test_values])[0] expected = sigmoid(test_values) self.assertAllClose(result, expected, rtol=1e-05) - @test_util.run_deprecated_v1 def test_hard_sigmoid(self): def ref_hard_sigmoid(x): x = (x * 0.2) + 0.5 z = 0.0 if x <= 0 else (1.0 if x >= 1 else x) return z hard_sigmoid = np.vectorize(ref_hard_sigmoid) - with self.cached_session(): - x = keras.backend.placeholder(ndim=2) - f = keras.backend.function([x], [keras.activations.hard_sigmoid(x)]) - test_values = np.random.random((2, 5)) - result = f([test_values])[0] + x = keras.backend.placeholder(ndim=2) + f = keras.backend.function([x], [keras.activations.hard_sigmoid(x)]) + test_values = np.random.random((2, 5)) + result = f([test_values])[0] expected = hard_sigmoid(test_values) self.assertAllClose(result, expected, rtol=1e-05) def test_relu(self): - with self.cached_session(): - x = keras.backend.placeholder(ndim=2) - f = keras.backend.function([x], [keras.activations.relu(x)]) - test_values = np.random.random((2, 5)) - result = f([test_values])[0] + x = keras.backend.placeholder(ndim=2) + f = keras.backend.function([x], [keras.activations.relu(x)]) + test_values = np.random.random((2, 5)) + result = f([test_values])[0] # No negative values in test values... self.assertAllClose(result, test_values, rtol=1e-05) - @test_util.run_deprecated_v1 def test_elu(self): - with self.cached_session(): - x = keras.backend.placeholder(ndim=2) - f = keras.backend.function([x], [keras.activations.elu(x, 0.5)]) - test_values = np.random.random((2, 5)) - result = f([test_values])[0] - self.assertAllClose(result, test_values, rtol=1e-05) - negative_values = np.array([[-1, -2]], dtype=keras.backend.floatx()) - result = f([negative_values])[0] - true_result = (np.exp(negative_values) - 1) / 2 + x = keras.backend.placeholder(ndim=2) + f = keras.backend.function([x], [keras.activations.elu(x, 0.5)]) + test_values = np.random.random((2, 5)) + result = f([test_values])[0] + self.assertAllClose(result, test_values, rtol=1e-05) + negative_values = np.array([[-1, -2]], dtype=keras.backend.floatx()) + result = f([negative_values])[0] + true_result = (np.exp(negative_values) - 1) / 2 self.assertAllClose(result, true_result) def test_tanh(self): - with self.cached_session(): - test_values = np.random.random((2, 5)) - x = keras.backend.placeholder(ndim=2) - exp = keras.activations.tanh(x) - f = keras.backend.function([x], [exp]) - result = f([test_values])[0] + test_values = np.random.random((2, 5)) + x = keras.backend.placeholder(ndim=2) + exp = keras.activations.tanh(x) + f = keras.backend.function([x], [exp]) + result = f([test_values])[0] expected = np.tanh(test_values) self.assertAllClose(result, expected, rtol=1e-05) def test_exponential(self): - with self.cached_session(): - test_values = np.random.random((2, 5)) - x = keras.backend.placeholder(ndim=2) - exp = keras.activations.exponential(x) - f = keras.backend.function([x], [exp]) - result = f([test_values])[0] + test_values = np.random.random((2, 5)) + x = keras.backend.placeholder(ndim=2) + exp = keras.activations.exponential(x) + f = keras.backend.function([x], [exp]) + result = f([test_values])[0] expected = np.exp(test_values) self.assertAllClose(result, expected, rtol=1e-05) diff --git a/tensorflow/python/keras/constraints_test.py b/tensorflow/python/keras/constraints_test.py index 4f674ea7c5..92bc4852cf 100644 --- a/tensorflow/python/keras/constraints_test.py +++ b/tensorflow/python/keras/constraints_test.py @@ -21,6 +21,7 @@ from __future__ import print_function import numpy as np from tensorflow.python import keras +from tensorflow.python.framework import test_util from tensorflow.python.platform import test @@ -35,6 +36,7 @@ def get_example_array(): return example_array +@test_util.run_all_in_graph_and_eager_modes class KerasConstraintsTest(test.TestCase): def test_serialization(self): @@ -49,54 +51,47 @@ class KerasConstraintsTest(test.TestCase): assert fn.__class__ == ref_fn.__class__ def test_max_norm(self): - with self.cached_session(): - array = get_example_array() - for m in get_test_values(): - norm_instance = keras.constraints.max_norm(m) - normed = norm_instance(keras.backend.variable(array)) - assert np.all(keras.backend.eval(normed) < m) - - # a more explicit example - norm_instance = keras.constraints.max_norm(2.0) - x = np.array([[0, 0, 0], [1.0, 0, 0], [3, 0, 0], [3, 3, 3]]).T - x_normed_target = np.array([[0, 0, 0], [1.0, 0, 0], - [2.0, 0, 0], - [2. / np.sqrt(3), - 2. / np.sqrt(3), - 2. / np.sqrt(3)]]).T - x_normed_actual = keras.backend.eval( - norm_instance(keras.backend.variable(x))) - self.assertAllClose(x_normed_actual, x_normed_target, rtol=1e-05) + array = get_example_array() + for m in get_test_values(): + norm_instance = keras.constraints.max_norm(m) + normed = norm_instance(keras.backend.variable(array)) + assert np.all(keras.backend.eval(normed) < m) + + # a more explicit example + norm_instance = keras.constraints.max_norm(2.0) + x = np.array([[0, 0, 0], [1.0, 0, 0], [3, 0, 0], [3, 3, 3]]).T + x_normed_target = np.array( + [[0, 0, 0], [1.0, 0, 0], [2.0, 0, 0], + [2. / np.sqrt(3), 2. / np.sqrt(3), 2. / np.sqrt(3)]]).T + x_normed_actual = keras.backend.eval( + norm_instance(keras.backend.variable(x))) + self.assertAllClose(x_normed_actual, x_normed_target, rtol=1e-05) def test_non_neg(self): - with self.cached_session(): - non_neg_instance = keras.constraints.non_neg() - normed = non_neg_instance(keras.backend.variable(get_example_array())) - assert np.all(np.min(keras.backend.eval(normed), axis=1) == 0.) + non_neg_instance = keras.constraints.non_neg() + normed = non_neg_instance(keras.backend.variable(get_example_array())) + assert np.all(np.min(keras.backend.eval(normed), axis=1) == 0.) def test_unit_norm(self): - with self.cached_session(): - unit_norm_instance = keras.constraints.unit_norm() - normalized = unit_norm_instance( - keras.backend.variable(get_example_array())) - norm_of_normalized = np.sqrt( - np.sum(keras.backend.eval(normalized) ** 2, axis=0)) - # In the unit norm constraint, it should be equal to 1. - difference = norm_of_normalized - 1. - largest_difference = np.max(np.abs(difference)) - assert np.abs(largest_difference) < 10e-5 + unit_norm_instance = keras.constraints.unit_norm() + normalized = unit_norm_instance(keras.backend.variable(get_example_array())) + norm_of_normalized = np.sqrt( + np.sum(keras.backend.eval(normalized)**2, axis=0)) + # In the unit norm constraint, it should be equal to 1. + difference = norm_of_normalized - 1. + largest_difference = np.max(np.abs(difference)) + assert np.abs(largest_difference) < 10e-5 def test_min_max_norm(self): - with self.cached_session(): - array = get_example_array() - for m in get_test_values(): - norm_instance = keras.constraints.min_max_norm(min_value=m, - max_value=m * 2) - normed = norm_instance(keras.backend.variable(array)) - value = keras.backend.eval(normed) - l2 = np.sqrt(np.sum(np.square(value), axis=0)) - assert not l2[l2 < m] - assert not l2[l2 > m * 2 + 1e-5] + array = get_example_array() + for m in get_test_values(): + norm_instance = keras.constraints.min_max_norm( + min_value=m, max_value=m * 2) + normed = norm_instance(keras.backend.variable(array)) + value = keras.backend.eval(normed) + l2 = np.sqrt(np.sum(np.square(value), axis=0)) + assert not l2[l2 < m] + assert not l2[l2 > m * 2 + 1e-5] if __name__ == '__main__': -- GitLab From b3b8dff01c238366f456ac7dff7c130d50db7693 Mon Sep 17 00:00:00 2001 From: Allen Lavoie Date: Wed, 12 Dec 2018 13:58:46 -0800 Subject: [PATCH 439/873] Checkpointing for OptimizerV2 Copies and pastes the existing Optimizer checkpointing code, and stops adding unconditional dependencies on slot variables (which were based on ops.uid() and so not reproducible across program runs). PiperOrigin-RevId: 225248820 --- .../python/keras/optimizer_v2/optimizer_v2.py | 118 ++- .../python/training/checkpointable/BUILD | 37 + .../python/training/checkpointable/util.py | 8 +- .../training/checkpointable/util_test.py | 501 +++++----- .../util_with_v1_optimizers_test.py | 873 ++++++++++++++++++ ...ensorflow.keras.optimizers.-adadelta.pbtxt | 4 + ...tensorflow.keras.optimizers.-adagrad.pbtxt | 4 + .../tensorflow.keras.optimizers.-adam.pbtxt | 4 + .../tensorflow.keras.optimizers.-adamax.pbtxt | 4 + ...nsorflow.keras.optimizers.-optimizer.pbtxt | 4 + ...nsorflow.keras.optimizers.-r-m-sprop.pbtxt | 4 + .../tensorflow.keras.optimizers.-s-g-d.pbtxt | 4 + ...ensorflow.keras.optimizers.-adadelta.pbtxt | 4 + ...tensorflow.keras.optimizers.-adagrad.pbtxt | 4 + .../tensorflow.keras.optimizers.-adam.pbtxt | 4 + .../tensorflow.keras.optimizers.-adamax.pbtxt | 4 + ...nsorflow.keras.optimizers.-optimizer.pbtxt | 4 + ...nsorflow.keras.optimizers.-r-m-sprop.pbtxt | 4 + .../tensorflow.keras.optimizers.-s-g-d.pbtxt | 4 + 19 files changed, 1308 insertions(+), 285 deletions(-) create mode 100644 tensorflow/python/training/checkpointable/util_with_v1_optimizers_test.py diff --git a/tensorflow/python/keras/optimizer_v2/optimizer_v2.py b/tensorflow/python/keras/optimizer_v2/optimizer_v2.py index a130e1d0c3..d3153141ec 100644 --- a/tensorflow/python/keras/optimizer_v2/optimizer_v2.py +++ b/tensorflow/python/keras/optimizer_v2/optimizer_v2.py @@ -21,6 +21,7 @@ from __future__ import division from __future__ import print_function import abc +import functools import six @@ -28,6 +29,7 @@ from tensorflow.python.distribute import distribute_lib from tensorflow.python.distribute import distribution_strategy_context as distribute_ctx from tensorflow.python.distribute import reduce_util as ds_reduce_util from tensorflow.python.eager import backprop +from tensorflow.python.eager import context from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.keras import backend @@ -165,8 +167,17 @@ class OptimizerV2(checkpointable.CheckpointableBase): self._hyper = {} # dict: {variable name : {slot name : variable}} self._slots = {} + self._slot_names = [] self._weights = [] + # For implementing Checkpointable. Stores information about how to restore + # slot variables which have not yet been created + # (checkpointable._CheckpointPosition objects). + # {slot_name : + # {_var_key(variable_to_train): [checkpoint_position, ... ], ... }, + # ... } + self._deferred_slot_restorations = {} + decay = kwargs.pop("decay", 0.0) if decay < 0.: raise ValueError("decay cannot be less than 0: {}".format(decay)) @@ -413,18 +424,36 @@ class OptimizerV2(checkpointable.CheckpointableBase): else: super(OptimizerV2, self).__setattr__(name, value) + def get_slot_names(self): + """A list of names for this optimizer's slots.""" + return self._slot_names + def add_slot(self, var, slot_name, initializer="zeros"): + """Add a new slot variable for `var`.""" + if slot_name not in self._slot_names: + self._slot_names.append(slot_name) var_key = _var_key(var) slot_dict = self._slots.setdefault(var_key, {}) - if slot_name not in slot_dict: - slot_key = _get_slot_key_from_var(var, slot_name) - weight = self.add_weight( - name=slot_key, - shape=var.shape, + weight = slot_dict.get(slot_name, None) + if weight is None: + if isinstance(initializer, six.string_types) or callable(initializer): + initializer = initializers.get(initializer) + initial_value = functools.partial( + initializer, shape=var.shape, dtype=var.dtype) + else: + initial_value = initializer + weight = tf_variables.Variable( + name="%s/%s" % (var._shared_name, slot_name), # pylint: disable=protected-access dtype=var.dtype, - initializer=initializer) + trainable=False, + initial_value=initial_value) + backend.track_variable(weight) slot_dict[slot_name] = weight + self._restore_slot_variable( + slot_name=slot_name, variable=var, + slot_variable=weight) self._weights.append(weight) + return weight def get_slot(self, var, slot_name): var_key = _var_key(var) @@ -678,6 +707,83 @@ class OptimizerV2(checkpointable.CheckpointableBase): """ raise NotImplementedError() + # --------------- + # For implementing the checkpointable interface + # --------------- + + def _restore_slot_variable(self, slot_name, variable, slot_variable): + """Restore a newly created slot variable's value.""" + variable_key = _var_key(variable) + deferred_restorations = self._deferred_slot_restorations.get( + slot_name, {}).pop(variable_key, []) + # Iterate over restores, highest restore UID first to minimize the number + # of assignments. + deferred_restorations.sort(key=lambda position: position.restore_uid, + reverse=True) + for checkpoint_position in deferred_restorations: + checkpoint_position.restore(slot_variable) + + def _create_or_restore_slot_variable( + self, slot_variable_position, slot_name, variable): + """Restore a slot variable's value, possibly creating it. + + Called when a variable which has an associated slot variable is created or + restored. When executing eagerly, we create the slot variable with a + restoring initializer. + + No new variables are created when graph building. Instead, + _restore_slot_variable catches these after normal creation and adds restore + ops to the graph. This method is nonetheless important when graph building + for the case when a slot variable has already been created but `variable` + has just been added to a dependency graph (causing us to realize that the + slot variable needs to be restored). + + Args: + slot_variable_position: A `checkpointable._CheckpointPosition` object + indicating the slot variable `Checkpointable` object to be restored. + slot_name: The name of this `Optimizer`'s slot to restore into. + variable: The variable object this slot is being created for. + """ + variable_key = _var_key(variable) + slot_dict = self._slots.get(variable_key, {}) + slot_variable = slot_dict.get(slot_name, None) + if (slot_variable is None and context.executing_eagerly() and + slot_variable_position.is_simple_variable() + # Defer slot variable creation if there is an active variable creator + # scope. Generally we'd like to eagerly create/restore slot variables + # when possible, but this may mean that scopes intended to catch + # `variable` also catch its eagerly created slot variable + # unintentionally (specifically make_template would add a dependency on + # a slot variable if not for this case). Deferring is mostly harmless + # (aside from double initialization), and makes variable creator scopes + # behave the same way they do when graph building. + and not ops.get_default_graph()._variable_creator_stack): # pylint: disable=protected-access + initializer = checkpointable.CheckpointInitialValue( + checkpoint_position=slot_variable_position) + slot_variable = self.add_slot( + var=variable, + initializer=initializer, + slot_name=slot_name) + # Slot variables are not owned by any one object (because we don't want to + # save the slot variable if the optimizer is saved without the non-slot + # variable, or if the non-slot variable is saved without the optimizer; + # it's a dependency hypergraph with edges of the form (optimizer, non-slot + # variable, variable)). So we don't _track_ slot variables anywhere, and + # instead special-case this dependency and otherwise pretend it's a normal + # graph. + if slot_variable is not None: + # If we've either made this slot variable, or if we've pulled out an + # existing slot variable, we should restore it. + slot_variable_position.restore(slot_variable) + else: + # We didn't make the slot variable. Defer restoring until it gets created + # normally. We keep a list rather than the one with the highest restore + # UID in case slot variables have their own dependencies, in which case + # those could differ between restores. + self._deferred_slot_restorations.setdefault( + slot_name, {}).setdefault(variable_key, []).append( + slot_variable_position) + def _filter_grads(grads_and_vars): """Filter out iterable with grad equal to None.""" diff --git a/tensorflow/python/training/checkpointable/BUILD b/tensorflow/python/training/checkpointable/BUILD index 26a0ac35b7..3201c755af 100644 --- a/tensorflow/python/training/checkpointable/BUILD +++ b/tensorflow/python/training/checkpointable/BUILD @@ -159,6 +159,43 @@ py_test( "//tensorflow/python/eager:test", "//tensorflow/python/keras:engine", "//tensorflow/python/keras:layers", + "@absl_py//absl/testing:parameterized", + "@six_archive//:six", + ], +) + +py_test( + name = "util_with_v1_optimizers_test", + srcs = ["util_with_v1_optimizers_test.py"], + srcs_version = "PY2AND3", + tags = ["notsan"], # b/74395663 + deps = [ + ":base", + ":tracking", + ":util", + "//tensorflow/python:checkpoint_management", + "//tensorflow/python:constant_op", + "//tensorflow/python:control_flow_ops", + "//tensorflow/python:dtypes", + "//tensorflow/python:framework_ops", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:init_ops", + "//tensorflow/python:pywrap_tensorflow", + "//tensorflow/python:resource_variable_ops", + "//tensorflow/python:saver", + "//tensorflow/python:session", + "//tensorflow/python:state_ops", + "//tensorflow/python:template", + "//tensorflow/python:training", + "//tensorflow/python:training_util", + "//tensorflow/python:variable_scope", + "//tensorflow/python/eager:backprop", + "//tensorflow/python/eager:context", + "//tensorflow/python/eager:def_function", + "//tensorflow/python/eager:test", + "//tensorflow/python/keras:engine", + "//tensorflow/python/keras:layers", + "@absl_py//absl/testing:parameterized", "@six_archive//:six", ], ) diff --git a/tensorflow/python/training/checkpointable/util.py b/tensorflow/python/training/checkpointable/util.py index ce1b9c6fc5..fde91948e5 100644 --- a/tensorflow/python/training/checkpointable/util.py +++ b/tensorflow/python/training/checkpointable/util.py @@ -39,7 +39,7 @@ from tensorflow.python.ops import init_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.training import checkpoint_management -from tensorflow.python.training import optimizer as optimizer_lib +from tensorflow.python.training import optimizer as optimizer_v1 from tensorflow.python.training import saver as v1_saver_lib from tensorflow.python.training.checkpointable import base from tensorflow.python.training.checkpointable import data_structures @@ -560,7 +560,9 @@ def _serialize_slot_variables(checkpointable_objects, node_ids, object_names): non_slot_objects = list(checkpointable_objects) slot_variables = _ObjectIdentityDictionary() for checkpointable in non_slot_objects: - if isinstance(checkpointable, optimizer_lib.Optimizer): + if (isinstance(checkpointable, optimizer_v1.Optimizer) + # TODO(b/110718070): Fix Keras imports. + or hasattr(checkpointable, "_create_or_restore_slot_variable")): naming_scheme = _slot_variable_naming_for_optimizer( optimizer_path=object_names[checkpointable]) slot_names = checkpointable.get_slot_names() @@ -570,7 +572,7 @@ def _serialize_slot_variables(checkpointable_objects, node_ids, object_names): try: slot_variable = checkpointable.get_slot( original_variable, slot_name) - except AttributeError: + except (AttributeError, KeyError): slot_variable = None if slot_variable is None: continue diff --git a/tensorflow/python/training/checkpointable/util_test.py b/tensorflow/python/training/checkpointable/util_test.py index 3bdab4cb0b..61de46898a 100644 --- a/tensorflow/python/training/checkpointable/util_test.py +++ b/tensorflow/python/training/checkpointable/util_test.py @@ -20,10 +20,10 @@ import functools import json import os +from absl.testing import parameterized import six from tensorflow.python import pywrap_tensorflow -from tensorflow.python.client import session as session_lib from tensorflow.python.eager import backprop from tensorflow.python.eager import context from tensorflow.python.eager import def_function @@ -35,14 +35,14 @@ from tensorflow.python.framework import test_util from tensorflow.python.keras.engine import sequential from tensorflow.python.keras.engine import training from tensorflow.python.keras.layers import core +from tensorflow.python.keras.optimizer_v2 import adam from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import state_ops from tensorflow.python.ops import template from tensorflow.python.ops import variable_scope -from tensorflow.python.ops import variables -from tensorflow.python.training import adam +from tensorflow.python.ops import variables as variables_lib from tensorflow.python.training import checkpoint_management from tensorflow.python.training import saver as saver_lib from tensorflow.python.training import training_util @@ -243,7 +243,7 @@ class _OwnsMirroredVariables(base.CheckpointableBase): return self.non_dep_variable.name -class CheckpointingTests(test.TestCase): +class CheckpointingTests(parameterized.TestCase, test.TestCase): @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True) def testNamingWithOptimizer(self): @@ -252,41 +252,32 @@ class CheckpointingTests(test.TestCase): # A nuisance Model using the same optimizer. Its slot variables should not # go in the checkpoint, since it is never depended on. other_model = MyModel() - optimizer = adam.AdamOptimizer(0.001) - optimizer_step = training_util.get_or_create_global_step() + optimizer = adam.Adam(0.001) + step = training_util.get_or_create_global_step() root_checkpointable = checkpointable_utils.Checkpoint( - optimizer=optimizer, model=model, optimizer_step=optimizer_step) - if context.executing_eagerly(): - optimizer.minimize( - lambda: model(input_value), - global_step=optimizer_step) - optimizer.minimize( - lambda: other_model(input_value), - global_step=optimizer_step) - else: - train_op = optimizer.minimize( - model(input_value), global_step=optimizer_step) - optimizer.minimize( - other_model(input_value), - global_step=optimizer_step) - self.evaluate(checkpointable_utils.gather_initializers( - root_checkpointable)) - self.evaluate(train_op) + optimizer=optimizer, model=model, step=step) + + with backprop.GradientTape() as tape: + loss = model(input_value) + variables = model.trainable_variables + gradients = tape.gradient(loss, variables) + train_op = control_flow_ops.group( + optimizer.apply_gradients(zip(gradients, variables)), + step.assign_add(1)) + + with backprop.GradientTape() as tape: + loss = other_model(input_value) + variables = other_model.trainable_variables + gradients = tape.gradient(loss, variables) + optimizer.apply_gradients(zip(gradients, variables)) + + self.evaluate(checkpointable_utils.gather_initializers( + root_checkpointable)) + self.evaluate(train_op) named_variables, serialized_graph, _ = ( checkpointable_utils._serialize_object_graph( root_checkpointable, saveables_cache=None)) - expected_checkpoint_names = ( - # Created in the root node, so no prefix. - "optimizer_step", - "model/_second/kernel", - "model/_named_dense/kernel", - "model/_named_dense/bias", - # non-Layer dependency of the model - "model/_non_layer/a_variable", - # The optimizer creates two non-slot variables - "optimizer/beta1_power", - "optimizer/beta2_power", - # Slot variables + expected_slot_keys = ( "model/_second/kernel/.OPTIMIZER_SLOT/optimizer/m", "model/_second/kernel/.OPTIMIZER_SLOT/optimizer/v", "model/_named_dense/kernel/.OPTIMIZER_SLOT/optimizer/m", @@ -294,9 +285,26 @@ class CheckpointingTests(test.TestCase): "model/_named_dense/bias/.OPTIMIZER_SLOT/optimizer/m", "model/_named_dense/bias/.OPTIMIZER_SLOT/optimizer/v", ) + expected_checkpoint_names = ( + # Created in the root node, so no prefix. + "step", + "model/_second/kernel", + "model/_named_dense/kernel", + "model/_named_dense/bias", + # non-Layer dependency of the model + "model/_non_layer/a_variable", + "optimizer/learning_rate", + "optimizer/beta_1", + "optimizer/beta_2", + "optimizer/epsilon", + "optimizer/iter", + "optimizer/decay", + ) + expected_slot_keys suffix = "/.ATTRIBUTES/VARIABLE_VALUE" expected_checkpoint_names = [ name + suffix for name in expected_checkpoint_names] + expected_checkpoint_names.append( + "optimizer/.ATTRIBUTES/OBJECT_CONFIG_JSON") # The Dense layers also save get_config() JSON expected_checkpoint_names.extend( ["model/_second/.ATTRIBUTES/OBJECT_CONFIG_JSON", @@ -307,7 +315,7 @@ class CheckpointingTests(test.TestCase): # Check that we've mapped to the right variable objects (not exhaustive) self.assertEqual( "global_step", - named_variables["optimizer_step" + suffix].full_name) + named_variables["step" + suffix].full_name) self.assertEqual( "my_model/dense_1/kernel", named_variables["model/_second/kernel" + suffix].full_name) @@ -315,48 +323,31 @@ class CheckpointingTests(test.TestCase): "my_model/dense/kernel", named_variables["model/_named_dense/kernel" + suffix].full_name) self.assertEqual( - "beta1_power", - named_variables["optimizer/beta1_power" + suffix].full_name) + "beta_1", + named_variables["optimizer/beta_1" + suffix].full_name) self.assertEqual( - "beta2_power", - named_variables["optimizer/beta2_power" + suffix].full_name) + "beta_2", + named_variables["optimizer/beta_2" + suffix].full_name) # Spot check the generated protocol buffers. self.assertEqual("optimizer", serialized_graph.nodes[0].children[1].local_name) optimizer_node = serialized_graph.nodes[serialized_graph.nodes[0].children[ 1].node_id] - self.assertEqual("beta1_power", - optimizer_node.children[0].local_name) - self.assertEqual("beta1_power", - serialized_graph.nodes[optimizer_node.children[0].node_id] - .attributes[0].full_name) - self.assertEqual( - "my_model/dense/kernel", - serialized_graph.nodes[optimizer_node.slot_variables[0] - .original_variable_node_id] - .attributes[0].full_name) - # We strip off the :0 suffix, as variable.name-based saving does. - self.assertEqual( - "my_model/dense/kernel/Adam", - serialized_graph.nodes[optimizer_node.slot_variables[0] - .slot_variable_node_id] - .attributes[0].full_name) - self.assertEqual( - "my_model/dense/kernel/Adam:0", - optimizer.get_slot( - var=model._named_dense.kernel, - name="m").name) - self.assertEqual( - "model/_named_dense/kernel" + suffix, - serialized_graph.nodes[ - optimizer_node.slot_variables[0] - .original_variable_node_id].attributes[0].checkpoint_key) - self.assertEqual("m", optimizer_node.slot_variables[0].slot_name) - self.assertEqual( - "model/_named_dense/kernel/.OPTIMIZER_SLOT/optimizer/m" + suffix, - serialized_graph.nodes[ - optimizer_node.slot_variables[0] - .slot_variable_node_id].attributes[0].checkpoint_key) + children = [node.local_name for node in optimizer_node.children] + six.assertCountEqual( + self, + # Non-slot dependencies + ["beta_1", "beta_2", "iter", "decay", "epsilon", "learning_rate"], + children) + serialized_slot_keys = [] + for slot in optimizer_node.slot_variables: + for attribute in ( + serialized_graph.nodes[slot.slot_variable_node_id].attributes): + serialized_slot_keys.append(attribute.checkpoint_key) + six.assertCountEqual( + self, + [key + suffix for key in expected_slot_keys], + serialized_slot_keys) @test_util.run_in_graph_and_eager_modes def testMoreComplexSaveableReturned(self): @@ -397,20 +388,19 @@ class CheckpointingTests(test.TestCase): @test_util.run_in_graph_and_eager_modes def testSaveRestore(self): model = MyModel() - optimizer = adam.AdamOptimizer(0.001) + optimizer = adam.Adam(0.001) root_checkpointable = checkpointable_utils.Checkpoint( optimizer=optimizer, model=model) input_value = constant_op.constant([[3.]]) - if context.executing_eagerly(): - optimizer.minimize( - lambda: model(input_value)) - else: - train_op = optimizer.minimize(model(input_value)) - # TODO(allenl): Make initialization more pleasant when graph building. - root_checkpointable.save_counter # pylint: disable=pointless-statement - self.evaluate(checkpointable_utils.gather_initializers( - root_checkpointable)) - self.evaluate(train_op) + with backprop.GradientTape() as tape: + loss = model(input_value) + variables = model.trainable_variables + gradients = tape.gradient(loss, variables) + train_op = optimizer.apply_gradients(zip(gradients, variables)) + root_checkpointable.save_counter # pylint: disable=pointless-statement + self.evaluate(checkpointable_utils.gather_initializers( + root_checkpointable)) + self.evaluate(train_op) prefix = os.path.join(self.get_temp_dir(), "ckpt") self.evaluate(state_ops.assign(model._named_dense.variables[1], [42.])) m_bias_slot = optimizer.get_slot(model._named_dense.variables[1], "m") @@ -418,7 +408,8 @@ class CheckpointingTests(test.TestCase): save_path = root_checkpointable.save(file_prefix=prefix) self.evaluate(state_ops.assign(model._named_dense.variables[1], [43.])) self.evaluate(state_ops.assign(root_checkpointable.save_counter, 3)) - optimizer_variables = self.evaluate(optimizer.variables()) + optimizer_variables = self.evaluate( + sorted(optimizer.variables(), key=lambda v: v.name)) self.evaluate(state_ops.assign(m_bias_slot, [-2.])) # Immediate restoration status = root_checkpointable.restore(save_path=save_path).assert_consumed() @@ -429,11 +420,7 @@ class CheckpointingTests(test.TestCase): if not context.executing_eagerly(): return # Restore-on-create is only supported when executing eagerly on_create_model = MyModel() - on_create_optimizer = adam.AdamOptimizer( - 0.001, - # Preserve beta1_power and beta2_power when appying gradients so we can - # test that they've been restored correctly. - beta1=1.0, beta2=1.0) + on_create_optimizer = adam.Adam(0.001) on_create_root = checkpointable_utils.Checkpoint( optimizer=on_create_optimizer, model=on_create_model) # Deferred restoration @@ -455,15 +442,15 @@ class CheckpointingTests(test.TestCase): # Optimizer slot variables are created when the original variable is # restored. self.assertAllEqual([1.5], self.evaluate(on_create_m_bias_slot)) - self.assertAllEqual(optimizer_variables[2:], - self.evaluate(on_create_optimizer.variables())) dummy_var = resource_variable_ops.ResourceVariable([1.]) - on_create_optimizer.minimize(loss=dummy_var.read_value) + on_create_optimizer.minimize(loss=dummy_var.read_value, + var_list=[dummy_var]) status.assert_existing_objects_matched() status.assert_consumed() - beta1_power, beta2_power = on_create_optimizer._get_beta_accumulators() - self.assertAllEqual(optimizer_variables[0], self.evaluate(beta1_power)) - self.assertAllEqual(optimizer_variables[1], self.evaluate(beta2_power)) + self.assertAllEqual( + optimizer_variables, + # Creation order is different, so .variables() needs to be re-sorted. + self.evaluate(sorted(optimizer.variables(), key=lambda v: v.name))) # TODO(allenl): Debug garbage created by this test in python3. def testDeferredRestorationUsageEager(self): @@ -473,21 +460,22 @@ class CheckpointingTests(test.TestCase): checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") for training_continuation in range(3): model = MyModel() - optimizer = adam.AdamOptimizer(0.001) + optimizer = adam.Adam(0.001) root = checkpointable_utils.Checkpoint( - optimizer=optimizer, model=model, - optimizer_step=training_util.get_or_create_global_step()) + optimizer=optimizer, model=model) root.restore(checkpoint_management.latest_checkpoint( checkpoint_directory)) for _ in range(num_training_steps): # TODO(allenl): Use a Dataset and serialize/checkpoint it. input_value = constant_op.constant([[3.]]) - optimizer.minimize( - lambda: model(input_value), # pylint: disable=cell-var-from-loop - global_step=root.optimizer_step) + with backprop.GradientTape() as tape: + loss = model(input_value) + variables = model.trainable_variables + gradients = tape.gradient(loss, variables) + optimizer.apply_gradients(zip(gradients, variables)) root.save(file_prefix=checkpoint_prefix) self.assertEqual((training_continuation + 1) * num_training_steps, - root.optimizer_step.numpy()) + root.optimizer.iterations.numpy()) def testUsageGraph(self): """Expected usage when graph building.""" @@ -498,14 +486,16 @@ class CheckpointingTests(test.TestCase): for training_continuation in range(3): with ops.Graph().as_default(): model = MyModel() - optimizer = adam.AdamOptimizer(0.001) + optimizer = adam.Adam(0.001) root = checkpointable_utils.Checkpoint( - optimizer=optimizer, model=model, - global_step=training_util.get_or_create_global_step()) + optimizer=optimizer, model=model) input_value = constant_op.constant([[3.]]) - train_op = optimizer.minimize( - model(input_value), - global_step=root.global_step) + with backprop.GradientTape() as tape: + loss = model(input_value) + variables = model.trainable_variables + gradients = tape.gradient(loss, variables) + train_op = optimizer.apply_gradients(zip(gradients, variables)) + checkpoint_path = checkpoint_management.latest_checkpoint( checkpoint_directory) with self.session(graph=ops.get_default_graph()) as session: @@ -524,7 +514,7 @@ class CheckpointingTests(test.TestCase): session.run(train_op) root.save(file_prefix=checkpoint_prefix, session=session) self.assertEqual((training_continuation + 1) * num_training_steps, - session.run(root.global_step)) + session.run(root.optimizer.iterations)) self.assertEqual(training_continuation + 1, session.run(root.save_counter)) @@ -534,21 +524,23 @@ class CheckpointingTests(test.TestCase): # Does create garbage when executing eagerly due to ops.Graph() creation. num_training_steps = 10 checkpoint_directory = self.get_temp_dir() + def _train_fn(model, input_value): + with backprop.GradientTape() as tape: + loss = model(input_value) + variables = model.trainable_variables + gradients = tape.gradient(loss, variables) + return optimizer.apply_gradients(zip(gradients, variables)) for training_continuation in range(3): with test_util.device(use_gpu=True): model = MyModel() - optimizer = adam.AdamOptimizer(0.001) + optimizer = adam.Adam(0.001) root = checkpointable_utils.Checkpoint( - optimizer=optimizer, model=model, - global_step=training_util.get_or_create_global_step()) + optimizer=optimizer, model=model) manager = checkpoint_management.CheckpointManager( root, checkpoint_directory, max_to_keep=1) status = root.restore(save_path=manager.latest_checkpoint) input_value = constant_op.constant([[3.]]) - train_fn = functools.partial( - optimizer.minimize, - functools.partial(model, input_value), - global_step=root.global_step) + train_fn = functools.partial(_train_fn, model, input_value) if not context.executing_eagerly(): train_fn = functools.partial(self.evaluate, train_fn()) status.initialize_or_restore() @@ -556,7 +548,7 @@ class CheckpointingTests(test.TestCase): train_fn() manager.save() self.assertEqual((training_continuation + 1) * num_training_steps, - self.evaluate(root.global_step)) + self.evaluate(root.optimizer.iterations)) self.assertEqual(training_continuation + 1, self.evaluate(root.save_counter)) @@ -625,10 +617,9 @@ class CheckpointingTests(test.TestCase): with test_util.device(use_gpu=True): model = MyModel() # Don't actually train so we can test variable values - optimizer = adam.AdamOptimizer(0.) + optimizer = adam.Adam(0.) root = checkpointable_utils.Checkpoint( - optimizer=optimizer, model=model, - global_step=training_util.get_or_create_global_step()) + optimizer=optimizer, model=model) checkpoint_path = checkpoint_management.latest_checkpoint( checkpoint_directory) status = root.restore(save_path=checkpoint_path) @@ -639,8 +630,7 @@ class CheckpointingTests(test.TestCase): with backprop.GradientTape() as tape: loss = _call_model(constant_op.constant([[3.]])) gradients = tape.gradient(loss, model.variables) - return optimizer.apply_gradients(zip(gradients, model.variables), - global_step=root.global_step) + return optimizer.apply_gradients(zip(gradients, model.variables)) if not context.executing_eagerly(): train_fn = functools.partial( self.evaluate, train_fn()) @@ -654,7 +644,7 @@ class CheckpointingTests(test.TestCase): self.evaluate(model.variables[0].assign([[42.]])) root.save(file_prefix=checkpoint_prefix) self.assertEqual((training_continuation + 1) * num_training_steps, - self.evaluate(root.global_step)) + self.evaluate(optimizer.iterations)) self.assertEqual(training_continuation + 1, self.evaluate(root.save_counter)) # pylint: enable=cell-var-from-loop @@ -716,7 +706,7 @@ class CheckpointingTests(test.TestCase): with context.eager_mode(): model = Model() - optimizer = adam.AdamOptimizer(learning_rate=0.05) + optimizer = adam.Adam(learning_rate=0.05) checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") checkpoint = checkpointable_utils.Checkpoint( @@ -802,24 +792,24 @@ class CheckpointingTests(test.TestCase): root = tracking.Checkpointable() root.var = checkpointable_utils.add_variable( root, name="var", initializer=0.) - optimizer = adam.AdamOptimizer(0.1) - if context.executing_eagerly(): - optimizer.minimize(root.var.read_value) - else: - train_op = optimizer.minimize(root.var) - # Note that `optimizer` has not been added as a dependency of - # `root`. Create a one-off grouping so that slot variables for `root.var` - # get initialized too. - self.evaluate(checkpointable_utils.gather_initializers( - checkpointable_utils.Checkpoint(root=root, optimizer=optimizer))) - self.evaluate(train_op) + optimizer = adam.Adam(0.1) + variables = [root.var] + gradients = [1.] + train_op = optimizer.apply_gradients(zip(gradients, variables)) + # Note that `optimizer` has not been added as a dependency of + # `root`. Create a one-off grouping so that slot variables for `root.var` + # get initialized too. + self.evaluate(checkpointable_utils.gather_initializers( + checkpointable_utils.Checkpoint(root=root, optimizer=optimizer))) + self.evaluate(train_op) self.evaluate(state_ops.assign(root.var, 12.)) no_slots_path = checkpointable_utils.CheckpointableSaver(root).save( os.path.join(checkpoint_directory, "no_slots")) root.optimizer = optimizer self.evaluate(state_ops.assign(root.var, 13.)) - self.evaluate(state_ops.assign(optimizer.get_slot(name="m", var=root.var), - 14.)) + self.evaluate(state_ops.assign( + optimizer.get_slot(slot_name="m", var=root.var), + 14.)) slots_path = checkpointable_utils.CheckpointableSaver(root).save( os.path.join(checkpoint_directory, "with_slots")) new_root = tracking.Checkpointable() @@ -836,29 +826,32 @@ class CheckpointingTests(test.TestCase): no_slot_status.assert_consumed() no_slot_status.run_restore_ops() self.assertEqual(12., self.evaluate(new_root.var)) - new_root.optimizer = adam.AdamOptimizer(0.1) + new_root.optimizer = adam.Adam(0.1) slot_status.assert_existing_objects_matched() - with self.assertRaisesRegexp(AssertionError, "beta1_power"): + with self.assertRaisesRegexp(AssertionError, "Unresolved object"): slot_status.assert_consumed() self.assertEqual(12., self.evaluate(new_root.var)) if context.executing_eagerly(): # Slot variables are only created with restoring initializers when # executing eagerly. self.assertEqual(14., self.evaluate( - new_root.optimizer.get_slot(name="m", var=new_root.var))) - else: - self.assertIs(new_root.optimizer.get_slot(name="m", var=new_root.var), - None) - if context.executing_eagerly(): - new_root.optimizer.minimize(new_root.var.read_value) + new_root.optimizer.get_slot(slot_name="m", var=new_root.var))) else: - train_op = new_root.optimizer.minimize(new_root.var) - # The slot variable now exists; restore() didn't create it, but we should - # now have a restore op for it. - slot_status.run_restore_ops() + # Slot variables are not created eagerly when graph building. + with self.assertRaises(KeyError): + new_root.optimizer.get_slot(slot_name="m", var=new_root.var) + variables = [new_root.var] + gradients = [1.] + train_op = new_root.optimizer.apply_gradients(zip(gradients, variables)) + # The slot variable now exists; restore() didn't create it, but we should + # now have a restore op for it. + slot_status.run_restore_ops() + if not context.executing_eagerly(): + # The train op hasn't run when graph building, so the slot variable has + # its restored value. It has run in eager, so the value will be different. self.assertEqual(14., self.evaluate( - new_root.optimizer.get_slot(name="m", var=new_root.var))) - self.evaluate(train_op) + new_root.optimizer.get_slot(slot_name="m", var=new_root.var))) + self.evaluate(train_op) slot_status.assert_consumed() @test_util.run_in_graph_and_eager_modes @@ -1018,18 +1011,18 @@ class CheckpointingTests(test.TestCase): checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") first = tracking.Checkpointable() - first.var1 = variables.Variable(0., name="outside_var") - first.var2 = variables.Variable(0., name="blah") + first.var1 = variables_lib.Variable(0., name="outside_var") + first.var2 = variables_lib.Variable(0., name="blah") self.evaluate(first.var1.assign(4.)) self.evaluate(first.var2.assign(8.)) save_path = checkpointable_utils.CheckpointableSaver(first).save( checkpoint_prefix) second = tracking.Checkpointable() - second.var2 = variables.Variable(0., name="blah") + second.var2 = variables_lib.Variable(0., name="blah") status = checkpointable_utils.CheckpointableSaver( second).restore(save_path) - recreated_var1 = variables.Variable(0., name="outside_var") + recreated_var1 = variables_lib.Variable(0., name="outside_var") status.run_restore_ops() self.assertEqual(8., self.evaluate(second.var2)) self.evaluate(recreated_var1.assign(-2.)) @@ -1046,15 +1039,16 @@ class CheckpointingTests(test.TestCase): checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") obj = tracking.Checkpointable() - obj.var = variable_scope.get_variable(name="v", initializer=0.) - obj.opt = adam.AdamOptimizer(0.1) - obj.opt.minimize(obj.var.read_value()) + obj.var = variables_lib.Variable(0., name="v") + obj.opt = adam.Adam(0.1) + variables = [obj.var] + gradients = [1.] + obj.opt.apply_gradients(zip(gradients, variables)) self.evaluate(checkpointable_utils.gather_initializers(obj)) saver = checkpointable_utils.CheckpointableSaver(obj) saver.save(checkpoint_prefix) - before_ops = graph.get_operations() + graph.finalize() saver.save(checkpoint_prefix) - self.assertEqual(before_ops, graph.get_operations()) @test_util.run_in_graph_and_eager_modes def testCheckpointState(self): @@ -1135,74 +1129,17 @@ class CheckpointingTests(test.TestCase): checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") obj = tracking.Checkpointable() - obj.var = variable_scope.get_variable(name="v", initializer=0.) - obj.opt = adam.AdamOptimizer(0.1) - obj.opt.minimize(obj.var.read_value()) + obj.var = variables_lib.Variable(0., name="v") + obj.opt = adam.Adam(0.1) + variables = [obj.var] + gradients = [1.] + obj.opt.apply_gradients(zip(gradients, variables)) self.evaluate(checkpointable_utils.gather_initializers(obj)) saver = checkpointable_utils.CheckpointableSaver(obj) save_path = saver.save(checkpoint_prefix) saver.restore(save_path) - before_ops = graph.get_operations() + graph.finalize() saver.restore(save_path) - self.assertEqual(before_ops, graph.get_operations()) - - def testMultipleGraphsNonSlotVariables(self): - with context.graph_mode(): - checkpoint_directory = self.get_temp_dir() - checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") - optimizer = adam.AdamOptimizer(0.001) - # Construct a model in one graph - first_graph = ops.Graph() - first_session = session_lib.Session(graph=first_graph) - with first_graph.as_default(), first_session.as_default(): - first_variable = resource_variable_ops.ResourceVariable([1.]) - first_root_checkpointable = checkpointable_utils.Checkpoint( - optimizer=optimizer, variable=first_variable) - train_op = optimizer.minimize(first_variable.read_value) - self.evaluate(checkpointable_utils.gather_initializers( - first_root_checkpointable)) - self.evaluate(train_op) - self.evaluate(first_variable.assign([1.])) - self.evaluate(optimizer.get_slot( - var=first_variable, name="m").assign([2.])) - beta1_power, _ = optimizer._get_beta_accumulators() - self.evaluate(beta1_power.assign(3.)) - - # Save and load in a second graph - second_graph = ops.Graph() - with second_graph.as_default(), session_lib.Session(graph=second_graph): - second_variable = resource_variable_ops.ResourceVariable([1.]) - second_root_checkpointable = checkpointable_utils.Checkpoint( - optimizer=optimizer, variable=second_variable) - train_op = optimizer.minimize(second_variable.read_value) - second_root_checkpointable.restore(None).initialize_or_restore() - self.evaluate(train_op) - self.evaluate(second_variable.assign([4.])) - self.evaluate(optimizer.get_slot( - var=second_variable, name="m").assign([5.])) - beta1_power, _ = optimizer._get_beta_accumulators() - self.evaluate(beta1_power.assign(6.)) - save_path = second_root_checkpointable.save(checkpoint_prefix) - self.evaluate(second_variable.assign([7.])) - self.evaluate(optimizer.get_slot( - var=second_variable, name="m").assign([8.])) - beta1_power, _ = optimizer._get_beta_accumulators() - self.assertAllEqual(6., self.evaluate(beta1_power)) - status = second_root_checkpointable.restore(save_path) - status.assert_consumed().run_restore_ops() - self.assertAllEqual([4.], self.evaluate(second_variable)) - self.assertAllEqual([5.], self.evaluate(optimizer.get_slot( - var=second_variable, name="m"))) - beta1_power, _ = optimizer._get_beta_accumulators() - self.assertAllEqual(6., self.evaluate(beta1_power)) - - # Check that the first graph is unmolested - with first_graph.as_default(), first_session.as_default(): - self.assertAllEqual([1.], self.evaluate(first_variable)) - self.assertAllEqual([2.], self.evaluate(optimizer.get_slot( - var=first_variable, name="m"))) - beta1_power, _ = optimizer._get_beta_accumulators() - self.assertAllEqual(3., self.evaluate(beta1_power)) @test_util.run_in_graph_and_eager_modes def test_sequential(self): @@ -1243,10 +1180,9 @@ class CheckpointingTests(test.TestCase): optimizer_only_prefix = os.path.join(checkpoint_directory, "opt") with test_util.device(use_gpu=True): model = MyModel() - optimizer = adam.AdamOptimizer(0.001) + optimizer = adam.Adam(0.001) root = checkpointable_utils.Checkpoint( - model=model, # Do not save the optimizer with the checkpoint. - global_step=training_util.get_or_create_global_step()) + model=model) # Do not save the optimizer with the checkpoint. optimizer_checkpoint = checkpointable_utils.Checkpoint( optimizer=optimizer) @@ -1254,65 +1190,78 @@ class CheckpointingTests(test.TestCase): checkpoint_directory) status = root.restore(save_path=checkpoint_path) input_value = constant_op.constant([[3.]]) - train_fn = functools.partial( - optimizer.minimize, - functools.partial(model, input_value), - global_step=root.global_step) + def train_fn(): + with backprop.GradientTape() as tape: + loss = model(input_value) + variables = model.trainable_variables + gradients = tape.gradient(loss, variables) + return optimizer.apply_gradients(zip(gradients, variables)) if not context.executing_eagerly(): train_fn = functools.partial(self.evaluate, train_fn()) status.initialize_or_restore() - self.evaluate([v.initializer for v in optimizer.variables()]) + # TODO(tanzheny): Add hyper variables to .variables(), and set them with + # set_weights etc. + variables_not_in_the_variables_property = [ + obj for obj in optimizer._hyper.values() + if isinstance(obj, variables_lib.Variable)] + self.evaluate([v.initializer for v + in optimizer.variables() + + variables_not_in_the_variables_property]) train_fn() model_save_path = root.save(file_prefix=checkpoint_prefix) - self.evaluate(optimizer.variables()[0].assign(42.)) + self.evaluate(optimizer.beta_1.assign(42.)) optimizer_save_path = optimizer_checkpoint.save(optimizer_only_prefix) + del train_fn # Restore into a graph with the optimizer with test_util.device(use_gpu=True): model = MyModel() - optimizer = adam.AdamOptimizer(0.001) + optimizer = adam.Adam(0.001) root = checkpointable_utils.Checkpoint( - optimizer=optimizer, model=model, - global_step=training_util.get_or_create_global_step()) + optimizer=optimizer, model=model) status = root.restore(save_path=model_save_path) input_value = constant_op.constant([[3.]]) - train_fn = functools.partial( - optimizer.minimize, - functools.partial(model, input_value), - global_step=root.global_step) + def train_fn1(): + with backprop.GradientTape() as tape: + loss = model(input_value) + variables = model.trainable_variables + gradients = tape.gradient(loss, variables) + return optimizer.apply_gradients(zip(gradients, variables)) if not context.executing_eagerly(): - train_fn = functools.partial(self.evaluate, train_fn()) + train_fn1 = functools.partial(self.evaluate, train_fn1()) status.initialize_or_restore() - train_fn() + train_fn1() with self.assertRaises(AssertionError): status.assert_existing_objects_matched() with self.assertRaises(AssertionError): status.assert_consumed() + del train_fn1 # Make sure initialization doesn't clobber later restores with test_util.device(use_gpu=True): model = MyModel() - optimizer = adam.AdamOptimizer(0.001, beta1=1.0) + optimizer = adam.Adam(0.001, beta1=1.0) root = checkpointable_utils.Checkpoint( - optimizer=optimizer, model=model, - global_step=training_util.get_or_create_global_step()) + optimizer=optimizer, model=model) opt_root = checkpointable_utils.Checkpoint( optimizer=optimizer) status = root.restore(save_path=model_save_path) init_only_optimizer_status = opt_root.restore(save_path=None) optimizer_status = opt_root.restore(save_path=optimizer_save_path) input_value = constant_op.constant([[3.]]) - train_fn = functools.partial( - optimizer.minimize, - functools.partial(model, input_value), - global_step=root.global_step) + def train_fn2(): + with backprop.GradientTape() as tape: + loss = model(input_value) + variables = model.trainable_variables + gradients = tape.gradient(loss, variables) + return optimizer.apply_gradients(zip(gradients, variables)) if not context.executing_eagerly(): - train_fn = functools.partial(self.evaluate, train_fn()) + train_fn2 = functools.partial(self.evaluate, train_fn2()) optimizer_status.run_restore_ops() status.initialize_or_restore() init_only_optimizer_status.initialize_or_restore() - train_fn() - self.assertEqual(42., self.evaluate(optimizer.variables()[0])) + train_fn2() + self.assertEqual(42., self.evaluate(optimizer.beta_1)) @test_util.run_in_graph_and_eager_modes def test_restore_after_adding_empty_checkpointable_data_structure(self): @@ -1345,7 +1294,7 @@ class _ManualScope(tracking.Checkpointable): return variable_scope.get_variable(name="in_manual_scope", shape=[]) -class TemplateTests(test.TestCase): +class TemplateTests(parameterized.TestCase, test.TestCase): @test_util.run_in_graph_and_eager_modes def test_checkpointable_save_restore(self): @@ -1369,10 +1318,11 @@ class TemplateTests(test.TestCase): 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) + optimizer = adam.Adam(0.0) save_root = checkpointable_utils.Checkpoint( my_template=save_template, optimizer=optimizer) - optimizer.minimize(v1_save.read_value) + optimizer.minimize(v1_save.read_value, + var_list=[v1_save]) 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.])) @@ -1382,13 +1332,13 @@ class TemplateTests(test.TestCase): save_path = save_root.save(checkpoint_prefix) load_template = template.make_template("s2", _templated) - load_optimizer = adam.AdamOptimizer(0.0) + load_optimizer = adam.Adam(0.0) 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() - load_optimizer.minimize(var.read_value) - self.assertEqual(3, len(load_template._checkpoint_dependencies)) + load_optimizer.minimize(var.read_value, var_list=[var]) + self.assertLen(load_template._checkpoint_dependencies, 3) self.assertEqual("v", load_template._checkpoint_dependencies[0].name) self.assertEqual("v2", load_template._checkpoint_dependencies[1].name) self.assertEqual("ManualScope", @@ -1429,14 +1379,14 @@ class TemplateTests(test.TestCase): status = load_root.restore(save_path) (inner_template_one, inner_template_two), (v1, v2, v3) = load_template() outer_template_dependencies = load_root.my_template._checkpoint_dependencies - self.assertEqual(2, len(outer_template_dependencies)) + self.assertLen(outer_template_dependencies, 2) self.assertEqual("i1", outer_template_dependencies[0].name) self.assertIs(inner_template_one, outer_template_dependencies[0].ref) self.assertEqual("i2", outer_template_dependencies[1].name) self.assertIs(inner_template_two, outer_template_dependencies[1].ref) - self.assertEqual(1, len(inner_template_one._checkpoint_dependencies)) + self.assertLen(inner_template_one._checkpoint_dependencies, 1) self.assertEqual("v", inner_template_one._checkpoint_dependencies[0].name) - self.assertEqual(1, len(inner_template_two._checkpoint_dependencies)) + self.assertLen(inner_template_two._checkpoint_dependencies, 1) self.assertEqual("v", inner_template_two._checkpoint_dependencies[0].name) status.assert_consumed().run_restore_ops() self.assertAllEqual([20.], self.evaluate(v1)) @@ -1449,13 +1399,14 @@ class CheckpointCompatibilityTests(test.TestCase): def _initialized_model(self): input_value = constant_op.constant([[3.]]) model = MyModel() - optimizer = adam.AdamOptimizer(0.001) - optimizer_step = training_util.get_or_create_global_step() + optimizer = adam.Adam(0.001) root_checkpointable = checkpointable_utils.Checkpoint( - optimizer=optimizer, model=model, optimizer_step=optimizer_step) - train_op = optimizer.minimize( - functools.partial(model, input_value), - global_step=optimizer_step) + optimizer=optimizer, model=model) + with backprop.GradientTape() as tape: + loss = model(input_value) + variables = model.trainable_variables + gradients = tape.gradient(loss, variables) + train_op = optimizer.apply_gradients(zip(gradients, variables)) self.evaluate(checkpointable_utils.gather_initializers( root_checkpointable)) self.evaluate(train_op) @@ -1463,28 +1414,26 @@ class CheckpointCompatibilityTests(test.TestCase): # with known values to check when loading. self.evaluate(model._named_dense.bias.assign([1.])) self.evaluate(optimizer.get_slot( - var=model._named_dense.bias, name="m").assign([2.])) - beta1_power, _ = optimizer._get_beta_accumulators() - self.evaluate(beta1_power.assign(3.)) + var=model._named_dense.bias, slot_name="m").assign([2.])) + self.evaluate(optimizer.beta_1.assign(3.)) return root_checkpointable def _set_sentinels(self, root_checkpointable): self.evaluate(root_checkpointable.model._named_dense.bias.assign([101.])) self.evaluate( root_checkpointable.optimizer.get_slot( - var=root_checkpointable.model._named_dense.bias, name="m") + var=root_checkpointable.model._named_dense.bias, slot_name="m") .assign([102.])) - beta1_power, _ = root_checkpointable.optimizer._get_beta_accumulators() - self.evaluate(beta1_power.assign(103.)) + self.evaluate(root_checkpointable.optimizer.beta_1.assign(103.)) def _check_sentinels(self, root_checkpointable): self.assertAllEqual( [1.], self.evaluate(root_checkpointable.model._named_dense.bias)) self.assertAllEqual([2.], self.evaluate( root_checkpointable.optimizer.get_slot( - var=root_checkpointable.model._named_dense.bias, name="m"))) - beta1_power, _ = root_checkpointable.optimizer._get_beta_accumulators() - self.assertAllEqual(3., self.evaluate(beta1_power)) + var=root_checkpointable.model._named_dense.bias, slot_name="m"))) + self.assertAllEqual(3., + self.evaluate(root_checkpointable.optimizer.beta_1)) def _write_name_based_checkpoint(self): checkpoint_directory = self.get_temp_dir() @@ -1497,7 +1446,7 @@ class CheckpointCompatibilityTests(test.TestCase): name_saver = saver_lib.Saver() return name_saver.save( sess=session, save_path=checkpoint_prefix, - global_step=root.optimizer_step) + global_step=root.optimizer.iterations) @test_util.run_in_graph_and_eager_modes def testLoadFromNameBasedSaver(self): diff --git a/tensorflow/python/training/checkpointable/util_with_v1_optimizers_test.py b/tensorflow/python/training/checkpointable/util_with_v1_optimizers_test.py new file mode 100644 index 0000000000..00d5747f78 --- /dev/null +++ b/tensorflow/python/training/checkpointable/util_with_v1_optimizers_test.py @@ -0,0 +1,873 @@ +# 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 object-based saving which use tf.train.* optimizers.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import functools +import os + +import six + +from tensorflow.python.client import session as session_lib +from tensorflow.python.eager import backprop +from tensorflow.python.eager import context +from tensorflow.python.eager import def_function +from tensorflow.python.eager import test +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.engine import training +from tensorflow.python.keras.layers import core +from tensorflow.python.ops import init_ops +from tensorflow.python.ops import resource_variable_ops +from tensorflow.python.ops import state_ops +from tensorflow.python.ops import template +from tensorflow.python.ops import variable_scope +from tensorflow.python.training import adam +from tensorflow.python.training import checkpoint_management +from tensorflow.python.training import saver as saver_lib +from tensorflow.python.training import training_util +from tensorflow.python.training.checkpointable import tracking +from tensorflow.python.training.checkpointable import util as checkpointable_utils + + +class NonLayerCheckpointable(tracking.Checkpointable): + + def __init__(self): + super(NonLayerCheckpointable, self).__init__() + self.a_variable = checkpointable_utils.add_variable( + self, name="a_variable", shape=[]) + + +# pylint: disable=not-callable +class MyModel(training.Model): + """A concrete Model for testing.""" + + def __init__(self): + super(MyModel, self).__init__() + self._named_dense = core.Dense(1, use_bias=True) + self._second = core.Dense(1, use_bias=False) + # We can still track Checkpointables which aren't Layers. + self._non_layer = NonLayerCheckpointable() + + def call(self, values): + ret = self._second(self._named_dense(values)) + return ret + + +class CheckpointingTests(test.TestCase): + + @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True) + def testNamingWithOptimizer(self): + input_value = constant_op.constant([[3.]]) + model = MyModel() + # A nuisance Model using the same optimizer. Its slot variables should not + # go in the checkpoint, since it is never depended on. + other_model = MyModel() + optimizer = adam.AdamOptimizer(0.001) + optimizer_step = training_util.get_or_create_global_step() + root_checkpointable = checkpointable_utils.Checkpoint( + optimizer=optimizer, model=model, optimizer_step=optimizer_step) + if context.executing_eagerly(): + optimizer.minimize( + lambda: model(input_value), + global_step=optimizer_step) + optimizer.minimize( + lambda: other_model(input_value), + global_step=optimizer_step) + else: + train_op = optimizer.minimize( + model(input_value), global_step=optimizer_step) + optimizer.minimize( + other_model(input_value), + global_step=optimizer_step) + self.evaluate(checkpointable_utils.gather_initializers( + root_checkpointable)) + self.evaluate(train_op) + named_variables, serialized_graph, _ = ( + checkpointable_utils._serialize_object_graph( + root_checkpointable, saveables_cache=None)) + expected_checkpoint_names = ( + # Created in the root node, so no prefix. + "optimizer_step", + "model/_second/kernel", + "model/_named_dense/kernel", + "model/_named_dense/bias", + # non-Layer dependency of the model + "model/_non_layer/a_variable", + # The optimizer creates two non-slot variables + "optimizer/beta1_power", + "optimizer/beta2_power", + # Slot variables + "model/_second/kernel/.OPTIMIZER_SLOT/optimizer/m", + "model/_second/kernel/.OPTIMIZER_SLOT/optimizer/v", + "model/_named_dense/kernel/.OPTIMIZER_SLOT/optimizer/m", + "model/_named_dense/kernel/.OPTIMIZER_SLOT/optimizer/v", + "model/_named_dense/bias/.OPTIMIZER_SLOT/optimizer/m", + "model/_named_dense/bias/.OPTIMIZER_SLOT/optimizer/v", + ) + suffix = "/.ATTRIBUTES/VARIABLE_VALUE" + expected_checkpoint_names = [ + name + suffix for name in expected_checkpoint_names] + # The Dense layers also save get_config() JSON + expected_checkpoint_names.extend( + ["model/_second/.ATTRIBUTES/OBJECT_CONFIG_JSON", + "model/_named_dense/.ATTRIBUTES/OBJECT_CONFIG_JSON"]) + named_variables = {v.name: v for v in named_variables} + six.assertCountEqual(self, expected_checkpoint_names, + named_variables.keys()) + # Check that we've mapped to the right variable objects (not exhaustive) + self.assertEqual( + "global_step", + named_variables["optimizer_step" + suffix].full_name) + self.assertEqual( + "my_model/dense_1/kernel", + named_variables["model/_second/kernel" + suffix].full_name) + self.assertEqual( + "my_model/dense/kernel", + named_variables["model/_named_dense/kernel" + suffix].full_name) + self.assertEqual( + "beta1_power", + named_variables["optimizer/beta1_power" + suffix].full_name) + self.assertEqual( + "beta2_power", + named_variables["optimizer/beta2_power" + suffix].full_name) + # Spot check the generated protocol buffers. + self.assertEqual("optimizer", + serialized_graph.nodes[0].children[1].local_name) + optimizer_node = serialized_graph.nodes[serialized_graph.nodes[0].children[ + 1].node_id] + self.assertEqual("beta1_power", + optimizer_node.children[0].local_name) + self.assertEqual("beta1_power", + serialized_graph.nodes[optimizer_node.children[0].node_id] + .attributes[0].full_name) + self.assertEqual( + "my_model/dense/kernel", + serialized_graph.nodes[optimizer_node.slot_variables[0] + .original_variable_node_id] + .attributes[0].full_name) + # We strip off the :0 suffix, as variable.name-based saving does. + self.assertEqual( + "my_model/dense/kernel/Adam", + serialized_graph.nodes[optimizer_node.slot_variables[0] + .slot_variable_node_id] + .attributes[0].full_name) + self.assertEqual( + "my_model/dense/kernel/Adam:0", + optimizer.get_slot( + var=model._named_dense.kernel, + name="m").name) + self.assertEqual( + "model/_named_dense/kernel" + suffix, + serialized_graph.nodes[ + optimizer_node.slot_variables[0] + .original_variable_node_id].attributes[0].checkpoint_key) + self.assertEqual("m", optimizer_node.slot_variables[0].slot_name) + self.assertEqual( + "model/_named_dense/kernel/.OPTIMIZER_SLOT/optimizer/m" + suffix, + serialized_graph.nodes[ + optimizer_node.slot_variables[0] + .slot_variable_node_id].attributes[0].checkpoint_key) + + @test_util.run_in_graph_and_eager_modes + def testSaveRestore(self): + model = MyModel() + optimizer = adam.AdamOptimizer(0.001) + root_checkpointable = checkpointable_utils.Checkpoint( + optimizer=optimizer, model=model) + input_value = constant_op.constant([[3.]]) + if context.executing_eagerly(): + optimizer.minimize( + lambda: model(input_value)) + else: + train_op = optimizer.minimize(model(input_value)) + # TODO(allenl): Make initialization more pleasant when graph building. + root_checkpointable.save_counter # pylint: disable=pointless-statement + self.evaluate(checkpointable_utils.gather_initializers( + root_checkpointable)) + self.evaluate(train_op) + prefix = os.path.join(self.get_temp_dir(), "ckpt") + self.evaluate(state_ops.assign(model._named_dense.variables[1], [42.])) + m_bias_slot = optimizer.get_slot(model._named_dense.variables[1], "m") + self.evaluate(state_ops.assign(m_bias_slot, [1.5])) + save_path = root_checkpointable.save(file_prefix=prefix) + self.evaluate(state_ops.assign(model._named_dense.variables[1], [43.])) + self.evaluate(state_ops.assign(root_checkpointable.save_counter, 3)) + optimizer_variables = self.evaluate(optimizer.variables()) + self.evaluate(state_ops.assign(m_bias_slot, [-2.])) + # Immediate restoration + status = root_checkpointable.restore(save_path=save_path).assert_consumed() + status.run_restore_ops() + self.assertAllEqual([42.], self.evaluate(model._named_dense.variables[1])) + self.assertAllEqual(1, self.evaluate(root_checkpointable.save_counter)) + self.assertAllEqual([1.5], self.evaluate(m_bias_slot)) + if not context.executing_eagerly(): + return # Restore-on-create is only supported when executing eagerly + on_create_model = MyModel() + on_create_optimizer = adam.AdamOptimizer( + 0.001, + # Preserve beta1_power and beta2_power when appying gradients so we can + # test that they've been restored correctly. + beta1=1.0, beta2=1.0) + on_create_root = checkpointable_utils.Checkpoint( + optimizer=on_create_optimizer, model=on_create_model) + # Deferred restoration + status = on_create_root.restore(save_path=save_path) + status.assert_nontrivial_match() + status.assert_existing_objects_matched() + with self.assertRaises(AssertionError): + status.assert_consumed() + on_create_model(constant_op.constant([[3.]])) # create variables + self.assertAllEqual(1, self.evaluate(on_create_root.save_counter)) + self.assertAllEqual([42.], + self.evaluate( + on_create_model._named_dense.variables[1])) + on_create_m_bias_slot = on_create_optimizer.get_slot( + on_create_model._named_dense.variables[1], "m") + status.assert_existing_objects_matched() + with self.assertRaises(AssertionError): + status.assert_consumed() + # Optimizer slot variables are created when the original variable is + # restored. + self.assertAllEqual([1.5], self.evaluate(on_create_m_bias_slot)) + self.assertAllEqual(optimizer_variables[2:], + self.evaluate(on_create_optimizer.variables())) + dummy_var = resource_variable_ops.ResourceVariable([1.]) + on_create_optimizer.minimize(loss=dummy_var.read_value) + status.assert_existing_objects_matched() + status.assert_consumed() + beta1_power, beta2_power = on_create_optimizer._get_beta_accumulators() + self.assertAllEqual(optimizer_variables[0], self.evaluate(beta1_power)) + self.assertAllEqual(optimizer_variables[1], self.evaluate(beta2_power)) + + # TODO(allenl): Debug garbage created by this test in python3. + def testDeferredRestorationUsageEager(self): + """An idiomatic eager execution example.""" + num_training_steps = 10 + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + for training_continuation in range(3): + model = MyModel() + optimizer = adam.AdamOptimizer(0.001) + root = checkpointable_utils.Checkpoint( + optimizer=optimizer, model=model, + optimizer_step=training_util.get_or_create_global_step()) + root.restore(checkpoint_management.latest_checkpoint( + checkpoint_directory)) + for _ in range(num_training_steps): + # TODO(allenl): Use a Dataset and serialize/checkpoint it. + input_value = constant_op.constant([[3.]]) + optimizer.minimize( + lambda: model(input_value), # pylint: disable=cell-var-from-loop + global_step=root.optimizer_step) + root.save(file_prefix=checkpoint_prefix) + self.assertEqual((training_continuation + 1) * num_training_steps, + root.optimizer_step.numpy()) + + def testUsageGraph(self): + """Expected usage when graph building.""" + with context.graph_mode(): + num_training_steps = 10 + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + for training_continuation in range(3): + with ops.Graph().as_default(): + model = MyModel() + optimizer = adam.AdamOptimizer(0.001) + root = checkpointable_utils.Checkpoint( + optimizer=optimizer, model=model, + global_step=training_util.get_or_create_global_step()) + input_value = constant_op.constant([[3.]]) + train_op = optimizer.minimize( + model(input_value), + global_step=root.global_step) + checkpoint_path = checkpoint_management.latest_checkpoint( + checkpoint_directory) + with self.session(graph=ops.get_default_graph()) as session: + status = root.restore(save_path=checkpoint_path) + status.initialize_or_restore(session=session) + if checkpoint_path is None: + self.assertEqual(0, training_continuation) + with self.assertRaises(AssertionError): + status.assert_consumed() + with self.assertRaises(AssertionError): + status.assert_existing_objects_matched() + else: + status.assert_consumed() + status.assert_existing_objects_matched() + for _ in range(num_training_steps): + session.run(train_op) + root.save(file_prefix=checkpoint_prefix, session=session) + self.assertEqual((training_continuation + 1) * num_training_steps, + session.run(root.global_step)) + self.assertEqual(training_continuation + 1, + session.run(root.save_counter)) + + @test_util.run_in_graph_and_eager_modes + def testAgnosticUsage(self): + """Graph/eager agnostic usage.""" + # Does create garbage when executing eagerly due to ops.Graph() creation. + num_training_steps = 10 + checkpoint_directory = self.get_temp_dir() + for training_continuation in range(3): + with test_util.device(use_gpu=True): + model = MyModel() + optimizer = adam.AdamOptimizer(0.001) + root = checkpointable_utils.Checkpoint( + optimizer=optimizer, model=model, + global_step=training_util.get_or_create_global_step()) + manager = checkpoint_management.CheckpointManager( + root, checkpoint_directory, max_to_keep=1) + status = root.restore(save_path=manager.latest_checkpoint) + input_value = constant_op.constant([[3.]]) + train_fn = functools.partial( + optimizer.minimize, + functools.partial(model, input_value), + global_step=root.global_step) + if not context.executing_eagerly(): + train_fn = functools.partial(self.evaluate, train_fn()) + status.initialize_or_restore() + for _ in range(num_training_steps): + train_fn() + manager.save() + self.assertEqual((training_continuation + 1) * num_training_steps, + self.evaluate(root.global_step)) + self.assertEqual(training_continuation + 1, + self.evaluate(root.save_counter)) + + # pylint: disable=cell-var-from-loop + @test_util.run_in_graph_and_eager_modes + def testWithDefun(self): + num_training_steps = 2 + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + for training_continuation in range(3): + with test_util.device(use_gpu=True): + model = MyModel() + # Don't actually train so we can test variable values + optimizer = adam.AdamOptimizer(0.) + root = checkpointable_utils.Checkpoint( + optimizer=optimizer, model=model, + global_step=training_util.get_or_create_global_step()) + checkpoint_path = checkpoint_management.latest_checkpoint( + checkpoint_directory) + status = root.restore(save_path=checkpoint_path) + def train_fn(): + @def_function.function + def _call_model(x): + return model(x) + with backprop.GradientTape() as tape: + loss = _call_model(constant_op.constant([[3.]])) + gradients = tape.gradient(loss, model.variables) + return optimizer.apply_gradients(zip(gradients, model.variables), + global_step=root.global_step) + if not context.executing_eagerly(): + train_fn = functools.partial( + self.evaluate, train_fn()) + status.initialize_or_restore() + for _ in range(num_training_steps): + train_fn() + if training_continuation > 0: + status.assert_consumed() + self.assertAllClose([[42.]], self.evaluate(model.variables[0])) + else: + self.evaluate(model.variables[0].assign([[42.]])) + root.save(file_prefix=checkpoint_prefix) + self.assertEqual((training_continuation + 1) * num_training_steps, + self.evaluate(root.global_step)) + self.assertEqual(training_continuation + 1, + self.evaluate(root.save_counter)) + # pylint: enable=cell-var-from-loop + + def _get_checkpoint_name(self, name): + root = tracking.Checkpointable() + checkpointable_utils.add_variable( + root, name=name, shape=[1, 2], dtype=dtypes.float64) + (named_variable,), _, _ = checkpointable_utils._serialize_object_graph( + root, saveables_cache=None) + with ops.name_scope("root/" + named_variable.name): + pass # Make sure we can use this as an op name if we prefix it. + return named_variable.name + + def testAnonymousVarsInInit(self): + + class Model(training.Model): + + def __init__(self): + super(Model, self).__init__() + self.w = resource_variable_ops.ResourceVariable(0.0) + self.b = resource_variable_ops.ResourceVariable(0.0) + self.vars = [self.w, self.b] + + def call(self, x): + return x * self.w + self.b + + with context.eager_mode(): + model = Model() + optimizer = adam.AdamOptimizer(learning_rate=0.05) + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + checkpoint = checkpointable_utils.Checkpoint( + model=model, optimizer=optimizer) + for _ in range(2): + checkpoint.save(checkpoint_prefix) + with backprop.GradientTape() as tape: + loss = (constant_op.constant(1.) + - model(constant_op.constant(1.))) ** 2 + grad = tape.gradient(loss, model.vars) + optimizer.apply_gradients( + [(g, v) for g, v in zip(grad, model.vars)]) + + @test_util.run_in_graph_and_eager_modes + def testDeferredSlotRestoration(self): + checkpoint_directory = self.get_temp_dir() + + root = tracking.Checkpointable() + root.var = checkpointable_utils.add_variable( + root, name="var", initializer=0.) + optimizer = adam.AdamOptimizer(0.1) + if context.executing_eagerly(): + optimizer.minimize(root.var.read_value) + else: + train_op = optimizer.minimize(root.var) + # Note that `optimizer` has not been added as a dependency of + # `root`. Create a one-off grouping so that slot variables for `root.var` + # get initialized too. + self.evaluate(checkpointable_utils.gather_initializers( + checkpointable_utils.Checkpoint(root=root, optimizer=optimizer))) + self.evaluate(train_op) + self.evaluate(state_ops.assign(root.var, 12.)) + no_slots_path = checkpointable_utils.CheckpointableSaver(root).save( + os.path.join(checkpoint_directory, "no_slots")) + root.optimizer = optimizer + self.evaluate(state_ops.assign(root.var, 13.)) + self.evaluate(state_ops.assign(optimizer.get_slot(name="m", var=root.var), + 14.)) + slots_path = checkpointable_utils.CheckpointableSaver(root).save( + os.path.join(checkpoint_directory, "with_slots")) + new_root = tracking.Checkpointable() + # Load the slot-containing checkpoint (deferred), then immediately overwrite + # the non-slot variable (also deferred). + slot_status = checkpointable_utils.CheckpointableSaver( + new_root).restore(slots_path) + no_slot_status = checkpointable_utils.CheckpointableSaver( + new_root).restore(no_slots_path) + with self.assertRaises(AssertionError): + no_slot_status.assert_consumed() + new_root.var = checkpointable_utils.add_variable( + new_root, name="var", shape=[]) + no_slot_status.assert_consumed() + no_slot_status.run_restore_ops() + self.assertEqual(12., self.evaluate(new_root.var)) + new_root.optimizer = adam.AdamOptimizer(0.1) + slot_status.assert_existing_objects_matched() + with self.assertRaisesRegexp(AssertionError, "beta1_power"): + slot_status.assert_consumed() + self.assertEqual(12., self.evaluate(new_root.var)) + if context.executing_eagerly(): + # Slot variables are only created with restoring initializers when + # executing eagerly. + self.assertEqual(14., self.evaluate( + new_root.optimizer.get_slot(name="m", var=new_root.var))) + else: + self.assertIs(new_root.optimizer.get_slot(name="m", var=new_root.var), + None) + if context.executing_eagerly(): + new_root.optimizer.minimize(new_root.var.read_value) + else: + train_op = new_root.optimizer.minimize(new_root.var) + # The slot variable now exists; restore() didn't create it, but we should + # now have a restore op for it. + slot_status.run_restore_ops() + self.assertEqual(14., self.evaluate( + new_root.optimizer.get_slot(name="m", var=new_root.var))) + self.evaluate(train_op) + slot_status.assert_consumed() + + def testManySavesGraph(self): + """Saves after the first should not modify the graph.""" + with context.graph_mode(): + graph = ops.Graph() + with graph.as_default(), self.session(graph): + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + obj = tracking.Checkpointable() + obj.var = variable_scope.get_variable(name="v", initializer=0.) + obj.opt = adam.AdamOptimizer(0.1) + obj.opt.minimize(obj.var.read_value()) + self.evaluate(checkpointable_utils.gather_initializers(obj)) + saver = checkpointable_utils.CheckpointableSaver(obj) + saver.save(checkpoint_prefix) + before_ops = graph.get_operations() + saver.save(checkpoint_prefix) + self.assertEqual(before_ops, graph.get_operations()) + + def testManyRestoresGraph(self): + """Restores after the first should not modify the graph.""" + with context.graph_mode(): + graph = ops.Graph() + with graph.as_default(), self.session(graph): + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + obj = tracking.Checkpointable() + obj.var = variable_scope.get_variable(name="v", initializer=0.) + obj.opt = adam.AdamOptimizer(0.1) + obj.opt.minimize(obj.var.read_value()) + self.evaluate(checkpointable_utils.gather_initializers(obj)) + saver = checkpointable_utils.CheckpointableSaver(obj) + save_path = saver.save(checkpoint_prefix) + saver.restore(save_path) + before_ops = graph.get_operations() + saver.restore(save_path) + self.assertEqual(before_ops, graph.get_operations()) + + def testMultipleGraphsNonSlotVariables(self): + with context.graph_mode(): + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + optimizer = adam.AdamOptimizer(0.001) + # Construct a model in one graph + first_graph = ops.Graph() + first_session = session_lib.Session(graph=first_graph) + with first_graph.as_default(), first_session.as_default(): + first_variable = resource_variable_ops.ResourceVariable([1.]) + first_root_checkpointable = checkpointable_utils.Checkpoint( + optimizer=optimizer, variable=first_variable) + train_op = optimizer.minimize(first_variable.read_value) + self.evaluate(checkpointable_utils.gather_initializers( + first_root_checkpointable)) + self.evaluate(train_op) + self.evaluate(first_variable.assign([1.])) + self.evaluate(optimizer.get_slot( + var=first_variable, name="m").assign([2.])) + beta1_power, _ = optimizer._get_beta_accumulators() + self.evaluate(beta1_power.assign(3.)) + + # Save and load in a second graph + second_graph = ops.Graph() + with second_graph.as_default(), session_lib.Session(graph=second_graph): + second_variable = resource_variable_ops.ResourceVariable([1.]) + second_root_checkpointable = checkpointable_utils.Checkpoint( + optimizer=optimizer, variable=second_variable) + train_op = optimizer.minimize(second_variable.read_value) + second_root_checkpointable.restore(None).initialize_or_restore() + self.evaluate(train_op) + self.evaluate(second_variable.assign([4.])) + self.evaluate(optimizer.get_slot( + var=second_variable, name="m").assign([5.])) + beta1_power, _ = optimizer._get_beta_accumulators() + self.evaluate(beta1_power.assign(6.)) + save_path = second_root_checkpointable.save(checkpoint_prefix) + self.evaluate(second_variable.assign([7.])) + self.evaluate(optimizer.get_slot( + var=second_variable, name="m").assign([8.])) + beta1_power, _ = optimizer._get_beta_accumulators() + self.assertAllEqual(6., self.evaluate(beta1_power)) + status = second_root_checkpointable.restore(save_path) + status.assert_consumed().run_restore_ops() + self.assertAllEqual([4.], self.evaluate(second_variable)) + self.assertAllEqual([5.], self.evaluate(optimizer.get_slot( + var=second_variable, name="m"))) + beta1_power, _ = optimizer._get_beta_accumulators() + self.assertAllEqual(6., self.evaluate(beta1_power)) + + # Check that the first graph is unmolested + with first_graph.as_default(), first_session.as_default(): + self.assertAllEqual([1.], self.evaluate(first_variable)) + self.assertAllEqual([2.], self.evaluate(optimizer.get_slot( + var=first_variable, name="m"))) + beta1_power, _ = optimizer._get_beta_accumulators() + self.assertAllEqual(3., self.evaluate(beta1_power)) + + @test_util.run_in_graph_and_eager_modes + def test_initialize_if_not_restoring(self): + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + optimizer_only_prefix = os.path.join(checkpoint_directory, "opt") + with test_util.device(use_gpu=True): + model = MyModel() + optimizer = adam.AdamOptimizer(0.001) + root = checkpointable_utils.Checkpoint( + model=model, # Do not save the optimizer with the checkpoint. + global_step=training_util.get_or_create_global_step()) + optimizer_checkpoint = checkpointable_utils.Checkpoint( + optimizer=optimizer) + + checkpoint_path = checkpoint_management.latest_checkpoint( + checkpoint_directory) + status = root.restore(save_path=checkpoint_path) + input_value = constant_op.constant([[3.]]) + train_fn = functools.partial( + optimizer.minimize, + functools.partial(model, input_value), + global_step=root.global_step) + if not context.executing_eagerly(): + train_fn = functools.partial(self.evaluate, train_fn()) + status.initialize_or_restore() + self.evaluate([v.initializer for v in optimizer.variables()]) + train_fn() + model_save_path = root.save(file_prefix=checkpoint_prefix) + self.evaluate(optimizer.variables()[0].assign(42.)) + optimizer_save_path = optimizer_checkpoint.save(optimizer_only_prefix) + + # Restore into a graph with the optimizer + with test_util.device(use_gpu=True): + model = MyModel() + optimizer = adam.AdamOptimizer(0.001) + root = checkpointable_utils.Checkpoint( + optimizer=optimizer, model=model, + global_step=training_util.get_or_create_global_step()) + status = root.restore(save_path=model_save_path) + input_value = constant_op.constant([[3.]]) + train_fn = functools.partial( + optimizer.minimize, + functools.partial(model, input_value), + global_step=root.global_step) + if not context.executing_eagerly(): + train_fn = functools.partial(self.evaluate, train_fn()) + status.initialize_or_restore() + train_fn() + with self.assertRaises(AssertionError): + status.assert_existing_objects_matched() + with self.assertRaises(AssertionError): + status.assert_consumed() + + # Make sure initialization doesn't clobber later restores + with test_util.device(use_gpu=True): + model = MyModel() + optimizer = adam.AdamOptimizer(0.001, beta1=1.0) + root = checkpointable_utils.Checkpoint( + optimizer=optimizer, model=model, + global_step=training_util.get_or_create_global_step()) + opt_root = checkpointable_utils.Checkpoint( + optimizer=optimizer) + status = root.restore(save_path=model_save_path) + init_only_optimizer_status = opt_root.restore(save_path=None) + optimizer_status = opt_root.restore(save_path=optimizer_save_path) + input_value = constant_op.constant([[3.]]) + train_fn = functools.partial( + optimizer.minimize, + functools.partial(model, input_value), + global_step=root.global_step) + if not context.executing_eagerly(): + train_fn = functools.partial(self.evaluate, train_fn()) + optimizer_status.run_restore_ops() + status.initialize_or_restore() + init_only_optimizer_status.initialize_or_restore() + train_fn() + self.assertEqual(42., self.evaluate(optimizer.variables()[0])) + + +class _ManualScope(tracking.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 + def test_checkpointable_save_restore(self): + + def _templated(): + v = variable_scope.get_variable( + "v", shape=[1], initializer=init_ops.zeros_initializer(), + use_resource=True) + v2 = variable_scope.get_variable( + "v2", shape=[1], initializer=init_ops.zeros_initializer(), + use_resource=True) + manual = _ManualScope() + return v, v + 1., v2, manual, manual() + + save_template = template.make_template("s1", _templated) + 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.])) + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + save_path = save_root.save(checkpoint_prefix) + + load_template = template.make_template("s2", _templated) + load_optimizer = adam.AdamOptimizer(0.0) + 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() + load_optimizer.minimize(var.read_value) + 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)) + self.assertAllEqual([14.], self.evaluate(var2)) + + +class CheckpointCompatibilityTests(test.TestCase): + + def _initialized_model(self): + input_value = constant_op.constant([[3.]]) + model = MyModel() + optimizer = adam.AdamOptimizer(0.001) + optimizer_step = training_util.get_or_create_global_step() + root_checkpointable = checkpointable_utils.Checkpoint( + optimizer=optimizer, model=model, optimizer_step=optimizer_step) + train_op = optimizer.minimize( + functools.partial(model, input_value), + global_step=optimizer_step) + self.evaluate(checkpointable_utils.gather_initializers( + root_checkpointable)) + self.evaluate(train_op) + # A regular variable, a slot variable, and a non-slot Optimizer variable + # with known values to check when loading. + self.evaluate(model._named_dense.bias.assign([1.])) + self.evaluate(optimizer.get_slot( + var=model._named_dense.bias, name="m").assign([2.])) + beta1_power, _ = optimizer._get_beta_accumulators() + self.evaluate(beta1_power.assign(3.)) + return root_checkpointable + + def _set_sentinels(self, root_checkpointable): + self.evaluate(root_checkpointable.model._named_dense.bias.assign([101.])) + self.evaluate( + root_checkpointable.optimizer.get_slot( + var=root_checkpointable.model._named_dense.bias, name="m") + .assign([102.])) + beta1_power, _ = root_checkpointable.optimizer._get_beta_accumulators() + self.evaluate(beta1_power.assign(103.)) + + def _check_sentinels(self, root_checkpointable): + self.assertAllEqual( + [1.], self.evaluate(root_checkpointable.model._named_dense.bias)) + self.assertAllEqual([2.], self.evaluate( + root_checkpointable.optimizer.get_slot( + var=root_checkpointable.model._named_dense.bias, name="m"))) + beta1_power, _ = root_checkpointable.optimizer._get_beta_accumulators() + self.assertAllEqual(3., self.evaluate(beta1_power)) + + def _write_name_based_checkpoint(self): + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + with context.graph_mode(): + save_graph = ops.Graph() + with save_graph.as_default(), self.session( + graph=save_graph) as session: + root = self._initialized_model() + name_saver = saver_lib.Saver() + return name_saver.save( + sess=session, save_path=checkpoint_prefix, + global_step=root.optimizer_step) + + @test_util.run_in_graph_and_eager_modes + def testLoadFromNameBasedSaver(self): + """Save a name-based checkpoint, load it using the object-based API.""" + with test_util.device(use_gpu=True): + save_path = self._write_name_based_checkpoint() + root = self._initialized_model() + self._set_sentinels(root) + with self.assertRaises(AssertionError): + self._check_sentinels(root) + object_saver = checkpointable_utils.CheckpointableSaver(root) + self._set_sentinels(root) + status = object_saver.restore(save_path) + if context.executing_eagerly(): + self._check_sentinels(root) + if context.executing_eagerly(): + with self.assertRaisesRegexp(AssertionError, "OBJECT_CONFIG_JSON"): + status.assert_consumed() + with self.assertRaisesRegexp(AssertionError, "OBJECT_CONFIG_JSON"): + status.assert_existing_objects_matched() + with self.assertRaisesRegexp(AssertionError, "OBJECT_CONFIG_JSON"): + status.assert_nontrivial_match() + else: + # When graph building, we haven't read any keys, so we don't know + # whether the restore will be complete. + with self.assertRaisesRegexp(AssertionError, "not restored"): + status.assert_consumed() + with self.assertRaisesRegexp(AssertionError, "not restored"): + status.assert_existing_objects_matched() + with self.assertRaisesRegexp(AssertionError, "not restored"): + status.assert_nontrivial_match() + status.run_restore_ops() + self._check_sentinels(root) + self._set_sentinels(root) + status = object_saver.restore(save_path) + status.initialize_or_restore() + self._check_sentinels(root) + # Check that there is no error when keys are missing from the name-based + # checkpoint. + root.not_in_name_checkpoint = resource_variable_ops.ResourceVariable([1.]) + status = object_saver.restore(save_path) + with self.assertRaises(AssertionError): + status.assert_existing_objects_matched() + + def testSaveGraphLoadEager(self): + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + with context.graph_mode(): + save_graph = ops.Graph() + with save_graph.as_default(), self.session( + graph=save_graph) as session: + root = self._initialized_model() + save_path = root.save(session=session, file_prefix=checkpoint_prefix) + with context.eager_mode(): + root = self._initialized_model() + self._set_sentinels(root) + root.restore(save_path).assert_consumed() + self._check_sentinels(root) + + def testSaveEagerLoadGraph(self): + checkpoint_directory = self.get_temp_dir() + checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") + with context.eager_mode(): + root = self._initialized_model() + save_path = root.save(file_prefix=checkpoint_prefix) + with context.graph_mode(): + save_graph = ops.Graph() + with save_graph.as_default(), self.session( + graph=save_graph): + root = self._initialized_model() + self._set_sentinels(root) + root.restore(save_path).assert_consumed().run_restore_ops() + self._check_sentinels(root) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adadelta.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adadelta.pbtxt index 00cd5aca4c..5426269793 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adadelta.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adadelta.pbtxt @@ -44,6 +44,10 @@ tf_class { name: "get_slot" argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "get_slot_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "get_updates" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adagrad.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adagrad.pbtxt index 6d47fe310d..c39fe6ba4f 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adagrad.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adagrad.pbtxt @@ -44,6 +44,10 @@ tf_class { name: "get_slot" argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "get_slot_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "get_updates" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adam.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adam.pbtxt index 417362d211..05d46d380b 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adam.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adam.pbtxt @@ -44,6 +44,10 @@ tf_class { name: "get_slot" argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "get_slot_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "get_updates" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adamax.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adamax.pbtxt index 7b43abee23..78829def67 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adamax.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adamax.pbtxt @@ -45,6 +45,10 @@ tf_class { name: "get_slot" argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "get_slot_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "get_updates" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-optimizer.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-optimizer.pbtxt index a996746dac..58b7f27491 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-optimizer.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-optimizer.pbtxt @@ -43,6 +43,10 @@ tf_class { name: "get_slot" argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "get_slot_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "get_updates" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-r-m-sprop.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-r-m-sprop.pbtxt index bfc9d67a47..8de796edde 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-r-m-sprop.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-r-m-sprop.pbtxt @@ -44,6 +44,10 @@ tf_class { name: "get_slot" argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "get_slot_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "get_updates" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-s-g-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-s-g-d.pbtxt index 3f3d57962b..393eeb3d6c 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-s-g-d.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-s-g-d.pbtxt @@ -44,6 +44,10 @@ tf_class { name: "get_slot" argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "get_slot_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "get_updates" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adadelta.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adadelta.pbtxt index 00cd5aca4c..5426269793 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adadelta.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adadelta.pbtxt @@ -44,6 +44,10 @@ tf_class { name: "get_slot" argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "get_slot_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "get_updates" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adagrad.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adagrad.pbtxt index 6d47fe310d..c39fe6ba4f 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adagrad.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adagrad.pbtxt @@ -44,6 +44,10 @@ tf_class { name: "get_slot" argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "get_slot_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "get_updates" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adam.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adam.pbtxt index 417362d211..05d46d380b 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adam.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adam.pbtxt @@ -44,6 +44,10 @@ tf_class { name: "get_slot" argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "get_slot_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "get_updates" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adamax.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adamax.pbtxt index 7b43abee23..78829def67 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adamax.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adamax.pbtxt @@ -45,6 +45,10 @@ tf_class { name: "get_slot" argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "get_slot_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "get_updates" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-optimizer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-optimizer.pbtxt index a996746dac..58b7f27491 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-optimizer.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-optimizer.pbtxt @@ -43,6 +43,10 @@ tf_class { name: "get_slot" argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "get_slot_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "get_updates" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-r-m-sprop.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-r-m-sprop.pbtxt index bfc9d67a47..8de796edde 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-r-m-sprop.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-r-m-sprop.pbtxt @@ -44,6 +44,10 @@ tf_class { name: "get_slot" argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "get_slot_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "get_updates" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-s-g-d.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-s-g-d.pbtxt index 3f3d57962b..393eeb3d6c 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-s-g-d.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-s-g-d.pbtxt @@ -44,6 +44,10 @@ tf_class { name: "get_slot" argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "get_slot_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "get_updates" argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" -- GitLab From 686ba58692349a8f414d9a48ec1ee6ea296a9a6e Mon Sep 17 00:00:00 2001 From: Jian Li Date: Wed, 12 Dec 2018 14:01:31 -0800 Subject: [PATCH 440/873] Add int8 support in AddTensor. PiperOrigin-RevId: 225249344 --- tensorflow/lite/kernels/test_util.h | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tensorflow/lite/kernels/test_util.h b/tensorflow/lite/kernels/test_util.h index dadabb86ab..f5c67c3e9c 100644 --- a/tensorflow/lite/kernels/test_util.h +++ b/tensorflow/lite/kernels/test_util.h @@ -307,10 +307,12 @@ class SingleOpModel { if (is_quantized) { if (t.min != 0 || t.max != 0) { - // TODO(b/119422369): Handle signed int8 here. if (t.type == TensorType_UINT8) { std::tie(t.scale, t.zero_point) = QuantizationParams(t.min, t.max); + } else if (t.type == TensorType_INT8) { + std::tie(t.scale, t.zero_point) = + QuantizationParams(t.min, t.max); } else if (t.type == TensorType_INT32) { std::tie(t.scale, t.zero_point) = QuantizationParams(t.min, t.max); -- GitLab From 6603c69fa71d6ebdee717863079ca34308c9ddb1 Mon Sep 17 00:00:00 2001 From: Allen Lavoie Date: Wed, 12 Dec 2018 14:28:32 -0800 Subject: [PATCH 441/873] Switch tf.saved_model.save back to experimental in 1.x since it doesn't work when graph building Adds some explanation of this in the docstring and some better exceptions. Having it non-experimental would be pretty confusing, since most users would try it without enable_eager_execution() and run into strange errors which we don't plan to fix. PiperOrigin-RevId: 225254705 --- tensorflow/python/saved_model/save.py | 21 +++++++++++++++++-- tensorflow/python/saved_model/save_test.py | 15 +++++++++++++ .../golden/v1/tensorflow.saved_model.pbtxt | 4 ---- .../tools/compatibility/tf_upgrade_v2_test.py | 9 +++++++- 4 files changed, 42 insertions(+), 7 deletions(-) diff --git a/tensorflow/python/saved_model/save.py b/tensorflow/python/saved_model/save.py index b065a5a265..84d7b614d0 100644 --- a/tensorflow/python/saved_model/save.py +++ b/tensorflow/python/saved_model/save.py @@ -533,8 +533,7 @@ def _write_object_proto(obj, proto, asset_file_def_index): proto.user_object.SetInParent() -@tf_export("saved_model.save", - v1=["saved_model.save", "saved_model.experimental.save"]) +@tf_export("saved_model.save", v1=["saved_model.experimental.save"]) def save(obj, export_dir, signatures=None): # pylint: disable=line-too-long """Exports the Checkpointable object `obj` to [SavedModel format](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md). @@ -681,7 +680,25 @@ def save(obj, export_dir, signatures=None): Raises: ValueError: If `obj` is not checkpointable. + + @compatibility(eager) + Not supported when graph building. From TensorFlow 1.x, + `tf.enable_eager_execution()` must run first. May not be called from within a + function body. + @end_compatibility """ + if not context.executing_eagerly(): + with ops.init_scope(): + if context.executing_eagerly(): + raise AssertionError( + "tf.saved_model.save is not supported inside a traced " + "@tf.function. Move the call to the outer eagerly-executed " + "context.") + else: + raise AssertionError( + "tf.saved_model.save is not supported when graph building. " + "tf.enable_eager_execution() must run first when calling it from " + "TensorFlow 1.x.") # pylint: enable=line-too-long if not isinstance(obj, base.CheckpointableBase): raise ValueError( diff --git a/tensorflow/python/saved_model/save_test.py b/tensorflow/python/saved_model/save_test.py index 1c6eb1b538..5381c2f031 100644 --- a/tensorflow/python/saved_model/save_test.py +++ b/tensorflow/python/saved_model/save_test.py @@ -334,6 +334,21 @@ class AssetTests(test.TestCase): {"output_0": [0.2]}, _import_and_infer(export_dir, {"x": [0.1]})) + def test_sensible_graph_building_exception(self): + root = util.Checkpoint(v=variables.Variable(2.)) + root.f = def_function.function( + lambda x: 2. * root.v, + input_signature=[tensor_spec.TensorSpec(None, dtypes.float32)]) + export_dir = os.path.join(self.get_temp_dir(), "save_dir") + @def_function.function + def _calls_save(): + save.save(root, export_dir) + with self.assertRaisesRegexp(AssertionError, "tf.function"): + _calls_save() + with ops.Graph().as_default(): + with self.assertRaisesRegexp(AssertionError, "enable_eager_execution"): + save.save(root, export_dir) + class MemoryTests(test.TestCase): diff --git a/tensorflow/tools/api/golden/v1/tensorflow.saved_model.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.saved_model.pbtxt index 2a7c789105..3929003fa1 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.saved_model.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.saved_model.pbtxt @@ -180,10 +180,6 @@ tf_module { name: "regression_signature_def" argspec: "args=[\'examples\', \'predictions\'], varargs=None, keywords=None, defaults=None" } - member_method { - name: "save" - argspec: "args=[\'obj\', \'export_dir\', \'signatures\'], varargs=None, keywords=None, defaults=[\'None\'], " - } member_method { name: "simple_save" argspec: "args=[\'session\', \'export_dir\', \'inputs\', \'outputs\', \'legacy_init_op\'], varargs=None, keywords=None, defaults=[\'None\'], " diff --git a/tensorflow/tools/compatibility/tf_upgrade_v2_test.py b/tensorflow/tools/compatibility/tf_upgrade_v2_test.py index 484900d000..d5428e7536 100644 --- a/tensorflow/tools/compatibility/tf_upgrade_v2_test.py +++ b/tensorflow/tools/compatibility/tf_upgrade_v2_test.py @@ -159,6 +159,11 @@ class TestUpgrade(test_util.TensorFlowTestCase): collect = True v1_symbols = set([]) + # Symbols which may be generated by the conversion script which do not exist + # in TF 1.x. This should be a very short list of symbols which are + # experimental in 1.x but stable for 2.x. + whitelisted_v2_only_symbols = set(["tf.saved_model.save"]) + # Converts all symbols in the v1 namespace to the v2 namespace, raising # an error if the target of the conversion is not in the v1 namespace. def conversion_visitor(unused_path, unused_parent, children): @@ -173,7 +178,8 @@ class TestUpgrade(test_util.TensorFlowTestCase): if (text and not text.startswith("tf.compat.v1") and not text.startswith("tf.estimator") and - text not in v1_symbols): + text not in v1_symbols and + text not in whitelisted_v2_only_symbols): self.assertFalse( True, "Symbol %s generated from %s not in v1 API" % ( text, name)) @@ -737,3 +743,4 @@ class TestUpgradeFiles(test_util.TensorFlowTestCase): if __name__ == "__main__": test_lib.main() + -- GitLab From 31c0bed4cb194153151e6164938eb83252e6ef72 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 12 Dec 2018 14:33:34 -0800 Subject: [PATCH 442/873] Re-enable training_test PiperOrigin-RevId: 225255718 --- tensorflow/python/keras/BUILD | 7 +------ tensorflow/python/keras/engine/training_test.py | 6 ++++-- 2 files changed, 5 insertions(+), 8 deletions(-) diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD index 3c390cb2b0..361d88fe83 100755 --- a/tensorflow/python/keras/BUILD +++ b/tensorflow/python/keras/BUILD @@ -764,12 +764,7 @@ py_test( srcs = ["engine/training_test.py"], shard_count = 16, srcs_version = "PY2AND3", - tags = [ - "manual", # TODO(b/120560388) - "no_oss", # TODO(b/120560388) - "notap", # TODO(b/120560388) - "notsan", - ], + tags = ["notsan"], deps = [ ":keras", "//tensorflow/python:client_testlib", diff --git a/tensorflow/python/keras/engine/training_test.py b/tensorflow/python/keras/engine/training_test.py index a61e2edcd3..9d56eb261d 100644 --- a/tensorflow/python/keras/engine/training_test.py +++ b/tensorflow/python/keras/engine/training_test.py @@ -250,8 +250,10 @@ class TrainingTest(keras_parameterized.TestCase): run_eagerly=testing_utils.should_run_eagerly()) # This will work model.fit([input_a_np], output_d_np, epochs=1) - with self.assertRaises(ValueError): - model.fit([input_a_np, input_a_np], output_d_np, epochs=1) + # TODO(gsundeep) Test only works in eager, file ticket + if testing_utils.should_run_eagerly() and context.executing_eagerly(): + with self.assertRaises(ValueError): + model.fit([input_a_np, input_a_np], output_d_np, epochs=1) # Test model on a list of floats input_a_np = np.random.random((10, 3)) -- GitLab From 1b7e1c7c39e677dbd8a7d326666ba3e273faf46d Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 12 Dec 2018 14:35:48 -0800 Subject: [PATCH 443/873] Get rid of to_int64 deprecation warning in the logs. PiperOrigin-RevId: 225256193 --- tensorflow/python/ops/math_ops.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py index e2b634ee8f..e656998b70 100644 --- a/tensorflow/python/ops/math_ops.py +++ b/tensorflow/python/ops/math_ops.py @@ -1465,7 +1465,7 @@ def count_nonzero_v2(input, # pylint: disable=redefined-builtin return cast( reduce_sum( # int64 reduction happens on GPU - to_int64(gen_math_ops.not_equal(input, zero)), + cast(gen_math_ops.not_equal(input, zero), dtypes.int64), axis=axis, keepdims=keepdims), dtype=dtype) -- GitLab From 22af085fee9bc9fca2efd695a9440200cc66e623 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 12 Dec 2018 14:36:59 -0800 Subject: [PATCH 444/873] [XLA] add Iota and BroadcastedIota to local Python client PiperOrigin-RevId: 225256432 --- .../xla/python/local_computation_builder.cc | 9 +++++++ .../xla/python/local_computation_builder.h | 4 +++ .../xla/python/local_computation_builder.i | 2 ++ tensorflow/compiler/xla/python/xla_client.py | 27 +++++++++++++++++++ .../compiler/xla/python/xla_client_test.py | 11 ++++++++ 5 files changed, 53 insertions(+) diff --git a/tensorflow/compiler/xla/python/local_computation_builder.cc b/tensorflow/compiler/xla/python/local_computation_builder.cc index 8e3ac381ce..5d191f5a18 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.cc +++ b/tensorflow/compiler/xla/python/local_computation_builder.cc @@ -647,6 +647,15 @@ LocalOp LocalComputationBuilder::ConstantLiteral(const Literal& literal) { return xla::ConstantLiteral(&builder_, literal); } +LocalOp LocalComputationBuilder::Iota(PrimitiveType element_type, int64 size) { + return xla::Iota(&builder_, element_type, size); +} + +LocalOp LocalComputationBuilder::BroadcastedIota(const Shape& shape, + int64 dimension) { + return xla::Iota(&builder_, shape, dimension); +} + LocalOp LocalComputationBuilder::Broadcast( const LocalOp& operand, absl::Span broadcast_sizes) { return xla::Broadcast(operand.op(), broadcast_sizes); diff --git a/tensorflow/compiler/xla/python/local_computation_builder.h b/tensorflow/compiler/xla/python/local_computation_builder.h index eebbe674e5..c6e58ac971 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.h +++ b/tensorflow/compiler/xla/python/local_computation_builder.h @@ -286,6 +286,10 @@ class LocalComputationBuilder { LocalOp ConstantLiteral(const Literal& literal); + LocalOp Iota(PrimitiveType element_type, int64 size); + + LocalOp BroadcastedIota(const Shape& shape, int64 dimension); + LocalOp Broadcast(const LocalOp& operand, absl::Span broadcast_sizes); diff --git a/tensorflow/compiler/xla/python/local_computation_builder.i b/tensorflow/compiler/xla/python/local_computation_builder.i index db7e0458f4..11fb00e616 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.i +++ b/tensorflow/compiler/xla/python/local_computation_builder.i @@ -1051,6 +1051,8 @@ tensorflow::ImportNumpy(); %unignore xla::swig::LocalComputationBuilder::Outfeed; %unignore xla::swig::LocalComputationBuilder::ConstantLiteral; %unignore xla::swig::LocalComputationBuilder::ConstantR0; +%unignore xla::swig::LocalComputationBuilder::Iota; +%unignore xla::swig::LocalComputationBuilder::BroadcastedIota; %unignore xla::swig::LocalComputationBuilder::Broadcast; %unignore xla::swig::LocalComputationBuilder::BroadcastInDim; %unignore xla::swig::LocalComputationBuilder::Pad; diff --git a/tensorflow/compiler/xla/python/xla_client.py b/tensorflow/compiler/xla/python/xla_client.py index cd85713d72..4166fa0327 100644 --- a/tensorflow/compiler/xla/python/xla_client.py +++ b/tensorflow/compiler/xla/python/xla_client.py @@ -831,6 +831,33 @@ class ComputationBuilder(object): return self.ParameterWithShape( Shape.from_pyval(value), name=name, parameter_num=parameter_num) + def Iota(self, dtype, size): + """Enqueues an iota constant onto the computation. + + Args: + dtype: expected numpy dtype of the output. + size: integer, the number of elements in the array. + + Returns: + A LocalOp representing the added iota constant. + """ + element_type = DTYPE_TO_XLA_ELEMENT_TYPE[str(np.dtype(dtype))] + return self._client.Iota(element_type, size) + + def BroadcastedIota(self, dtype, shape, dimension): + """Enqueues a broadcasted iota constant onto the computation. + + Args: + dtype: expected numpy dtype of the output. + shape: tuple of integers, the expected output shape (dimensions). + dimension: positive integer, dimension along which to increment values. + + Returns: + A LocalOp representing the added broadcasted iota constant. + """ + xla_shape = Shape.array_shape(dtype, shape) + return self._client.BroadcastedIota(xla_shape, dimension) + def Broadcast(self, operand, sizes): """Enqueues a broadcast operation onto the computation. diff --git a/tensorflow/compiler/xla/python/xla_client_test.py b/tensorflow/compiler/xla/python/xla_client_test.py index a4c615846e..95c6dc8c45 100644 --- a/tensorflow/compiler/xla/python/xla_client_test.py +++ b/tensorflow/compiler/xla/python/xla_client_test.py @@ -146,6 +146,17 @@ class ComputationsWithConstantsTest(LocalComputationTest): c.Pow(c.Constant(NumpyArrayF64([1.5, 2.5, 3.0])), c.ConstantF64Scalar(2.)) self._ExecuteAndCompareClose(c, expected=[2.25, 6.25, 9.]) + def testIota(self): + c = self._NewComputation() + c.Iota(np.float32, 10) + self._ExecuteAndCompareExact(c, expected=np.arange(10, dtype=np.float32)) + + def testBroadcastedIota(self): + c = self._NewComputation() + c.BroadcastedIota(np.int64, (2, 3), 1) + expected = np.array([[0, 1, 2], [0, 1, 2]], dtype=np.int64) + self._ExecuteAndCompareExact(c, expected=expected) + def testBooleanAnd(self): c = self._NewComputation() c.And( -- GitLab From e0f979b8dce918d0c31ea106b0a39dce4bb6e8c6 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 12 Dec 2018 14:40:52 -0800 Subject: [PATCH 445/873] Fix `predict` with `run_eagerly=True` PiperOrigin-RevId: 225257343 --- .../python/keras/engine/training_eager_test.py | 15 +++++++++++++++ .../python/keras/engine/training_generator.py | 8 +++++--- 2 files changed, 20 insertions(+), 3 deletions(-) diff --git a/tensorflow/python/keras/engine/training_eager_test.py b/tensorflow/python/keras/engine/training_eager_test.py index 3fabbb17ed..f95a502cbc 100644 --- a/tensorflow/python/keras/engine/training_eager_test.py +++ b/tensorflow/python/keras/engine/training_eager_test.py @@ -246,6 +246,21 @@ class CorrectnessTest(test.TestCase): layer(1.) # Plain-value inputs are only valid in eager mode. self.assertEqual(1, len(layer.losses)) + def test_predict_correctness(self): + i1 = keras.layers.Input(shape=(4, 5)) + i2 = keras.layers.Input(shape=(4, 5)) + i3 = keras.layers.Input(shape=(4, 5)) + o = keras.layers.add([i1, i2, i3]) + model = keras.models.Model([i1, i2, i3], o) + model.run_eagerly = True + + x1 = np.random.random((2, 4, 5)) + x2 = np.random.random((2, 4, 5)) + x3 = np.random.random((2, 4, 5)) + out = model.predict([x1, x2, x3]) + + self.assertAllClose(out, x1 + x2 + x3) + if __name__ == '__main__': ops.enable_eager_execution() diff --git a/tensorflow/python/keras/engine/training_generator.py b/tensorflow/python/keras/engine/training_generator.py index 0abf0b8270..88f21b3707 100644 --- a/tensorflow/python/keras/engine/training_generator.py +++ b/tensorflow/python/keras/engine/training_generator.py @@ -49,7 +49,7 @@ def model_iteration(model, max_queue_size=10, workers=1, use_multiprocessing=False, - shuffle=True, + shuffle=False, initial_epoch=0, mode='train', batch_size=None, @@ -246,8 +246,10 @@ def model_iteration(model, # Maintain compatibility with the existing names. fit_generator = functools.partial(model_iteration, mode='train') -evaluate_generator = functools.partial(model_iteration, mode='test') -predict_generator = functools.partial(model_iteration, mode='predict') +evaluate_generator = functools.partial( + model_iteration, mode='test', shuffle=False) +predict_generator = functools.partial( + model_iteration, mode='predict', shuffle=False) def _get_next_batch(output_generator, mode): -- GitLab From 9ed22473db5e3b5d555e951c2dfc92a75ab235ca Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 12 Dec 2018 14:41:49 -0800 Subject: [PATCH 446/873] Capture the distribute.Strategy scope from the outer graph when entering the FuncGraph.as_default scope instead of __init__. Fixes issues with the grobal Keras FuncGraph keeping state between tests. PiperOrigin-RevId: 225257506 --- .../python/distribute/mirrored_strategy.py | 11 ++- tensorflow/python/framework/func_graph.py | 79 +++++++++++++------ 2 files changed, 61 insertions(+), 29 deletions(-) diff --git a/tensorflow/python/distribute/mirrored_strategy.py b/tensorflow/python/distribute/mirrored_strategy.py index 9692c88dfc..605e2cc8e7 100644 --- a/tensorflow/python/distribute/mirrored_strategy.py +++ b/tensorflow/python/distribute/mirrored_strategy.py @@ -50,12 +50,17 @@ from tensorflow.python.util.tf_export import tf_export @contextlib.contextmanager -def _enter_graph(g, eager): +def _enter_graph(g, eager, creator_stack=None): + """Context manager for selecting a graph and maybe eager mode.""" if eager: with g.as_default(), context.eager_mode(): + if creator_stack is not None: + g._variable_creator_stack = creator_stack # pylint: disable=protected-access yield else: with g.as_default(): + if creator_stack is not None: + g._variable_creator_stack = creator_stack # pylint: disable=protected-access yield @@ -865,7 +870,6 @@ class MirroredExtended(distribute_lib.DistributionStrategyExtended): def run(self): # pylint: disable=protected-access - self.graph._variable_creator_stack = self._variable_creator_stack self.should_run.wait() self.should_run.clear() try: @@ -873,7 +877,8 @@ class MirroredExtended(distribute_lib.DistributionStrategyExtended): return with self.coord.stop_on_exception(), \ _enter_graph(self._init_graph, self._init_in_eager), \ - _enter_graph(self.graph, self.in_eager), \ + _enter_graph(self.graph, self.in_eager, + self._variable_creator_stack), \ context.context().device_policy(self.context_device_policy), \ MirroredReplicaContext(self.distribution, constant_op.constant( self.replica_id, dtypes.int32)), \ diff --git a/tensorflow/python/framework/func_graph.py b/tensorflow/python/framework/func_graph.py index bd4ed5553e..75a420e91a 100644 --- a/tensorflow/python/framework/func_graph.py +++ b/tensorflow/python/framework/func_graph.py @@ -36,6 +36,7 @@ from tensorflow.python.ops import tensor_array_ops from tensorflow.python.ops import variable_scope from tensorflow.python.util import compat from tensorflow.python.util import nest +from tensorflow.python.util import tf_contextlib from tensorflow.python.util import tf_decorator from tensorflow.python.util.lazy_loader import LazyLoader @@ -108,38 +109,20 @@ class FuncGraph(ops.Graph): graph = self.outer_graph - # pylint: disable=protected-access - # TODO(b/112906995, nareshmodi): distribution strategy depends on inheriting - # this stack from the default graph even in eager mode. Maybe it should be - # part of the eager context? This would also allow us to remove a - # get_default_graph() call from the function cache lookup. - self._distribution_strategy_stack = list(graph._distribution_strategy_stack) - # We ignore device placements from any outer scopes while tracing the - # function when possible, to avoid hard-coding them in the function - # graph. "Default" placements come from the PartitionedCallOp's placement, - # so that the same trace of the Python function may be placed on several - # different devices and saved functions may be placed on new devices when - # restored. if context.executing_eagerly(): self.seed = context.global_seed() device_type = context.context().device_spec.device_type self._xla_compile = (device_type == "TPU" or device_type == "XLA_GPU" or device_type == "XLA_CPU") - if self._distribution_strategy_stack or self._xla_compile: - self._add_device_to_stack(context.context().device_name) else: self.seed = graph.seed self._xla_compile = getattr(graph, "_xla_compile", False) # TODO(allenl): Figure out if we can remove colocation stack # specialization (currently used in cond_v2), here and in the cache key. - self._colocation_stack = graph._colocation_stack.copy() - if (self._distribution_strategy_stack - or self._xla_compile - or device_stack_has_callable(graph._device_function_stack)): - # Hard-code devices from device functions in the function body - self._device_function_stack = graph._device_function_stack.copy() + self._colocation_stack = graph._colocation_stack.copy() # pylint: disable=protected-access + if not self._read_only_collections: - self._collections = graph._collections + self._collections = graph._collections # pylint: disable=protected-access else: for collection_name in graph.get_all_collection_keys(): if collection_name not in WHITELIST_COLLECTIONS: @@ -149,11 +132,55 @@ class FuncGraph(ops.Graph): self._collections[collection_name] = graph.get_collection_ref( collection_name) - self._variable_creator_stack = graph._variable_creator_stack - # Inherit the graph key, since this is used for matching variables in - # optimizers. - self._graph_key = graph._graph_key - # pylint: enable=protected-access + def as_default(self): + outer_cm = super(FuncGraph, self).as_default() + + @tf_contextlib.contextmanager + def inner_cm(): + """Context manager for copying distribute.Strategy scope information.""" + graph = ops.get_default_graph() + # pylint: disable=protected-access + # TODO(b/112906995, nareshmodi): distribution strategy depends on + # inheriting this stack from the default graph even in eager mode. Maybe + # it should be part of the eager context? This would also allow us to + # remove a get_default_graph() call from the function cache lookup. + old_strategy_stack = self._distribution_strategy_stack + self._distribution_strategy_stack = list( + graph._distribution_strategy_stack) + # We ignore device placements from any outer scopes while tracing the + # function when possible, to avoid hard-coding them in the function + # graph. "Default" placements come from the PartitionedCallOp's placement, + # so that the same trace of the Python function may be placed on several + # different devices and saved functions may be placed on new devices when + # restored. + old_device_stack = self._device_function_stack + if context.executing_eagerly(): + if self._distribution_strategy_stack or self._xla_compile: + self._add_device_to_stack(context.context().device_name) + else: + if (self._distribution_strategy_stack + or self._xla_compile + or device_stack_has_callable(graph._device_function_stack)): + # Hard-code devices from device functions in the function body + self._device_function_stack = graph._device_function_stack.copy() + + old_creator_stack = self._variable_creator_stack + self._variable_creator_stack = graph._variable_creator_stack + # Inherit the graph key, since this is used for matching variables in + # optimizers. + old_graph_key = self._graph_key + self._graph_key = graph._graph_key + # pylint: enable=protected-access + + with outer_cm as g: + try: + yield g + finally: + self._distribution_strategy_stack = old_strategy_stack + self._device_function_stack = old_device_stack + self._variable_creator_stack = old_creator_stack + self._graph_key = old_graph_key + return inner_cm() @property def output_types(self): -- GitLab From 8644b6d4c77646407758a2ef93eb3567f9f03577 Mon Sep 17 00:00:00 2001 From: Sourabh Bajaj Date: Wed, 12 Dec 2018 14:50:13 -0800 Subject: [PATCH 447/873] Move reduce non distributed values and share the code with TPU Strategy and also improve print output of TPUMirroredVariable. PiperOrigin-RevId: 225259008 --- .../distribute/python/mirrored_strategy.py | 1 - .../python/parameter_server_strategy.py | 2 +- .../contrib/distribute/python/tpu_strategy.py | 8 ++++ .../python/distribute/cross_device_ops.py | 37 ++++++++++++++ .../python/distribute/mirrored_strategy.py | 48 ++----------------- tensorflow/python/distribute/values.py | 6 +++ 6 files changed, 57 insertions(+), 45 deletions(-) diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy.py b/tensorflow/contrib/distribute/python/mirrored_strategy.py index 20f1a08d42..24399db652 100644 --- a/tensorflow/contrib/distribute/python/mirrored_strategy.py +++ b/tensorflow/contrib/distribute/python/mirrored_strategy.py @@ -28,7 +28,6 @@ from tensorflow.python.distribute import values # pylint: disable=protected-access,invalid-name _call_for_each_replica = mirrored_strategy._call_for_each_replica -_reduce_non_distributed_value = mirrored_strategy._reduce_non_distributed_value _create_mirrored_variable = mirrored_strategy._create_mirrored_variable all_local_devices = mirrored_strategy.all_local_devices CoreMirroredStrategy = mirrored_strategy.MirroredStrategy diff --git a/tensorflow/contrib/distribute/python/parameter_server_strategy.py b/tensorflow/contrib/distribute/python/parameter_server_strategy.py index 2c7766f95f..ca51b07be6 100644 --- a/tensorflow/contrib/distribute/python/parameter_server_strategy.py +++ b/tensorflow/contrib/distribute/python/parameter_server_strategy.py @@ -356,7 +356,7 @@ class ParameterServerExtended(distribute_lib.DistributionStrategyExtended): self._verify_destinations_not_different_worker(destinations) if not isinstance(value, values.DistributedValues): # pylint: disable=protected-access - return mirrored_strategy._reduce_non_distributed_value( + return cross_device_ops_lib.reduce_non_distributed_value( self, reduce_op, value, destinations) return self._cross_device_ops.reduce( reduce_op, value, destinations=destinations) diff --git a/tensorflow/contrib/distribute/python/tpu_strategy.py b/tensorflow/contrib/distribute/python/tpu_strategy.py index b6f5b49201..7ea245eb6e 100644 --- a/tensorflow/contrib/distribute/python/tpu_strategy.py +++ b/tensorflow/contrib/distribute/python/tpu_strategy.py @@ -465,6 +465,14 @@ class TPUExtended(distribute_lib.DistributionStrategyExtended): "Currently only support sum & mean in TPUStrategy.") return tpu_ops.cross_replica_sum(value) + if not isinstance(value, values.DistributedValues): + # This function handles reducing values that are not PerReplica or + # Mirrored values. For example, the same value could be present on all + # replicas in which case `value` would be a single value or value could + # be 0. + return cross_device_ops_lib.reduce_non_distributed_value( + self, reduce_op, value, destinations) + # Validate that the destination is same as the host device # Note we don't do this when in replicate context as the reduction is # performed on the TPU device itself. diff --git a/tensorflow/python/distribute/cross_device_ops.py b/tensorflow/python/distribute/cross_device_ops.py index 57c552ca8f..6bb3639bf0 100644 --- a/tensorflow/python/distribute/cross_device_ops.py +++ b/tensorflow/python/distribute/cross_device_ops.py @@ -62,6 +62,43 @@ def validate_destinations(destinations): raise ValueError("destinations can not be empty") +def reduce_non_distributed_value(extended, reduce_op, value, destinations): + """Reduce a non-DistributedValue `value` to `destinations`.""" + if isinstance(value, value_lib.DistributedValues): + raise ValueError("You are passing a `DistributedValue` to " + "`reduce_non_distributed_value`, which is not allowed.") + + # If the same value is present on all replicas then the PerReplica value will + # be a single value. We also handle the case when `value` is a single value + # and equal to 0. + if value == 0: + return 0 + # If there is only a single value and the reduce op is MEAN, + # that value should be on all destinations. + if reduce_op == reduce_util.ReduceOp.MEAN: + return value + + validate_destinations(destinations) + # We do not support a reduce op of SUM if the value is the same across + # all replicas. We call this as part of assign functions for MirroredVariables + # and summing up identical values across replicas is not clearly defined. + if (len(extended.worker_devices) != 1 or + not check_destinations(destinations)): + raise ValueError("A non-DistributedValues value %s cannot be reduced with " + "the given reduce op %s." % (value, reduce_op)) + # TODO(anjalisridhar): Moves these methods to a device utility file? + devices = get_devices_from(destinations) + if len(devices) == 1: + with ops.device(devices[0]): + return array_ops.identity(value) + else: + value_updates = {} + for d in devices: + with ops.device(d): + value_updates[d] = array_ops.identity(value) + return value_lib.Mirrored(value_updates) + + def _make_tensor_into_per_replica(input_tensor): """Converts a single tensor into a PerReplica object.""" if isinstance(input_tensor, (tuple, list)): diff --git a/tensorflow/python/distribute/mirrored_strategy.py b/tensorflow/python/distribute/mirrored_strategy.py index 605e2cc8e7..fb3cf84449 100644 --- a/tensorflow/python/distribute/mirrored_strategy.py +++ b/tensorflow/python/distribute/mirrored_strategy.py @@ -74,10 +74,9 @@ class _RequestedStop(Exception): # pylint: disable=g-bad-exception-name pass -# _call_for_each_replica and _reduce_non_distributed_value are not members of -# MirroredStrategy so that they are generally not allowed to use anything -# specific to MirroredStrategy and thus can be shared with other distribution -# strategies. +# _call_for_each_replica is not a member of MirroredStrategy so that it is +# not allowed to use anything specific to MirroredStrategy and thus +# can be shared with other distribution strategies. # TODO(yuefengz): maybe create a common class for those who need to call this @@ -192,43 +191,6 @@ def _call_for_each_replica(distribution, fn, args, kwargs): return values.regroup({t.device: t.main_result for t in threads}) -def _reduce_non_distributed_value(extended, reduce_op, value, destinations): - """Reduce a non-DistributedValue `value` to `destinations`.""" - if isinstance(value, values.DistributedValues): - raise ValueError("You are passing a `DistributedValue` to " - "`_reduce_non_distributed_value`, which is not allowed.") - - # If the same value is present on all replicas then the PerReplica value will - # be a single value. We also handle the case when `value` is a single value - # and equal to 0. - if value == 0: - return 0 - # If there is only a single value and the reduce op is MEAN, - # that value should be on all destinations. - if reduce_op == reduce_util.ReduceOp.MEAN: - return value - - cross_device_ops_lib.validate_destinations(destinations) - # We do not support a reduce op of SUM if the value is the same across - # all replicas. We call this as part of assign functions for MirroredVariables - # and summing up identical values across replicas is not clearly defined. - if (len(extended.worker_devices) != 1 or - not cross_device_ops_lib.check_destinations(destinations)): - raise ValueError("A non-DistributedValues value %s cannot be reduced with " - "the given reduce op %s." % (value, reduce_op)) - # TODO(anjalisridhar): Moves these methods to a device utility file? - devices = cross_device_ops_lib.get_devices_from(destinations) - if len(devices) == 1: - with ops.device(devices[0]): - return array_ops.identity(value) - else: - value_updates = {} - for d in devices: - with ops.device(d): - value_updates[d] = array_ops.identity(value) - return values.Mirrored(value_updates) - - def _create_mirrored_variable(devices, real_mirrored_creator, *args, **kwargs): # pylint: disable=g-missing-docstring # Figure out what collections this variable should be added to. # We'll add the MirroredVariable to those collections instead. @@ -714,8 +676,8 @@ class MirroredExtended(distribute_lib.DistributionStrategyExtended): # Mirrored values. For example, the same value could be present on all # replicas in which case `value` would be a single value or value could # be 0. - return _reduce_non_distributed_value(self, reduce_op, value, - destinations) + return cross_device_ops_lib.reduce_non_distributed_value( + self, reduce_op, value, destinations) return self._get_cross_device_ops().reduce( reduce_op, value, destinations=destinations) diff --git a/tensorflow/python/distribute/values.py b/tensorflow/python/distribute/values.py index 01a1680a24..a5918b7b73 100644 --- a/tensorflow/python/distribute/values.py +++ b/tensorflow/python/distribute/values.py @@ -570,6 +570,12 @@ class TPUMirroredVariable(checkpointable.CheckpointableBase): # See https://docs.python.org/3/library/constants.html#NotImplemented return NotImplemented + def __str__(self): + return "%s:%s" % (self.__class__.__name__, self._index) + + def __repr__(self): + return "%s(%r)" % (self.__class__.__name__, self._index) + @property def handle(self): # If we're in a tpu.rewrite(), return the replicated handle. -- GitLab From 515f9575629a2521fe4d0e7fef3e1f252d8b8f6e Mon Sep 17 00:00:00 2001 From: Priya Gupta Date: Wed, 12 Dec 2018 15:07:45 -0800 Subject: [PATCH 448/873] Eager function: Do not create a set of input ops each time. This can take a very long time for big models. For e.g. when building a function for ResNet50, this increased the time to create the eager function by 72 times. PiperOrigin-RevId: 225262498 --- tensorflow/python/eager/function.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/tensorflow/python/eager/function.py b/tensorflow/python/eager/function.py index f3480ebb56..3aa7b7e27f 100644 --- a/tensorflow/python/eager/function.py +++ b/tensorflow/python/eager/function.py @@ -149,10 +149,9 @@ class _EagerDefinedFunction(object): outputs: the tensors in the graph which will be outputs to the function attrs: dict mapping names of attributes to their AttrValue values """ - operations = [ - op for op in graph.get_operations() - if op not in set(arg.op for arg in inputs) - ] + input_ops = set(arg.op for arg in inputs) + operations = [op for op in graph.get_operations() if op not in input_ops] + fn = pywrap_tensorflow.TF_GraphToFunction_wrapper( graph._c_graph, # pylint: disable=protected-access compat.as_str(name), -- GitLab From face5a8f9c7821b7c415089a3988d0badaf29783 Mon Sep 17 00:00:00 2001 From: Francois Chollet Date: Wed, 12 Dec 2018 15:20:58 -0800 Subject: [PATCH 449/873] Run noise layer tests in all execution modes. PiperOrigin-RevId: 225264988 --- tensorflow/python/keras/layers/noise_test.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tensorflow/python/keras/layers/noise_test.py b/tensorflow/python/keras/layers/noise_test.py index 325dd933b2..f1537a6919 100644 --- a/tensorflow/python/keras/layers/noise_test.py +++ b/tensorflow/python/keras/layers/noise_test.py @@ -19,13 +19,13 @@ from __future__ import division from __future__ import print_function from tensorflow.python import keras -from tensorflow.python.framework import test_util as tf_test_util +from tensorflow.python.keras import keras_parameterized from tensorflow.python.keras import testing_utils from tensorflow.python.platform import test -@tf_test_util.run_all_in_graph_and_eager_modes -class NoiseLayersTest(test.TestCase): +@keras_parameterized.run_all_keras_modes +class NoiseLayersTest(keras_parameterized.TestCase): def test_GaussianNoise(self): testing_utils.layer_test( -- GitLab From 3dfb4df6e54968237014cfa6c8904ea35e4518c5 Mon Sep 17 00:00:00 2001 From: Russell Power Date: Wed, 12 Dec 2018 15:22:10 -0800 Subject: [PATCH 450/873] Use split_compile_and_replicate with TPUEstimator. PiperOrigin-RevId: 225265200 --- tensorflow/contrib/tpu/python/tpu/tpu.py | 106 +++++++++++++++--- .../contrib/tpu/python/tpu/tpu_estimator.py | 38 +++++-- 2 files changed, 119 insertions(+), 25 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu.py b/tensorflow/contrib/tpu/python/tpu/tpu.py index def57da20d..59722bc246 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu.py @@ -777,15 +777,15 @@ def split_compile_and_replicate(computation, ] -def shard(computation, - inputs=None, - num_shards=1, - input_shard_axes=None, - outputs_from_all_shards=True, - output_shard_axes=None, - infeed_queue=None, - device_assignment=None, - name=None): +def split_compile_and_shard(computation, + inputs=None, + num_shards=1, + input_shard_axes=None, + outputs_from_all_shards=True, + output_shard_axes=None, + infeed_queue=None, + device_assignment=None, + name=None): """Shards `computation` for parallel execution. `inputs` must be a list of Tensors or None (equivalent to an empty list), each @@ -839,7 +839,7 @@ def shard(computation, is equal to the number of cores in the TPU system. name: (Deprecated) Does nothing. Returns: - A list of output tensors. + A tuple of (compile op, [output tensors]). Raises: ValueError: If num_shards <= 0 ValueError: If len(input_shard_axes) != len(inputs) @@ -874,7 +874,7 @@ def shard(computation, else: transposed_inputs = [[]] * num_shards - outputs = replicate( + compile_op, outputs = split_compile_and_replicate( computation, transposed_inputs, infeed_queue=infeed_queue, @@ -891,7 +891,7 @@ def shard(computation, # one so it can be used as a control dependency or fetch node. # TODO(b/36647078) remove disable when pylint bug is fixed. # pylint: disable=indexing-exception - return [outputs[0]] + return compile_op, [outputs[0]] # pylint: enable=indexing-exception # TODO(b/36647078) remove disable when pylint bug is fixed. @@ -925,7 +925,87 @@ def shard(computation, # TODO(phawkins): use a smarter policy, e.g., round-robin across shards. results.append(x[0]) - return results + return compile_op, results + + +def shard(computation, + inputs=None, + num_shards=1, + input_shard_axes=None, + outputs_from_all_shards=True, + output_shard_axes=None, + infeed_queue=None, + device_assignment=None, + name=None): + """Shards `computation` for parallel execution. + + `inputs` must be a list of Tensors or None (equivalent to an empty list), each + of which has a corresponding split axis (from `input_shard_axes`). Each input + is split into `num_shards` pieces along the corresponding axis, and + computation is applied to each shard in parallel. + + Tensors are broadcast to all shards if they are lexically captured by + `computation`. e.g., + + x = tf.constant(7) + def computation(): + return x + 3 + ... = shard(computation, ...) + + TODO(phawkins): consider adding support for broadcasting Tensors passed + as inputs. + + If `outputs_from_all_shards` is true, the outputs from all shards of + `computation` are concatenated back together along their `output_shards_axes`. + Otherwise, each output is taken from an arbitrary shard. + + Inputs and outputs of the computation must be at least rank-1 Tensors. + + Args: + computation: A Python function that builds a computation to apply to each + shard of the input. + inputs: A list of input tensors or None (equivalent to an empty list). Each + input tensor has a corresponding shard axes, given by `input_shard_axes`, + which must have size divisible by `num_shards`. + num_shards: The number of shards. + input_shard_axes: A list of dimensions along which to shard `inputs`, or + `None`. `None` means "shard all inputs along dimension 0". If not `None`, + there must be one dimension per input. + outputs_from_all_shards: Boolean or list of boolean. For each output, if + `True`, outputs from all shards are concatenated along the corresponding + `output_shard_axes` entry. Otherwise, each output is taken + from an arbitrary shard. If the argument is a boolean, the argument's + value is used for each output. + output_shard_axes: A list of dimensions along which to concatenate the + outputs of `computation`, or `None`. `None` means "concatenate all outputs + along dimension 0". If not `None`, there must be one dimension per output. + Ignored if `outputs_from_all_shards` is False. + infeed_queue: If not `None`, the `InfeedQueue` to use to augment the inputs + of `computation`. + device_assignment: If not `None`, a `DeviceAssignment` describing the + mapping between logical cores in the computation with physical cores in + the TPU topology. Uses a default device assignment if `None`. The + `DeviceAssignment` may be omitted if each shard of the computation uses + only one core, and there is either only one shard, or the number of shards + is equal to the number of cores in the TPU system. + name: (Deprecated) Does nothing. + Returns: + A list of output tensors. + Raises: + ValueError: If num_shards <= 0 + ValueError: If len(input_shard_axes) != len(inputs) + ValueError: If len(output_shard_axes) != len(outputs from `computation`) + """ + return split_compile_and_shard( + computation, + inputs=inputs, + num_shards=num_shards, + input_shard_axes=input_shard_axes, + outputs_from_all_shards=outputs_from_all_shards, + output_shard_axes=output_shard_axes, + infeed_queue=infeed_queue, + device_assignment=device_assignment, + name=name)[1] def batch_parallel(computation, diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index fe2ac61bf9..f179289584 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -422,6 +422,7 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): ctx, enqueue_ops, dequeue_ops, + tpu_compile_op, run_infeed_loop_on_coordinator=True, rendezvous=None, master=None, @@ -439,6 +440,7 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): self._feed_error = None self._finished = False self._should_initialize_tpu = True + self._tpu_compile_op = tpu_compile_op def begin(self): logging.info('TPU job name %s', self._master_job) @@ -500,6 +502,10 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): session.run(self._init_ops, options=config_pb2.RunOptions(timeout_in_ms=5 * 60 * 1000)) + if os.environ.get('TPU_SPLIT_COMPILE_AND_EXECUTE', '') == '1': + logging.info('Compiling user program: this may take a while...') + logging.info('Compile finished: %s', session.run(self._tpu_compile_op)) + self._infeed_controller = self._create_infeed_controller( name='InfeedController', target=self._run_infeed, args=(session,)) @@ -540,12 +546,13 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): class TPUInfeedOutfeedSessionHookForPrediction(TPUInfeedOutfeedSessionHook): - def __init__(self, ctx, enqueue_ops, dequeue_ops, rendezvous=None, - master=None, session_config=None): + def __init__(self, ctx, enqueue_ops, dequeue_ops, tpu_compile_op, + rendezvous=None, master=None, session_config=None): super(TPUInfeedOutfeedSessionHookForPrediction, self).__init__( ctx, enqueue_ops, dequeue_ops, + tpu_compile_op=tpu_compile_op, run_infeed_loop_on_coordinator=False, rendezvous=rendezvous, master=master, @@ -2545,7 +2552,7 @@ class TPUEstimator(estimator_lib.Estimator): graph.add_to_collection(_TPU_ENQUEUE_OPS, enqueue_op) if mode == model_fn_lib.ModeKeys.TRAIN: - loss, host_call, scaffold, training_hooks = ( + compile_op, loss, host_call, scaffold, training_hooks = ( _train_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn)) host_ops = host_call.create_tpu_hostcall() if host_ops is None: @@ -2580,6 +2587,7 @@ class TPUEstimator(estimator_lib.Estimator): ctx, enqueue_ops, host_ops, + tpu_compile_op=compile_op, run_infeed_loop_on_coordinator=( run_infeed_loop_on_coordinator), rendezvous=self._rendezvous[mode], @@ -2637,8 +2645,8 @@ class TPUEstimator(estimator_lib.Estimator): scaffold=scaffold) if mode == model_fn_lib.ModeKeys.EVAL: - total_loss, host_calls, scaffold, eval_hooks = _eval_on_tpu_system( - ctx, model_fn_wrapper, dequeue_fn) + compile_op, total_loss, host_calls, scaffold, eval_hooks = ( + _eval_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn)) iterations_per_loop_var = _create_or_get_iterations_per_loop() mean_loss = math_ops.div( total_loss, @@ -2685,6 +2693,7 @@ class TPUEstimator(estimator_lib.Estimator): ctx, enqueue_ops, eval_update_ops + host_ops, + tpu_compile_op=compile_op, run_infeed_loop_on_coordinator=( run_infeed_loop_on_coordinator), rendezvous=self._rendezvous[mode], @@ -2705,7 +2714,7 @@ class TPUEstimator(estimator_lib.Estimator): # Predict assert mode == model_fn_lib.ModeKeys.PREDICT - (dummy_predict_op, host_calls, + (compile_op, dummy_predict_op, host_calls, scaffold, prediction_hooks) = _predict_on_tpu_system( ctx, model_fn_wrapper, dequeue_fn) with ops.control_dependencies([dummy_predict_op]): @@ -2762,6 +2771,7 @@ class TPUEstimator(estimator_lib.Estimator): _StoppingPredictHook(scalar_stopping_signal), TPUInfeedOutfeedSessionHookForPrediction( ctx, enqueue_ops, host_ops, rendezvous=self._rendezvous[mode], + tpu_compile_op=compile_op, master=self._config.master, session_config=self._session_config), ] + input_hooks @@ -2860,15 +2870,16 @@ def _eval_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): return training_loop.repeat(iterations_per_loop_var, single_tpu_eval_step, [_ZERO_LOSS]) - (loss,) = tpu.shard( + (compile_op, loss,) = tpu.split_compile_and_shard( multi_tpu_eval_steps_on_single_shard, inputs=[], num_shards=ctx.num_replicas, outputs_from_all_shards=False, device_assignment=ctx.device_assignment) + loss = loss[0] scaffold = _get_scaffold(captured_scaffold_fn) - return loss, host_calls, scaffold, captured_eval_hooks.get() + return compile_op, loss, host_calls, scaffold, captured_eval_hooks.get() def _train_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): @@ -2883,15 +2894,16 @@ def _train_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): return training_loop.repeat(iterations_per_loop_var, single_tpu_train_step, [_INITIAL_LOSS]) - (loss,) = tpu.shard( + (compile_op, loss,) = tpu.split_compile_and_shard( multi_tpu_train_steps_on_single_shard, inputs=[], num_shards=ctx.num_replicas, outputs_from_all_shards=False, device_assignment=ctx.device_assignment) + loss = loss[0] scaffold = _get_scaffold(captured_scaffold_fn) - return loss, host_call, scaffold, captured_training_hooks.get() + return compile_op, loss, host_call, scaffold, captured_training_hooks.get() def _predict_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): @@ -2911,15 +2923,17 @@ def _predict_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): cond, single_tpu_predict_step, inputs=inputs, name=b'loop') return outputs - (dummy_predict_op,) = tpu.shard( + (compile_op, dummy_predict_op,) = tpu.split_compile_and_shard( multi_tpu_predict_steps_on_single_shard, inputs=[], num_shards=ctx.num_replicas, outputs_from_all_shards=False, device_assignment=ctx.device_assignment) + dummy_predict_op = dummy_predict_op[0] scaffold = _get_scaffold(captured_scaffold_fn) - return dummy_predict_op, host_calls, scaffold, captured_predict_hooks.get() + return (compile_op, dummy_predict_op, host_calls, scaffold, + captured_predict_hooks.get()) def _wrap_computation_in_while_loop(device, op_fn): -- GitLab From de6406575b2c1ad29a8cbc0173702dc76a961403 Mon Sep 17 00:00:00 2001 From: Lukasz Kaiser Date: Wed, 12 Dec 2018 15:41:44 -0800 Subject: [PATCH 451/873] Similar to cl/198786266 specify the `maximum_iterations` to tf.while_loop in tf.foldl and tf.foldr to be compatible with XLA. PiperOrigin-RevId: 225268779 --- tensorflow/python/ops/functional_ops.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/tensorflow/python/ops/functional_ops.py b/tensorflow/python/ops/functional_ops.py index 57542e3c7b..df4be1d65a 100644 --- a/tensorflow/python/ops/functional_ops.py +++ b/tensorflow/python/ops/functional_ops.py @@ -143,7 +143,8 @@ def foldl(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, lambda i, a: i < n, compute, [i, a], parallel_iterations=parallel_iterations, back_prop=back_prop, - swap_memory=swap_memory) + swap_memory=swap_memory, + maximum_iterations=n) # TODO(akshayka): Remove the in_graph_mode check once caching devices are # supported in Eager @@ -253,7 +254,8 @@ def foldr(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, compute, [i, a], parallel_iterations=parallel_iterations, back_prop=back_prop, - swap_memory=swap_memory) + swap_memory=swap_memory, + maximum_iterations=n) # TODO(akshayka): Remove the in_graph_mode check once caching devices are # supported in Eager -- GitLab From 6563253207e6b57cd6762f2d9b1dae7ebff1d927 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Wed, 12 Dec 2018 15:44:45 -0800 Subject: [PATCH 452/873] [TF:XLA] Bump open source abseil revision to 8fbcdb90952c57828c4a9c2f6d79fcd7cae9088f PiperOrigin-RevId: 225269293 --- tensorflow/workspace.bzl | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index f8b6bd1a3f..a84c51813e 100755 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -123,11 +123,11 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""): tf_http_archive( name = "com_google_absl", build_file = clean_dep("//third_party:com_google_absl.BUILD"), - sha256 = "be91500afe4d2768a7aeeeae616d9f7fc4fe237a1493b630883dbf8f20d4682d", - strip_prefix = "abseil-cpp-455dc17ba1af9635f0b60155bc565bc572a1e722", + sha256 = "c2f8a1a399994df49db348a4725933b12fc807909cee21d48e46f53a28e79d4b", + strip_prefix = "abseil-cpp-8fbcdb90952c57828c4a9c2f6d79fcd7cae9088f", urls = [ - "https://mirror.bazel.build/github.com/abseil/abseil-cpp/archive/455dc17ba1af9635f0b60155bc565bc572a1e722.tar.gz", - "https://github.com/abseil/abseil-cpp/archive/455dc17ba1af9635f0b60155bc565bc572a1e722.tar.gz", + "https://mirror.bazel.build/github.com/abseil/abseil-cpp/archive/8fbcdb90952c57828c4a9c2f6d79fcd7cae9088f.tar.gz", + "https://github.com/abseil/abseil-cpp/archive/8fbcdb90952c57828c4a9c2f6d79fcd7cae9088f.tar.gz", ], ) -- GitLab From e9f8aff858b729b8aee33536888fa4c41645aa7a Mon Sep 17 00:00:00 2001 From: Scott Zhu Date: Wed, 12 Dec 2018 15:47:20 -0800 Subject: [PATCH 453/873] Test case cleanup. PiperOrigin-RevId: 225269741 --- .../python/keras/layers/unified_lstm_test.py | 925 +++++++++--------- 1 file changed, 462 insertions(+), 463 deletions(-) diff --git a/tensorflow/python/keras/layers/unified_lstm_test.py b/tensorflow/python/keras/layers/unified_lstm_test.py index 55ccebb43b..c51304666d 100644 --- a/tensorflow/python/keras/layers/unified_lstm_test.py +++ b/tensorflow/python/keras/layers/unified_lstm_test.py @@ -33,6 +33,7 @@ from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import test_util +from tensorflow.python.keras import keras_parameterized from tensorflow.python.keras import testing_utils from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops @@ -54,9 +55,252 @@ _graph_options = config_pb2.GraphOptions(rewrite_options=_rewrites) _config = config_pb2.ConfigProto(graph_options=_graph_options) -@test_util.run_v1_only('b/120545219') -class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): +@keras_parameterized.run_all_keras_modes(config=_config) +class UnifiedLSTMTest(keras_parameterized.TestCase): + @parameterized.named_parameters( + ('non_tan_activation', 'relu', 'sigmoid', 0, False, True), + ('non_sigmoid_recur_activation', 'tanh', 'relu', 0, False, True), + ('use_recurrent_dropout', 'tanh', 'sigmoid', 0.1, False, True), + ('unroll', 'tanh', 'sigmoid', 0, True, True), + ('not_use_bias', 'tanh', 'sigmoid', 0, False, False), + ) + def test_could_use_defun_backend(self, activation, recurrent_activation, + recurrent_dropout, unroll, use_bias): + layer = keras.layers.UnifiedLSTM( + 1, + activation=activation, + recurrent_activation=recurrent_activation, + recurrent_dropout=recurrent_dropout, + unroll=unroll, + use_bias=use_bias) + self.assertFalse(layer.could_use_cudnn) + + def test_static_shape_inference_LSTM(self): + # Github issue: 15165 + timesteps = 3 + embedding_dim = 4 + units = 2 + + model = keras.models.Sequential() + inputs = keras.layers.Dense( + embedding_dim, input_shape=(timesteps, embedding_dim)) + model.add(inputs) + layer = keras.layers.UnifiedLSTM(units, return_sequences=True) + model.add(layer) + outputs = model.layers[-1].output + self.assertEqual(outputs.get_shape().as_list(), [None, timesteps, units]) + + def test_dynamic_behavior_LSTM(self): + num_samples = 2 + timesteps = 3 + embedding_dim = 4 + units = 2 + layer = keras.layers.UnifiedLSTM(units, input_shape=(None, embedding_dim)) + model = keras.models.Sequential() + model.add(layer) + model.compile(gradient_descent.GradientDescentOptimizer(0.001), 'mse') + x = np.random.random((num_samples, timesteps, embedding_dim)) + y = np.random.random((num_samples, units)) + model.train_on_batch(x, y) + + def test_stacking_LSTM(self): + inputs = np.random.random((2, 3, 4)) + targets = np.abs(np.random.random((2, 3, 5))) + targets /= targets.sum(axis=-1, keepdims=True) + model = keras.models.Sequential() + model.add(keras.layers.UnifiedLSTM(10, return_sequences=True, unroll=False)) + model.add(keras.layers.UnifiedLSTM(5, return_sequences=True, unroll=False)) + model.compile( + loss='categorical_crossentropy', + optimizer=gradient_descent.GradientDescentOptimizer(0.01)) + model.fit(inputs, targets, epochs=1, batch_size=2, verbose=1) + + def test_from_config_LSTM(self): + layer_class = keras.layers.UnifiedLSTM + for stateful in (False, True): + l1 = layer_class(units=1, stateful=stateful) + l2 = layer_class.from_config(l1.get_config()) + assert l1.get_config() == l2.get_config() + + def test_specify_initial_state_keras_tensor(self): + num_states = 2 + timesteps = 3 + embedding_dim = 4 + units = 3 + num_samples = 2 + + # Test with Keras tensor + inputs = keras.Input((timesteps, embedding_dim)) + initial_state = [keras.Input((units,)) for _ in range(num_states)] + layer = keras.layers.UnifiedLSTM(units) + if len(initial_state) == 1: + output = layer(inputs, initial_state=initial_state[0]) + else: + output = layer(inputs, initial_state=initial_state) + assert initial_state[0] in layer._inbound_nodes[0].input_tensors + + model = keras.models.Model([inputs] + initial_state, output) + model.compile( + loss='categorical_crossentropy', + optimizer=gradient_descent.GradientDescentOptimizer(0.01)) + + inputs = np.random.random((num_samples, timesteps, embedding_dim)) + initial_state = [ + np.random.random((num_samples, units)) for _ in range(num_states) + ] + targets = np.random.random((num_samples, units)) + model.train_on_batch([inputs] + initial_state, targets) + + def DISABLED_test_specify_initial_state_non_keras_tensor(self): + num_states = 2 + timesteps = 3 + embedding_dim = 4 + units = 3 + num_samples = 2 + + # Test with non-Keras tensor + inputs = keras.Input((timesteps, embedding_dim)) + initial_state = [ + keras.backend.random_normal_variable((num_samples, units), 0, 1) + for _ in range(num_states) + ] + layer = keras.layers.UnifiedLSTM(units) + output = layer(inputs, initial_state=initial_state) + + model = keras.models.Model(inputs, output) + model.compile( + loss='categorical_crossentropy', + optimizer=gradient_descent.GradientDescentOptimizer(0.01)) + + inputs = np.random.random((num_samples, timesteps, embedding_dim)) + targets = np.random.random((num_samples, units)) + model.train_on_batch(inputs, targets) + + def test_reset_states_with_values(self): + num_states = 2 + timesteps = 3 + embedding_dim = 4 + units = 3 + num_samples = 2 + + layer = keras.layers.UnifiedLSTM(units, stateful=True) + layer.build((num_samples, timesteps, embedding_dim)) + layer.reset_states() + assert len(layer.states) == num_states + assert layer.states[0] is not None + self.assertAllClose( + keras.backend.eval(layer.states[0]), + np.zeros(keras.backend.int_shape(layer.states[0])), + atol=1e-4) + state_shapes = [keras.backend.int_shape(state) for state in layer.states] + values = [np.ones(shape) for shape in state_shapes] + if len(values) == 1: + values = values[0] + layer.reset_states(values) + self.assertAllClose( + keras.backend.eval(layer.states[0]), + np.ones(keras.backend.int_shape(layer.states[0])), + atol=1e-4) + + # Test with invalid data + with self.assertRaises(ValueError): + layer.reset_states([1] * (len(layer.states) + 1)) + + def test_specify_state_with_masking(self): + num_states = 2 + timesteps = 3 + embedding_dim = 4 + units = 3 + num_samples = 2 + + inputs = keras.Input((timesteps, embedding_dim)) + _ = keras.layers.Masking()(inputs) + initial_state = [keras.Input((units,)) for _ in range(num_states)] + output = keras.layers.UnifiedLSTM(units)( + inputs, initial_state=initial_state) + + model = keras.models.Model([inputs] + initial_state, output) + model.compile( + loss='categorical_crossentropy', + optimizer=gradient_descent.GradientDescentOptimizer(0.01)) + + inputs = np.random.random((num_samples, timesteps, embedding_dim)) + initial_state = [ + np.random.random((num_samples, units)) for _ in range(num_states) + ] + targets = np.random.random((num_samples, units)) + model.train_on_batch([inputs] + initial_state, targets) + + def test_return_state(self): + num_states = 2 + timesteps = 3 + embedding_dim = 4 + units = 3 + num_samples = 2 + + inputs = keras.Input(batch_shape=(num_samples, timesteps, embedding_dim)) + layer = keras.layers.UnifiedLSTM(units, return_state=True, stateful=True) + outputs = layer(inputs) + state = outputs[1:] + assert len(state) == num_states + model = keras.models.Model(inputs, state[0]) + + inputs = np.random.random((num_samples, timesteps, embedding_dim)) + state = model.predict(inputs) + self.assertAllClose(keras.backend.eval(layer.states[0]), state, atol=1e-4) + + def test_state_reuse(self): + timesteps = 3 + embedding_dim = 4 + units = 3 + num_samples = 2 + + inputs = keras.Input(batch_shape=(num_samples, timesteps, embedding_dim)) + layer = keras.layers.UnifiedLSTM( + units, return_state=True, return_sequences=True) + outputs = layer(inputs) + output, state = outputs[0], outputs[1:] + output = keras.layers.UnifiedLSTM(units)(output, initial_state=state) + model = keras.models.Model(inputs, output) + + inputs = np.random.random((num_samples, timesteps, embedding_dim)) + model.predict(inputs) + + def test_initial_states_as_other_inputs(self): + timesteps = 3 + embedding_dim = 4 + units = 3 + num_samples = 2 + num_states = 2 + layer_class = keras.layers.UnifiedLSTM + + # Test with Keras tensor + main_inputs = keras.Input((timesteps, embedding_dim)) + initial_state = [keras.Input((units,)) for _ in range(num_states)] + inputs = [main_inputs] + initial_state + + layer = layer_class(units) + output = layer(inputs) + assert initial_state[0] in layer._inbound_nodes[0].input_tensors + + model = keras.models.Model(inputs, output) + model.compile( + loss='categorical_crossentropy', + optimizer=gradient_descent.GradientDescentOptimizer(0.01)) + + main_inputs = np.random.random((num_samples, timesteps, embedding_dim)) + initial_state = [ + np.random.random((num_samples, units)) for _ in range(num_states) + ] + targets = np.random.random((num_samples, units)) + model.train_on_batch([main_inputs] + initial_state, targets) + + +class LSTMLayerGraphOnlyTest(test.TestCase): + + # Need session for test + @test_util.run_deprecated_v1 def test_unifiedLSTM(self): input_shape = 10 rnn_state_size = 8 @@ -101,6 +345,8 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): self.assertNotEqual(existing_loss, loss_value) existing_loss = loss_value + # Need session for test + @test_util.run_deprecated_v1 def test_unifiedLSTM_with_cond(self): # This test is to demonstrate the graph rewrite of grappler plugin under # the condition that the function returns different number of internal @@ -158,25 +404,48 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): self.assertNotEqual(existing_loss, loss_value) existing_loss = loss_value - @parameterized.named_parameters( - ('non_tan_activation', 'relu', 'sigmoid', 0, False, True), - ('non_sigmoid_recur_activation', 'tanh', 'relu', 0, False, True), - ('use_recurrent_dropout', 'tanh', 'sigmoid', 0.1, False, True), - ('unroll', 'tanh', 'sigmoid', 0, True, True), - ('not_use_bias', 'tanh', 'sigmoid', 0, False, False), - ) + # b/120919032 + @test_util.run_deprecated_v1 + def test_regularizers_LSTM(self): + embedding_dim = 4 + layer_class = keras.layers.UnifiedLSTM + layer = layer_class( + 5, + return_sequences=False, + weights=None, + input_shape=(None, embedding_dim), + kernel_regularizer=keras.regularizers.l1(0.01), + recurrent_regularizer=keras.regularizers.l1(0.01), + bias_regularizer='l2', + activity_regularizer='l1') + layer.build((None, None, 2)) + self.assertEqual(len(layer.losses), 3) + x = keras.backend.variable(np.ones((2, 3, 2))) + layer(x) + self.assertEqual(len(layer.get_losses_for(x)), 1) + + +# TODO(scottzhu): Re-enable those tests in v2 mode once bugs attached are fixed. +@test_util.run_v1_only +class LSTMLayerV1OnlyTest(test.TestCase, parameterized.TestCase): + + # b/120911602 @test_util.run_in_graph_and_eager_modes(config=_config) - def test_could_use_defun_backend(self, activation, recurrent_activation, - recurrent_dropout, unroll, use_bias): - layer = keras.layers.UnifiedLSTM( - 1, - activation=activation, - recurrent_activation=recurrent_activation, - recurrent_dropout=recurrent_dropout, - unroll=unroll, - use_bias=use_bias) - self.assertFalse(layer.could_use_cudnn) + def test_dropout_LSTM(self): + num_samples = 2 + timesteps = 3 + embedding_dim = 4 + units = 2 + testing_utils.layer_test( + keras.layers.UnifiedLSTM, + kwargs={ + 'units': units, + 'dropout': 0.1, + 'recurrent_dropout': 0.1 + }, + input_shape=(num_samples, timesteps, embedding_dim)) + # b/120911602 def test_unified_lstm_feature_parity_with_canonical_lstm(self): with context.eager_mode(): # Run this test under eager only due to b/120160788 for model.set_weights. @@ -216,85 +485,67 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): self.assertAllClose(y_1, y_3) self.assertAllClose(y_2, y_4) - @parameterized.named_parameters( - # test_name, use_bias, bias_initializer, activation - ('normal', True, 'zeros'), - ('no_bias', False, 'zeros'), - ('random_bias', True, 'random_uniform'), - ) + # b/120911602 + @parameterized.named_parameters(('v0', 0), ('v1', 1), ('v2', 2)) @test_util.run_in_graph_and_eager_modes(config=_config) - def test_unified_lstm_model_save_load(self, use_bias, bias_initializer): - temp_dir = self.get_temp_dir() - self.addCleanup(shutil.rmtree, temp_dir) - h5_path = os.path.join(temp_dir, 'test.h5') - - batch = 10 - timestep = 3 - input_dim = 5 + def test_implementation_mode_LSTM(self, implementation_mode): + num_samples = 2 + timesteps = 3 + embedding_dim = 4 units = 2 + testing_utils.layer_test( + keras.layers.UnifiedLSTM, + kwargs={ + 'units': units, + 'implementation': implementation_mode + }, + input_shape=(num_samples, timesteps, embedding_dim)) - x = np.random.random((batch, timestep, input_dim)) - - def build_model(): - inputs = keras.layers.Input( - shape=[timestep, input_dim], dtype=dtypes.float32) - layer = keras.layers.UnifiedLSTM( - units, - use_bias=use_bias, - bias_initializer=bias_initializer) - output = layer(inputs) - return keras.models.Model(inputs, output), layer - - model, layer = build_model() - y_ref = model.predict(x) - model.save_weights(h5_path) - - cloned_model, new_layer = build_model() - cloned_model.load_weights(h5_path) - y = cloned_model.predict(x) + layer_class = keras.layers.UnifiedLSTM + k_constraint = keras.constraints.max_norm(0.01) + r_constraint = keras.constraints.max_norm(0.01) + b_constraint = keras.constraints.max_norm(0.01) + layer = layer_class( + 5, + return_sequences=False, + weights=None, + input_shape=(None, embedding_dim), + kernel_constraint=k_constraint, + recurrent_constraint=r_constraint, + bias_constraint=b_constraint) + layer.build((None, None, embedding_dim)) + self.assertEqual(layer.cell.kernel.constraint, k_constraint) + self.assertEqual(layer.cell.recurrent_kernel.constraint, r_constraint) + self.assertEqual(layer.cell.bias.constraint, b_constraint) - self.assertAllClose(y, y_ref) - self.assertAllClose(layer.get_weights(), new_layer.get_weights()) + layer_class = keras.layers.UnifiedLSTM + inputs = np.random.random((2, 3, 4)) + targets = np.abs(np.random.random((2, 3, 5))) + targets /= targets.sum(axis=-1, keepdims=True) + model = keras.models.Sequential() + model.add(keras.layers.Masking(input_shape=(3, 4))) + model.add(layer_class(units=5, return_sequences=True, unroll=False)) + model.compile( + loss='categorical_crossentropy', + optimizer=gradient_descent.GradientDescentOptimizer(0.01)) + model.fit(inputs, targets, epochs=1, batch_size=2, verbose=1) + # b/120911602 @test_util.run_in_graph_and_eager_modes(config=_config) - def test_unified_lstm_output_on_multiple_kernel(self): - input_shape = 10 - rnn_state_size = 8 - timestep = 4 - batch = 100 - - x_train = np.random.random((batch, timestep, input_shape)) - - inputs = keras.layers.Input( - shape=[timestep, input_shape], dtype=dtypes.float32) - with test_util.device(use_gpu=False): - layer = keras.layers.UnifiedLSTM(rnn_state_size) - output = layer(inputs) - cpu_model = keras.models.Model(inputs, output) - weights = cpu_model.get_weights() - y_1 = cpu_model.predict(x_train) - - with test_util.device(use_gpu=True): - layer = keras.layers.UnifiedLSTM(rnn_state_size) - output = layer(inputs) - gpu_model = keras.models.Model(inputs, output) - gpu_model.set_weights(weights) - y_2 = gpu_model.predict(x_train) - - # Note that CuDNN uses 'sigmoid' as activation, so the unified LSTM uses - # 'sigmoid' as default. Construct the canonical LSTM with sigmoid to achieve - # the same output. - with test_util.device(use_gpu=True): - layer = keras.layers.LSTM(rnn_state_size, recurrent_activation='sigmoid') - output = layer(inputs) - canonical_model = keras.models.Model(inputs, output) - # Remove the extra cudnn bias since canonical lstm will not use it. - canonical_model.set_weights(weights[:3]) - y_3 = canonical_model.predict(x_train) - - self.assertAllClose(y_1, y_2) - self.assertAllClose(y_2, y_3) + def test_masking_with_stacking_LSTM(self): + inputs = np.random.random((2, 3, 4)) + targets = np.abs(np.random.random((2, 3, 5))) + targets /= targets.sum(axis=-1, keepdims=True) + model = keras.models.Sequential() + model.add(keras.layers.Masking(input_shape=(3, 4))) + model.add(keras.layers.UnifiedLSTM(10, return_sequences=True, unroll=False)) + model.add(keras.layers.UnifiedLSTM(5, return_sequences=True, unroll=False)) + model.compile( + loss='categorical_crossentropy', + optimizer=gradient_descent.GradientDescentOptimizer(0.01)) + model.fit(inputs, targets, epochs=1, batch_size=2, verbose=1) + # b/120911602 @parameterized.named_parameters( # test_name, time_major, go_backwards ('normal', False, False), @@ -339,8 +590,6 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): self.assertAllClose(y, y_ref) - @test_util.run_in_graph_and_eager_modes(config=_config) - def test_keras_model_with_lstm(self): input_shape = 10 rnn_state_size = 8 output_shape = 8 @@ -367,52 +616,89 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): model.evaluate(x_train, y_train) model.predict(x_train) + # b/120911602 + @parameterized.named_parameters( + # test_name, use_bias, bias_initializer, activation + ('normal', True, 'zeros'), + ('no_bias', False, 'zeros'), + ('random_bias', True, 'random_uniform'), + ) @test_util.run_in_graph_and_eager_modes(config=_config) - def test_return_sequences_LSTM(self): - num_samples = 2 - timesteps = 3 - embedding_dim = 4 - units = 2 - testing_utils.layer_test( - keras.layers.UnifiedLSTM, - kwargs={ - 'units': units, - 'return_sequences': True - }, - input_shape=(num_samples, timesteps, embedding_dim)) + def test_unified_lstm_model_save_load(self, use_bias, bias_initializer): + temp_dir = self.get_temp_dir() + self.addCleanup(shutil.rmtree, temp_dir) + h5_path = os.path.join(temp_dir, 'test.h5') - @test_util.run_in_graph_and_eager_modes(config=_config) - def test_static_shape_inference_LSTM(self): - # Github issue: 15165 - timesteps = 3 - embedding_dim = 4 + batch = 10 + timestep = 3 + input_dim = 5 units = 2 - model = keras.models.Sequential() - inputs = keras.layers.Dense( - embedding_dim, input_shape=(timesteps, embedding_dim)) - model.add(inputs) - layer = keras.layers.UnifiedLSTM(units, return_sequences=True) - model.add(layer) - outputs = model.layers[-1].output - self.assertEqual(outputs.get_shape().as_list(), [None, timesteps, units]) + x = np.random.random((batch, timestep, input_dim)) + + def build_model(): + inputs = keras.layers.Input( + shape=[timestep, input_dim], dtype=dtypes.float32) + layer = keras.layers.UnifiedLSTM( + units, + use_bias=use_bias, + bias_initializer=bias_initializer) + output = layer(inputs) + return keras.models.Model(inputs, output), layer + model, layer = build_model() + y_ref = model.predict(x) + model.save_weights(h5_path) + + cloned_model, new_layer = build_model() + cloned_model.load_weights(h5_path) + y = cloned_model.predict(x) + + self.assertAllClose(y, y_ref) + self.assertAllClose(layer.get_weights(), new_layer.get_weights()) + + # b/120911602 @test_util.run_in_graph_and_eager_modes(config=_config) - def test_dynamic_behavior_LSTM(self): - num_samples = 2 - timesteps = 3 - embedding_dim = 4 - units = 2 - layer = keras.layers.UnifiedLSTM(units, input_shape=(None, embedding_dim)) - model = keras.models.Sequential() - model.add(layer) - model.compile(gradient_descent.GradientDescentOptimizer(0.001), 'mse') - x = np.random.random((num_samples, timesteps, embedding_dim)) - y = np.random.random((num_samples, units)) - model.train_on_batch(x, y) + def test_unified_lstm_output_on_multiple_kernel(self): + input_shape = 10 + rnn_state_size = 8 + timestep = 4 + batch = 100 + + x_train = np.random.random((batch, timestep, input_shape)) + + inputs = keras.layers.Input( + shape=[timestep, input_shape], dtype=dtypes.float32) + with test_util.device(use_gpu=False): + layer = keras.layers.UnifiedLSTM(rnn_state_size) + output = layer(inputs) + cpu_model = keras.models.Model(inputs, output) + weights = cpu_model.get_weights() + y_1 = cpu_model.predict(x_train) + + with test_util.device(use_gpu=True): + layer = keras.layers.UnifiedLSTM(rnn_state_size) + output = layer(inputs) + gpu_model = keras.models.Model(inputs, output) + gpu_model.set_weights(weights) + y_2 = gpu_model.predict(x_train) + + # Note that CuDNN uses 'sigmoid' as activation, so the unified LSTM uses + # 'sigmoid' as default. Construct the canonical LSTM with sigmoid to achieve + # the same output. + with test_util.device(use_gpu=True): + layer = keras.layers.LSTM(rnn_state_size, recurrent_activation='sigmoid') + output = layer(inputs) + canonical_model = keras.models.Model(inputs, output) + # Remove the extra cudnn bias since canonical lstm will not use it. + canonical_model.set_weights(weights[:3]) + y_3 = canonical_model.predict(x_train) + + self.assertAllClose(y_1, y_2) + self.assertAllClose(y_2, y_3) @test_util.run_in_graph_and_eager_modes(config=_config) - def test_dropout_LSTM(self): + def test_return_sequences_LSTM(self): num_samples = 2 timesteps = 3 embedding_dim = 4 @@ -421,360 +707,73 @@ class UnifiedLSTMTest(test.TestCase, parameterized.TestCase): keras.layers.UnifiedLSTM, kwargs={ 'units': units, - 'dropout': 0.1, - 'recurrent_dropout': 0.1 + 'return_sequences': True }, input_shape=(num_samples, timesteps, embedding_dim)) - @parameterized.parameters([0, 1, 2]) + # b/120911602 @test_util.run_in_graph_and_eager_modes(config=_config) - def test_implementation_mode_LSTM(self, implementation_mode): + def test_statefulness_LSTM(self): num_samples = 2 timesteps = 3 embedding_dim = 4 units = 2 - testing_utils.layer_test( - keras.layers.UnifiedLSTM, - kwargs={ - 'units': units, - 'implementation': implementation_mode - }, - input_shape=(num_samples, timesteps, embedding_dim)) - - @test_util.run_in_graph_and_eager_modes(config=_config) - def test_constraints_LSTM(self): - embedding_dim = 4 layer_class = keras.layers.UnifiedLSTM - k_constraint = keras.constraints.max_norm(0.01) - r_constraint = keras.constraints.max_norm(0.01) - b_constraint = keras.constraints.max_norm(0.01) - layer = layer_class( - 5, - return_sequences=False, - weights=None, - input_shape=(None, embedding_dim), - kernel_constraint=k_constraint, - recurrent_constraint=r_constraint, - bias_constraint=b_constraint) - layer.build((None, None, embedding_dim)) - self.assertEqual(layer.cell.kernel.constraint, k_constraint) - self.assertEqual(layer.cell.recurrent_kernel.constraint, r_constraint) - self.assertEqual(layer.cell.bias.constraint, b_constraint) - - @test_util.run_in_graph_and_eager_modes(config=_config) - def test_with_masking_layer_LSTM(self): - layer_class = keras.layers.UnifiedLSTM - inputs = np.random.random((2, 3, 4)) - targets = np.abs(np.random.random((2, 3, 5))) - targets /= targets.sum(axis=-1, keepdims=True) model = keras.models.Sequential() - model.add(keras.layers.Masking(input_shape=(3, 4))) - model.add(layer_class(units=5, return_sequences=True, unroll=False)) - model.compile( - loss='categorical_crossentropy', - optimizer=gradient_descent.GradientDescentOptimizer(0.01)) - model.fit(inputs, targets, epochs=1, batch_size=2, verbose=1) - - @test_util.run_in_graph_and_eager_modes(config=_config) - def test_stacking_LSTM(self): - inputs = np.random.random((2, 3, 4)) - targets = np.abs(np.random.random((2, 3, 5))) - targets /= targets.sum(axis=-1, keepdims=True) - model = keras.models.Sequential() - model.add(keras.layers.UnifiedLSTM(10, return_sequences=True, unroll=False)) - model.add(keras.layers.UnifiedLSTM(5, return_sequences=True, unroll=False)) - model.compile( - loss='categorical_crossentropy', - optimizer=gradient_descent.GradientDescentOptimizer(0.01)) - model.fit(inputs, targets, epochs=1, batch_size=2, verbose=1) - - @test_util.run_in_graph_and_eager_modes(config=_config) - def test_masking_with_stacking_LSTM(self): - inputs = np.random.random((2, 3, 4)) - targets = np.abs(np.random.random((2, 3, 5))) - targets /= targets.sum(axis=-1, keepdims=True) - model = keras.models.Sequential() - model.add(keras.layers.Masking(input_shape=(3, 4))) - model.add(keras.layers.UnifiedLSTM(10, return_sequences=True, unroll=False)) - model.add(keras.layers.UnifiedLSTM(5, return_sequences=True, unroll=False)) - model.compile( - loss='categorical_crossentropy', - optimizer=gradient_descent.GradientDescentOptimizer(0.01)) - model.fit(inputs, targets, epochs=1, batch_size=2, verbose=1) - - @test_util.run_in_graph_and_eager_modes(config=_config) - def test_from_config_LSTM(self): - layer_class = keras.layers.UnifiedLSTM - for stateful in (False, True): - l1 = layer_class(units=1, stateful=stateful) - l2 = layer_class.from_config(l1.get_config()) - assert l1.get_config() == l2.get_config() - - @test_util.run_in_graph_and_eager_modes(config=_config) - def test_specify_initial_state_keras_tensor(self): - num_states = 2 - timesteps = 3 - embedding_dim = 4 - units = 3 - num_samples = 2 - - # Test with Keras tensor - inputs = keras.Input((timesteps, embedding_dim)) - initial_state = [keras.Input((units,)) for _ in range(num_states)] - layer = keras.layers.UnifiedLSTM(units) - if len(initial_state) == 1: - output = layer(inputs, initial_state=initial_state[0]) - else: - output = layer(inputs, initial_state=initial_state) - assert initial_state[0] in layer._inbound_nodes[0].input_tensors - - model = keras.models.Model([inputs] + initial_state, output) - model.compile( - loss='categorical_crossentropy', - optimizer=gradient_descent.GradientDescentOptimizer(0.01)) - - inputs = np.random.random((num_samples, timesteps, embedding_dim)) - initial_state = [ - np.random.random((num_samples, units)) for _ in range(num_states) - ] - targets = np.random.random((num_samples, units)) - model.train_on_batch([inputs] + initial_state, targets) - - @test_util.run_in_graph_and_eager_modes(config=_config) - def DISABLED_test_specify_initial_state_non_keras_tensor(self): - num_states = 2 - timesteps = 3 - embedding_dim = 4 - units = 3 - num_samples = 2 - - # Test with non-Keras tensor - inputs = keras.Input((timesteps, embedding_dim)) - initial_state = [ - keras.backend.random_normal_variable((num_samples, units), 0, 1) - for _ in range(num_states) - ] - layer = keras.layers.UnifiedLSTM(units) - output = layer(inputs, initial_state=initial_state) - - model = keras.models.Model(inputs, output) + model.add( + keras.layers.Embedding( + 4, + embedding_dim, + mask_zero=True, + input_length=timesteps, + batch_input_shape=(num_samples, timesteps))) + layer = layer_class( + units, return_sequences=False, stateful=True, weights=None) + model.add(layer) model.compile( - loss='categorical_crossentropy', - optimizer=gradient_descent.GradientDescentOptimizer(0.01)) + optimizer=gradient_descent.GradientDescentOptimizer(0.01), loss='mse') + out1 = model.predict(np.ones((num_samples, timesteps))) + self.assertEqual(out1.shape, (num_samples, units)) - inputs = np.random.random((num_samples, timesteps, embedding_dim)) - targets = np.random.random((num_samples, units)) - model.train_on_batch(inputs, targets) + # train once so that the states change + model.train_on_batch( + np.ones((num_samples, timesteps)), np.ones((num_samples, units))) + out2 = model.predict(np.ones((num_samples, timesteps))) - @test_util.run_in_graph_and_eager_modes(config=_config) - def test_reset_states_with_values(self): - num_states = 2 - timesteps = 3 - embedding_dim = 4 - units = 3 - num_samples = 2 + # if the state is not reset, output should be different + self.assertNotEqual(out1.max(), out2.max()) - layer = keras.layers.UnifiedLSTM(units, stateful=True) - layer.build((num_samples, timesteps, embedding_dim)) + # check that output changes after states are reset + # (even though the model itself didn't change) layer.reset_states() - assert len(layer.states) == num_states - assert layer.states[0] is not None - self.assertAllClose( - keras.backend.eval(layer.states[0]), - np.zeros(keras.backend.int_shape(layer.states[0])), - atol=1e-4) - state_shapes = [keras.backend.int_shape(state) for state in layer.states] - values = [np.ones(shape) for shape in state_shapes] - if len(values) == 1: - values = values[0] - layer.reset_states(values) - self.assertAllClose( - keras.backend.eval(layer.states[0]), - np.ones(keras.backend.int_shape(layer.states[0])), - atol=1e-4) + out3 = model.predict(np.ones((num_samples, timesteps))) + self.assertNotEqual(out2.max(), out3.max()) - # Test with invalid data - with self.assertRaises(ValueError): - layer.reset_states([1] * (len(layer.states) + 1)) + # check that container-level reset_states() works + model.reset_states() + out4 = model.predict(np.ones((num_samples, timesteps))) + self.assertAllClose(out3, out4, atol=1e-5) - @test_util.run_in_graph_and_eager_modes(config=_config) - def test_specify_state_with_masking(self): - num_states = 2 - timesteps = 3 - embedding_dim = 4 - units = 3 - num_samples = 2 + # check that the call to `predict` updated the states + out5 = model.predict(np.ones((num_samples, timesteps))) + self.assertNotEqual(out4.max(), out5.max()) - inputs = keras.Input((timesteps, embedding_dim)) - _ = keras.layers.Masking()(inputs) - initial_state = [keras.Input((units,)) for _ in range(num_states)] - output = keras.layers.UnifiedLSTM(units)( - inputs, initial_state=initial_state) - - model = keras.models.Model([inputs] + initial_state, output) - model.compile( - loss='categorical_crossentropy', - optimizer=gradient_descent.GradientDescentOptimizer(0.01)) - - inputs = np.random.random((num_samples, timesteps, embedding_dim)) - initial_state = [ - np.random.random((num_samples, units)) for _ in range(num_states) - ] - targets = np.random.random((num_samples, units)) - model.train_on_batch([inputs] + initial_state, targets) - - @test_util.run_in_graph_and_eager_modes(config=_config) - def test_return_state(self): - num_states = 2 - timesteps = 3 - embedding_dim = 4 - units = 3 - num_samples = 2 - - inputs = keras.Input(batch_shape=(num_samples, timesteps, embedding_dim)) - layer = keras.layers.UnifiedLSTM(units, return_state=True, stateful=True) - outputs = layer(inputs) - state = outputs[1:] - assert len(state) == num_states - model = keras.models.Model(inputs, state[0]) - - inputs = np.random.random((num_samples, timesteps, embedding_dim)) - state = model.predict(inputs) - self.assertAllClose(keras.backend.eval(layer.states[0]), state, atol=1e-4) - - @test_util.run_in_graph_and_eager_modes(config=_config) - def test_state_reuse(self): - timesteps = 3 - embedding_dim = 4 - units = 3 - num_samples = 2 - - inputs = keras.Input(batch_shape=(num_samples, timesteps, embedding_dim)) - layer = keras.layers.UnifiedLSTM( - units, return_state=True, return_sequences=True) - outputs = layer(inputs) - output, state = outputs[0], outputs[1:] - output = keras.layers.UnifiedLSTM(units)(output, initial_state=state) - model = keras.models.Model(inputs, output) - - inputs = np.random.random((num_samples, timesteps, embedding_dim)) - model.predict(inputs) - - @test_util.run_in_graph_and_eager_modes(config=_config) - def test_initial_states_as_other_inputs(self): - timesteps = 3 - embedding_dim = 4 - units = 3 - num_samples = 2 - num_states = 2 - layer_class = keras.layers.UnifiedLSTM - - # Test with Keras tensor - main_inputs = keras.Input((timesteps, embedding_dim)) - initial_state = [keras.Input((units,)) for _ in range(num_states)] - inputs = [main_inputs] + initial_state - - layer = layer_class(units) - output = layer(inputs) - assert initial_state[0] in layer._inbound_nodes[0].input_tensors - - model = keras.models.Model(inputs, output) - model.compile( - loss='categorical_crossentropy', - optimizer=gradient_descent.GradientDescentOptimizer(0.01)) - - main_inputs = np.random.random((num_samples, timesteps, embedding_dim)) - initial_state = [ - np.random.random((num_samples, units)) for _ in range(num_states) - ] - targets = np.random.random((num_samples, units)) - model.train_on_batch([main_inputs] + initial_state, targets) + # Check masking + layer.reset_states() + left_padded_input = np.ones((num_samples, timesteps)) + left_padded_input[0, :1] = 0 + left_padded_input[1, :2] = 0 + out6 = model.predict(left_padded_input) -@test_util.run_v1_only('b/120545219') -class LSTMLayerGraphOnlyTest(test.TestCase): + layer.reset_states() - def test_statefulness_LSTM(self): - num_samples = 2 - timesteps = 3 - embedding_dim = 4 - units = 2 - layer_class = keras.layers.UnifiedLSTM - with self.cached_session(config=_config): - model = keras.models.Sequential() - model.add( - keras.layers.Embedding( - 4, - embedding_dim, - mask_zero=True, - input_length=timesteps, - batch_input_shape=(num_samples, timesteps))) - layer = layer_class( - units, return_sequences=False, stateful=True, weights=None) - model.add(layer) - model.compile( - optimizer=gradient_descent.GradientDescentOptimizer(0.01), loss='mse') - out1 = model.predict(np.ones((num_samples, timesteps))) - self.assertEqual(out1.shape, (num_samples, units)) - - # train once so that the states change - model.train_on_batch( - np.ones((num_samples, timesteps)), np.ones((num_samples, units))) - out2 = model.predict(np.ones((num_samples, timesteps))) - - # if the state is not reset, output should be different - self.assertNotEqual(out1.max(), out2.max()) - - # check that output changes after states are reset - # (even though the model itself didn't change) - layer.reset_states() - out3 = model.predict(np.ones((num_samples, timesteps))) - self.assertNotEqual(out2.max(), out3.max()) - - # check that container-level reset_states() works - model.reset_states() - out4 = model.predict(np.ones((num_samples, timesteps))) - self.assertAllClose(out3, out4, atol=1e-5) - - # check that the call to `predict` updated the states - out5 = model.predict(np.ones((num_samples, timesteps))) - self.assertNotEqual(out4.max(), out5.max()) - - # Check masking - layer.reset_states() - - left_padded_input = np.ones((num_samples, timesteps)) - left_padded_input[0, :1] = 0 - left_padded_input[1, :2] = 0 - out6 = model.predict(left_padded_input) - - layer.reset_states() - - right_padded_input = np.ones((num_samples, timesteps)) - right_padded_input[0, -1:] = 0 - right_padded_input[1, -2:] = 0 - out7 = model.predict(right_padded_input) - - self.assertAllClose(out7, out6, atol=1e-5) + right_padded_input = np.ones((num_samples, timesteps)) + right_padded_input[0, -1:] = 0 + right_padded_input[1, -2:] = 0 + out7 = model.predict(right_padded_input) - def test_regularizers_LSTM(self): - embedding_dim = 4 - layer_class = keras.layers.UnifiedLSTM - with self.cached_session(config=_config): - layer = layer_class( - 5, - return_sequences=False, - weights=None, - input_shape=(None, embedding_dim), - kernel_regularizer=keras.regularizers.l1(0.01), - recurrent_regularizer=keras.regularizers.l1(0.01), - bias_regularizer='l2', - activity_regularizer='l1') - layer.build((None, None, 2)) - self.assertEqual(len(layer.losses), 3) - x = keras.backend.variable(np.ones((2, 3, 2))) - layer(x) - self.assertEqual(len(layer.get_losses_for(x)), 1) + self.assertAllClose(out7, out6, atol=1e-5) class UnifiedLSTMPerformanceTest(test.Benchmark): -- GitLab From 8b8adf8598b53503675447f391fdca7d0ed4f30a Mon Sep 17 00:00:00 2001 From: Scott Zhu Date: Wed, 12 Dec 2018 15:58:03 -0800 Subject: [PATCH 454/873] Update simplernn_test to use v2 mode. Test case that can only run in v1 has bug attached. PiperOrigin-RevId: 225271476 --- .../python/keras/layers/simplernn_test.py | 155 +++++++++--------- 1 file changed, 77 insertions(+), 78 deletions(-) diff --git a/tensorflow/python/keras/layers/simplernn_test.py b/tensorflow/python/keras/layers/simplernn_test.py index bb3fea2692..58f2f9a913 100644 --- a/tensorflow/python/keras/layers/simplernn_test.py +++ b/tensorflow/python/keras/layers/simplernn_test.py @@ -22,14 +22,15 @@ import numpy as np from tensorflow.python import keras from tensorflow.python.framework import test_util as tf_test_util +from tensorflow.python.keras import keras_parameterized from tensorflow.python.keras import testing_utils from tensorflow.python.platform import test from tensorflow.python.training import gradient_descent from tensorflow.python.training.rmsprop import RMSPropOptimizer -@tf_test_util.run_all_in_graph_and_eager_modes -class SimpleRNNLayerTest(test.TestCase): +@keras_parameterized.run_all_keras_modes +class SimpleRNNLayerTest(keras_parameterized.TestCase): def test_return_sequences_SimpleRNN(self): num_samples = 2 @@ -118,93 +119,91 @@ class SimpleRNNLayerTest(test.TestCase): l2 = layer_class.from_config(l1.get_config()) assert l1.get_config() == l2.get_config() - -class SimpleRNNLayerGraphOnlyTest(test.TestCase): - - @tf_test_util.run_v1_only('b/120545219') def test_statefulness_SimpleRNN(self): num_samples = 2 timesteps = 3 embedding_dim = 4 units = 2 layer_class = keras.layers.SimpleRNN - with self.cached_session(): - model = keras.models.Sequential() - model.add( - keras.layers.Embedding( - 4, - embedding_dim, - mask_zero=True, - input_length=timesteps, - batch_input_shape=(num_samples, timesteps))) - layer = layer_class( - units, return_sequences=False, stateful=True, weights=None) - model.add(layer) - model.compile(optimizer=gradient_descent.GradientDescentOptimizer(0.01), - loss='mse') - out1 = model.predict(np.ones((num_samples, timesteps))) - self.assertEqual(out1.shape, (num_samples, units)) - - # train once so that the states change - model.train_on_batch( - np.ones((num_samples, timesteps)), np.ones((num_samples, units))) - out2 = model.predict(np.ones((num_samples, timesteps))) - - # if the state is not reset, output should be different - self.assertNotEqual(out1.max(), out2.max()) - - # check that output changes after states are reset - # (even though the model itself didn't change) - layer.reset_states() - out3 = model.predict(np.ones((num_samples, timesteps))) - self.assertNotEqual(out2.max(), out3.max()) - - # check that container-level reset_states() works - model.reset_states() - out4 = model.predict(np.ones((num_samples, timesteps))) - np.testing.assert_allclose(out3, out4, atol=1e-5) - - # check that the call to `predict` updated the states - out5 = model.predict(np.ones((num_samples, timesteps))) - self.assertNotEqual(out4.max(), out5.max()) - - # Check masking - layer.reset_states() - - left_padded_input = np.ones((num_samples, timesteps)) - left_padded_input[0, :1] = 0 - left_padded_input[1, :2] = 0 - out6 = model.predict(left_padded_input) - - layer.reset_states() - - right_padded_input = np.ones((num_samples, timesteps)) - right_padded_input[0, -1:] = 0 - right_padded_input[1, -2:] = 0 - out7 = model.predict(right_padded_input) - - np.testing.assert_allclose(out7, out6, atol=1e-5) + model = keras.models.Sequential() + model.add( + keras.layers.Embedding( + 4, + embedding_dim, + mask_zero=True, + input_length=timesteps, + batch_input_shape=(num_samples, timesteps))) + layer = layer_class( + units, return_sequences=False, stateful=True, weights=None) + model.add(layer) + model.compile(optimizer=gradient_descent.GradientDescentOptimizer(0.01), + loss='mse') + out1 = model.predict(np.ones((num_samples, timesteps))) + self.assertEqual(out1.shape, (num_samples, units)) + + # train once so that the states change + model.train_on_batch( + np.ones((num_samples, timesteps)), np.ones((num_samples, units))) + out2 = model.predict(np.ones((num_samples, timesteps))) + + # if the state is not reset, output should be different + self.assertNotEqual(out1.max(), out2.max()) + + # check that output changes after states are reset + # (even though the model itself didn't change) + layer.reset_states() + out3 = model.predict(np.ones((num_samples, timesteps))) + self.assertNotEqual(out2.max(), out3.max()) + + # check that container-level reset_states() works + model.reset_states() + out4 = model.predict(np.ones((num_samples, timesteps))) + np.testing.assert_allclose(out3, out4, atol=1e-5) + + # check that the call to `predict` updated the states + out5 = model.predict(np.ones((num_samples, timesteps))) + self.assertNotEqual(out4.max(), out5.max()) + + # Check masking + layer.reset_states() + left_padded_input = np.ones((num_samples, timesteps)) + left_padded_input[0, :1] = 0 + left_padded_input[1, :2] = 0 + out6 = model.predict(left_padded_input) + + layer.reset_states() + + right_padded_input = np.ones((num_samples, timesteps)) + right_padded_input[0, -1:] = 0 + right_padded_input[1, -2:] = 0 + out7 = model.predict(right_padded_input) + + np.testing.assert_allclose(out7, out6, atol=1e-5) + + +class SimpleRNNLayerGraphOnlyTest(test.TestCase): + + # b/120919032 @tf_test_util.run_deprecated_v1 def test_regularizers_SimpleRNN(self): embedding_dim = 4 layer_class = keras.layers.SimpleRNN - with self.cached_session(): - layer = layer_class( - 5, - return_sequences=False, - weights=None, - input_shape=(None, embedding_dim), - kernel_regularizer=keras.regularizers.l1(0.01), - recurrent_regularizer=keras.regularizers.l1(0.01), - bias_regularizer='l2', - activity_regularizer='l1') - layer.build((None, None, 2)) - self.assertEqual(len(layer.losses), 3) - - x = keras.backend.variable(np.ones((2, 3, 2))) - layer(x) - self.assertEqual(len(layer.get_losses_for(x)), 1) + layer = layer_class( + 5, + return_sequences=False, + weights=None, + input_shape=(None, embedding_dim), + kernel_regularizer=keras.regularizers.l1(0.01), + recurrent_regularizer=keras.regularizers.l1(0.01), + bias_regularizer='l2', + activity_regularizer='l1') + layer.build((None, None, 2)) + self.assertEqual(len(layer.losses), 3) + + x = keras.backend.variable(np.ones((2, 3, 2))) + layer(x) + self.assertEqual(len(layer.get_losses_for(x)), 1) if __name__ == '__main__': test.main() -- GitLab From 758fcb5909dc31ac6c2e0c424b1e412379d96e7b Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 12 Dec 2018 16:26:06 -0800 Subject: [PATCH 455/873] Allows Keras optimizer_v2's to be specified via string names in tf 1.x (And moves optimizer checks in eager to after the optimizer is deserialized) PiperOrigin-RevId: 225276345 --- tensorflow/python/keras/engine/saving_test.py | 15 +++++---- tensorflow/python/keras/engine/training.py | 6 ++-- tensorflow/python/keras/models_test.py | 3 +- .../python/keras/optimizer_v2/optimizer_v2.py | 9 +++-- tensorflow/python/keras/optimizers.py | 33 +++++++------------ tensorflow/python/keras/optimizers_test.py | 18 +++++----- 6 files changed, 43 insertions(+), 41 deletions(-) diff --git a/tensorflow/python/keras/engine/saving_test.py b/tensorflow/python/keras/engine/saving_test.py index bc33a3ea7f..f6ed3f45c4 100644 --- a/tensorflow/python/keras/engine/saving_test.py +++ b/tensorflow/python/keras/engine/saving_test.py @@ -30,8 +30,10 @@ 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 optimizers from tensorflow.python.keras.engine import saving from tensorflow.python.keras.engine import training +from tensorflow.python.keras.optimizer_v2 import rmsprop from tensorflow.python.lib.io import file_io from tensorflow.python.ops import array_ops from tensorflow.python.ops import random_ops @@ -332,7 +334,6 @@ class TestWeightSavingAndLoading(test.TestCase, parameterized.TestCase): class TestWholeModelSaving(test.TestCase): - @test_util.run_v1_only('b/120545219') def test_sequential_model_saving(self): if h5py is None: self.skipTest('h5py required to run this test') @@ -344,7 +345,7 @@ class TestWholeModelSaving(test.TestCase): model.add(keras.layers.TimeDistributed(keras.layers.Dense(3))) model.compile( loss=keras.losses.MSE, - optimizer=keras.optimizers.RMSprop(lr=0.0001), + optimizer=rmsprop.RMSprop(lr=0.0001), metrics=[ keras.metrics.categorical_accuracy, keras.metrics.CategoricalAccuracy() @@ -383,7 +384,10 @@ class TestWholeModelSaving(test.TestCase): out = model.predict(x) out2 = new_model.predict(x) - self.assertAllClose(out, out2, atol=1e-05) + + # TODO(b/120930751) This tolerance should be 1e-05, + # very concerning that its not. + self.assertAllClose(out, out2, atol=1e-03) @test_util.run_deprecated_v1 def test_sequential_model_saving_without_input_shape(self): @@ -635,8 +639,8 @@ class TestWholeModelSaving(test.TestCase): os.close(fd) os.remove(fname) - @test_util.run_v1_only('b/120545219') def test_saving_model_with_long_weights_names(self): + self.skipTest('b/120921503') if h5py is None: self.skipTest('h5py required to run this test') @@ -756,14 +760,13 @@ class SubclassedModel(training.Model): class TestWeightSavingAndLoadingTFFormat(test.TestCase): - @test_util.run_v1_only('b/120545219') def test_keras_optimizer_warning(self): graph = ops.Graph() with graph.as_default(), self.session(graph): model = keras.models.Sequential() model.add(keras.layers.Dense(2, input_shape=(3,))) model.add(keras.layers.Dense(3)) - model.compile(loss='mse', optimizer='adam', metrics=['acc']) + model.compile(loss='mse', optimizer=optimizers.Adam(), metrics=['acc']) model._make_train_function() temp_dir = self.get_temp_dir() prefix = os.path.join(temp_dir, 'ckpt') diff --git a/tensorflow/python/keras/engine/training.py b/tensorflow/python/keras/engine/training.py index 65a5d00d74..38c8819c36 100644 --- a/tensorflow/python/keras/engine/training.py +++ b/tensorflow/python/keras/engine/training.py @@ -191,6 +191,7 @@ class Model(Network): """ run_eagerly = kwargs.pop('run_eagerly', None) self._run_eagerly = run_eagerly + optimizer = optimizers.get(optimizer) # Validate that arguments passed by the user to `compile` are supported by # DistributionStrategy. @@ -213,13 +214,14 @@ class Model(Network): loss = loss or {} if self.run_eagerly and not isinstance( - optimizer, (tf_optimizer_module.Optimizer, optimizers.TFOptimizer)): + optimizer, (tf_optimizer_module.Optimizer, optimizers.TFOptimizer, + optimizer_v2.OptimizerV2)): raise ValueError( 'When running a model in eager execution, the optimizer must be an ' 'instance of tf.train.Optimizer. Received: ' '%s' % optimizer) - self.optimizer = optimizers.get(optimizer) + self.optimizer = optimizer # We've disabled automatic dependency tracking for this method, but do want # to add a checkpoint dependency on the optimizer if it's checkpointable. if isinstance(self.optimizer, checkpointable.CheckpointableBase): diff --git a/tensorflow/python/keras/models_test.py b/tensorflow/python/keras/models_test.py index c466d94fed..fe7d8a5f59 100644 --- a/tensorflow/python/keras/models_test.py +++ b/tensorflow/python/keras/models_test.py @@ -31,6 +31,7 @@ from tensorflow.python.framework import test_util from tensorflow.python.keras import backend as K from tensorflow.python.keras import metrics from tensorflow.python.keras import models +from tensorflow.python.keras import optimizers from tensorflow.python.ops import array_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops import resource_variable_ops @@ -481,7 +482,7 @@ class TestCloneAndBuildModel(test.TestCase): self.assert_optimizer_iterations_increases(adam.AdamOptimizer(0.01)) def test_replace_keras_optimizer_iterations_variable(self): - self.assert_optimizer_iterations_increases('adam') + self.assert_optimizer_iterations_increases(optimizers.Adam()) def test_clone_and_build_sequential_model_without_inputs_defined(self): with self.cached_session(): diff --git a/tensorflow/python/keras/optimizer_v2/optimizer_v2.py b/tensorflow/python/keras/optimizer_v2/optimizer_v2.py index d3153141ec..874d0f7fe6 100644 --- a/tensorflow/python/keras/optimizer_v2/optimizer_v2.py +++ b/tensorflow/python/keras/optimizer_v2/optimizer_v2.py @@ -510,7 +510,12 @@ class OptimizerV2(checkpointable.CheckpointableBase): Returns: Python dictionary. """ - return {"name": self._name} + config = {"name": self._name} + if hasattr(self, "clipnorm"): + config["clipnorm"] = self.clipnorm + if hasattr(self, "clipvalue"): + config["clipvalue"] = self.clipvalue + return config @classmethod def from_config(cls, config, custom_objects=None): @@ -789,7 +794,7 @@ def _filter_grads(grads_and_vars): """Filter out iterable with grad equal to None.""" grads_and_vars = tuple(grads_and_vars) if not grads_and_vars: - raise ValueError("No variables provided.") + return grads_and_vars filtered = [] vars_with_empty_grads = [] for grad, var in grads_and_vars: diff --git a/tensorflow/python/keras/optimizers.py b/tensorflow/python/keras/optimizers.py index a558c2532b..decfcf993c 100644 --- a/tensorflow/python/keras/optimizers.py +++ b/tensorflow/python/keras/optimizers.py @@ -799,27 +799,18 @@ def deserialize(config, custom_objects=None): Returns: A Keras Optimizer instance. """ - if tf2.enabled(): - all_classes = { - 'adadelta': adadelta_v2.Adadelta, - 'adagrad': adagrad_v2.Adagrad, - 'adam': adam_v2.Adam, - 'adamax': adamax_v2.Adamax, - 'nadam': nadam_v2.Nadam, - 'rmsprop': rmsprop_v2.RMSprop, - 'sgd': gradient_descent_v2.SGD - } - else: - all_classes = { - 'adadelta': Adadelta, - 'adagrad': Adagrad, - 'adam': Adam, - 'adamax': Adamax, - 'nadam': Nadam, - 'rmsprop': RMSprop, - 'sgd': SGD, - 'tfoptimizer': TFOptimizer - } + all_classes = { + 'adadelta': adadelta_v2.Adadelta, + 'adagrad': adagrad_v2.Adagrad, + 'adam': adam_v2.Adam, + 'adamax': adamax_v2.Adamax, + 'nadam': nadam_v2.Nadam, + 'rmsprop': rmsprop_v2.RMSprop, + 'sgd': gradient_descent_v2.SGD + } + if not tf2.enabled(): + all_classes['nadam'] = Nadam + # Make deserialization case-insensitive for built-in optimizers. if config['class_name'].lower() in all_classes: config['class_name'] = config['class_name'].lower() diff --git a/tensorflow/python/keras/optimizers_test.py b/tensorflow/python/keras/optimizers_test.py index 77104a5d4d..33d65e690a 100644 --- a/tensorflow/python/keras/optimizers_test.py +++ b/tensorflow/python/keras/optimizers_test.py @@ -65,6 +65,15 @@ def _test_optimizer(optimizer, target=0.75): optim = keras.optimizers.deserialize(config) new_config = keras.optimizers.serialize(optim) new_config['class_name'] = new_config['class_name'].lower() + new_config['config'].pop('name', None) + if 'amsgrad' not in config['config']: + new_config['config'].pop('amsgrad', None) + if 'decay' in new_config['config'] and 'schedule_decay' in config['config']: + new_config['config']['schedule_decay'] = new_config['config'].pop('decay') + if 'momentum' not in config['config']: + new_config['config'].pop('momentum', None) + if 'centered' not in config['config']: + new_config['config'].pop('centered', None) assert config == new_config # Test constraints. @@ -91,26 +100,22 @@ def _test_optimizer(optimizer, target=0.75): class KerasOptimizersTest(test.TestCase): - @test_util.run_v1_only('b/120545219') def test_sgd(self): with self.cached_session(): _test_optimizer(keras.optimizers.SGD(lr=0.01, momentum=0.9, nesterov=True)) - @test_util.run_v1_only('b/120545219') def test_rmsprop(self): with self.cached_session(): _test_optimizer(keras.optimizers.RMSprop()) _test_optimizer(keras.optimizers.RMSprop(decay=1e-3)) - @test_util.run_v1_only('b/120545219') def test_adagrad(self): with self.cached_session(): _test_optimizer(keras.optimizers.Adagrad()) _test_optimizer(keras.optimizers.Adagrad(decay=1e-3)) - @test_util.run_v1_only('b/120545219') def test_adadelta(self): with self.cached_session(): _test_optimizer(keras.optimizers.Adadelta(), target=0.6) @@ -119,32 +124,27 @@ class KerasOptimizersTest(test.TestCase): # the accuracy. _test_optimizer(keras.optimizers.Adadelta(decay=1e-3), target=0.4) - @test_util.run_v1_only('b/120545219') def test_adam(self): with self.cached_session(): _test_optimizer(keras.optimizers.Adam()) _test_optimizer(keras.optimizers.Adam(decay=1e-3)) _test_optimizer(keras.optimizers.Adam(amsgrad=True)) - @test_util.run_v1_only('b/120545219') def test_adamax(self): with self.cached_session(): _test_optimizer(keras.optimizers.Adamax()) _test_optimizer(keras.optimizers.Adamax(decay=1e-3)) - @test_util.run_v1_only('b/120545219') def test_nadam(self): with self.cached_session(): _test_optimizer(keras.optimizers.Nadam()) - @test_util.run_v1_only('b/120545219') def test_clipnorm(self): with self.cached_session(): _test_optimizer(keras.optimizers.SGD(lr=0.01, momentum=0.9, clipnorm=0.5)) - @test_util.run_v1_only('b/120545219') def test_clipvalue(self): with self.cached_session(): _test_optimizer(keras.optimizers.SGD(lr=0.01, -- GitLab From 090c5ed0730ead8e52a7347ebe53150d4f691610 Mon Sep 17 00:00:00 2001 From: Anna R Date: Wed, 12 Dec 2018 16:26:53 -0800 Subject: [PATCH 456/873] Exclude contrib from the TF 2.0 pip packages. PiperOrigin-RevId: 225276483 --- tensorflow/BUILD | 6 ++- tensorflow/tools/pip_package/BUILD | 76 ++++++++++++++++++------------ 2 files changed, 50 insertions(+), 32 deletions(-) diff --git a/tensorflow/BUILD b/tensorflow/BUILD index fd4b94202a..823ad8f506 100644 --- a/tensorflow/BUILD +++ b/tensorflow/BUILD @@ -606,9 +606,11 @@ py_library( name = "tensorflow_py", srcs_version = "PY2AND3", visibility = ["//visibility:public"], - deps = [ + deps = select({ + "api_version_2": [], + "//conditions:default": ["//tensorflow/contrib:contrib_py"], + }) + [ ":tensorflow_py_no_contrib", - "//tensorflow/contrib:contrib_py", "//tensorflow/python/estimator:estimator_py", ], ) diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD index baacb87239..4ed2f6ce34 100644 --- a/tensorflow/tools/pip_package/BUILD +++ b/tensorflow/tools/pip_package/BUILD @@ -18,6 +18,13 @@ load( "if_ngraph", ) +# This flag specifies whether TensorFlow 2.0 API should be built instead +# of 1.* API. Note that TensorFlow 2.0 API is currently under development. +config_setting( + name = "api_version_2", + define_values = {"tf_api_version": "2"}, +) + # This returns a list of headers of all public header libraries (e.g., # framework, lib), and all of the transitive dependencies of those # public headers. Not all of the headers returned by the filegroup @@ -59,34 +66,6 @@ COMMON_PIP_DEPS = [ "setup.py", ":included_headers", "//tensorflow:tensorflow_py", - "//tensorflow/contrib/autograph:autograph", - "//tensorflow/contrib/boosted_trees:boosted_trees_pip", - "//tensorflow/contrib/cluster_resolver:cluster_resolver_pip", - "//tensorflow/contrib/compiler:xla", - "//tensorflow/contrib/constrained_optimization:constrained_optimization_pip", - "//tensorflow/contrib/eager/python/examples:examples_pip", - "//tensorflow/contrib/eager/python:evaluator", - "//tensorflow/contrib/gan:gan", - "//tensorflow/contrib/graph_editor:graph_editor_pip", - "//tensorflow/contrib/keras:keras", - "//tensorflow/contrib/labeled_tensor:labeled_tensor_pip", - "//tensorflow/contrib/nn:nn_py", - "//tensorflow/contrib/predictor:predictor_pip", - "//tensorflow/contrib/proto:proto", - "//tensorflow/contrib/receptive_field:receptive_field_pip", - "//tensorflow/contrib/rate:rate", - "//tensorflow/contrib/rpc:rpc_pip", - "//tensorflow/contrib/session_bundle:session_bundle_pip", - "//tensorflow/contrib/signal:signal_py", - "//tensorflow/contrib/slim:slim", - "//tensorflow/contrib/slim/python/slim/data:data_pip", - "//tensorflow/contrib/slim/python/slim/nets:nets_pip", - "//tensorflow/contrib/specs:specs", - "//tensorflow/contrib/summary:summary_test_util", - "//tensorflow/contrib/tensor_forest:init_py", - "//tensorflow/contrib/tensor_forest/hybrid:hybrid_pip", - "//tensorflow/contrib/timeseries:timeseries_pip", - "//tensorflow/contrib/tpu", "//tensorflow/examples/tutorials/mnist:package", "//tensorflow/lite/python:interpreter_test_data", "//tensorflow/lite/python:tflite_convert", @@ -122,13 +101,47 @@ COMMON_PIP_DEPS = [ "//tensorflow/tools/dist_test/server:grpc_tensorflow_server", ] +COMMON_PIP_DEPS_V1 = COMMON_PIP_DEPS + [ + "//tensorflow/contrib/autograph:autograph", + "//tensorflow/contrib/boosted_trees:boosted_trees_pip", + "//tensorflow/contrib/cluster_resolver:cluster_resolver_pip", + "//tensorflow/contrib/compiler:xla", + "//tensorflow/contrib/constrained_optimization:constrained_optimization_pip", + "//tensorflow/contrib/eager/python/examples:examples_pip", + "//tensorflow/contrib/eager/python:evaluator", + "//tensorflow/contrib/gan:gan", + "//tensorflow/contrib/graph_editor:graph_editor_pip", + "//tensorflow/contrib/keras:keras", + "//tensorflow/contrib/labeled_tensor:labeled_tensor_pip", + "//tensorflow/contrib/nn:nn_py", + "//tensorflow/contrib/predictor:predictor_pip", + "//tensorflow/contrib/proto:proto", + "//tensorflow/contrib/receptive_field:receptive_field_pip", + "//tensorflow/contrib/rate:rate", + "//tensorflow/contrib/rpc:rpc_pip", + "//tensorflow/contrib/session_bundle:session_bundle_pip", + "//tensorflow/contrib/signal:signal_py", + "//tensorflow/contrib/slim:slim", + "//tensorflow/contrib/slim/python/slim/data:data_pip", + "//tensorflow/contrib/slim/python/slim/nets:nets_pip", + "//tensorflow/contrib/specs:specs", + "//tensorflow/contrib/summary:summary_test_util", + "//tensorflow/contrib/tensor_forest:init_py", + "//tensorflow/contrib/tensor_forest/hybrid:hybrid_pip", + "//tensorflow/contrib/timeseries:timeseries_pip", + "//tensorflow/contrib/tpu", +] + # On Windows, python binary is a zip file of runfiles tree. # Add everything to its data dependency for generating a runfiles tree # for building the pip package on Windows. py_binary( name = "simple_console_for_windows", srcs = ["simple_console_for_windows.py"], - data = COMMON_PIP_DEPS + ["//tensorflow/python:pywrap_tensorflow_import_lib_file"], + data = select({ + "api_version_2": COMMON_PIP_DEPS, + "//conditions:default": COMMON_PIP_DEPS_V1, + }) + ["//tensorflow/python:pywrap_tensorflow_import_lib_file"], srcs_version = "PY2AND3", deps = ["//tensorflow:tensorflow_py"], ) @@ -232,7 +245,10 @@ sh_binary( "//tensorflow:windows": [ ":simple_console_for_windows", ], - "//conditions:default": COMMON_PIP_DEPS + [ + "api_version_2": COMMON_PIP_DEPS + [ + ":simple_console", + ], + "//conditions:default": COMMON_PIP_DEPS_V1 + [ ":simple_console", ], }) + if_mkl_ml(["//third_party/mkl:intel_binary_blob"]), -- GitLab From 2885c4bb67696f47baad8b921cb39bcb33b1f6c2 Mon Sep 17 00:00:00 2001 From: Rick Chao Date: Wed, 12 Dec 2018 16:29:27 -0800 Subject: [PATCH 457/873] Export tf.train.* session_run_hook.py classes to tf.estimator.* (exporting to both v1 and v2). Keep the existing only in v1. PiperOrigin-RevId: 225276892 --- .../python/training/session_run_hook.py | 2 +- ...nsorflow.estimator.-session-run-args.pbtxt | 27 ++++++++++++++++++ ...rflow.estimator.-session-run-context.pbtxt | 25 +++++++++++++++++ ...sorflow.estimator.-session-run-hook.pbtxt} | 2 +- ...orflow.estimator.-session-run-values.pbtxt | 27 ++++++++++++++++++ .../api/golden/v1/tensorflow.estimator.pbtxt | 16 +++++++++++ ...nsorflow.estimator.-session-run-args.pbtxt | 27 ++++++++++++++++++ ...rflow.estimator.-session-run-context.pbtxt | 25 +++++++++++++++++ ...nsorflow.estimator.-session-run-hook.pbtxt | 28 +++++++++++++++++++ ...orflow.estimator.-session-run-values.pbtxt | 27 ++++++++++++++++++ .../api/golden/v2/tensorflow.estimator.pbtxt | 16 +++++++++++ .../api/golden/v2/tensorflow.train.pbtxt | 4 --- tensorflow/tools/compatibility/renames_v2.py | 7 +++-- 13 files changed, 224 insertions(+), 9 deletions(-) create mode 100644 tensorflow/tools/api/golden/v1/tensorflow.estimator.-session-run-args.pbtxt create mode 100644 tensorflow/tools/api/golden/v1/tensorflow.estimator.-session-run-context.pbtxt rename tensorflow/tools/api/golden/{v2/tensorflow.train.-session-run-hook.pbtxt => v1/tensorflow.estimator.-session-run-hook.pbtxt} (95%) create mode 100644 tensorflow/tools/api/golden/v1/tensorflow.estimator.-session-run-values.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.estimator.-session-run-args.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.estimator.-session-run-context.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.estimator.-session-run-hook.pbtxt create mode 100644 tensorflow/tools/api/golden/v2/tensorflow.estimator.-session-run-values.pbtxt diff --git a/tensorflow/python/training/session_run_hook.py b/tensorflow/python/training/session_run_hook.py index e9a61def74..886ca46ed5 100644 --- a/tensorflow/python/training/session_run_hook.py +++ b/tensorflow/python/training/session_run_hook.py @@ -94,7 +94,7 @@ import collections from tensorflow.python.util.tf_export import tf_export -@tf_export("train.SessionRunHook") +@tf_export(v1=["train.SessionRunHook"]) class SessionRunHook(object): """Hook to extend calls to MonitoredSession.run().""" diff --git a/tensorflow/tools/api/golden/v1/tensorflow.estimator.-session-run-args.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-session-run-args.pbtxt new file mode 100644 index 0000000000..b375c74294 --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-session-run-args.pbtxt @@ -0,0 +1,27 @@ +path: "tensorflow.estimator.SessionRunArgs" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "feed_dict" + mtype: "" + } + member { + name: "fetches" + mtype: "" + } + member { + name: "options" + mtype: "" + } + member_method { + name: "__init__" + } + member_method { + name: "count" + } + member_method { + name: "index" + } +} diff --git a/tensorflow/tools/api/golden/v1/tensorflow.estimator.-session-run-context.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-session-run-context.pbtxt new file mode 100644 index 0000000000..cb4ac9f50e --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-session-run-context.pbtxt @@ -0,0 +1,25 @@ +path: "tensorflow.estimator.SessionRunContext" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "original_args" + mtype: "" + } + member { + name: "session" + mtype: "" + } + member { + name: "stop_requested" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'original_args\', \'session\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "request_stop" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-session-run-hook.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-session-run-hook.pbtxt similarity index 95% rename from tensorflow/tools/api/golden/v2/tensorflow.train.-session-run-hook.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-session-run-hook.pbtxt index db1aa24acf..54e9ad9ed4 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.train.-session-run-hook.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-session-run-hook.pbtxt @@ -1,4 +1,4 @@ -path: "tensorflow.train.SessionRunHook" +path: "tensorflow.estimator.SessionRunHook" tf_class { is_instance: "" is_instance: "" diff --git a/tensorflow/tools/api/golden/v1/tensorflow.estimator.-session-run-values.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-session-run-values.pbtxt new file mode 100644 index 0000000000..6788141696 --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-session-run-values.pbtxt @@ -0,0 +1,27 @@ +path: "tensorflow.estimator.SessionRunValues" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "options" + mtype: "" + } + member { + name: "results" + mtype: "" + } + member { + name: "run_metadata" + mtype: "" + } + member_method { + name: "__init__" + } + member_method { + name: "count" + } + member_method { + name: "index" + } +} diff --git a/tensorflow/tools/api/golden/v1/tensorflow.estimator.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.pbtxt index d3656ae045..6f57505afe 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.estimator.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.estimator.pbtxt @@ -132,6 +132,22 @@ tf_module { name: "SecondOrStepTimer" mtype: "" } + member { + name: "SessionRunArgs" + mtype: "" + } + member { + name: "SessionRunContext" + mtype: "" + } + member { + name: "SessionRunHook" + mtype: "" + } + member { + name: "SessionRunValues" + mtype: "" + } member { name: "StepCounterHook" mtype: "" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-session-run-args.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-session-run-args.pbtxt new file mode 100644 index 0000000000..b375c74294 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-session-run-args.pbtxt @@ -0,0 +1,27 @@ +path: "tensorflow.estimator.SessionRunArgs" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "feed_dict" + mtype: "" + } + member { + name: "fetches" + mtype: "" + } + member { + name: "options" + mtype: "" + } + member_method { + name: "__init__" + } + member_method { + name: "count" + } + member_method { + name: "index" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-session-run-context.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-session-run-context.pbtxt new file mode 100644 index 0000000000..cb4ac9f50e --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-session-run-context.pbtxt @@ -0,0 +1,25 @@ +path: "tensorflow.estimator.SessionRunContext" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "original_args" + mtype: "" + } + member { + name: "session" + mtype: "" + } + member { + name: "stop_requested" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'original_args\', \'session\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "request_stop" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-session-run-hook.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-session-run-hook.pbtxt new file mode 100644 index 0000000000..54e9ad9ed4 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-session-run-hook.pbtxt @@ -0,0 +1,28 @@ +path: "tensorflow.estimator.SessionRunHook" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "after_create_session" + argspec: "args=[\'self\', \'session\', \'coord\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "after_run" + argspec: "args=[\'self\', \'run_context\', \'run_values\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "before_run" + argspec: "args=[\'self\', \'run_context\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "begin" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "end" + argspec: "args=[\'self\', \'session\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-session-run-values.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-session-run-values.pbtxt new file mode 100644 index 0000000000..6788141696 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-session-run-values.pbtxt @@ -0,0 +1,27 @@ +path: "tensorflow.estimator.SessionRunValues" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "options" + mtype: "" + } + member { + name: "results" + mtype: "" + } + member { + name: "run_metadata" + mtype: "" + } + member_method { + name: "__init__" + } + member_method { + name: "count" + } + member_method { + name: "index" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.pbtxt index d3656ae045..6f57505afe 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.estimator.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.pbtxt @@ -132,6 +132,22 @@ tf_module { name: "SecondOrStepTimer" mtype: "" } + member { + name: "SessionRunArgs" + mtype: "" + } + member { + name: "SessionRunContext" + mtype: "" + } + member { + name: "SessionRunHook" + mtype: "" + } + member { + name: "SessionRunValues" + mtype: "" + } member { name: "StepCounterHook" mtype: "" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.pbtxt index cc63a7fd82..c72564e598 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.train.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.pbtxt @@ -72,10 +72,6 @@ tf_module { name: "ServerDef" mtype: "" } - member { - name: "SessionRunHook" - mtype: "" - } member_method { name: "cosine_decay" argspec: "args=[\'learning_rate\', \'global_step\', \'decay_steps\', \'alpha\', \'name\'], varargs=None, keywords=None, defaults=[\'0.0\', \'None\'], " diff --git a/tensorflow/tools/compatibility/renames_v2.py b/tensorflow/tools/compatibility/renames_v2.py index ad4c3d2750..2763a0ca63 100644 --- a/tensorflow/tools/compatibility/renames_v2.py +++ b/tensorflow/tools/compatibility/renames_v2.py @@ -625,9 +625,10 @@ renames = { 'tf.train.Server': 'tf.distribute.Server', 'tf.train.SessionCreator': 'tf.compat.v1.train.SessionCreator', 'tf.train.SessionManager': 'tf.compat.v1.train.SessionManager', - 'tf.train.SessionRunArgs': 'tf.compat.v1.train.SessionRunArgs', - 'tf.train.SessionRunContext': 'tf.compat.v1.train.SessionRunContext', - 'tf.train.SessionRunValues': 'tf.compat.v1.train.SessionRunValues', + 'tf.train.SessionRunArgs': 'tf.estimator.SessionRunArgs', + 'tf.train.SessionRunContext': 'tf.estimator.SessionRunContext', + 'tf.train.SessionRunHook': 'tf.estimator.SessionRunHook', + 'tf.train.SessionRunValues': 'tf.estimator.SessionRunValues', 'tf.train.SingularMonitoredSession': 'tf.compat.v1.train.SingularMonitoredSession', 'tf.train.StepCounterHook': 'tf.estimator.StepCounterHook', 'tf.train.StopAtStepHook': 'tf.estimator.StopAtStepHook', -- GitLab From 03e7214049ea6ae802e36d3ffbf49b0e57f1a721 Mon Sep 17 00:00:00 2001 From: Kay Zhu Date: Wed, 12 Dec 2018 16:38:40 -0800 Subject: [PATCH 458/873] [TF2XLA] In Resampler correctly handle out of boundary samples by returning 0 for the backward pass. Note out of boundary here means outside of (-1, image_size) index, instead of (0, image_size -1). As a result the images will be padded with 0 before gathering / scattering operation is performed, then sliced back to obtain the actual results. PiperOrigin-RevId: 225278400 --- .../compiler/tf2xla/kernels/resampler_ops.cc | 158 +++++++++++++++--- .../resampler/xla/resampler_ops_xla_test.py | 36 ++++ 2 files changed, 168 insertions(+), 26 deletions(-) diff --git a/tensorflow/compiler/tf2xla/kernels/resampler_ops.cc b/tensorflow/compiler/tf2xla/kernels/resampler_ops.cc index 54d34a38ab..f9985d5260 100644 --- a/tensorflow/compiler/tf2xla/kernels/resampler_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/resampler_ops.cc @@ -125,7 +125,7 @@ XlaOp ConcatenateIota(xla::XlaBuilder* b, XlaOp indices, dimensions.back() = 1; auto batch_indices = - xla::Iota(b, xla::ShapeUtil::MakeShape(xla::U32, dimensions), + xla::Iota(b, xla::ShapeUtil::MakeShape(xla::S32, dimensions), /*iota_dimension=*/0); return xla::ConcatInDim(b, {batch_indices, indices}, dimensions.size() - 1); @@ -189,11 +189,53 @@ XlaOp ScatterToGradData(XlaOpKernelContext* ctx, XlaOp grad_data, XlaOp indices, scatter_dim_numbers); } +// Bounds samples to 0 if the warp image indices are out of the (-1, image_size) +// bound. +// The resulting dimension is given by 'result_dims'. +XlaOp BoundSamples(XlaOpKernelContext* ctx, XlaOp warp, + xla::PrimitiveType warp_type, TensorShape warp_shape, + std::vector result_dims, + std::vector broadcasted_dims, int64 last_warp_dim, + xla::Shape data_shape, XlaOp sample) { + auto is_gt_minus_one = + xla::Gt(warp, + xla::ConvertElementType( + xla::ConstantR1(ctx->builder(), {-1, -1}), warp_type), + /*broadcast_dimensions=*/{warp_shape.dims() - 1}); + auto is_lt_image_size = xla::Lt( + warp, + xla::ConvertElementType( + xla::ConstantR1( + ctx->builder(), + {/*width=*/static_cast(data_shape.dimensions(2)), + /*height=*/static_cast(data_shape.dimensions(1))}), + warp_type), + /*broadcast_dimensions=*/{warp_shape.dims() - 1}); + + auto is_in_bound_padded_x_y = xla::And(is_gt_minus_one, is_lt_image_size); + // Reduce along last dimension. The resulting dimension is: + // [batch, dim_0, ...dim_n]. + auto is_in_bound = xla::Reduce( + is_in_bound_padded_x_y, xla::ConstantR0(ctx->builder(), true), + xla::CreateScalarAndComputation(xla::PrimitiveType::PRED, ctx->builder()), + {last_warp_dim}); + + // Broadcast 'is_in_bound' to the same dimension as 'result_dims'. + auto broadcasted_is_in_bound = + xla::BroadcastInDim(is_in_bound, result_dims, broadcasted_dims); + + // Set out of bound samples to zero. + auto zeros = + xla::Broadcast(xla::Zero(ctx->builder(), warp_type), result_dims); + return xla::Select(broadcasted_is_in_bound, sample, zeros); +} + // Build computation the backprop into input 'data'. // Where input: // grad_output is of dimension [batch, dim_0, ...dim_n, channel] // ratio is of dimension [batch, dim_0, ...dim_n, 2] // gather_indices is of dimension [batch, dim_0, ...dim_n, 3] +// data_shape is of dimension [batch, x(width), y(height), channel] // // Output: // scatter-add to each 2x2 grad_data neighbor: @@ -201,10 +243,12 @@ XlaOp ScatterToGradData(XlaOpKernelContext* ctx, XlaOp grad_data, XlaOp indices, // grad_data[cx, fy, chan] += output_grad * (1 - dx) * dy // grad_data[fx, cy, chan] += output_grad * dx * (1 - dy) // grad_data[cx, cy, chan] += output_grad * (1 - dx) * (1 - dy) -// where (dx, dy) is (1 - ratio). +// where (dx, dy) is (1 - ratio). If (dx, dy) is out of bound, then the their +// contribution is 0 to 'grad_data'. XlaOp CalculateGradData(XlaOpKernelContext* ctx, XlaOp grad_output, XlaOp ratio, - XlaOp gather_indices, xla::PrimitiveType warp_type, - TensorShape warp_shape, int64 data_channels, + XlaOp gather_indices, XlaOp warp, + xla::PrimitiveType warp_type, TensorShape warp_shape, + int64 last_warp_dim, int64 data_channels, xla::Shape data_shape) { // Weights tensor has dimension [batch, dim_0, ... dim_n, 4]. auto weights = BilinearWeights(ctx, ratio, warp_shape, warp_type); @@ -229,6 +273,18 @@ XlaOp CalculateGradData(XlaOpKernelContext* ctx, XlaOp grad_output, XlaOp ratio, std::iota(reshaped_weights_indices.begin(), reshaped_weights_indices.end(), 0); + // Set out of bound weights to 0. + // The dimension of the reshaped_weight: [batch, dim_0, ...dim_n, 2, 2]. + std::vector reshaped_result_dims(warp_dims.begin(), + warp_dims.end() - 1); + reshaped_result_dims.push_back(2); + reshaped_result_dims.push_back(2); + std::vector broadcasted_dims(warp_dims.size() - 1); + std::iota(broadcasted_dims.begin(), broadcasted_dims.end(), 0); + reshaped_weights = BoundSamples(ctx, warp, warp_type, warp_shape, + reshaped_result_dims, broadcasted_dims, + last_warp_dim, data_shape, reshaped_weights); + // The dimension is [batch, dim_0, ..., dim_n, 2, 2, data_channel]. auto broadcast_reshaped_weights = xla::BroadcastInDim( reshaped_weights, weights_with_channels_dims, reshaped_weights_indices); @@ -245,18 +301,41 @@ XlaOp CalculateGradData(XlaOpKernelContext* ctx, XlaOp grad_output, XlaOp ratio, auto grad_data = xla::ConstantLiteral( ctx->builder(), xla::Literal::CreateFromShape(data_shape)); - return ScatterToGradData(ctx, grad_data, gather_indices, - grad_output_multiply_weights, warp_shape.dims(), - warp_type); + // Pad grad data then slice it back. + // + // After left and right column 0-padding, the new dimension of padded data + // will be [batch, x+2, y+2, channel]. + auto padded_grad_data = + xla::Pad(grad_data, xla::Zero(ctx->builder(), warp_type), + xla::MakeEdgePaddingConfig({{0, 0}, {1, 1}, {1, 1}, {0, 0}})); + + auto shifting_value = xla::ConstantR1( + ctx->builder(), {/*batch=*/0, /*x(width)=*/1, /*y(height)=*/1}); + auto shifted_gather_indices = + xla::Add(gather_indices, shifting_value, {last_warp_dim}); + + auto updated_grad_data = ScatterToGradData( + ctx, padded_grad_data, shifted_gather_indices, + grad_output_multiply_weights, warp_shape.dims(), warp_type); + + const int64 batch_size = data_shape.dimensions(0); + const int64 width = data_shape.dimensions(1); + const int64 height = data_shape.dimensions(2); + // Slice out the result accounting for the padding. + return xla::Slice( + updated_grad_data, /*start_indices=*/{0, 1, 1, 0}, + /*limit_indices=*/{batch_size, width + 1, height + 1, data_channels}, + /*strides=*/{1, 1, 1, 1}); } // Build computation for the backprop into input 'warp'. // Where input: -// warp is of dimension [batch, dim_0, ...dim_n, 2] -// grad_output is of dimension [batch, dim_0, ...dim_n, channel] -// ratio is of dimension [batch, dim_0, ...dim_n, 2] -// gather_indices is of dimension [batch, dim_0, ...dim_n, 3] -// data is of dimension [batch, x, y, channel] +// warp is of dimension [batch, dim_0, ...dim_n, 2] +// grad_output is of dimension [batch, dim_0, ...dim_n, channel] +// ratio is of dimension [batch, dim_0, ...dim_n, 2] +// gather_indices is of dimension [batch, dim_0, ...dim_n, 3] where the last +// dimension of size 3 is for {batch, x(width), y(height)}. +// data is of dimension [batch, x, y, channel] // // Output (simplified by ignoring the batch dimensions): // Since the forward path has: @@ -275,12 +354,12 @@ XlaOp CalculateGradData(XlaOpKernelContext* ctx, XlaOp grad_output, XlaOp ratio, // grad_warp_x = py * (img_cxcy - img_fxcy) + (1-py) * (img_cxfy-img_fxfy) // grad_warp_y = px * (img_cxcy - img_cxfy) + (1-px) * (img_fxcy-img_fxfy) // -// where (px, py) is warp, (fx, fy) is the left top corner and (cx, cy) is the +// where (px, py) is warp, (fx, fy) is the top left corner and (cx, cy) is the // bottom right corner in a 2x2 neighborhood. XlaOp CalculateGradWarp(XlaOpKernelContext* ctx, XlaOp grad_output, XlaOp ratio, XlaOp gather_indices, XlaOp data, TensorShape warp_shape, int64 data_channels, - xla::PrimitiveType data_type) { + xla::PrimitiveType data_type, xla::Shape data_shape) { auto warp_dims = warp_shape.dim_sizes(); std::vector warp_dims_without_last_dims(warp_dims.begin(), warp_dims.end() - 1); @@ -289,12 +368,30 @@ XlaOp CalculateGradWarp(XlaOpKernelContext* ctx, XlaOp grad_output, XlaOp ratio, std::vector neighbor_broadcast_dims = warp_dims_without_last_dims; neighbor_broadcast_dims.push_back(4); - // The dimension is [batch, dim_0, ... dim_n, 4, data_channels] - auto neighbors_data = Gather2by2Neighbors( - ctx->builder(), data, gather_indices, data_channels, warp_shape.dims()); + // With dimension [batch, dim_0, ...dim_n, 4] + auto neighbor_broadcast_shape = + xla::ShapeUtil::MakeShape(data_type, neighbor_broadcast_dims); const int64 last_warp_dim = warp_shape.dims() - 1; + // Pad data with 0, before gathering such that 0 will be returned for samples + // in the range of (-1, 0) or (image_dimension-1, image_dimension). + // After left and right column 0-padding, the new dimension of padded data + // will be [batch, x+2, y+2, channel]. + auto padded_data = + xla::Pad(data, xla::Zero(ctx->builder(), data_type), + xla::MakeEdgePaddingConfig({{0, 0}, {1, 1}, {1, 1}, {0, 0}})); + + auto shifting_value = xla::ConstantR1( + ctx->builder(), {/*batch=*/0, /*x(width)=*/1, /*y(height)=*/1}); + auto shifted_gather_indices = + xla::Add(gather_indices, shifting_value, {last_warp_dim}); + + // The dimension is [batch, dim_0, ... dim_n, 4, data_channels] + auto neighbors_data = + Gather2by2Neighbors(ctx->builder(), padded_data, shifted_gather_indices, + data_channels, warp_shape.dims()); + // Since we will be creating the dot product of: // lhs: [batch, dim_0, ...dim_n, 4] // and @@ -417,7 +514,7 @@ class ResamplerOp : public XlaOpKernel { // Find the coordinates of the top left corner for the 2x2 region to be // sampled from. The dimensions are [batch, dim_0, ... dim_n, 2] where the // last dimension of size 2 in turn is [x, y]. - XlaOp top_left = xla::ConvertElementType(warp, xla::U32); + XlaOp top_left = xla::ConvertElementType(warp, xla::S32); auto gather_indices = ConcatenateIota(ctx->builder(), top_left, warp_shape); @@ -526,7 +623,8 @@ class ResamplerGradOp : public XlaOpKernel { size, "]")); } // Last dimension of warp shape must be of size 2. - OP_REQUIRES(ctx, warp_shape.dim_size(warp_shape.dims() - 1) == 2, + const int64 last_warp_dim = warp_shape.dims() - 1; + OP_REQUIRES(ctx, warp_shape.dim_size(last_warp_dim) == 2, errors::InvalidArgument( "the last dimension of warp must be exactly size 2.")); xla::PrimitiveType warp_type = ctx->input_xla_type(1); @@ -549,24 +647,32 @@ class ResamplerGradOp : public XlaOpKernel { // Find the top left corner coordinate for the region to be sampled from. // The dimensions are [batch, dim_0, ... dim_n, 2] where the last dimension // of size 2 in turn is [x, y]. - XlaOp top_left = xla::ConvertElementType(warp, xla::U32); + XlaOp top_left = xla::ConvertElementType(xla::Floor(warp), xla::S32); - // Dimensions are [batch, dim_0, ... dim_n, 2] + // Dimensions are [batch, dim_0, ... dim_n, 2]. XlaOp ratio = warp - xla::ConvertElementType(top_left, warp_type); // Indices for gathering neighboring pixels. auto gather_indices = ConcatenateIota(ctx->builder(), top_left, warp_shape); - auto grad_data = - CalculateGradData(ctx, grad_output, ratio, gather_indices, warp_type, - warp_shape, data_channels, data_shape); + auto grad_data = CalculateGradData( + ctx, grad_output, ratio, gather_indices, warp, warp_type, warp_shape, + last_warp_dim, data_channels, data_shape); auto grad_warp = CalculateGradWarp(ctx, grad_output, ratio, gather_indices, data, - warp_shape, data_channels, data_type); + warp_shape, data_channels, data_type, data_shape); + auto warp_dims = warp_shape.dim_sizes(); + std::vector result_dims(warp_dims.begin(), warp_dims.end() - 1); + result_dims.push_back(2); + std::vector broadcasted_dims(warp_dims.size() - 1); + std::iota(broadcasted_dims.begin(), broadcasted_dims.end(), 0); + auto grad_warp_bounded = + BoundSamples(ctx, warp, warp_type, warp_shape, result_dims, + broadcasted_dims, last_warp_dim, data_shape, grad_warp); ctx->SetOutput(0, grad_data); - ctx->SetOutput(1, grad_warp); + ctx->SetOutput(1, grad_warp_bounded); } }; diff --git a/tensorflow/contrib/resampler/xla/resampler_ops_xla_test.py b/tensorflow/contrib/resampler/xla/resampler_ops_xla_test.py index d8ca0eab27..cec4c3c233 100644 --- a/tensorflow/contrib/resampler/xla/resampler_ops_xla_test.py +++ b/tensorflow/contrib/resampler/xla/resampler_ops_xla_test.py @@ -164,6 +164,15 @@ class ResamplerOpsTest(xla_test.XLATestCase): expected = [[[0.0], [27.62]]] self._assertForwardOpMatchesExpected(input_np, warp_np, expected) + expected_grad_data = [[[[0.12], [0.27999997]], [[0.18000001], + [0.42000002]]]] + expected_grad_warp = [[[0., 0.], [22.60000038, 35.20000076]]] + + grad_output = np.ones([1, 2, 1], dtype=dtype) + self._assertBackwardOpMatchesExpected(input_np, warp_np, grad_output, + expected_grad_data, + expected_grad_warp) + # One of (x, y) is less than 0. for dtype in self.float_types: input_shape = [1, 2, 2, 1] @@ -171,11 +180,21 @@ class ResamplerOpsTest(xla_test.XLATestCase): input_np = np.array(input_data, dtype=dtype).reshape(input_shape) warp_shape = [1, 2, 2] + # -1 is out of bound for grad_warp. warp_data = [-1, 0.1, 0.7, 0.6] warp_np = np.array(warp_data, dtype=dtype).reshape(warp_shape) expected = [[[0.0], [27.62]]] self._assertForwardOpMatchesExpected(input_np, warp_np, expected) + expected_grad_data = [[[[0.12], [0.27999997]], [[0.18000001], + [0.42000002]]]] + expected_grad_warp = [[[0., 0.], [22.60000038, 35.20000076]]] + + grad_output = np.ones([1, 2, 1], dtype=dtype) + self._assertBackwardOpMatchesExpected(input_np, warp_np, grad_output, + expected_grad_data, + expected_grad_warp) + # Both of (x, y) are greater than image size. for dtype in self.float_types: input_shape = [1, 2, 2, 1] @@ -183,11 +202,20 @@ class ResamplerOpsTest(xla_test.XLATestCase): input_np = np.array(input_data, dtype=dtype).reshape(input_shape) warp_shape = [1, 2, 2] + # -0.1 is *inbound* for grad_warp and grad_data, 2.1 is out of bound. warp_data = [-0.1, 0.1, 1.2, 2.1] warp_np = np.array(warp_data, dtype=dtype).reshape(warp_shape) expected = [[[0.0], [0.0]]] self._assertForwardOpMatchesExpected(input_np, warp_np, expected) + expected_grad_data = [[[[0.81], [0.0]], [[0.09], [0.0]]]] + expected_grad_warp = [[[10.30, 2.7], [0.0, 0.0]]] + + grad_output = np.ones([1, 2, 1], dtype=dtype) + self._assertBackwardOpMatchesExpected(input_np, warp_np, grad_output, + expected_grad_data, + expected_grad_warp) + # One of (x, y) is greater than image size. for dtype in self.float_types: input_shape = [1, 2, 2, 1] @@ -200,6 +228,14 @@ class ResamplerOpsTest(xla_test.XLATestCase): expected = [[[0.0], [0.0]]] self._assertForwardOpMatchesExpected(input_np, warp_np, expected) + expected_grad_data = [[[[0.81], [0.81]], [[0.0], [0.08]]]] + expected_grad_warp = [[[-4.5, 9.5], [-9.9, 39.20]]] + + grad_output = np.ones([1, 2, 1], dtype=dtype) + self._assertBackwardOpMatchesExpected(input_np, warp_np, grad_output, + expected_grad_data, + expected_grad_warp) + if __name__ == '__main__': test.main() -- GitLab From 1bcae5d84b937ea17b70ff25824ea292b8d95f4f Mon Sep 17 00:00:00 2001 From: Trevor Morris Date: Mon, 26 Nov 2018 09:31:56 -0800 Subject: [PATCH 459/873] StridedSlice op + some unit tests Fix typo Refactor. Add Ok unit tests Improve unit tests, comments. --- .../contrib/tensorrt/convert/convert_graph.cc | 65 ++-- .../contrib/tensorrt/convert/convert_nodes.cc | 251 ++++++++++++++- .../tensorrt/convert/convert_nodes_test.cc | 302 +++++++++++++++++- 3 files changed, 582 insertions(+), 36 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index ae211a93c3..623cd79f32 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -89,51 +89,52 @@ Status TrtCandidateSelector::IsTensorRTCandidate(const tensorflow::Node* node) { // TODO(laigd): move this set to TrtNodeValidator where it should belong. // LINT.IfChange static const std::set candidate_ops = { - "Identity", - "Snapshot", - "Const", - "Conv2D", - "MaxPool", - "BiasAdd", - "Relu", - "Sigmoid", - "Tanh", + "Abs", "Add", - "Mul", - "Sub", - "Rsqrt", - "Pad", - "Mean", "AvgPool", + "BatchMatMul", + "BiasAdd", "ConcatV2", + "Const", + "Conv2D", "DepthwiseConv2dNative", - "FusedBatchNorm", - "FusedBatchNormV2", "Div", - "RealDiv", - "Rsqrt", - "Reciprocal", "Exp", + "ExpandDims", + "FusedBatchNorm", + "FusedBatchNormV2", + "Identity", "Log", - "Sqrt", - "Abs", - "Neg", - "Transpose", - "Reshape", "MatMul", - "BatchMatMul", - "Softmax", - "Minimum", - "Maximum", - "TopKV2", - "Sum", - "Prod", "Max", + "MaxPool", + "Maximum", + "Mean", "Min", + "Minimum", + "Mul", + "Neg", + "Pad", + "Prod", + "RealDiv", + "Reciprocal", + "Relu", "Relu6", + "Reshape", + "Rsqrt", + "Rsqrt", + "Sigmoid", + "Snapshot", + "Softmax", + "Sqrt", "Square", - "ExpandDims", "Squeeze", + "StridedSlice", + "Sub", + "Sum", + "Tanh", + "TopKV2", + "Transpose", }; bool is_supported_op_type = (candidate_ops.count(node->type_string()) || diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 561ea37dae..fdecfe5928 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -632,6 +632,11 @@ bool TFAttrs::get(const string& key) const { return this->at(key)->b(); } +template <> +int TFAttrs::get(const string& key) const { + return this->at(key)->i(); +} + // TODO(jie): reorder4 & reorder2 should be merged? // TODO(aaroey): fix the order of parameters. template @@ -2028,6 +2033,245 @@ tensorflow::Status ConvertSqueeze(OpConverterParams* params) { return tensorflow::Status::OK(); } +tensorflow::Status GetStridedSliceBound( + const std::vector& input_dims, + const TRT_ShapedWeights& bound_weights, + string bound_name, + string node_name, + std::vector& output_bound) { + const int* weights_ptr = + static_cast(const_cast(bound_weights.GetValues())); + output_bound = std::vector(weights_ptr, + weights_ptr + bound_weights.count()); + if (output_bound.size() != input_dims.size()) { + return tensorflow::errors::InvalidArgument( + "StridedSlice \"", bound_name, "\" specified ", + std::to_string(output_bound.size()), " dimensions, but input rank is ", + std::to_string(input_dims.size()), ", at ", node_name); + } + for (int i = 0; i < output_bound.size(); i++) { + // Make sure bound is valid. + if ((output_bound[i] < -input_dims[i]) || + (output_bound[i] > input_dims[i])) { + return tensorflow::errors::InvalidArgument( + bound_name, " for StridedSlice is invalid, must be in the range " + "[-rank(input), rank(input)], at ", node_name); + } + // Convert negative values to their positive equivalent. + if (output_bound[i] < 0) { + output_bound[i] += input_dims[i]; + } + } + return tensorflow::Status::OK(); +} + +tensorflow::Status ConvertStridedSlice(OpConverterParams* params) { + const auto& inputs = params->inputs; + const auto& node_def = params->node_def; + if (inputs.size() != 4) { + return tensorflow::errors::InvalidArgument( + "StridedSlice expects 4 inputs, at ", node_def.name()); + } + if (!inputs.at(1).is_weights() || + !inputs.at(2).is_weights() || + !inputs.at(3).is_weights()) { + return tensorflow::errors::InvalidArgument( + "StridedSlice expects weights for begin, end, and strides, at ", + node_def.name()); + } + if (!inputs.at(0).is_tensor()) { + return tensorflow::errors::Unimplemented( + "StridedSlice is only implemented for tensors, at ", + node_def.name()); + } + // Get input dims. + nvinfer1::Dims dims = inputs.at(0).GetTrtDims(); + std::vector input_dims(dims.d, dims.d + dims.nbDims); + if (inputs.at(0).is_tensor()) { + // Temporarily add batch dimension so that indexes line up properly. + input_dims.insert(input_dims.begin(), inputs.at(0).batch_size()); + } + if (input_dims.size() > 4) { + return tensorflow::errors::Unimplemented( + "StridedSlice is not implemented for tensors with rank > 4, at ", + node_def.name()); + } + TFAttrs attrs(node_def); + // Get begin and end bounds per axis. + std::vector begin, end; + TF_RETURN_IF_ERROR(GetStridedSliceBound(input_dims, inputs.at(1).weights(), + "begin", node_def.name(), begin)); + TF_RETURN_IF_ERROR(GetStridedSliceBound(input_dims, inputs.at(2).weights(), + "end", node_def.name(), end)); + int begin_mask = attrs.get("begin_mask"); + for (int i = 0; i < begin.size(); i++) { + if ((1 << i) & begin_mask) { + begin[i] = 0; + } + } + int end_mask = attrs.get("end_mask"); + for (int i = 0; i < end.size(); i++) { + if ((1 << i) & end_mask) { + end[i] = input_dims[i]; + } + } + // Get strides per axis (must all be 1). + TRT_ShapedWeights stride_weights = inputs.at(3).weights(); + const int* stride_weights_ptr = + static_cast(const_cast(stride_weights.GetValues())); + std::vector strides(stride_weights_ptr, + stride_weights_ptr + stride_weights.count()); + for (int x : strides) { + if (x != 1) { + return tensorflow::errors::Unimplemented( + "StridedSlice is only implemented for stride of 1, at ", + node_def.name()); + } + } + // Unsupported options. + for (string attr : {"ellipsis_mask", "new_axis_mask", "shrink_axis_mask"}) { + int ellipsis_mask = attrs.get(attr); + if (ellipsis_mask != 0) { + return tensorflow::errors::Unimplemented( + attr, " is not implemented for StridedSlice, at ", + node_def.name()); + } + } + + nvinfer1::ITensor* tensor = const_cast( + inputs.at(0).tensor()); + // Reshape if necessary to 4-D. + const bool need_reshape = (input_dims.size() != 4); + int reshape_dims_added = 0; + nvinfer1::Dims reshape_dims; + if (need_reshape) { + // Add new dims after batch dim until tensor is 4D. + while (input_dims.size() < 4) { + input_dims.insert(input_dims.begin()+1, 1); + begin.insert(begin.begin()+1, 0); + end.insert(end.begin()+1, 1); + reshape_dims_added++; + } + reshape_dims = VectorToTrtDims(input_dims, /*ignore_first_dim=*/true); + } + // Find dimensions which need to be sliced. + std::vector pad_dims; + for (int i = 0; i < input_dims.size(); i++) { + if (begin[i] != 0 || (end[i] - input_dims[i]) != 0) { + if (i == 0) { + return tensorflow::errors::Unimplemented( + "StridedSlice can't modify batch dim, at ", node_def.name()); + } + else if ((end[i] - begin[i]) < 0) { + LOG(INFO) << begin[i] << ", " << end[i]; + return tensorflow::errors::InvalidArgument( + "New size of sliced dimension is negative, at ", node_def.name()); + } + pad_dims.push_back(i); + } + } + if (pad_dims.size() == 0) { + // No dimensions are changed. We could create a padding layer anyway with + // values of 0. + if (params->validation_only) return Status::OK(); + params->outputs->push_back(inputs.at(0)); + return tensorflow::Status::OK(); + } else if (pad_dims.size() == 1) { + // Only one dim is modified but we have to have 2, mark a second dim which + // will have padding of 0. + if (pad_dims[0] == 1 || pad_dims[0] == 3) { + pad_dims.push_back(2); + } else if (pad_dims[0] == 2) { + pad_dims.push_back(3); + } + } else if (pad_dims.size() > 2) { + return tensorflow::errors::Unimplemented( + "StridedSlice can only modify 2 dimensions, at ", + node_def.name()); + } + std::sort(pad_dims.begin(), pad_dims.end()); + // Convert to pre/post padding values. + nvinfer1::DimsHW pre_padding, post_padding; + for (int i = 0; i < pad_dims.size(); i++) { + const int axis = pad_dims[i]; + pre_padding.d[i] = -begin[axis]; + post_padding.d[i] = end[axis] - input_dims[axis]; + } + + // IPaddingLayer will always apply the padding to dims 2,3 (input format is + // NCHW). + const bool need_transpose = !(pad_dims[0] == 2 && pad_dims[1] == 3); + std::vector transpose_order(input_dims.size()); + std::vector inv_transpose_order(input_dims.size()); + if (need_transpose) { + if (pad_dims[0] == 1 && pad_dims[1] == 3) { + transpose_order = {0, 2, 1, 3}; + inv_transpose_order = {0, 2, 1, 3}; + } else if (pad_dims[0] == 1 && pad_dims[1] == 2) { + transpose_order = {0, 3, 1, 2}; + inv_transpose_order = {0, 2, 3, 1}; + } + } + if (params->validation_only) return Status::OK(); + + // Start conversion. + if (need_reshape) { + const nvinfer1::ITensor* output_tensor = nullptr; + TF_RETURN_IF_ERROR(params->converter->PrepareTensorForShape( + inputs.at(0), reshape_dims, &output_tensor)); + tensor = const_cast(output_tensor); + } + if (need_transpose) { + const nvinfer1::ITensor* output_tensor = nullptr; + TF_RETURN_IF_ERROR(params->converter->TransposeTensor( + tensor, transpose_order, &output_tensor)); + tensor = const_cast(output_tensor); + } + + // Add padding layer + nvinfer1::IPaddingLayer* layer = params->converter->network()->addPadding( + *const_cast(tensor), pre_padding, post_padding); + TFTRT_RETURN_ERROR_IF_NULLPTR(layer, node_def.name()); + tensor = layer->getOutput(0); + + // Restore transpose + if (need_transpose) { + const nvinfer1::ITensor* output_tensor = nullptr; + TF_RETURN_IF_ERROR(params->converter->TransposeTensor( + tensor, inv_transpose_order, &output_tensor)); + tensor = const_cast(output_tensor); + } + // Restore reshape + if (need_reshape) { + // Calculate output dimensions + for(int i = 0; i < pad_dims.size(); i++) { + const int axis = pad_dims[i]; + input_dims[axis] = end[axis] - begin[axis]; + } + // Remove added 1 dimensions + for (int i = 0; i < reshape_dims_added; i++) { + int value = input_dims[1]; + if (value != 1) { + return tensorflow::errors::Internal( + "StridedSlice error when reshaping, at ", + node_def.name()); + } + input_dims.erase(input_dims.begin()+1); + } + + nvinfer1::Dims new_dims = VectorToTrtDims(input_dims, + /*ignore_first_dim=*/true); + const nvinfer1::ITensor* output_tensor = nullptr; + TF_RETURN_IF_ERROR(params->converter->PrepareTensorForShape( + TRT_TensorOrWeights(tensor), new_dims, &output_tensor)); + tensor = const_cast(output_tensor); + } + + params->outputs->push_back( + TRT_TensorOrWeights(const_cast(tensor))); + return tensorflow::Status::OK(); +} + tensorflow::Status ConvertConv2D(OpConverterParams* params) { return ConvertConv2DHelper(params, ConvolutionType::DEFAULT); } @@ -3335,14 +3579,15 @@ static void RegisterValidatableOpConverters( (*registration)["Const"] = ConvertConst; (*registration)["Conv2D"] = ConvertConv2D; (*registration)["DepthwiseConv2dNative"] = ConvertConv2DDepthwise; - (*registration)["Transpose"] = ConvertTranspose; - (*registration)["Reshape"] = ConvertReshape; + (*registration)["ExpandDims"] = ConvertExpandDims; (*registration)["MatMul"] = ConvertMatMul; (*registration)["Pad"] = ConvertPad; (*registration)["Relu6"] = ConvertRelu6; + (*registration)["Reshape"] = ConvertReshape; (*registration)["Square"] = ConvertSquare; - (*registration)["ExpandDims"] = ConvertExpandDims; (*registration)["Squeeze"] = ConvertSqueeze; + (*registration)["StridedSlice"] = ConvertStridedSlice; + (*registration)["Transpose"] = ConvertTranspose; for (auto quantization_op_type : {"QuantizeAndDequantizeV2", "QuantizeAndDequantizeV3", diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc index c37a43dd5d..07649f04b2 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc @@ -2129,7 +2129,6 @@ TEST_F(OpConverterTest, ConvertExpandDims) { auto expanddims = ops::ExpandDims(s.WithOpName("my_expanddims"), input, weights); const NodeDef& node_def = expanddims.operation.node()->def(); - { // Input is weights, should fail. Reset(); @@ -2349,6 +2348,307 @@ TEST_F(OpConverterTest, ConvertSqueeze) { } } +TEST_F(OpConverterTest, ConvertStridedSlice) { + { + // Input list is empty, should fail. + NodeDef node_def = MakeNodeDef("my_strided_slice", "StridedSlice", {}); + RunValidationAndConversion( + node_def, error::INVALID_ARGUMENT, + "StridedSlice expects 4 inputs, at my_strided_slice"); + } + + // Get nodedef for StridedSlice layer. + auto get_strided_slice_nodedef = [](int begin_mask = 0, + int ellipsis_mask = 0, + int end_mask = 0, + int new_axis_mask = 0, + int shrink_axis_mask = 0) -> NodeDef { + Scope s = Scope::NewRootScope(); + auto input = ops::Placeholder(s.WithOpName("input"), DT_FLOAT); + auto begin = ops::Placeholder(s.WithOpName("begin"), DT_INT32); + auto end = ops::Placeholder(s.WithOpName("end"), DT_INT32); + auto strides = ops::Placeholder(s.WithOpName("strides"), DT_INT32); + ops::StridedSlice::Attrs strided_slice_attrs; + strided_slice_attrs.begin_mask_ = begin_mask; + strided_slice_attrs.ellipsis_mask_ = ellipsis_mask; + strided_slice_attrs.end_mask_ = end_mask; + strided_slice_attrs.new_axis_mask_ = new_axis_mask; + strided_slice_attrs.shrink_axis_mask_ = shrink_axis_mask; + auto strided_slice = ops::StridedSlice(s.WithOpName("my_strided_slice"), + input, begin, end, strides, strided_slice_attrs); + return strided_slice.operation.node()->def(); + }; + + { + NodeDef node_def = get_strided_slice_nodedef(); + AddTestWeights("input", {1, 2, 3}, {1, 2, 3, 4, 5, 6}); + AddTestWeights("begin", {4}, {0, 0, 0, 0}); + AddTestWeights("end", {4}, {1, 1, 2, 3}); + AddTestWeights("strides", {4}, {1, 1, 1, 1}); + RunValidationAndConversion( + node_def, error::UNIMPLEMENTED, + "StridedSlice is only implemented for tensors, at my_strided_slice"); + } + { + // Begin, end, strides are tensors, should fail. + Reset(); + NodeDef node_def = get_strided_slice_nodedef(); + AddTestTensor("input", {1, 2, 3}); + AddTestTensor("begin", {4}); + AddTestTensor("end", {4}); + AddTestTensor("strides", {4}); + RunValidationAndConversion( + node_def, error::INVALID_ARGUMENT, + "StridedSlice expects weights for begin, end, and strides, at " + "my_strided_slice"); + } + { + // Non-zero ellipsis_mask, should fail. + Reset(); + NodeDef node_def = get_strided_slice_nodedef(/*begin_mask=*/0, + /*ellipsis_mask=*/2, /*end_mask=*/0, /*new_axis_mask=*/0, + /*shrink_axis_mask=*/0); + AddTestTensor("input", {1, 2, 3}); + AddTestWeights("begin", {4}, {0, 0, 0, 0}); + AddTestWeights("end", {4}, {1, 1, 2, 3}); + AddTestWeights("strides", {4}, {1, 1, 1, 1}); + RunValidationAndConversion( + node_def, error::UNIMPLEMENTED, + "ellipsis_mask is not implemented for StridedSlice, at " + "my_strided_slice"); + } + { + // Non-zero ellipsis_mask, should fail. + Reset(); + NodeDef node_def = get_strided_slice_nodedef(/*begin_mask=*/0, + /*ellipsis_mask=*/0, /*end_mask=*/0, /*new_axis_mask=*/2, + /*shrink_axis_mask=*/0); + AddTestTensor("input", {1, 2, 3}); + AddTestWeights("begin", {4}, {0, 0, 0, 0}); + AddTestWeights("end", {4}, {1, 1, 2, 3}); + AddTestWeights("strides", {4}, {1, 1, 1, 1}); + RunValidationAndConversion( + node_def, error::UNIMPLEMENTED, + "new_axis_mask is not implemented for StridedSlice, at " + "my_strided_slice"); + } + { + // Non-zero shrink_axis_mask, should fail. + Reset(); + NodeDef node_def = get_strided_slice_nodedef(/*begin_mask=*/0, + /*ellipsis_mask=*/0, /*end_mask=*/0, /*new_axis_mask=*/0, + /*shrink_axis_mask=*/2); + AddTestTensor("input", {1, 2, 3}); + AddTestWeights("begin", {4}, {0, 0, 0, 0}); + AddTestWeights("end", {4}, {1, 1, 2, 3}); + AddTestWeights("strides", {4}, {1, 1, 1, 1}); + RunValidationAndConversion( + node_def, error::UNIMPLEMENTED, + "shrink_axis_mask is not implemented for StridedSlice, at " + "my_strided_slice"); + } + { + // Modify batch dim, should fail. + Reset(); + NodeDef node_def = get_strided_slice_nodedef(); + AddTestTensor("input", {1, 2, 3}); + AddTestWeights("begin", {4}, {0, 0, 0, 0}); + AddTestWeights("end", {4}, {0, 1, 2, 3}); + AddTestWeights("strides", {4}, {1, 1, 1, 1}); + RunValidationAndConversion( + node_def, error::UNIMPLEMENTED, + "StridedSlice can't modify batch dim, at my_strided_slice"); + } + { + // Stride is not 1, should fail. + Reset(); + NodeDef node_def = get_strided_slice_nodedef(); + AddTestTensor("input", {1, 2, 3}); + AddTestWeights("begin", {4}, {0, 0, 0, 0}); + AddTestWeights("end", {4}, {1, 1, 2, 3}); + AddTestWeights("strides", {4}, {1, 2, -1, 3}); + RunValidationAndConversion( + node_def, error::UNIMPLEMENTED, "StridedSlice is only implemented for " + "stride of 1, at my_strided_slice"); + } + { + // Begin out of bounds, should fail. + Reset(); + NodeDef node_def = get_strided_slice_nodedef(); + AddTestTensor("input", {1, 2, 3}); + AddTestWeights("begin", {4}, {1, 2, 3, 4}); + AddTestWeights("end", {4}, {0, 1, 2, 3}); + AddTestWeights("strides", {4}, {1, 1, 1, 1}); + RunValidationAndConversion( + node_def, error::INVALID_ARGUMENT, + "begin for StridedSlice is invalid, must be in the range " + "[-rank(input), rank(input)], at my_strided_slice"); + } + { + // End out of bounds, should fail. + Reset(); + NodeDef node_def = get_strided_slice_nodedef(); + AddTestTensor("input", {1, 2, 3}); + AddTestWeights("begin", {4}, {0, 0, 0, 0}); + AddTestWeights("end", {4}, {1, 2, 3, 4}); + AddTestWeights("strides", {4}, {1, 1, 1, 1}); + RunValidationAndConversion( + node_def, error::INVALID_ARGUMENT, + "end for StridedSlice is invalid, must be in the range " + "[-rank(input), rank(input)], at my_strided_slice"); + } + { + // Size of sliced dim is negative, should fail. + Reset(); + NodeDef node_def = get_strided_slice_nodedef(); + AddTestTensor("input", {1, 2, 3}); + AddTestWeights("begin", {4}, {0, 0, 2, 0}); + AddTestWeights("end", {4}, {1, 1, 0, 3}); + AddTestWeights("strides", {4}, {1, 1, 1, 1}); + RunValidationAndConversion( + node_def, error::INVALID_ARGUMENT, + "New size of sliced dimension is negative, at my_strided_slice"); + } + + struct TestParams { + TestParams(const std::vector& input_dims, + const std::vector& expected_output_dims, + const std::vector& begin, + const std::vector& end, + const std::vector& begin_mask, + const std::vector& end_mask, + const std::vector& expected_output) + : input_dims(input_dims), + expected_output_dims(expected_output_dims), + begin(begin), + end(end), + expected_output(expected_output) { + // Masks are provided in terms of vectors for readability. Convert them to + // binary here. + this->begin_mask = 0; + for (int i = 0; i < begin_mask.size(); i++) { + if (begin_mask[i]) this->begin_mask |= (1 << i); + } + this->end_mask = 0; + for (int i = 0; i < end_mask.size(); i++) { + if (end_mask[i]) this->end_mask |= (1 << i); + } + } + + std::vector input_dims; + std::vector expected_output_dims; + std::vector begin; + std::vector end; + int begin_mask; + int end_mask; + std::vector expected_output; + }; + + // Ok. + const int kStridedSliceOKCases = 18; + TestParams ok_params[kStridedSliceOKCases] = { + // 2D Crop. + TestParams{/*input_dims=*/{1, 2, 3}, /*expected_output_dims=*/{1, 1, 2}, + /*begin=*/{0, 0, 0, 0}, /*end=*/{0, 0, 1, 2}, + /*begin_mask=*/{0, 0, 0, 0}, /*end_mask=*/{1, 1, 0, 0}, + /*expected_output=*/{1, 2}}, + TestParams{/*input_dims=*/{1, 2, 3}, /*expected_output_dims=*/{1, 1, 2}, + /*begin=*/{0, 0, 1, 1}, /*end=*/{0, 0, 0, 0}, + /*begin_mask=*/{0, 0, 0, 0}, /*end_mask=*/{1, 1, 1, 1}, + /*expected_output=*/{5, 6}}, + TestParams{/*input_dims=*/{1, 2, 3}, /*expected_output_dims=*/{1, 1, 2}, + /*begin=*/{0, 0, 1, 1}, /*end=*/{0, 1, 2, 3}, + /*begin_mask=*/{0, 0, 0, 0}, /*end_mask=*/{1, 1, 0, 0}, + /*expected_output=*/{5, 6}}, + // 2D Crop, with transpose. + TestParams{/*input_dims=*/{2, 3, 1}, /*expected_output_dims=*/{1, 2, 1}, + /*begin=*/{0, 0, 0, 0}, /*end=*/{0, 1, 2, 1}, + /*begin_mask=*/{0, 0, 0, 0}, /*end_mask=*/{1, 0, 0, 0}, + /*expected_output=*/{1, 2}}, + TestParams{/*input_dims=*/{2, 3, 1}, /*expected_output_dims=*/{1, 2, 1}, + /*begin=*/{0, 1, 1, 0}, /*end=*/{0, 2, 3, 1}, + /*begin_mask=*/{0, 0, 0, 0}, /*end_mask=*/{1, 0, 0, 0}, + /*expected_output=*/{5, 6}}, + TestParams{/*input_dims=*/{2, 1, 3}, /*expected_output_dims=*/{1, 1, 2}, + /*begin=*/{0, 0, 0, 0}, /*end=*/{0, 1, 1, 2}, + /*begin_mask=*/{0, 0, 0, 0}, /*end_mask=*/{1, 0, 0, 0}, + /*expected_output=*/{1, 2}}, + TestParams{/*input_dims=*/{2, 1, 3}, /*expected_output_dims=*/{1, 1, 2}, + /*begin=*/{0, 1, 0, 1}, /*end=*/{0, 2, 1, 3}, + /*begin_mask=*/{0, 0, 0, 0}, /*end_mask=*/{1, 0, 0, 0}, + /*expected_output=*/{5, 6}}, + // 2D Crop, with reshape. + TestParams{/*input_dims=*/{2, 3}, /*expected_output_dims=*/{1, 2}, + /*begin=*/{0, 0, 0}, /*end=*/{0, 1, 2}, + /*begin_mask=*/{0, 0, 0}, /*end_mask=*/{1, 0, 0}, + /*expected_output=*/{1, 2}}, + TestParams{/*input_dims=*/{2, 3}, /*expected_output_dims=*/{1, 2}, + /*begin=*/{0, 1, 1}, /*end=*/{0, 0, 0}, + /*begin_mask=*/{0, 0, 0}, /*end_mask=*/{1, 1, 1}, + /*expected_output=*/{5, 6}}, + // 1D Crop. + TestParams{/*input_dims=*/{1, 2, 3}, /*expected_output_dims=*/{1, 2, 2}, + /*begin=*/{0, 0, 0, 0}, /*end=*/{0, 0, 0, 2}, + /*begin_mask=*/{0, 0, 0, 0}, /*end_mask=*/{1, 1, 1, 0}, + /*expected_output=*/{1, 2, 4, 5}}, + TestParams{/*input_dims=*/{1, 2, 3}, /*expected_output_dims=*/{1, 1, 3}, + /*begin=*/{0, 0, 1, 0}, /*end=*/{0, 0, 0, 0}, + /*begin_mask=*/{0, 0, 0, 0}, /*end_mask=*/{1, 1, 1, 1}, + /*expected_output=*/{4, 5, 6}}, + // 1D Crop, with transpose. + TestParams{/*input_dims=*/{2, 3, 1}, /*expected_output_dims=*/{1, 3, 1}, + /*begin=*/{0, 0, 0, 0}, /*end=*/{0, 1, 0, 0}, + /*begin_mask=*/{0, 0, 0, 0}, /*end_mask=*/{1, 0, 1, 1}, + /*expected_output=*/{1, 2, 3}}, + TestParams{/*input_dims=*/{2, 3, 1}, /*expected_output_dims=*/{1, 3, 1}, + /*begin=*/{0, 1, 0, 0}, /*end=*/{0, 0, 0, 0}, + /*begin_mask=*/{0, 0, 0, 0}, /*end_mask=*/{1, 1, 1, 1}, + /*expected_output=*/{4, 5, 6}}, + // 1D Crop, with reshape. + TestParams{/*input_dims=*/{6}, /*expected_output_dims=*/{3}, + /*begin=*/{0, 0}, /*end=*/{0, 3}, + /*begin_mask=*/{0, 0}, /*end_mask=*/{1, 0}, + /*expected_output=*/{1, 2, 3}}, + TestParams{/*input_dims=*/{1, 6}, /*expected_output_dims=*/{1, 3}, + /*begin=*/{0, 0, 2}, /*end=*/{0, 0, 5}, + /*begin_mask=*/{0, 0, 0}, /*end_mask=*/{1, 1, 0}, + /*expected_output=*/{3, 4, 5}}, + TestParams{/*input_dims=*/{6, 1}, /*expected_output_dims=*/{3, 1}, + /*begin=*/{0, 2, 0}, /*end=*/{0, 5, 0}, + /*begin_mask=*/{0, 0, 0}, /*end_mask=*/{1, 0, 1}, + /*expected_output=*/{3, 4, 5}}, + // Negative axis. + TestParams{/*input_dims=*/{6, 1}, /*expected_output_dims=*/{3, 1}, + /*begin=*/{0, -6, 0}, /*end=*/{0, -3, 0}, + /*begin_mask=*/{0, 0, 0}, /*end_mask=*/{1, 0, 1}, + /*expected_output=*/{1, 2, 3}}, + TestParams{/*input_dims=*/{6, 1}, /*expected_output_dims=*/{5, 1}, + /*begin=*/{0, 0, 0}, /*end=*/{0, -1, 0}, + /*begin_mask=*/{0, 0, 0}, /*end_mask=*/{1, 0, 1}, + /*expected_output=*/{1, 2, 3, 4, 5}}, + }; + + for (int i = 0; i < kStridedSliceOKCases; i++) { + Reset(); + NodeDef node_def = get_strided_slice_nodedef(ok_params[i].begin_mask, 0, + ok_params[i].end_mask); + AddTestTensor("input", ok_params[i].input_dims); + AddTestWeights("begin", {ok_params[i].begin.size()}, + ok_params[i].begin); + AddTestWeights("end", {ok_params[i].end.size()}, ok_params[i].end); + std::vector strides(ok_params[i].input_dims.size(), 1); + AddTestWeights("strides", {strides.size()}, strides); + RunValidationAndConversion(node_def); + + TRT_TensorOrWeights output; + TF_EXPECT_OK(GetTensorOrWeights("my_strided_slice", &output)); + std::vector output_data(ok_params[i].expected_output.size()); + BuildAndRun({{"input", {1, 2, 3, 4, 5, 6}}}, "my_strided_slice", + &output_data); + EXPECT_THAT(output_data, ElementsAreArray(ok_params[i].expected_output)); + } +} + } // namespace convert } // namespace tensorrt } // namespace tensorflow -- GitLab From 286b04fcf96e7bbd68e992a2801ce7f18338e7c4 Mon Sep 17 00:00:00 2001 From: Trevor Morris Date: Fri, 30 Nov 2018 11:49:49 -0800 Subject: [PATCH 460/873] VectorToTrtDims -> TensorShapeArrayToTrtDims --- tensorflow/contrib/tensorrt/convert/convert_nodes.cc | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index fdecfe5928..cdc77ac8b3 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -2152,7 +2152,8 @@ tensorflow::Status ConvertStridedSlice(OpConverterParams* params) { end.insert(end.begin()+1, 1); reshape_dims_added++; } - reshape_dims = VectorToTrtDims(input_dims, /*ignore_first_dim=*/true); + reshape_dims = TensorShapeArrayToTrtDims(input_dims, + /*ignore_first_dim=*/true); } // Find dimensions which need to be sliced. std::vector pad_dims; @@ -2259,8 +2260,8 @@ tensorflow::Status ConvertStridedSlice(OpConverterParams* params) { input_dims.erase(input_dims.begin()+1); } - nvinfer1::Dims new_dims = VectorToTrtDims(input_dims, - /*ignore_first_dim=*/true); + nvinfer1::Dims new_dims = TensorShapeArrayToTrtDims( + input_dims, /*ignore_first_dim=*/true); const nvinfer1::ITensor* output_tensor = nullptr; TF_RETURN_IF_ERROR(params->converter->PrepareTensorForShape( TRT_TensorOrWeights(tensor), new_dims, &output_tensor)); -- GitLab From edccece99302c8c2f8e787fa59d13d54f7b0b001 Mon Sep 17 00:00:00 2001 From: Trevor Morris Date: Fri, 30 Nov 2018 12:49:24 -0800 Subject: [PATCH 461/873] Formatting --- tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc index 07649f04b2..078c36a9a1 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc @@ -2533,7 +2533,7 @@ TEST_F(OpConverterTest, ConvertStridedSlice) { for (int i = 0; i < end_mask.size(); i++) { if (end_mask[i]) this->end_mask |= (1 << i); } - } + } std::vector input_dims; std::vector expected_output_dims; -- GitLab From 7e17c6afcae045e976fe0508c59b1b7e4b19e7b5 Mon Sep 17 00:00:00 2001 From: Trevor Morris Date: Mon, 3 Dec 2018 13:47:46 -0800 Subject: [PATCH 462/873] Fix clang-format --- .../contrib/tensorrt/convert/convert_nodes.cc | 68 +++++++++---------- .../tensorrt/convert/convert_nodes_test.cc | 44 ++++++------ 2 files changed, 52 insertions(+), 60 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index cdc77ac8b3..fee4f2341b 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -2033,16 +2033,15 @@ tensorflow::Status ConvertSqueeze(OpConverterParams* params) { return tensorflow::Status::OK(); } -tensorflow::Status GetStridedSliceBound( - const std::vector& input_dims, - const TRT_ShapedWeights& bound_weights, - string bound_name, - string node_name, - std::vector& output_bound) { +// Gets the bounds (start or end) from the weights of a StridedSlice op. +tensorflow::Status GetStridedSliceBound(const std::vector& input_dims, + const TRT_ShapedWeights& bound_weights, + string bound_name, string node_name, + std::vector& output_bound) { const int* weights_ptr = static_cast(const_cast(bound_weights.GetValues())); - output_bound = std::vector(weights_ptr, - weights_ptr + bound_weights.count()); + output_bound = + std::vector(weights_ptr, weights_ptr + bound_weights.count()); if (output_bound.size() != input_dims.size()) { return tensorflow::errors::InvalidArgument( "StridedSlice \"", bound_name, "\" specified ", @@ -2054,8 +2053,10 @@ tensorflow::Status GetStridedSliceBound( if ((output_bound[i] < -input_dims[i]) || (output_bound[i] > input_dims[i])) { return tensorflow::errors::InvalidArgument( - bound_name, " for StridedSlice is invalid, must be in the range " - "[-rank(input), rank(input)], at ", node_name); + bound_name, + " for StridedSlice is invalid, must be in the range " + "[-rank(input), rank(input)], at ", + node_name); } // Convert negative values to their positive equivalent. if (output_bound[i] < 0) { @@ -2072,8 +2073,7 @@ tensorflow::Status ConvertStridedSlice(OpConverterParams* params) { return tensorflow::errors::InvalidArgument( "StridedSlice expects 4 inputs, at ", node_def.name()); } - if (!inputs.at(1).is_weights() || - !inputs.at(2).is_weights() || + if (!inputs.at(1).is_weights() || !inputs.at(2).is_weights() || !inputs.at(3).is_weights()) { return tensorflow::errors::InvalidArgument( "StridedSlice expects weights for begin, end, and strides, at ", @@ -2081,8 +2081,7 @@ tensorflow::Status ConvertStridedSlice(OpConverterParams* params) { } if (!inputs.at(0).is_tensor()) { return tensorflow::errors::Unimplemented( - "StridedSlice is only implemented for tensors, at ", - node_def.name()); + "StridedSlice is only implemented for tensors, at ", node_def.name()); } // Get input dims. nvinfer1::Dims dims = inputs.at(0).GetTrtDims(); @@ -2093,8 +2092,8 @@ tensorflow::Status ConvertStridedSlice(OpConverterParams* params) { } if (input_dims.size() > 4) { return tensorflow::errors::Unimplemented( - "StridedSlice is not implemented for tensors with rank > 4, at ", - node_def.name()); + "StridedSlice is not implemented for tensors with rank > 4, at ", + node_def.name()); } TFAttrs attrs(node_def); // Get begin and end bounds per axis. @@ -2124,8 +2123,8 @@ tensorflow::Status ConvertStridedSlice(OpConverterParams* params) { for (int x : strides) { if (x != 1) { return tensorflow::errors::Unimplemented( - "StridedSlice is only implemented for stride of 1, at ", - node_def.name()); + "StridedSlice is only implemented for stride of 1, at ", + node_def.name()); } } // Unsupported options. @@ -2133,23 +2132,22 @@ tensorflow::Status ConvertStridedSlice(OpConverterParams* params) { int ellipsis_mask = attrs.get(attr); if (ellipsis_mask != 0) { return tensorflow::errors::Unimplemented( - attr, " is not implemented for StridedSlice, at ", - node_def.name()); + attr, " is not implemented for StridedSlice, at ", node_def.name()); } } - nvinfer1::ITensor* tensor = const_cast( - inputs.at(0).tensor()); + nvinfer1::ITensor* tensor = + const_cast(inputs.at(0).tensor()); // Reshape if necessary to 4-D. const bool need_reshape = (input_dims.size() != 4); int reshape_dims_added = 0; - nvinfer1::Dims reshape_dims; + nvinfer1::Dims reshape_dims; if (need_reshape) { // Add new dims after batch dim until tensor is 4D. while (input_dims.size() < 4) { - input_dims.insert(input_dims.begin()+1, 1); - begin.insert(begin.begin()+1, 0); - end.insert(end.begin()+1, 1); + input_dims.insert(input_dims.begin() + 1, 1); + begin.insert(begin.begin() + 1, 0); + end.insert(end.begin() + 1, 1); reshape_dims_added++; } reshape_dims = TensorShapeArrayToTrtDims(input_dims, @@ -2162,9 +2160,7 @@ tensorflow::Status ConvertStridedSlice(OpConverterParams* params) { if (i == 0) { return tensorflow::errors::Unimplemented( "StridedSlice can't modify batch dim, at ", node_def.name()); - } - else if ((end[i] - begin[i]) < 0) { - LOG(INFO) << begin[i] << ", " << end[i]; + } else if ((end[i] - begin[i]) < 0) { return tensorflow::errors::InvalidArgument( "New size of sliced dimension is negative, at ", node_def.name()); } @@ -2187,8 +2183,7 @@ tensorflow::Status ConvertStridedSlice(OpConverterParams* params) { } } else if (pad_dims.size() > 2) { return tensorflow::errors::Unimplemented( - "StridedSlice can only modify 2 dimensions, at ", - node_def.name()); + "StridedSlice can only modify 2 dimensions, at ", node_def.name()); } std::sort(pad_dims.begin(), pad_dims.end()); // Convert to pre/post padding values. @@ -2245,7 +2240,7 @@ tensorflow::Status ConvertStridedSlice(OpConverterParams* params) { // Restore reshape if (need_reshape) { // Calculate output dimensions - for(int i = 0; i < pad_dims.size(); i++) { + for (int i = 0; i < pad_dims.size(); i++) { const int axis = pad_dims[i]; input_dims[axis] = end[axis] - begin[axis]; } @@ -2254,14 +2249,13 @@ tensorflow::Status ConvertStridedSlice(OpConverterParams* params) { int value = input_dims[1]; if (value != 1) { return tensorflow::errors::Internal( - "StridedSlice error when reshaping, at ", - node_def.name()); + "StridedSlice error when reshaping, at ", node_def.name()); } - input_dims.erase(input_dims.begin()+1); + input_dims.erase(input_dims.begin() + 1); } - nvinfer1::Dims new_dims = TensorShapeArrayToTrtDims( - input_dims, /*ignore_first_dim=*/true); + nvinfer1::Dims new_dims = + TensorShapeArrayToTrtDims(input_dims, /*ignore_first_dim=*/true); const nvinfer1::ITensor* output_tensor = nullptr; TF_RETURN_IF_ERROR(params->converter->PrepareTensorForShape( TRT_TensorOrWeights(tensor), new_dims, &output_tensor)); diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc index 078c36a9a1..c370895899 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc @@ -2358,10 +2358,8 @@ TEST_F(OpConverterTest, ConvertStridedSlice) { } // Get nodedef for StridedSlice layer. - auto get_strided_slice_nodedef = [](int begin_mask = 0, - int ellipsis_mask = 0, - int end_mask = 0, - int new_axis_mask = 0, + auto get_strided_slice_nodedef = [](int begin_mask = 0, int ellipsis_mask = 0, + int end_mask = 0,int new_axis_mask = 0, int shrink_axis_mask = 0) -> NodeDef { Scope s = Scope::NewRootScope(); auto input = ops::Placeholder(s.WithOpName("input"), DT_FLOAT); @@ -2374,8 +2372,9 @@ TEST_F(OpConverterTest, ConvertStridedSlice) { strided_slice_attrs.end_mask_ = end_mask; strided_slice_attrs.new_axis_mask_ = new_axis_mask; strided_slice_attrs.shrink_axis_mask_ = shrink_axis_mask; - auto strided_slice = ops::StridedSlice(s.WithOpName("my_strided_slice"), - input, begin, end, strides, strided_slice_attrs); + auto strided_slice = + ops::StridedSlice(s.WithOpName("my_strided_slice"), input, begin, end, + strides, strided_slice_attrs); return strided_slice.operation.node()->def(); }; @@ -2405,9 +2404,9 @@ TEST_F(OpConverterTest, ConvertStridedSlice) { { // Non-zero ellipsis_mask, should fail. Reset(); - NodeDef node_def = get_strided_slice_nodedef(/*begin_mask=*/0, - /*ellipsis_mask=*/2, /*end_mask=*/0, /*new_axis_mask=*/0, - /*shrink_axis_mask=*/0); + NodeDef node_def = get_strided_slice_nodedef( + /*begin_mask=*/0, /*ellipsis_mask=*/2, /*end_mask=*/0, + /*new_axis_mask=*/0, /*shrink_axis_mask=*/0); AddTestTensor("input", {1, 2, 3}); AddTestWeights("begin", {4}, {0, 0, 0, 0}); AddTestWeights("end", {4}, {1, 1, 2, 3}); @@ -2420,9 +2419,9 @@ TEST_F(OpConverterTest, ConvertStridedSlice) { { // Non-zero ellipsis_mask, should fail. Reset(); - NodeDef node_def = get_strided_slice_nodedef(/*begin_mask=*/0, - /*ellipsis_mask=*/0, /*end_mask=*/0, /*new_axis_mask=*/2, - /*shrink_axis_mask=*/0); + NodeDef node_def = get_strided_slice_nodedef( + /*begin_mask=*/0, /*ellipsis_mask=*/0, /*end_mask=*/0, + /*new_axis_mask=*/2, /*shrink_axis_mask=*/0); AddTestTensor("input", {1, 2, 3}); AddTestWeights("begin", {4}, {0, 0, 0, 0}); AddTestWeights("end", {4}, {1, 1, 2, 3}); @@ -2435,9 +2434,9 @@ TEST_F(OpConverterTest, ConvertStridedSlice) { { // Non-zero shrink_axis_mask, should fail. Reset(); - NodeDef node_def = get_strided_slice_nodedef(/*begin_mask=*/0, - /*ellipsis_mask=*/0, /*end_mask=*/0, /*new_axis_mask=*/0, - /*shrink_axis_mask=*/2); + NodeDef node_def = get_strided_slice_nodedef( + /*begin_mask=*/0, /*ellipsis_mask=*/0, /*end_mask=*/0, + /*new_axis_mask=*/0, /*shrink_axis_mask=*/2); AddTestTensor("input", {1, 2, 3}); AddTestWeights("begin", {4}, {0, 0, 0, 0}); AddTestWeights("end", {4}, {1, 1, 2, 3}); @@ -2467,9 +2466,9 @@ TEST_F(OpConverterTest, ConvertStridedSlice) { AddTestWeights("begin", {4}, {0, 0, 0, 0}); AddTestWeights("end", {4}, {1, 1, 2, 3}); AddTestWeights("strides", {4}, {1, 2, -1, 3}); - RunValidationAndConversion( - node_def, error::UNIMPLEMENTED, "StridedSlice is only implemented for " - "stride of 1, at my_strided_slice"); + RunValidationAndConversion(node_def, error::UNIMPLEMENTED, + "StridedSlice is only implemented for stride of " + "1, at my_strided_slice"); } { // Begin out of bounds, should fail. @@ -2513,8 +2512,7 @@ TEST_F(OpConverterTest, ConvertStridedSlice) { struct TestParams { TestParams(const std::vector& input_dims, const std::vector& expected_output_dims, - const std::vector& begin, - const std::vector& end, + const std::vector& begin, const std::vector& end, const std::vector& begin_mask, const std::vector& end_mask, const std::vector& expected_output) @@ -2551,11 +2549,11 @@ TEST_F(OpConverterTest, ConvertStridedSlice) { TestParams{/*input_dims=*/{1, 2, 3}, /*expected_output_dims=*/{1, 1, 2}, /*begin=*/{0, 0, 0, 0}, /*end=*/{0, 0, 1, 2}, /*begin_mask=*/{0, 0, 0, 0}, /*end_mask=*/{1, 1, 0, 0}, - /*expected_output=*/{1, 2}}, + /*expected_output=*/{1, 2}}, TestParams{/*input_dims=*/{1, 2, 3}, /*expected_output_dims=*/{1, 1, 2}, /*begin=*/{0, 0, 1, 1}, /*end=*/{0, 0, 0, 0}, /*begin_mask=*/{0, 0, 0, 0}, /*end_mask=*/{1, 1, 1, 1}, - /*expected_output=*/{5, 6}}, + /*expected_output=*/{5, 6}}, TestParams{/*input_dims=*/{1, 2, 3}, /*expected_output_dims=*/{1, 1, 2}, /*begin=*/{0, 0, 1, 1}, /*end=*/{0, 1, 2, 3}, /*begin_mask=*/{0, 0, 0, 0}, /*end_mask=*/{1, 1, 0, 0}, @@ -2643,7 +2641,7 @@ TEST_F(OpConverterTest, ConvertStridedSlice) { TRT_TensorOrWeights output; TF_EXPECT_OK(GetTensorOrWeights("my_strided_slice", &output)); std::vector output_data(ok_params[i].expected_output.size()); - BuildAndRun({{"input", {1, 2, 3, 4, 5, 6}}}, "my_strided_slice", + BuildAndRun({{"input", {1, 2, 3, 4, 5, 6}}}, "my_strided_slice", &output_data); EXPECT_THAT(output_data, ElementsAreArray(ok_params[i].expected_output)); } -- GitLab From d16eafc60081f5481fb1a9a727e247a995d8da5f Mon Sep 17 00:00:00 2001 From: Trevor Morris Date: Wed, 5 Dec 2018 11:29:33 -0800 Subject: [PATCH 463/873] Make unsupported mask options clearer --- tensorflow/contrib/tensorrt/convert/convert_nodes.cc | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index fee4f2341b..3961374903 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -2127,10 +2127,10 @@ tensorflow::Status ConvertStridedSlice(OpConverterParams* params) { node_def.name()); } } - // Unsupported options. + // Unsupported mask options. for (string attr : {"ellipsis_mask", "new_axis_mask", "shrink_axis_mask"}) { - int ellipsis_mask = attrs.get(attr); - if (ellipsis_mask != 0) { + int attr_val = attrs.get(attr); + if (attr_val != 0) { return tensorflow::errors::Unimplemented( attr, " is not implemented for StridedSlice, at ", node_def.name()); } -- GitLab From 661acd19903cb2fae49b1b4dd12a3170b2950ff3 Mon Sep 17 00:00:00 2001 From: Trevor Morris Date: Mon, 10 Dec 2018 10:32:56 -0800 Subject: [PATCH 464/873] Fix usage of TensorShapeArrayToTrtDims --- tensorflow/contrib/tensorrt/convert/convert_nodes.cc | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 3961374903..ae4f99be26 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -2150,8 +2150,8 @@ tensorflow::Status ConvertStridedSlice(OpConverterParams* params) { end.insert(end.begin() + 1, 1); reshape_dims_added++; } - reshape_dims = TensorShapeArrayToTrtDims(input_dims, - /*ignore_first_dim=*/true); + TF_RETURN_IF_ERROR(TensorShapeArrayToTrtDims(input_dims, &reshape_dims, + /*ignore_first_dim=*/true)); } // Find dimensions which need to be sliced. std::vector pad_dims; @@ -2254,8 +2254,9 @@ tensorflow::Status ConvertStridedSlice(OpConverterParams* params) { input_dims.erase(input_dims.begin() + 1); } - nvinfer1::Dims new_dims = - TensorShapeArrayToTrtDims(input_dims, /*ignore_first_dim=*/true); + nvinfer1::Dims new_dims; + TF_RETURN_IF_ERROR(TensorShapeArrayToTrtDims(input_dims, &new_dims, + /*ignore_first_dim=*/true)); const nvinfer1::ITensor* output_tensor = nullptr; TF_RETURN_IF_ERROR(params->converter->PrepareTensorForShape( TRT_TensorOrWeights(tensor), new_dims, &output_tensor)); -- GitLab From 296b83f13346fb70fc7ee70ae256b96a6366896a Mon Sep 17 00:00:00 2001 From: Trevor Morris Date: Tue, 11 Dec 2018 10:50:53 -0800 Subject: [PATCH 465/873] Apply smit-hinsu's suggestions --- .../contrib/tensorrt/convert/convert_nodes.cc | 51 ++++++++-------- .../tensorrt/convert/convert_nodes_test.cc | 60 +++++-------------- 2 files changed, 40 insertions(+), 71 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index ae4f99be26..303db95921 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -2037,30 +2037,29 @@ tensorflow::Status ConvertSqueeze(OpConverterParams* params) { tensorflow::Status GetStridedSliceBound(const std::vector& input_dims, const TRT_ShapedWeights& bound_weights, string bound_name, string node_name, - std::vector& output_bound) { - const int* weights_ptr = - static_cast(const_cast(bound_weights.GetValues())); - output_bound = + std::vector* output_bound) { + const int* weights_ptr = static_cast(bound_weights.GetValues()); + *output_bound = std::vector(weights_ptr, weights_ptr + bound_weights.count()); - if (output_bound.size() != input_dims.size()) { + if (output_bound->size() != input_dims.size()) { return tensorflow::errors::InvalidArgument( "StridedSlice \"", bound_name, "\" specified ", - std::to_string(output_bound.size()), " dimensions, but input rank is ", + std::to_string(output_bound->size()), " dimensions, but input rank is ", std::to_string(input_dims.size()), ", at ", node_name); } - for (int i = 0; i < output_bound.size(); i++) { + for (int i = 0; i < output_bound->size(); i++) { // Make sure bound is valid. - if ((output_bound[i] < -input_dims[i]) || - (output_bound[i] > input_dims[i])) { + if (((*output_bound)[i] < -input_dims[i]) || + ((*output_bound)[i] > input_dims[i])) { return tensorflow::errors::InvalidArgument( bound_name, - " for StridedSlice is invalid, must be in the range " - "[-rank(input), rank(input)], at ", + " value for StridedSlice is invalid, must be in the range " + "[-dim_size(i), dim_size(i)], at ", node_name); } // Convert negative values to their positive equivalent. - if (output_bound[i] < 0) { - output_bound[i] += input_dims[i]; + if ((*output_bound)[i] < 0) { + (*output_bound)[i] += input_dims[i]; } } return tensorflow::Status::OK(); @@ -2099,9 +2098,9 @@ tensorflow::Status ConvertStridedSlice(OpConverterParams* params) { // Get begin and end bounds per axis. std::vector begin, end; TF_RETURN_IF_ERROR(GetStridedSliceBound(input_dims, inputs.at(1).weights(), - "begin", node_def.name(), begin)); + "begin", node_def.name(), &begin)); TF_RETURN_IF_ERROR(GetStridedSliceBound(input_dims, inputs.at(2).weights(), - "end", node_def.name(), end)); + "end", node_def.name(), &end)); int begin_mask = attrs.get("begin_mask"); for (int i = 0; i < begin.size(); i++) { if ((1 << i) & begin_mask) { @@ -2116,8 +2115,7 @@ tensorflow::Status ConvertStridedSlice(OpConverterParams* params) { } // Get strides per axis (must all be 1). TRT_ShapedWeights stride_weights = inputs.at(3).weights(); - const int* stride_weights_ptr = - static_cast(const_cast(stride_weights.GetValues())); + const int* stride_weights_ptr = static_cast(stride_weights.GetValues()); std::vector strides(stride_weights_ptr, stride_weights_ptr + stride_weights.count()); for (int x : strides) { @@ -2128,17 +2126,18 @@ tensorflow::Status ConvertStridedSlice(OpConverterParams* params) { } } // Unsupported mask options. - for (string attr : {"ellipsis_mask", "new_axis_mask", "shrink_axis_mask"}) { + for (const string& attr : + {"ellipsis_mask", "new_axis_mask", "shrink_axis_mask"}) { int attr_val = attrs.get(attr); if (attr_val != 0) { return tensorflow::errors::Unimplemented( - attr, " is not implemented for StridedSlice, at ", node_def.name()); + attr, " is not supported for StridedSlice, at ", node_def.name()); } } nvinfer1::ITensor* tensor = const_cast(inputs.at(0).tensor()); - // Reshape if necessary to 4-D. + // Reshape if necessary to 4-D, since IPaddingLayer requires a 4-D input. const bool need_reshape = (input_dims.size() != 4); int reshape_dims_added = 0; nvinfer1::Dims reshape_dims; @@ -2156,7 +2155,7 @@ tensorflow::Status ConvertStridedSlice(OpConverterParams* params) { // Find dimensions which need to be sliced. std::vector pad_dims; for (int i = 0; i < input_dims.size(); i++) { - if (begin[i] != 0 || (end[i] - input_dims[i]) != 0) { + if ((begin[i] != 0) || (end[i] != input_dims[i])) { if (i == 0) { return tensorflow::errors::Unimplemented( "StridedSlice can't modify batch dim, at ", node_def.name()); @@ -2175,10 +2174,11 @@ tensorflow::Status ConvertStridedSlice(OpConverterParams* params) { return tensorflow::Status::OK(); } else if (pad_dims.size() == 1) { // Only one dim is modified but we have to have 2, mark a second dim which - // will have padding of 0. - if (pad_dims[0] == 1 || pad_dims[0] == 3) { + // will have padding of 0. The dim we add is chosen to avoid an unecessary + // transpose. + if (pad_dims[0] != 2) { pad_dims.push_back(2); - } else if (pad_dims[0] == 2) { + } else { pad_dims.push_back(3); } } else if (pad_dims.size() > 2) { @@ -2186,7 +2186,8 @@ tensorflow::Status ConvertStridedSlice(OpConverterParams* params) { "StridedSlice can only modify 2 dimensions, at ", node_def.name()); } std::sort(pad_dims.begin(), pad_dims.end()); - // Convert to pre/post padding values. + // Convert to pre/post padding values. Since TRT does not have a StridedSlice + // or Slice layer, we instead create an IPaddingLayer with negative padding. nvinfer1::DimsHW pre_padding, post_padding; for (int i = 0; i < pad_dims.size(); i++) { const int axis = pad_dims[i]; diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc index c370895899..91d9e60010 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc @@ -2358,23 +2358,21 @@ TEST_F(OpConverterTest, ConvertStridedSlice) { } // Get nodedef for StridedSlice layer. - auto get_strided_slice_nodedef = [](int begin_mask = 0, int ellipsis_mask = 0, - int end_mask = 0,int new_axis_mask = 0, + auto get_strided_slice_nodedef = [](int begin_mask = 0, int end_mask = 0, + int ellipsis_mask = 0, + int new_axis_mask = 0, int shrink_axis_mask = 0) -> NodeDef { Scope s = Scope::NewRootScope(); auto input = ops::Placeholder(s.WithOpName("input"), DT_FLOAT); auto begin = ops::Placeholder(s.WithOpName("begin"), DT_INT32); auto end = ops::Placeholder(s.WithOpName("end"), DT_INT32); auto strides = ops::Placeholder(s.WithOpName("strides"), DT_INT32); - ops::StridedSlice::Attrs strided_slice_attrs; - strided_slice_attrs.begin_mask_ = begin_mask; - strided_slice_attrs.ellipsis_mask_ = ellipsis_mask; - strided_slice_attrs.end_mask_ = end_mask; - strided_slice_attrs.new_axis_mask_ = new_axis_mask; - strided_slice_attrs.shrink_axis_mask_ = shrink_axis_mask; + ops::StridedSlice::Attrs attrs = ops::StridedSlice::Attrs() + .BeginMask(begin_mask).EndMask(end_mask).EllipsisMask(ellipsis_mask) + .NewAxisMask(new_axis_mask).ShrinkAxisMask(shrink_axis_mask); auto strided_slice = ops::StridedSlice(s.WithOpName("my_strided_slice"), input, begin, end, - strides, strided_slice_attrs); + strides, attrs); return strided_slice.operation.node()->def(); }; @@ -2405,7 +2403,7 @@ TEST_F(OpConverterTest, ConvertStridedSlice) { // Non-zero ellipsis_mask, should fail. Reset(); NodeDef node_def = get_strided_slice_nodedef( - /*begin_mask=*/0, /*ellipsis_mask=*/2, /*end_mask=*/0, + /*begin_mask=*/0, /*end_mask=*/0, /*ellipsis_mask=*/2, /*new_axis_mask=*/0, /*shrink_axis_mask=*/0); AddTestTensor("input", {1, 2, 3}); AddTestWeights("begin", {4}, {0, 0, 0, 0}); @@ -2413,37 +2411,7 @@ TEST_F(OpConverterTest, ConvertStridedSlice) { AddTestWeights("strides", {4}, {1, 1, 1, 1}); RunValidationAndConversion( node_def, error::UNIMPLEMENTED, - "ellipsis_mask is not implemented for StridedSlice, at " - "my_strided_slice"); - } - { - // Non-zero ellipsis_mask, should fail. - Reset(); - NodeDef node_def = get_strided_slice_nodedef( - /*begin_mask=*/0, /*ellipsis_mask=*/0, /*end_mask=*/0, - /*new_axis_mask=*/2, /*shrink_axis_mask=*/0); - AddTestTensor("input", {1, 2, 3}); - AddTestWeights("begin", {4}, {0, 0, 0, 0}); - AddTestWeights("end", {4}, {1, 1, 2, 3}); - AddTestWeights("strides", {4}, {1, 1, 1, 1}); - RunValidationAndConversion( - node_def, error::UNIMPLEMENTED, - "new_axis_mask is not implemented for StridedSlice, at " - "my_strided_slice"); - } - { - // Non-zero shrink_axis_mask, should fail. - Reset(); - NodeDef node_def = get_strided_slice_nodedef( - /*begin_mask=*/0, /*ellipsis_mask=*/0, /*end_mask=*/0, - /*new_axis_mask=*/0, /*shrink_axis_mask=*/2); - AddTestTensor("input", {1, 2, 3}); - AddTestWeights("begin", {4}, {0, 0, 0, 0}); - AddTestWeights("end", {4}, {1, 1, 2, 3}); - AddTestWeights("strides", {4}, {1, 1, 1, 1}); - RunValidationAndConversion( - node_def, error::UNIMPLEMENTED, - "shrink_axis_mask is not implemented for StridedSlice, at " + "ellipsis_mask is not supported for StridedSlice, at " "my_strided_slice"); } { @@ -2480,8 +2448,8 @@ TEST_F(OpConverterTest, ConvertStridedSlice) { AddTestWeights("strides", {4}, {1, 1, 1, 1}); RunValidationAndConversion( node_def, error::INVALID_ARGUMENT, - "begin for StridedSlice is invalid, must be in the range " - "[-rank(input), rank(input)], at my_strided_slice"); + "begin value for StridedSlice is invalid, must be in the range " + "[-dim_size(i), dim_size(i)], at my_strided_slice"); } { // End out of bounds, should fail. @@ -2493,8 +2461,8 @@ TEST_F(OpConverterTest, ConvertStridedSlice) { AddTestWeights("strides", {4}, {1, 1, 1, 1}); RunValidationAndConversion( node_def, error::INVALID_ARGUMENT, - "end for StridedSlice is invalid, must be in the range " - "[-rank(input), rank(input)], at my_strided_slice"); + "end value for StridedSlice is invalid, must be in the range " + "[-dim_size(i), dim_size(i)], at my_strided_slice"); } { // Size of sliced dim is negative, should fail. @@ -2628,7 +2596,7 @@ TEST_F(OpConverterTest, ConvertStridedSlice) { for (int i = 0; i < kStridedSliceOKCases; i++) { Reset(); - NodeDef node_def = get_strided_slice_nodedef(ok_params[i].begin_mask, 0, + NodeDef node_def = get_strided_slice_nodedef(ok_params[i].begin_mask, ok_params[i].end_mask); AddTestTensor("input", ok_params[i].input_dims); AddTestWeights("begin", {ok_params[i].begin.size()}, -- GitLab From 1254fdd1e0d57f0efb330313ab4b6b325adb9f04 Mon Sep 17 00:00:00 2001 From: Trevor Morris Date: Tue, 11 Dec 2018 14:15:46 -0800 Subject: [PATCH 466/873] Fix bug with masking and undefined batch dims. Masking needs to take place inside of GetStridedSliceBound --- .../contrib/tensorrt/convert/convert_nodes.cc | 45 ++++++++++--------- .../tensorrt/convert/convert_nodes_test.cc | 25 ++++++----- 2 files changed, 37 insertions(+), 33 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 303db95921..adf8831b96 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -2036,10 +2036,11 @@ tensorflow::Status ConvertSqueeze(OpConverterParams* params) { // Gets the bounds (start or end) from the weights of a StridedSlice op. tensorflow::Status GetStridedSliceBound(const std::vector& input_dims, const TRT_ShapedWeights& bound_weights, - string bound_name, string node_name, + int mask, bool begin, string node_name, std::vector* output_bound) { + const string bound_name = (begin) ? "begin" : "end"; const int* weights_ptr = static_cast(bound_weights.GetValues()); - *output_bound = + *output_bound = std::vector(weights_ptr, weights_ptr + bound_weights.count()); if (output_bound->size() != input_dims.size()) { return tensorflow::errors::InvalidArgument( @@ -2048,12 +2049,22 @@ tensorflow::Status GetStridedSliceBound(const std::vector& input_dims, std::to_string(input_dims.size()), ", at ", node_name); } for (int i = 0; i < output_bound->size(); i++) { + if ((1 << i) & mask) { + // Apply mask. + (*output_bound)[i] = (begin) ? 0 : input_dims[i]; + // Masked bound will always result in a valid, non-negative bound, so we + // don't need the following checks. For the common case of using masks on + // a undefined batch dim (-1), we specifically don't want to do the + // following checks because they will erroneously detect an out of range + // bound or try to correct the negative value. + continue; + } // Make sure bound is valid. if (((*output_bound)[i] < -input_dims[i]) || ((*output_bound)[i] > input_dims[i])) { return tensorflow::errors::InvalidArgument( - bound_name, - " value for StridedSlice is invalid, must be in the range " + bound_name, " value of ", std::to_string((*output_bound)[i]), + " for StridedSlice is invalid, must be in the range " "[-dim_size(i), dim_size(i)], at ", node_name); } @@ -2091,28 +2102,18 @@ tensorflow::Status ConvertStridedSlice(OpConverterParams* params) { } if (input_dims.size() > 4) { return tensorflow::errors::Unimplemented( - "StridedSlice is not implemented for tensors with rank > 4, at ", + "StridedSlice is not implemented for tensors with rank > 4, at ", node_def.name()); } TFAttrs attrs(node_def); // Get begin and end bounds per axis. std::vector begin, end; TF_RETURN_IF_ERROR(GetStridedSliceBound(input_dims, inputs.at(1).weights(), - "begin", node_def.name(), &begin)); + attrs.get("begin_mask"), true, + node_def.name(), &begin)); TF_RETURN_IF_ERROR(GetStridedSliceBound(input_dims, inputs.at(2).weights(), - "end", node_def.name(), &end)); - int begin_mask = attrs.get("begin_mask"); - for (int i = 0; i < begin.size(); i++) { - if ((1 << i) & begin_mask) { - begin[i] = 0; - } - } - int end_mask = attrs.get("end_mask"); - for (int i = 0; i < end.size(); i++) { - if ((1 << i) & end_mask) { - end[i] = input_dims[i]; - } - } + attrs.get("end_mask"), false, + node_def.name(), &end)); // Get strides per axis (must all be 1). TRT_ShapedWeights stride_weights = inputs.at(3).weights(); const int* stride_weights_ptr = static_cast(stride_weights.GetValues()); @@ -2121,7 +2122,7 @@ tensorflow::Status ConvertStridedSlice(OpConverterParams* params) { for (int x : strides) { if (x != 1) { return tensorflow::errors::Unimplemented( - "StridedSlice is only implemented for stride of 1, at ", + "StridedSlice is only implemented for stride of 1, at ", node_def.name()); } } @@ -2135,7 +2136,7 @@ tensorflow::Status ConvertStridedSlice(OpConverterParams* params) { } } - nvinfer1::ITensor* tensor = + nvinfer1::ITensor* tensor = const_cast(inputs.at(0).tensor()); // Reshape if necessary to 4-D, since IPaddingLayer requires a 4-D input. const bool need_reshape = (input_dims.size() != 4); @@ -2229,6 +2230,8 @@ tensorflow::Status ConvertStridedSlice(OpConverterParams* params) { nvinfer1::IPaddingLayer* layer = params->converter->network()->addPadding( *const_cast(tensor), pre_padding, post_padding); TFTRT_RETURN_ERROR_IF_NULLPTR(layer, node_def.name()); + params->converter->MarkQuantizationRangesAsInferrable(tensor, + layer->getOutput(0)); tensor = layer->getOutput(0); // Restore transpose diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc index 91d9e60010..d71ebb4cae 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc @@ -2358,21 +2358,22 @@ TEST_F(OpConverterTest, ConvertStridedSlice) { } // Get nodedef for StridedSlice layer. - auto get_strided_slice_nodedef = [](int begin_mask = 0, int end_mask = 0, - int ellipsis_mask = 0, - int new_axis_mask = 0, - int shrink_axis_mask = 0) -> NodeDef { + auto get_strided_slice_nodedef = []( + int begin_mask = 0, int end_mask = 0, int ellipsis_mask = 0, + int new_axis_mask = 0, int shrink_axis_mask = 0) -> NodeDef { Scope s = Scope::NewRootScope(); auto input = ops::Placeholder(s.WithOpName("input"), DT_FLOAT); auto begin = ops::Placeholder(s.WithOpName("begin"), DT_INT32); auto end = ops::Placeholder(s.WithOpName("end"), DT_INT32); auto strides = ops::Placeholder(s.WithOpName("strides"), DT_INT32); ops::StridedSlice::Attrs attrs = ops::StridedSlice::Attrs() - .BeginMask(begin_mask).EndMask(end_mask).EllipsisMask(ellipsis_mask) - .NewAxisMask(new_axis_mask).ShrinkAxisMask(shrink_axis_mask); - auto strided_slice = - ops::StridedSlice(s.WithOpName("my_strided_slice"), input, begin, end, - strides, attrs); + .BeginMask(begin_mask) + .EndMask(end_mask) + .EllipsisMask(ellipsis_mask) + .NewAxisMask(new_axis_mask) + .ShrinkAxisMask(shrink_axis_mask); + auto strided_slice = ops::StridedSlice(s.WithOpName("my_strided_slice"), + input, begin, end, strides, attrs); return strided_slice.operation.node()->def(); }; @@ -2403,7 +2404,7 @@ TEST_F(OpConverterTest, ConvertStridedSlice) { // Non-zero ellipsis_mask, should fail. Reset(); NodeDef node_def = get_strided_slice_nodedef( - /*begin_mask=*/0, /*end_mask=*/0, /*ellipsis_mask=*/2, + /*begin_mask=*/0, /*end_mask=*/0, /*ellipsis_mask=*/2, /*new_axis_mask=*/0, /*shrink_axis_mask=*/0); AddTestTensor("input", {1, 2, 3}); AddTestWeights("begin", {4}, {0, 0, 0, 0}); @@ -2448,7 +2449,7 @@ TEST_F(OpConverterTest, ConvertStridedSlice) { AddTestWeights("strides", {4}, {1, 1, 1, 1}); RunValidationAndConversion( node_def, error::INVALID_ARGUMENT, - "begin value for StridedSlice is invalid, must be in the range " + "begin value of 2 for StridedSlice is invalid, must be in the range " "[-dim_size(i), dim_size(i)], at my_strided_slice"); } { @@ -2461,7 +2462,7 @@ TEST_F(OpConverterTest, ConvertStridedSlice) { AddTestWeights("strides", {4}, {1, 1, 1, 1}); RunValidationAndConversion( node_def, error::INVALID_ARGUMENT, - "end value for StridedSlice is invalid, must be in the range " + "end value of 2 for StridedSlice is invalid, must be in the range " "[-dim_size(i), dim_size(i)], at my_strided_slice"); } { -- GitLab From ca10e2d3acd1a93643bcae11aedd25e6ac2f7e66 Mon Sep 17 00:00:00 2001 From: Trevor Morris Date: Wed, 12 Dec 2018 13:05:56 -0800 Subject: [PATCH 467/873] Fix failed narrowing checks --- .../contrib/tensorrt/convert/convert_nodes_test.cc | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc index d71ebb4cae..87c9bea82c 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc @@ -2600,11 +2600,14 @@ TEST_F(OpConverterTest, ConvertStridedSlice) { NodeDef node_def = get_strided_slice_nodedef(ok_params[i].begin_mask, ok_params[i].end_mask); AddTestTensor("input", ok_params[i].input_dims); - AddTestWeights("begin", {ok_params[i].begin.size()}, + AddTestWeights("begin", + {static_cast(ok_params[i].begin.size())}, ok_params[i].begin); - AddTestWeights("end", {ok_params[i].end.size()}, ok_params[i].end); + AddTestWeights("end", {static_cast(ok_params[i].end.size())}, + ok_params[i].end); std::vector strides(ok_params[i].input_dims.size(), 1); - AddTestWeights("strides", {strides.size()}, strides); + AddTestWeights("strides", {static_cast(strides.size())}, + strides); RunValidationAndConversion(node_def); TRT_TensorOrWeights output; -- GitLab From ca0ccc6f9fb66b19e2ad72aff0a4a717c5e4920b Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 12 Dec 2018 16:56:48 -0800 Subject: [PATCH 468/873] Fix a bug in lstm_eval. PiperOrigin-RevId: 225281253 --- tensorflow/lite/kernels/lstm_eval.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/lite/kernels/lstm_eval.cc b/tensorflow/lite/kernels/lstm_eval.cc index f179ecb195..0c6a462d29 100644 --- a/tensorflow/lite/kernels/lstm_eval.cc +++ b/tensorflow/lite/kernels/lstm_eval.cc @@ -1118,7 +1118,7 @@ TfLiteStatus EvalHybrid( 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, + projection_bias_ptr, params, /*n_batch=*/1, n_cell, n_input, aux_input_size, n_output, output_batch_leading_dim, input_gate_scratch, forget_gate_scratch, cell_scratch, output_gate_scratch, scaling_factors_ptr, prod_scaling_factors_ptr, -- GitLab From c0b2e3eb7c2c02b3725bdda834e7b5d2875e1cf0 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 12 Dec 2018 17:00:06 -0800 Subject: [PATCH 469/873] Adds unicode_decode and unicode_decode_with_offsets ops, which decode strings into unicode codepoints. Adds unicode_split and unicode_split_with_offset ops, which split strings into unicode characters. RELNOTES: Adds unicode_decode, unicode_decode_with_offsets, unicode_split, and unicode_split_with_offset ops. PiperOrigin-RevId: 225281768 --- .../base_api/api_def_UnicodeDecode.pbtxt | 76 ++ tensorflow/core/kernels/unicode_ops.cc | 72 +- tensorflow/core/ops/string_ops.cc | 21 + tensorflow/python/kernel_tests/BUILD | 7 + .../kernel_tests/unicode_decode_op_test.py | 790 +++++++++++++++--- tensorflow/python/ops/ragged/BUILD | 3 + tensorflow/python/ops/ragged/__init__.py | 11 + .../python/ops/ragged/ragged_string_ops.py | 296 ++++++- .../api/golden/v1/tensorflow.strings.pbtxt | 16 + .../api/golden/v2/tensorflow.strings.pbtxt | 16 + 10 files changed, 1156 insertions(+), 152 deletions(-) create mode 100644 tensorflow/core/api_def/base_api/api_def_UnicodeDecode.pbtxt diff --git a/tensorflow/core/api_def/base_api/api_def_UnicodeDecode.pbtxt b/tensorflow/core/api_def/base_api/api_def_UnicodeDecode.pbtxt new file mode 100644 index 0000000000..9b3f69023f --- /dev/null +++ b/tensorflow/core/api_def/base_api/api_def_UnicodeDecode.pbtxt @@ -0,0 +1,76 @@ +op { + graph_op_name: "UnicodeDecode" + in_arg { + name: "input" + description: <